Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-to-event modeling of hypertension reveals the nonexistence of true controls

Given a lifetime risk of ~90% by the ninth decade of life, it is unknown if there are true controls for hypertension in epidemiological and genetic studies. Here, we use the Bayesian framework to compare logistic and time-to-event approaches to modeling hypertension. Using a proportional hazards model, we explored nonparametric and parametric models of the baseline hazard function, accounting for interval censoring. In the Howard University Family Study (HUFS), a population-based study of African Americans from Washington, D.C., the median age at hypertension was 48 years, baseline hazard rates increased with age until 55 years, and the probability of being free of hypertension at 85 years of age was 12.2%. In the nationwide NHANES study, the median age at hypertension was 42 years in African Americans, in contrast to 57 years in European Americans and 56 years in Mexican Americans. Baseline hazard rates increased with age until 58 years in African Americans, comparable to 60 years in European Americans and 58 years in Mexican Americans. The probability of being free of hypertension at 85 years of age was 8.4% in African Americans, in contrast to 21.4% in European Americans and 20.6% in Mexican Americans. In all four groups, baseline hazard rates decreased but did not reach zero, consistent with the nonexistence of controls. Model fits were comparable for a proportional hazards model based on gamma-distributed hazard rates under a correlated prior process and a logistic model adjusted for age and age2. Last, using an agnostic model screening approach of 38 potential covariates, we identified and replicated in all groups a model that included chloride, low-density lipoprotein cholesterol, uric acid, and weight. With age and age2, the variance explained by these covariates was 40.9% in African Americans, 34.8% in European Americans, and 28.3% in Mexican Americans. Taken together, modeling of the baseline hazard function of hypertension suggests that there are no true controls and that controls in logistic regression are cases with a late age of onset. These findings shed considerable insights into the design of genetic and epidemiological studies of hypertension with implications for ethnic health disparities.
Reflux esophagitis is associated with higher risks of acute stroke and transient ischemic attacks in patients hospitalized with atrial fibrillation

Supplemental Digital Content is available in the text Abstract Reflux esophagitis (RE) is a subset of gastroesophageal reflux disease (GERD) with endoscopic evidence of esophageal inflammation, which has been linked to an increased incidence of atrial fibrillation (AF). However, data on the effect of RE on patient outcomes is limited. We sought to examine the potential association of RE with outcomes of patients with AF in a nationwide study. The National Inpatient Sample (NIS) database was queried to identify hospitalized adult patients with AF and RE between 2010 and 2014. Primary outcomes included inpatient mortality, length of stay (LOS), and total hospital charges. AF related complications such as acute stroke, transient ischemic attack (TIA) and acute heart failure were assessed as secondary outcomes. Propensity score matching and multivariate regression analysis were used. Six lakh sixty seven thousands five hundred twenty patients were admitted for primary diagnosis of AF out of which 5396 had a secondary diagnosis of RE. In the AF with RE cohort, the average age was 73.6 years, 41.5% were male, and 79.9% were Caucasian. There was a greater prevalence of concomitant dyslipidemia, chronic liver disease and chronic pulmonary disease (P < .01) when compared to the AF without RE cohort. Patients with AF and RE also had higher incidence of acute strokes and TIAs (P < .05), longer LOS (P < .001), and higher hospital charges (P < .05) with no difference in acute heart failure (P = .08), hospital mortality (P = .12), or CHA2DS2-VASc score (P = .67). In hospitalized patients with AF, RE was associated with a higher rate of acute stroke and TIAs, longer LOS, and greater hospital charges.
MAXIMUM POWER POINT TRAKING TECHNICAL BASED ON FUZZY LOGIC CONTROLLER FOR PHOTOVOLTAIC SYSTEM

This paper presents an intelligent method of maximum power point tracking (MPPT) technique using fuzzy logic controller (FLC) for photovoltaic system, which consists of the PV panel coupled with dc-dc buckboost controlled by a MPPT algorithm techniques, permits to feed a storage battery. The proposed algorithms will be simulated in Matlab/Simulink environment. The PV system has been tested with KC200GT solar panel under various operating conditions with and without MPPT technique.
Adaptation Across Extreme Variations using Unlabeled Bridges

We tackle an unsupervised domain adaptation problem for which the domain discrepancy between labeled source and unlabeled target domains is large, due to many factors of inter-and intra-domain variation. While deep domain adaptation methods have been realized by reducing the domain discrepancy, these are difﬁcult to apply when domains are signiﬁcantly different. We propose to decompose domain discrepancy into multiple but smaller, and thus easier to minimize, discrepancies by introducing unlabeled bridging domains that connect the source and target domains. We realize our proposed approach through an extension of the domain adversarial neural network with multiple discriminators, each of which accounts for reducing discrepancies between unlabeled (bridge, target) domains and a mix of all precedent domains including source. We validate the effectiveness of our method on several adaptation tasks including object recognition and semantic segmentation.
MILLIPEDES (DIPLOPODA) OF MARAMUREŞ (ROMANIA)

Here we present data of 21 diplopod species from 71 sampling sites of Maramureş, Romania, which belongs to the Eastern Carpathian Mountains. The species list can not be regarded complete since a systematic diplopod-summary of whole Romania is still lacking, but anyway it represents 12% of the presently known diplopod fauna of the country. The most abundant species was Polydesmus complanatus which is common in whole Eastern Europe, but characteristic montane species occurred in remarkable proportion, too, such as: P. polonicus, P. tatranus rodnaensis, P. dadayi, Glomeris tetrasticha and Trachysphaera acutula.
Histological Studies on the Prenatal Development of Endocrine Pancreas in Goats

The present study was conducted on the prenatal development of endocrine pancreas in goat foetuses. The development of endocrine pancreas was observed in goat embryos from 52 days of prenatal life. The endocrine cells appeared as large, solitary cells among the ductular cells. Along with the development of these cells in the ductular wall, a space developed in the mesenchyme around these cells. Later these cells increased in number and were pushed out of the ductular wall with corresponding increase in the size of the space. By 69 days of age, a few well defined encapsulated islets were observed and A-cells and B-cells could be identified. From 71 days onwards, lobulation of pancreatic parenchyma became evident and developing islets of varying size were noticed within lobules as well as in the interlobular connective tissue. Some islets showed blood-filled spaces among endocrine cells. Pancreas of 134 days old foetuses showed thick nerve bundles or peri-insular plexuses in close contact with islets located in the interlobular septa. From 140 days of development onwards, some of the large islets showed a central sinus. In 148 days-old foetuses, most of the large islets contained a large central sinus with peripherally arranged groups of cells or several small sinuses among groups or cords of cells.
Regulation of vascular smooth muscle cell phenotype

REGULATION OF VASCULAR SMOOTH MUSCLE CELL PHENOTYPE Joshua K Salabei, October 30, 2012 Vascular injury and chronic arterial diseases such as atherosclerosis and restenosis result in exposure of vascular smooth muscle cells (VSMCs) to increased concentrations of growth factors triggering a change from a contractile to a synthetic phenotype. The mechanisms by which growth factors trigger VSMC phenotype transitions remain unclear. Because phenotype switching involves the removal of contractile proteins, we hypothesized that autophagy is an essential modulator of VSMC phenotype. Also, the conversion to the synthetic phenotype is accompanied by cellular changes that trigger metabolic changes to meet the increasing demands for cellular energy. Little is known however about the contributions of different substrates metabolized for energy production and how these metabolic changes integrate with phenotype switching and VSMC proliferation. We further hypothesize that metabolic changes induced by PDGF are required for phenotype switching and proliferation. Studies presented here show
Robust Defense Against L p -Norm-Based Attacks by Learning Robust Representations

Image classifiers are vulnerable to adversarial perturbations. Previous work [8, 9] proposed restrictions of the feature space as a defense mechanism against L p -Norm attacks. In this work we evaluate the claim that adversarial examples are caused by the difficulty of DNN to learn robust representations. We propose a regularization objective based on the Euclidean distance between classes in the hypothetical latent space found in the features of the penultimate layer. Our novel objective increases the size of perturbation epsilon ε requiredtogeneratetheaforementionedadversarialexamples. Weevaluateour approachandconcludethatsuccessfulattacksrequireanexceedinglylargeperturbationbudget ε that puts into question the true class of the adversarial image. Our approach is significantly more robust than that of the previous state of the art, and entails an insignificant fine-tuning of existing models.
The GFDL Earth System Model Version 4.1 (GFDL‐ESM 4.1): Overall Coupled Model Description and Simulation Characteristics

We describe the baseline coupled model configuration and simulation characteristics of GFDL's Earth System Model Version 4.1 (ESM4.1), which builds on component and coupled model developments at GFDL over 2013–2018 for coupled carbon‐chemistry‐climate simulation contributing to the sixth phase of the Coupled Model Intercomparison Project. In contrast with GFDL's CM4.0 development effort that focuses on ocean resolution for physical climate, ESM4.1 focuses on comprehensiveness of Earth system interactions. ESM4.1 features doubled horizontal resolution of both atmosphere (2° to 1°) and ocean (1° to 0.5°) relative to GFDL's previous‐generation coupled ESM2‐carbon and CM3‐chemistry models. ESM4.1 brings together key representational advances in CM4.0 dynamics and physics along with those in aerosols and their precursor emissions, land ecosystem vegetation and canopy competition, and multiday fire; ocean ecological and biogeochemical interactions, comprehensive land‐atmosphere‐ocean cycling of CO2, dust and iron, and interactive ocean‐atmosphere nitrogen cycling are described in detail across this volume of JAMES and presented here in terms of the overall coupling and resulting fidelity. ESM4.1 provides much improved fidelity in CO2 and chemistry over ESM2 and CM3, captures most of CM4.0's baseline simulations characteristics, and notably improves on CM4.0 in (1) Southern Ocean mode and intermediate water ventilation, (2) Southern Ocean aerosols, and (3) reduced spurious ocean heat uptake. ESM4.1 has reduced transient and equilibrium climate sensitivity compared to CM4.0. Fidelity concerns include (1) moderate degradation in sea surface temperature biases, (2) degradation in aerosols in some regions, and (3) strong centennial scale climate modulation by Southern Ocean convection.
Seed mycoflora of tomato (Lycopersicon Esculentum Mill.) cultivars collected from different locations of Gujarat

The seed mycoflora of four cultivars of tomato seed samples collected from different locations of Gujarat was investigated in 2009–11. Agar plate method was found more suitable for detection of seed-borne mycoflora of tomato, since twelve fungal species were isolated by agar plate method as against only eight fungal species by standard blotter method. Frequencies of fungi increased with the increasing storage time in both methods. Aspergillus amstelodami and Cunnninghamelia echinulata were new records of association with tomato seeds in Gujarat. Alternaria alternata, Aspergillus flavus and Aspergillus niger were found predominant.
Burden of chronic diseases among sarcoma survivors treated with anthracycline chemotherapy: results from an observational study

Aim: Cardiovascular disease is a leading cause of mortality among long-term cancer survivors treated with large total doses of doxorubicin. An increase in coronary artery disease (CAD) among childhood cancer survivors by age 45 has been observed and is driven by primarily anthracycline chemotherapy and to a lesser extent chest radiation that includes the heart in the radiation field. The risk factors and associated chronic diseases (hypertension, etc.) are well known for CAD and can be often prevented or treated, thus reducing the risk of CAD in these patients. We piloted a risk-based survivorship clinic in an academic medical center to characterize the distribution of risk factors for CAD and improve the quality of life in a population of sarcoma survivors treated with doxorubicin. Methods: We followed a prospective cohort of sixty-one survivors of bone and soft tissue sarcoma treated with doxorubicin chemotherapy (> 400 mg/m2) and at least 2 years post-therapy attending the sarcoma survivorship clinic. We collected clinical, demographic data, and patient reported outcomes via PROMIS questionnaires annually. Results: We demonstrated a high burden of chronic diseases in this population. Among six chronic conditions that are known risk factors for CAD (hypertension, diabetes, obesity, chronic inflammation, kidney disease and dyslipidemia), more than one-fourth (26%, 16/61) of patients had three or more of these risk factors at baseline visit, and 49% (30/61) had two or more. Conclusion: The results of this pilot study support the presence of modifiable CAD risk factors in this population of sarcoma survivors. Evidence-based guidelines for high-risk survivors of rare cancers are needed.
FINITE ELEMENT MODAL ANALYSIS OF A HYBRID STIFFENED PLATE

Stiffened plates under longitudinal compression is outlined using several codes. The finite element analysis (FEA) is a powerful technique which is used for the dynamic response of structures. The dynamic behaviour of the hybrid stiffened plate can be investigated further by our modal analyses. The modal analysis helps clarify the causes of resonant vibrations which may occur in application of stiffened plates. The idea of the hybrid plate is to change the Young’s modulus of the base plate and the stiffeners, thus assuming that they were made of different materials.
RUL Predict of IGBT Based on DeepAR Using Transient Switch Features

The insulated gate bipolar transformer (IGBT) has been widely used in power electrical system such as wind power converter and drive converter. However, the high load and capricious working environment make IGBT become the most vulnerable component in the electrical system. In this paper, a method based on probabilistic forecasting with auto-regressive recurrent networks (DeepAR) is proposed to predict the remaining useful life (RUL) of IGBT. Firstly, the transient data of the collector-emitter voltage signal are acquired when IGBT is turned off. Then, the different characteristics are extracted from transient data and the features that can represent the health state of IGBT best are chosen as the input of the DeepAR model to predict the remaining life of IGBT. Experiment results show that the log-log ratio of transient data can be an accurate precursor to predict RUL, and compared with other similar series predict models such as Auto-Regressive Integrated Moving Average (ARIMA) and Simple Exponential Smoothing (SES), DeepAR can get higher accuracy.
Monitoring of Aerosol and Other Particulate Matter in Air Using Aerial Monitored Sensors and Real Time Data Monitoring and Processing

Identification of the concentration of dust particles and monitoring the content of harmful toxic a site to monitor and analyze atmospheric aerosols, methane, CO2, SO2 is required for urban cities with dense population centers and identify results in the emission of particulate material and gases potentially hazardous to health. We often use air quality indexing system but it is always not possible to identify monitoring methods that rely on limited numbers of fixed sampling locations to validate a complex environment and collect sufficient data to confirm model effectiveness. We can use an aerial monitoring instrument to collect data transmitted as telemetry from the UAV for instantaneous micro-meteorological data that can assist interpretation of concentrations detected by on-board air quality sensors. We have also explained the design for ionizing filter based dust sensor that will send information based on the distribution of the gas concentration at 12 points using quad copter sensing system.
Team RUC AI · M 3 Technical Report at VMT Challenge 2020: Enhancing Neural Machine Translation with Multimodal Rewards

This notebook paper presents our model in the VATEX video-guided machine translation (VMT) challenge. We propose an objective-side VMT model by improving the translation system with multimodal rewards. The visual contexts are used in an additional ob-jective to correct visually-discrepant translations and generally make the learned language embeddings more visually grounded. The proposed model signiﬁcantly outperforms the state-of-the-art NMT baseline and achieves 35.28 BLEU4 score on the testing set which ranks the third place at the VATEX VMT challenge leaderboard 2020.
Patient Centered Care: Focus on Low and Middle Income Countries and Proposition of New Conceptual Model

The health system is a fundamental building block of a country's social project. Approaches and strategies differ but some concepts such as Patient Centred Care (PCC) seem to be more and more popular nowadays as the customer centric advocated in the industry. The research has focused on this trend of PCC with several different definitions but without having a particular focus on the context of Low and Middle Income Countries (LMICs). The objective of this study is, through an extensive literature review, to analyze this concept, to unify the dimensions described by different authors, to define a concept that can be proposed to LMICs and to determine facilitating factors as well as obstacles for its implementation. Keywords: Conceptual model, Health system, Low and middle income countries (LMICs), Patient Centered Care
Integrated Risk-Based Strategy for Real-Time Decision Making and Mission Reconfiguration in Unmanned Aerial Vehicles

Small unmanned aerial vehicles (UAVs) have been increasingly popular in the last years, being employed in a wide range of applications in diverse areas, including, for instance, military, medicine, and package delivery. These aerial vehicles are commonly energized by rechargeable batteries, and, as a result, their autonomy can be significantly affected by several uncertainty sources, including variable ambient conditions or mechanical mishaps. For this reason, the ability to predict their electric consumption for a determined path is critical to design UAV missions and complete them successfully. Additionally, due to the potential occurrence of unexpected events (e.g., mechanical failures or changes in the weather conditions), airborne implementation of real-time decision-making schemes for mission replanning is of utmost importance. Hence, this paper presents an integrated Risk-based strategy utilizing a State-of-Charge (SOC) prognostics algorithm to quantify the risk associated with a given path in terms of future consumption, intending to make real-time decisions on the UAV operation. More specifically, we consider a UAV mission framework with a discrete set of possible targets, where each one has costs, rewards, and failure risks, which is characterized by calculating the total probability using sample-resampling methods. Then, we seek the optimal choice by employing a Bayesian-Risk inspired approach whose outcome results in a compromise between risks and rewards.
Continue or SHIFT: Learning Conversational Patterns for Dialogue Generation

In dialogues, it is often the case that the response could be either relevant or irrelevant to the given conversation context, depending on the speaker’s intention of either topic continuity or topic shift. However, this aspect of dialogues is less explored in existing generative dialogue systems, because the widely-used encoder-decoder-based attention models are built upon the assumption that the target sequence is and must be relevant to the source sequence. In this work, we propose the loose coupling approach (LCA) to directly address the learning of such conversational patterns, which includes two parts: a high-level information selecting mechanism that enables the model to ignore the context or the previous part of the response, and a sampling-based response guide mechanism that mitigates the exposure bias problem in training. Human and automatic evaluation results demonstrate the effectiveness of our approach on capturing and describing both topic shift and continuity.
Fighting Copycat Agents in Behavioral Cloning from Multiple Observations

Imitation learning trains policies to map from input observations to the actions that an expert would choose. In this setting, distribution shift frequently exacerbates the effect of misattributing expert actions to nuisance correlates among the observed variables. We observe that a common instance of this causal confusion occurs in partially observed settings when expert actions are strongly correlated over time: the imitator learns to cheat by predicting the expert’s previous action, rather than the next action. To combat this “copy-cat problem”, we propose an adversarial approach to learn a feature representation that removes excess information about the previous expert action nuisance correlate, while retaining the information necessary to predict the next action. In our experiments, our approach improves performance signiﬁcantly across a variety of partially observed imitation learning tasks.
Use of Text Mining Technique in Doing Trend Analysis of the Internet Articles for Nuclear Energy

Because use of nuclear energy is highly influenced by the public acceptance, it is necessary to identify the people’s perception in establishing the policy for the use of nuclear energy [1]. However, there is actually no way to check if the public’s first perceptions are kept without changes or how they vary as time goes on. Since the public’s perceptions are subject to change by the mass media, such as SNS (Social Networking Service), newspapers and news, the trend analysis of those media could be a useful method to predict the people’s perception of nuclear energy [2]. This study analyzed the internet articles posted on ‘NAVER’, a Korean internet portal site, to figure out the trend of those articles on nuclear energy for the past four years from January 1, 2016 to December 31, 2019. For this, we used a big data analysis program ‘R’ and performed text mining technique.