Miracle-dz/UniFAR-FTUs huggingface dataset:
Dispersal limitations and long‐term persistence drive differentiation from haplotypes to communities within a tropical sky‐island: Evidence from community metabarcoding

Neutral theory proposes that dispersal stochasticity is one of the main drivers of local diversity. Haplotypes‐level genetic variation can now be efficiently sampled from across whole communities, thus making it possible to test neutral predictions from the genetic to species‐level diversity, and higher. However, empirical data is still limited, with the few studies to date coming from temperate latitudes. Here, we focus on a tropical mountain within the Transmexican Volcanic Belt to evaluate spatially fine‐scale patterns of arthropod community assembly to understand the role of dispersal limitation and landscape features as drivers of diversity. We sampled whole‐communities of arthropods for eight orders at a spatial scale ranging from 50 m to 19 km, using whole community metabarcoding. We explored multiple hierarchical levels, from individual haplotypes to lineages at 0.5, 1.5, 3, 5, and 7.5% similarity thresholds, to evaluate patterns of richness, turnover, and distance decay of similarity with isolation‐by‐distance and isolation‐by‐resistance (costs to dispersal given by landscape features) approaches. Our results showed that distance and altitude influence distance decay of similarity at all hierarchical levels. This holds for arthropod groups of contrasting dispersal abilities, but with different strength depending on the spatial scale. Our results support a model where local‐scale differentiation mediated by dispersal constraints, combined with long‐term persistence of lineages, is an important driver of diversity within tropical sky islands.
ABO(H) and Lewis blood group substances and disease treatment

Since the early 20th century, scientists have determined that blood group antigens can be inherited. With more and more studies have been devoted to finding the relationship between blood groups and diseases, the relationship of ABO(H) and Lewis blood groups and the development of human diseases have been summarised. In addition, many studies have shown that blood group substances, such as blood group antigen or related antibody, play an important role in disease prevention and treatment. This review focuses on the advances of ABO(H), Lewis blood group substances in the treatment of diseases, which has important significance for the development of novel therapeutic methods.
Visual Search Asymmetry Due to the Relative Magnitude Represented by Number Symbols

In visual search tasks, physically large target stimuli are more easily identified among small distractors than are small targets among large distractors. The present study extends this finding by presenting preliminary evidence of a new search asymmetry: stimuli that symbolically represent larger magnitude are identified more easily among featurally equivalent distractors that represent smaller magnitude. Participants performed a visual search task using line-segment digits representing the numbers 2 and 5, and the numbers 6 and 9, as well as comparable non-numeric control stimuli. In three experiments, we found that search times are faster when the target is a digit that represents a larger magnitude than the distractor, although this pattern was not evident in one additional experiment. The results provide suggestive evidence that the magnitude of a number symbol can affect perceptual comparisons between number symbols, and that the semantic meaning of a target stimulus can systematically affect visual search.
A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper’s main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.
Modeling the Adverse Childhood Experiences Questionnaire–International Version

Adverse childhood experiences (ACEs) are related to a host of deleterious physical and mental health outcomes. The ACE–International Questionnaire (ACE-IQ) was developed to assess categories of ACEs (e.g., sexual, emotional, and physical abuse) in internationally representative samples. Though the ACE-IQ has been used world-wide, little work has examined the structure of this measure. Further, much of the modeling techniques implemented lacked theoretical rationale. The present work used two principal components analyses (PCA) to evaluate the ACE-IQ structure using both the identified ACE categories as defined by the World Health Organization (WHO) and using the ACE-IQ items as individual indicators. Using the WHO method, a two-component structure was indicated. Alternatively, a PCA of the individual items yielded a six-component structure. Results highlight the importance of theoretically grounded measure evaluation and the potential distinctions amongst types of ACEs. Implications and future directions for research and practice are discussed.
Responding to COVID-19 Through Interhospital Resource Coordination: A Mixed-Methods Evaluation

Objectives The COVID-19 pandemic stressed hospital operations, requiring rapid innovations to address rise in demand and specialized COVID-19 services while maintaining access to hospital-based care and facilitating expertise. We aimed to describe a novel hospital system approach to managing the COVID-19 pandemic, including multihospital coordination capability and transfer of COVID-19 patients to a single, dedicated hospital. Methods We included patients who tested positive for SARS-CoV-2 by polymerase chain reaction admitted to a 12-hospital network including a dedicated COVID-19 hospital. Our primary outcome was adherence to local guidelines, including admission risk stratification, anticoagulation, and dexamethasone treatment assessed by differences-in-differences analysis after guideline dissemination. We evaluated outcomes and health care worker satisfaction. Finally, we assessed barriers to safe transfer including transfer across different electronic health record systems. Results During the study, the system admitted a total of 1209 patients. Of these, 56.3% underwent transfer, supported by a physician-led System Operations Center. Patients who were transferred were older (P = 0.001) and had similar risk-adjusted mortality rates. Guideline adherence after dissemination was higher among patients who underwent transfer: admission risk stratification (P < 0.001), anticoagulation (P < 0.001), and dexamethasone administration (P = 0.003). Transfer across electronic health record systems was a perceived barrier to safety and reduced quality. Providers positively viewed our transfer approach. Conclusions With standardized communication, interhospital transfers can be a safe and effective method of cohorting COVID-19 patients, are well received by health care providers, and have the potential to improve care quality.
The emerging role of immune checkpoint inhibitors for the treatment of breast cancer

ABSTRACT Introduction Breast cancer has traditionally been viewed as immunogenically ‘cold,’ but two immune checkpoint inhibitors have been approved in combination with chemotherapy for PD-L1 positive advanced triple-negative breast cancer (TNBC), and pembrolizumab was also recently approved for early stage TNBC. As the landscape is rapidly evolving, a comprehensive review of checkpoint inhibitors in breast cancer is needed to aid clinicians in selecting appropriate candidates for therapy, and to highlight ongoing promising studies in this area and topics in need of further investigation. Area covered This review summarizes the latest evidence from completed and ongoing trials of immune checkpoint inhibitors. Ongoing studies were identified using a search of ClinicalTrials.gov with the term ‘breast cancer’ along with specific checkpoint inhibitor agents. Expert opinion A number of novel combination strategies are under investigation to enhance response and overcome resistance to immunotherapy, with promising preliminary data from checkpoint inhibitors targeting TIGIT, combinations with small molecule inhibitors such as lenvatinib, and injectable agents directly influencing the immune microenvironment. As immunotherapy enters into the curative setting, biomarkers predictive of immunotherapy benefit are needed, as PD-L1 status has not been a helpful discriminator in completed trials in early-stage breast cancer.
Evaluation of One- and Two-Box Models as Particle Exposure Prediction Tools at Industrial Scale

One- and two-box models have been pointed out as useful tools for modelling indoor particle exposure. However, model performance still needs further testing if they are to be implemented as trustworthy tools for exposure assessment. The objective of this work is to evaluate the performance, applicability and reproducibility of one- and two-box models on real-world industrial scenarios. A study on filling of seven materials in three filling lines with different levels of energy and mitigation strategies was used. Inhalable and respirable mass concentrations were calculated with one- and two-box models. The continuous drop and rotating drum methods were used for emission rate calculation, and ranges from a one-at-a-time methodology were applied for local exhaust ventilation efficiency and inter-zonal air flows. When using both dustiness methods, large differences were observed for modelled inhalable concentrations but not for respirable, which showed the importance to study the linkage between dustiness and processes. Higher model accuracy (ratio modelled vs. measured concentrations 0.5–5) was obtained for the two- (87%) than the one-box model (53%). Large effects on modelled concentrations were seen when local exhausts ventilation and inter-zonal variations where parametrized in the models. However, a certain degree of variation (10–20%) seems acceptable, as similar conclusions are reached.
Sono-Fenton regeneration of granular activated carbon saturated with Rhodamine B: Optimization using response surface methodology

ABSTRACT This study investigated the combination of chemical and ultrasonic regeneration (Sono-Fenton), used for regenerating granular activated carbon (GAC) saturated with Rhodamine B (RB). This process was modeled and optimized using response surface methodology (RSM) based on central composite design (CCD). Effective parameters on regeneration efficiency such as pH, concentration of hydrogen peroxide (H2O2), and time of ultrasonic irradiation were optimized and modeled using the reduced quadratic method. The fitness of the model was checked by the determination coefficient (R2 = 0.9978). At optimum condition, the effective parameters of pH = 3.84, concentration of H2O2 = 38.28 (mM), and time of ultrasonic irradiation = 23.11(min), maximum regeneration efficiency (87.88%) was achieved, the results of which were obtained after four-time sequential adsorption–regeneration cycles were acceptable. Desorption and degradation of RB were conducted through the generation of active species, including hydroxyl radicals and high-energy ultrasound. The regeneration efficiency was increased using this combination method. In conclusion, the Sono-Fenton method is suggested to be used more widely in regeneration processes.
Numeral Recognition using Artificial Neural Networks

 Abstract —Handwritten Numeral recognition plays a vital role in postal automation services especially in countries like India where multiple languages and scripts are used . Because of intermixing of these languages; it is very difficult to understand the script in which the pin code is written. Objective of this paper is to resolve this problem through Multilayer feed-forward back-propagation algorithm using two hidden layer. This work has been tested on five different popular Indian scripts namely Devnagri, English, Urdu, Tamil and Telugu. Network was trained to learn its behavior by adjusting the connection strengths on every iteration. The resultant of each presented training pattern was calculated to identify the minima on the error surface for each training pattern. Experiments were performed on samples by using two hidden layers and the results revealed that as the number of hidden layers is increased, more accuracy is achieved in large number of epochs
The birth of motion capture: Transcribing the phenomena of bodily movement through the “graphic method”

How is the movement of bodies recorded, traced, captured? How is the perception of movement decomposed, analyzed, and then reconstructed through signs, lines, and diagrams? This article traces how, with the help of engineers and collaborators, Etienne-Jules Marey’s self-styled “graphic method” innovated upon existing instruments and photographic apparatuses in order to capture not just the movement of horses’ legs but something of the biomechanical essence of animal movement through the technique of “chronophotographie. ” Although inspired by Edward Muybridge’s photographs of horses in motion, for Marey the photographs were not the end result. What he achieved were new ways of transcribing the phenomena of bodily motion. Unlike previous physiologists who thrived on vivisection in the laboratory, Marey took ever greater pains to examine the principles of animal movement in the wild, and built an open-air “station physiologique” in a Parisian park for this purpose. One legacy of Marey’s chronophotographic technique was in the documentation and dissection of human movement, and became acknowledged precursors of the wave of Taylorism which would sweep industrial research in the early 20th century. But another legacy is the capacity to transcribe the phenomena of movement into other forms, externalizing perception across other media.
ArgueBERT: How To Improve BERT Embeddings for Measuring the Similarity of Arguments

Argumentation is an important tool within human interaction, not only in law and politics but also for discussing issues, expressing and exchanging opinions and coming to decisions in our everyday life. Applications for argumentation often require the measurement of the arguments’ similarity, to solve tasks like clustering, paraphrase identiﬁcation or summarization. In our work, BERT embeddings are pre-trained on novel training objectives and afterwards ﬁne-tuned in a siamese architecture, similar to Reimers and Gurevych (2019b), to measure the similarity of arguments. The experiments conducted in our work show that a change in BERT’s pre-training process can improve the performance on measuring argument similarity.
Examining the Effects of Preprocessing on the Detection of Offensive Language in German Tweets

Preprocessing is essential for creating more effective features and reducing noise in classiﬁ-cation, especially in user-generated data (e.g. Twitter). How each individual preprocessing decision changes an individual classiﬁer’s behavior is not universal. We perform a series of ablation experiments in which we examine how classiﬁers behave based on individual pre-processing steps when detecting offensive language in German. While preprocessing decisions for traditional classiﬁer approaches are not as varied, we note that pre-trained BERT models are far more sensitive to each decision and do not behave identically to each other. We ﬁnd that the cause of much variation be-tween classiﬁers has to do with the interactions speciﬁc preprocessing steps have on the over-all vocabulary distributions, and, in the case of BERT models, how this interacts with the WordPiece tokenization.
A contextual approach to improve the user's experience in interactive recommendation systems

Recommendation Systems have concerned about the online environment of real-world scenarios where the system should continually learn and predict new recommendations. Current works have handled it as a Multi-Armed Bandit (MAB) problem by proposing parametric bandit models based on the main recommendation concepts to handle the exploitation and exploration dilemma. However, recent works identified a new problem about the way these models handle the user cold-start. Due to the lack of information about the user, these models have intrinsically delivered naive non-personalized recommendations in their first recommendations until the system learns more about the user. The first recommendations of these bandit models are equivalent to a random selection around the items (i.e., a pure-exploration approach) or a biased selection by the most popular items in the system (i.e., a pure-exploitation approach). Thus, to mitigate this problem, we propose a new contextual approach to initialize the bandit models. This context is made by the information available about the items: their popularity and entropy. The idea is to address both exploration and exploitation goals since the first recommendations by mixing entropic and popular items. Indeed, this approach maximizes the user's satisfaction in the long-term run. By a strong experimental evaluation, comparing our proposal with seven state-of-the-art methods in three real datasets, we demonstrate this context achieves statistically significant improvements by outperforming all baselines.
A Sentiment-Based Multimodal Method to Detect Fake News

The dissemination of news through digital media has amplified Fake News proliferation. In the face of this scenario, sentiment-based methods have presented promising results in Fake News detection. Although sentiment-based methods can extract sentiment (i.e., polarity and/or emotion) from either texts or images available in news, the ones applied to Portuguese-written news have considered sentiment exclusively extracted from texts. Thus, this study proposes a multimodal method that, besides the polarity and emotions extracted from texts, also considers sentiment extracted from news' images in order to detect Fake News written in Portuguese. The proposed method showed promising results in experimental data, overcoming the baseline methods in 8 p.p.
A Systematic Process for Computing Bourdieusian Social Capital within Institutional Profiles on Twitter

This work presents a systematic process for computing Bourdieusian Social Capital (BSC) within institutional Twitter profiles. Based on the observation of sociability practices (social interactions) with the support of Pierre Bourdieu's Theory, we capture data related to these practices, crystallized through performing actions such as Tweet, Comment, Retweet and Like. Next, social interactions are extracted from the captured data as sequences of actions and they are used to compute the effective mobilization capacity of each profile. Finally, the BSC volume is measured by using the effective mobilization capacity, the number of social interactions and the number of followers. The results obtained are aligned with the Bourdieu theory and the systematic process allows to compute the continuous sociability effort of each profile to maintain and expand its BSC. Moreover, the results show that polarities positive and neutral impacts in the composition of the BSC volume.
Effects of Pressure- and Volume-controlled Ventilation on Pulmonary Ventilation and Gas Exchange in Obstructive Lung Disease: A Comparative Study

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death in the world. Its pathophysiological hallmark is expiratory flow limitation. But it's unclear whether pressure-controlled ventilation (PCV) or volume-controlled ventilation (VCV) has a lower risk of barotrauma and which one can provide more adequate gas exchange for the patients. In this study, the effects of PCV and VCV on pulmonary ventilation and gas exchange were compared for COPD patients with expiratory flow limitation by modeling the pulmonary ventilation and the gas exchange during mechanical ventilation. It is found the peak transpulmonary pressure was similar between PCV and VCV, indicating the two modes may have similar risk of barotrauma. The transmural pressure of collapsible airways increased more rapidly at the beginning of inspiration in PCV than in VCV. And the partial pressures of oxygen and carbon dioxide were similar between PCV and VCV. The results suggest that the two modes seem to be equally suited for COPD patients with expiratory flow limitation. But PCV may open collapsed airways earlier.
Research on TDA-Effective Analytical Methods For Modern Biology

At present, the research of biology is faced with some difficulties in data processing. Topological data analysis has shown good description capabilities in cancer gene markers, bioinformatics, and other fields. Therefore, this article mainly introduces basic topological data analysis. The process(from the most basic definition of distance), how to construct a simple complex, the advantages of topological data analysis, and the two most commonly used topological data analysis methods (Persistent Homology, Mapper) are introduced, and provides some visualized examples to facilitate readers understanding, and finally, some research content applied in the fields of biomedicine is provided for a recommendation. Besides, the author provides a new idea for future research.
ReVera Framework: A Framework for Fact Checking Traceability

Professional fact-checking tends to be costly and scalability challenging. For this reason, a series of methods to automate this process has been emerging. Unfortunately, these methods have been created with monolithic architectures, not making use of pre-built parts, and are harder to be understood by others. This work aims to propose a framework, where the development of methods can be done in a modular manner, creating workflows based on key steps that can be linked together to generate the input classification. For this, a study of the literature was conducted to identify the main processing steps of the methods, seeking to develop a solution that would guarantee flexibility of execution and interoperability between the different processing components. With this, the automatic verification process was described in a limited set of steps that can be performed, creating independent components that implement each of them. The framework makes use of a proposed data traceability ontology to map all generated data during execution under a unified vocabulary, which facilitates communication between these independent components.