Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulation of PD-L1: Emerging Routes for Targeting Tumor Immune Evasion

Immune checkpoint blockade therapies (ICBTs) targeting programmed cell death 1 (PD-1) and its ligand programmed death ligand-1 (PD-L1/B7-H1/CD274) have exhibited momentous clinical benefits and durable responses in multiple tumor types. However, primary resistance is found in considerable number of cancer patients, and most responders eventually develop acquired resistance to ICBT. To tackle these challenges, it is essential to understand how PD-L1 is controlled by cancer cells to evade immune surveillance. Recent research has shed new light into the mechanisms of PD-L1 regulation at genetic, epigenetic, transcriptional, translational, and posttranslational levels. In this work, we systematically discuss the mechanisms that control the gene amplification, epigenetic alteration, transcription, subcellular transportation and posttranscriptional modification of PD-L1 in cancer cells. We further categorize posttranscriptional PD-L1 regulations by the molecular modification of PD-L1, including glycosylation, phosphorylation, ubiquitination, deubiquitination, and lysosomal degradation. These findings may provide new routes for targeting tumor immune escape and catalyze the development of small molecular inhibitors of PD-L1 in addition to existing antibody drugs.
Bayesian Joint Spike-and-Slab Graphical Lasso

In this article, we propose a new class of priors for Bayesian inference with multiple Gaussian graphical models. We introduce Bayesian treatments of two popular procedures, the group graphical lasso and the fused graphical lasso, and extend them to a continuous spike-and-slab framework to allow self-adaptive shrinkage and model selection simultaneously. We develop an EM algorithm that performs fast and dynamic explorations of posterior modes. Our approach selects sparse models efficiently and automatically with substantially smaller bias than would be induced by alternative regularization procedures. The performance of the proposed methods are demonstrated through simulation and two real data examples.
Test Case Generation from Android Mobile Applications Focusing on Context Events

Nowadays mobile apps are developed to address more critical areas of people's daily computing needs, which bring concern on the applications' quality. Today's Mobile apps processed not only the traditional GUI events but also accept and react to constantly varying context events which may have an impact on the application's behaviour. To build high quality and more reliable applications, there is a need for effective testing techniques to test apps before release. Most of recent testing technique focuses on GUI events only making it difficult to identify other defects in the changes that can be inclined by the context in which an application runs. This paper proposed an approach for testing mobile apps considering the two sets of events: GUI events which we identified through static analysis of bytecode and context events obtained from analysis of manifest.xml file. Results from the experimental evaluation indicated that our approach is effective in identifying and testing context events.
Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Mapless Navigation by Leveraging Prior Demonstrations

This letter presents a case study of a learning-based approach for target-driven mapless navigation. The underlying navigation model is an end-to-end neural network, which is trained using a combination of expert demonstrations, imitation learning (IL) and reinforcement learning (RL). While RL and IL suffer from a large sample complexity and the distribution mismatch problem, respectively, we show that leveraging prior expert demonstrations for pretraining can reduce the training time to reach at least the same level of the performance compared to plain RL by a factor of 5. We present a thorough evaluation of different combinations of expert demonstrations, different RL algorithms, and reward functions, both in simulation and on a real robotic platform. Our results show that the final model outperforms both standalone approaches in the amount of successful navigation tasks. In addition, the RL reward function can be significantly simplified when using pretraining, e.g., by using a sparse reward only. The learned navigation policy is able to generalize to unseen and real-world environments.
Assessing the Relationships Among Forgiveness by God, God Images, and Death Anxiety

Previous research suggests that people who feel forgiven by God may experience lower levels of death anxiety. The purpose of the current study is to contribute to this work by assessing whether the relationship between forgiveness by God and death anxiety varies according to how people view God. Three images of God are assessed: a pantheistic view of God, a theistic view of God, and a deistic view of God. Data from nationwide survey that was conducted in 2014 (N = 2,650) suggest that the relationship between forgiveness by God and death anxiety is strongest among people with a theistic view of God, significantly weaker among people with a pantheistic view of God, and not significant among individuals with a deistic view of God. The findings point to the importance of taking views of God into account when assessing the relationship between forgiveness by God and death anxiety.
Water-carbon interactions. III: The influence of surface and fluid impurities

Molecular dynamics simulations are performed to study the influence of surface and fluid impurities on water–carbon interactions. In order to quantify these interactions we consider the canonical problem of wetting of a doped flat graphitic surface by a water system with impurities. As model fluid impurities we consider aqueous solutions of potassium–chloride with molar concentrations up to 1.8 M. Quantum chemistry calculations are performed to derive pair potentials for the ion–graphite interactions. The contact angle is found to decrease weakly with increasing ionic concentration, from 90° at 0 M to 81° at 1.8 M concentration. The influence of solid impurities is found to be more significant. Thus, 10, 15, and 20% coverages of chemisorbed hydrogen result in contact angles of 90°, 74° and 60°, respectively.
A human iPSC line capable of differentiating into functional macrophages expressing ZsGreen: a tool for the study and in vivo tracking of therapeutic cells

We describe the production of a human induced pluripotent stem cell (iPSC) line, SFCi55-ZsGr, that has been engineered to express the fluorescent reporter gene, ZsGreen, in a constitutive manner. The CAG-driven ZsGreen expression cassette was inserted into the AAVS1 locus and a high level of expression was observed in undifferentiated iPSCs and in cell lineages derived from all three germ layers including haematopoietic cells, hepatocytes and neurons. We demonstrate efficient production of terminally differentiated macrophages from the SFCi55-ZsGreen iPSC line and show that they are indistinguishable from those generated from their parental SFCi55 iPSC line in terms of gene expression, cell surface marker expression and phagocytic activity. The high level of ZsGreen expression had no effect on the ability of macrophages to be activated to an M(LPS + IFNγ), M(IL10) or M(IL4) phenotype nor on their plasticity, assessed by their ability to switch from one phenotype to another. Thus, targeting of the AAVS1 locus in iPSCs allows for the production of fully functional, fluorescently tagged human macrophages that can be used for in vivo tracking in disease models. The strategy also provides a platform for the introduction of factors that are predicted to modulate and/or stabilize macrophage function. This article is part of the theme issue ‘Designer human tissue: coming to a lab near you’.
Impairment Severity and Evaluative and Experienced Well-being Among Older Adults: Assessing the Role of Daily Activities

Abstract Background and Objectives Physical impairments affect a substantial number of older adults in the United States, with rates increasing with advancing age. Impairment is linked with compromised well-being, although the reasons are not fully understood. We explore the extent to which linkages between impairment severity and well-being are accounted for by older adults’ daily activities. We speculate that activities may influence global appraisals of well-being by offering the opportunity to fulfill productive and social roles and may influence daily emotions by shaping the context (places, people) in which life occurs. Research Design and Method We examine the effects of impairment severity on life satisfaction and four diary-based experienced well-being measures (happiness, frustration, worry, and sadness). Data are from the Disability and Use of Time supplement to the Panel Study of Income Dynamics (n = 1,606), a national sample of adults ages 60 years and older in the United States. We estimate nested regression models, taking into account within-person correlations for experienced well-being. Results Impairment severity is associated with poorer assessments of life satisfaction and all four dimensions of experienced well-being. Activity measures, which encompass eight productive (e.g., household chores) and three leisure (e.g., socializing) activities, account for 10% of the association between impairment and life satisfaction, and virtually none of the association between impairment and experienced well-being. However, psychosocial factors including higher neuroticism, lower self-efficacy, and poorer quality social relationships account for a sizeable share of the associations. Discussion and Implications Role-fulfilling aspects of activities appear to be more central than contextual aspects of activities to the impairment-well-being relationship. However, potentially modifiable psychosocial factors account for a much greater share of this relationship. Further research is needed on whether interventions targeting these psychosocial factors might bolster emotional well-being for older adults experiencing impairments.
Morphomic Malnutrition Score: A Standardized Screening Tool for Severe Malnutrition in Adults

BACKGROUND Granular diagnostic criteria for adult malnutrition are lacking. OBJECTIVE This study uses analytic morphomics to define the Morphomic Malnutrition Score (MMS), a robust screening tool for severe malnutrition. METHODS The study population (n = 643) consisted of 2 cohorts: 1) 124 emergency department patients diagnosed with severe malnutrition by a registered dietitian (RD) and an available computed tomography (CT) scan within 2 days of RD evaluation, and 2) 519 adult kidney donor candidates to represent a healthy cohort. Body composition markers of muscle area and abdominal adiposity were measured from patient CT scans using analytic morphomic assessment, and then converted to sex- and age-adjusted percentiles using the Reference Analytic Morphomics Population (RAMP). RAMP consists of 6000 patients chosen to be representative of the general population. The combined cohort was then randomly divided into training (n = 453) and validation (n = 190) sets. MMS was derived using logistic regression. The model coefficients were transformed into a score, normalized from 0 to 10 (10 = most severe). RESULTS Severely malnourished patients had lower amounts of muscle and fat than kidney donors, specifically for dorsal muscle group area at the twelfth thoracic vertebral level (P < 0.001), psoas muscle area at the fourth lumbar vertebral level (P < 0.001), and subcutaneous fat area at the third lumbar vertebral level (P < 0.001)-all parameters in MMS. MMS for severely malnourished patients was higher than kidney donors (7.7 ± 2.2 vs 3.8 ± 2.0, respectively; P-value < 0.001). An MMS > 6.1 was accurate in determining nutrition diagnosis (82.1% sensitivity; 88.3% specificity; 85.2% balanced accuracy). CONCLUSIONS MMS provides an evidence-based, granular assessment to distinguish severely malnourished adults from a healthy population.
Neural User Simulation for Corpus-based Policy Optimisation of Spoken Dialogue Systems

User Simulators are one of the major tools that enable offline training of task-oriented dialogue systems. For this task the Agenda-Based User Simulator (ABUS) is often used. The ABUS is based on hand-crafted rules and its output is in semantic form. Issues arise from both properties such as limited diversity and the inability to interface a text-level belief tracker. This paper introduces the Neural User Simulator (NUS) whose behaviour is learned from a corpus and which generates natural language, hence needing a less labelled dataset than simulators generating a semantic output. In comparison to much of the past work on this topic, which evaluates user simulators on corpus-based metrics, we use the NUS to train the policy of a reinforcement learning based Spoken Dialogue System. The NUS is compared to the ABUS by evaluating the policies that were trained using the simulators. Cross-model evaluation is performed i.e. training on one simulator and testing on the other. Furthermore, the trained policies are tested on real users. In both evaluation tasks the NUS outperformed the ABUS.
Spatial Temporal Prediction of Malaria Risk in Western Kenya using Bayesian Geostatistical Approach

: Malaria is a vector borne disease that occurs in areas where the climatic and environmental conditions are suitable for survival of Anopheles mosquitoes. The environmental and climatic factors that affect malaria transmission are, rainfall, temperature, humidity and vegetation. Deforestation, agricultural activities and population movements are an anthropogenic factor that affects malaria transmission. Despite implementation of several strategies in controlling and management of malaria in western parts of Kenya, high numbers of malaria cases are still being recorded due to changing environmental and climatic factors. The aim of this study was to apply the geostatistical modelling to estimate and map the spatial and temporal changes in malaria risk by using the available time series climatic and environmental data and to estimate the population at risk at different time epochs. The data was prepared using python scripts and different ArcGIS tools. A spatial temporal model based on Bayesian approach was used to estimate malaria risk and was implemented in R using Integrated Nested Laplace Approximation (INLA) package to estimate the malaria risk. INLA was preferred to Monte Carlo Markov Chain (MCMC) due to its efficient computation advantage.
Monocular Depth Reconstruction using Geometry and Deep Convolutional Networks

In this thesis, we explore methods of building dense depth map from monocular video using geometry and deep convolutional networks. We focus on a particular challenging case: small motion videos, in which depth error grows large as camera movement reduces. First, we introduce our multiview stereo pipeline. Our pipeline consists of four stages: point tracking, bundle adjustment, photometric bundle adjustment, and densification. Here we demonstrate that using photometric bundle adjustment helps in getting accurate depth of textured regions from small motion video. The traditional multiview stereo approaches rely on heuristic local smoothness priors for low-texture regions. We improve the depth estimation of low-texture region by fusing deep convolutional network predictions. We categorize the depth fusion methods into two categories: late integration and early integration. Late integration uses highly confident partial depth from pure geometric methods as anchor points to refine the dense depth map generated by deep convolutional networks. However in this case, the network output is not guaranteed to be aligned with confident partial depth, thus the fusion process might be problematic. To improve this issue, we propose early integration, which uses confident partial depths as constraints for deep convolutional networks. This method ensures the two depth sources to be well-aligned and thus has better depth accuracy than previous methods.
Demonstrating vistiles: visual data exploration using mobile devices

We demonstrate the prototype of the conceptual VisTiles framework. VisTiles allows exploring multivariate data sets by using multiple coordinated views that are distributed across a set of mobile devices. This setup allows users to benefit from dynamic and user-defined interface arrangements and to easily initiate co-located data exploration sessions. The current web-based prototype runs on commodity devices and is able to determine the spatial device arrangement by either a cross-device pinch gesture or an external tracking system. Multiple data sets are provided that can be explored by different visualizations (e.g., scatterplots, parallel coordinate plots, stream graphs). With this demonstration, we showcase the general concepts of VisTiles and discuss ideas for enhancements as well the potential for application cases beyond data analysis.
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation

Deep learning techniques have been widely applied, achieving state-of-the-art results in various fields of study. This survey focuses on deep learning solutions that target learning control policies for robotics applications. We carry out our discussions on the two main paradigms for learning control with deep networks: deep reinforcement learning and imitation learning. For deep reinforcement learning (DRL), we begin from traditional reinforcement learning algorithms, showing how they are extended to the deep context and effective mechanisms that could be added on top of the DRL algorithms. We then introduce representative works that utilize DRL to solve navigation and manipulation tasks in robotics. We continue our discussion on methods addressing the challenge of the reality gap for transferring DRL policies trained in simulation to real-world scenarios, and summarize robotics simulation platforms for conducting DRL research. For imitation leaning, we go through its three main categories, behavior cloning, inverse reinforcement learning and generative adversarial imitation learning, by introducing their formulations and their corresponding robotics applications. Finally, we discuss the open challenges and research frontiers.
Fault Tolerant Distributed Stream Processing based on Backtracking

Since distributed stream analytics is treated as a kind of cloud service, there exists a pressing need for its reliability and fault-tolerance, to guarantee the streaming data tuples to be processed in the order of their generation in every dataflow path, with each tuple processed once and only once. Currently there exist two kind approaches: one treats the whole process as a single transaction, and therefore suffers from the loss of intermediate results during failures; the other relies on the receipt of acknowledgement (ACK) to decide whether moving forward to emit the next resulting tuple or resending the current one after timeout, on the per-tuple basis, thus incurs extremely high latency penalty. In contradistinction to the above, we propose the backtrack mechanism for failure recovery, which allows a task to process tuples continuously without waiting for ACKs and without resending tuples in the failure-free case, but to request (ASK) the source tasks to resend the missing tuples only when it is restored from a failure which is a rare case thus has limited impact on the overall performance. The specific hard problem for building a transaction layer on-top of an existing stream processing platform consists in how to keep track the physical input/output messaging channels in order to realize re-messaging during failure recovery. Our solution is characterized by tracking physical messaging channels logically, for that we introduce the notions of virtual channel, task alias and messageId-set in reasoning, recording and communicating the channel information. We also provide a designated messaging channel, separated from the regular dataflow channel, for signaling ACK/ASK messages and for resending tuples, in order to avoid interrupting the regular order of data transfer. We have implemented the proposed mechanisms on Fontainebleau, the distributed stream analytics infrastructure we developed on top of Storm. As a principle, we ensure all the transactional properties to be system supported and transparent to users. Our experience shows the novelty and efficiency of the proposed mechanisms.
Structure of an EIIC sugar transporter trapped in an inward-facing conformation

Significance The phosphoenolpyruvate-dependent phosphotransferase system (PTS) is a multiprotein system unique to bacteria. The PTS transports sugars into bacteria and then phosphorylates the sugars. Phosphorylation prevents sugars from escaping the cell and primes them for metabolic consumption. As a major component of the PTS, Enzyme IIC (EIIC) transports sugar across the membrane and assists the phosphorylation process, but the molecular mechanism of EIIC-mediated sugar transport is unclear. Results from this study allow visualization of conformational changes during sugar transport and establish the mechanism of transport at the atomic level. The knowledge will facilitate development of inhibitors against EIIC and provide a foundation for understanding the phosphorylation process. The phosphoenolpyruvate-dependent phosphotransferase system (PTS) transports sugar into bacteria and phosphorylates the sugar for metabolic consumption. The PTS is important for the survival of bacteria and thus a potential target for antibiotics, but its mechanism of sugar uptake and phosphorylation remains unclear. The PTS is composed of multiple proteins, and the membrane-embedded Enzyme IIC (EIIC) component transports sugars across the membrane. Crystal structures of two members of the glucose superfamily of EIICs, bcChbC and bcMalT, were solved in the inward-facing and outward-facing conformations, and the structures suggest that sugar translocation could be achieved by movement of a structured domain that contains the sugar-binding site. However, different conformations have not been captured on the same transporter to allow precise description of the conformational changes. Here we present a crystal structure of bcMalT trapped in an inward-facing conformation by a mercury ion that bridges two strategically placed cysteine residues. The structure allows direct comparison of the outward- and inward-facing conformations and reveals a large rigid-body motion of the sugar-binding domain and other conformational changes that accompany the rigid-body motion. All-atom molecular dynamics simulations show that the inward-facing structure is stable with or without the cross-linking. The conformational changes were further validated by single-molecule Föster resonance energy transfer (smFRET). Combined, these results establish the elevator-type mechanism of transport in the glucose superfamily of EIIC transporters.