Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical probing of orexin/hypocretin receptor antagonists

Study Objectives The present study investigated the function of Hypocretin (Hcrt or Orexin/OX) receptor antagonists in sleep modulation and memory function with optical methods in transgenic mice. Methods We used Hcrt-IRES-Cre knock-in mice and AAV vectors expressing channelrhodopsin-2 (ChR2) to render Hcrt neurons sensitive to blue light stimulation. We optogenetically stimulated Hcrt neurons and measured latencies to wakefulness in the presence or absence of OX1/2R antagonists and Zolpidem. We also examined endogenous Hcrt neuronal activity with fiber photometry. Changes in memory after optogenetic sleep disruption were evaluated by the novel object recognition test (NOR) and compared for groups treated with vehicle, OX1/2R antagonists, or Zolpidem. We also analyzed electroencephalogram (EEG) power spectra of wakefulness, rapid eye movement (REM) sleep, and non-REM (NREM) sleep following the injections of vehicle, OX1/2R antagonists, and Zolpidem in young adult mice. Results Acute optogenetic stimulation of Hcrt neurons at different frequencies resulted in wakefulness. Treatment with dual OX1/2R antagonists (DORAs) DORA12 and MK6096, as well as selective OX2R antagonist MK1064 and Zolpidem, but not selective OX1R antagonist 1SORA1, significantly reduced the bout length of optogenetic stimulation-evoked wakefulness episode. Fiber photometry recordings of GCaMP6f signals showed that Hcrt neurons are active during wakefulness, even in the presence of OXR antagonists. Treatment with dual OX1/2R antagonists improved memory function despite optogenetic sleep fragmentation caused impaired memory function in a NOR test. Conclusions Our results show DORAs and selective OX2R antagonists stabilize sleep and improve sleep-dependent cognitive processes even when challenged by optogenetic stimulation mimicking highly arousing stimuli.
Inflation versus filling-in: why we feel we see more than we actually do in peripheral vision

Do we perceive fine details in the visual periphery? Here, we propose that phenomenology in the visual periphery can be characterized by an inflated sense of perceptual capacity, as observers overestimate the quality of their perceptual inputs. Distinct from the well-known perceptual phenomenon of “filling-in” where perceptual content is generated or completed endogenously, inflation can be characterized by incorrect introspection at the subjective level. The perceptual content itself may be absent or weak (i.e., not necessarily filled-in), and yet such content is mistakenly regarded by the system as rich. Behaviorally, this can be reflected by metacognitive deficits in the degree to which confidence judgments track task accuracy, and decisional biases for observers to think particular items are present, even when they are not. In two experiments using paradigms which exploit unique attributes of peripheral vision (crowding and summary statistics), we provide evidence that both types of deficits are present in peripheral vision, as observers’ reports are marked by overconfidence in discrimination judgments and high numbers of false alarms in detection judgments. We discuss potential mechanisms which may be the cause of inflation and propose future experiments to further explore this unique sensory phenomenon.
Listeria monocytogenes Colonizes Pseudomonas fluorescens Biofilms and Induces Matrix Over-Production

In food facilities, biofilms or their debris might act as helpers for attracting free floating microorganisms. In this sense, Pseudomonas fluorescens, a dense biofilm producer frequently isolated from food contact surfaces, could be a good candidate for sheltering other microorganisms, such as Listeria monocytogenes. The main objective of this work was to evaluate the ability of L. monocytogenes to colonize pre-established Pseudomonas biofilms. For this, the movement throughout mature Pseudomonas biofilms of a green fluorescent protein (GFP) – tagged strain of L. monocytogenes was tracked for 24 h by confocal laser scanning microscopy (CLSM). Moreover, in order to check the effect of the incorporation of Listeria on the overall matrix production, attached populations of both microorganisms and total biomass (cells + matrix) of the resulting biofilms were measured over time. Planktonic cells of L. monocytogenes efficiently migrated to preformed P. fluorescens biofilms. Moreover, they moved preferentially toward the bottom layers of these structures, suggesting some kind of tropism. When preformed P. fluorescens biofilms were conditioning the surfaces, the L. monocytogenes attached population was on average, 1–2 Log higher than when this organism grew on bare coupons. Furthermore, the arrival of L. monocytogenes to the already established P. fluorescens biofilms led to a matrix over-production. Indeed, biomass values [optical density (OD595 nm)] of the resulting biofilms were double those of the ordinary L. monocytogenes–P. fluorescens mixed biofilms (1.40 vs. 0.6). The fact that L. monocytogenes cells accumulate in the bottom layers of preformed biofilms provides this microorganism an extra protection toward physical–chemical damages. This might partly explain why this microorganism can persist in food industry environments.
PCStream: Automatic Stream Allocation Using Program Contexts

We propose a fully automatic stream management technique, called PCStream , for multi-streamed SSDs. PC-Stream is based on our observation that data lifetimes can be reliably predicted using write program contexts. By extracting program contexts during runtime, PC-Stream automates the data-to-stream mapping. When data mapped to the same stream show large differences in their lifetimes, PCStream moves the long-lived data of the current stream to its substream during garbage collection. Our experimental results show that PCStream can reduce the garbage collection overhead as much as a highly-optimized manual stream management technique while no code modiﬁcation is necessary.
Interleukin-33 Contributes to the Induction of Th9 Cells and Antitumor Efficacy by Dectin-1-Activated Dendritic Cells

We recently discovered that dectin-1-activated dendritic cells (DCs) drive potent T helper (Th) 9 cell responses and antitumor immunity. However, the underlying mechanisms need to be further defined. The cytokine microenvironment is critical for Th cell differentiation. Here, we show that dectin-1 activation enhances interleukin (IL)-33 expression in DCs. We found that blocking IL-33/ST2 inhibits dectin-1-activated DC-induced Th9 cell differentiation. More importantly, the addition of IL-33 further promotes Th9 cell priming and antitumor efficacy induced by dectin-1-activated DCs. Mechanistically, in addition to the promotion of Th9 and Th1 cells, dectin-1-activated DCs combined with IL-33 abolish the activity of IL-33 in the induction of regulatory T cells. Furthermore, the combined treatment of dectin-1-activated DCs and IL-33 increases the frequencies of CD4+ T cells by fostering their proliferation and inhibiting their exhaustive differentiation. Thus, our results demonstrate the important role of IL-33 in dectin-1-activated DC-induced Th9 cell differentiation and antitumor efficacy, and suggest that the combination of dectin-1-activated DCs and IL-33 may present a new effective modality of DC-based vaccines in tumor immunotherapy.
A Direct Imaging Method for Inverse Scattering by Unbounded Rough Surfaces

This paper is concerned with the inverse scattering problem by an unbounded rough surface. A direct imaging method is proposed to reconstruct the rough surface from the scattered near-field Cauchy data generating by point sources and measured on a horizontal straight line segment at a finite distance above the rough surface. Theoretical analysis of the imaging algorithm is given for the case of a penetrable rough surface, but the imaging algorithm also works for impenetrable surfaces with Dirichlet or impedance boundary conditions. Numerical experiments are presented to show that the direct imaging algorithm is fast, accurate and very robust with respect to noise in the data.
Albis: High-Performance File Format for Big Data Systems

Over the last decade, a variety of external file formats such as Parquet, ORC, Arrow, etc., have been developed to store large volumes of relational data in the cloud. As high-performance networking and storage devices are used pervasively to process this data in frameworks like Spark and Hadoop, we observe that none of the popular file formats are capable of delivering data access rates close to the hardware. Our analysis suggests that multiple antiquated notions about the nature of I/O in a distributed setting, and the preference for the "storage efficiency" over performance is the key reason for this gap. In this paper we present Albis, a high-performance file format for storing relational data on modern hardware. Albis is built upon two key principles: (i) reduce the CPU cost by keeping the data/metadata storage format simple; (ii) use a binary API for an efficient object management to avoid unnecessary object materialization. In our evaluation, we demonstrate that in micro-benchmarks Albis delivers 1.9-21.4× faster bandwidths than other formats. At the workload-level, Albis in Spark/SQL reduces the runtimes of TPC-DS queries up to a margin of 3×.
Tackling the Antibiotic Resistance Caused by Class A β-Lactamases through the Use of β-Lactamase Inhibitory Protein

β-Lactams are the most widely used and effective antibiotics for the treatment of infectious diseases. Unfortunately, bacteria have developed several mechanisms to combat these therapeutic agents. One of the major resistance mechanisms involves the production of β-lactamase that hydrolyzes the β-lactam ring thereby inactivating the drug. To overcome this threat, the small molecule β-lactamase inhibitors (e.g., clavulanic acid, sulbactam and tazobactam) have been used in combination with β-lactams for treatment. However, the bacterial resistance to this kind of combination therapy has evolved recently. Therefore, multiple attempts have been made to discover and develop novel broad-spectrum β-lactamase inhibitors that sufficiently work against β-lactamase producing bacteria. β-lactamase inhibitory proteins (BLIPs) (e.g., BLIP, BLIP-I and BLIP-II) are potential inhibitors that have been found from soil bacterium Streptomyces spp. BLIPs bind and inhibit a wide range of class A β-lactamases from a diverse set of Gram-positive and Gram-negative bacteria, including TEM-1, PC1, SME-1, SHV-1 and KPC-2. To the best of our knowledge, this article represents the first systematic review on β-lactamase inhibitors with a particular focus on BLIPs and their inherent properties that favorably position them as a source of biologically-inspired drugs to combat antimicrobial resistance. Furthermore, an extensive compilation of binding data from β-lactamase–BLIP interaction studies is presented herein. Such information help to provide key insights into the origin of interaction that may be useful for rationally guiding future drug design efforts.
Harvesting-Throughput Trade-Off for Wireless-Powered Smart Grid IoT Applications: An Experimental Study

Sensor nodes, one of the most crucial elements of Internet of Things (IoT), sense the environment and send their observations to a remote Access Point (AP). One drawback of sensor nodes in an IoT setting is their limited battery supply. Hereby, energy harvesting (EH) stands as a promising solution to reduce or even completely eliminate lifetime constraints of sensors with exploitation of available resources. In this paper, we propose an electric-field EH (EFEH) method to enable battery-less execution of sensor-based IoT services for Smart Grid (SG) context. For this purpose, for the first time in the literature, harvestable energy through EFEH method is investigated with a transformer room experimental set-up. Our experiments reveal that 40 mJ of energy can be harvested in a period of 900 sec with the proposed EFEH method. Building on this energy profile, we define a throughput objective function θ for a "harvest-then-transmit" type system model, to shed light on the harvesting- throughput trade-off specific to IoT-assisted SG applications. Numerical results disclose non- trivial relationships between optimal harvesting period T_H, optimal transmission period T_T and critical network parameters such as node-AP hop distance, path loss exponent and minimum reporting frequency requirement.
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems

Thompson sampling (TS) is an effective approach to trade off exploration and exploration in reinforcement learning. Despite its empirical success and recent advances, its theoretical analysis is often limited to the Bayesian setting, finite stateaction spaces, or finite-horizon problems. In this paper, we study an instance of TS in the challenging setting of the infinite-horizon linear quadratic (LQ) control, which models problems with continuous state-action variables, linear dynamics, and quadratic cost. In particular, we analyze the regret in the frequentist sense (i.e., for a fixed unknown environment) in one-dimensional systems. We derive the first O( √ T ) frequentist regret bound for this problem, thus significantly improving the O(T ) bound of Abeille & Lazaric (2017) and matching the frequentist performance derived by Abbasi-Yadkori & Szepesvári (2011) for an optimistic approach and the Bayesian result of Ouyang et al. (2017). We obtain this result by developing a novel bound on the regret due to policy switches, which holds for LQ systems of any dimensionality and it allows updating the parameters and the policy at each step, thus overcoming previous limitations due to lazy updates. Finally, we report numerical simulations supporting the conjecture that our result extends to multi-dimensional systems.
Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes

Synchronized stochastic gradient descent (SGD) optimizers with data parallelism are widely used in training large-scale deep neural networks. Although using larger mini-batch sizes can improve the system scalability by reducing the communication-to-computation ratio, it may hurt the generalization ability of the models. To this end, we build a highly scalable deep learning training system for dense GPU clusters with three main contributions: (1) We propose a mixed-precision training method that significantly improves the training throughput of a single GPU without losing accuracy. (2) We propose an optimization approach for extremely large mini-batch size (up to 64k) that can train CNN models on the ImageNet dataset without losing accuracy. (3) We propose highly optimized all-reduce algorithms that achieve up to 3x and 11x speedup on AlexNet and ResNet-50 respectively than NCCL-based training on a cluster with 1024 Tesla P40 GPUs. On training ResNet-50 with 90 epochs, the state-of-the-art GPU-based system with 1024 Tesla P100 GPUs spent 15 minutes and achieved 74.9\% top-1 test accuracy, and another KNL-based system with 2048 Intel KNLs spent 20 minutes and achieved 75.4\% accuracy. Our training system can achieve 75.8\% top-1 test accuracy in only 6.6 minutes using 2048 Tesla P40 GPUs. When training AlexNet with 95 epochs, our system can achieve 58.7\% top-1 test accuracy within 4 minutes, which also outperforms all other existing systems.
Preoperative Prognostic Nutritional Index Predicts Long-term Outcome in Gastric Cancer: A Propensity Score-matched Analysis

Background/Aim: Recent evidence suggests that preoperative malnutrition may lead to poor survival in cancer patients. This study aimed to determine the ability of the prognostic nutritional index (PNI) to predict survival in gastric cancer patients. Patients and Methods: Two hundred and eighteen patients who had undergone laparoscopic gastrectomy were retrospectively reviewed via propensity score-matched analysis. Results: In multivariate analysis of overall patients, pTNM stage, carcinoembryonic antigen, and PNI were independent predictors of overall survival (OS), and pTNM stage and PNI were independent predictors of cancer-specific survival (CSS). Among the 92 non-elderly patients, pTNM stage and PNI were independent predictors of OS, and pTNM stage, PNI, and adjuvant chemotherapy were independent predictors of CSS in multivariate analysis. On the other hand, among the 126 elderly patients, low PNI value was identified as a significant predictor of shorter OS in univariate analysis. Conclusion: PNI is associated with OS and CSS in gastric cancer patients, especially non-elderly patients.
PedsQL 3.2 Diabetes Module for Children, Adolescents, and Young Adults: Reliability and Validity in Type 1 Diabetes

OBJECTIVE The objective of the study was to report on the measurement properties of the revised and updated Pediatric Quality of Life Inventory (PedsQL) 3.2 Diabetes Module for children, adolescents, and young adults with type 1 diabetes. RESEARCH DESIGN AND METHODS The 33-item PedsQL 3.2 Diabetes Module and PedsQL Generic Core Scales were completed in a 10-site national field test study by 656 families of patients ages 2–25 years with type 1 diabetes. RESULTS The 15-item Diabetes Symptoms Summary Score and 18-item Diabetes Management Summary Score were derived from the factor analysis of the items. The Diabetes Symptoms and Diabetes Management Summary Scores evidenced excellent reliability (patient self-report α = 0.88–0.90; parent proxy report α = 0.89–0.90). The Diabetes Symptoms and Diabetes Management Summary Scores demonstrated construct validity through medium to large effect size correlations with the Generic Core Scales Total Scale Score (r = 0.43–0.67, P < 0.001). HbA1c was significantly correlated with the Diabetes Symptoms and Diabetes Management Summary Scores (r = −0.21 to −0.29, P < 0.001). Minimal clinically important difference scores ranged from 5.05 to 5.55. CONCLUSIONS The PedsQL 3.2 Diabetes Module Diabetes Symptoms and Diabetes Management Summary Scores demonstrated excellent measurement properties and may be useful as standardized patient-reported outcomes of diabetes symptoms and diabetes management in clinical research, clinical trials, and practice in children, adolescents, and young adults with type 1 diabetes.
Learning in Reproducing Kernel Kreı̆n Spaces

We formulate a novel regularized risk minimization problem for learning in reproducing kernel Krein spaces and show that the strong representer theorem applies to it. As a result of the latter, the learning problem can be expressed as the minimization of a quadratic form over a hypersphere of constant radius. We present an algorithm that can find a globally optimal solution to this nonconvex optimization problem in time cubic in the number of instances. Moreover, we derive the gradient of the solution with respect to its hyperparameters and, in this way, provide means for efficient hyperparameter tuning. The approach comes with a generalization bound expressed in terms of the Rademacher complexity of the corresponding hypothesis space. The major advantage over standard kernel methods is the ability to learn with various domain specific similarity measures for which positive definiteness does not hold or is difficult to establish. The approach is evaluated empirically using indefinite kernels defined on structured as well as vectorial data. The empirical results demonstrate a superior performance of our approach over the state-of-the-art baselines.
An Empirical Study of Building a Strong Baseline for Constituency Parsing

This paper investigates the construction of a strong baseline based on general purpose sequence-to-sequence models for constituency parsing. We incorporate several techniques that were mainly developed in natural language generation tasks, e.g., machine translation and summarization, and demonstrate that the sequence-to-sequence model achieves the current top-notch parsers’ performance (almost) without requiring any explicit task-specific knowledge or architecture of constituent parsing.
Behind Closed Doors: The Priorities of the Alcohol Industry as Communicated in a Trade Magazine

Background: Efforts to reduce alcohol-related harm face strong resistance from the alcohol industry. It is important to monitor industry actions over time to assist in developing appropriate responses to this resistance. Monitoring can enable public health to identify industry positions on alcohol policy issues, stay abreast of current and emerging marketing tactics, and inform the development of possible counter-actions. One form of monitoring is the examination of industry trade publications where the industry converses with itself. The aim of this study was to assess industry strategic approaches as communicated in articles published in a leading Australian alcohol trade magazine to provide insights for policy makers and advocacy groups. Methods: Thematic analysis of 362 articles published in a trade magazine over a one-year period. Results: Three primary themes were evident in the articles: (1) the legitimization of alcohol as an important social and economic product, (2) the portrayal of the industry as trustworthy and benign, and (3) the strategic embedding of alcohol in various facets of everyday life. Conclusions: There was a general failure to acknowledge the substantial burden of disease caused by alcohol products, and instead much effort was expended on legitimizing the product and the companies responsible for its production, distribution, and promotion. The level of denial exhibited shows that additional regulation of the industry and its tactics will need to proceed without industry acceptance. Clear resistance to increasing consumer protections also points to the futility of inviting industry members to the policy table.
Comprehensive Snake Venomics of the Okinawa Habu Pit Viper, Protobothrops flavoviridis, by Complementary Mass Spectrometry-Guided Approaches

The Asian world is home to a multitude of venomous and dangerous snakes, which are used to induce various medical effects in the preparation of traditional snake tinctures and alcoholics, like the Japanese snake wine, named Habushu. The aim of this work was to perform the first quantitative proteomic analysis of the Protobothrops flavoviridis pit viper venom. Accordingly, the venom was analyzed by complimentary bottom-up and top-down mass spectrometry techniques. The mass spectrometry-based snake venomics approach revealed that more than half of the venom is composed of different phospholipases A2 (PLA2). The combination of this approach and an intact mass profiling led to the identification of the three main Habu PLA2s. Furthermore, nearly one-third of the total venom consists of snake venom metalloproteinases and disintegrins, and several minor represented toxin families were detected: C-type lectin-like proteins (CTL), cysteine-rich secretory proteins (CRISP), snake venom serine proteases (svSP), l-amino acid oxidases (LAAO), phosphodiesterase (PDE) and 5′-nucleotidase. Finally, the venom of P. flavoviridis contains certain bradykinin-potentiating peptides and related peptides, like the svMP inhibitors, pEKW, pEQW, pEEW and pENW. In preliminary MTT cytotoxicity assays, the highest cancerous-cytotoxicity of crude venom was measured against human neuroblastoma SH-SY5Y cells and shows disintegrin-like effects in some fractions.