Miracle-dz/UniFAR-FTUs huggingface dataset:
Validating a Machine Learning Algorithm to Predict 30-Day Re-Admissions in Patients With Heart Failure: Protocol for a Prospective Cohort Study

Background Big data solutions, particularly machine learning predictive algorithms, have demonstrated the ability to unlock value from data in real time in many settings outside of health care. Rapid growth in electronic medical record adoption and the shift from a volume-based to a value-based reimbursement structure in the US health care system has spurred investments in machine learning solutions. Machine learning methods can be used to build flexible, customized, and automated predictive models to optimize resource allocation and improve the efficiency and quality of health care. However, these models are prone to the problems of overfitting, confounding, and decay in predictive performance over time. It is, therefore, necessary to evaluate machine learning–based predictive models in an independent dataset before they can be adopted in the clinical practice. In this paper, we describe the protocol for independent, prospective validation of a machine learning–based model trained to predict the risk of 30-day re-admission in patients with heart failure. Objective This study aims to prospectively validate a machine learning–based predictive model for inpatient admissions in patients with heart failure by comparing its predictions of risk for 30-day re-admissions against outcomes observed prospectively in an independent patient cohort. Methods All adult patients with heart failure who are discharged alive from an inpatient admission will be prospectively monitored for 30-day re-admissions through reports generated by the electronic medical record system. Of these, patients who are part of the training dataset will be excluded to avoid information leakage to the algorithm. An expected sample size of 1228 index admissions will be required to observe a minimum of 100 30-day re-admission events. Deidentified structured and unstructured data will be fed to the algorithm, and its prediction will be recorded. The overall model performance will be assessed using the concordance statistic. Furthermore, multiple discrimination thresholds for screening high-risk patients will be evaluated according to the sensitivity, specificity, predictive values, and estimated cost savings to our health care system. Results The project received funding in April 2017 and data collection began in June 2017. Enrollment was completed in July 2017. Data analysis is currently underway, and the first results are expected to be submitted for publication in October 2018. Conclusions To the best of our knowledge, this is one of the first studies to prospectively evaluate a predictive machine learning algorithm in a real-world setting. Findings from this study will help to measure the robustness of predictions made by machine learning algorithms and set a realistic benchmark for expectations of gains that can be made through its application to health care. Registered Report Identifier RR1-10.2196/9466
Effects of Transmission Bottlenecks on the Diversity of Influenza A Virus

We investigate the fate of de novo mutations that occur during the in-host replication of a pathogenic virus, predicting the probability that such mutations are passed on during disease transmission to a new host. Using influenza A virus as a model organism, we develop a life-history model of the within-host dynamics of the infection, deriving a multitype branching process with a coupled deterministic model to capture the population of available target cells. We quantify the fate of neutral mutations and mutations affecting five life-history traits: clearance, attachment, budding, cell death, and eclipse phase timing. Despite the severity of disease transmission bottlenecks, our results suggest that in a single transmission event, several mutations that appeared de novo in the donor are likely to be transmitted to the recipient. Even in the absence of a selective advantage for these mutations, the sustained growth phase inherent in each disease transmission cycle generates genetic diversity that is not eliminated during the transmission bottleneck.
Nanoscale remodeling of ryanodine receptor cluster size underlies cerebral microvascular dysfunction in Duchenne muscular dystrophy

Significance Duchenne muscular dystrophy (DMD) is a hereditary neuromuscular disease that results from mutations in the gene encoding dystrophin. The effects of the disease on cardiac and skeletal muscle have been intensely investigated, but much less is known about how DMD impacts vascular smooth muscle cells (SMCs). Using superresolution nanoscopy, we demonstrate that clusters of ryanodine receptors (RyR2s) on the sarcoplasmic reticulum (SR) of cerebral artery SMCs from the mdx mouse model of DMD are larger compared with controls. Increased RyR2 cluster size is associated with augmented SR Ca2+ release and Ca2+-activated K+ channel activity, resulting in impaired vasoconstriction of cerebral microvessels. Our findings demonstrate that remodeling of RyR2 clusters at the molecular level results in cerebral microvascular dysfunction during DMD. Duchenne muscular dystrophy (DMD) results from mutations in the gene encoding dystrophin which lead to impaired function of skeletal and cardiac muscle, but little is known about the effects of the disease on vascular smooth muscle cells (SMCs). Here we used the mdx mouse model to study the effects of mutant dystrophin on the regulation of cerebral artery and arteriole SMC contractility, focusing on an important Ca2+-signaling pathway composed of type 2 ryanodine receptors (RyR2s) on the sarcoplasmic reticulum (SR) and large-conductance Ca2+-activated K+ (BK) channels on the plasma membrane. Nanoscale superresolution image analysis revealed that RyR2 and BKα were organized into discrete clusters, and that the mean size of RyR2 clusters that colocalized with BKα was larger in SMCs from mdx mice (∼62 RyR2 monomers) than in controls (∼40 RyR2 monomers). We further found that the frequency and signal mass of spontaneous, transient Ca2+-release events through SR RyR2s (“Ca2+ sparks”) were greater in SMCs from mdx mice. Patch-clamp electrophysiological recordings indicated a corresponding increase in Ca2+-dependent BK channel activity. Using pressure myography, we found that cerebral pial arteries and parenchymal arterioles from mdx mice failed to develop appreciable spontaneous myogenic tone. Inhibition of RyRs with tetracaine and blocking of BK channels with paxilline restored myogenic tone to control levels, demonstrating that enhanced RyR and BK channel activity is responsible for the diminished pressure-induced constriction of arteries and arterioles from mdx mice. We conclude that increased size of RyR2 protein clusters in SMCs from mdx mice increases Ca2+ spark and BK channel activity, resulting in cerebral microvascular dysfunction.
Data Augmentation for Neural Online Chats Response Selection

Data augmentation seeks to manipulate the available data for training to improve the generalization ability of models. We investigate two data augmentation proxies, permutation and flipping, for neural dialog response selection task on various models over multiple datasets, including both Chinese and English languages. Different from standard data augmentation techniques, our method combines the original and synthesized data for prediction. Empirical results show that our approach can gain 1 to 3 recall-at-1 points over baseline models in both full-scale and small-scale settings.
Pay One, Get Hundreds for Free: Reducing Cloud Costs through Shared Query Execution

Cloud-based data analysis is nowadays common practice because of the lower system management overhead as well as the pay-as-you-go pricing model. The pricing model, however, is not always suitable for query processing as heavy use results in high costs. For example, in query-as-a-service systems, where users are charged per processed byte, collections of queries accessing the same data frequently can become expensive. The problem is compounded by the limited options for the user to optimize query execution when using declarative interfaces such as SQL. In this paper, we show how, without modifying existing systems and without the involvement of the cloud provider, it is possible to significantly reduce the overhead, and hence the cost, of query-as-a-service systems. Our approach is based on query rewriting so that multiple concurrent queries are combined into a single query. Our experiments show the aggregated amount of work done by the shared execution is smaller than in a query-at-a-time approach. Since queries are charged per byte processed, the cost of executing a group of queries is often the same as executing a single one of them. As an example, we demonstrate how the shared execution of the TPC-H benchmark is up to 100x and 16x cheaper in Amazon Athena and bigquery than using a query-at-a-time approach while achieving a higher throughput.
Preclinical hazard evaluation strategy for nanomedicines

Abstract The increasing nanomedicine usage has raised concerns about their possible impact on human health. Present evaluation strategies for nanomaterials rely on a case-by-case hazard assessment. They take into account material properties, biological interactions, and toxicological responses. Authorities have also emphasized that exposure route and intended use should be considered in the safety assessment of nanotherapeutics. In contrast to an individual assessment of nanomaterial hazards, we propose in the present work a novel and unique evaluation strategy designed to uncover potential adverse effects of such materials. We specifically focus on spherical engineered nanoparticles used as parenterally administered nanomedicines. Standardized assay protocols from the US Nanotechnology Characterization Laboratory as well as the EU Nanomedicine Characterisation Laboratory can be used for experimental data generation. We focus on both cellular uptake and intracellular persistence as main indicators for nanoparticle hazard potentials. Based on existing regulatory specifications defined by authorities such as the European Medicines Agency and the United States Food and Drug Administration, we provide a robust framework for application-oriented classification paired with intuitive decision making. The Hazard Evaluation Strategy (HES) for injectable nanoparticles is a three-tiered concept covering physicochemical characterization, nanoparticle (bio)interactions, and hazard assessment. It is cost-effective and can assist in the design and optimization of nanoparticles intended for therapeutic use. Furthermore, this concept is designed to be adaptable for alternative exposure and application scenarios. To the knowledge of the authors, the HES is unique in its methodology based on exclusion criteria. It is the first hazard evaluation strategy designed for nanotherapeutics.
Acquisition of WNT Pathway Gene Alterations Coincides With the Transition From Precursor Polyps to Traditional Serrated Adenomas

Colorectal traditional serrated adenomas (TSAs) are often associated with precursor polyps, including hyperplastic polyps and sessile serrated adenoma/polyps. To elucidate the molecular mechanisms involved in the progression from precursor polyps to TSAs, the present study analyzed 15 precursor polyp-associated TSAs harboring WNT pathway gene mutations. Laser microdissection-based sequencing analysis showed that BRAF or KRAS mutations were shared between TSA and precursor polyps in all lesions. In contrast, the statuses of WNT pathway gene mutations were different between the 2 components. In 8 lesions, RNF43, APC, or CTNNB1 mutations, were exclusively present in TSA. RNF43 mutations were shared between the TSA and precursor components in 3 lesions; however, they were heterozygous in the precursor polyps whereas homozygous in the TSA. In 4 lesions with PTPRK-RSPO3 fusions, RNA in situ hybridization demonstrated that overexpression of RSPO3, reflecting PTPRK-RSPO3 fusion transcripts, was restricted to TSA components. Consistent with the results of the genetic and in situ hybridization analyses, nuclear &bgr;-catenin accumulation and MYC overexpression were restricted to the TSA component in 13 and 12 lesions, respectively. These findings indicate that the WNT pathway gene alterations are acquired during the progression from the precursor polyps to TSAs and that the activation of the WNT pathway plays a critical role in the development of TSA rather than their progression to high-grade lesions.
One-step generation of monoclonal B cell receptor mice capable of isotype switching and somatic hypermutation

We developed a method for rapid generation of B cell receptor (BCR) monoclonal mice expressing prerearranged Igh and Igk chains monoallelically from the Igh locus by CRISPR-Cas9 injection into fertilized oocytes. B cells from these mice undergo somatic hypermutation (SHM), class switch recombination (CSR), and affinity-based selection in germinal centers. This method combines the practicality of BCR transgenes with the ability to study Ig SHM, CSR, and affinity maturation.
Nonprecious Alloy vs Precious Alloy Telescopic Crown-Retained Removable Partial Dentures: Survival and Maintenance Needs.

PURPOSE The purpose of this retrospective clinical study was to evaluate the clinical outcomes of nonprecious alloy (NPA) vs precious alloy (PA) telescopic crown-retained removable partial dentures (TRPDs), the factors influencing survival, and the type and number of maintenance procedures required during the observation period. MATERIALS AND METHODS This retrospective clinical study is based on 462 patients with a total of 572 TRPDs on a total of 1,946 abutment teeth. The following parameters were analyzed with Kaplan-Meier analysis: sex; alloy type; denture location; number and distribution of abutment teeth (Kennedy Class); vitality of abutment teeth; dentition in the opposing arch; and participation in follow-up visits. RESULTS The mean observation period was 3.87 ± 3.15 years (2.99 ± 2.52 years for NPATRPDs and 5.36 ± 3.53 years for PA-TRPDs; maximum 11.01 years). During the observation period, 4.2% (n = 24) of the TRPDs ceased functioning and 8.3% (n = 161) of the abutment teeth had to be removed. The calculated 5- to 10-year survival probabilities were 96.1% and 84.0%, respectively, for the TRPDs. The number of abutment teeth was the only parameter that significantly (P < .05) impacted this probability, whereas the vitality of the abutment teeth and the type of alloy significantly (P < .05) impacted the survival probability of the abutment teeth. NPA-TRPDs needed significantly earlier initial treatment than PA-TRPDs (P < .05). CONCLUSION The type of alloy seems to have no impact on TRPD survival, but seems to have an influence on the survival of the abutment teeth. There are also differences in maintenance between NPA- and PA-TRPDs.
Natural Language Person Search Using Deep Reinforcement Learning

Recent success in deep reinforcement learning is having an agent learn how to play Go and beat the world champion without any prior knowledge of the game. In that task, the agent has to make a decision on what action to take based on the positions of the pieces. Person Search is recently explored using natural language based text description of images for video surveillance applications (S.Li et.al). We see (Fu.et al) provides an end to end approach for object-based retrieval using deep reinforcement learning without constraints placed on which objects are being detected. However, we believe for real-world applications such as person search defining specific constraints which identify a person as opposed to starting with a general object detection will have benefits in terms of performance and computational resources required. In our task, Deep reinforcement learning would localize the person in an image by reshaping the sizes of the bounding boxes. Deep Reinforcement learning with appropriate constraints would look only for the relevant person in the image as opposed to an unconstrained approach where each individual objects in the image are ranked. For person search, the agent is trying to form a tight bounding box around the person in the image who matches the description. The bounding box is initialized to the full image and at each time step, the agent makes a decision on how to change the current bounding box so that it has a tighter bound around the person based on the description of the person and the pixel values of the current bounding box. After the agent takes an action, it will be given a reward based on the Intersection over Union (IoU) of the current bounding box and the ground truth box. Once the agent believes that the bounding box is covering the person, it will indicate that the person is found.
Functional Connectivity Alterations in Children with Spastic and Dyskinetic Cerebral Palsy

Cerebral palsy (CP) has long been investigated to be associated with a range of motor and cognitive dysfunction. As the two most common CP subtypes, spastic cerebral palsy (SCP) and dyskinetic cerebral palsy (DCP) may share common and distinct elements in their pathophysiology. However, the common and distinct dysfunctional characteristics between SCP and DCP on the brain network level are less known. This study aims to detect the alteration of brain functional connectivity in children with SCP and DCP based on resting-state functional MRI (fMRI). Resting-state networks (RSNs) were established based on the independent component analysis (ICA), and the functional network connectivity (FNC) was performed on the fMRI data from 16 DCP, 18 bilateral SCP, and 18 healthy children. Compared with healthy controls, altered functional connectivity within the cerebellum network, sensorimotor network (SMN), left frontoparietal network (LFPN), and salience network (SN) were found in DCP and SCP groups. Furthermore, the disconnections of the FNC consistently focused on the visual pathway; covariance of the default mode network (DMN) with other networks was observed both in DCP and SCP groups, while the DCP group had a distinct connectivity abnormality in motor pathway and self-referential processing-related connections. Correlations between the functional disconnection and the motor-related clinical measurement in children with CP were also found. These findings indicate functional connectivity impairment and altered integration widely exist in children with CP, suggesting that the abnormal functional connectivity is a pathophysiological mechanism of motor and cognitive dysfunction of CP.
Effects of Systemic Simvastatin on the Concentrations of Visfatin, Tumor Necrosis Factor-α, and Interleukin-6 in Gingival Crevicular Fluid in Patients with Type 2 Diabetes and Chronic Periodontitis

Purpose The objective of this study is to explore the relationship between the levels of interleukin- (IL-) 6, tumor necrosis factor- (TNF-) α, and visfatin and simvastatin usage, in the gingival crevicular fluids (GCFs) of diabetic patients afflicted with chronic periodontitis. Methods Eighty outpatients at the Periodontology Department, Faculty of Dentistry, University Dental Hospital (King Abdulaziz University), were categorized into 4 groups (20 patients per group), on the basis of radiological evaluation of bone loss, clinical attachment levels (CAL), probing depth (PD), and gingival indices: group 1 (healthy periodontium), group 2 (chronic periodontitis + type 2 diabetes), group 3 (chronic periodontitis), and group 4 (type 2 diabetes + chronic periodontitis + simvastatin). Enzyme-linked immunosorbent assays were used to measure IL-6, TNF-α, and visfatin levels. Results Significantly elevated levels of IL-6, TNF-α, and visfatin were seen in group 2 in comparison to groups 1 and 3. Reduced levels were seen in group 4 due to simvastatin usage. Positive association was seen between periodontal variables and the levels of IL-6, TNF-α, and visfatin. Conclusion Periodontal destruction and diabetes have a synergistic effect on the elevation of inflammatory cytokine levels. Simvastatin may be beneficial in improving periodontal health among diabetic patients.
A Minimum Discounted Reward Hamilton–Jacobi Formulation for Computing Reachable Sets

We propose a novel formulation for approximating reachable sets through a minimum discounted reward optimal control problem. The formulation yields a continuous solution that can be obtained by solving a Hamilton–Jacobi equation. Furthermore, the numerical approximation to this solution is the unique fixed-point to a contraction mapping. This allows for more efficient solution methods that are not applicable under traditional formulations for solving reachable sets. Lastly, this formulation provides a link between reinforcement learning and learning reachable sets for systems with unknown dynamics, allowing algorithms from the former to be applied to the latter. We use two benchmark examples, double integrator, and pursuit-evasion games, to show the correctness of the formulation as well as its strengths in comparison to previous work.
Small-Molecule Modulation of Lipid-Dependent Cellular Processes against Cancer: Fats on the Gunpoint

Lipid cell membrane composed of various distinct lipids and proteins act as a platform to assemble various signaling complexes regulating innumerous cellular processes which are strongly downregulated or altered in cancer cells emphasizing the still-underestimated critical function of lipid biomolecules in cancer initiation and progression. In this review, we outline the current understanding of how membrane lipids act as signaling hot spots by generating distinct membrane microdomains called rafts to initiate various cellular processes and their modulation in cancer phenotypes. We elucidate tangible drug targets and pathways all amenable to small-molecule perturbation. Ranging from targeting membrane rafts organization/reorganization to rewiring lipid metabolism and lipid sorting in cancer, the work summarized here represents critical intervention points being attempted for lipid-based anticancer therapy and future directions.
Cognitive Impairment in Chronic Obstructive Pulmonary Disease (COPD): Possible Utility of Marine Bioactive Compounds

Chronic obstructive pulmonary disease (COPD) is characterized by long-term airflow limitation. Early-onset COPD in non-smoker subjects is ≥60 years and in the elderly is often associated with different comorbidities. Cognitive impairment is one of the most common feature in patients with COPD, and is associated with COPD severity and comorbidities. Cognitive impairment in COPD enhances the assistance requirement in different aspects of daily living, treatment adherence, and effectual self-management.This review describes various bioactive compounds of natural marine sources that modulate different targets shared by both COPD and cognitive impairment and hypothesizes a possible link between these two syndromes.
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions

In this paper, we propose a new kernel-based co-occurrence measure that can be applied to sparse linguistic expressions (e.g., sentences) with a very short learning time, as an alternative to pointwise mutual information (PMI). As well as deriving PMI from mutual information, we derive this new measure from the Hilbert–Schmidt independence criterion (HSIC); thus, we call the new measure the pointwise HSIC (PHSIC). PHSIC can be interpreted as a smoothed variant of PMI that allows various similarity metrics (e.g., sentence embeddings) to be plugged in as kernels. Moreover, PHSIC can be estimated by simple and fast (linear in the size of the data) matrix calculations regardless of whether we use linear or nonlinear kernels. Empirically, in a dialogue response selection task, PHSIC is learned thousands of times faster than an RNN-based PMI while outperforming PMI in accuracy. In addition, we also demonstrate that PHSIC is beneficial as a criterion of a data selection task for machine translation owing to its ability to give high (low) scores to a consistent (inconsistent) pair with other pairs.