Miracle-dz/UniFAR-FTUs huggingface dataset:
Blood Pressure Reactions to Stress and the Prediction of Future Blood Pressure: Effects of Sex, Age, and Socioeconomic Position

Objective This epidemiological study examined whether the magnitude of blood pressure reactions to mental stress was associated with future blood pressure and whether the strength of association was affected by sex, age, and socioeconomic position. Materials and Methods Resting blood pressure was recorded at initial baseline and in response to mental stress. Five-year follow-up resting blood pressure data were available for 990 (68%) of the participants; 333 were 23 years old at the time of stress testing, 427 were 43, and 230 were 63. There were 541 women and 449 men; 440 came from manual and 550 from nonmanual occupation households. Results Systolic blood pressure reactions to stress correlated positively with follow-up systolic blood pressure; no association was found for diastolic blood pressure reactions and follow-up diastolic blood pressure. In multivariate tests, systolic reactivity remained predictive of follow-up systolic blood pressure and accounted for 2.3% of the variance not explained by age, body mass index, and initial baseline systolic blood pressure. Systolic and diastolic reactivity predicted 5-year upward drift in systolic and diastolic blood pressure respectively, accounting for an additional 3.6% and 2.9% of variance, respectively, in multivariate models. The predictive value of reactivity was greater for participants from manual occupation households and tended to be greater for men. Conclusions The results of this study indicate that blood reactions to mental stress predict future blood pressure status and the increase in resting blood pressure over time. The magnitude of the prediction appears to vary with socioeconomic position and sex.
Effects of short‐term metformin treatment on insulin sensitivity of blood glucose and free fatty acids

Aim: Based on the known effect of metformin (MET) in improving insulin sensitivity in type 2 diabetes, with the scope to focus the effects on glycaemic and free fatty acids (FFA) levels, we studied the effects of a short‐term treatment with this drug in obese subjects and obese patients with diabetes or family history of diabetes (FHD). We used a method to allow us to evaluate the possible difference of insulin sensibility with regard to the insulin action on glycaemia and blood FFA, both in the basal state and during oral glucose tolerance test (OGTT).
Superpixel Filtering for Mean Field Inference in CRFs Integrated with Convolutional Neural Networks

Low level computer vision tasks such as per pixel labelling have recently been approached from the perspective of representation learning. Using deep architectures, strong, characteristic features can be learnt through applying a series of operations such as convolutions, resampling and nonlinearities. These learnt representations are then used in a classifier to assign labels to pixels, yielding a segmentation. These pixel classifications can be refined through the use of variational Mean Field inference, in which the observation that similar pixels are likely to have agreeing labels is accounted for. Variational Mean Field inference is performed on a Conditional Random Field, modelling the per pixel label distributions and the agreement between labels of pairs of pixels. The initial class distributions are provided by a Convolutional Neural Network with a stacked classifier. Central to Mean Field inference in the CRF model is the use of high dimensional filtering for message passing. Existing methods use the convolutional data structure, the Permutohedral Lattice. This work presents an alternative method of approximating the high dimensional filter output, by using a superpixel segmented representation of the image. As with previous work, the inference and training(of parameters) phases are incorporated in to the forward and backward passes of the Artificial Neural Network training.
Platelet Indices as a Predictor of Microvascular Complications in Type 2 Diabetes

Background: Type 2 diabetes mellitus (T2DM) patients have a higher risk of developing micro- and macrovascular complications, which lead to decrease in the quality of life and increase in morbidity. Platelet indices have been available in the laboratory routine using blood cell counters for several years. These indices could alert us regarding endothelial dysfunction and in turn regarding the microvascular complications. Hence, this study was done to prove the correlation between platelet indices and microvascular complications in T2DM. Materials and Methods: In total, 125 diabetic patients attending diabetes OPD and admitted in medicine department along with age and sex-matched non-diabetic controls were studied. A detailed history was taken regarding duration of diabetes, medication, past history of stroke, IHD, and hypertension. Patients with T2DM were specially evaluated for microvascular complications. Platelet indices, fasting blood glucose, Post prandial blood glucose, HbA1C, and Sr. Creatinine were obtained from venous blood samples. All parameters were then subjected to statistical analysis using SPSS 17.0. Results: Platelet indices, namely MPV, PCT, PDW, and P/LCR were significantly higher in diabetic individuals than those in age and gender-matched controls. Moreover, the increase in MPV, PDW, and P/LCR was more significant in diabetic subjects with microvascular complications when compared with those without microvascular complications. Platelet dysfunction also showed a positive association with HbA1C, retinopathy, nephropathy, and neuropathy individually. Conclusions: Changes in platelet indices were found to be statistically associated with diabetes and its complications.
Refractory Lactic Acidosis and an Approach to its Management - A Case Report

Abstract Background Lactic acidosis (LA) is a complication of diseases commonly seen in intensive care patients which carries an increased risk of mortality. It is classified by its pathophysiology; Type A results from tissue hypo-perfusion and hypoxia, and Type B results from abnormal metabolic activity in the absence of hypoxia. Reports of the co-occurrence of both types have been rarely reported in the literature relating to intensive care patients. This case report describes the challenging management of a patient diagnosed with both Type A and Type B LA. Case presentation A 55-year-old female with newly diagnosed diffuse large B-cell lymphoma (DLBCL) developed hospital-acquired pneumonia, respiratory failure, shock and intra-abdominal septicaemia from a bowel perforation. Blood gases revealed a mixed picture lactic acidosis. Correction of septic shock, respiratory failure and surgical repair caused initial improvement to the lactic acidosis, but this gradually worsened in the intensive care unit. Only upon starting chemotherapy and renal replacement therapy was full resolution of the lactic acidosis achieved. The patient was discharged but succumbed to her DLBCL several months later. Conclusion Type A and Type B LA can co-occur, making management difficult. A systematic approach can help diagnose any underlying pathology and aid in early management.
Platelet-derived growth factor-beta as a new marker of deep venous thrombosis

Background: Deep venous thrombosis (DVT) is associated with significant morbidity and mortality. Thus, there is a great need to demonstrate a more efficient biomarker that would confirm the diagnosis of DVT. Our work aimed to evaluate the role of platelet-derived growth factor-beta (PDGF-B) as a new marker of DVT and its correlation with other radiological and laboratory tools used for the diagnosis. Materials and Methods: A case–control study enrolled forty patients selected from our university hospital between April 2018 and August 2018, who divided into two groups: Group I (n = 20) consisted of patients diagnosed with acute venous thrombosis and Group II (n = 20) consisted of patients diagnosed with chronic venous thrombosis. Twenty samples were collected from age- and gender-matched apparently healthy controls to be used as a control. Venous duplex ultrasonography, routine laboratory investigations, D-dimer (DD), and protein expression of PDGF-B were performed on all patients. Results: There was a highly significant increase in a protein expression of PDFG-B in all cases of acute and chronic venous thrombosis compared to the control group with P < 0.001; furthermore, it was more specific than DD for the detection of DVT (specificity 95% and 90%, respectively). Conclusion: Our study submits a novel association of PDGF-B plasma levels with DVT, and PDGF-B is considered to be a more specific indicator for DVT than is DD.
A particle-field representation unifies paradigms in active matter

Active matter research focuses on the emergent behavior among interacting self-propelled particles. Unification of seemingly disconnected paradigms -- active phase-separation of repulsive discs and collective motion of self-propelled rods -- is a major challenge in contemporary active matter. Inspired by the quanto-mechanical wave-particle duality, we develop an approach based on the representation of active particles by smoothed continuum fields. On the basis of the collision kinetics, we demonstrate analytically and numerically how nonequilibrium stresses acting among self-driven, anisotropic objects hinder the formation of phase-separated states as observed for self-propelled discs and facilitate the emergence of orientational order. Besides particle shape, the rigidity of self-propelled objects controlling the symmetry of emergent ordered states is as a crucial parameter: impenetrable, anisotropic rods are found to form polar, moving clusters, whereas large-scale nematic structures emerge for soft rods, notably separated by a bistable coexistence regime. These results indicate that the symmetry of the ordered state is not dictated by the symmetry of the interaction potential but is rather a dynamical, emergent property of active systems. This unifying theoretical framework can represent a variety of active systems: living cell tissues, bacterial colonies, cytoskeletal extracts as well as shaken granular media.
Learning Representations by Maximizing Mutual Information Across Views

We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, one could produce multiple views of a local spatio-temporal context by observing it from different locations (e.g., camera positions within a scene), and via different modalities (e.g., tactile, auditory, or visual). Or, an ImageNet image could provide a context from which one produces multiple views by repeatedly applying data augmentation. Maximizing mutual information between features extracted from these views requires capturing information about high-level factors whose influence spans multiple views -- e.g., presence of certain objects or occurrence of certain events. Following our proposed approach, we develop a model which learns image representations that significantly outperform prior methods on the tasks we consider. Most notably, using self-supervised learning, our model learns representations which achieve 68.1% accuracy on ImageNet using standard linear evaluation. This beats prior results by over 12% and concurrent results by 7%. When we extend our model to use mixture-based representations, segmentation behaviour emerges as a natural side-effect. Our code is available online: this https URL.
Vitamin B12 Deficiency is Endemic in Indian Population: A Perspective from North India

Background: Vitamin B12 deficiency is believed to be widespread in Indian population. However, more data is needed to fuel a meaningful debate on preventive and therapeutic strategies. Aims and Objectives: Objective of the current study is to evaluate status of vitamin B12 levels in people from a tier 3 city and among people living in an urban area with or without diabetes. Settings and Design: Retrospective, cross-sectional study. Methodology: Data captured in electronic medical records (EMR) of an endocrine practice and from a diagnostic laboratory was analysed. Statistical Analysis Used: Statistical analysis was done using open source software “Jamovi”. Results: Prevalence of vitamin B12 deficiency (Vitamin B12 levels <200 pg/ml) in tier 3 city was 47.19% (n = 267). From an urban endocrine practice, database of 11913 patients was searched for reports of vitamin B12 levels. Prevalence of vitamin B12 deficiency was 37.76% in people with pre-diabetes (n = 92), 31.23% in people with endocrine problems other than diabetes and pre-diabetes (n = 285) and 18.25% in people with diabetes (n = 378). Tier 3 city population had significantly lower vitamin B12 levels than people living in an urban area and attending an endocrine clinic. Vitamin B12 levels were significantly higher in people with diabetes as compared to people with other endocrine problems. Conclusion: Prevalence of vitamin B12 deficiency is 47% in north Indian population. People with diabetes have higher vitamin B12 levels than general population though still have high prevalence of deficiency. This data shows that Vitamin B12 deficiency is widespread in Indian population.
Differentiation of adult human mesenchymal stem cells into dopaminergic neurons

The striatal dopamine (DA) deficiency is known as the main cause of the clinical picture of Parkinson’s disease (PD). The disease is a progressive degeneration of dopaminergic neurons in the striatum. The treatment of PD is based on compensation for the brain’s supply of DA lost by drug therapy, deep brain stimulation, surgery, gene and cell therapies. Clinical studies have focused on the utility of stem cell-based therapies in PD. Embryonic and mesenchymal stem cells (MSCs) are widely used. Recently, human adipose derived stem cells (hADSCs) have been considered as a suitable source of tissue for this purpose. In this project, hADSCs differentiated into dopaminergic neurons and the specificity of the cell preparations was examined. Human adipose tissues were collected from healthy volunteers undergoing liposuction and hADSCs were isolated by collagenase-based enzymatic method. Flow cytometry was performed using the surface cluster of differentiation (CD) markers to confirm the cell typical properties. Then hADSCs were differentiated to dopaminergic neurons in neurobasal medium in the presence of differentiation factors and confirmed by immunocytochemistry via neuronal and dopaminergic markers. The isolated hADSCs were cultured and identified by the expression of MSCs surface markers including CD90, and CD44. These cells did not express hematopoietic surface markers such as CD45 and CD14. Differentiated cells express neuronal marker NeuN and dopaminergic marker tyrosine hydroxylase (TH). It is concluded that hADSCs can be easily taken from the patient’s own body and differentiated into dopaminergic cells having a lower risk of transplant rejection.
Prevalence and characterization of beta-lactamase-producing Escherichia coli isolates from a tertiary care hospital in India

BACKGROUND: The purpose of the study was to determine the prevalence and characterize the resistance profiles of Escherichia coli isolated from various clinical specimens by various phenotypic and genotypic methods. MATERIALS AND METHODS: A total of 196 consecutive, nonduplicate strains of clinically significant E. coli isolated from various clinical specimens were included in the study. Identification and antimicrobial susceptibility testing was performed by using Vitek-2 system (Biomerieux, France). Phenotypic detection of extended spectrum beta-lactamase (ESBLs), Amp-C-β lactamase (Amp C), and carbapenemase production was done by various combination of disc diffusion methods, minimum inhibitory concentration determination by E-test, followed by polymerase-chain-reaction for the detection of β-lactamase-encoding genes. RESULTS: Overall prevalence of ESBLs, Amp C, and carbapenemase production was found to be 88.3%, 42.2%, and 65.1% by the phenotypic detection methods. Our study also revealed high resistance rates against other antibiotics such as cefepime (89%), cefotaxime (95.4%), ceftazidime (85.4%), ceftriaxone (91.8%), cefpodoxime (92.7%), aztreonam (56.3%), piperacillin/tazobactam (89.2%), and ticarcillin/clavulanic acid (76.3%). The most prevalent ESBL gene was blaTEM(67.30%), and least prevalent ESBL gene was blaVEB(2.61%). In case of Amp C, blaFOXgene (21.9%) was predominant. Among the genes encoding for carbapenemases, the most common gene was blaNDM(61.7%) followed by blaVIM(30.8%), blaKPC(10.6%), blaOXA-48 (5.3%), and blaIMP(2.1%). CONCLUSION: Our findings suggest a high rate of ESBLs, Amp C, and carbapenemase production among the E. coli isolates. A combination of both phenotypic and genotypic methods would be ideal for better characterization of resistance patterns among the E. coli isolates.
Automated Machine Learning with Monte-Carlo Tree Search (Extended Version)

The AutoML approach aims to deliver peak performance from a machine learning portfolio on the dataset at hand. A Monte-Carlo Tree Search Algorithm Selection and Configuration (Mosaic) approach is presented to tackle this mixed (combinatorial and continuous) expensive optimization problem on the structured search space of ML pipelines. Extensive lesion studies are conducted to independently assess and compare: i) the optimization processes based on Bayesian Optimization or Monte Carlo Tree Search (MCTS); ii) its warm-start initialization based on meta-features or random runs; iii) the ensembling of the solutions gathered along the search. Mosaic is assessed on the OpenML 100 benchmark and the Scikit-learn portfolio, with statistically significant gains over AutoSkLearn, winner of all former AutoML challenges.
Adversarially Robust Generalization Just Requires More Unlabeled Data

Neural network robustness has recently been highlighted by the existence of adversarial examples. Many previous works show that the learned networks do not perform well on perturbed test data, and significantly more labeled data is required to achieve adversarially robust generalization. In this paper, we theoretically and empirically show that with just more unlabeled data, we can learn a model with better adversarially robust generalization. The key insight of our results is based on a risk decomposition theorem, in which the expected robust risk is separated into two parts: the stability part which measures the prediction stability in the presence of perturbations, and the accuracy part which evaluates the standard classification accuracy. As the stability part does not depend on any label information, we can optimize this part using unlabeled data. We further prove that for a specific Gaussian mixture problem illustrated by [35], adversarially robust generalization can be almost as easy as the standard generalization in supervised learning if a sufficiently large amount of unlabeled data is provided. Inspired by the theoretical findings, we propose a new algorithm called PASS by leveraging unlabeled data during adversarial training. We show that in the transductive and semi-supervised settings, PASS achieves higher robust accuracy and defense success rate on the Cifar-10 task.
How to Best Use Syntax in Semantic Role Labelling

There are many different ways in which external information might be used in a NLP task. This paper investigates how external syntactic information can be used most effectively in the Semantic Role Labeling (SRL) task. We evaluate three different ways of encoding syntactic parses and three different ways of injecting them into a state-of-the-art neural ELMo-based SRL sequence labelling model. We show that using a constituency representation as input features improves performance the most, achieving a new state-of-the-art for non-ensemble SRL models on the in-domain CoNLL’05 and CoNLL’12 benchmarks.
Multiuser Diversity Scheduling in Free-Space Optical Communications

A point-to-multipoint system consisting of an optical transmitter (known as the central node) equipped with apertures, and users communicating over free-space optical (FSO) channels is considered. It is assumed that each link suffers from weak-turbulence conditions. The main goal of this paper is to analyze the performance of the system for various multiuser diversity scheduling schemes in terms of the turbulence strength, from the throughput and the latency perspectives. It is demonstrated that for the proposed system with low turbulence strength, the greedy scheduling scheme (i.e., the central node serves at each time slot the user with the best channel characteristics) achieves the maximum system throughput among various scheduling algorithms at the cost of a large latency. We extend the well-known RF concepts of multiuser diversity to FSO communications. In particular, we show that the Proportional Fair Scheduling with Exponential Rule (PFS/ER) scheme achieves the minimum latency. In addition, the results demonstrate that for point-to-multipoint FSO systems with low turbulence strengths, using the proportional fair scheduling (PFS) has no diversity gain over other schemes. Finally, two new scheduling algorithms are introduced to achieve a tradeoff between the throughput and the latency in FSO-based systems.
Multiple Drones driven Hexagonally Partitioned Area Exploration: Simulation and Evaluation

In this paper, we simulated a distributed, cooperative path planning technique for multiple drones (~200) to explore an unknown region (~10,000 connected units) in the presence of obstacles. The map of an unknown region is dynamically created based on the information obtained from sensors and other drones. The unknown area is considered a connected region made up of hexagonal unit cells. These cells are grouped to form larger cells called sub-areas. We use long range and short range communication. The short-range communication within drones in smaller proximity helps avoid re-exploration of cells already explored by companion drones located in the same subarea. The long-range communication helps drones identify next subarea to be targeted based on weighted RNN (Reverse nearest neighbor). Simulation results show that weighted RNN in a hexagonal representation makes exploration more efficient, scalable and resilient to communication failures.
Two New HATNet Hot Jupiters around A Stars and the First Glimpse at the Occurrence Rate of Hot Jupiters from TESS

Wide-field surveys for transiting planets are well suited to searching diverse stellar populations, enabling a better understanding of the link between the properties of planets and their parent stars. We report the discovery of HAT-P-69 b (TOI 625.01) and HAT-P-70 b (TOI 624.01), two new hot Jupiters around A stars from the Hungarian-made Automated Telescope Network (HATNet) survey that have also been observed by the Transiting Exoplanet Survey Satellite. HAT-P-69 b has a mass of MJup and a radius of RJup and resides in a prograde 4.79 day orbit. HAT-P-70 b has a radius of RJup and a mass constraint of MJup and resides in a retrograde 2.74 day orbit. We use the confirmation of these planets around relatively massive stars as an opportunity to explore the occurrence rate of hot Jupiters as a function of stellar mass. We define a sample of 47,126 main-sequence stars brighter than Tmag = 10 that yields 31 giant planet candidates, including 18 confirmed planets, 3 candidates, and 10 false positives. We find a net hot Jupiter occurrence rate of 0.41 ± 0.10% within this sample, consistent with the rate measured by Kepler for FGK stars. When divided into stellar mass bins, we find the occurrence rate to be 0.71 ± 0.31% for G stars, 0.43 ± 0.15% for F stars, and 0.26 ± 0.11% for A stars. Thus, at this point, we cannot discern any statistically significant trend in the occurrence of hot Jupiters with stellar mass.