Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of lifestyle interventions and overweight on infertility: a systematic review, meta-analysis, and meta-regression of randomized controlled trials

This study aimed to investigate the effect of lifestyle intervention (LSI) on diagnosed infertility in overweight and obese women. A systematic review and meta-analysis were conducted. A literature search was performed on the following databases from September 2022 to December 2022: PubMed, Web of Science, and SPORTDiscus. The inclusion criteria were the following: women between 18 and 45 years of age, BMI over 25.0 kg/m2, diagnosed with infertility, a weight loss intervention, and control group part of RCTs. In total, 15 studies were identified and included. The meta-analysis shows a beneficial effect of LSI on reducing weight, waist circumference, and BMI and increasing infertility. A significantly beneficial effect of lifestyle intervention on weight reduction was observed for participants who initially had a higher BMI, while a non-significant effect was observed for individuals with a BMI above 35 kg/m2. The meta-analysis showed a beneficial effect of lifestyle intervention on ovulation incidence and sex hormone-binding globulin. The lifestyle intervention group had 11.23 times more ovulatory incidence than the control group, which in turn increased the ability to conceive. As robust evidence for the effect of lifestyle interventions on infertility in obese and overweight women was found, it is advised to integrate similar interventions into future infertility treatment processes.
The Study of Flood Effects on The Environment and Livelihoods in Ghana

Purpose: This study investigates the repercussions of flooding on the environment and the livelihoods within the Haatso community and its immediate surroundings, situated in the Ga East Municipality of the Greater Accra Region, Ghana. Design/ Methodology/ Approach: We employed purposive sampling to conduct household-based interviews with 61 individuals across five residential areas. We used semi-structured questionnaires to collect their perspectives on the 2022 flooding situation within the Haatso communities. Descriptive statistics were used to analyze disaggregated data such as demographic characteristics and other relevant themes directly bearing on flooding and flood vulnerability. Findings: The Haatso community and its immediate environs would continue to experience floods recurring annually due mainly to drain-related and unplanned infrastructure issues. Hence, the detrimental consequences of floods on infrastructure and businesses, resulting in hardships in the community are expected to persist. Research Limitation/Implication: The approach utilized in this study can be adapted for similar endeavours aimed at mitigating the effects of flooding on both the environment and livelihoods within various communities and their immediate environs. Practical Implication: The causes, rate of occurrence, and adaptive and coping mechanisms of floods ought to be known to provide additional knowledge on combatting floods. Social Implication: Providing early warning alerts increases survival schemes and hence decreases the direct and indirect losses caused by annual floods on communities. Originality/Value: This research submits a more pragmatic, holistic, and realistic survival strategy for the Haatso community and its immediate environs as compared to generalized studies, which did not consider directly local settings.
Initial Study on Physiochemical Property and Antibacterial Activity against Skin-Infecting Bacteria of Silver Nanoparticles Biologically Produced Using Crude Melanin from Xylaria sp.

Silver nanoparticles (AgNPs) produced by biological methods are safer for biomedical applications. Melanins were initially reported to facilitate AgNPs synthesis. Our research found that the stromata of some Xylaria species contained significant amounts of melanins, which had strong antioxidant and anti-ultraviolet activities without toxicity toward human skin cells. This study reported the characteristics and antibacterial activities against skin-infecting bacteria (Staphylococcus aureus and Cutibacterium acnes) of AgNPs synthesized using crude melanin extracted from stromata of Xylaria sp. AgNPs were successfully synthesized by mixing the crude melanin solution with 0.1 M AgNO3 (25:1, v/v) and incubating for 3 h at 100 °C. The SEM found that the average size of the synthesized AgNPs was 18.85 ± 3.75 nm. The melanin-mediated AgNPs displayed significantly higher antibacterial activities against the tested acne-causing bacteria compared to the positive control (Erythromycin). Specifically, the melanin-mediated AgNPs inhibited 90% of S. aureus and C. acnes at 62.5 (µg/mL) and 15.625 (µg/mL), respectively, whereas it required erythromycin up to 4000 (µg/mL) to achieve the same activities. This research illustrated the feasibility of using crude melanin of Xylaria sp. for the direct synthesis of AgNPs and the potential use of the synthesized AgNPs for treating acne-causing bacteria (with further investigation needed).
Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models

The data explosion driven by advancements in genomic research, such as high-throughput sequencing techniques, is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in various fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning, since we expect a superhuman intelligence that explores beyond our knowledge to interpret the genome from deep learning. A powerful deep learning model should rely on the insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with proper deep learning-based architecture, and we remark on practical considerations of developing deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research and point out current challenges and potential research directions for future genomics applications. We believe the collaborative use of ever-growing diverse data and the fast iteration of deep learning models will continue to contribute to the future of genomics.
Genetic diversity of the Rhododendron subsection Ledum species in Sakhalin Island: data from chloroplast DNA markers variability

A high genetic variability was revealed in the populations of the Rhododendron subsection Ledum species complex in Sakhalin. In total, 94 individuals from natural populations of the four sympatric species of Labrador tea (R. tomentosum, R. subarcticum, R. tolmachevii and R. hypoleucum) were analyzed to evaluate the genetic variation in the territory of the Sakhalin Island and in two mainland populations. The haplotype diversity of five chloroplast intergenic spacers (trnH–psbA, petB–petD, 3’trnV (UAC)–ndhC, K2R–K707, atpB–rbcL) was quite high. There were 16 haplotypes (h1–h16) identified; h1, h2 and h4 are the most common, the rest were rare and no species-specific ones were found. Analysis of Molecular Variance (AMOVA) indicate that about 37 % of the genetic diversity is due to among population differences (FST = 0.37, p < 0.001). Molecular phylogenetic analysis based on chloroplast DNA data did not reveal a clear species structure, but revealed a trend towards the isolation of two genetic groups: including a group of samples from the southern and the middle parts of Sakhalin Island.
Migration and Crime in Ecuador

This paper evaluates the casual impact of migration on homicides rates for Ecuadorian provinces for the period 1999-2012. Our main measure of migration is the stock of asylum seekers. In order to address the endogeneity of the migration variable, we exploit the implementation of the Plan Colombia, which caused thousands of Colombian people to seek asylum in Ecuador. Results show a modest but significant crime-reducing effect of migration on homicides rates. In particular, we are able to conclude that an increase of the asylum seekers stock by about 2,000 people (per 100,000) lead to a reduction of about 1.37 murder per 100,000 people. These results are in line with the previous literature on developed countries which found no effect or little crimereducing effect (Bianchi et al., 2012, Bell and Machin, 2012, and Bell et al., 2013).
Embracing the Emotion in Emotional Intelligence Measurement: Insights from Emotion Theory and Research

Emotional intelligence (EI) has gained significant popularity as a scientific construct over the past three decades, yet its conceptualization and measurement still face limitations. Applied EI research often overlooks its components, treating it as a global characteristic, and there are few widely used performance-based tests for assessing ability EI. The present paper proposes avenues for advancing ability EI measurement by connecting the main EI components to models and theories from the emotion science literature and related fields. For emotion understanding and emotion recognition, we discuss the implications of basic emotion theory, dimensional models, and appraisal models of emotion for creating stimuli, scenarios, and response options. For the regulation and management of one’s own and others’ emotions, we discuss how the process model of emotion regulation and its extensions to interpersonal processes can inform the creation of situational judgment items. In addition, we emphasize the importance of incorporating context, cross-cultural variability, and attentional and motivational factors into future models and measures of ability EI. We hope this article will foster exchange among scholars in the fields of ability EI, basic emotion science, social cognition, and emotion regulation, leading to an enhanced understanding of the individual differences in successful emotional functioning and communication.
The extreme flood event of Lago Maggiore in September 1993

In September 1993, the Valais and Ticino regions of Switzerland were affected by extreme flooding triggered by heavy precipitation. The meteorological situation leading to this event is studied in the Twentieth Century Reanalysis (20CR) data set. A strong cut-off low development is found to be the driving synoptic-scale atmospheric circulation pattern. The agreement with previous studies highlights the applicability of 20CR for extreme event analysis.
The geometry of synchronization: quantifying the coupling direction of physiological signals of stress between individuals using inter-system recurrence networks

In the study of synchronization dynamics between interacting systems, several techniques are available to estimate coupling strength and coupling direction. Currently, there is no general ‘best’ method that will perform well in most contexts. Inter-system recurrence networks (IRN) combine auto-recurrence and cross-recurrence matrices to create a graph that represents interacting networks. The method is appealing because it is based on cross-recurrence quantification analysis, a well-developed method for studying synchronization between 2 systems, which can be expanded in the IRN framework to include N > 2 interacting networks. In this study we examine whether IRN can be used to analyze coupling dynamics between physiological variables (acceleration, blood volume pressure, electrodermal activity, heart rate and skin temperature) observed in a client in residential care with severe to profound intellectual disabilities (SPID) and their professional caregiver. Based on the cross-clustering coefficients of the IRN conclusions about the coupling direction (client or caregiver drives the interaction) can be drawn, however, deciding between bi-directional coupling or no coupling remains a challenge. Constructing the full IRN, based on the multivariate time series of five coupled processes, reveals the existence of potential feedback loops. Further study is needed to be able to determine dynamics of coupling between the different layers.
Music production and its role in coalition signaling during foraging contexts in a hunter-gatherer society

Music is a cultural activity universally present in all human societies. Several hypotheses have been formulated to understand the possible origins of music and the reasons for its emergence. Here, we test two hypotheses: (1) the coalition signaling hypothesis which posits that music could have emerged as a tool to signal cooperative intent and signal strength of alliances and (2) music as a strategy to deter potential predators. In addition, we further explore the link between tactile cues and the propensity of mothers to sing toward infants. For this, we investigated the singing behaviors of hunter-gatherer mothers during daily foraging trips among the Mbendjele BaYaka in the Republic of the Congo. Although singing is a significant component of their daily activities, such as when walking in the forest or collecting food sources, studies on human music production in hunter-gatherer societies are mostly conducted during their ritual ceremonies. In this study, we collected foraging and singing behavioral data of mothers by using focal follows of five BaYaka women during their foraging trips in the forest. In accordance with our predictions for the coalition signaling hypothesis, women were more likely to sing when present in large groups, especially when group members were less familiar. However, predictions of the predation deterrence hypothesis were not supported as the interaction between group size and distance from the village did not have a significant effect on the likelihood of singing. The latter may be due to limited variation in predation risk in the foraging areas, because of the intense bush meat trade, and hence, future studies should include foraging areas with higher densities of wild animals. Lastly, we found that mothers were more likely to sing when they were carrying infants compared to when infants were close, but carried by others, supporting the prediction that touch plays an important prerequisite role in musical interaction between the mother and child. Our study provides important insight into the role of music as a tool in displaying the intent between or within groups to strengthen potentially conflict-free alliances during joint foraging activities.
A Process Optimization Method of the Mini-LOCOS Field Plate Profile for Improving Electrical Characteristics of LDMOS Device

In this work, the effects of the mini-local oxidation of silicon (LOCOS) field plate’s bottom physical profile on the devices’ breakdown performance are analyzed through technology computer-aided design simulations. It is indicated that the “abrupt” bottom profile could certainly do with an optimization. This paper introduces an effective process improvement method by etching bias power adjustment and time reduction. The upgradation of the field plate physical profile has been proved by transmission electron microscope cross-section analysis. The angle for the bottom surface of mini-LOCOS field plate θ2 is improved from 11.9° to 12.6°, and the thickness ratio of Hup/Hbottom (field plate oxide thickness for the upper and bottom, respectively) is increased from 71.8% to 76.6%. Finally, the optimized laterally diffused metal oxide semiconductor devices have been fabricated, and both figure of merit curves and safe operation area curves are measured. The specific on-resistance Ron,sp could achieve as low as 11.3 mΩ mm2, while breakdown voltage BVds,max arrives at 37.4 V, which is nearly 19.3% improved.
IN SILICO MINING AND VALIDATION OF SIMPLE SEQUENCE REPEAT (SSR) MARKERS DERIVED FROM THE FRAGARIA X ANANASSA GENOME ASSEMBLY

Strawberry (Fragaria x ananassa Duchesne ex Rozier) is an economically important fruit crop grown commercially worldwide due to its known nutritional value. The demand for strawberries in the Philippines has been increasing but varietal development is slow due to the lack of genetic information on locally available germplasm. In addition, molecular marker resources for strawberries are limited. Here, we developed Simple Sequence Repeats (SSR) markers to characterize strawberries cultivated in tropical environments, particularly in the Philippines. In silico mining generated a total of 219,239 SSR markers, from which 160,025 unique markers were identified. Polymorphism scoring and the variation on the expected alleles resulting to the synthesis of the top 160 markers were validated using representative strawberry cultivars from the core collection. All 70 markers produced successful amplicons from all the genotypes, but only 67 markers displayed polymorphism. Using UPGMA, genetic similarities among the cultivars were calculated generating a dendrogram which categorized the strawberry cultivars into 3 groups. The grouping clearly showed the separation of the diploid Fragaria vesca from the octoploid F. x ananassa cultivars, considered as group 1. Furthermore, Groups 2 and 3 contained cultivars from different geographical origins (US, Japan, and North America). The eight novel markers developed and validated here can be added to the genetic marker resource for cultivar verification, assessment of genetic diversity, and most importantly, marker-assisted breeding.
Meta-Analysis of the Efficacy of Levosimendan in the Treatment of Severe Sepsis Complicated with Septic Cardiomyopathy.

INTRODUCTION Sepsis is a medical condition characterized by acute organ dysfunction and uncontrolled inflammation. Organ dysfunction in sepsis is the primary cause of mortality in patients with myocardial dysfunction. Levosimendan is a vasodilating and inotropic agent used in patients with acute heart failure and has resulted in decreased morbidity and mortality in these patients. Our main objective is to examine levosimendan's efficacy in treating severe sepsis complicated with septic cardiomyopathy. METHODS We systematically searched five databases, PubMed, Web of Science, Embase, Cochrane Library and BioMed Central, for articles and publications from their inception to 2023. Our study adopted the PICOS approach in identifying suitable publications during the systematic search. Inclusion criteria included randomized, controlled trials utilizing levosimendan in adult patients diagnosed with septic shock or severe sepsis. We excluded non-English publications and non-randomized controlled trials. The Newcastle-Ottawa scale (NOS) scale was used to assess the methodological quality, while the risk of bias was assessed through the Cochrane Risk of bias tool. All statistical analyses were performed using RevMan version 5.4. RESULTS Eight studies met the eligibility criteria and were included in the analysis. There was a statistically significant positive effect on cardiac input in patients treated with levosimendan compared to those treated with dobutamine (p < 0.001). Similarly, there were positive effects on left ventricular ejection fraction (LVEF) (p < 0.001) and left ventricular stroke work index (LVSWI) (p < 0.001). We observed a significant reduction in mortality (p < 0.01) and serum levels of lactic acid (p < 0.01). DISCUSSION Levosimendan is a calcium sensitizer associated with an influx of calcium ions and activation of ATP-dependent potassium channels that increases myocardial contractility contractions, enhances vasodilation and improves oxygen supply to the cells and tissues. CONCLUSION Levosimendan is highly efficacious and safe in the management of sepsis and sepsis-induced cardiomyopathy.
Progress of geological study in the Middle-Lower Yangtze River Valley metallogenic belt

The Middle-Lower Yangtze River Valley metallogenic belt is one of the most important metallogenic belts in China,which experienced a long history of mining exploration and utilization.Many geologists have carried out researchs on this area since the 1920s and have published hundreds of scientific papers and more than 50 books.According to statistics,there are 888 scientific papers published in Chinese Journals focusing on the Middle-Lower Yangtze River Valley metallogenic belt from 1959 to 2012,and 185 SCI papers were published from 1990 to 2012.The research about ore deposits and mineralization is nearly occupying half of the total number of the published papers.The amount of published papers about the Tongling ore district in the belt is the highest.The number of published papers about Middle-Lower Yangtze River Valley metallogenic belt formed two peaks in the early 1990s and 2010 ～ 2012 and still exhibit increasing trend,which shows that the Middle-Lower Yangtze River Valley metallogenic belt has been one of the hotspots for geological research in recent years.The researchers focus on four fields:(1) Mesozoic tectonic evolution and dynamic background of magmatism and mineralization,(2) magmatism and plutonic activity,(3) ore-forming system and evolution,(4) metallogenic potential.This edition contains 26 papers generally reflect the latest research progress of the Middle-Lower Yangtze River Valley metallogenic belt and its adjacent areas.Those papers report many new geological and petrological observations,elemental and isotopic geochemical data and high-precision U-Pb zircon age data etc.These documents offer important evidences for further understanding of the origin and evolvement of the Middle-Lower Yangtze River Valley metallogenic belt and also have important significance on prospecting in the area.
Convolutional neural network in rice disease recognition: accuracy, speed and lightweight

There are many rice diseases, which have very serious negative effects on rice growth and final yield. It is very important to identify the categories of rice diseases and control them. In the past, the identification of rice disease types was completely dependent on manual work, which required a high level of human experience. But the method often could not achieve the desired effect, and was difficult to popularize on a large scale. Convolutional neural networks are good at extracting localized features from input data, converting low-level shape and texture features into high-level semantic features. Models trained by convolutional neural network technology based on existing data can extract common features of data and make the framework have generalization ability. Applying ensemble learning or transfer learning techniques to convolutional neural network can further improve the performance of the model. In recent years, convolutional neural network technology has been applied to the automatic recognition of rice diseases, which reduces the manpower burden and ensures the accuracy of recognition. In this paper, the applications of convolutional neural network technology in rice disease recognition are summarized, and the fruitful achievements in rice disease recognition accuracy, speed, and mobile device deployment are described. This paper also elaborates on the lightweighting of convolutional neural networks for real-time applications as well as mobile deployments, and the various improvements in the dataset and model structure to enhance the model recognition performance.