Miracle-dz/UniFAR-FTUs huggingface dataset:
Molting and Salmonella enterica serovar enteritidis infection: the problem and some solutions.

Induced molting is an important economic tool used by the egg industry to recycle an aging layer flock. It is estimated that approximately 70% of the flocks nationwide and almost 100% in California are molted annually. Considering that there are approximately 240 million hens in production in the U.S., a rough estimate of the numbers of hens molted every year would be between 144 and 168 million birds, a substantial number. There are many methods to induce molt, but feed removal until hens lose a specific weight is the most prevalent molt strategy in the U.S. However, experimental studies in our laboratory have shown that induced molting via feed removal depresses the immune system of hens and exacerbates a Salmonella enteritidis (SE) problem in a simulated flock situation. Molted hens excreted significantly higher SE numbers in the feces, had higher numbers of SE in internal organs, and exhibited more intestinal inflammation. Molted hens were 100- to 1,000-fold more susceptible to infection by SE and therefore more readily transmitted the organism to uninfected hens in neighboring cages. With the problems identified, solutions were sought, and several were successful in ameliorating the SE issue. Antibiotic therapy, vaccination, and use of low-energy, low-calcium diets to molt hens all dramatically decreased SE shedding during molt. All of the solutions provide the producer with many potential solutions to the SE food safety issue and still allow them to recycle their hens.
Energy-Efficient Power Allocation for Point-to-Point MIMO Systems over the Rayleigh Fading Channel

It is well-established that transmitting at full power is the most spectral-efficient power allocation strategy for point-to-point (P2P) multi-input multi-output (MIMO) systems, however, can this strategy be energy efficient as well? In this letter, we address the most energy-efficient power allocation policy for symmetric P2P MIMO systems by accurately approximating in closed-form their optimal transmit power when a realistic MIMO power consumption model is considered. In most cases, being energy efficient implies a reduction in transmit and overall consumed powers at the expense of a lower spectral efficiency.
An L1 criterion for dictionary learning by subspace identification

We propose an ℓ1 criterion for dictionary learning for sparse signal representation. Instead of directly searching for the dictionary vectors, our dictionary learning approach identifies vectors that are orthogonal to the subspaces in which the training data concentrate. We study conditions on the coefficients of training data that guarantee that ideal normal vectors deduced from the dictionary are local optima of the criterion. We illustrate the behavior of the criterion on a 2D example, showing that the local minima correspond to ideal normal vectors when the number of training data is sufficient. We conclude by describing an algorithm that can be used to optimize the criterion in higher dimension.
Relationship between disease activity and patient-reported outcomes in rheumatoid arthritis: Post hoc analyses of overall and Japanese results from two phase 3 clinical trials

Abstract Objective: To examine patient-reported outcomes (PROs) in patients with different rheumatoid arthritis (RA) disease activity levels and identify residual symptoms. Methods: Post hoc analyses of overall and Japanese data from two randomized controlled trials including RA patients with previous inadequate responses to methotrexate (NCT01710358) or no/minimal previous disease-modifying antirheumatic drug treatment (NCT01711359) (sponsor: Eli Lilly and Company). Week 24 assessments were disease activity (Simplified Disease Activity Index, Disease Activity Score/Disease Activity Score 28 joints-erythrocyte sedimentation rate) and PROs (pain visual analog scale [VAS], morning joint stiffness [MJS], Health Assessment Questionnaire-Disability Index, Functional Assessment of Chronic Illness Therapy-Fatigue, and Medical Outcomes Study Short Form 36 Health Survey Physical and Mental Component Scores). Results: Patients achieving remission/low disease activity (LDA) at Week 24 had larger/significant improvements from baseline in pain, MJS, disability, fatigue, and physical and emotional quality of life versus patients with high/moderate disease activity. Some patients achieving remission and LDA, reported residual pain (pain VAS >10 mm): 20.8–39.3% and 48.7–70.0% (overall study populations), 16.0–34.5% and 47.1–62.0% (Japanese patients). Residual MJS and fatigue were also reported. Conclusion: Remission/LDA were associated with improvements in PROs in overall and Japanese patient populations; however, some patients achieving remission had residual symptoms, including pain.
Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture

In this paper, we propose a deep learning based vehicle trajectory prediction technique which can generate the future trajectory sequence of surrounding vehicles in real time. We employ the encoder-decoder architecture which analyzes the pattern underlying in the past trajectory using the long short-term memory (LSTM) based encoder and generates the future trajectory sequence using the LSTM based decoder. This structure produces the $K$ most likely trajectory candidates over occupancy grid map by employing the beam search technique which keeps the $K$ locally best candidates from the decoder output. The experiments conducted on highway traffic scenarios show that the prediction accuracy of the proposed method is significantly higher than the conventional trajectory prediction techniques.
Intrinsic Noise Profoundly Alters the Dynamics and Steady State of Morphogen-Controlled Bistable Genetic Switches

During tissue development, patterns of gene expression determine the spatial arrangement of cell types. In many cases, gradients of secreted signalling molecules—morphogens—guide this process by controlling downstream transcriptional networks. A mechanism commonly used in these networks to convert the continuous information provided by the gradient into discrete transitions between adjacent cell types is the genetic toggle switch, composed of cross-repressing transcriptional determinants. Previous analyses have emphasised the steady state output of these mechanisms. Here, we explore the dynamics of the toggle switch and use exact numerical simulations of the kinetic reactions, the corresponding Chemical Langevin Equation, and Minimum Action Path theory to establish a framework for studying the effect of gene expression noise on patterning time and boundary position. This provides insight into the time scale, gene expression trajectories and directionality of stochastic switching events between cell states. Taking gene expression noise into account predicts that the final boundary position of a morphogen-induced toggle switch, although robust to changes in the details of the noise, is distinct from that of the deterministic system. Moreover, the dramatic increase in patterning time close to the boundary predicted from the deterministic case is substantially reduced. The resulting stochastic switching introduces differences in patterning time along the morphogen gradient that result in a patterning wave propagating away from the morphogen source with a velocity determined by the intrinsic noise. The wave sharpens and slows as it advances and may never reach steady state in a biologically relevant time. This could explain experimentally observed dynamics of pattern formation. Together the analysis reveals the importance of dynamical transients for understanding morphogen-driven transcriptional networks and indicates that gene expression noise can qualitatively alter developmental patterning.
Faster Distributed Shortest Path Approximations via Shortcuts

A long series of recent results and breakthroughs have led to faster and better distributed approximation algorithms for single source shortest paths (SSSP) and related problems in the CONGEST model. The runtime of all these algorithms, however, is $\tilde{\Omega}(\sqrt{n})$, regardless of the network topology, even on nice networks with a (poly)logarithmic network diameter $D$. While this is known to be necessary for some pathological networks, most topologies of interest are arguably not of this type. We give the first distributed approximation algorithms for shortest paths problems that adjust to the topology they are run on, thus achieving significantly faster running times on many topologies of interest. The running time of our algorithms depends on and is close to $Q$, where $Q$ is the quality of the best shortcut that exists for the given topology. While $Q = \tilde{\Theta}(\sqrt{n} + D)$ for pathological worst-case topologies, many topologies of interest have $Q = \tilde{\Theta}(D)$, which results in near instance optimal running times for our algorithm, given the trivial $\Omega(D)$ lower bound. The problems we consider are as follows: (1) an approximate shortest path tree and SSSP distances, (2) a polylogarithmic size distance label for every node such that from the labels of any two nodes alone one can determine their distance (approximately), and (3) an (approximately) optimal flow for the transshipment problem. Our algorithms have a tunable tradeoff between running time and approximation ratio. Our fastest algorithms have an arbitrarily good polynomial approximation guarantee and an essentially optimal $\tilde{O}(Q)$ running time. On the other end of the spectrum, we achieve polylogarithmic approximations in $\tilde{O}(Q \cdot n^{\epsilon})$ rounds for any $\epsilon > 0$. It seems likely that eventually, our non-trivial approximation algorithms for the.
Too good to be true?

In this not-for-publication Supplementary Appendix we start by studying the extensions described in Section 6.6 of the main text. We then fill some gaps in the proof of our main result, Proposition 1, and provide proofs of Propositions 5, 6 and 7. We give a numerical example of bounded replacement equilibria in the costly noise model (Section 6.2), and provide other missing details, such as the shape of the optimal choice of noise for a generic type, depicted in Figure 3 of the paper.
Polarization rotation compensation in POLMUX-DD OFDMA-PON using ICA and adaptive filter

OFDMA-PON is being intensively studied to realize high-speed communication and improve bandwidth efficiency. POLMUX-DD OFDMA-PON should enable even higher communication speeds by supporting direct detection and polarization multiplexing, but a key problem is its noise created by orthogonal polarization interference. This paper examines the relationship between fiber twist and polarization rotation. We propose that this noise be removed by a combination of ICA and RLS filter; tests confirm the BER improvements.
Association between GSTP1 polymorphisms and prognosis of osteosarcoma in patients treated with chemotherapy: a meta-analysis

Objective The aim of this study was to evaluate the relationship between GSTP1 polymorphisms and prognosis of osteosarcoma in patients treated with chemotherapy, by performing a meta-analysis. Methods The studies of effects of GSTP1 gene polymorphisms on osteosarcoma survival after chemotherapy were collected. STATA (version 12.0) was used to perform data synthesis. Results Six studies involving 898 participants were included. A meta-analysis was performed on studies in GSTP1 313A>G(rs1695) assessing the association between tumor response and the polymorphisms in GSTP1 (AA vs AG, AA vs GG), the pooled odds ratios (ORs) were 2.06 (95% confidence interval [CI]: 1.48–2.86, P=0.628, I2=0.0%). There was significant association between the polymorphisms in GSTP1 (AA vs AG, AA vs GG) and the events that happened, the pooled ORs were 1.86 (95% CI: 1.14–3.06, P=0.034, I2=58.6%), and there was significant association between the polymorphisms in GSTP1 (AA vs AG, AA vs GG) and survival times (overall survival and progression-free survival) in osteosarcoma patients treated with chemotherapy, and the pooled ORs were 2.14 (95% CI: 1.51–3.04, P=0.675, I2=0.0%) and 2.77 (95% CI: 1.56–4.91, P=0.347, I2=9.3%), respectively. Two studies assessed the association of polymorphisms in GSTP1 I105V (IIe/IIe vs IIe/Val, IIe/IIe vs Val/Val) with overall survival in human osteosarcoma. The pooled ORs were 1.20 (95% CI: 0.64–2.27, P=0.010, I2=73.5%). The study showed an insignificant difference in overall survival for the polymorphisms in GSTP1 (IIe/IIe vs IIe/Val, IIe/IIe vs Val/Val). Conclusion This meta-analysis indicated that GSTP1 polymorphisms might influence osteosarcoma risk and suggests that GSTP1 polymorphisms may be an important risk factor for osteosarcoma.
ω-3 and ω-6 Fatty Acid Supplementation May Reduce Autism Symptoms Based on Parent Report in Preterm Toddlers.

Background Children born preterm are at increased risk of autism spectrum disorder (ASD). n-3 (ω-3) Combined with n-6 (ω-6) fatty acids including γ-linolenic acid (GLA) may benefit children born preterm showing early signs of ASD. Previous trials have reported that docosahexaenoic acid (DHA) promotes cognitive development in preterm neonates and n-3 fatty acids combined with GLA improve attention-deficit-hyperactivity disorder. Objectives The objectives of the pilot Preemie Tots Trial were 1) to confirm the feasibility of a full-scale trial in toddlers born very preterm and exhibiting ASD symptoms and 2) to explore the effects of supplementation on parent-reported ASD symptoms and related behaviors. Methods This was a 90-d randomized, fully blinded, placebo-controlled trial in 31 children 18-38 mo of age who were born at ≤29 wk of gestation. One group was assigned to daily Omega-3-6-9 Junior (Nordic Naturals, Inc.) treatment (including 338 mg eicosapentaenoic acid, 225 mg DHA, and 83 mg GLA), and the other group received canola oil (124 mg palmitic acid, 39 mg stearic acid, 513 mg linoleic acid, 225 mg α-linolenic acid, and 1346 mg oleic acid). Mixed-effects regression analyses followed intent-to-treat analysis and explored effects on parent-reported ASD symptoms and related behaviors. Results Of 31 children randomly assigned, 28 had complete outcome data. After accounting for baseline scores, those assigned to treatment exhibited a greater reduction in ASD symptoms per the Brief Infant Toddler Social Emotional Assessment ASD scale than did those assigned to placebo (difference in change = - 2.1 points; 95% CI: - 4.1, - 0.2 points; standardized effect size = - 0.71). No other outcome measure reflected a similar magnitude or a significant effect. Conclusions This pilot trial confirmed adequate numbers of children enrolled and participated fully in the trial. No safety concerns were noted. It also found clinically-significant improvements in ASD symptoms for children randomly assigned to receive Omega-3-6-9 Junior, but effects were confined to one subscale. A future full-scale trial is warranted given the lack of effective treatments for this population. This trial was registered at www.clinicaltrials. gov as NCT01683565.
Translating Research into Improved Patient Care in Pulmonary Arterial Hypertension

Abstract Despite important advances in its therapeutic management, pulmonary arterial hypertension (PAH) remains an incurable disease. Although numerous drugs exhibited beneficial effects in preclinical settings, only few have reached clinical trial phases, highlighting the challenges of translating preclinical investigations into clinical trials. Potential reasons for delayed PAH drug development may include the inherent limitations of the currently available animal and in vitro models, potential lack of appropriate standardization of the experimental design, regulatory agencies requirements, competing clinical trials and insufficient funding. Although this is not unique to PAH, there is urgency for reducing the number of false positive signals in preclinical studies and optimizing the development of innovative therapeutic targets through performance of clinical trials based on more robust experimental data. The current review discusses the challenges and opportunities in preclinical research to foster drug development in PAH.
Molecular angiogenic signaling in angiofibromas after embolization: implications for therapy.

OBJECTIVES To examine (1) the molecular angiogenic relationship between endothelial and stromal cells of angiofibromas and how this may elucidate the pathogenesis of angiofibromas and (2) the effects of embolization on the expression of angiotrophic factors and proapoptotic and antiapoptotic factors within the tumor. DESIGN The expression of mesenchymal and endothelial stem/progenitor cell-associated proteins (MECAPs) such as proangiogenic cytokine vascular endothelial growth factor (VEGF), VEGF receptors (VEGFR1, VEGFR2, and VEGFR3), angiopoietin receptors (Tie-1 and Tie-2), and stem cell subset marker CD133 was assessed by immunohistological staining in 7 embolized angiofibroma specimens. Expression of proapoptotic Bax, antiapoptotic Bcl-2 and Bcl-xL, nuclear proliferation protein MiB-1, and hypoxia-inducible factor 1alpha (Hif-1alpha) in peri-ischemic areas of the embolized angiofibromas was also assessed. SETTING A single pediatric institution. PATIENTS Seven patients (identified from medical records, January 1, 2001, through December 31, 2005) who were diagnosed as having juvenile angiofibroma and who underwent surgical treatment. Archival tissues were retrieved for immunostaining. MAIN OUTCOME MEASURES The immunostaining results were evaluated by microscopy and the staining intensities were also recorded. RESULTS All angiofibroma specimens expressed the stem cell subset marker CD133 and MECAPs except VEGFR3 (a few cases). In the only case tested, we found evidence of VEGF-induced angiogenic signaling as the expression of phosphorylated VEGFR2 (Tyr951). Endothelial cells expressed VEGFR1 and VEGFR2 and angiopoietin receptors Tie-1 and Tie-2 but not VEGF. In contrast, VEGF was expressed within stromal cells. Viable tumor adjacent to the ischemic areas demonstrated increased staining intensities to VEGFR2, Tie-1, Tie-2 (all cases), and VEGFR3 (2 cases) and increased nuclear proliferation (5%-20%). All cases expressed proapoptotic and antiapoptotic factors, and the expression of Hif-1alpha was unaffected by ischemia. CONCLUSIONS Stromal cells appear to be similar to mesenchymal stem cells with endothelial differentiation potential in umbilical cord blood cells. Stromal cells support endothelial growth by providing VEGF as a paracrine factor. Under ischemic stress, the embolized tumor tissues show upregulation of angiogenic receptors, retention of Hif-1alpha, and increased nuclear proliferation rates. Specific angiogenesis blockers may represent a novel treatment strategy for angiofibromas.
Foundations of Homomorphic Secret Sharing

Homomorphic secret sharing (HSS) is the secret sharing analogue of homomorphic encryption. An HSS scheme supports a local evaluation of functions on shares of one or more secret inputs, such that the resulting shares of the output are short. Some applications require the stronger notion of additive HSS, where the shares of the output add up to the output over some finite Abelian group. While some strong positive results for HSS are known under specific cryptographic assumptions, many natural questions remain open. We initiate a systematic study of HSS, making the following contributions. - A definitional framework. We present a general framework for defining HSS schemes that unifies and extends several previous notions from the literature, and cast known results within this framework. - Limitations. We establish limitations on information-theoretic multi-input HSS with short output shares via a relation with communication complexity. We also show that additive HSS for non-trivial functions, even the AND of two input bits, implies non-interactive key exchange, and is therefore unlikely to be implied by public-key encryption or even oblivious transfer. - Applications. We present two types of applications of HSS. First, we construct 2-round protocols for secure multiparty computation from a simple constant-size instance of HSS. As a corollary, we obtain 2-round protocols with attractive asymptotic efficiency features under the Decision Diffie Hellman (DDH) assumption. Second, we use HSS to obtain nearly optimal worst-case to average-case reductions in P. This in turn has applications to fine-grained average-case hardness and verifiable computation.
OTFS: A New Generation of Modulation Addressing the Challenges of 5G

In this paper, we introduce a new 2D modulation scheme referred to as OTFS (Orthogonal Time Frequency & Space) that multiplexes information QAM symbols over new class of carrier waveforms that correspond to localized pulses in a signal representation called the delay-Doppler representation. OTFS constitutes a far reaching generalization of conventional time and frequency modulations such as TDM and FDM and, from a broader perspective, it establishes a conceptual link between Radar and communication. The OTFS waveforms couple with the wireless channel in a way that directly captures the underlying physics, yielding a high-resolution delay-Doppler Radar image of the constituent reflectors. As a result, the time-frequency selective channel is converted into an invariant, separable and orthogonal interaction, where all received QAM symbols experience the same localized impairment and all the delay-Doppler diversity branches are coherently combined. The high resolution delay-Doppler separation of the reflectors enables OTFS to approach channel capacity with optimal performance-complexity tradeoff through linear scaling of spectral efficiency with the MIMO order and robustness to Doppler and multipath channel conditions. OTFS is an enabler for realizing the full promise of MUMIMO gains even in challenging 5G deployment settings where adaptation is unrealistic.
Deep Learning Models Delineates Multiple Nuclear Phenotypes in H&E Stained Histology Sections

Nuclear segmentation is an important step for profiling aberrant regions of histology sections. However, segmentation is a complex problem as a result of variations in nuclear geometry (e.g., size, shape), nuclear type (e.g., epithelial, fibroblast), and nuclear phenotypes (e.g., vesicular, aneuploidy). The problem is further complicated as a result of variations in sample preparation. It is shown and validated that fusion of very deep convolutional networks overcomes (i) complexities associated with multiple nuclear phenotypes, and (ii) separation of overlapping nuclei. The fusion relies on integrating of networks that learn region- and boundary-based representations. The system has been validated on a diverse set of nuclear phenotypes that correspond to the breast and brain histology sections.