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Context 1: Paper Info

Title: An CUSUM Test with Observation-Adjusted Control Limits in Change Detection
Publish Date: March 9, 2023
Author List: Fuquan Tang (from Department of Statistics, Shanghai Jiao Tong University), Dong Han (from Department of Statistics, Shanghai Jiao Tong University)

Figure

exp{−cg(µ)(θ − x Hv (θ) + o(1))} for 1 ≤ k ≤ ac − 1, bc ≤ n ≤ m, where Zi = −g ′ (µ)(Z i − µ)/a and Hv (θ) = ln hv (θ) + ( ac k − 1) ln ĥv (θ), ĥv (θ) = E v (e θ Zi ) 
i < cg(µ)(1 + o(1))) exp{−cg(µ)θ * v (1 + o(1))} (A  5) for ac ≤ k ≤ bc − 1, bc ≤ n ≤ m,andP v (
i + g ′ (µ)a −1 Tc(g)−1 i=Tc(g)−ac (Z i − µ)] −→ µas c → ∞ By the uniform integrability of {T c (g)/c} and using Theorem A 1 1 in Gut's book(1988), we haveE v (T c (g)) = (1 + o(1)) cg(µ) µfor a large c This completes the proof of Theorem 2 Proof of Theorem 4  Since g(x) < 0 for x > a * , a * ≤ µ * and µ * ≥ 0, it follows thatP v m Ẑm < cg( Ẑm ), Ẑm > a * ≤ P v ( Ẑm < µ * )andP v (T c (g) > m) = P v n i=n−k+1 Z i < cg( Ẑn ), 1 ≤ k ≤ n, 1 ≤ n ≤ m ≤ P v m Ẑm < cg( Ẑm ) = P v m Ẑm < cg( Ẑm ), Ẑm ≤ a * + P v m Ẑm < cg( Ẑm ), Ẑm > a * ≤ 2P v ( Ẑm < µ * ) Furthermore,P v ( Ẑm < µ * ) = P v ( m i −Z i > −mµ * ) = P v ( m i (µ − Z i ) > m(µ − µ * )) = P v (e θ m i (µ−Z i ) > e θm(µ−µ * ) ) ≤ e −m[θ(µ−µ * )−ln M (θ)] ,whereM(θ) = E v (e θ(µ−Z 1 )) and the last inequality follows from Chebychev's inequality Note thath(θ) = θ(µ − µ * ) − ln M(θ) attains its maximum value h(θ * ) = θ * (µ − µ * ) − ln M(θ * ) > 0 at θ = θ * > 0, where h ′ (θ * ) = 0  So, E v (T c (g)) = 1 + ∞ m=1 P v (T c (g) > m) ≤ 1 + m=1 −m[θ * (µ−µ * )−ln M (θ * )] = e θ * (µ−µ * )−ln M (θ * ) + 1 e θ * (µ−µ * )−ln M (θ * ) − Let k > 1 It follows that E vk (T c (g) − k + 1) + = ∞ m=1 P vk (T c (g) > m + k − 1, T c (g) > k − 1) ≤ (a 0 + 1)(k − 1)P 0 (T c (g) > k − 1) + ∞ m≥(a 0 +1)(k−1) P vk (T c (g) > m + k − 1) Similarly, we haveP vk (T c (g) > m + k − 1) = P vk n i=n−k+1 Z i < cg( Ẑn ), 1 ≤ k ≤ n, 1 ≤ n ≤ m + k − 1 ≤ 2P vk ( Ẑm+k−1 < µ * ) − Z i ) > m(µ − µ * ) + (k − 1)(µ 0 − µ * ) ≤ 2 exp{−m θ * (µ − µ * ) − ln M(θ * ) + k − 1 m [µ 0 − µ * − ln M 0 (θ * )] } ≤ e −mb for m ≥ (a 0 + 1)(k − 1), since θ * (µ − µ * ) − ln M(θ * ) + k − 1 m [µ 0 − µ * − ln M 0 (θ * )] ≥ b for m ≥ (a 0 + 1)(k−1) Thus, E vk (T c (g) − k + 1) + ≤ (a 0 + 1)(k − 1)P 0 (T c (g) ≥ k) + m≥(a 0 +1)(k−1) e −mb ≤ (a 0 + 1)(k − 1)P 0 (T c (g) >≥ k) + 2e −(a 0 +1)(k−1)b 1 − e −b  
Simulation of E τ i ,v and J ACE for detecting two mean shifts v = 0 1, v = 1 The parameters for T * M are k1=1, k2=150, r 1 = 5 2 * 10 −5 , r 2 = 1 1 * 10 −5 , and the expectation and standard deviation in both cases are 1717 06with 13459 80 and 3918 33 with 16893 25,respectively 

abstract

In this paper, we not only propose an new optimal sequential test of sum of logarithmic likelihood ratio (SLR) but also present the CUSUM sequential test (control chart, stopping time) with the observation-adjusted control limits (CUSUM-OAL) for monitoring quickly and adaptively the change in distribution of a sequential observations 
Two limiting relationships between the optimal test and a series of the CUSUM-OAL tests are established  Moreover, we give the estimation of the in-control and the out-of-control average run lengths (ARLs) of the CUSUM-OAL test  The theoretical results are illustrated by numerical simulations in detecting mean shifts of the observations sequence 

INTRODUCTION

In order to quickly detect a change in distribution of observations sequence without exceeding a certain false alarm rate, a great variety of sequential tests have been proposed, developed and applied to various fields since proposed a control chart method, see, for example, , , One of popular used sequential tests is the following upper-sided CUSUM test which was proposed by  
where c > 0 is a constant control limit, Z i = log[p v 1 (X i )/p v 0 (X i )], p v 0 (x) and p v 1 (x) are prechange and post-change probability density functions respectively for a sequence of mutually independent observations {X i , i ≥ 1}, that is, there is a unknown change-point τ ≥ 1 such that X 1 ,    , X τ −1 have the probability density function p v 0 , whereas, X τ , X τ +1 ,     have the probability density function p v 1  
By the renewal property of the CUSUM test T C we have , where E 1 (T C ) is the out-of-control average run length (ARL 1 ), P k and E k denote the probability and expectation respectively when the change from p v 0 to p v 1 occurs at the change-point τ = k for k ≥ 1  Though we know that the CUSUM test is optimal under Lorden's measure (see Moustakides 1986 and Ritov 1990), the out-of-control ARL 1 of the CUSUM test is not small, especially in detecting small mean shifts ( see Table in Section 4) 
In other words, the CUSUM test is insensitive in detecting small mean shifts  Then, how to increase the sensitivity of the CUSUM test ? Note that the control limit in the CUSUM test is a constant c which does not depend on the observation samples  Intuitively, if the control limit of the CUSUM test can become low as the samples mean of the observation sequence increases, then the alarm time of detecting the increasing mean shifts will be greatly shortened 
Based on this idea, by selecting a decreasing function g(x) we may define the ( upper-sided ) CUSUM chart T C (cg) with the observation-adjusted control limits cg( Ẑn ) ( abbreviated to the CUSUM-OAL chart ) in the following where c > 0 is a constant and Ẑn = n i=1 Z i /n  In other words, the control limits cg( Ẑn ) of the CUSUM-OAL test can be adjusted adaptively according to the observation information { Ẑn } 
Note that the control limits cg( Ẑn ) may be negative  In the special case, the CUSUM-OAL chart T C (cg) becomes into the conventional CUSUM chart T C (c) in (1) when g ≡ 1  Similarly, we can define a down-sided CUSUM-OAL test  In this paper, we consider only the upper-sided CUSUM-OAL test since the properties of the down-sided CUSUM-OAL test can be obtained by the similar method 
The main purpose of the present paper is to show the good detection performance of the CUSUM-OAL test and to give the estimation of its the in-control and out-of-control ARLs  The paper is organized as follows  In Section 2, we first present an optimal SLR sequential test, then define two sequences of the CUSUM-OAL tests and prove that one of the two sequences of CUSUM-OAL tests converges to the optimal test, another sequences of CUSUM-OAL tests converges to a combination of the optimal test and the CUSUM test 
The estimation of the in-control and out-of-control ARLs of the CUSUM-OAL tests and their comparison are given in Section 3  The detection performances of the three CUSUM-OAL tests and the conventional CUSUM test are illustrated in Section 4 by comparing their numerical out-ofcontrol ARLs  Section 5 provides some concluding remarks 
Proofs of the theorems are given in the Appendix 

AN OPTIMAL SLR TEST, TWO CUSUM-OAL TESTS AND THEIR LIMITING RELATIONSHIPS

Let P 0 and E 0 denote the probability and the expectation respectively with the probability density p v 0 when there is no change for all the time  It is known that It follows from Proposition 2 38 in and (5 8)-(5 9) in Chow et al, P 108) that the following sequence test of sum of logarithmic likelihood ratio (SLR)
for B > 1, is optimal in the following sense min for P 0 (T SLR < ∞) = α, where c = log B and 0 < α < 1  In particular, if P 0 is the standard normal distribution with mean shift µ > 0 after changepoint, we have Z j − µ 0 = µX j , where µ 0 = −µ 2 /2  It follows from proposition 4 in that the SLR test T SLR in (4) is also optimal (minimal ARL 1 ) with the same false alarm probability P 0 (T < τ ) 
It can be seen that the in-control average run length of T SLR is infinite, that is, ARL 0 = E 0 (T SLR ) = ∞  However, the minimal ARL 1 with finite ARL 0 is a widely used optimality criterion in statistical quality control (see ) and detection of abrupt changes (see   In order to get finite ARL 0 for T SLR , we replace the constant control limit c of T SLR in (3) or (4) with the dynamic control limit n(µ 0 − r) and obtain a modified SLR test T SLR (r) in the following
for r ≥ 0  For comparison, the in-control ARL 0 of all candidate sequential tests are constrained to be equal to the same desired level of type I error, the test with the lowest out-of-control ARL v has the highest power or the fastest monitoring (detection) speed  In the following example 1, the numerical simulations of the out-of-control ARLs of the CUSUM-OAL tests T C (cg u,0 ) in detecting the mean shifts of observations with normal distribution will be compared with that of the SLR tests T * (r) and T * (0), and that of the CUSUM-SLR test T C (c) ∧ T * (0) := min{T C (c), T * (0)} in the following Table  
These comparisons lead us to guess that there are some limiting relationships between T C (cg u,r ) and T * (r), and T C (c g u ) and T C (c) ∧ T * (0), respectively  Example 1  Let X 1 , X 2 ,      be mutually independent following the normal distribution N(0, 1) if there is no change  After the change-point τ = 1, the mean E µ (X k ) ( k ≥ 1 ) will change from v 0 = 0 to v = 0 1, 0 25, 0 5, 0 75, 1, 1 25, 1 5, 3  Here, we let
, where v 1 = 1 is a given reference value which for the CUSUM test is the magnitude of a shift in the process mean to be detected quickly  We conducted the numerical simulation based on 1,000,000 repetitions  The following Table lists the simulation results of the ARLs of the tests T C (c), T C (c g u ) for u = 1, 10, 10 2 , 10 3 , 10 4 , T * (0 0007), T C (c) ∧ T * (0) and T * (0) for detecting the mean shifts, where the mean shift 0 0 means that there is no change which corresponds to the in-control ARL 0 and all tests have the common ARL 0 ≈ 1000 except the test T * (0) which has ARL 0 = ∞ 
The values in the parameters are the standard deviations of the tests  From the last row in Table , it's a little surprising that though the ARL 0 of T * (0) is infinite, that is, E 0 (T * (0)) = ∞, the detection speed of T * (0) is faster than that of the CUSUM chart T C for all mean shifts, in particular, for detecting the small mean shift 0 1, the speed of T * (0) is only 7 47 which is very faster than the speed, 439, of the CUSUM test 
Moreover, both control charts T * (0 0007) and T C (11 9271) ∧ T * (0) not only have the nearly same detection performance as T * (0) but also can have the finite in-control ARL 0   Note particularly that when the number u in g u is taken from 0 to 1, 10, 10 2 , 10 3 , 10 4 , the detection speed of T C (c g u ) is getting faster and faster, approaching to that of T C (c) ∧ T * (0) 
This inspires us to prove the following theoretic results  Let τ = 1 and {X k , k ≥ 1} be an i i d  observations sequence with Theorem 2 shows that when the constant control limit c of the CUSUM test T C (c) is replaced with the observation-adjusted control limits {cg u,r ( Ẑn )} and {c g u ( Ẑn )} respectively, the corresponding two CUSUM-OAL tests {T C (cg u,r )} and {T C (c g u )} will converge to the optimal SLR test T * (r) and the CUSUM-SLR test T C (c) ∧ T * (0) as u → ∞, respectively 
In other words, the fastest alarm times that {T C (cg u,r )} and {T C (c g u )} can be reached are T * (r) and T C (c) ∧ T * (0), respectively  u ≥ 0} can be seen as two "long bridges" connecting T C (c) and T * (r), and T C (c) and T C (c) ∧ T * (0), respectively 

ESTIMATION AND COMPARISON OF ARL OF THE CUSUM-OAL TEST

In this section we will give an estimation of the ARLs of the following CUSUM-OAL test that can be written as where g( ) is a decreasing function, Ẑn (ac x] denotes the smallest integer greater than or equal to x  Here Ẑn (ac) is a sliding average of the statistics, Next we discuss on the the post-change probability distribution in order to estimate the ARLs of T C (cg) 
Usually we rarely know the post-change probability distribution P v of the observation process before it is detected  But the possible change domain and its boundary (including the size and form of the boundary) about v may be determined by engineering knowledge, practical experience or statistical data 
So we may assume that the region of parameter space V and a probability distribution Q on V are known  If we have no prior knowledge of the possible value of v after the change time τ , we may assume that v occurs equally on V , that is, the probability distribution Q is an equal probability distribution (or uniform distribution ) on V  
For example, let P v be the normal distribution and v = (µ, σ), where µ and σ denote the mean and standard deviation respectively, we can take the set V = {(µ, σ) : and Q is subject to the uniform distribution U(V ) on V if v occurs equally on V , where the numbers µ 1 , µ 2 , σ 1 and σ 2 are known  It means that we know the domain of the possible post-change distributions, P v , v ∈ V , i e , the boundary ∂V of the parameter space V is known 
Next we shall divide the parameter space V into three subsets V + , V 0 and V − by the Kullback-Leibler information distance  Let and are two Kullblak-Leibler information distances between P v , P v 0 and P v , P v 1   Since I(p|q) = 0 if and only if p = q, where p and q are two probability measures, it follows that
, it means that P v is closer to P v 0 than to P v 1 according to the Kullblak-Leibler information distance  There is a similar explanation for v ∈ V + or ∈ V 0   Suppose the post-change distribution P v and the function g(x) satisfy the following conditions: (I) The probability P v is not a point mass at E v (Z 1 ) and P v (Z 1 > 0) > 0 
(II) The moment-generating function h v (θ) = E v (e θZ 1 ) satisfies h v (θ) < ∞ for some θ > 0  (III) The function g(x) is decreasing, its second order derivative function g ′′ (x) is continuous and bounded, and there is a positive number x * such that g(x * ) = 0  ) and and therefore, Θ ′ (θ(u)) = −H(θ(u)) = −H(θ * v ) = 0, Θ ′ (θ(1/x)) > 0 for x > 1/u and Θ ′ (θ(1/x)) < 0 for x > 1/u 
Hence, there exists a positive number b defined in (??)  It can be seen, the main part of ARL v (T c (g)) will be an exponential function, square function, and linear function of c when the process {Z k : k ≥ 0} has no change or a "small change", a "medium change" and a "large change" from P v 0 to P v , respectively 
Here, the "small change" (v ∈ V − ) means that P v is closer to P v 0 than to P v 1 , i e , I(P v |P v 0 ) < I(P v |P v 1 ), and the "large change" is just the opposite  The "medium change" (v ∈ V 0 ) corresponds to In this paper, we will use another method to prove Theorem 3 since Wald's identity and the martingale method do not hold or can not work for showing the ARLs estimation of the test T c (g) when g is not constant 
Next we compare the detection performance of the CUSUM-OAL test (ARL v (T c ′ (g))) with that of the CUSUM test (ARL v (T C (c))) by using (??) in Theorem 4 1  ) when µ 0 < µ < 0 and for θ * v 0 > g(µ)/g(µ 0 ) when µ ≥ 0  This means that ARL v (T c (g)) can be smaller than ARL v (T C (c)) as long as g(µ)/g(µ 0 ) is small for all µ > µ 0  

NUMERICAL SIMULATION AND A REAL EX-AMPLE ILLUSTRATION

4 1 Numerical Simulation of ARLs for τ ≥ 1 By the simulation results of ARLs in Table , we see that the detection performance of T * (r), T C (c)∧T * (0), T * (0) and T C (c g u ) for large u is much better than that of the conventional CUSUM test T C for τ = 1  The following Table illustrates the simulation values of E τ i ,v and J ACE of nine tests in detecting two mean shifts v = 0 1 and v = 1 after six change-points, τ i , 1 ≤ i ≤ 6 with ARL 0 (T ) = E 0 (T ) ≈ 500 
Note that H v (θ) is a convex function and H ′ v (0) = µ < 0  This means that there is a unique positive number   It follows from (A 9) that for a large c  Taking θ ց θ * v and u ′ ց u, we have for a large c  Thus, by (A 11) we have as c → ∞  By the properties of exponential distribution, we have for a large c 
To prove the downward inequality of (A 10), let where b is defined in (??) and without loss of generality, we assume that b > a  Obviously, Let k = xcg(µ)  By Chebyshev's inequality, we have Since Hv (θ) and H v (θ) are two convex functions and Let m = tcg(µ)θ * v /bc for t > 0  By (A 13), (A 14), (A 15) and Theorem 5 1 in Esary, Proschan and Walkup (1967) we have
Finally, as c → +∞, where θ 0 > 0 satisfies h v (θ 0 ) = 1  Thus as c → ∞  This implies that for a large c  This completes the proof of (A 10)  Let v ∈ V 0   Let m 1 = (cg(0)) 2 /σ 2   It follows that Note that for a large c, where A = |g ′ (0)|/a, and , where Φ( ) is the standard normal distribution  Let m 2 = (cg(0)) 2 /(8σ 2 ln c) 
Note that as c → ∞, where the third inequality comes from Theorem 5 1 in Esary, Proschan and Walkup (1967)  Thus, we have Let v ∈ V + and let The uniform integrability of {T c (g)/c} for c ≥ 1, follows from the well-known uniform integrability of {T 0 /c} (see Gut (1988)) 
Context 2: Current address: Division of Brain Sciences, Department of Medicine, Imperial College London, London, United Kingdom 
In a variety of species, reduced food intake, and in particular protein or amino acid (AA) restriction, extends lifespan and healthspan  However, the underlying epigenetic and/or transcriptional mechanisms are largely unknown, and dissection of specific pathways in cultured cells may contribute to filling this gap  We have previously shown that, in mammalian cells, deprivation of essential AAs (methionine/cysteine or tyrosine) leads to the transcriptional reactivation of integrated silenced transgenes, including plasmid and retroviral vectors and latent HIV-1 provirus, by a process involving epigenetic chromatic remodeling and histone acetylation  Here we show that the deprivation of methionine/cysteine also leads to the transcriptional upregulation of endogenous retroviruses, suggesting that essential AA starvation affects the expression not only of exogenous non-native DNA sequences, but also of endogenous anciently-integrated and silenced parasitic elements of the genome  Moreover, we show that the transgene reactivation response is highly conserved in different mammalian cell types, and it is reproducible with deprivation of most essential AAs  The General Control Non-derepressible 2 (GCN2) kinase and the downstream integrated stress response represent the best candidates mediating this process; however, by pharmacological approaches, RNA interference and genomic editing, we demonstrate that they are not implicated  Instead, the response requires MEK/ERK and/or JNK activity and is reproduced by ribosomal inhibitors, suggesting that it is triggered by a novel nutrient-sensing and signaling pathway, initiated by translational block at the ribosome, and independent of mTOR and GCN2  Overall, these findings point to a general transcriptional response to essential AA deprivation, which affects the expression of non-native genomic sequences, with relevant implications for the epigenetic/transcriptional effects of AA restriction in health and disease 
Copyright: © 2018 De Vito et al  This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited 
Data Availability: All relevant data are within the paper and its Supporting Information files  RNAseq data are available in the ArrayExpress database under the accession number E-MTAB-6452 
Funding: This study was funded by the Ajinomoto Innovation Alliance Program, (AIAP; https://www ajinomoto com/en/rd/AIAP/index html#aiap) (to M V S and D G), which is a joint research initiative of Ajinomoto Co , Inc , Japan  One of the authors [M B ] is an employee of Ajinomoto Co , and his specific roles are articulated in the ‘author contributions’ section  The commercial funder provided support in the form of salary for author [M B ] and some of the necessary research materials (medium for cell culture), but did not have any additional role in the study design, data collection and analysis, or preparation of the manuscript, and the authors had unrestricted access to the data  Due to a confidentiality agreement, the commercial funder participated only in the decision to publish the data obtained during the study, without any restriction 
Competing interests: This study was funded by Ajinomoto Co , Inc , Japan and one of the authors [M B ] is an employee of this commercial funder  No other employment or consultancy relationships exist with the commercial funder, and no patents, products in development, or marketed products result from this study  The authors declare that no competing interests exist and that the commercial affiliation of one of the authors does not alter the adherence of authors to all PLOS ONE policies on sharing data and materials 
In animals, excessive, insufficient, or imbalanced nutrient availability is known to strongly impact on phenotype and health, both short and long-term, and across generations [1, 2]  In particular, studies in yeast, animal models and humans have shown that reduced food intake, reducing either overall calories, or only sugars, proteins, or even single amino acids (AA), such as Methionine (Met), may extend lifespan and healthspan, and reduce the risk of cancer and other age-related diseases [3–9]  In addition, fasting or specific AA deprivation have shown potential therapeutic applications, owing to their ability to directly reduce the growth of some tumor types [10, 11], sensitize cancer cells to chemo- or immunotherapy [12, 13], and allow efficient hematopoietic stem cell engraftment   However, little is known about the specific processes and molecular mechanisms mediating the roles of nutrient restriction in human health and longevity 
A properly balanced diet in metazoans contains optimal amounts of a subset of AA, which cannot be synthetized de novo and are therefore named essential amino acids (EAAs)  In humans these include Met, Histidine (His), Isoleucine (Ile), Leucine (Leu), Lysine (Lys), Phenylalanine (Phe), Threonine (Thr), Tryptophan (Trp), and Valine (Val), while a few others are considered as semi-essential, such as Glutamine (Gln) and Tyrosine (Tyr) [15, 16]  Consistently, EAA deprivation triggers a cell-autonomous adaptive response, characterized by extensive metabolic and gene expression modifications, implementing biosynthetic, catabolic, and plasma membrane transport processes, aimed at reconstituting the full AA complement [17, 18]  The best known and conserved pathways responding to AA deprivation are triggered by mechanistic Target of Rapamycin Complex 1 (mTORC1) and General amino acid Control Non-derepressible 2 (GCN2) protein kinases [15, 19, 20]  Activation of mTORC1 requires in particular the presence of Gln, Arg and Leu, but also Met , which activate the kinase through sensors mainly acting upstream of Rag GTPases at lysosomal membranes   In turn, mTORC1 promotes cell growth, proliferation and anabolism upon activation, and translational attenuation and autophagy upon inhibition [19, 20] 
By contrast, GCN2 is activated by deprivation of any individual EAA, by means of its histidyl-tRNA synthetase-related domain, which binds uncharged tRNAs accumulating during AA limitation [23, 24]  Upon activation, GCN2 phosphorylates and inhibits its only known downstream target, namely the eukaryotic Initiation Factor 2 α (eIF2α), thereby initiating the Integrated Stress Response (ISR)  This leads to attenuation of general translation, and induction of a transcriptional/translational program, aimed at increasing stress resistance and restoring cell homeostasis, by upregulating a specific subset of genes, including Activating Transcription Factor 4 (ATF4) and C/EBP-Homologous Protein (CHOP) [25–27]  Thus, inhibition of mTORC1 and activation of GCN2 by AA restriction cooperate to attenuate general translation at the initiation step, increase catabolism and turnover, and enhance stress resistance to promote adaptation   However, how these processes eventually induce protective mechanisms against the alterations associated with aging, which include pervasive epigenetic and transcriptional changes [28, 29], remains largely unknown 
We previously reported the unexpected observation that prolonged deprivation of either Tyr, or of both Methionine and Cysteine (Met/Cys), triggers the selective and reversible reactivation of exogenous transcriptional units, including plasmids, retroviral vectors and proviruses, integrated into the genome and transcriptionally repressed by defensive mechanisms against non-native DNA sequences [30, 31]  This phenomenon was observed both in HeLa epithelial and ACH-2 lymphocytic human cells, and was independent of the transgene or provirus (Ocular Albinism type 1, OA1; Green Fluorescent Protein, GFP; Lysosomal-Associated Membrane Protein 1, LAMP1; Human Immunodeficiency Virus-1, HIV-1), or of the exogenous promoter driving their transcription, either viral (cytomegalovirus, CMV; Long Terminal Repeat, LTR) or human (Phospho-Glycerate Kinase 1, PGK1; Elongation Factor-1α, EF-1α)   Furthermore, this transgene reactivation response was not reproduced by serum starvation, activation of p38, or pharmacological inhibitors of mTOR (PP242 or rapamycin), sirtuins and DNA methylation  By contrast, it was induced by pan histone deacetylase (HDAC) inhibitors, and by selective inhibitors of class II HDACs   Consistently, we found that the mechanism responsible involves epigenetic modifications at the transgene promoter, including reduced nucleosome occupancy and increased histone acetylation, and is mediated in part by reduced expression of a class II HDAC, namely HDAC4  
These findings indicate that AA deprivation induces a specific epigenetic and transcriptional response, affecting the expression of newly-integrated exogenous transgenes and proviruses, and suggesting that endogenous sequences sharing similar structural and functional features may represent a transcriptional target as well [30, 31]  In particular, transposable elements, such as LTR-retrotransposons (or endogenous retroviruses, ERVs), are genomic “parasites” anciently-integrated into the genome, and silenced by epigenetic mechanisms of mammalian cells against the spreading of mobile elements, eventually becoming "endogenized" during evolution [32, 33]  This raises the question of whether their expression is also sensitive to AA restriction  In addition, it remains unclear whether or not the transgene reactivation response is related to specific AA deprivations, and most importantly which is the AA sensing/signaling pathway involved, in particular whether the GCN2 kinase is implicated  Thus, here we used the reactivation of silenced transgenes in cultured cells, as a model to investigate a novel molecular pathway induced by imbalanced EAA starvation, implicated in the epigenetic/transcriptional regulation of exogenous non-native DNA sequences and possibly of other endogenous anciently-integrated genomic elements 
HeLa human epithelial carcinoma, HepG2 human hepatocellular carcinoma and C2C12 mouse skeletal muscle cells were maintained in DMEM containing glutaMAX (Invitrogen) and supplemented with 10% FBS (Sigma), 100 U/ml penicillin G (Invitrogen), 100 mg/ml streptomycin (Invitrogen), at 37°C in a 5% CO2 humidified atmosphere  Cell lines carrying integrated and partially silenced transgenes were also maintained in 600–1000 μg/ml G418 
The C2C12 cell line was provided by ATCC  HeLa and HepG2 cells were obtained by Drs  F  Blasi and G  Tonon at San Raffaele Scientific Institute, Milan, Italy, respectively, and were authenticated by Short Tandem Repeat (STR) profiling, using the Cell ID System kit (Promega), according to the manufacturer’s instructions  Briefly, STR-based multiplex PCR was carried out in a final volume of 25 μL/reaction, including 5 μL Cell ID Enzyme Mix 5X, 2 5 μL Cell ID Primer Mix 10X and 3 ng of template DNA  The thermal cycling conditions were: 1 cycle at 96°C for 2 min, followed by 32 cycles at 94°C for 30 sec, 62°C for 90 sec, and 72°C for 90 sec, and 1 cycle at 60°C for 45 sec  The following STR loci were amplified: AMEL, CSF1PO, D13S317, D16S539, D21S11, D5S818, D7S820, TH01, TPOX, vWA  Fragment length analysis of STR-PCR products was performed by Eurofins Genomics, using standard procedures of capillary electrophoresis on the Applied Biosystems 3130 XL sequencing machine, and assessment of the STR profile was performed at the online STR matching analysis service provided at http://www dsmz de/fp/cgi-bin/str html 
Stable cell clones, expressing myc-tagged human OA1 (GPR143) or GFP transcripts, were generated using pcDNA3 1/OA1myc-His or pcDNA3 1/EGFP vectors   Briefly, HeLa, HepG2 and C2C12 cells were transfected using FuGENE 6 (Roche) and selected with 800, 1000, and 650 μg/ml of G418 (Sigma), respectively, which was maintained thereafter to avoid loss of plasmid integration  G418-resistant clones were isolated and analyzed for protein expression by epifluorescence and/or immunoblotting 
Full DMEM-based medium, carrying the entire AA complement, and media deprived of Met/Cys (both AAs), Met (only), Cys (only), Alanine (Ala), Thr, Gln, Val, Leu, Tyr, Trp, Lys and His were prepared using the Nutrition free DMEM (cat #09077–05, from Nacalai Tesque, Inc , Kyoto, Japan), by adding Glucose, NaHCO3, and either all 20 AAs (for full medium) or 18–19 AAs only (for deprivations of two-one AAs)  Single AAs, Glucose, and NaHCO3 were from Sigma  Further details and amounts utilized are indicated in S1 Table  All media were supplemented with 10% dialyzed FBS (Invitrogen), 100 U/ml penicillin G (Invitrogen), 100 mg/ml streptomycin (Invitrogen), and G418 as required  HBSS was from Invitrogen  Cells were seeded at 10–30% of confluency; cells to be starved for 48 h were plated 2–3 times more confluent compared to the control  The following day, cells were washed and cultured in the appropriate medium, with or without EAA, for 24–48 h 
L-Histidinol (HisOH), PP242, Integrated Stress Response Inhibitor (ISRIB), SP600125, Cycloheximide (CHX) were from Sigma; Salubrinal was from Tocris Bioscience; U0126 was from Promega  Drugs were used at the following final concentrations: HisOH at 4–16 mM; PP242 at 1–3 μM; ISRIB at 100 nM; SP600125 at 20 μM in HepG2 cells and 50 μM in HeLa cells; Cycloheximide (CHX) at 50 ug/ml in HepG2 cells and 100 ug/ml in HeLa cells; Salubrinal at 75 μM; U0126 at 50 μM  Vehicle was used as mock control  Treatments with drugs to be tested for their ability to inhibit transgene reactivation (ISRIB, SP600125 and U0126) were initiated 1h before the subsequent addition of L-Histidinol (ISRIB) or the subsequent depletion of Met/Cys (SP600125 and U0126) 
Total RNA was purified using the RNeasy Mini kit (Qiagen), according to manufacturer’s instructions  RNA concentration was determined by Nanodrop 8000 Spectrophotometer (Thermo Scientific)  Equal amount (1 μg) of RNA from HeLa, HepG2 and C2C12 cells was reverse transcribed using the SuperScript First-Strand Synthesis System for RT-PCR (Invitrogen) using oligo-dT as primers, and diluted to 5 ng/μl  The cDNA (2 μl) was amplified by real-time PCR using SYBR green Master Mix on a Light Cycler 480 (Roche), according to manufacturer’s instructions  The thermal cycling conditions were: 1 cycle at 95°C for 5 min, followed by 40–45 cycles at 95° for 20 sec, 56° for 20 sec and 72° for 20 sec  The sequences, efficiencies and annealing temperatures of the primers are provided in S2 Table  Data were analyzed with Microsoft Excel using the formula EtargetΔct target (control-sample) /EreferenceΔct reference (control-sample)   Reference genes for normalizations were ARPC2 (actin-related protein 2/3 complex, subunit 2) for HeLa and HepG2 cells; and Actb (actin beta) for C2C12 cells, unless otherwise indicated 
siRNA (Mission esiRNA, 200 ng/μL; Sigma) against ATF4 and GCN2 were designed against the targeted sequences NM_001675 and NM_001013703, respectively  Cells seeded in 6-well plates were transfected with 1 μg of siRNAs and 5 μL of Lipofectamine 2000 (Invitrogen), following manufacturer’s instructions, at day 1 post-plating for ATF4 and at day 1 and 2 post-plating for GCN2  At day 2 (ATF4) or 3 (GCN2) post-plating, cells were washed and cultured in medium in the absence or presence of HisOH 4 mM for 6 h  siRNAs against RLuc (Sigma), targeting Renilla Luciferase, were used as negative control  For CRISPR/Cas9 experiments, we used the “all-in-one Cas9-reporter” vector, expressing GFP (Sigma), which is characterized by a single vector format including the Cas9 protein expression cassette and gRNA (guide RNA)  GFP is co-expressed from the same mRNA as the Cas9 protein, enabling tracking of transfection efficiency and enrichment of transfected cells by fluorescence activated cell sorting (FACS)  The human U6 promoter drives gRNA expression, and the CMV promoter drives Cas9 and GFP expression  The oligonucleotide sequences for the three gRNAs targeting GCN2 exon 1 or 6 are listed in S2 Table  We transfected HeLa and HepG2 cells with these plasmids individually (one plasmid one guide) and sorted the GFP-positive, transfected cells by FACS  Screening GCN2-KO clones was performed by western blotting  In the case of HepG2-OA1 cells, two rounds of selection were necessary to obtain three GCN2-KO clones by using a guide RNA against exon 1  Compared to the original HepG2-OA1 cell line and to the clone resulting from the first round of selection (185#27), the selected clones E23, F22 and F27 showed a very low amount—if any—of residual GCN2 protein (see results) 
Genomic DNA of HeLa and HepG2 cells was purified using DNeasy Blood and Tissue kit (Qiagen), according to the manufacturer’s instructions  DNA concentration was determined by Nanodrop 8000 Spectrophotometer (Thermo Scientific)  PCR conditions for amplification of GCN2 exon 1 and 6 were as follows: 1 cycle at 94°C for 5 min, followed by 35 cycles at 94°C for 40 sec, 56°C for 40 sec, and 72°C for 40 sec; and a final extension step of 5 min at 72°C  The primer sequences are provided in S2 Table 
For OA1, western immunoblotting was carried out as described   For GCN2, cells were lysed in RIPA buffer, boiled at 95°C for 5 min and resolved on a 7 5% polyacrylamide gel; immunoblotting was then performed following standard procedures  Primary Abs were as follows: anti-human OA1, previously developed by our group in rabbits ; anti-GCN2 (Cell Signaling, Cat  #3302) 
Statistical analyses were performed using Microsoft Excel for Mac (version 15 32, Microsoft) for Student’s t-test; or GraphPad Prism (version 5 0d for Mac, GraphPad Software, Inc ) for one-way analysis of variance (ANOVA), followed by Dunnett’s or Tukey’s multiple comparisons post-tests  T-test was used when only two means, typically sample versus control, were compared, as specified in the figure legends  One way ANOVA was used for multiple comparisons, followed by either a Dunnett’s (to compare every mean to a control mean), or a Tukey’s (to compare every mean with every other mean) post-test, by setting the significance level at 0 05 (95% confidence intervals)  Both tests compare the difference between means to the amount of scatter, quantified using information from all the groups  Specifically, Prism computes the Tukey-Kramer test, allowing unequal sample sizes  P values in Figures are generally referred to comparison between a sample and the control (full medium/mock), and are indicated as follows: *P<0 05, **P<0 01, ***P<0 001  Comparisons not involving the control are similarly indicated, by a horizontal line at the top of the graphs, encompassing the two samples under analysis  Additional details regarding the specific experiments are reported in the Figure Legends 
To examine the expression behavior of genomic repeats upon AA starvation, we performed a transcriptomic analysis taking advantage of an intramural sequencing facility  HeLa-OA1 cells were cultured in normal medium (for 6-30-120 hours) or in absence of Met/Cys (for 6-15-30-72-120 hours)  Total RNA was prepared using Trizol (Sigma) to preserve transcripts of both small and long sizes (from Alu, of about 0 3 kb, to Long Interspersed Nuclear Elements, LINEs, and ERVs, up to 6–8 kb long), DNase treated to avoid contamination of genomic DNA, and processed for NGS sequencing by Ovation RNA-Seq System V2 protocol and HiSeq 2000 apparatus  Raw sequence data (10–20 M reads/sample) were aligned to the human genome (build hg19) with SOAPSplice   Read counts over repeated regions, defined by RepeatMasker track from UCSC genome browser , were obtained using bedtools suite   Normalization factors and read dispersion (d) were estimated with edgeR , variation of abundance during time was analyzed using maSigPro package , fitting with a negative binomial distribution (Θ = 1/d, Q = 0 01), with a cutoff on stepwise regression fit r2 = 0 7  Read counts were transformed to RPKM for visualization purposes  The expression of the OA1 transgene and HDAC4, which are progressively up- and down-regulated during starvation, respectively , were used as internal controls 
For genomic repeat analysis, reads belonging to repetitive elements were classified according to RepeatMasker and assigned to repeat classes (total number in the genome = 21), families (total number in the genome = 56) and finally subfamilies (total number in the genome = 1396), each including a variable number of genomic loci (from a few hundred for endogenous retroviruses, up to several thousand for Alu)  Repeat subfamilies were then clustered according to their expression pattern in starved vs control cells, by maSigPro using default parameters, and repeats classes or families that are significantly enriched in each cluster, compared to all genomic repeats, were identified by applying a Fisher Exact test (using scipy stats, a statistical module of Python)  Alternatively, differentially expressed repeat subfamilies were identified by averaging three time points of starvation (15-30-72 h) and controls  Repeats significantly up- or downregulated (104 and 77, respectively) were selected based on a P value <0 05 (unpaired two-tailed Student’s t-test, assuming equal variance), and analyzed for their class enrichment by a Fisher Exact test as described above 
For gene set enrichment analysis of Met/Cys deprived vs control HeLa cells, differentially expressed genes were selected considering three time points of starvation (15-30-72 h) and controls, based on a P value <0 05 (unpaired two-tailed Student’s t-test, assuming equal variance) and a fold change >2  This led to a total of 2033 differentially expressed genes, 996 upregulated and 1037 downregulated  The enrichment analysis was performed separately for up and down regulated genes, or with all differentially expressed genes together (both), using the KEGG database  The analysis was performed with correction for the background of all expressed genes (about 13600 genes showing an average expression over 3 starvation and 3 control samples of at least 5 counts) and by using default parameters (adjusted P value and q-value cut-off of <0 05 and 0 2, respectively)  Differentially expressed genes were also selected considering all starvation time points, as with genomic repeats, by maSigPro using default parameters, and a fold change of at least 1 5, leading to similar enrichment results (not shown)  RNAseq gene expression data are available in the ArrayExpress database under the accession number E-MTAB-6452 
To provide proof-of-principle that AA starvation may affect the expression of transposable elements, we performed an RNAseq analysis of the previously described HeLa-OA1 cells, carrying an integrated and partially silenced OA1 transgene   Since the reactivation of the transgene by starvation is a progressive phenomenon , we performed a time-course experiment, where each time point represents one biological sample, rather than a biological triplicate of a single time point  To this aim, cells were cultured either in normal medium, or in absence of Met/Cys for different time points (6-15-30-72-120 hours), resulting in the progressive upregulation of the OA1 transgene during starvation (Fig 1A and 1B), consistent with previously published results   The expression of genomic repeats was determined according to RepeatMasker annotation and classification into classes, families, and subfamilies  Repeat species were then subjected to differential expression and enrichment analyses in starved vs control conditions  Out of 1396 annotated repeat subfamilies, 172 species displayed a differential expression profile during starvation 
Fig 1
Context 3: The future of mobile CPUs, part 1: Today’s fork in the road | Ars Technica
2013 may be a big year for the evolution of smartphones and tablets 
Mobile computing's rise from niche market to the mainstream is among the most significant technological trends in our lifetimes  And to a large extent, it's been driven by the bounty of Moore’s Law—the rule that transistor density doubles every 24 months  Initially, most mobile devices relied on highly specialized hardware to meet stringent power and size budgets  But with so many transistors available, devices inevitably grew general-purpose capabilities  Most likely, that wasn't even the real motivation  The initial desire was probably to reduce costs by creating a more flexible software ecosystem with better re-use and faster time to market  As such, the first smartphones were very much a novelty, and it took many years before the world realized the potential of such devices  Apple played a major role by creating innovative smartphones that consumers craved and quickly adopted 
To some extent, this is where we still stand today  Smartphones are still (relatively) expensive and primarily interesting to the developed world  But over the next 10 years, this too will change  As Moore’s Law rolls on, the cost of a low-end smartphone will decline  At some point, the incremental cost will be quite minimal and many feature phones of today will be supplanted by smartphones  A $650 unsubsidized phone is well beyond the reach of most of the world compared to a $20 feature phone, but a $30 to $40 smartphone would naturally be very popular 
In this grand progression, 2013 will certainly be a significant milestone for mobile devices, smartphones and beyond  It's likely to be the first year in which tablets out-ship notebooks in the US  And in the coming years, this will lead to a confluence of high-end tablets and ultra-mobile notebooks as the world figures out how these devices co-exist, blend, hybridize, and/or merge 
Against this backdrop, in this two-part series, we'll explore the major trends and evolution for mobile SoCs  More importantly, we'll look to where the major vendors are likely going in the next several years 
Tablet and phone divergence
While phones and tablets are mobile devices that often share a great deal of software, it's becoming increasingly clear the two are very different products  These two markets have started to diverge and will continue doing so over time 
From a technical perspective, smartphones are far more compact and power constrained  Smartphone SoCs are limited to around 1W, both by batteries and by thermal dissipation  The raison d’etre of a smartphone is connectivity, so a cellular modem is an absolute necessity  For the cost sensitive-models that make up the vast majority of the market, the modem is integrated into the SoC itself  High-end designs favor discrete modems with a greater power budget instead  The main smartphone OSes today are iOS and Android, though Windows is beginning to make an appearance (perhaps with Linux or BlackBerry on the horizon)  Just as importantly, phone vendors like HTC must pass government certification and win the approval of carriers  There is very much a walled-garden aspect, where carriers control which devices can be attached to their networks, and in some cases devices can only be sold through a certain carrier  The business model places consumers quite far removed from the actual hardware 
In contrast, tablets are far more akin to the PC both technically and economically  The power budget for tablet SoCs is much greater, up to 4W for a passively cooled device and as high as 7-8W for systems with fans  This alone means there is a much wider range of tablet designs than smartphones  Moreover, the default connectivity for tablets is Wi-Fi rather than a cellular modem  The vast majority of tablets do not have cellular modems, and even fewer customers actually purchase a wireless data plan  As a result, cellular modems are almost always optional discrete components of the platform  The software ecosystem is relatively similar, with Microsoft, Apple, and Google OSes available  Because tablets eschew cellular modems, the time to market is faster, and they are much more commonly sold directly to consumers rather than through carriers  In terms of usage models, tablets are much more PC-like, with reasonable-sized screens that make games and media more attractive 
Looking forward, these distinctions will likely become more pronounced  Many tablets today use high-end smartphone SoCs, but the difference in power targets and expected performance is quite large  As the markets grow in volume, SoCs will inevitably bifurcate to focus on one market or the other  Even today, Apple is doing so, with the A6 for phones and the larger A6X for tablets  Other vendors may need to wait a few years to have the requisite volume, but eventually the two markets will be clearly separate 
Horizontal business model evolution
Another aspect of the mobile device market that is currently in flux and likely to change in the coming years is the business model for the chip and system vendors  Currently, Apple is the only company truly pursuing a vertically integrated model, where all phones and tablets are based on Apple’s own SoC designs and iOS  The tight integration between hardware and software has been a huge boon for Apple, and it has yielded superb products 
Samsung is one of the few others companies that takes a vertically integrated approach to phones and tablets, although in truth its strategy seems to be ambivalent on that point  Unlike Apple, Samsung’s SoCs are readily available to third parties, and some Samsung devices, such as the S7562 Galaxy S Duos, use SoCs from competitors  More recently though, there has been a trend of Samsung devices using Samsung SoCs, at least for the premier products  For the moment, Samsung’s approach is best characterized as a hybrid, particularly as the company lacks a bespoke OS 
The rest of the major SoC vendors (e g , Intel, Qualcomm, Nvidia, TI, Mediatek, etc ) have stayed pretty far away from actual mobile devices  These companies tend to focus on horizontal business models that avoid competing with customers or suppliers 
In the long term, mobile devices are likely to evolve similarly to the PC and favor a horizontal business model  The real advantage is one of flexibility; as costs drop and the market expands, it will be increasingly necessary for vendors like HTC to offer a wide range of phones based on radically different SoCs  While a vertically integrated company like Apple can focus and maintain leadership in a specific (and highly lucrative) niche, it would be very difficult to expand in many growing areas of the market  The differences between an iPhone 6 and a $20 feature phone are tremendous and would be very difficult for a single company to bridge 
However, SoC vendors will attempt to reap the benefits of vertical integration by providing complete reference platforms to OEMs  Conceptually, this is a form of "optional" system integration, where the phone vendor or carrier can get the entire platform from the SoC supplier  This has the principal advantages of reducing time to market while also providing a baseline quality and experience for consumers  Currently, this approach has mostly been tested in emerging markets, but it's likely to become more common over time  There is a crucial distinction between reference platforms and vertical integration  Namely, OEMs can always choose to customize a platform to differentiate, and the SoC vendor avoids dealing with consumers directly  Typically, most of the customization is in terms of software on top of a base operating system 
Quote:Moreover, that will make the transition to a 10nm node even more difficult, as the foundries will have to move from 20nm interconnects to 10nm interconnects and skip a generation The advances in technology lately allowing components on such a small scale to even be envisioned, much less planned for, are truly amazing 
Off topic: show
I present the first generation 'non-technical' rock:
I don't think your horizontal market development theory is supported by facts  Samsung and Apple are more vertically oriented than their competition, for starters  I know this article is narrowly focused on the hardware, but MS and Intel getting into hardware, Amazon getting into hardware, Google buying Moto, this is all vertical integration  How can you support the idea that this trend will be reversed with no real justification? I'm sure mobile chips will continue to specialize, but I don't think this means what you think it means  Automobile companies started making their own engines and with rare exceptions, never went back to being more horizontal  Same with retail and their store brands  Same with cloud companies and their servers  Same with mobile companies and their OSs  The horizontal market of PCs created by long-lasting standards and loose hegemony is the exception, not the norm 
Why wouldn't the foundries be able close the process gap with Intel? Is it a matter of money? Scale?
I'm not so sure about several things:1- Moore's law's relevance  Moore's Law is about ICs  ICs are not as big a part of mobile computers as they are of desktops, even of laptops: screens, batteries, radios are a huge part of tablets' and phones' costs, as opposed to the bare SoC + RAM 2- The tablet vs phone dichotomy  For some reason (probably price insensitivity due to subsidies), Phones have a tendency to be more powerful than Tablets, ie phone SoCs are more than good enough for tablets  Since the OS and peripherals are the same, it makes more sense to design and build just one type of SoC, and just disable the phone-modem part of it (even the other radios are still required: BT, Wifi, GPS   ), same as Intel disable cache and cores for their entry-level CPUs  Once you're fabbing a SoC, it makes more sense to make more of the same than to setup a separate run of a cut-down SoC on an older process, unless volumes are huge  We might still be getting previous-generation, well amortized SoCs in cheaper tablets, though 3- On the contrary, I see a tablet and phone convergence (the ugly phablet)  I'm patiently waiting for the new 6"+ phones to replace my Nook Color and Galaxy Note 1 with a single device 4- The advantage of diversity ? Software is becoming ever more important than hardware  Multiplying SoCs means multiplying product development costs, making support and updates more difficult    Again, unless volumes are huge, OEMs are probaly better off going the way of the car industry and using modular "platforms" housed in different chassis with various screen sizes, keyboards, radios, digitizers   I'm wondering why the "single device" trend does not figure in your analysis  Is it stillborn ? Does it have no impact nor dependency on/with SoCs ?
Samsung has its own bespoke OS: Bada and it is used on an extensive line of devices  I think there are numbers somewhere that it outsold Windows Phone 7 for a time 
gypsumfantastic wrote:Why wouldn't the foundries be able close the process gap with Intel? Is it a matter of money? Scale?First mover advantage 
SoC? System on a Chip I guess?
You're way off on the Moore's Law/cost of smartphones point  The processors used in today's high-end smartphones are already cheap, around $25  And there are less expensive options if you want a lower end product  In fact, the hardware in the whole smartphone is relatively cheap  Analyst's estimate the Z10's materials cost around $160, the iPhone 5 around $140  They're using expensive glass and metals, then there's the battery, memory, etc  which means the processor is a small factor of the cost And then there's the jump from $140 in materials to the unsubsidized costs  The reason these phones cost $650 is because of the high margins these companies are able to get and the high cost of hardware design and/or software development  But the point is that making the processors 4 times better/cheaper isn't going to change the economics of the smartphone  What will change the economics is commoditized designs and software and cheaper materials all around  Then you'll have a $50 smartphone that's decent 
Last edited by ggeezz on Wed Feb 13, 2013 9:17 am
bigterp wrote:SoC? System on a Chip I guess?Yup 
gypsumfantastic wrote:Why wouldn't the foundries be able close the process gap with Intel? Is it a matter of money? Scale?Money and momentum, the x86 market is a huge money maker for Intel so it is able to recoup its huge investments for advanced foundries 
Quote:Currently, the only products using 3D integration are FPGAs from Xilinx,Doesn't Sony use it in the PS Vita? I thought I read somewhere that they had the CPU, main memory (2 dies) and video memory, so 4 dies in total, sitting on top of each other all on the same chip 
renoX wrote:gypsumfantastic wrote:Why wouldn't the foundries be able close the process gap with Intel? Is it a matter of money? Scale?Money and momentum, the x86 market is a huge money maker for Intel so it is able to recoup its huge investments for advanced foundries Exactly and I would clarify that it's all about margins, the difference between what it costs to make a chip and what it sells for  The margins for desktop and server processors is huge because a) the whole product is expensive so $200 to $1000 for the chip is acceptable, and b) Intel has huge advantages in that space and little competition So Intel can afford to do the R&D to stay ahead of the curve and keep their position  When your smartphone chip sells for $25 you can't do the R&D to leapfrog a company that sells Xeons for $1000 and Core i5's for $200 
I am happy to see Kanter here at Ars, I like his writing and he maintains Real World Tech, where Linus Torvalds often shows up to comment on CPU arch and other interesting topics 
ggeezz wrote:renoX wrote:gypsumfantastic wrote:Why wouldn't the foundries be able close the process gap with Intel? Is it a matter of money? Scale?Money and momentum, the x86 market is a huge money maker for Intel so it is able to recoup its huge investments for advanced foundries Exactly and I would clarify that it's all about margins, the difference between what it costs to make a chip and what it sells for  The margins for desktop and server processors is huge because a) the whole product is expensive so $200 to $1000 for the chip is acceptable, and b) Intel has huge advantages in that space and little competition So Intel can afford to do the R&D to stay ahead of the curve and keep their position  When your smartphone chip sells for $25 you can't do the R&D to leapfrog a company that sells Xeons for $1000 and Core i5's for $200 Spot on Intel are able to piggyback other development efforts off the highly lucrative mainstream x86 market which generates the huge sums of money to fund their amazing fab technology The question for the future is how the economics will stack up when overall device costs fall significantly and there is a big downward pressure on SoC prices  In that situation, can Intel still justify bringing their A-game to a market where products are essentially commoditised and you have processors selling for a only a few dollars each?The lesson from their previous involvement in the DRAM market is that they probably won't want to be involved because there isn't enough money to be made to justify manufacturing phone SoCs on a cutting edge, or near cutting edge process  In that scenario, Intel may not totally abandon the market but they might just stick to manufacturing SoCs on nodes that are a step or two behind the state of the art



ground_truth: The three subsets are V+, V0, and V-, determined by the Kullback-Leibler information distance.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: Is the ISR necessary for transgene reactivation?

Read the following text and answer briefly.

Context 2:  Exogenous transgene and endogenous retroviruses are upregulated in Met/Cys-deprived HeLa cells 
(A,B) Exogenous integrated transgene (OA1) mRNA abundance in HeLa-OA1 cells, cultured in Met/Cys-deprived medium for the indicated time points, and analyzed by RNAseq (A), or RT-qPCR (B), compared to full medium  Data represent RPKM (A), or mean ± SD of 2 technical replicates, expressed as fold change vs  control (full medium at 6 h = 1) (B)  (C) Clustering of 172 genomic repeat subfamilies, differentially expressed upon starvation, according to their expression profile  (D) Class distribution of repeat subfamilies belonging to differential expression clusters, compared to all genomic repeat subfamilies (first column)  Class DNA includes DNA transposons; SINE includes Alu; LINE includes L1 an L2; LTR includes endogenous retroviruses and solitary LTRs; Satellite includes centromeric acrosomal and telomeric satellites; Others includes SVA, simple repeats, snRNA, and tRNAs  LTR-retroelements are significantly enriched among repeats that are upregulated upon starvation, while LINEs are significantly enriched among repeats that are downregulated  *P<0 05, ***P<0 001 (Fisher exact test) 
As shown in Fig 1C, the clustering of differentially expressed repeats, according to their expression pattern, reveals profiles comparable to the behavior of the transgene in the same conditions, i e  upregulation upon starvation and no change in regular medium (Cluster 1 and 2)  In particular, Cluster 1 contains sequences that, similarly to the OA1 transgene, are progressively upregulated upon starvation (Fig 1A and 1C) , while Cluster 2 contains sequences that are upregulated at early time points  Interestingly, repeat families that are significantly enriched in these two clusters belong mostly to the group of LTR-retrotransposons, including ERV1, ERVK, ERVL, ERVL-MaLR and other LTR sequences (Fig 1D; S1A and S2A Figs)  By contrast, DNA transposons (such as TcMar-Tigger) and L1 non-LTR retrotransposons are enriched among repeats that are downregulated during starvation, particularly at late time points (Clusters 3 and 4) (Fig 1D; S1A and S2A Figs)  Consistent results were obtained by selecting significantly up- or downregulated genomic repeats (overall 181 species), based on their average expression out of three time points of starvation (15-30-72 h, when the transgene upregulation is more homogeneous) and controls, and on a P value <0 05 (S1B and S2B Figs)  These findings suggest that EAA starvation induces genome-wide effects involving repetitive elements, and that—among major repeat classes—it upregulates in particular the expression of ERVs 
In addition, to obtain a general overview of main gene pathways changing their expression together with the transgene during AA starvation, we performed gene expression and enrichment analyses of regular genes, by considering three time points of starvation (15-30-72 h) and controls  Differentially expressed genes were selected based on a P value <0 05 and a fold change between means of at least 2, and analyzed with the EnrichR tool   As shown in Fig 2 and S1 File, enrichment analyses against the KEGG and Reactome databases reveals a predominance of downregulated pathways, namely ribosome and translation, proteasome, AA metabolism, oxidative phosphorylation and other pathways related to mitochondrial functions, which are affected in Huntington, Alzheimer and Parkinson diseases (http://www genome jp/kegg/pathway html)  In particular, a large fraction of ribosomal protein mRNAs is downregulated upon Met/Cys starvation (Fig 2A and 2C; S1 File), consistent with the notion that their genes–despite being scattered throughout the genome—are coordinately expressed in a variety of conditions   This reduced expression may depend on multiple pathways that control ribosome biogenesis in response to external stimuli, including the downregulation of Myc activity , the downregulation of mTORC1 [42, 44], or possibly the activation of the ISR, as described in yeast   By contrast, upregulated genes show a significant enrichment for transcription and gene expression (Fig 2B)  Similar results were obtained by the Gene Ontology Biological Process (GO-BP) database (S1 File), overall indicating a general downregulation of translation and metabolism, and upregulation of transcription, during the time interval of Met/Cys starvation corresponding to the transgene upregulation 
Fig 2  Gene set enrichment analysis of Met/Cys-deprived HeLa cells 
Differentially expressed genes between three time points of starvation (15-30-72 h) and controls were selected based on a P value <0 05 and a fold change of at least 2, leading to a total of 996 upregulated, and 1037 downregulated genes  The enrichment analysis was performed separately for up and down regulated genes, using the EnrichR tool and the KEGG (A) and REACTOME (B, C) databases  Ranking is based on the combined score provided by EnrichR, and categories are displayed up to 20 items with an Adjusted P value <0 05  No significant categories were found with upregulated genes against the KEGG database  All data are shown in S1 File  The enrichment analysis using all differentially expressed genes together did not reveal any additional enriched process 
To characterize the pathway leading to the reactivation of silenced transgenes, we used HeLa-OA1 and HeLa-GFP cells, as described   In addition, to test cell types relevant for AA metabolism, such as liver and muscle, we generated clones of HepG2 human hepatoma and C2C12 mouse skeletal muscle cells, stably transfected with plasmids for OA1 and GFP transgenes, respectively (HepG2-OA1 and C2C12-GFP cells; endogenous OA1 is not expressed in any of these cell types)  In all cases, the integrated transgenes are under the control of the CMV promoter in the context of a pcDNA3 1 plasmid, are partially silenced, and can be efficiently upregulated by HDAC inhibitors (trichostatin A, TSA; ref   and S3A, S3B and S4A Figs), indicating that their expression is controlled at least in part by epigenetic mechanisms, as previously described  
To establish whether the reactivation response results from the shortage of specific AAs only, such as Met/Cys, or it is triggered by any AA deprivations, we cultured HeLa-OA1, HeLa-GFP, HepG2-OA1 and C2C12-GFP cells for 24–48 hours with a battery of media deprived of EAAs or semi-EAAs, including Met/Cys, Thr, Gln, Val, Leu, Tyr, Trp, Lys, and His  As negative controls, cells were cultured in full medium, carrying the entire AA complement, and in a medium deprived of Ala, a non-essential AA  The expression of the transgene transcript was then evaluated by RT-qPCR  As shown in Fig 3, and in S3C and S4B Figs, most EAA-deficiencies induced reactivation of the OA1 or GFP transgenes in all four cell lines, with the notable exception of Trp deprivation, which consistently resulted in no or minimal reactivation of the transgenes  Indeed, despite some variability, Met/Cys deficiency, but also Thr, Val, Tyr, and His deprivation always gave an efficient response, while Leu, Gln and Lys elicited evident responses in some cases, but not in others  Depletion of Phe gave results comparable to Tyr deprivation, however it significantly altered multiple reference genes used for normalization and therefore was eventually omitted from the analysis (not shown)  Finally, in the above experiments we used a combined Met/Cys deficiency, to avoid the potential sparing of Met by Cys  and for consistency with our previous studies   Nevertheless, the analysis of single Met or Cys starvation, both at the protein and transcript levels, revealed an exclusive role of Met deprivation in transgene reactivation, consistent with the notion that Cys is not an EAA (S3D and S3E Fig) 
Fig 3  EAA deprivation induces reactivation of silent transgenes in HeLa and HepG2 cells 
Relative transgene (OA1) and CHOP mRNA abundance in HeLa-OA1 (A) and HepG2-OA1 (B) cells, cultured in various AA-deprived media for 48 h and 24 h, respectively, compared to full medium  Mean ± SEM of 3 independent experiments  Data are expressed as fold change vs  control (full medium = 1)  *P<0 05, **P<0 01, ***P<0 001 (one way ANOVA, followed by Dunnett’s post-test vs  full medium) 
Collectively, these results indicate that transgene reactivation by EAA starvation is reproducible with most EAAs, shared by different cell types (epithelium, liver, and skeletal muscle), and conserved in different mammalian species (human, mouse) 
mTORC1 inhibition and GCN2 activation trigger the best-known signaling pathways responding to AA starvation   We previously showed that inhibition of mTORC1 is not sufficient to reproduce transgene reactivation in HeLa cells   By contrast, the involvement of GCN2 and the ISR, including the downstream effectors ATF4 and CHOP, has never been tested  In addition, this pathway has been typically assessed in transient assays, lasting for a few hours, which may not be comparable with the prolonged starvation conditions necessary to reactivate the transgene expression (at least 15–24 h)  Thus, we tested whether CHOP expression was upregulated upon incubation of HeLa-OA1, HepG2-OA1 and C2C12-GFP cells in media deprived of different EAAs for 24–48 h 
As shown in Fig 3 and S4B Fig, we found that CHOP expression is increased in all EAA-starvation conditions, but not in the absence of Ala, in all tested cell lines  Similar, yet less pronounced, results were obtained with ATF4, consistent with the notion that activation of this transcription factor is mainly mediated by translational upregulation (not shown) [15, 26]  However, the upregulation of CHOP does not parallel quantitatively that of the transgene, neither appears sufficient to induce it  In fact, CHOP is highly upregulated even upon Trp starvation, which consistently results in no or minimal reactivation of the transgenes (compare CHOP with OA1 or GFP expression; Fig 3 and S4B Fig)  Thus, while the ISR appears widely activated upon EAA starvation, the upregulation of its downstream effector CHOP only partly correlates with transgene reactivation and may not be sufficient to induce it 
The activation of the ISR upon AA starvation suggests that GCN2 may be involved in the transgene reactivation response  Therefore, we tested whether direct pharmacological activation of this kinase is sufficient to trigger the transgene reactivation similarly to starvation  In addition, we used pharmacological inhibitors of mTOR to corroborate previous negative results in HeLa cells  in the other cell lines under study  To this aim, HeLa-OA1 or GFP, HepG2-OA1 and C2C12-GFP cells were cultured in the presence of different concentrations of PP242 (mTOR inhibitor) or L-Histidinol (GCN2 activator, inhibiting tRNAHis charging by histidyl-tRNA synthetase), either alone or in combination for 24 h, compared to Met/Cys-deprived and full medium  As shown in Fig 4 and S5 Fig, while inhibition of mTORC1 consistently leads to minor or no effects, in agreement with previous findings , treatment with L-Histidinol results in efficient reactivation of the transgene in HepG2-OA1 and C2C12-GFP cells, but not in HeLa cells 
Fig 4  mTOR inhibition and GCN2 activation differently affect transgene expression in HeLa and HepG2 cells 
Relative transgene (OA1) and CHOP mRNA abundance in HeLa-OA1 (A) and HepG2-OA1 (B) cells, cultured in Met/Cys-deprived medium, or in the presence of PP242 (mTOR inhibitor; 1–3 μM) or L-Histidinol (HisOH, GCN2 activator; 4–16 mM), either alone or in combination for 24–48 h, compared to full medium  Mean ± SEM of 4 (A) or 3 (B) independent experiments  Data are expressed as fold change vs  control (full medium = 1)  *P<0 05, **P<0 01, ***P<0 001 (one way ANOVA, followed by Dunnett’s post-test vs  full medium)  PP-1 and PP-3, PP242 at 1 and 3 μM, respectively; HisOH-4 and HisOH-16, L-Histidinol at 4 and 16 mM, respectively 
Specifically, L-Histidinol is not effective in HeLa-OA1 and HeLa-GFP cells, either alone or in combination with PP242 (Fig 4A and S5A Fig), or by using different concentrations of the drug, with or without serum (not shown)  In these cells, L-Histidinol appears also unable to trigger the ISR, as indicated by lack of CHOP upregulation, possibly due to their different sensitivity to the drug  These findings are consistent with previous reports, describing the use of L-Histidinol in HeLa cells in conditions of low His concentration in the culture medium , which would resemble AA starvation in our system and therefore may not be applicable  Thus, even though the amount of the amino alcohol was adapted to exceed 20 to 80 times that of the amino acid, as described , HeLa cells may be resistant or able to compensate 
In contrast, in other cell types, L-Histidinol has been utilized in regular DMEM, to mimic the AA response triggered by DMEM lacking His [48, 49]  Consistently, in HepG2-OA1 cells, L-Histidinol is sufficient to elicit extremely high levels of transgene reactivation, and its combination with PP242 results in additive or even synergistic effects, possibly due to an indirect effect of mTOR inhibition on GCN2 activity (Fig 4B) [50, 51]  Similarly, C2C12-GFP cells efficiently reactivate the transgene upon treatment with L-Histidinol, but not PP242 (S5B Fig)  However, differently from HepG2-OA1 cells, simultaneous treatment of C2C12-GFP cells with L-Histidinol and PP242 does not lead to synergistic effects  Consistent with stimulation of the ISR, CHOP and to a minor extent ATF4 are upregulated by L-Histidinol in both cell lines, yet their expression levels show only an incomplete correlation with those of the transgene (Fig 4B, S5B Fig, and not shown) 
The finding that GCN2 activation by L-Histidinol is sufficient to reactivate the transgenes in both HepG2-OA1 and C2C12-GFP cells pointed to this kinase, and to the downstream ISR, as the pathway possibly involved in the EAA starvation response  Thus, we investigated whether the ISR is sufficient to trigger upregulation of the OA1 transgene in HepG2-OA1 cells by pharmacological means  As CHOP expression does not correspond quantitatively and is not sufficient to induce transgene reactivation, we tested the role of the core upstream event of the ISR, namely the phosphorylation of eIF2α , which can be induced by pharmacological treatments, independent of GCN2 (Fig 5A)  To this aim, we used Salubrinal, a specific phosphatase inhibitor that blocks both constitutive and ER stress-induced phosphatase complexes against eIF2α, thereby increasing its phosphorylation   We found that, while the ISR is activated upon Salubrinal treatment, as shown by increased CHOP expression, it does not induce OA1 transgene reactivation (Fig 5B) 
Fig 5  The ISR is neither sufficient nor necessary to induce transgene reactivation in HepG2 cells 
(A) Schematic representation of GCN2 activation by AA starvation, resulting in phosphorylation of eIF2a and initiation of the downstream ISR  In addition to GCN2, the ISR may be activated by other eIF2a kinases (PKR, HRI and PERK; not shown in the picture)  (B) Relative transgene (OA1) and CHOP mRNA abundance in HepG2-OA1 cells treated for 24 h with Salubrinal (a drug that induces the ISR by inhibiting the dephosphorylation of eIF2α; 75 μM), compared to full medium  Mean ± range of two experiments  Data are expressed as fold change vs  control (DMEM = 1)  *P<0 05 (paired two-tailed Student’s t-test vs  control)  (C) Relative transgene (OA1) and CHOP mRNA abundance in HepG2-OA1 cells treated for 6 h with L-Histidinol (HisOH, GCN2 activator; 4 mM), in the absence or presence of ISRIB (a drug that bypasses the phosphorylation of eIF2α, inhibiting triggering of the ISR; 100 nM)  Mean ± range of two experiments  Data are expressed as fold change vs  control (DMEM = 1)  **P<0 01, ***P<0 001 (one way ANOVA, followed by Tukey’s post-test; P values refer to comparisons vs  control, unless otherwise indicated)  (D) Relative transgene (OA1) and ATF4 mRNA abundance in HepG2-OA1 cells transfected with control (CTRL) or anti-ATF4 siRNAs, and incubated in the presence or absence of L-Histidinol (HisOH, GCN2 activator; 4 mM) for 6 h  Mean ± range of two experiments  Data are expressed as fold change vs  control (w/o HisOH = 1, top; control siRNA = 1, bottom)  *P<0 05 (one way ANOVA, followed by Tukey’s post-test; P values refer to comparisons vs  control, unless otherwise indicated) 
To test whether the ISR is necessary to trigger the transgene response to L-Histidinol, we used the chemical compound ISRIB, which inhibits the activation of the ISR, even in the presence of phosphorylated eIF2α, likely by boosting the activity of the guanine-nucleotide exchange factor (GEF) for eIF2α, namely eIF2B [53, 54]  HepG2-OA1 cells were stimulated with L-Histidinol, either in the presence or absence of ISRIB  As shown in Fig 5C, while the expression of CHOP is inhibited by ISRIB, as expected, the reactivation of the OA1 transgene is not affected  In addition, knockdown of the closest eIF2α downstream effector ATF4 by siRNAs does not interfere with the reactivation of the OA1 transgene by L-Histidinol (Fig 5D)  Together, these data suggest that eIF2α phosphorylation and the downstream ISR pathway are neither sufficient nor necessary to induce transgene reactivation 
To definitively establish if GCN2 is necessary to trigger the transgene reactivation response to EAA starvation, we directly suppressed its expression by CRISPR/Cas9-mediated knock-out (KO)  We generated three independent GCN2-KO clones from the parental HeLa-OA1 cell line, by using three different guide RNAs, two against exon 1 (clones 183#11 and 185#5), and one against exon 6 (clone 239#1) of the GCN2 gene  Genomic characterization confirmed the presence of mutations on both alleles of exon 1 of the GCN2 gene in clone 183#11, and on both alleles of exon 6 in clone 239#1; by contrast, clone 185#5 showed multiple alleles in exon 1, consistent with the presence of two cell populations, and was not characterized further at the genomic level (S6 Fig)  None of these clones express GCN2 at the protein level, as shown by immunoblotting (Fig 6A)  To test the GCN2-KO cells for their ability to respond to EAA starvation, parental HeLa-OA1 cells and the three GCN2-KO clones were cultured in media deprived of Met/Cys or Thr (corresponding to the most effective treatments in this cell line; see Fig 3A) for 24–48 h and transgene expression was assessed by RT-qPCR  We found that the reactivation of the OA1 transgene is neither abolished, nor reduced by KO of GCN2, thus excluding that this kinase is necessary for the response to EAA starvation in HeLa-OA1 cells (Fig 6B and 6C) 
Fig 6  GCN2 knockout does not interfere with transgene reactivation in HeLa cells 
(A) Immunoblotting of protein extracts from the HeLa-OA1 parental cell line and GCN2-KO clones 183#11, 185#5 and 239#1, immunodecorated with anti-GCN2 antibody  Arrow, GCN2 specific band  Ponceau staining was used as loading control  (B, C) Relative transgene (OA1) mRNA abundance in HeLa-OA1 cells and GCN2-KO clones, cultured in Met/Cys (B) or Thr (C) deprived medium for 24 h or 48 h, respectively, compared to full medium  Mean ± SD of 3 technical replicates from 1 experiment  Data are expressed as fold change vs  control (full medium = 1)  Since independent clones may display variable reactivation responses (e g  due to different levels of transgene expression in basal conditions), the results are not shown as means of the three clones, but as separate replicates 
Similarly, we generated GCN2-KO clones from the parental HepG2-OA1 cell line by the same strategy  By using a guide RNA against exon 1 of the GCN2 gene, we obtained three independent GCN2-KO clones, namely E23, F22 and F27  Genomic characterization confirmed the presence of mutations on both alleles of exon 1 of the GCN2 gene in clone F27 (S7 Fig) and all three clones showed a very low amount—if any—of residual GCN2 protein, compared to the original HepG2-OA1 cell line (Fig 7A)  To assess the ability of GCN2-KO cells to reactivate the transgene upon starvation, we cultured parental HepG2-OA1 cells and the three GCN2-KO clones in media deprived of Met/Cys or His (corresponding to the most effective treatments in this cell line; see Fig 3B) for 24 h, and evaluated the transgene expression by RT-qPCR  As shown in Fig 7B and 7C, we found that the reactivation of the OA1 transgene is neither abolished, nor reduced by KO of GCN2, as in HeLa cells  To further confirm this result, we knocked-down GCN2 by RNA interference (RNAi), and incubated the cells with or without L-Histidinol for 6 h  As shown in Fig 8, treatment of HepG2-OA1 cells with L-Histidinol results in efficient transgene reactivation, even upon significant GCN2 downregulation, both at the mRNA and protein levels  Taken together, these data strongly support the conclusion that GCN2 is not necessary for transgene reactivation in response to EAA starvation, either in HeLa or in HepG2 cells 
Fig 7  GCN2 knockout does not interfere with transgene reactivation in HepG2 cells 
(A) Immunoblotting of protein extracts from the HepG2-OA1 parental cell line and GCN2-KO clones 185#27, E23, F22, F27, immunodecorated with anti-GCN2 antibody  Clone 185#27 results from the first round of selection, and was used to generate clones E23, F22, F27  Arrow, GCN2 specific band  For GCN2 protein quantification, Ponceau staining was used as loading control and data are expressed as fold change vs  parental cell line (= 1)  (B, C) Relative transgene (OA1) mRNA abundance in HepG2-OA1 cells and GCN2-KO clones, cultured in Met/Cys (B) or His (C) deprived medium for 24 h, compared to full medium  Mean ± SD of 3 technical replicates from 1 experiment 
Context 3: 
solomonrex wrote:I don't think your horizontal market development theory is supported by facts  Samsung and Apple are more vertically oriented than their competition, for starters  I know this article is narrowly focused on the hardware, but MS and Intel getting into hardware, Amazon getting into hardware, Google buying Moto, this is all vertical integration  How can you support the idea that this trend will be reversed with no real justification? I'm sure mobile chips will continue to specialize, but I don't think this means what you think it means  Automobile companies started making their own engines and with rare exceptions, never went back to being more horizontal  Same with retail and their store brands  Same with cloud companies and their servers  Same with mobile companies and their OSs  The horizontal market of PCs created by long-lasting standards and loose hegemony is the exception, not the norm Yea, each year Amazon, MS, Apple and Google look more and more the same 
Intel cannot abandon the phone/tablet market  Desktop/laptop sales are stagnating/decreasing and phones/tablets are on the rise  This trend is only going to increase going forward But you're right, they're going to have use their fabs that are a step or two behind the cutting the edge  But they're going to have to up their game in the tablet space to even be able to do that 
gypsumfantastic wrote:Why wouldn't the foundries be able close the process gap with Intel? Is it a matter of money? Scale?Intel's called Chipzilla for a reason up
Lagrange wrote:The question for the future is how the economics will stack up when overall device costs fall significantly and there is a big downward pressure on SoC prices  In that situation, can Intel still justify bringing their A-game to a market where products are essentially commoditised and you have processors selling for a only a few dollars each?The lesson from their previous involvement in the DRAM market is that they probably won't want to be involved because there isn't enough money to be made to justify manufacturing phone SoCs on a cutting edge, or near cutting edge process  In that scenario, Intel may not totally abandon the market but they might just stick to manufacturing SoCs on nodes that are a step or two behind the state of the art I think the processing is a bigger advantage than many realize  If Intel can stay ahead in process design - which this article seems to indicate - they should have a major advantage  All else being equal a 14nm chip should be significantly faster and more efficient than the same chip at 22nm  Add in the fact that yields increase geometrically - you can fit a lot more 14nm chips on a given wafer size vs 22nm (or 32nm for the other manufacturers ) and you have a very appealing proposition  And then add in the fact that Intel actually has a pretty good graphics stack and IP  It's not a sure thing by any means, but I suspect ARM may have just prodded a sleeping giant edit: Also worth noting, Intel, TSMC, and Samsung are the only manufacturers who are building out 450nm wafers  This will increase yields dramatically  Of course Samsung and TSMC will build ARM out, but it definitely puts quite a bit of pressure on all other manufacturers  As the article mentions Intel and Samsung are the only ones who control production top to bottom, and Samsung must share some of the benefits with ARM 
As someone who has worked in the semiconductor industry for longer than contemporary fanboys have existed, I'm getting a bit fed-up seeing many of these articles which rewrite history with analyses distorted by the lens of the contemporary Apple or Samsung fanboy The mobile industry moved to horizontal integration a long time ago  Better indicators than these odd contemporary obsessions with the relatively non-innovative Samsung and Apple where when Motorola spun out its semiconductor division as Freescale, Nokia stopped making it's own custom designs with TI and ST, and Ericsson spun out it's Ericsson Mobile Platforms division and formed ST-Ericsson with ST The true innovation in the mobile market was done a decade or more ago mainly by Moto/Nokia/Ericsson/TI/Qualcomm, and Samsung and Apple had little to do with it  Under the hood most stuff has been on a simple linear evolutionary path by the SoC providers since then  The phone manufacturers have then mainly been simply sticking these off-the-shelf SoCs (and their included low-level software stacks) in a box, a job made all the easier with the SoC manufacturer collaboration providing the bulk of the work for the AOSP 
Last edited by paul5ra on Wed Feb 13, 2013 11:06 am
introiboad wrote:I am happy to see Kanter here at Ars, I like his writing and he maintains Real World Tech, where Linus Torvalds often shows up to comment on CPU arch and other interesting topics Indeed  Most tech writing in this area is atrocious  This piece is one of the few well informed articles I've read in a long time 
ggeezz wrote:Intel cannot abandon the phone/tablet market  Desktop/laptop sales are stagnating/decreasing and phones/tablets are on the rise  This trend is only going to increase going forward But you're right, they're going to have use their fabs that are a step or two behind the cutting the edge  But they're going to have to up their game in the tablet space to even be able to do that The word you're looking for is Haswell, as far as I know 
Mabsark
ggeezz wrote:Intel cannot abandon the phone/tablet market  Desktop/laptop sales are stagnating/decreasing and phones/tablets are on the rise  This trend is only going to increase going forward But you're right, they're going to have use their fabs that are a step or two behind the cutting the edge  But they're going to have to up their game in the tablet space to even be able to do that Actually, that trend will not simply keep increasing going forward  The reason desktop/laptop sales are stagnating/decreasing is due to the fact that most people already have one and therefore don't need to buy another one  The exact same thing will happen with tablets as well  Sales are increasing now because people without tablets are buying them  When most people already own a tablet, they won't be buying a new one every year and therefore sales will stagnate/decrease The PC market is saturated and in a couple of years, the tablet market will be saturated too  Basically, in order to increase sales in a saturated market, you need to increase the population growth or decrease the longevity of the product 
gypsumfantastic wrote:Why wouldn't the foundries be able close the process gap with Intel? Is it a matter of money? Scale?Probably a mix of a lot of things  One big thing was during this recession, Intel was the ONLY fab company that didn't scale back their R&D  That alone gave Intel a large advantage Intel has almost always been ahead  One of the reasons could be that Intel works with much higher margins than many of the commodity companies like Samsung and TSMC Outside of the P4 flop and some of the monopolistic abuses, Intel has typically been selling to high end customers that are willing to pay a premium for "the best" Intel has a large benefit of having a relatively "good name" when it comes to CPUs, so they can effectively charge a brand-name premium I'm sure there are other reasons, and probably better reasons, but these are the main ones that I think of 
Mabsark wrote:Actually, that trend will not simply keep increasing going forward  The reason desktop/laptop sales are stagnating/decreasing is due to the fact that most people already have one and therefore don't need to buy another one  The exact same thing will happen with tablets as well  Sales are increasing now because people without tablets are buying them  When most people already own a tablet, they won't be buying a new one every year and therefore sales will stagnate/decrease The PC market is saturated and in a couple of years, the tablet market will be saturated too  Basically, in order to increase sales in a saturated market, you need to increase the population growth or decrease the longevity of the product That's true as long as most people are still buying both a tablet and a laptop when each needs to be replaced  I think the assumption is that, as you say, the tablet market will saturate, with people just replacing existing ones, but the desktop/laptop market could decrease much farther than that, if most people stop replacing them at all  I'm not sure of the likelihood of that, but I think that's where this idea comes from 
ggeezz wrote:Intel cannot abandon the phone/tablet market  Desktop/laptop sales are stagnating/decreasing and phones/tablets are on the rise  This trend is only going to increase going forward But you're right, they're going to have use their fabs that are a step or two behind the cutting the edge  But they're going to have to up their game in the tablet space to even be able to do that The upcoming Haswell chip is showing to consume 1/3 the power of IvyBridge at peak, consumes 1/20th the power at idle, all the while maintaining Identical or better performance This chip should actually compete with ARM CPUs on both power/performance and idle I am expecting a large war 
Apple once again is dictating the performance in the mobile industry  Nice to see others being able to keep the pace, as well 
paul5ra wrote:As someone who has worked in the semiconductor industry for longer than contemporary fanboys have existed, I'm getting a bit fed-up seeing many of these articles which rewrite history with analyses distorted by the lens of the contemporary Apple or Samsung fanboy The mobile industry moved to horizontal integration a long time ago  Better indicators than these odd contemporary obsessions with the relatively non-innovative Samsung and Apple where when Motorola spun out its semiconductor division as Freescale, Nokia stopped making it's own custom designs with TI and ST, and Ericsson spun out it's Ericsson Mobile Platforms division and formed ST-Ericsson with ST The true innovation in the mobile market was done a decade or more ago mainly by Moto/Nokia/Ericsson/TI/Qualcomm, and Samsung and Apple had little to do with it  Under the hood most stuff has been on a simple evolutionary path by the SoC providers since then Yeah, and most of the innovation in the automobile industry came about before Henry Ford came into the business  Doesn't change the fact that cars would probably have been an asterisk in the history books under "toys for rich people" if it weren't for him The same applies to to mobile computing for Apple, Samsung, et al 
SheldonRoss wrote:Lagrange wrote:The question for the future is how the economics will stack up when overall device costs fall significantly and there is a big downward pressure on SoC prices  In that situation, can Intel still justify bringing their A-game to a market where products are essentially commoditised and you have processors selling for a only a few dollars each?The lesson from their previous involvement in the DRAM market is that they probably won't want to be involved because there isn't enough money to be made to justify manufacturing phone SoCs on a cutting edge, or near cutting edge process  In that scenario, Intel may not totally abandon the market but they might just stick to manufacturing SoCs on nodes that are a step or two behind the state of the art I think the processing is a bigger advantage than many realize  If Intel can stay ahead in process design - which this article seems to indicate - they should have a major advantage  All else being equal a 14nm chip should be significantly faster and more efficient than the same chip at 22nm  Add in the fact that yields increase geometrically - you can fit a lot more 14nm chips on a given wafer size vs 22nm (or 32nm for the other manufacturers ) and you have a very appealing proposition  And then add in the fact that Intel actually has a pretty good graphics stack and IP  My point was that Intel might have a one or two process advantage over the rest of the industry at the cutting edge but that doesn't mean that they can afford to manufacture on those processes for very low margin parts  If the SoC market becomes increasingly commoditised, there isn't going to be the money to justify making them in a state of the art fab Remember that one of the big selling points of Itanium was that it would make use of process advantages that were effectively paid for by the mainstream x86 market  That didn't quite work out in practice and Itanium processors were often well behind Xeons in process technology 
paul5ra wrote:As someone who has worked in the semiconductor industry for longer than contemporary fanboys have existed, I'm getting a bit fed-up seeing many of these articles which rewrite history with analyses distorted by the lens of the contemporary Apple or Samsung fanboy The mobile industry moved to horizontal integration a long time ago  Better indicators than these odd contemporary obsessions with the relatively non-innovative Samsung and Apple where when Motorola spun out its semiconductor division as Freescale, Nokia stopped making it's own custom designs with TI and ST, and Ericsson spun out it's Ericsson Mobile Platforms division and formed ST-Ericsson with ST The true innovation in the mobile market was done a decade or more ago mainly by Moto/Nokia/Ericsson/TI/Qualcomm, and Samsung and Apple had little to do with it  Under the hood most stuff has been on a simple linear evolutionary path by the SoC providers since then  The phone manufacturers have then mainly been simply sticking these off-the-shelf SoCs (and their included low-level software stacks) in a box, a job made all the easier with the SoC manufacturer collaboration providing the bulk of the work for the AOSP Just goes to show that people who have worked in an industry for a long time don't always understand what that industry is doing You haven't been working in it long enough to seem to know that it was Acorn and Apple that invented the mobile ARM CPU in the first place  All those companies you've mentioned have just been working off Acorn and Apple's pioneering work  Now, Apple is back at it again, very successfully, and all the companies you mentioned that produce chips with ARM IP in them are licensing them from the company Acorn and Apple formed—ARM 
Last edited by melgross on Wed Feb 13, 2013 11:13 am
Mark Havel wrote:ggeezz wrote:Intel cannot abandon the phone/tablet market  Desktop/laptop sales are stagnating/decreasing and phones/tablets are on the rise  This trend is only going to increase going forward But you're right, they're going to have use their fabs that are a step or two behind the cutting the edge  But they're going to have to up their game in the tablet space to even be able to do that The word you're looking for is Haswell, as far as I know If tablets move into the $100-200 range, is there going to be room for Haswell?So long as there is a higher-end tablet market, then Haswell will be able to shine, but it's going to be a much more powerful and costly part than the sort of ARM based hardware that often runs tablets  If we see a race to the bottom where price is the dominant motivator behind purchases, then a high performance SoC will struggle to make its mark 
melgross wrote:paul5ra wrote:As someone who has worked in the semiconductor industry for longer than contemporary fanboys have existed, I'm getting a bit fed-up seeing many of these articles which rewrite history with analyses distorted by the lens of the contemporary Apple or Samsung fanboy The mobile industry moved to horizontal integration a long time ago  Better indicators than these odd contemporary obsessions with the relatively non-innovative Samsung and Apple where when Motorola spun out its semiconductor division as Freescale, Nokia stopped making it's own custom designs with TI and ST, and Ericsson spun out it's Ericsson Mobile Platforms division and formed ST-Ericsson with ST The true innovation in the mobile market was done a decade or more ago mainly by Moto/Nokia/Ericsson/TI/Qualcomm, and Samsung and Apple had little to do with it  Under the hood most stuff has been on a simple linear evolutionary path by the SoC providers since then  The phone manufacturers have then mainly been simply sticking these off-the-shelf SoCs (and their included low-level software stacks) in a box, a job made all the easier with the SoC manufacturer collaboration providing the bulk of the work for the AOSP Just goes to show that people who have worked in an industry for a long time don't always understand what that industry is doing You haven't been working in it long enough to seem to know that it was Acorn and Apple that invented the mobile ARM CPU in the first place  All those companies you've mentioned have just been working off Acorn and Apple's pioneering work  Now, Apple is back at it again, very successfully, and all the companies you mentioned that produce chips with ARM IP in them are licensing them from the company Acorn and Apple formed—ARM



ground_truth: No, it is not necessary.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: How are smartphones and tablets different from a technical perspective?

Read the following text and answer briefly.

Context 3: Of course I realise ARM IP has indeed been a major driving factor too (though only one if several architectures before ARM became dominant), though I see ARM's influence on the mobile industry as having nothing to do with modern day Apple and only one small piece of the puzzle  My point is that the hard electrical engineering, mathematics, DSP, semiconductor physics/chemistry, RF engineering, analogue design, CAD etc  that make modern telecommunications possible has very little to do with the fashion companies who consumers (and unfortunately much of the tech media) associate with it and give the credit (though in this respect Samsung does deserve a bit more credit for their work on NAND flash and displays)  The industry simply would not exist TODAY without the overwhelming horizontal integration that already dominates 
Quote:In the long term, mobile devices are likely to evolve similarly to the PC and favor a horizontal business model  The real advantage is one of flexibility; as costs drop and the market expands, it will be increasingly necessary for vendors like HTC to offer a wide range of phones based on radically different SoCs  You don't mention in the article that each SoC necessarily requires a bit of parallel dev work unlike the PC  In the PC world there is a standard BIOS and HW architecture that allows for pluggable designs  On a highly integrated SoC this is untrue  HTC suffers because it has to support radically different SoCs, their drivers and boot loaders, etc  Quote:While a vertically integrated company like Apple can focus and maintain leadership in a specific (and highly lucrative) niche, it would be very difficult to expand in many growing areas of the market  The differences between an iPhone 6 and a $20 feature phone are tremendous and would be very difficult for a single company to bridge It's only difficult because Apple chooses to ignore that market, not because they can't  If they can release a $99 Apple TV, they can surely cobble together a $20 feature phone if they chose to eschew 8GB of NAND, BT, WiFi, a specialized dock connector, LTE, and their specialized processors  In other words, build the equivalent of an iPod shuffle with a horrible screen and no OS to speak of 
paul5ra wrote:melgross wrote:paul5ra wrote:As someone who has worked in the semiconductor industry for longer than contemporary fanboys have existed, I'm getting a bit fed-up seeing many of these articles which rewrite history with analyses distorted by the lens of the contemporary Apple or Samsung fanboy The mobile industry moved to horizontal integration a long time ago  Better indicators than these odd contemporary obsessions with the relatively non-innovative Samsung and Apple where when Motorola spun out its semiconductor division as Freescale, Nokia stopped making it's own custom designs with TI and ST, and Ericsson spun out it's Ericsson Mobile Platforms division and formed ST-Ericsson with ST The true innovation in the mobile market was done a decade or more ago mainly by Moto/Nokia/Ericsson/TI/Qualcomm, and Samsung and Apple had little to do with it  Under the hood most stuff has been on a simple linear evolutionary path by the SoC providers since then  The phone manufacturers have then mainly been simply sticking these off-the-shelf SoCs (and their included low-level software stacks) in a box, a job made all the easier with the SoC manufacturer collaboration providing the bulk of the work for the AOSP Just goes to show that people who have worked in an industry for a long time don't always understand what that industry is doing You haven't been working in it long enough to seem to know that it was Acorn and Apple that invented the mobile ARM CPU in the first place  All those companies you've mentioned have just been working off Acorn and Apple's pioneering work  Now, Apple is back at it again, very successfully, and all the companies you mentioned that produce chips with ARM IP in them are licensing them from the company Acorn and Apple formed—ARM Of course I realise ARM IP has indeed been a major driving factor too (though only one if several architectures before ARM became dominant), though I see ARM's influence on the mobile industry as having nothing to do with modern day Apple and only one piece of the puzzle  My point is that the hard electrical engineering, mathematics, DSP, semiconductor physics/chemistry, RF engineering, analogue design,etc  that make modern telecommunications possible has very little to do with the fashion companies who consumers (and unfortunately much of the tech media) associate with it and give the credit (though in this respect Samsung does deserve a bit more credit for their work on NAND flash and displays)  The industry simply would not exist TODAY without the overwhelming horizontal integration that already dominates Yes the efforts of these companies getting cellular communications standardized were immense  And the technology matured  And then they didn't do much with it  It took some youngin's to look at the problem fresh and add the UI that make today's smartphones work  As we have all seen, the moment your technology has matured is the moment you are screwed because someone else now has the opportunity to look at it as a black box and make something new  Each of those manufacturers knew that smartphones would eventually be awesome, but none of them had the UI and software design to make a truly breakout product  Imagine if Motorola would have been smart enough to buy the Android guys instead of Google  Instead, Google bought a bunch of patents on that cellular black box to try to defend it's platform And when you think about it, which consumes more man years of engineering effort per year at this point     iterating that cellular black box or developing the OS, services and apps that power today's smartphones?
Intel had better decide that they are competing in this space "for real", or they are screwed  They've already let the Atom languish for five years, during which ARM has completely caught up in performance Just like Tim Cook said, if you don't cannibalize your own markets someone else will do it for you Whether Intel will embrace that concept in time remains to be seen  Personally, I hope they don't; if Intel transforms into a chipless Fab company (like TSMC) everyone benefits 
I still think Samsung has the advantage long term because they have both the SOC and the memory products  As mentioned in the article, TSV's (Through Silicon Via's) are going to be quite a disruption  Today, people normally stack an LPDDR2 package on top of their SOC package (POP or Package On Package)  Within the LPDDR2 package, you could have a stack of DRAM die typically with wire bonding connecting the die within the package Once you more to TSV's, you can have a LOT more connections between the SOC and its DRAM's  While this is being standardized through JEDEC (http://www jedec org/category/technolog     a/3d-ics-0), Samsung has all the pieces in house to do whatever they want  You could see a 512 bit or higher bus from the SOC to the memory  The trick is that the memory and the SOC need to line up with each other when you stack them  This gives Samsung an inherent advantage This isn't just going to impact mobile either  Take a look at that JEDEC link  It also lists High Bandwidth Memory (HBM)  This is a 1024 bit bus that provides 128GBytes/s to 256GBytes/s of bandwidth to a stack of up to 8 DRAM's  Here is your processor that includes 8-16 cores and 4GBytes of really, really, fast DRAM    No DIMMs required  How many of them do you want in your server rack?If I was Intel or Apple, I would be thinking seriously about making some investments in Micron to guarantee they make some compelling DRAM's to integrate with their SOC's and processors    otherwise Samsung is going to blow them out of the water on bandwidth 
Great_Scott wrote:Intel had better decide that they are competing in this space "for real", or they are screwed  They've already let the Atom languish for five years, during which ARM has completely caught up in performance Just like Tim Cook said, if you don't cannibalize your own markets someone else will do it for you Whether Intel will embrace that concept in time remains to be seen  Personally, I hope they don't; if Intel transforms into a chipless Fab company (like TSMC) everyone benefits It's true that Atom has stood still for too long, but honestly it's pretty amazing how Atom is still competitive with current ARM chips  The Z2760 is even 32nm vs 28nm of the latest Krait and A15 chips But that's all changing with Atom moving to the tick tock schedule this year  It wouldn't even surprise me to see Apple move to Intel chips for IOS And I don't see how Intel moving to a chipless Fab company would help everyone  It certainly wouldn't help Intel 
Mabsark wrote:ggeezz wrote:Intel cannot abandon the phone/tablet market  Desktop/laptop sales are stagnating/decreasing and phones/tablets are on the rise  This trend is only going to increase going forward But you're right, they're going to have use their fabs that are a step or two behind the cutting the edge  But they're going to have to up their game in the tablet space to even be able to do that Actually, that trend will not simply keep increasing going forward  The reason desktop/laptop sales are stagnating/decreasing is due to the fact that most people already have one and therefore don't need to buy another one  The exact same thing will happen with tablets as well  Sales are increasing now because people without tablets are buying them  When most people already own a tablet, they won't be buying a new one every year and therefore sales will stagnate/decrease The PC market is saturated and in a couple of years, the tablet market will be saturated too  Basically, in order to increase sales in a saturated market, you need to increase the population growth or decrease the longevity of the product Yes and no  I'm not sure the tablet market will saturate in a "couple of years " It may be more like 5 years  But that's a quibble Here's the real issue  Right now Apple wants you to own an iPhone AND iPad AND Macbook AND iWatch AND Apple TV  Microsoft, OTOH, is making the Surface so that you could ditch your laptop and just use a Surface  Not everyone, but some people If 5 years from now, we're in a world where a significant number of people use a Surface-type device instead of a laptop, then the PC market is going to contract significantly  Maybe some of the tablet-like devices will use moderately expensive Intel chips, but some of them are going to use cheaper chips 
GravyGraphics wrote:I still think Samsung has the advantage long term because they have both the SOC and the memory products  As mentioned in the article, TSV's (Through Silicon Via's) are going to be quite a disruption  Today, people normally stack an LPDDR2 package on top of their SOC package (POP or Package On Package)  Within the LPDDR2 package, you could have a stack of DRAM die typically with wire bonding connecting the die within the package Once you more to TSV's, you can have a LOT more connections between the SOC and its DRAM's  While this is being standardized through JEDEC (http://www jedec org/category/technolog     a/3d-ics-0), Samsung has all the pieces in house to do whatever they want  You could see a 512 bit or higher bus from the SOC to the memory  The trick is that the memory and the SOC need to line up with each other when you stack them  This gives Samsung an inherent advantage This isn't just going to impact mobile either  Take a look at that JEDEC link  It also lists High Bandwidth Memory (HBM)  This is a 1024 bit bus that provides 128GBytes/s to 256GBytes/s of bandwidth to a stack of up to 8 DRAM's  Here is your processor that includes 8-16 cores and 4GBytes of really, really, fast DRAM    No DIMMs required  How many of them do you want in your server rack?If I was Intel or Apple, I would be thinking seriously about making some investments in Micron to guarantee they make some compelling DRAM's to integrate with their SOC's and processors    otherwise Samsung is going to blow them out of the water on bandwidth Why not AMD? Last I checked they still made memory   and processors/GPUs 



ground_truth: Smartphones are more compact and power constrained.