Miracle-dz/UniFAR-FTUs huggingface dataset:
Does the Available Literature about Hyperhomocysteinemia Cause Confusion to Clinicians?

Background—Aim: Hyperhomocysteinemia (HHcy) (typically defined as serum total homocysteine/tHcy levels >15 μmol/L) has been associated with more than 100 diseases, syndromes, or outcomes. However, the current literature about the testing for or the treatment of HHcy causes confusion to clinicians. The aim of this study was to present and comment on the main causes of this confusion. Discussion: The main causes of the above confusion are the important limitations of clinical trials related to the management of HHcy, the false impression that measurement of serum tHcy levels is not useful generally in clinical practice, the inability of modern diet (poor in folate and rich in methionine) to reduce the serum tHcy levels, and, finally, the consequent exclusion of identifying individuals with genetic causes of HHcy, as MTHFR C677T gene mutation. Conclusion: The recommendations about the testing for or the treatment of HHcy must be clarified.
Capsule-train stability

Elastic capsules flowing in small enough tubes, such as red blood cells in capillaries, are well known to line up into regular single-file trains. The stability of such trains in somewhat wider channels, where this organization is not observed, is studied in a two-dimensional model system that includes full coupling between the viscous flow and suspended capsules. A diverse set of linearly amplifying disturbances, both long-time asymptotic (modal) and transient (nonmodal) perturbations, is identified and analyzed. These have a range of amplification rates and their corresponding forms are wavelike, typically dominated by one of five principal perturbation classes: longitudinal and transverse translations, tilts, and symmetric and asymmetric shape distortions. Finite-amplitude transiently amplifying perturbations are shown to provide a mechanism that can bypass slower asymptotic modal linear growth and precipitate the onset of nonlinear effects. Direct numerical simulations are used to verify the linear analysis and track the subsequent transition of the regular capsule trains into an apparently chaotic flow.
The Burden of Frequency of Hospitalization and Length of Hospital Stay by Children with Major Congenital Abnormalities That Require Surgical Treatment during the First Year of Life – a Case-control Study

Objective − To determine how much children with “major” congenital abnormalities during the first year of life, compared to those without them, burden the health care system more in terms of length of stay and number of admissions and discharges.Methods − The electronic records of 57 patients with major congenital abnormalities (study group) and 57 patients who were surgically treated for another condition in the first year of life (control group) were reviewed retrospectively.Results − Children with congenital abnormalities were first hospitalized at the age of 1 (IQR=5) day, compared to 121 (IQR=278) days in children without congenital abnormalities (P<0.001). The total number of hospitalizations in the study group was 2.39±1.57, while in the control group, it was 1.14±0.35 (P<0.001). The length of stay in the hospital in children with congenital abnormalities was 48 (IQR=74) days, compared to the control group, which was 3 (IQR=15) days (P<0.001). The average length of stay in children with congenital abnormalities was 22.5 (IQR=29) days, while in the control group, it was 3 (IQR=11) days (P<0.001). The average share of the length of stay in the intensive care unit in relation to the total length of stay in the hospital for the group with congenital abnormalities was 46.6%, while for the control group, it was 23.3% (P<0.001). Conclusion − Children with congenital abnormalities had a 4.9× overall longer length of hospital stay and a 3.1× longer length of stay in the intensive care unit.
Constructing Depression Prediction Model using ChatGPT and Machine Learning Algorithms

Depression is currently one of the main causes of self-harm in human society. However, the insufficient diagnosis of depression has been a long-standing problem. Traditional diagnostic methods heavily rely on the patient’s current emotions, and patients often hesitate to seek help, which frequently delays intervention. With the deep expansion of social media, individuals with suicidal thoughts often express their perspectives and thoughts on these platforms. Relevant studies have also found that people with depression are more likely to disclose their condition. Therefore, there is a potential to enhance the identification of users at risk of suicide by analyzing social media posts. Although machine learning has been successfully applied to build depression prediction models, the brevity of social media posts often affects the classification performance of these models. Hence, this study aims to introduce ChatGPT to automatically generate experimental corpus using information from short comments. Natural language processing (NLP), Support Vector Machines (SVM), and Naive Bayes (NB) classifiers are then employed to enhance the classification accuracy of the depression prediction model. The experimental results validate the effectiveness of the proposed method.
Identifying Crucial Factors for E-students’ Satisfaction in Vietnamese Emengency Remote Learning Using Machine Learning Methods

E-students’ satisfaction significantly affects the success of e-learning adoption and leads to quality improvement of e-courses. E-students’ satisfaction also highly impacts student learning performance and learning activities in e-courses. However, many previous studies focused on e-student satisfaction factors in developed countries and in some big cities in Vietnam in normal educational contexts. Limited research emphasized top-ranking factors for e-students’ satisfaction in emergency remote learning (ERL) in universities in rural regions in Mekong Delta using machine learning methods. Thus, this study aimed to identify crucial factors affecting e-students’ satisfaction in ERL in a public university in rural areas of Mekong Delta in Vietnam by using Random Forests (RF) and Support Vector Machine (SVM) algorithms. This study used a 5-point Likert e-survey including 28 items allocated to four scales: 1) Course designs, (2) Infrastructure and technology, (3) Teacher-student interaction; and (4) Lecturers to collect research data. The employed data included responses of 916 e-students from multi departments of the target university during the second semester of the 2021/2022 academic year. The results displayed that RF (94.47%) outperformed SVM (91.15%). The crucial factors were mainly related to infrastructure and technology, materials, and teacher-student interaction, especially feedback and evaluation. The outcomes of this study provide sustainable e-learning in the post-pandemic educational context. The proposed prediction models inform policy-makers and e-learning program designers in a long-term strategy that meets e-leaners’ needs in order to attract more e-students and maintain e-student retention.
Acumulação de microcistinas no mexilhão dourado Limnoperna fortunei e riscos para a biota aquática

Cyanobacterias are natural components of the phytoplankton community, but they are often associated with eutrophic waters. Rivers in the state of São Paulo are in a continuous process of eutrophication, which favors the frequent cyanobacteria blooms in these environments, compromises the quality of the water for human consumption and promotes risks to the aquatic biota. The golden mussel (Limnoperna fortune) is an invasive alien species that can be a vector in the transfer of cyanobacterial toxins. The aim of this study was to evaluate the cyanobacteria blooms occurrence in São José dos Dourados River, their cyanotoxins and the possibility of phycotoxins bioaccumulating in the Limnoperna fortunei. Toxic cyanobacterias and cyanotoxins (microcystins) were detected in the samples. The results indicated that L. fortunei accumulated microcystins. The cyanobacterial blooms in the water of the studied river indicated a damage to the environment associated with eutrophication and risks of cyanotoxins presence to the aquatic biota.
Data science competition for cross-site individual tree species identification from airborne remote sensing data

Data on individual tree crowns from remote sensing have the potential to advance forest ecology by providing information about forest composition and structure with a continuous spatial coverage over large spatial extents. Classifying individual trees to their taxonomic species over large regions from remote sensing data is challenging. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a data science competition to help identify effective methods for the task of classification of individual crowns to species identity. The competition included data from three sites to assess each methods’ ability to generalize patterns across two sites simultaneously and apply methods to an untrained site. Three different metrics were used to assess and compare model performance. Six teams participated, representing four countries and nine individuals. The highest performing method from a previous competition in 2017 was applied and used as a baseline to understand advancements and changes in successful methods. The best species classification method was based on a two-stage fully connected neural network that significantly outperformed the baseline random forest and gradient boosting ensemble methods. All methods generalized well by showing relatively strong performance on the trained sites (accuracy = 0.46–0.55, macro F1 = 0.09–0.32, cross entropy loss = 2.4–9.2), but generally failed to transfer effectively to the untrained site (accuracy = 0.07–0.32, macro F1 = 0.02–0.18, cross entropy loss = 2.8–16.3). Classification performance was influenced by the number of samples with species labels available for training, with most methods predicting common species at the training sites well (maximum F1 score of 0.86) relative to the uncommon species where none were predicted. Classification errors were most common between species in the same genus and different species that occur in the same habitat. Most methods performed better than the baseline in detecting if a species was not in the training data by predicting an untrained mixed-species class, especially in the untrained site. This work has highlighted that data science competitions can encourage advancement of methods, particularly by bringing in new people from outside the focal discipline, and by providing an open dataset and evaluation criteria from which participants can learn.
Occurrence of Toxoplasma gondii and other gastrointestinal parasites in free-roaming cats from the Rio de Janeiro zoo

Abstract This communication aimed to determine the frequency of infection by T. gondii and other gastrointestinal parasites in free-living cats captured in BioParque do Rio (Fundação RioZoo), Brazil. To this end, 58 blood and 51 fecal samples were collected from 68 cats from July 2019 to September 2020. The serum obtained was analyzed by indirect fluorescent antibody test for anti-T. gondii IgG. Fecal samples were examined by microscopic parasitological techniques. Of the total, 10.3% of the animals presented anti-T. gondii IgG. Parasitic structures were observed in 39.2% of the fecal samples. Hookworms were the most detected parasites (19.6%) followed by Cystoisospora sp. (11.7%), Dipylidium caninum (7.8%) and Toxocara cati (3.9%). These results indicated the exposure of this population of stray cats to potentially zoonotic parasites, which in addition to causing possible damage to domestic and wild animal health, also pose risks to public health.
Soft Driving Epicyclical Mechanism for Robotic Finger

Nowadays, the development or improvement of soft actuation mechanisms is a crucialtopic for the achievement of dexterous manipulation using. Then, a primary target of research is thedesign of actuation and driving devices. Consequently, in this paper, we introduce a soft drivingepicyclical mechanism that mimics human muscle behavior and fulfills motion requirements toachieve grasping gestures using a robotic finger. The prototype is experimentally assessed, andresults show that our approach has enough performance for the implementation in grasping tasks.Furthermore, we introduce the basis for a new soft epicyclical mechanism merger with shape memoryalloys to allow active stiffness control of the mechanism.
Automatic non-contact grinding surface roughness measurement based on multi-focused sequence images and CNN

Microscopic images of surfaces can be used for non-contact roughness measurement by visual methods. However, the images are usually acquired manually and need to be as sharp as possible, which limits the general application of the method. This manuscript provides an automatic roughness measurement method that can apply to automatic industrial sites. This method first automatically acquires the sharpest image and then feeds the image into a convolutional neural network (CNN) model for roughness measurement. In this method, the weighted window enhanced sharpness evaluation algorithm based on the sharpness evaluation function is proposed to automatically extract the sharpest image. Then, a CNN model, CFEN, suitable for the roughness measurement task was designed and pre-trained. The results demonstrate that the measurement accuracy of the method reaches 91.25% and the time is within a few seconds. It is proved that the method has high accuracy and efficiency and is feasible in practical applications.
Annual and Interannual Variability of the Tropical Instability Vortices in the Equatorial Eastern Pacific Observed from Lagrangian Surface Drifters

AbstractThis study documents the spatial distributions and temporal variations of anticyclonic eddies with identified radii ≥100 km in the equatorial eastern tropical Pacific Ocean [viz., tropical instability vortices (TIVs)] using Lagrangian surface drifters. The TIVs identified from Lagrangian surface drifters are distributed in a band along 5°N and are closely associated with latitudinal barotropically unstable shear between the westward South Equatorial Current (SEC) and the eastward North Equatorial Countercurrent (NECC). Fewer TIVs are identified from February to June when the shear between the SEC and NECC is weak, whereas more TIVs are found from July to January when the shear is enhanced. The number of identified TIVs also exhibits substantial interannual variability, with fewer TIVs identified during El Nino events and more TIVs found during La Nina events. This relationship is likely associated with the interannual variations of the zonal circulation in the equatorial Pacific modulated by El Ni.
Oxygen Annealing of GdBa2Cu3O7−$\delta$ Superconducting Thin Films: Influence of Annealing Time

GdBa<sub>2</sub>Cu<sub>3</sub>O<sub>7−<inline-formula><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula></sub> (GdBCO) superconducting thin films, grown by Pulsed Laser Deposition (PLD) on MgO(100) single crystals are oxygen annealed using two different approaches: <italic>in-situ</italic> (oxygen annealing in PLD chamber) and <italic>ex-situ</italic> (oxygen annealing in tubular furnace). Their structural and transport properties are investigated in relation to annealing time (<italic>t</italic><sub>ann</sub>) at 450 <inline-formula><tex-math notation="LaTeX">${^\circ }\text{C}$</tex-math></inline-formula>. <italic>In-situ</italic> annealed GdBCO thin films require at least 15 min of annealing to achieve high in-field critical current densities (<italic>J</italic><sub>c</sub>), while <italic>ex-situ</italic> annealed samples achieve high in-field <italic>J</italic><sub>c</sub> already after 5 min of oxygen annealing at 450 <inline-formula><tex-math notation="LaTeX">${^\circ }\text{C}$</tex-math></inline-formula> and 1 bar of oxygen pressure.
Characterization of a thermophilic and glucose-tolerant GH1 β-glucosidase from hot springs and its prospective application in corn stover degradation

Introduction β-Glucosidase serves as the pivotal rate-limiting enzyme in the cellulose degradation process, facilitating the hydrolysis of cellobiose and cellooligosaccharides into glucose. However, the widespread application of numerous β-glucosidases is hindered by their limited thermostability and low glucose tolerance, particularly in elevated-temperature and high-glucose environments. Methods This study presents an analysis of a β-glucosidase gene belonging to the GH1 family, denoted lqbg8, which was isolated from the metagenomic repository of Hehua hot spring located in Tengchong, China. Subsequently, the gene was cloned and heterologously expressed in Escherichia coli BL21(DE3). Post expression, the recombinant β-glucosidase (LQBG8) underwent purification through a Ni affinity chromatography column, thereby enabling the in-depth exploration of its enzymatic properties. Results LQBG8 had an optimal temperature of 70°C and an optimum pH of 5.6. LQBG8 retained 100 and 70% of its maximum activity after 2-h incubation periods at 65°C and 70°C, respectively. Moreover, even following exposure to pH ranges of 3.0–10.0 for 24 h, LQBG8 retained approximately 80% of its initial activity. Notably, the enzymatic prowess of LQBG8 remained substantial at glucose concentrations of up to 3 M, with a retention of over 60% relative activity. The kinetic parameters of LQBG8 were characterized using cellobiose as substrate, with Km and Vmax values of 28 ± 1.9 mg/mL and 55 ± 3.2 μmol/min/mg, respectively. Furthermore, the introduction of LQBG8 (at a concentration of 0.03 mg/mL) into a conventional cellulase reaction system led to an impressive 43.7% augmentation in glucose yield from corn stover over a 24-h period. Molecular dynamics simulations offered valuable insights into LQBG8’s thermophilic nature, attributing its robust stability to reduced fluctuations, conformational changes, and heightened structural rigidity in comparison to mesophilic β-glucosidases. Discussion In summation, its thermophilic, thermostable, and glucose-tolerant attributes, render LQBG8 ripe for potential applications across diverse domains encompassing food, feed, and the production of lignocellulosic ethanol.
Road Traffic Accident Pattern Analysis with Data Mining

The discovery of Road Traffic Accident (RTA) patterns is vital to formulate mitigation strategies based on the characteristics of RTA. Various studies have applied association rule mining for RTA pattern discovery. However, limited research has conducted a comparative analysis of RTA patterns in different regions. This work is necessary to identify the similarities and differences of RTA characteristics between different regions, especially the developed and developing countries with different RTA trends in fatality rates. This work explored the application of association rule mining (FP-Growth algorithm) to discover and compare the RTA patterns for developed and developing countries. The Orange Data Mining software is used to discover RTA patterns for Ethiopia (12,317 samples) and the UK (United Kingdom) (1,048,575 samples), representing developing and developed countries. Eventually, findings showed similarities and differences in RTA patterns between Ethiopia and the UK. Road characteristics and conditions are the common factors contributing to RTA severity levels, while other crucial factors include the light condition and speed limit. In conclusion, the discovery of RTA patterns in different regions is beneficial to assist in formulating strategies to reduce the risk of RTA. Future work is necessary to discover the RTA patterns from different perspectives, such as seasonal or periodical variations of RTA patterns.
THE EFFECTS OF MIGRATION ON ASSISTED ELDERLY PEOPLE IN RESIDENTIAL SERVICES IN IASI. CASE STUDY

The issue of migration from the Romanian space has multiple approaches, sometimes dilemmatic, of the active social support potential offered by community actors. In this frame of reference, we propose a study that highlights a number of issues concerning the effects of migration on elderly people in the community of Iasi. The study is based on the focus group method, applied to a sample of twenty elderly beneficiaries of the residential services offered by Saint Pious Paraskeva - nursinghome for retired people owned by the City Hall of Iasi. The conclusions of the three focus groups converge on the need to maintain the emotional balance through constant communication, the preservation of the proximate habitus and to assume the serenity condition of the threshold peak age. Existent Romanian regulatory framework for social services for the elders requires efficient connection to the one in the European Union and the assumption of good practice from intra-community or extra-community countries may structure a specific pattern, desirable for social services development.
Biodiversity and mite population fluctuation in different apple orchards in southern Brazil

Abstract The aim of this work was to know the population fluctuation of the mites in apple (Malus domestica Borkh: Rosaceae) orchards with different crop management strategies, with emphasis on Aculus schlechtendali Nalepa (Eriophyidae) and Panonychus ulmi (Koch) (Tetranychidae) and their natural enemies. The studies were carried out in 2020-21, in three organic orchards (Eva, Fuji and Gala), from Antônio Prado, two conventional (Fuji and Gala), from Muitos Capões, RS, Brazil, and two of regenerative agriculture (Fuji and Gala), in São Joaquim, SC, Brazil. Forty plants/orchard were sampled, from which three leaves/plant from the apical, median and basal offshoot were detached. The material was screened and identified in the laboratory. A total of 7.193 specimens, belonging to 14 families, 29 genera, and 40 species, were collected. The most common phytophagous mites were A. schlechtendali (46.2%), P. ulmi (16.9%) and Polyphagotarsonemus latus Banks (Tarsonemidae) (10.9%). Panonychus ulmi was observed in all orchards, being abundant in conventional and regenerative orchards. The most common predatory mites were Neoseiulus californicus (McGregor) (Phytoseiidae), Agistemus riograndensis Johann and Ferla (Stigmaeidae) and Agistemus brasiliensis Matioli, Ueckermann and Oliveira (Stigmaeidae). Among predators, greater diversity was observed in organic orchards, while in conventional and regenerative orchards N. californicus was the most common. Greater acarine abundance was observed when there was an increase in temperatures, and the predatory mites increased their populations in response to the presence of phytophagous mites. The results suggest that the mite populations were influenced by the cultivar and mainly by the management used.