Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Evaluation of Factors Affecting Temporal Variability of Water Samples Obtained from Long‐Screened Wells

As a well is pumped through time, concentrations of specific constituents in the water discharging from the well may change as a result of their transport within the well and the aquifer. A series of experiments conducted at a research site on Cape Cod, Massachusetts, examined the effects of transport on the chemistry of water samples obtained from a long‐screened well. Analyses of time series of constituent concentrations in water pumped from the long‐screened well showed persistent temporal trends during the first experiment. Iron concentrations decreased over a five‐hour test (15 casing volumes), whereas the calcium and magnesium concentrations increased. In contrast, the time series of constituent concentrations of water discharging from the same well showed less change with time during a later experiment. Numerical simulations were undertaken to test the relative importance of several possible factors affecting the temporal variations of these constituents. During the process of quantitatively explaining the changes in concentrations over time observed in the two experiments, different system conceptualizations were used, including (1) flow and transport in the aquifer without wellbore transport, (2) flow and transport in the aquifer with advective flow and transport in the wellbore, and (3) flow and transport in the aquifer with advective flow and transport in the wellbore and a thin layer (skin) of water surrounding the well with constituent concentrations that had been altered by the presence of the well. The conjectured skin of wellbore water, which could have invaded the aquifer because of nearby sampling or dispersion and diffusion near the wellbore, in conjunction with flow and transport in the aquifer and advective transport within the wellbore, produced a reasonable match between the simulated and observed concentrations. The data analysis confirms the known fact that long‐screened wells provide mechanisms for the redistribution of chemical constituents in the aquifer. The analysis provides new insight into the mechanisms responsible for the observed changes in concentrations during the sampling of long‐screened wells. The analysis quantifies the effects of the various conceptual models of flow and transport in and around wells on the interpretation and meaning of chemical analyses of water withdrawn from long‐screened wells.
Vaccine Immunity in Children After Hematologic Cancer Treatment: A Retrospective Single-center Study

Background: Children lose their vaccine-induced protection and are particularly vulnerable to vaccine-preventable diseases after chemotherapy. However, revaccination guidelines are heterogeneous, and there is often a lack of revaccination post-treatment. Aims: We conducted a retrospective study of children with hematologic cancer to evaluate vaccine immunity before and after the end of treatment and to determine whether the current institutional revaccination program based on vaccine serology results was followed and effective. Materials and Methods: Data of all children treated by chemotherapy between April 2015 and July 2021 were extracted from hospital medical records for analysis. Serum antibody levels and time of vaccination were evaluated for diphtheria, tetanus, Streptococcus pneumoniae, Haemophilus influenzae type b (Hib), measles, varicella, and hepatitis B. Results: We included 31 patients (median age, 9 years). At cancer diagnosis, 90% of children were protected against tetanus, diphtheria, and measles; 65% to 67% were protected against pneumococcus and varicella; and 25% against hepatitis B. At the end of chemotherapy, 67% to 71% of patients were protected against tetanus, varicella, and measles; 40% remained protected against hepatitis B; and 27% to 33% against pneumococcus and diphtheria. Patients were revaccinated at various times after the end of treatment but not systematically. During the first-year post-treatment, 20% to 25% of children remained unprotected against pneumococcus, measles, and hepatitis B, one third against diphtheria, but all were protected against tetanus and varicella. Conclusions: An effective individualized vaccination program post-cancer based on serology results should be accompanied by an appropriate serology tracking method and follow-up to assess if booster doses are necessary. Our study supports vaccinating all children with a dose of the 13-valent pneumococcal conjugate at cancer diagnosis and at 3 months post-treatment with the combined diphtheria-tetanus-acellular pertussis/poliomyelitis vaccine/hepatitis B virus plus or minus Hib and 13-valent pneumococcal conjugate and meningococcal vaccine, including measles/mumps/rubella-varicella zoster virus vaccine if good immune reconstitution is present.
New-Onset of Herpes Simplex Keratitis After Blepharoplasty, Case Series and Review of the Literature

Purpose: To report 3 cases of new-onset herpes simplex keratitis (HSK) after uncomplicated extraocular plastic surgery and discuss potential risk factors. Methods: This case series includes 3 patients who underwent uncomplicated blepharoplastic surgery. Within 2 weeks postoperatively, all patients reported ocular discomfort, and their ophthalmic examinations revealed corneal lesions suspicious of HSK. One case was confirmed as an active herpes infection, and the other 2 cases were clinically diagnosed with HSK. The patients were treated with oral acyclovir and followed up for up to 6 weeks. Results: All patients demonstrated improvement without sequelae at follow-up visits from 5 days to 4 weeks after initiating acyclovir treatment. Conclusions: Risk factors for new-onset HSK after uncomplicated extraocular surgeries may be related to an immunocompromised state, postoperative administration of topical or periocular corticosteroids, or environmental factors such as psychological stress. Ophthalmologists, particularly plastic surgeons, should be vigilant for ocular discomfort following eyelid surgeries and consider the possibility of herpes infection. This report highlights the importance of recognizing and managing HSK in the context of extraocular plastic surgery. Ocular discomfort and relevant ocular surface findings after blepharoplasty should warn plastic surgeons of the presence of herpes simplex keratitis.
Nutritional therapy in chronic wound management for older adults

Purpose of review We provided an updated overview of recent data on the value of nutritional therapy in the management of chronic wounds in older adults. Recent findings In the last years, advances in this area were limited, but new data suggest considering nutritional care (screening and assessment of malnutrition and nutritional interventions) also in patients with chronic wounds other than pressure ulcers, namely venous leg and diabetic foot ulcers, as in these patients, nutritional derangements can be present despite overweight/obesity and their management is beneficial. Summary Chronic wounds are wounds in which the process of repair does not progress normally due to a disruption in one or more of the healing phases. Nutritional therapy is aimed at recovering the process of repair. General principles of nutritional care in geriatrics apply to these patients but disease-specific recommendations are available, particularly for pressure ulcers. Interventions should address nutritional status, comorbidities, hydration and should provide key nutrients playing an active role in the healing process (arginine, zinc, and antioxidants) but always within the context of an individual care plan addressing patients requirements, particularly protein needs. Further evidence of efficacy in vascular and diabetic foot ulcers is warranted.
Bed form evolution under unsteady discharge, flume versus field

Introduction Accurate forecasts of flood levels are essential for flood management. During floods, bed forms develop on the river bed. Dunes have heights in the order of 10–30% of the water depth and lengths in the order of 10 times their height. River bed forms act as roughness to the flow, thereby significantly influencing the (flood) water levels. It is essential to predict the time evolution of bed forms and assess their influence on the hydraulic roughness. Field observations have shown that dunes of different lengths and amplitude co-exist (Carling et al., 2000; Wilbers & Ten Brinke, 2003; Frings & Kleinhans, 2008). Secondary bed forms and their interaction with primary bed forms are not recorded in most flume data sets. This raises the question if there is a difference in bed form evolution between flume and field. Therefore, the objective of this paper is to assess the differences in bed form evolution under unsteady discharge between flume and field and explain the interaction between primary and secondary bed forms.
Explore Your Network in Minutes: A Rapid Prototyping Toolkit for Understanding Neural Networks with Visual Analytics

Neural networks attract significant attention in almost every field due to their widespread applications in various tasks. However, developers often struggle with debugging due to the black-box nature of neural networks. Visual analytics provides an intuitive way for developers to understand the hidden states and underlying complex transformations in neural networks. Existing visual analytics tools for neural networks have been demonstrated to be effective in providing useful hints for debugging certain network architectures. However, these approaches are often architecture-specific with strong assumptions of how the network should be understood. This limits their use when the network architecture or the exploration goal changes. In this paper, we present a general model and a programming toolkit, Neural Network Visualization Builder (NNVisBuilder), for prototyping visual analytics systems to understand neural networks. NNVisBuilder covers the common data transformation and interaction model involved in existing tools for exploring neural networks. It enables developers to customize a visual analytics interface for answering their specific questions about networks. NNVisBuilder is compatible with PyTorch so that developers can integrate the visualization code into their learning code seamlessly. We demonstrate the applicability by reproducing several existing visual analytics systems for networks with NNVisBuilder. The source code and some example cases can be found at https://github.com/sysuvis/NVB.
Multi-Level Content-Aware Boundary Detection for Temporal Action Proposal Generation

It is challenging to generate temporal action proposals from untrimmed videos. In general, boundary-based temporal action proposal generators are based on detecting temporal action boundaries, where a classifier is usually applied to evaluate the probability of each temporal action location. However, most existing approaches treat boundaries and contents separately, which neglect that the context of actions and the temporal locations complement each other, resulting in incomplete modeling of boundaries and contents. In addition, temporal boundaries are often located by exploiting either local clues or global information, without mining local temporal information and temporal-to-temporal relations sufficiently at different levels. Facing these challenges, a novel approach named multi-level content-aware boundary detection (MCBD) is proposed to generate temporal action proposals from videos, which jointly models the boundaries and contents of actions and captures multi-level (i.e., frame level and proposal level) temporal and context information. Specifically, the proposed MCBD preliminarily mines rich frame-level features to generate one-dimensional probability sequences, and further exploits temporal-to-temporal proposal-level relations to produce two-dimensional probability maps. The final temporal action proposals are obtained by a fusion of the multi-level boundary and content probabilities, achieving precise boundaries and reliable confidence of proposals. The extensive experiments on the three benchmark datasets of THUMOS14, ActivityNet v1.3 and HACS demonstrate the effectiveness of the proposed MCBD compared to state-of-the-art methods. The source code of this work can be found in https://mic.tongji.edu.cn.
Global remapping emerges as the mechanism for renewal of context-dependent behavior in a reinforcement learning model

The hippocampal formation exhibits complex and context-dependent activity patterns and dynamics, e.g., place cell activity during spatial navigation in rodents or remapping of place fields when the animal switches between contexts. Furthermore, rodents show context-dependent renewal of extinguished behavior. However, the link between context-dependent neural codes and context-dependent renewal is not fully understood. We use a reinforcement learning agent based on deep neural networks to study the learning dynamics that occur during spatial learning and context switching in a simulated ABA extinction and renewal paradigm in a 3D virtual environment. Despite its simplicity, the network exhibits a number of features typically found in the CA1 and CA3 regions of the hippocampus. A significant proportion of neurons in deeper layers of the network are tuned to a specific spatial position of the agent in the environment - similar to place cells in the hippocampus. These spatial representations exhibit global remapping when the agent is exposed to a new context. The spatial maps are restored when the agent returns to the previous context, accompanied by renewal of the conditioned behavior. Remapping is facilitated by memory replay of experiences during training. These complex spatial representations and dynamics occur spontaneously in the hidden layer of a deep network during learning. Our results show that integrated codes that jointly represent spatial and task-relevant contextual variables are the mechanism underlying renewal in a simulated DQN agent.
A Simple Bias Reduction Algorithm for RNA Sequencing Datasets

RNA sequencing (RNA-seq) is the conventional genome-scale approach used to capture the expression levels of all detectable genes in a biological sample. This is now regularly used in the clinical diagnostic space for cancer patients. While the information gained is intended to impact treatment decisions, numerous technical and quality issues remain. This includes inaccuracies in the dissemination of gene-gene relationships. For such reasons, clinical decisions are still mostly driven by DNA biomarkers, such as gene mutations or fusions. In this study, we aimed to correct for systemic bias based on RNA-sequencing platforms in order to improve our understanding of the gene-gene relationships. To do so, we examined standard pre-processed RNA-seq datasets obtained from three studies conducted by two consortium efforts including The Cancer Genome Atlas (TCGA) and Stand Up 2 Cancer (SU2C). We particularly examined the TCGA Bladder Cancer (n = 408) and Prostate Cancer (n = 498) studies as well as the SU2C Prostate Cancer study (n = 208). Using various statistical tests, we detected expression-level dependent, per-sample biases in all datasets. Using simulations, we show that these biases corrupt the results of t-tests designed to identify expression level differences between subpopulations. Importantly, these biases introduce large errors into estimates of gene-gene correlations. To mitigate these biases, we introduce Local Leveling as a novel mathematical approach that transforms count level data and corrects these observed biases. Local Leveling specifically corrects for the bias due to the inherent differential detection of transcripts that is driven by differential expression levels. Based on standard forms of count data (Raw counts, transcripts per million, fragments per kilobase of exon per million), we demonstrate that local leveling effectively removes the observed per-sample biases, and improves the accuracy in simulated statistical tests. Importantly, this led to systemic changes of gene-gene relationships when examining the correlation of key oncogenes, such as the Androgen Receptor, with all other detectable genes. Altogether, Local Leveling improves our capacity towards understanding gene-gene relationships, which may lead to novel ways to utilize the information derived from clinical tests.
The Research Developing on Evaluation of the Multi effect of the Water-saving Agriculture

According to introduce the development of the international and internal research on evaluation of the multieffect of the watersaving agriculture, this paper analyses the main traits and questions on this research field currently. Furthermore, based on these analyses the paper discusses the research direction in the future on this field in China. 
EL EXPERIMENTO NATURAL COMO UN NUEVO DISEÑO CUASI-EXPERIMENTAL EN INVESTIGACION SOCIAL Y DE SALUD. NATURAL EXPERIMENT AS A NEW QUASI-XPERIMENTAL DESIGN IN SOCIAL AND hEALTh-RELATED SCIENCES.

RESUMEN En las ciencias sociales y de la salud actualmente comienza a ser conocido un diseno denominado como “experi-mento natural”, que se caracteriza por ser un tipo de cuasi-experimento en que la asignacion de tratamiento no es hecha por el investigador sino por un agente exogeno tal como una ley, una politica sectorial o un fenomeno natural. Esa intervencion exogena implicita permite que se formen grupos con bajos niveles de sesgos de selec-cion inicial, por lo que la comparacion de resultados encontrados en esos grupos (grupos afectados por la ley, la politica o fenomeno natural respecto del grupo contrafactico que no es afectado) permite predecir los efectos de la ley, politica sectorial o fenomeno natural casi tan eficientemente como en la creacion de grupos aleatorios de tratamiento y control. Palabras claves: experimento natural, diseno metodologico, sesgos de seleccion ABSTRACT Currently in social science research it is used a new research design defined as a “natural experiment”, where the researcher has none participation in the treatment assignment because it has been directly done for an exogenous agent such as a law, a social policy or a natural event. This implicit exogenous intervention allows that low-level pre-determined selection bias groups be formed. Therefore, the comparison on results being found between these groups (law-affected group, social policy-affected group, or natural event-affected group versus contrafactic groups not being affected) allow to predict the effect of this law, social policy, or natural event almost so efficiently like happen in randomly created treatment-and-control groups.
Seroprevalence of Rift Valley fever virus specific antibodies in livestock in Egypt and expression studies of virus related proteins in mammalian and arthropod cells

Background: Rift Valley fever virus (RVFV) caused several outbreaks throughout the African continent and the Arabian Peninsula posing significant threat to human and animal health. In Egypt the first and most important Rift Valley fever epidemic occurred during 1977/78 with a multitude of infected humans and huge economic losses in livestock. After this major outbreak, RVF epidemics re-occurred in irregular intervals between 1993 and 2003. Seroprevalence of anti-RVFV antibodies in livestock during inter-epidemic periods can be used for supporting the evaluation of the present risk exposure for animal and public health. A serosurvey was conducted during 2014/2015 in non-vaccinated livestock including camels, sheep, goats and buffalos in different areas of the Nile River Delta as well as the furthermost southeast of Egypt to investigate the presence of anti-RVFV antibodies for further evaluating of the risk exposure for animal and human health. All animals integrated in this study were born after the last Egyptian RVF epidemic in 2003 and sampled buffalos and small ruminants were not imported from other endemic countries. Results: A total of 873 serum samples from apparently healthy animals from different host species (camels: n =221; sheep: n = 438; goats: n =26; buffalo: n = 188) were tested serologically using RVFV competition ELISA, virus neutralization test and/or an indirect immunofluorescence assay, depending on available serum volume. Sera were assessed positive when virus neutralization test alone or least two assays produced consistent positive results. The overall seroprevalence was 2.29% (95%CI: 1.51–3.07) ranging from 0% in goats, 0.46% in sheep (95%CI: 0.41–0.5), and 3.17% in camels (95%CI: 0.86–5.48) up to 5.85% in buffalos (95%CI: 2.75–8.95).
Deep Label Distribution Learning for Apparent Age Estimation

In the age estimation competition organized by ChaLearn, apparent ages of images are provided. Uncertainty of each apparent age is induced because each image is labeled by multiple individuals. Such uncertainty makes this age estimation task different from common chronological age estimation tasks. In this paper, we propose a method using deep CNN (Convolutional Neural Network) with distribution-based loss functions. Using distributions as the training tasks can exploit the uncertainty induced by manual labeling to learn a better model than using ages as the target. To the best of our knowledge, this is one of the first attempts to use the distribution as the target of deep learning. In our method, two kinds of deep CNN models are built with different architectures. After pre-training each deep CNN model with different datasets as one corresponding stream, the competition dataset is then used to fine-tune both deep CNN models. Moreover, we fuse the results of two streams as the final predicted ages. In the final testing dataset provided by competition, the age estimation performance of our method is 0.3057, which is significantly better than the human-level performance (0.34) provided by the competition organizers.
AMRA: Augmented Reality Assistance for Train Maintenance Tasks

The AMRA project, carried out by a consortium including industrials and research partners, aims at implementing an Augmented Reality (AR) system for mobile use in industrial applications such as train maintenance and repairs in industrial sites. The adopted solution is a video see-through system where a tablet-PC is used as an augmented window. The overall architecture of a prototype is unfolded, and its key points are detailed. For instance, a visual registration system has been developed to accurately overlay a video stream with information. A robust, real time registration, using a single camera tied to the tablet-PC, is performed. Besides, a hierarchical description of maintenance procedure is set up and enriched by new media such as photos, video and/or 3D models. These 3D models have been specially tailored to meet maintenance tasks requirements. The obtained multimedia contents allow easy access to technical documentation through a man machine interface managing a multimedia engine. All these features have been combined in the AMRA prototype which have been evaluated by a maintenance operator.
Towards Eﬀicient Verification of Systems with Dynamic Process Creation

Modelling and analysis of dynamic multi-threaded state systems often encounters obstacles when one wants to use automated ver-iﬁcation methods, such as model checking. Our aim in this paper is to develop a technical device for coping with one such obstacle, namely that caused by dynamic process creation. We ﬁrst introduce a general class of coloured Petri nets—not tied to any particular syntax or approach—allowing one to capture systems with dynamic (and concurrent) process creation as well as capable of manipulating data. Following this, we introduce the central notion of our method which is a marking equivalence that can be eﬃciently computed and then used, for instance, to aggregate markings in a reachability graph. In some situations, such an aggregation may produce a ﬁnite representation of an inﬁnite state system which still allows one to establish the relevant behavioural properties. We show feasibility of the method on an example and provide initial experimental results.