Miracle-dz/UniFAR-FTUs huggingface dataset:
1H NMR Serum Metabolomic Change of Trimethylamine N-oxide (TMAO) Is Associated with Alcoholic Liver Disease Progression

Without early detection and treatment, chronic and excessive alcohol consumption can lead to the development of alcoholic liver disease (ALD). With this in mind, we exploit the recent concept of the liver–gut axis and analyze the serum profile of ALD patients for identification of microbiome-derived metabolites that can be used as diagnostic biomarkers for onset of ALD. 1H-NMR was used to analyze serum metabolites of 38 ALD patients that were grouped according to their Child–Turcotte–Pugh scores (CTP): class A (CTP-A; 19), class B(CTP-B; 10), and class C (CTP-C; 9). A partial least squares-discriminant analysis (PLS-DA) and a variable importance of projection (VIP) score were used to identify significant metabolites. A receiver operating characteristic (ROC) curve and correlation heatmap were used to evaluate the predictability of identified metabolites as ALD biomarkers. Among 42 identified metabolites, 6 were significantly correlated to exacerbation of ALD. As ALD progressed in CTP-C, the levels of trimethylamine N-oxide (TMAO), malate, tyrosine, and 2-hydroxyisovalerate increased, while isobutyrate and isocitrate decreased. Out of six metabolites, elevated levels of TMAO and its precursors (carnitine, betaine, choline) were associated with severity of ALD. This indicates that TMAO can be used as an effective biomarker for the diagnosis of ALD progression.
Agricultura familiar-Agronegocios: Disputas, interrelaciones y proyectos * Family Farming-Agribusiness: Disputes, Relationships and Projects Agricultura familiar-Agronegócios: Disputas, inter-relações e projetos

This article identifies the aspects where tensions between different ways of carrying out the agricultural activity emerge, their local impacts and the responses of social agents both in material and discursive terms. It analyzes a district of the Argentinean Pampas region where traditionally labor intensive crops with participation of family farmers and oriented to the domestic market have developed, and in which there is a growing expansion of extensive agriculture oriented to world markets. Often regarded as an expression of the agribusiness paradigm, the latter is associated to large-scale agriculture seeking short-term benefits, and with the dislocation of production of the place where surpluses are invested. While there are elements that reflect the consolidation of the so-called agribusiness model, its features are not reproduced mechanically and evenly in the rural space. There are hybrid situations and processes not directly attributed to the influence that one of them takes over the other. The importance of family farmers and the nature of their productive behavior may represent potential barriers to this. Nevertheless, their weakness as social actors to position their interests is an indicator of their difficulties to persist within the agrarian structure that is emerging. resumo Este artigo identifica aspectos onde emergem tensões entre diferentes firmas de levar a cabo a atividade agrária, seus impactos territoriais e as respostas dos agentes sociais tanto desde o plano material quanto o plano discursivo. Para isso analisa um partido da região pampeana argentina onde tradicionalmente têm se desenvolvido produções de trabalho intensivo orientadas ao mercado interno com participação de produtores familiares, no qual se constata a expansão da agricultura extensiva orientada aos mercados mundiais. Frequentemente considerada como ilustrativa do paradigma dos
Intracranial recordings reveal high-frequency activity in the human temporal-parietal cortex supporting non-literal language processing

Objective Non-literal expressions such as sarcasm, metaphor and simile refer to words and sentences that convey meanings or intentions that are different and more abstract than literal expressions. Neuroimaging studies have shown activations in a variety of frontal, parietal and temporal brain regions implicated in non-literal language processing. However, neurophysiological correlates of these brain areas underlying non-literal processing remain underexplored. Methods To address this, we investigated patterns of intracranial EEG activity during non-literal processing by leveraging a unique patient population. Seven neurosurgical patients with invasive electrophysiological monitoring of superficial brain activity were recruited. Intracranial neural responses were recorded over the temporal-parietal junction (TPJ) and its surrounding areas while patients performed a language task. Participants listened to vignettes that ended with non-literal or literal statements and were then asked related questions to which they responded verbally. Results We found differential neurophysiological activity during the processing of non-literal statements as compared to literal statements, especially in low-Gamma (30–70 Hz) and delta (1–4 Hz) bands. In addition, we found that neural responses related to non-literal processing in the high-gamma band (>70 Hz) were significantly more prominent at TPJ electrodes as compared to non-TPJ (i.e., control) electrodes in most subjects. Moreover, in half of patients, high-gamma activity related to non-literal processing was accompanied by delta-band modulation. Conclusion These results suggest that both low- and high-frequency electrophysiological activities in the temporal-parietal junction play a crucial role during non-literal language processing in the human brain. The current investigation, utilizing better spatial and temporal resolution of human intracranial electrocorticography, provides a unique opportunity to gain insights into the localized brain dynamics of the TPJ during the processing of non-literal language expressions.
Characterizing Isotopic Composition and Trajectories of Atmospheric River Events

Landfalling atmospheric rivers (LARs) are important drivers of mid-latitude climate; however, our understanding of the water vapour sources, storm trajectories, and receiving waters of ARs is limited. This study aims to characterize LARs in southwest British Columbia by their isotopic composition and storm track trajectories and to better understand how AR-derived precipitation is manifested in watershed waters. ARs were depleted (−11.71‰ δ18O, −85.80‰ δ2H, n = 19) compared to non-ARs (−9.47‰ δ18O, −69.58‰ δ2H, n = 32) (p = 0.03); however, the difference is minimal. LAR storm tracks did not show any obvious correlation to their isotopic composition, despite the large variability in their source regions across the Pacific Ocean. The lack of correlation is attributed to mixing air parcels, thereby incorporating moisture with different isotopic compositions into the main transport mechanism. D-excess values for ARs and non-ARs were statistically similar, although seasonal differences were observed. ARs with higher d-excess were sourced from the central Pacific, whereas ARs with lower d-excess had storm tracks through the northern Pacific. Watershed water d-excess values (mean = 8.58 ± 2.97‰) were more similar to winter precipitation (mean = 10.1 ± 5.1‰), compared to summer (mean = 2.8 ± 4.3‰), likely due to their source of winter precipitation at high elevation. A greater range in AR d-excess winter values relative to summer values (3.6–16.6‰, −0.3–6.0‰, respectively) is attributed to storm track variability.
Gene modules associated with human diseases revealed by network analysis

Despite many genes associated with human diseases have been identified, disease mechanisms often remain elusive due to the lack of understanding how disease genes are connected functionally at pathways level. Within biological networks, disease genes likely map to modules whose identification facilitates etiology studies but remains challenging. We describe a systematic approach to identify disease-associated gene modules. A gene co-expression network based on the graphical Gaussian model (GGM) was constructed using the GTEx dataset and assembled into 652 gene modules. Screening these modules identified those with disease genes enrichment for obesity, cardiomyopathy, hypertension, and autism, which illuminated the molecular pathways underlying their pathogenesis. Using mammalian phenotypes derived from mouse models, potential disease candidate genes were identified from these modules. Also analyzed were epilepsy, schizophrenia, bipolar disorder, and depressive disorder, revealing shared and distinct disease modules among brain disorders. Thus, disease genes converge on modules within our GGM gene co-expression network, which provides a general framework to dissect genetic architecture of human diseases.
Efficient graph representation framework for chemical molecule similarity tasks

Graph data has emerged in numerous scientific domains and machine learning techniques have been widely used for analysis and learning of diverse data for prediction and decision. Machine learning techniques can readily address complex problems by leveraging their structural information. But graphs cannot be directly used for existing machine learning algorithms unless encoded as vectors. The problem of efficient representation of graphs is a substantial challenge in graph machine learning. In this paper, we propose a novel two-stage framework for the representation of chemical molecule graphs based on the strengths of Graph Isomorphism Networks (GINs) and Siamese autoencoders. In the first stage, the GIN model is constructed and trained using the structural information of chemical molecule graphs. Node attributes, edge attributes, and edge indices are used as input data, while graph attributes are used as labels. The GIN model effectively captures the structural characteristics of graphs and can accurately predict graph attributes, i.e., molecular properties. It also generates Graph Embeddings, represented as vectors that encode the structural information of graphs. In the second stage, Graph Embedding vectors are further optimized for downstream similarity tasks while preserving the graph structural information. The Siamese autoencoder is constructed and trained, which reduces the dimensionality of the Graph Embedding vectors, while maximizing the preservation of structural information in the original high-dimensional vectors. The resulting low-dimensional Graph Embeddings can be effectively utilized for tasks such as approximate nearest neighbor search. The experimental results demonstrate the effectiveness of our proposed framework in accurately predicting graph similarity.
Coal fly ash-slag and slag-based geopolymer as an absorbent for the removal of methylene blue in wastewater

The fly ash and slag from coal burning were attractive byproducts to prepare geopolymers for the adsorption of methyelen blue in wastewater due to their availability and low cost. Various mixing amounts between them were conducted during geopolymerization with a Na2SiO3/NaOH ratio of 10 M of 2.5 and a curing temperature of 60°C for 24 h. When the amount of coal slag in the geopolymer composition was increased from 0 to 51 %, the surface structures of the resultant geopolymers were much softer and more porous due to the lack of initial material, causing a reduction in the surface area of geopolymers to 119,23 m2/g for 0 % and 5,29 m2/g for 51 %. The adsorption amount of methylene blue performed at pH 12 showed different tendencies on the dependence of contact time for the indivually prepared geopolymer. The uptake amount decreased from 36.2 mg/g to 34.2 mg/g with the enhancement of coal slag in the geopolymer from 0 to 51 % after 180 minutes of immersion in methylene blue solution. In addition, the adsorption mechanism evaluated by FT-IR spectroscopy was observed to involve electrostatic forces formed by hydrogen bonding between hydroxyl groups (Si–OH) and nitrogen atoms in the structure of methylene molecules. This study indicated that coal slag could be a potential material to prepare geopolymers for removing dye pollutants.
Laryngeal Mask Airway and Adenotonsillectomy

Objective: To compare the use of flexible laryngeal mask airway (LMA) and endotracheal tube (ETT) in pediatricadenotonsillectomy.Design: Prospective randomized trial.Setting: Tertiary care hospital.Patients: One hundred thirty-one children (aged 2-12 years). Exclusion criteria were body mass index (calculatedas the weight in kilograms divided by the height in meters squared) greater than 35 and craniofacial anomalies.Obstructive sleep apnea was the most common indication for surgery.Intervention: Children undergoing adenotonsillectomy were randomized to use of an LMA or ETT. A standardizedanesthesia protocol was used.Main Outcome Measures: Primary outcome measure was laryngospasm. Secondary measures included anesthesia,operative, and recovery times.Results: Sixty children were randomized to the LMA group and 71 to the ETT group. There was no differencebetween groups with regard to age (P = .76), ethnicity (P = .75), body mass index (P = .99), or American Societyof Anesthesiologists grade (P = .46). Incidence of postoperative laryngospasm between LMA (12.5%) and ETT(9.6%) was similar (P = .77). In 10 patients, the LMA was changed to ETT intraoperatively owing to tube kinkingor difficulty with visualization. Mean (SD) surgical times for LMA and ETT groups were 33.35 (13.39) and 37.76(18.26) minutes, respectively (P = .15). Time from surgery end to extubation was significantly shorter in patientswho used LMA (P = .01) by 4.06 minutes. There were no differences (P = .49) in postanesthesia care unit recoverytimes.Conclusions: An LMA is an efficient alternative to ETT in pediatric adenotonsillectomy. When comparing LMAand ETT, there is no difference in rates of laryngospasm. Time to extubation is significantly shorter in patientsusing LMA. Before adopting the routine use of LMA in pediatric adenotonsillectomy, further study is needed toaddress visualization and kinking issues associated with this device.
Evolution of in-hospital patient characteristics and predictors of death in the COVID-19 pandemic across four waves: are they moving targets with implications for patient care?

Objectives The aim of this work was to study characteristics, outcomes and predictors of all-cause death in inpatients with SARS-CoV-2 infection across the pandemic waves in one large teaching hospital in Italy to optimize disease management. Methods All patients with SARS-CoV-2 infection admitted to our center from March 2020 to June 2022 were included in this retrospective observational cohort study. Both descriptive and regression tree analyses were applied to identify factors influencing all-cause mortality. Results 527 patients were included in the study (65.3% with moderate and 34.7% with severe COVID-19). Significant evolutions of patient characteristics were found, and mortality increased in the last wave with respect to the third wave notwithstanding vaccination. Regression tree analysis showed that in-patients with severe COVID-19 had the greatest mortality across all waves, especially the older adults, while prognosis depended on the pandemic waves in patients with moderate COVID-19: during the first wave, dyspnea was the main predictor, while chronic kidney disease emerged as determinant factor afterwards. Conclusion Patients with severe COVID-19, especially the older adults during all waves, as well as those with moderate COVID-19 and concomitant chronic kidney disease during the most recent waves require more attention for monitoring and care. Therefore, our study drives attention towards the importance of co-morbidities and their clinical impact in patients with COVID-19 admitted to hospital, indicating that the healthcare system should adapt to the evolving features of the epidemic.
A novel ensemble learning method for crop leaf disease recognition

Deep learning models have been widely applied in the field of crop disease recognition. There are various types of crops and diseases, each potentially possessing distinct and effective features. This brings a great challenge to the generalization performance of recognition models and makes it very difficult to build a unified model capable of achieving optimal recognition performance on all kinds of crops and diseases. In order to solve this problem, we have proposed a novel ensemble learning method for crop leaf disease recognition (named ELCDR). Unlike the traditional voting strategy of ensemble learning, ELCDR assigns different weights to the models based on their feature extraction performance during ensemble learning. In ELCDR, the models’ feature extraction performance is measured by the distribution of the feature vectors of the training set. If a model could distinguish more feature differences between different categories, then it receives a higher weight during ensemble learning. We conducted experiments on the disease images of four kinds of crops. The experimental results show that in comparison to the optimal single model recognition method, ELCDR improves by as much as 1.5 (apple), 0.88 (corn), 2.25 (grape), and 1.5 (rice) percentage points in accuracy. Compared with the voting strategy of ensemble learning, ELCDR improves by as much as 1.75 (apple), 1.25 (corn), 0.75 (grape), and 7 (rice) percentage points in accuracy in each case. Additionally, ELCDR also has improvements on precision, recall, and F1 measure metrics. These experiments provide evidence of the effectiveness of ELCDR in the realm of crop leaf disease recognition.
Effects of Oral Cavity Stem Cell Sources and Serum-Free Cell Culture on Hydrogel Encapsulation of Mesenchymal Stem Cells for Bone Regeneration: An In Vitro Investigation

Introduction: To develop a stem cell delivery model and improve the safety of stem cell transplantation for bone regeneration, this study aimed to determine the effects of stem cell sources, serum-free cell culture, and hydrogel cell encapsulation on the growth and osteogenic differentiation of mesenchymal stem cells (MSCs) from the oral cavity. Methods: The study groups were categorized according to stem cell sources into buccal fat pad adipose (hBFP-ADSCs) (Groups 1, 4, and 7), periodontal ligament (hPDLSCs) (Groups 2, 5, and 8), and dental pulp-derived stem cells (hDPSCs) (Groups 3, 6, and 9). MSCs from each source were isolated and expanded in three types of sera: fetal bovine serum (FBS) (Groups 1–3), human serum (HS) (Groups 4–6), and synthetic serum (SS) (StemPro™ MSC SFM) (Groups 7–9) for monolayer (m) and hydrogel cell encapsulation cultures (e). Following this, the morphology, expression of MSC cell surface antigens, growth, and osteogenic differentiation potential of the MSCs, and the expression of adhesion molecules were analyzed and compared. Results: SS decreased variations in the morphology and expression levels of cell surface antigens of MSCs from three cell sources (Groups 7m–9m). The levels of osteoblastic differentiation of the hPDLSCs and hBFP-ADSCs were increased in SS (Groups 8m and 7m) and the cell encapsulation model (Groups 1e, 4e, 7e–9e), but the promoting effects of SS were decreased in a cell encapsulation model (Groups 7e–9e). The expression levels of the alpha v beta 3 (ITG-αVβ3) and beta 1 (ITG-β1) integrins in the encapsulated cells in FBS (Group 1e) were higher than those in the SS (Group 7e). Conclusions: Human PDLSCs and BFP-ADSCs were the optimum stem cell source for stem cell encapsulation by using nanohydroxyapatite–calcium carbonate microcapsule–chitosan/collagen hydrogel in serum-free conditions.
Structure and assembly process of fungal communities in the Yangtze River Estuary

Marine fungi are essential for the ecological function of estuarine ecosystems. However, limited studies have reported on the structure and assembly pattern of the fungal communities in estuaries. The purpose of this study is to reveal the structure and the ecological process of the fungal community in the Yangtze River Estuary (YRE) by using the amplicon sequencing method. Phyla of Ascomycota, Basidiomycota, and Chytridiomycota were dominant in the seawater and sediment samples from YRE. The null model analysis, community-neutral community model (NCM), and phylogenetic normalized stochasticity ratio (pNST) showed that the stochastic process dominated the assembly of fungal communities in YRE. Drift and homogeneous dispersal were the predominant stochastic processes for the fungal community assembly in seawater and sediment samples, respectively. The co-occurrence network analysis showed that fungal communities were more complex and closely connected in the sediment than in the seawater samples. Phyla Ascomycota, Basidiomycota, and Mucoromycota were the potential keystone taxa in the network. These findings demonstrated the importance of stochastic processes for the fungal community assembly, thereby widening our knowledge of the community structure and dynamics of fungi for future study and utilization in the YRE ecosystem.
La Formación Basada en Competencias Aplicada a la Formación de los Entrenadores de Tenis

El continuo proceso de educación para los entrenadores en el área de investigación esta ganando mucho interés en el mundo del deporte, de una manera significativa en el desarrollo profesional. Este artículo discute algunas de las más comunes herramientas en la educación de los entrenadores de tenis que se usan hoy en día, teniendo España como caso de estudio.
Choosing to study geography and the career aspirations of future graduates and postgraduates

Several factors are bound to influence a young person’s decision to study a particular study programme, including his or her interests and commitment, abilities and skills, career prospects, family expectations, social pressure and peer influence, the information and advice available to him or her and financial constraints. To find out what factors contribute to adolescents’ decision to study geography and the career aspirations of future graduates and postgraduates, a survey was conducted in 2017 and 2020 at the Department of Geography, Faculty of Arts, University of Ljubljana involving a total of 155 students in their first year of the first cycle and the first and the second year of the second cycle. The results will be of great help in the further development of the geography study, as knowledge of the factors influencing their decisions to study, as well as their career aspirations and expectations, will allow us to understand the low enrolment figures in geography studies in recent years (which are not comparable to those of the past in all three Slovenian departments of geography) and to find potential solutions or ways to improve the current situation.
Differential behavioral response to predator odor in neuropathic pain in mice

Neuropathic pain, a type of chronic pain caused by injury or disease of the somatosensory system, affects ∼10% of the general population and is difficult to treat. It is strongly associated with mood disorder comorbidities and impairs quality of life. It was recently suggested that hypervigilance caused by chronic pain might be of advantage in some species, helping them avoid predators during injury when they are most vulnerable. Here, we sought to confirm the hypervigilance hypothesis by using two predator odor (PO) paradigms, one with transient and one with continuous odor presentation. We observed behavioral responses to PO in neuropathic and control mice in an open field setting. We find that neuropathic mice show hypervigilance to PO, confirming previous results. However, we also find increased anxiety responses to neutral odor in neuropathic mice, which manifests as maladaptive pain. This demonstrates that this maladaptive nature of pain could be an evolutionary adaptation aimed at reducing injury-induced vulnerability.