Miracle-dz/UniFAR-FTUs huggingface dataset:
Selective Permeability of Carboxysome Shell Pores to Anionic Molecules

Carboxysomes are closed polyhedral cellular microcompartments that increase the efficiency of carbon fixation in autotrophic bacteria. Carboxysome shells consist of small proteins that form hexameric units with semi-permeable central pores containing binding sites for anions. This feature is thought to selectively allow access to RuBisCO enzymes inside the carboxysome by (the dominant form of CO2 in the aqueous solution at pH 7.4) but not O2, which leads to a non-productive reaction. To test this hypothesis, here we use molecular dynamics simulations to characterize the energetics and permeability of CO2, O2, and through the central pores of two different shell proteins, namely, CsoS1A of α–carboxysome and CcmK4 of β-carboxysome shells. We find that the central pores are in fact selectively permeable to anions such as , as predicted by the model.
A survey of author name disambiguation techniques: 2010–2016

Abstract Digital libraries content and quality of services are badly affected by the author name ambiguity problem in the citations and it is considered as one of the hardest problems faced by the digital library researchers. Several techniques have been proposed in the literature for the author name ambiguity problem. In this paper, we reviewed some recently presented author name disambiguation techniques and give some challenges and future research directions. We analyze the recent advancements in this field and classify these techniques into supervised, unsupervised, semi-supervised, graph-based and heuristic-based techniques according to their problem formulation that is mainly used for the author name disambiguation. A few surveys have been conducted to review different techniques for the author name disambiguation. These surveys highlighted only the methodology adopted for author name disambiguation but did not critically review their shortcomings. This survey provides a detailed review of author name disambiguation techniques available in the literature, makes a comparison of these techniques at an abstract level and discusses their limitations.
Diagnosis and Management of Mild Traumatic Brain Injury in Children: A Systematic Review

Importance In recent years, there has been an exponential increase in the research guiding pediatric mild traumatic brain injury (mTBI) clinical management, in large part because of heightened concerns about the consequences of mTBI, also known as concussion, in children. The CDC National Center for Injury Prevention and Control’s (NCIPC) Board of Scientific Counselors (BSC), a federal advisory committee, established the Pediatric Mild TBI Guideline workgroup to complete this systematic review summarizing the first 25 years of literature in this field of study. Objective To conduct a systematic review of the pediatric mTBI literature to serve as the foundation for an evidence-based guideline with clinical recommendations associated with the diagnosis and management of pediatric mTBI. Evidence Review Using a modified Delphi process, the authors selected 6 clinical questions on diagnosis, prognosis, and management or treatment of pediatric mTBI. Two consecutive searches were conducted on PubMed, Embase, ERIC, CINAHL, and SportDiscus. The first included the dates January 1, 1990, to November 30, 2012, and an updated search included December 1, 2012, to July 31, 2015. The initial search was completed from December 2012 to January 2013; the updated search, from July 2015 to August 2015. Two authors worked in pairs to abstract study characteristics independently for each article selected for inclusion. A third author adjudicated disagreements. The risk of bias in each study was determined using the American Academy of Neurology Classification of Evidence Scheme. Conclusion statements were developed regarding the evidence within each clinical question, and a level of confidence in the evidence was assigned to each conclusion using a modified GRADE methodology. Data analysis was completed from October 2014 to May 2015 for the initial search and from November 2015 to April 2016 for the updated search. Findings Validated tools are available to assist clinicians in the diagnosis and management of pediatric mTBI. A significant body of research exists to identify features that are associated with more serious TBI-associated intracranial injury, delayed recovery from mTBI, and long-term sequelae. However, high-quality studies of treatments meant to improve mTBI outcomes are currently lacking. Conclusions and Relevance This systematic review was used to develop an evidence-based clinical guideline for the diagnosis and management of pediatric mTBI. While an increasing amount of research provides clinically useful information, this systematic review identified key gaps in diagnosis, prognosis, and management.
Multiple functionalities of molecular chaperones revealed through systematic mapping of their interaction networks

Chaperones are a highly interactive group of proteins that function globally in many cellular processes involved in maintaining protein homeostasis. Traditional biochemical assays typically do not provide a complete view of the intricate networks through which chaperones collaborate to promote proteostasis. Recent advances in high-throughput systematic analyses of chaperone interactions have uncovered that chaperones display a remarkable cooperativity in their interactions with numerous client proteins. This cooperativity has been found to be a fundamental aspect of a properly functioning cell. Aberrant formation or improper regulation of these interactions can easily lead to disease states. Herein, we provide an overview of the use of large-scale interaction assays, whether physical (protein–protein) or genetic (epistatic), to study chaperone interaction networks. Importantly, we discuss the ongoing need for such studies to determine the mechanisms by which protein homeostasis is controlled in the cell.
Constructing Cognitive Profiles for Simulation-Based Hiring Assessments

Imbellus is an assessment company that builds immersive simulation-based assessments designed to evaluate cognitive processes. The work described here explores our partnership with McKinsey & Company, a best-in-class management-consulting firm, to build a simulation-based assessment that evaluates incoming applicants' cognitive skills and abilities. Our simulationbased assessments are designed to produce a substantial amount of information about the incoming applicants, including metacognitive skills, decision-making processes, and situational awareness (to name a few of the constructs we measure). This paper will explore the rich telemetry data we collect and quantify, as well as the novel scoring and exploratory techniques we are conducting to gain insight into applicants’ cognitive profiles. We will present our initial findings and describe implications of our current work for the fields of artificial intelligence, educational data mining, and assessment.
Structure of Asp‐bound peptidase E from Salmonella enterica: Active site at dimer interface illuminates Asp recognition

Peptidase‐E, a nonclassical serine peptidase, is specific for dipeptides with an N‐terminal aspartate. This stringent substrate specificity remains largely unexplained. We report an aspartate‐bound structure of peptidase‐E at 1.83 Å resolution. In contrast to previous reports, the enzyme forms a dimer, and the active site is located at the dimer interface, well shielded from the solvent. Our findings further suggest that the stringent aspartate specificity of the enzyme is due to electrostatics and molecular complementarity in the active site. The new structural information presented herein may provide insights into the role of functionally important residues in peptidase‐E.
Differentially Private Continual Release of Graph Statistics

Motivated by understanding the dynamics of sensitive social networks over time, we consider the problem of continual release of statistics in a network that arrives online, while preserving privacy of its participants. For our privacy notion, we use differential privacy -- the gold standard in privacy for statistical data analysis. The main challenge in this problem is maintaining a good privacy-utility tradeoff; naive solutions that compose across time, as well as solutions suited to tabular data either lead to poor utility or do not directly apply. In this work, we show that if there is a publicly known upper bound on the maximum degree of any node in the entire network sequence, then we can release many common graph statistics such as degree distributions and subgraph counts continually with a better privacy-accuracy tradeoff. Code available at this https URL
Clarithromycin Enhances the Antibacterial Activity and Wound Healing Capacity in Type 2 Diabetes Mellitus by Increasing LL-37 Load on Neutrophil Extracellular Traps

Background: Type 2 diabetes mellitus (T2D) is characterized by susceptibility to bacterial infections and impaired wound healing. Neutrophil extracellular traps (NETs) and the cathelicidin antimicrobial peptide LL-37 have been implicated both in defense against bacterial infections and in wound healing process. Recently, it was shown that macrolide antibiotic clarithromycin induces the release of LL-37-bearing NETs. In T2D there has not been identified any link between NETs and LL-37 and the effect of clarithromycin in neutrophils/NETs is unknown yet. Methods: Peripheral blood neutrophils were obtained from treatment-naive hyperglycemic T2D patients (naive), normoglycemic T2D patients under antidiabetic treatment (well-controlled) and healthy donors (controls). NET release and NET proteins were studied. Co-culture systems of NET structures with E. coli NCTC 9001 and primary skin fibroblasts were deployed to examine the in vitro antibacterial and fibrotic NET properties, respectively. The effect of clarithromycin was also investigated. Analysis was performed using immunofluorescence confocal microscopy, myeloperoxidase-DNA complex and LL-37 ELISA, immunoblotting and qRT-PCR. Results: NETs were characterized by the presence of LL-37, however they lacked antibacterial activity, in both groups of T2D patients. Clarithromycin significantly increased the externalization of LL-37 on NETs generated from well-controlled T2D neutrophils, thus restoring NET antibacterial capacity and promoting the wound healing process via fibroblast activation and differentiation. Conclusion: This study suggests that clarithromycin may add further advantage to well-controlled T2D patients, by enhancing their antibacterial defense and improving wound healing capacity of fibroblasts, through upregulation of LL-37 on NET structures.
MixUp as Locally Linear Out-Of-Manifold Regularization

MixUp (Zhang et al. 2017) is a recently proposed dataaugmentation scheme, which linearly interpolates a random pair of training examples and correspondingly the one-hot representations of their labels. Training deep neural networks with such additional data is shown capable of significantly improving the predictive accuracy of the current art. The power of MixUp, however, is primarily established empirically and its working and effectiveness have not been explained in any depth. In this paper, we develop an understanding for MixUp as a form of “out-of-manifold regularization”, which imposes certain “local linearity” constraints on the model’s input space beyond the data manifold. This analysis enables us to identify a limitation of MixUp, which we call “manifold intrusion”. In a nutshell, manifold intrusion in MixUp is a form of under-fitting resulting from conflicts between the synthetic labels of the mixed-up examples and the labels of original training data. Such a phenomenon usually happens when the parameters controlling the generation of mixing policies are not sufficiently fine-tuned on the training data. To address this issue, we propose a novel adaptive version of MixUp, where the mixing policies are automatically learned from the data using an additional network and objective function designed to avoid manifold intrusion. The proposed regularizer, AdaMixUp, is empirically evaluated on several benchmark datasets. Extensive experiments demonstrate that AdaMixUp improves upon MixUp when applied to the current art of deep classification models.
Adversarial Feature-Mapping for Speech Enhancement

Feature-mapping with deep neural networks is commonly used for single-channel speech enhancement, in which a feature-mapping network directly transforms the noisy features to the corresponding enhanced ones and is trained to minimize the mean square errors between the enhanced and clean features. In this paper, we propose an adversarial feature-mapping (AFM) method for speech enhancement which advances the feature-mapping approach with adversarial learning. An additional discriminator network is introduced to distinguish the enhanced features from the real clean ones. The two networks are jointly optimized to minimize the feature-mapping loss and simultaneously mini-maximize the discrimination loss. The distribution of the enhanced features is further pushed towards that of the clean features through this adversarial multi-task training. To achieve better performance on ASR task, senone-aware (SA) AFM is further proposed in which an acoustic model network is jointly trained with the feature-mapping and discriminator networks to optimize the senone classification loss in addition to the AFM losses. Evaluated on the CHiME-3 dataset, the proposed AFM achieves 16.95% and 5.27% relative word error rate (WER) improvements over the real noisy data and the feature-mapping baseline respectively and the SA-AFM achieves 9.85% relative WER improvement over the multi-conditional acoustic model.
Using the common-sense model to understand health outcomes for medically unexplained symptoms: a meta-analysis

ABSTRACT Consistent with the common-sense model of self-regulation, illness representations are considered the key to improving health outcomes for medically unexplained symptoms and illnesses (MUS). Which illness representations are related to outcomes and how they are related is not well understood. In response, we conducted a meta-analysis of the relationship between illness representations, self-management/coping, and health outcomes (perceived disease state, psychological distress, and quality of life) for patients with MUS. We reviewed 23 studies and found that threat-related illness representations and emotional representations were related to worse health outcomes and more negative coping (moderate to large effect). Generally, increases in negative coping mediated (with a moderate to large effect) the relationship of threat/emotional illness representations and health outcomes. Protective illness representations were related to better health outcomes, less use of negative coping and greater use of positive coping (small to moderate effect). The relationship of protective illness representations to better health outcomes was mediated by decreases in negative coping (moderate to large effect) and increases in positive coping (moderate effect). Perceiving a psychological cause to the MUS was related to more negative health outcomes (moderate to large effect) and more negative emotional coping (small effect). The relationship of perceiving a psychological cause and more negative health outcomes was mediated by increases in negative emotional coping. Improving our understanding of how illness representations impact health outcomes can inform efforts to improve treatments for MUS. Our results suggest behavioural treatments should focus on reducing threat-related illness representations and negative coping.
Mitochondrial ubiquinone–mediated longevity is marked by reduced cytoplasmic mRNA translation

This study uses polysomal RNA sequencing to show that the translational efficiency of specific mRNA changes in long-lived Caenorhabditis elegans with reduced ubiquinone synthesis. Mutations in the clk-1 gene impair mitochondrial ubiquinone biosynthesis and extend the lifespan in Caenorhabditis elegans. We demonstrate here that this life extension is linked to the repression of cytoplasmic mRNA translation, independent of the alleged nuclear form of CLK-1. Clk-1 mutations inhibit polyribosome formation similarly to daf-2 mutations that dampen insulin signaling. Comparisons of total versus polysomal RNAs in clk-1(qm30) mutants reveal a reduction in the translational efficiencies of mRNAs coding for elements of the translation machinery and an increase in those coding for the oxidative phosphorylation and autophagy pathways. Knocking down the transcription initiation factor TATA-binding protein-associated factor 4, a protein that becomes sequestered in the cytoplasm during early embryogenesis to induce transcriptional silencing, ameliorates the clk-1 inhibition of polyribosome formation. These results underscore a prominent role for the repression of cytoplasmic protein synthesis in eukaryotic lifespan extension and suggest that mutations impairing mitochondrial function are able to exploit this repression similarly to reductions of insulin signaling. Moreover, this report reveals an unexpected role for TATA-binding protein-associated factor 4 as a repressor of polyribosome formation when ubiquinone biosynthesis is compromised.
Fish, Coral, and Sponge Assemblages Associated With Altiphotic and Mesophotic Reefs Along the Guánica Biosphere Reserve Continental Shelf Edge, Southwest Puerto Rico

The benthic and fish communities of the central portion of the Guanica, Puerto Rico shelf edge were studied to determine species abundance, distributions and species overlap between two depth stratifications, 20 and 45 m, at eight sites. A total of 67 fish species belonging to 21 families were identified. Similar species richness estimates were observed between depths, though fish assemblage composition differed significantly, with observable changes in feeding guild contributions of herbivore and omnivore (20 m) to a deeper assemblage composed of piscivores and planktivores (45 m). Coral assemblages consisted of 31 species at 20 m and 11 species at 45 m, accounting for 17.0% (±1.76 SE) and 2.6% (±0.89 SE) benthic cover for the altiphotic and mesophotic surveys, respectively. The altiphotic and mesophotic coral reef communities support different scleractinian coral assemblages with minimal species overlap. Altiphotic surveys of sponges yielded a higher species richness than mesophotic, with 60 and 54 species respectively, and an overall total of 71 species identified from both depths, with 45 species overlapping (63.0%). The percent cover of sponges surveyed at altiphotic reefs accounted for 9.0% (±1.04 SE), while the percent cover of sponges surveyed at mesophotic reefs was 14.0% (±1.96 SE). Our data show fish, coral, and sponge assemblages are differentiated between 20 and 45 m along thes provides an annotated species list of the local sponge fauna. Combined, these results highlight the need for con Guanica shelf edge offshore of the Guanica Biosphere Reserve. This study represents the first observations for species distributions of adjacent altiphotic and mesophotic coral reef habitats along the Guanica shelf edge, as well atinued environmental stewardship and conservation in the area.
Analysis of clinical outcomes of upper and lower extremity reconstructions in patients with soft‐tissue sarcoma

Primary amputations have been the standard of treatment for extremity soft tissue sarcomas. However, over the last few decades, this mode of treatment has been increasingly replaced by limb‐sparing procedures.1 Our goal is to report the clinical outcomes of upper and lower extremity reconstructions in patients with a history of soft‐tissue sarcoma.
Obstacle Detection Quality as a Problem-Oriented Approach to Stereo Vision Algorithms Estimation in Road Situation Analysis

In this work we present a method for performance evaluation of stereo vision based obstacle detection techniques that takes into account the specifics of road situation analysis to minimize the effort required to prepare a test dataset. This approach has been designed to be implemented in systems such as self-driving cars or driver assistance and can also be used as problem-oriented quality criterion for evaluation of stereo vision algorithms.
Occupational exposures and parkinsonism among Shanghai women textile workers

BACKGROUND Endotoxin, a contaminant of cotton dust, is an experimental model for parkinsonism (PS). METHODS We investigated associations between exposures to endotoxin, solvents, magnetic fields, and night shift work, and neurologist-determined PS among Shanghai women textile workers, including 537 retired cotton factory workers ages ≥50 years and an age-matched reference group of 286 retired textile workers not exposed to cotton dust. Repeat exams were conducted 2.5 years after enrollment among 467 cotton workers and 229 reference workers. RESULTS We identified 39 prevalent PS cases and 784 non-cases. No consistent or statistically significant associations were observed for endotoxin, solvents, magnetic fields, or shift work with PS risk, severity, or progression. CONCLUSIONS Despite the null findings, additional studies of endotoxin exposure and risk of PS in other well-characterized occupational cohorts are warranted in view of toxicological evidence that endotoxin is a pathogenic agent and its widespread occurrence in multiple industries worldwide.
Monte Carlo Tree Search with Scalable Simulation Periods for Continuously Running Tasks

Monte Carlo Tree Search (MCTS) is particularly adapted to domains where the potential actions can be represented as a tree of sequential decisions. For an effective action selection, MCTS performs many simulations to build a reliable tree representation of the decision space. As such, a bottleneck to MCTS appears when enough simulations cannot be performed between action selections. This is particularly highlighted in continuously running tasks, for which the time available to perform simulations between actions tends to be limited due to the environment's state constantly changing. In this paper, we present an approach that takes advantage of the anytime characteristic of MCTS to increase the simulation time when allowed. Our approach is to effectively balance the prospect of selecting an action with the time that can be spared to perform MCTS simulations before the next action selection. For that, we considered the simulation time as a decision variable to be selected alongside an action. We extended the Hierarchical Optimistic Optimization applied to Tree (HOOT) method to adapt our approach to environments with a continuous decision space. We evaluated our approach for environments with a continuous decision space through OpenAI gym's Pendulum and Continuous Mountain Car environments and for environments with discrete action space through the arcade learning environment (ALE) platform. The evaluation results show that, with variable simulation times, the proposed approach outperforms the conventional MCTS in the evaluated continuous decision space tasks and improves the performance of MCTS in most of the ALE tasks.