Miracle-dz/UniFAR-FTUs huggingface dataset:
Open Access Capture of Patients With Gastroesophageal Reflux Disease Using an Online Patient-Reported Outcomes Instrument

Background Persons with gastroesophageal reflux disease (GERD) frequently search online for information about causes and treatment options. The GerdQ self-assessment questionnaire can be used for diagnosis of GERD and follow-up of symptoms. Objectives To assess whether it is feasible (1) to study the prevalence and impact of GERD in persons visiting a GERD information website, and (2) to identify partial responsiveness to proton pump inhibitor (PPI) therapy using the GerdQ. Methods All visitors (aged 18–79 years) to a GERD information website between November 2008 and May 2011 were invited to complete the GerdQ online. The GerdQ questionnaire consists of 6 questions (score per question: 0–3). In respondents who did not use PPIs, we used the questionnaire to identify those with GERD (total score ≥8) and assess the influence of these symptoms on their daily life, divided into low (total score <3 on impact questions) and high impact (total score ≥3 on impact questions). In PPI users, we used the GerdQ to quantify partial responsiveness by any report of heartburn, regurgitation, sleep disturbance, or over-the-counter medication use for more than 1 day in the preceding week. We subsequently asked GerdQ respondents scoring ≥8 to complete the disease-specific Quality of Life in Reflux and Dyspepsia (QOLRAD) questionnaire. Results A total of 131,286 visitors completed the GerdQ, of whom 80.23% (n = 105,329) did not use a PPI. Of these, we identified 67,379 respondents (63.97%) to have GERD (n = 32,935; 48.88% high impact). We invited 14,028 non-PPI users to complete the QOLRAD questionnaire, of whom 1231 (8.78%) completed the questionnaire. Mean total QOLRAD scores were 5.14 (SEM 0.04) for those with high-impact GERD and 5.77 (SEM 0.04) for those with low-impact GERD (P < .001). In PPI users, 22,826 of 25,957 respondents (87.94%) reported partial responsiveness. We invited 6238 PPI users to complete the QOLRAD questionnaire, of whom 599 (9.60%) completed the disease-specific quality-of-life questionnaire. Mean total QOLRAD scores were 4.62 (SEM 0.05) for partial responders and 5.88 (SEM 0.14) for adequate responders (P < .001). Conclusions The GerdQ identified GERD in many website respondents and measured partial responsiveness in the majority of PPI users. Both non-PPI users with GERD and PPI users with partial responsiveness were associated with a decreased health-related quality of life. We have shown the feasibility of GERD patient identification online.
Data Augmentation based Cross-Lingual Multi-Speaker TTS using DL with Sentiment Analysis

Text to Speech (TTS) algorithms have made tremendous strides in recent years in terms of their ability to generate speech in a single language that sounds as natural as possible. However, because to a lack of available training data, the synthesis of speech from the same person in various languages continues to be difficult. It might be challenging to locate people who are proficient in numerous languages at a level equivalent to native speakers. Voice conversion is one method that may be used to create a polyglot corpus, which can then be used to solve this problem. This entails making use of a voice representation model that has been trained on 53 different languages through the application of hybrid deep learning in order to capture speaker-invariant qualities. In this study, we present a novel approach for the conversion of voices across different languages by employing Generated Adversarial Networks (GANs) to train a multilingual TTS system. The concept of individual likeness loss in order to address the special difficulty of maintaining one's individual speaking identity during the training process can be offered. This work is focused to provide the impression that voice data coming from a variety of languages and speakers was produced by the same individual, and one way to do so is by using this word. In order to determine the extent to which our model is useful, two experiments that compared it against benchmarks that made use of varying degrees of parameter sharing between languages are carried out. The purpose of these experiments was to evaluate the accuracy of pronunciation as well as the caliber of the synthetic voice during transitions between different languages.
AF2BIND: Predicting ligand-binding sites using the pair representation of AlphaFold2

Predicting ligand-binding sites, particularly in the absence of previously resolved homologous structures, presents a significant challenge in structural biology. Here, we leverage the internal pairwise representation of AlphaFold2 (AF2) to train a model, AF2BIND, to accurately predict small-molecule-binding residues given only a target protein. AF2BIND uses 20 “bait” amino acids to optimally extract the binding signal in the absence of a small-molecule ligand. We find that the AF2 pair representation outperforms other neural-network representations for binding-site prediction. Moreover, unique combinations of the 20 bait amino acids are correlated with chemical properties of the ligand.
Recombinant biosensors for multiplex and super-resolution imaging of phosphoinositides

Phosphoinositides are a small family of phospholipids, acting as signalling hubs and key regulators of cellular function. Detecting their subcellular distribution is crucial to gain insights into membrane organisation and is most commonly done by over-expression of biosensors. However, this leads to perturbations of phosphoinositide signalling and is challenging in systems that cannot be transfected. Here, we present a toolkit for the reliable, fast, multiplex, and super-resolution detection of all 8 phosphoinositides using a unifying staining approach for fixed cells and tissue, based on recombinant biosensors with self-labelling SNAP tags. These recombinant biosensors are highly specific, and reliably visualise the subcellular distributions of phosphoinositides across scales, ranging from 2D or 3D cell culture to Drosophila tissue. Using stimulated emission depletion (STED) microscopy, we reveal the nanoscale organisation of PI(3)P on endosomes and PI(4)P on the Golgi and confirm the preservation of subcellular membranes. Multiplex staining enables the investigation of phosphoinositide conversions and reveals an unexpected presence of residual PI(3,5)P2 positive membranes in swollen lysosomes following PIKfyve inhibition. This approach enables the versatile, high-resolution visualisation of multiple phosphoinositide species in an unprecedented manner.
Towards Interpretable Deep Metric Learning with Structural Matching Supplementary Material

In all of our experiments, we use ResNet50 [6] as our backbone. Therefore, the size of the feature map before the pooling layer is 7 × 7. To reduce computational costs, we first use ROI Align [5] to pool the feature map to G×G and G = 4 in most of our experiments unless otherwise noted. According to the multi-scale matching algorithm, for each image as a query, we first sort the images in the gallery using the standard cosine similarity to obtain the indices of top-K candidates IK (we useK = 100 in most of the experiments). We then calculate the proposed structural similarity of all the images in IK . To combine both global and structural information, we use the sum of the cosine similarity and the structural similarity for the top-K images to compute their ranks. The regularization parameter λ in Equation (6) is set to 0.05.
SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation Supplementary Material

We choose the BPNet [6] as our backbone, which consists of a 3D sparse convolution encoder-decoder branch and a 2D vanilla convolution encoder-decoder branch. The 3D branch implements the sparse convolution MinkowskiEngine [2] to build a ResNet-style architecture [5], while the 2D branch exploits the ResNet34 model as the encoder. The decoder blocks of these two branches are composed of residual blocks with interpolation operation for the 2D branch and transpose convolution for the 3D branch. Skip connections are added between the corresponding layers of encoders and decoders to pass lowlevel information. The bidirectional links between layers of the 2D branch and the 3D branch from the BPNet are kept to communicate knowledge between the two modalities. Please refer to the BPNet paper for further details about the bidirectional projections between 2D and 3D features.
Human-in-the-Loop Automatic Program Repair

learn2fix is a human-in-the-loop interactive program repair technique, which can be applied when no bug oracle—except the user who is reporting the bug—is available. This approach incrementally learns the condition under which the bug is observed by systematic negotiation with the user. In this process, learn2fix generates alternative test inputs and sends some of those to the user for obtaining their labels. A limited query budget is assigned to the user for this task. A query is a Yes/No question: “When executing this alternative test input, the program under test produces the following output; is the bug observed? ”. Using the labelled test inputs, learn2fix incrementally learns an automatic bug oracle to predict the user's response. A classification algorithm in machine learning is used for this task. Our key challenge is to maximise the oracle's accuracy in predicting the tests that expose the bug given a practical, small budget of queries. After learning the automatic oracle, an existing program repair tool attempts to repair the bug using the alternative tests that the user has labelled. Our experiments demonstrate that learn2fix trains a sufficiently accurate automatic oracle with a reasonably low labelling effort (lt. 20 queries), and the oracles represented by interpolation-based classifiers produce more accurate predictions than those represented by approximation-based classifiers. Given the user-labelled test inputs, generated using the interpolation-based approach, the GenProg and Angelix automatic program repair tools produce patches that pass a much larger proportion of validation tests than the manually constructed test suites provided by the repair benchmark.
Hemorrhagic meningioma with pulmonary metastasis: Case report and literature review

Abstract Meningiomas are extra-axial neoplasms that originate from the arachnoid cap cells located on the inner surface of the meninges. Approximately 36% of central nervous system tumors are meningiomas. Based on earlier findings to be benign in most cases, they are categorized as slow-growing tumors that form gradually over time. Meningiomas are usually asymptomatic and discovered inadvertently. They rarely present with immediate clinical symptoms or abrupt hemorrhagic strokes. However, tumor hemorrhage can be fatal in high-grade meningiomas, particularly those with vascularization. We describe a 58-year-old man who was hospitalized after experiencing an unexpectedly acute headache. The right cerebellar hemisphere and vermis cerebellar hemorrhage were detected on computed tomography (CT), and the cerebellar hemorrhage was explained by a diagnosis of hypertension. When additional analysis of the patient’s chest CT indicated lung mass lesions, we assumed that the lung cancer had spread to the brain. However, the pathological outcomes of a guided definite pulmonary aspiration biopsy, in conjunction with resection of the cerebellar tumor, suggested a subtentorial meningioma with ruptured hemorrhage and pulmonary meningioma metastasis. The patient was transferred to a hospital closer to home for ongoing follow-up and, after 2 months, he had recovered well.
HUMAN RABIES IN SOUTH AFRICA: A MOLECULAR STUDY OF CASES FROM THE PREVIOUS 25 YEARS

Human rabies is caused by viruses from the Lyssavirus genus. Only genotypes 1 and 4 have caused human rabies in South Africa. Genotype 1 can further be subdivided into canid and mongoose biotypes. Most laboratory confirmed cases of human rabies in South Africa occur in Kwa-Zulu Natal, with an additional outbreak occurring in the Limpopo province in 2006. Phylogenetic analysis of the G-L region revealed that there have been no other genotypes causing human rabies and that most of the human cases are caused by the canid biotype of genotype 1. In addition to this, the viruses grouped by location, with Eastern Cape and Kwa-Zulu Natal coastal viruses grouping together and Free State viruses grouping in this cluster as well, Mpumalanga and Kwa-Zulu Natal inland viruses grouping together, and Limpopo viruses forming their own cluster. One mongoose rabies case was found, from a man bitten by a cat in the Northern Cape province.
Les difficultés de régénération naturelle du Sapin (Abies alba Mill.) dans les Vosges. Etude écologique

La régénération naturelle est un phénomène complexe, qui marque une étape cruciale dans la vie d'une forêt . Pour la mener à bien, le forestier doit appliquer une sylviculture bien adaptée à l'écologie de l'essence dominante, ce qui nécessite en premier lieu une bonne connaissance de tous les facteurs du milieu et de la façon dont ils agissent . Ce principe de base, enseigné depuis longtemps dans les écoles forestières, n'est en pratique malheureusement pas toujours appliqué, et la régénération des forêts est encore trop souvent menée de façon assez empirique.
Projected Climate Change in the Northwestern Arid Regions of China: An Ensemble of Regional Climate Model Simulations

Abstract The projected temperature and precipitation-change under different emissions scenarios using Coupled Model Intercomparison Project Phase 5 models over the northwestern arid regions of China (NWAC) were analyzed using the ensemble of three high-resolution dynamical downscaling simulations: the simulation of the Regional Climate Model version 4.0 (RegCM4) forced by the Beijing Climate Center Climate System Model version 1.1 (BCC_CSM1.1); the Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA) forced by the Atmosphere-Ocean coupled HadGEM version 2 (HadGEM2-AO); and the Weather Research and Forecasting (WRF) model forced by the Norwegian community Earth System Model (NorESM1-M). Model validation indicated that the multimodel simulations reproduce the spatial and temporal distribution of temperature and precipitation well. The temperature is projected to increase over NWAC under both the 4.5 and 8.5 Representative Concentration Pathways scenarios (RCP4.5 and RCP8.5, respectively) in the middle of the 21st century, but the warming trend is larger under the RCP8.5 scenario. Precipitation shows a significant increasing trend in spring and winter under both RCP4.5 and RCP8.5; but in summer, precipitation is projected to decrease in the Tarim Basin and Junggar Basin. The regional averaged temperature and precipitation show increasing trends in the future over NWAC; meanwhile, the large variability of the winter mean temperature and precipitation may induce more extreme cold events and intense snowfall events in these regions in the future.
Influence of the Sierra Leone mantle plume on the equatorial Mid-Atlantic Ridge : a Nd-Sr-Pb isotopic study

We report on a Pb-Nd-Sr isotope and rare earth study of Mid-Atlantic Ridge (MAR) basalt glasses collected across the equatorial fracture zones from 7°S to 5°N (65 stations). The 1600-km-long profile reveals two mixing zones in the mantle that are isotopically distinct but cover the same range of (La/Sm)n ratios (0.3–2), with a gradational boundary between the Romanche and the Chain fracture zones. The potential mantle temperature profile inferred from Na2O content is also quite distinct. The north zone is dominated by a major, La/Sm and HIMU type Pb isotope anomaly centered at 1.7°N±300 km, which is flanked by two zones mildly radiogenic in Pb but depleted in light REE. A kinematic and evolutionary model describing the dispersion and interaction of the Sierra Leone plume with the asthenosphere and the MAR in the last 75 m.y. is proposed for this zone, which includes St. Paul and St. Peter's Rocks. In contrast, over the south zone the isotope/geochemical profiles are well correlated at all length scales and opposite in sign from the inferred potential mantle temperature profile and mean percent fusion. Broad negative gradients are observed between the Romanche and the Charcot fracture zones, superimposed by spikelike anomalies at the intersection with the eastern part of the Romanche and Chain transform faults, where cold plate edge effects prevail. The heterogeneous mantle model of Sleep [1984] and Langmuir and Bender [1984] is applicable to this zone, that is the volatile and radiogenic Pb-rich lumps are preferentially melted during mantle decompression and passively sampled. The lumps may reflect the early dispersion of the St. Helena or Ascension mantle plumes under a thick lithosphere, followed by redistribution due to intense shearing, continental lithosphere delamination, and secondary mantle convection. The presence of a depleted asthenosphere unpolluted by plumes along the 400-km-long MAR segment between the Charcot and Ascension fracture zones is also apparent in the data.
Circulation and Modification of Warm Deep Water on the Central Amundsen Shelf

AbstractThe circulation pathways and subsurface cooling and freshening of warm deep water on the central Amundsen Sea shelf are deduced from hydrographic transects and four subsurface moorings. The Amundsen Sea continental shelf is intersected by the Dotson trough (DT), leading from the outer shelf to the deep basins on the inner shelf. During the measurement period, warm deep water was observed to flow southward on the eastern side of DT in approximate geostrophic balance. A northward outflow from the shelf was also observed along the bottom in the western side of DT. Estimates of the flow rate suggest that up to one-third of the inflowing warm deep water leaves the shelf area below the thermocline in this deep outflow. The deep current was 1.2°C colder and 0.3 psu fresher than the inflow, but still warm, salty, and dense compared to the overlying water mass. The temperature and salinity properties suggest that the cooling and freshening process is induced by subsurface melting of glacial ice, possibly f.
Estimating the effect of climate on water demand: Towards strategic policy analysis

Abstract There is an expectation that many cities in Australia may experience higher temperatures and reduced rainfall in the future. Hence, to support water policy analysis there is a need for predicting water demand and how it changes in response to climate change. This paper describes a methodology to develop a model that explains month-to-month variability in water demand due to climate and weather, on the basis of using statistical methods. To achieve this, we use time series data of historical water use volumes and climate data from relevant weather stations. Applying this model together with hypothetical climate change scenarios, but without community adaptation scenarios, the results indicate that water demand will change moderately and, as may be expected, it is most sensitive to evaporation and temperature, followed by rainfall. With such scenarios, demand is shown to increase by as much as 10–20%, but more realistic climate sequences are required to provide reliable estimates. Given availability of data, this statistical method can be used to support policy analyses in other cities.
Peer review in linguistics journals: Best practices and emerging standards

Abstract:We present new data on peer review practices in linguistics journals, reporting the results of an online survey of editors. This paper aims to increase understanding of the processes and practices of peer review for everyone involved—editors, authors, reviewers, and readers. Apprehending concretely how peer review happens from beginning to end and how editors think about it should help to demystify the process, especially for graduate students and early career researchers, and make the experience somewhat less stressful across the board. Editors, authors, and reviewers all share, we trust, a desire for high professional standards and best practices. We hope to stimulate further discussion of these issues in the field and development of field-wide standards. *
Att-FMI: A Fusing Multi-Information Model with Self-Attentive Strategy for Relation Extraction

—Relation classification is an important task in information extraction that involves identifying potential semantic connections between two entities in a sentence. Most relation extraction models either use semantic or structure information as the relation representation for classification. Although some researchers have tried to fuse both types of information, they simply combine different information as relation representation, which ignores semantic and structure information contribute unequally to the relation representation at the instance level, leading to degradation of performance and generalization capability. To address this issue, we propose a fusing multi-information model with a self-attentive strategy (Att-FMI) for relation classification. Our approach utilizes the pre-trained BERT model and dependency syntactic parser to obtain semantic and structure representations from the sequence, respectively and efficiently combines the two with support the self-attention mechanism. Experimental results on four commonly used datasets, including TACRED, TACREV, KBP-37 and SemEval-2010 Task8, demonstrate that our approach yields significant improvements over baseline methods. Additionally, we observe that the Att-FMI model exhibits greater robustness against information interference in the extraction of entity relations from longer sentences than previous methods.
Adaptive Continual Learning: Rapid Adaptation and Knowledge Refinement

Continual learning (CL) is an emerging research area aiming to emulate human learning throughout a lifetime. Most existing CL approaches primarily focus on mitigating catastrophic forgetting, a phenomenon where performance on old tasks declines while learning new ones. However, human learning involves not only retaining knowledge but also quickly recognizing the current environment, recalling related knowledge, and refining it for improved performance. In this work, we introduce a new problem setting, Adaptive CL, which captures these aspects in an online, recurring task environment without explicit task boundaries or identities. We propose the LEARN algorithm to efficiently explore, recall, and refine knowledge in such environments. We provide theoretical guarantees from two perspectives: online prediction with tight regret bounds and asymptotic consistency of knowledge. Additionally, we present a scalable implementation that requires only first-order gradients for training deep learning models. Our experiments demonstrate that the LEARN algorithm is highly effective in exploring, recalling, and refining knowledge in adaptive CL environments, resulting in superior performance compared to competing methods.
Collecting data for quantitative research on pluvial flooding

Urban pluvial flood management requires detailed spatial and temporal information on flood characteristics and damaging consequences. There is lack of quantitative field data on pluvial flooding resulting in large uncertainties in urban flood model calculations and ensuing decisions for investments in flood protection. In this paper four different data sources are discussed, based on literature and expert consultation, that are believed to be of value for the acquisition of quantitative data on pluvial flooding. Data assembled by insurance agencies on flood damage, call databases held by water authorities and emergency services and remote sensing images cover years of observational data that can be mined to obtain data on flood characteristics and occurrence. Flood monitoring using sensor technology can be effective to collect additional pluvial flood data, that is not captured by existing data sources.