Miracle-dz/UniFAR-FTUs huggingface dataset:
Retinoids signal directly to zebraﬁsh endoderm to specify insulin -expressing (cid:2) -cells

studies have revealed that RA signaling is important for the development of derivatives of all germ layers (reviewed by Ross et al., 2000). The expression of RARs in a given tissue does not During vertebrate development, the endodermal germ layer becomes regionalized along its anteroposterior axis to give rise to a variety of organs, including the pancreas. Genetic studies in zebraﬁsh and mice have established that the signaling molecule retinoic acid (RA) plays a crucial role in endoderm patterning and promotes pancreas development. To identify how RA signals to pancreatic progenitors in the endoderm, we have developed a novel cell transplantation technique, using the ability of the SOX32 transcription factor to confer endodermal identity, to selectively target reagents to (or exclude them from) the endodermal germ layer of the zebraﬁsh. We show that RA synthesized in the anterior paraxial mesoderm adjacent to the foregut is necessary for the development of insulin -expressing (cid:2) -cells. Conversely, RA receptor function is required in the foregut endoderm for insulin expression, but not in mesoderm or ectoderm. We further show that activation of RA signal transduction in endoderm alone is sufficient to induce insulin expression. Our results reveal that RA is an instructive signal from the mesoderm that directly induces precursors of the endocrine pancreas. These ﬁndings suggest that RA will have important applications in the quest to induce islets from stem cells for therapeutic uses.
Detection of genotoxic effect of cadmium on tomato ( Lycopersicon esculentum M ill . ) using RAPD markers

: Tomato ( Lycopersicon esculentum Mill.) seeds were germinated in Petri dishes for 7-10 days. Seedlings were transferred to pots, grown in greenhouse and Cadmium (Cd) treatments were applied at concentrations of 0.0, 10.0, 50.0, 70.0 and 100.0 µM for two weeks. Genomic DNA from tomato leaves was used for RAPD analysis using four random primers (OPB05, OPB07, OPB08, and OPB09). RAPD profiles showed great variations in banding patterns, particularly in intensity, number, thickness and mobility of the PCR generated fragments. Appearance of new RAPD markers may occur because some oligonucleotide priming sites could become accessible to primers due to changes in DNA sequence. DNA might be to structural of the of of In other words, cadmium pollution can cause genotoxic effects on tomato plants that may be regarded as a bioindicator of environmental pollution.
SUSTAINABLE INSTITUTIONAL QUALITIES ON STANDARD OF LIVING IN SUB-SAHARA AFRICAN COUNTRIES BY

Despite the abundant research on institutional quality and development, little has been done to examine sustainable institutional quality on standard of living especially in SSA countries.The problem facing SSA can be ascertained in the area of weak institutions which metamorphose to poor level of standard of living, in terms of economic growth, low level of educational attainment and low level of life expectancy which sum up to high level of poverty in the region.The objective of this study is to evaluate the relationship between institutional quality and standard of living in SSA countries from 2005 to 2013. The research used secondary data sourced from World Bank governance indicators, Transparency International and Heritage Foundation. The method of analysis employed is panel data using the panel corrected standard error (PCSE) and generalized method of moments (GMM). The analysis produced diverse results. The findings are compatible with theory. Both the panel data analysis and the generalized method of moments of institutional quality and standard of living model indicated that most of the countries investigated exhibits mixed performance in terms of the institutional quality such as rule of law, corruption, bureaucratic quality and property rights. The study recommends policies to reduce corruption in all levels of economic activities. In addition, rule of law need to be strengthened and the educational sector should be fine-toned to train manpower in all aspect of economic growth vis-à-vis standard of living in the
Evaluation of hepatic fibrosis in patients with chronic hepatitis B virus by noninvasive methods: Aspartate-to-platelet ratio index, Fibrosis-4, fibrotest and fibroscan

Background: The process of hepatic fibrosis is common to the various etiologies of chronic liver disease such as viral hepatitis B. Objective: To evaluate hepatic fibrosis by non-invasive markers such as Aspartate-to-Platelet Ratio Index (APRI), fibrosis-4 (FIB-4), fibrotest and fibroscan. Patients and Method: This was a descriptive study during a period of 32 months. Included in our study were the records of outpatients, chronic carriers of hepatitis B virus without viral co-infection C, D or HIV, followed in the Gastroenterology unit of the Campus Teaching Hospital of Lome-Togo. Results: We retained 222 patients. Among the patients, 148 patients (66.67%) were classified in Phase 3 (inactive carrying). Only 10 patients (4.50%) had a APRI score indicating a fibrosis stage ≥ F4 (presence of cirrhosis). A FIB-4 score indicating the presence of cirrhosis was found in 12 patients (5.40%). The most represented stage at fibrotest was the F0 stage (45.45%). Cirrhosis was noted in 6.06% of cases at fibroscan. Patients with APRI score ≤ 2 (96.23%) had a FIB-4 score ≤ 3.25, (p = 0.0088). Conclusion: The evaluation of hepatic fibrosis during chronic hepatopathies is essential for patients care because it influences therapeutic decisions.
The Structure of Models of Peano Arithmetic

Preface 1. Basics 2. Extensions 3. Minimal and other types 4. Substructure lattices 5. How to control types 6. Generics and forcing 7. Cuts 8. Automorphisms of recursively saturated models 9. Automorphism groups of recursively saturated models 10. Omega 1-like models 11. Order types 12. Twenty questions References Index
LYMPHOID NEOPLASIA miR-181a negatively regulates NF- k B signaling and affects activated B-cell – like diffuse large B-cell lymphoma pathogenesis

Distinct subgroups of diffuse large B-cell lymphoma (DLBCL) genetically resemble specific mature B-cell populations that are blocked at different stages of the immune response in germinal centers (GCs). The activated B-cell (ABC)–like subgroup resembles post-GC plasmablasts undergoing constitutive survival signaling, yet knowledge of the mechanisms that negatively regulate this oncogenic signaling remains incomplete. In this study, we report that microRNA (miR)-181a is a negative regulator of nuclear factor k –light-chain enhancer of activated B-cells (NF- k B) signaling. miR-181a overexpression significantly decreasestheexpression and activityofkeyNF- k Bsignalingcomponents. Moreover,miR-181a decreases DLBCL tumor cell proliferation and survival, and anti–miR-181a abrogates these effects. Remarkably, these effects are augmented in the NF- k B dependent ABC-like subgroupcomparedwiththeGCB-cell(GCB)–likeDLBCLsubgroup.Concordantly,invivo analyses of miR-181a induction in xenografts results in slower tumor growth rate and prolonged survival in the ABC-like DLBCL xenografts compared withthe GCB-like DLBCL. We link these outcomes to relatively lower endogenous miR-181a expression and to NF- k B signaling dependency in the ABC-like DLBCL subgroup. Our findings indicate that miR-181a inhibits NF- k B activity, and that manipulation of miR-181a expression in the ABC-like DLBCL genetic background may result in a significant change in the proliferation and survival phenotype of this malignancy. ( Blood . 2016;127(23):2856-2866)
Conditional b 1-integrin gene deletion in neural crest cells causes severe developmental alterations of the peripheral nervous system

extracellular matrix and to be required for migration, proliferation, differentiation and apoptosis. We have generated mice with a neural crest cell-speciﬁc deletion of the b 1-integrin gene to analyse the role of b 1-integrins in neural crest cell migration and differentiation. This targeted mutation caused death within a month of birth. The loss of b 1-integrins from the embryo delayed the migration of Schwann cells along axons and induced multiple defects in spinal nerve arborisation and morphology. There was an almost complete absence of Schwann cells and sensory axon segregation and defective maturation in neuromuscular synaptogenesis. Thus, b 1-integrins are important for the control of embryonic and postnatal peripheral nervous system development.
Reweighting the RCT for generalization: ﬁnite sample analysis and variable selection

The limited scope of Randomized Controlled Trials (RCT) is increasingly under scrutiny, in particular when samples are unrepresentative. Indeed, some RCTs over- or under- sample individuals with certain characteristics compared to the target population, for which one want to draw conclusions on treatment eﬀectiveness. Re-weighting trial individuals to match the target population helps to improve the treatment eﬀect estimation. Such procedures require an estimation of the ratio of the two densities (trial and target distributions). In this work, we establish the exact expressions of the bias and variance of such reweighting procedures - also called Inverse Propensity of Sampling Weighting (IPSW) - in presence of categorical covariates for any sample size. Such results allow us to compare the theoretical performance of diﬀerent versions of IPSW estimates. Besides, our results show how the performance (bias, variance and quadratic risk) of IPSW estimates depends on the two sample sizes (RCT and target population). A by-product of our work is the proof of consistency of IPSW estimates. A reﬁned analysis also shows that IPSW performances are improved when the trial probability to be treated is estimated (rather than using its oracle counterpart). In addition, we study how including covariates that are unnecessary to a proper estimation of the weights may impact the asymptotic variance. We illustrate all the takeaways twice: in a toy and didactic example, and on a semi-synthetic simulation inspired from critical care medicine.
QuickSkill: Novice Skill Estimation in Online Multiplayer Games

Matchmaking systems are vital for creating fair matches in online multiplayer games, which directly affects players' satisfactions and game experience. Most of the matchmaking systems largely rely on precise estimation of players' game skills to construct equitable games. However, the skill rating of a novice is usually inaccurate, as current matchmaking rating algorithms require considerable amount of games for learning the true skill of a new player. Using these unreliable skill scores at early stages for matchmaking usually leads to disparities in terms of team performance, which causes negative game experience. This is known as the "cold-start" problem for matchmaking rating algorithms. To overcome this conundrum, this paper proposes QuickSKill, a deep learning based novice skill estimation framework to quickly probe abilities of new players in online multiplayer games. QuickSKill extracts sequential performance features from initial few games of a player to predict his/her future skill rating with a dedicated neural network, thus delivering accurate skill estimation at the player's early game stage. By employing QuickSKill for matchmaking, game fairness can be dramatically improved in the initial cold-start period. We conduct experiments in a popular mobile multiplayer game in both offline and online scenarios. Results obtained with two real-world anonymized gaming datasets demonstrate that proposed QuickSKill delivers precise estimation of game skills for novices, leading to significantly lower team skill disparities and better player game experience. To the best of our knowledge, proposed QuickSKill is the first framework that tackles the cold-start problem for traditional skill rating algorithms.
Magnetocaloric effect in multilayers studied by membrane-based calorimetry

We study magnetic multilayers, incorporating dilute ferromagnetic spacers between strongly-ferromagnetic layers exhibiting a proximity-enhanced magnetocaloric effect. Using magnetometry and direct measurements of the adiabatic temperature change based on a nanomembrane-calorimetry, we find that the magnetocaloric effect in the studied multilayer is indeed enhanced compared to that in the bulk spacer material. We develop a phenomenological numerical model of the studied trilayer and find that a long-range exchange interaction through the weakly-ferromagnetic spacer is required to adequately describe the magnetic and magnetocaloric properties of the system.
Quantum Mechanics reconstruction from invariance of the laws of nature under tensor composition

Quantum and classical mechanics are derived using four natural physical principles: (1) the laws of nature are invariant under time evolution, (2) the laws of nature are invariant under tensor composition, (3) the laws of nature are relational, and (4) positivity (the ability to define a physical state). Quantum mechanics is singled out by a fifth experimentally justified postulate: nature violates Bell's inequalities.
Extending the variational quantum eigensolver to finite temperatures

We present a variational quantum thermalizer (VQT), called quantum-VQT (qVQT), which extends the variational quantum eigensolver to finite temperatures. The qVQT makes use of an intermediate measurement between two variational circuits to encode a density matrix on a quantum device. A classical optimization provides the thermal state and, simultaneously, all associated excited states of a quantum mechanical system. We demonstrate the capabilities of the qVQT for two different spin systems. First, we analyze the performance of qVQT as a function of the circuit depth and the temperature for a one-dimensional Heisenberg chain. Second, we use the excited states to map the complete, temperature dependent phase diagram of a two-dimensional J1–J2 Heisenberg model. Numerical experiments on both quantum simulators and real quantum hardware demonstrate the efficiency of our approach, which can be readily applied to study various quantum many-body systems at finite temperatures on currently available noisy intermediate-scale quantum devices.
$L^p$ sampling numbers for the Fourier-analytic Barron space

In this paper, we consider Barron functions $f : [0,1]^d \to \mathbb{R}$ of smoothness $\sigma>0$, which are functions that can be written as \[ f(x) = \int_{\mathbb{R}^d} F(\xi) \, e^{2 \pi i \langle x, \xi \rangle} \, d \xi \quad \text{with} \quad \int_{\mathbb{R}^d} |F(\xi)| \cdot (1 + |\xi|)^{\sigma} \, d \xi<\infty. \] For $\sigma = 1$, these functions play a prominent role in machine learning, since they can be efficiently approximated by (shallow) neural networks without suffering from the curse of dimensionality. For these functions, we study the following question: Given $m$ point samples $f(x_1),\dots,f(x_m)$ of an unknown Barron function $f : [0,1]^d \to \mathbb{R}$ of smoothness $\sigma$, how well can $f$ be recovered from these samples, for an optimal choice of the sampling points and the reconstruction procedure? Denoting the optimal reconstruction error measured in $L^p$ by $s_m (\sigma; L^p)$, we show that \[ m^{- \frac{1}{\max \{ p,2 \}} - \frac{\sigma}{d}} \lesssim s_m(\sigma;L^p) \lesssim (\ln (e + m))^{\alpha(\sigma,d) / p} \cdot m^{- \frac{1}{\max \{ p,2 \}} - \frac{\sigma}{d}} , \] where the implied constants only depend on $\sigma$ and $d$ and where $\alpha(\sigma,d)$ stays bounded as $d \to \infty$.
Demonstrating A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning

—Deep reinforcement learning is a promising approach to learning policies in unstructured environments. Due to its sample inefficiency, though, deep RL applications have primarily focused on simulated environments. In this work, we demonstrate that the recent advancements in machine learning algorithms and libraries combined with careful MDP formulation lead to learning quadruped locomotion in only 20 minutes in the real world. We evaluate our approach on several indoor and outdoor terrains that are known to be challenging for classical, model-based controllers and observe that the robot consistently learns a walking gait on all of these terrains. Finally, we evaluate our design decisions in a simulated environment. We provide videos of all real-world training and code to reproduce our results on our website: https://sites.google. com/berkeley.
Learning Operators with Ignore Effects for Bilevel Planning in Continuous Domains

Bilevel planning, in which a high-level search over an abstraction of an environment is used to guide low-level decision making, is an effective approach to solving long-horizon tasks in continuous state and action spaces. Recent work has shown that action abstractions that enable such bilevel planning can be learned in the form of symbolic operators and neural samplers given symbolic predicates and demonstrations that achieve known goals. In this work, we show that existing approaches fall short in environments where actions tend to cause a large number of predicates to change. To address this issue, we propose to learn operators with ignore effects . The key idea motivating our approach is that modeling every observed change in the predicates is unnecessary; the only changes that need be modeled are those that are necessary for high-level search to achieve the speciﬁed goal. Ex-perimentally, we show that our approach is able to learn operators with ignore effects across six hybrid robotic domains that enable an agent to solve novel variations of a task, with different initial states, goals, and numbers of objects, signiﬁ-cantly more efﬁciently than several baselines.
Role of Data Augmentation in Unsupervised Anomaly Detection

Self-supervised learning (SSL) has emerged as a promising alternative to create supervisory signals to real-world tasks, avoiding extensive cost of careful labeling. SSL is particu- larly attractive for unsupervised problems such as anomaly detection (AD), where labeled anomalies are costly to secure, difﬁcult to simulate, or even nonexistent. A large catalog of augmentation functions have been used for SSL-based AD (SSAD), and recent works have observed that the type of augmentation has a signiﬁcant impact on performance. Motivated by those, this work sets out to put SSAD under a larger lens and carefully investigate the role of data augmentation in AD through extensive experiments on many testbeds. Our main ﬁnding is that self-supervision acts as a yet-another model hyperparameter, and should be chosen carefully in regards to the nature of true anomalies in the data. That is, the align- ment between the augmentation and the underlying anomaly-generating mechanism is the key for the success of SSAD, and in the lack thereof, SSL can even impair (! ) detection performance. Moving beyond proposing another SSAD method, our study contributes to the better understanding of this growing area and lays out new directions for future research.
Analysing Factors Affecting the Adoption of Ride-Hailing Services (RHS) in India? A Step-Wise LCCA-MCDM Modeling Approach

The study examines heterogeneity in travel behaviour among ride-hailing services (RHS) users by including attitudes, in order to reinforce conventional user-segmentation approaches. Simultaneously, prioritization of ride-hailing specific attributes was carried out to assess how RHS will operate in a sustainable way. The study initially examines latent heterogeneity in users through a LCCA model, and subsequently prioritizes key RHS-specific attributes for each cluster using and MCDM techniques. Three clusters were identified, based on individuals’ attitudes and covariates (socio-demographics, land-use attributes, and travel habits). The largest cluster is termed as Tech-savvy ride-hailing-ready individuals (48%), who have higher technological ability, and show maximum acceptance towards RHS. The second largest cluster, termed as Traditional active-travelling individuals (28%), display least proclivity towards RHS, probably due to their technological inhibition coupled with their greater attachment to traditional travel modes. Lastly, the Multimodal PV-loving individuals (24%) are mostly vehicle-owners but also prefer RHS for occasional trips. The final ranking obtained from the analysis revealed that travel time, reliability, and flexibility ( motivators ); travel cost, and waiting time ( deterrents ) are perceived as the key attributes influencing RHS adoption in the Indian context.
A Hydro-based MCMC Analysis of SNR 0509−67.5: Revealing the Explosion Properties from Fluid Discontinuities Alone

Using Hubble Space Telescope/ACS Hα images of SNR 0509−67.5 taken ∼10 yr apart, we measure the forward shock (FS) proper motions (PMs) at 231 rim locations. The average shock radius and velocity are 3.66 ± 0.036 pc and 6315 ± 310 km s−1. Hydrodynamic simulations, recast as similarity solutions, provide models for the supernova remnant’s expansion into a uniform ambient medium. These are coupled to a Markov chain Monte Carlo (MCMC) analysis to determine explosion parameters, constrained by the FS measurements. For our baseline model, the MCMC posteriors yield an age of 315.5 ± 1.8 yr, a dynamical explosion center at 5h09m31. ˢ16, −67°31′17. ″1 and ambient medium densities at each azimuth ranging over 3.7–8.0 × 10−25 g cm−3. The age uncertainty can be an order of magnitude larger when considering other models, or subsets of the data. We detect stellar PMs corresponding to speeds in the Large Magellanic Cloud ≥ 770 km s−1. Five stars in the remnant show measurable PMs but none are moving radially from the dynamical center. There are four stars 1. ″4 from the center, including three faint, previously unidentified ones. Using coronal [Fe xiv] λ5303 emission as a proxy for the reverse shock location, we constrain the explosion energy (for a compression factor of 4) to a value of E = (1.30 ± 0.41) × 1051 erg for the first time from shock kinematics alone. Higher compression factors (7 or more) are strongly disfavored based on multiple criteria, arguing for inefficient particle acceleration in the Balmer shocks of SNR 0509−67.5.
Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level context memory

—Semantic segmentation is a key technique involved in automatic interpretation of high-resolution remote sensing (HRS) imagery and has drawn much attention in the remote sensing community. Deep convolutional neural networks (DCNNs) have been successfully applied to the HRS imagery semantic segmentation task due to their hierarchical representation ability. However, the heavy dependency on a large number of training data with dense annotation and the sensitiveness to the variation of data distribution severely restrict the potential application of DCNNs for the semantic segmentation of HRS imagery. This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery. MemoryAdaptNet constructs an output space adversarial learning scheme to bridge the domain distribution discrepancy between source domain and target domain and to narrow the influence of domain shift. Specifically, we embed an invariant feature memory module to store invariant domain-level context information because the features obtained from adversarial learning only tend to represent the variant feature of current limited inputs. This module is integrated by a category attention-driven invariant domain-level context aggregation module to current pseudo invariant feature for further augmenting the pixel representations. An entropy-based pseudo label filtering strategy is used to update the memory module with high-confident pseudo invariant feature of current target images. Extensive experiments under three cross-domain tasks indicate that our proposed MemoryAdaptNet is remarkably superior to the state-of-the-art methods.