Miracle-dz/UniFAR-FTUs huggingface dataset:
Zonation-based landslide hazard assessment using artificial neural networks in the China-Pakistan Economic Corridor

Distribution of landslide is controlled by various causative factors that have different impacts on the occurrence of landslide in different regions. Using one single model to build the hazard assessment is not enough to fully reflect the spatial differences of landslide controlling factors especially for large area. Landslide hazard assessment based on zonation was therefore proposed in this study with an attempt to take effective measures to address this problem. The China–Pakistan Economic Corridor was taken as the study area where landslide hazard assessment was carried out. Based on the features of geological structure, topography, and climate, the study area was divided into three zones. The controlling factors were further analyzed by the geographical detectors method. It was found that the main controlling factors for landslides in these three zones were related to the site’s topography (altitude, slope gradient, and relief amplitude), land use, and distance to an earthquake epicenter. Furthermore, different factors for landslide hazard assessment were selected based on the result of a controlling factor analysis. An artificial neural network model was employed to build the hazard assessment models, and hazard assessment maps were generated. Validations were conducted, showing that the accuracy of hazard assessment maps by zones was higher than that by the whole study area, despite there was no significant difference during the modeling process.
Efficacy of nutrition education for the increase of symbiotic intake on nutritional and metabolic status in schizophrenic spectrum disorders: A two-arm protocol

Background/Objectives The microbiota plays a vital role in the two-way communication between the gastrointestinal tract and numerous neuropsychiatric disorders, such as schizophrenia. Besides, the microbiota modulation through the use of psychobiotics (prebiotics and probiotics with nutraceutical action) is related to the improvement of the physical and psychopathological health. The objective to this study was to test the efficacy of prebiotic/probiotic dietary modulation in patients diagnosed with schizophrenia, attending to the nutritional and cardio-metabolic impact. Methods Two-arms, double-blind, randomized in balanced blocks clinical trial of 6 months of intervention, will be developed in a group of 50 individuals. The control group will receive conventional dietary advice individually from specialized mental health nurses. In the intervention group, an individual dietetic-nutritional education program with high prebiotic and probiotic content (dairy and fermented foods, green leafy vegetables, high-fiber fruit, whole grains, etc.) will be developed by these nurses. Data will be collected on the psychopathological state, and blood test (at the beginning, at 3 and 6 months). The estimation of intestinal microbiota and the usual nutritional pattern will also be assessed at the beginning and 6 months, using a stool test. To evaluate the degree of adherence, the intervention group will fill a specific weekly record of the main dishes/food consumed. Anthropometric parameters will also be analyzed monthly. Discussion The study is anticipated to establish feasibility an adequate dietary modulation with a high simbiotic content, leads to a significant improvement in the nutritional status and cardio-metabolic. Furthermore, it is presumed to reach a degree of evidence that allows establishing nutritional management as an effective therapeutic intervention in the psychopathological treatment of patients with schizophrenia spectrum disorders. Clinical Trial Registration [www.ClinicalTrials. gov], identifier [NCT04366401].
Responses Toward Injustice Shaped by Justice Sensitivity – Evidence From Germany

Anger, indignation, guilt, rumination, victim compensation, and perpetrator punishment are considered primary responses associated with justice sensitivity (JS). However, injustice and high JS may predispose to further responses. We had N = 293 adults rate their JS, 17 potential responses toward 12 unjust scenarios from the victim’s, observer’s, beneficiary’s, and perpetrator’s perspectives, and several control variables. Unjust situations generally elicited many affective, cognitive, and behavioral responses. JS generally predisposed to strong affective responses toward injustice, including sadness, pity, disappointment, and helplessness. It impaired trivialization, victim-blaming, or justification, which may otherwise help cope with injustice. It predisposed to conflict solutions and victim compensation. Particularly victim and beneficiary JS had stronger effects in unjust situations from the corresponding perspective. These findings add to a better understanding of the main and interaction effects of unjust situations from different perspectives and the JS facets, differences between the JS facets, as well as the links between JS and behavior and well-being.
The role of lncRNAs and XIST in oral cancer

Long non-coding RNA (lncRNA) plays a significant role in the pathogenesis of many human malignant tumors, including oral cancer. LncRNA can act as a gene regulator in a variety of cancers. It regulates the growth of malignant cells via many cellular signal pathways such as the PI3K (phosphoinositide 3-kinase)/AKT (α-serine/threonine-protein kinase) pathway. In this review, we have analyzed the role of lncRNAs, such as lncRNA X inactive specific transcript (XIST), in oral cancer, including its effects on the proliferation, apoptosis, invasion, migration, and resistance to chemotherapy of oral cancer. We have also focused on the role of lncRNA XIST as the core of X chromosome inactivation. Here, we provide a brief overview of the role of many kinds of lncRNAs, including XIST, which provides a theoretical basis for the study of the role of XIST in oral cancer. Our review may provide a new direction for the study of the occurrence, development, and prognosis of oral cancer and provide a new target for its treatment.
Spatial organization and early signaling of the B-cell receptor in CLL

Most chronic lymphocytic leukemia (CLL) clones express B-cell receptors (BcR) of both IgM/IgD isotypes; however, 5%–10% of CLL cases express isotype-switched immunoglobulin G (IgG). The early signaling and spatial patterning of the various BcRs at steady state and after activation are still fully unresolved. Herein, we show higher expression of the BcR signalosome elements and a more robust constitutive cell-intrinsic proximal BcR signaling in CLL with unmutated IGHV expressing IgM isotype (IgM U-CLL), compared with IGHV-mutated CLL (M-CLL) expressing either IgM or IgG isotypes. IgM in U-CLL is frequently located in the membrane plane in polarized patches, occasionally in caps, and sometimes inside the cells. Among M-CLL, IgM is scattered laterally in the membrane plane in a similar pattern as seen in normal B cells, whereas IgG is dispersed around the cell membrane in smaller clusters than in IgM U-CLL. Upon BcR engagement, both IgG and IgM expressing M-CLL showed attenuated signaling and only slight spatial reorganization dynamics of BcR microclusters and internalization, compared with the extensive reorganization and internalization of the BcR in IgM expressing U-CLL. The global gene signature of IgG M-CLL was closely related to that of IgM M-CLL rather than IgM U-CLL. Overall, we report fundamental differences in the basal composition, biochemical status, and spatial organization of the BcR in the three examined immunogenetic CLL subtypes that correlate with their clinical behavior. On the basis of our findings, IgG class-switched M-CLL likely represents the same disease as IgM M-CLL rather than a different biological and/or clinical entity.
Comparison on different traditional Chinese medicine therapies for chronic hepatitis B liver fibrosis

Background and Aims: Although different kinds of traditional Chinese medicines could reportedly improve the efficacy of antiviral therapy on liver fibrosis caused by HBV, the problem of clinicians on how to choose the appropriate treatment strategies for the patients fails to be solved. This study aims at comparing and ranking different traditional Chinese medicine (TCM) therapies in the treatment of liver fibrosis due to chronic hepatitis B (CHB). Methods: Eight electronic databases were searched from their establishment to 17 Aug 2021. All included data and pooled odds ratio were used for network meta-analysis (NMA) and statistical analysis. The consistency was evaluated by the node-splitting analysis. The stability of results and source of heterogeneity were tested by sensitivity analysis. Different treatment strategies (regimens) in this network meta-analysis were ranked with the aid of surface under the cumulative ranking curve (SUCRA) probability value. Results: A total of 29 articles with 3,106 sufferers were recruited in this NMA. Results of SUCRA value rankings indicated that Fuzheng Huayu therapy or combined with entecavir had preferable effects in improving the clinical efficacy, recovering the level of hyaluronic acid, IV-C, ALT, ALB, and TBil, relieving the TCM symptoms including hypochondriac pain and poor appetite, regaining the width of portal vein and thickness of spleen, and lessening side effects. Apart from these, Ziyin Shugan therapy or combined with ETV could also be suitable to regain the level of laminin, PC-III, and AST, relieve fatigue and HBV-DNA conversion. Conclusion: This NMA confirmed the efficacy and safety of different treatment therapies for improving CHB liver fibrosis, including the serum biomarkers of live fibrosis and serum parameters for liver function, TCM symptoms, imaging indexes, HBV-DNA conversion rate, which offered the TCM practitioners crucial reference value on clinical medication.
Dry eye disease and tear film assessment through a novel non-invasive ocular surface analyzer: The OSA protocol

We describe the role of OSA as a new instrument in the study of dry eye, and we recommend a protocol for conducting the tests as well as describe the advantages and disadvantages compared with other instruments. A comparison with other ocular surface devices (Tearscope Plus, Keratograph 5M, anterior-segment ocular coherence tomography, Easy Tear View-Plus, LipiView, IDRA, and LacryDiag) were presented due to manual or automatic procedure and objective or subjective measurements. The purpose of this study was to describe the OSA as new non-invasive dry eye disease diagnostic device. The OSA is a device that can provide accurate, non-invasive and easy-to-use parameters to specifically interpret distinct functions of the tear film. This OSA protocol proposed a lesser to higher non-invasive ocular surface dry eye disease tear film diagnostic methodology. A complete and exhaustive OSA and OSA Plus examination protocol was presented within the subjective questionnaire (Dry Eye Questionnaire 5, DEQ5), limbal and bulbar redness classification (within the Efron grade Scale, interferometry lipid layer thickness (LLT) (according to Guillon pattern), tear meniscus height (manually or automatic), first and mean non-invasive break up time (objective and automatic) and meibomian gland (MG) dysfunction grade and percentage (objective and automatic). The OSA and OSA Plus devices are novel and relevant dry eye disease diagnostic tools; however, the automatization and objectivity of the measurements can be increased in future software or device updates. The new non-invasive devices supposed represent a renewal in the dry eye disease diagnosis and introduce a tendency to replace the classic invasive techniques that supposed less reliability and reproducibility.
Neutrophil to lymphocyte ratio predicting poor outcome after aneurysmal subarachnoid hemorrhage: A retrospective study and updated meta-analysis

Background The relationship between neutrophil to lymphocyte ratio (NLR) and poor outcome of aneurysmal subarachnoid hemorrhage (aSAH) is controversial. We aim to evaluate the relationship between NLR on admission and the poor outcome after aSAH. Method Part I: Retrospective analysis of aSAH patients in our center. Baseline characteristics of patients were collected and compared. Multivariate analysis was used to evaluate parameters independently related to poor outcome. Receiver operating characteristic (ROC) curve analysis was used to determine the best cut-off value of NLR. Part II: Systematic review and meta-analysis of relevant literature. Related literature was selected through the database. The pooled odds ratio (OR) and corresponding 95% confidence interval (CI) were calculated to evaluate the correlation between NLR and outcome measures. Results Part I: A total of 240 patients with aSAH were enrolled, and 52 patients had a poor outcome. Patients with poor outcome at 3 months had a higher admission NLR, Hunt & Hess score, Barrow Neurological Institute (BNI) scale score, Subarachnoid Hemorrhage Early Brain Edema Score (SEBES), and proportion of hypertension history. After adjustment, NLR at admission remained an independent predictor of poor outcome in aSAH patients (OR 0.76, 95% CI 0.69-0.83; P < 0.001). The best cut-off value of NLR in ROC analysis is 12.03 (area under the curve 0.805, 95% CI 0.735 - 0.875; P < 0.001). Part II: A total of 16 literature were included. Pooled results showed that elevated NLR was significantly associated with poor outcome (OR 1.31, 95% CI 1.14-1.49; P < 0.0001) and delayed cerebral ischemia (DCI) occurrence (OR 1.32, 95% CI 1.11-1.56; P = 0.002). The results are more reliable in large sample sizes, low NLR cut-off value, multicenter, or prospective studies. Conclusion Elevated NLR is an independent predictor of poor outcome and DCI occurrence in aSAH.
Innovation in physical education: The role of cognitive factors and self-efficacy

Among the beliefs related to teaching work, self-efficacy stands out and encourage innovation across the global education systems. Specifically, the lack of interest among instructors in introducing innovative techniques in physical education is a concern across China. Therefore, this study intends to investigate the role of cognitive indicators (mental workload, decision-making process, innovation in physical education, and self-efficacy) of innovation in physical education across China. This study opted for quantitative techniques, including using a structured questionnaire to collect data from targeted respondents through the survey techniques. Moreover, 800 questionnaires were circulated, and as a result, 420 usable responses were attained, making the overall response rate stand at 40%. The results indicate that the above-stated cognitive factors, along with self-efficacy, have a positive role in causing innovation across the physical education exchequer of China. Likewise, self-efficacy played the mediating role between cognitive indicators and innovation in physical education in China. The study has notable theoretical and practical implications for the policymakers in terms of introducing policies that could help increase the cognitive state of educationists, which in turn possibly will help make them pursue innovation within the education system of China.
Leveraging a multidimensional linguistic analysis of constructed responses produced by college readers

The goal of this study was to assess the relationships between computational approaches to analyzing constructed responses made during reading and individual differences in the foundational skills of reading in college readers. We also explored if these relationships were consistent across texts and samples collected at different institutions and texts. The study made use of archival data that involved college participants who produced typed constructed responses under thinking aloud instructions reading history and science texts. They also took assessments of vocabulary knowledge and proficiency in comprehension. The protocols were analyzed to assess two different ways to determine their cohesion. One approach involved assessing how readers established connections with themselves (i.e., to other constructed responses they produced). The other approach involved assessing connections between the constructed responses and the texts that were read. Additionally, the comparisons were made by assessing both lexical (i.e., word matching) and semantic (i.e., high dimensional semantic spaces) comparisons. The result showed that both approaches for analyzing cohesion and making the comparisons were correlated with vocabulary knowledge and comprehension proficiency. The implications of the results for theory and practice are discussed.
A machine-learning approach for predicting the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome

The present study aimed to explore the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome (PCOS) and predict an appropriate dosage schedule using a machine-learning approach. Data were obtained from literature mining and the rates of body weight change from the initial values were selected as the therapeutic index. The maximal effect (Emax) model was built up as the machine-learning model. A total of 242 patients with PCOS were included for analysis. In the machine-learning model, the Emax of carnitine supplementation on body weight was −3.92%, the ET50 was 3.6 weeks, and the treatment times to realize 25%, 50%, 75%, and 80% (plateau) Emax of carnitine supplementation on body weight were 1.2, 3.6, 10.8, and 14.4 weeks, respectively. In addition, no significant relationship of dose-response was found in the dosage range of carnitine supplementation used in the present study, indicating the lower limit of carnitine supplementation dosage, 250 mg/day, could be used as a suitable dosage. The present study first explored the effect of carnitine supplementation on body weight in patients with PCOS, and in order to realize the optimal therapeutic effect, carnitine supplementation needs 250 mg/day for at least 14.4 weeks.
The VoxWorld Platform for Multimodal Embodied Agents

We present a five-year retrospective on the development of the VoxWorld platform, first introduced as a multimodal platform for modeling motion language, that has evolved into a platform for rapidly building and deploying embodied agents with contextual and situational awareness, capable of interacting with humans in multiple modalities, and exploring their environments. In particular, we discuss the evolution from the theoretical underpinnings of the VoxML modeling language to a platform that accommodates both neural and symbolic inputs to build agents capable of multimodal interaction and hybrid reasoning. We focus on three distinct agent implementations and the functionality needed to accommodate all of them: Diana, a virtual collaborative agent; Kirby, a mobile robot; and BabyBAW, an agent who self-guides its own exploration of the world.
Nutritional potentiality and antinutritional properties of Telfairia occidentalis from Côte d’lvoire

The present study sought to valorize Telfairia occidentalis in Côte d’Ivoire for a more recurrent use and to determine the biochemical and nutritional characteristics of the powdered almonds of this plant , then to determine the phytochemical compounds and the anti-nutritional factors as well as the chemical characteristics of the oil extracted from the almonds. Regarding the biochemical and nutritive composition of Telfairia occidentalis almond powder, the results showed that the powders were low in moisture (2.83 ± 0.28 %). Lipids and proteins were very important (35.62 ± 0.06 % and 29.15 ± 0.03 %) respectively. The most present amino acids were: Methionine (3.7 mg/100 g), Cystine (4.44 mg/100 g), lysine, aspartic acid, leucine and arginine varied from 0.15 to 0.37 mg/100 g. Several phytochemicals were measured in the powder of Telfairia occidentalis almonds. The results indicated that polyphenols were 272.68 ± 0.46 mg/100 g, flavonoids were 8.32 ± 0.3 9 mg/100 g, tannins were 165.09 ± 0.19 mg/100 g and alkaloids were 1.1 ± 0.1 %. The powder also contained some anti-nutritional factors such as phytates (49.39±0.71 mg/100 g) and oxalates (43.5 ± 0.86 mg/100 g).
One Document, Many Revisions: A Dataset for Classification and Description of Edit Intents

Document authoring involves a lengthy revision process, marked by individual edits that are frequently linked to comments. Modeling the relationship between edits and comments leads to a better understanding of document evolution, potentially benefiting applications such as content summarization, and task triaging. Prior work on understanding revisions has primarily focused on classifying edit intents, but falling short of a deeper understanding of the nature of these edits. In this paper, we present explore the challenge of describing an edit at two levels: identifying the edit intent, and describing the edit using free-form text. We begin by defining a taxonomy of general edit intents and introduce a new dataset of full revision histories of Wikipedia pages, annotated with each revision’s edit intent. Using this dataset, we train a classifier that achieves a 90% accuracy in identifying edit intent. We use this classifier to train a distantly-supervised model that generates a high-level description of a revision in free-form text. Our experimental results show that incorporating edit intent information aids in generating better edit descriptions. We establish a set of baselines for the edit description task, achieving a best score of 28 ROUGE, thus demonstrating the effectiveness of our layered approach to edit understanding.
OVERVIEW OF THE MACHINE LEARNING AND NUMERICAL OPTIMISER APPLICATIONS ON BEAM TRANSFER SYSTEMS FOR LHC AND ITS INJECTORS

Machine learning and numerical optimisation algorithms are getting more and more popular in the accelerator physics community and, thanks to the computing power available, their application in daily operation more likely. In the CERN accelerator complex, and specifically on the beam transfer systems, many promising numerical tools have been put in place in the last years. Some of the state-of-the-art machine learning models have been explored and used to solve problems that were never fully addressed in the past. In this paper, the most recent results of application of machine learning and numerical optimisation for injection, extraction and transfer of beam from machine and to experimental areas are presented. An overview of the possible next steps and shortcomings is finally discussed.
RELATE: Generating a linguistically inspired Knowledge Graph for fine-grained emotion classification

Several existing resources are available for sentiment analysis (SA) tasks that are used for learning sentiment specific embedding (SSE) representations. These resources are either large, common-sense knowledge graphs (KG) that cover a limited amount of polarities/emotions or they are smaller in size (e.g.: lexicons), which require costly human annotation and cover fine-grained emotions. Therefore using knowledge resources to learn SSE representations is either limited by the low coverage of polarities/emotions or the overall size of a resource. In this paper, we first introduce a new directed KG called ‘RELATE’, which is built to overcome both the issue of low coverage of emotions and the issue of scalability. RELATE is the first KG of its size to cover Ekman’s six basic emotions that are directed towards entities. It is based on linguistic rules to incorporate the benefit of semantics without relying on costly human annotation. The performance of ‘RELATE’ is evaluated by learning SSE representations using a Graph Convolutional Neural Network (GCN).