Miracle-dz/UniFAR-FTUs huggingface dataset:
Emerging roles of fibroblast growth factor 21 in critical disease

In spite of the great progress in the management of critical diseases in recent years, its associated prevalence and mortality of multiple organ failure still remain high. As an endocrine hormone, fibroblast growth factor 21 (FGF21) functions to maintain homeostasis in the whole body. Recent studies have proved that FGF21 has promising potential effects in critical diseases. FGF21 has also been found to have a close relationship with the progression of critical diseases and has a great predictive function for organ failure. The level of FGF21 was elevated in both mouse models and human patients with sepsis or other critical illnesses. Moreover, it is a promising biomarker and has certain therapeutic roles in some critical diseases. We focus on the emerging roles of FGF21 and its potential effects in critical diseases including acute lung injury/acute respiratory distress syndrome (ALI/ARDS), acute myocardial injury (AMI), acute kidney injury (AKI), sepsis, and liver failure in this review. FGF21 has high application value and is worth further studying. Focusing on FGF21 may provide a new perspective for the management of the critical diseases.
The rational design of iron-sulfur cluster binding site for prolonged stability in magnetoreceptor MagR

Iron-sulfur proteins play essential roles in a wide variety of cellular processes such as respiration, photosynthesis, nitrogen fixation and magnetoreception. The stability of iron-sulfur clusters varies significantly between anaerobic and aerobic conditions due to their intrinsic sensitivity to oxygen. Iron-sulfur proteins are well suited to various practical applications as molecular redox sensors or molecular “wires” for electron transfer. Various technologies have been developed recently using one particular iron-sulfur protein, MagR, as a magnetic tag. However, the limited protein stability and low magnetic sensitivity of MagR hindered its wide application. Here in this study, the iron-sulfur binding site of pigeon clMagR was rationally re-designed. One such mutation, T57C in pigeon MagR, showed improved iron-sulfur binding efficiency and higher iron content, as well as prolonged thermostability. Thus, clMagRT57C can serve as a prototype for further design of more stable and sensitive magnetic toolbox for magnetogenetics in the future.
Magnetic Field Oscillations Observed by Swarm Satellites in the Nightside Upper Ionosphere During Low‐Latitude Pi2 Pulsations

Pi2 oscillations (40–150 s) in the nightside upper ionosphere are studied using magnetic field data acquired by multiple Swarm spacecraft in low‐Earth orbit and at the low‐latitude Bohyun (BOH, L = 1.3) station on 22 October 2014. Four Pi2 events were identified from the BOH data near midnight (magnetic local time = 1.5 hr), while Swarm‐A, Swarm‐B, and Swarm‐C spacecraft were orbiting in the premidnight (magnetic local time = 21–22 hr) meridian from 70° to −60° in magnetic latitude at ∼450‐ to 500‐km altitudes. Unlike previous low‐Earth orbit studies, which used a single point observation, the latitudinal structure of the amplitude and phase of ionospheric magnetic field perturbations can be determined by simultaneous multipoint observations along the latitude at a constant radial distance. We observed that the horizontal H component of BOH data is well correlated with the compressional (Bz) component of ionospheric magnetic fields when Swarm spacecraft were at |magnetic latitude| < 30° with or without an accompanying ionospheric field perturbation in the radial (Bx) component, depending on the latitude of the spacecraft. It is found that the phase and amplitude relationship between Bx and Bz along the latitude is consistent with the model ionospheric field perturbations at 500‐km altitude, which are associated with a plasmaspheric resonance excited in a dipole numerical simulation. This indicates that the latitudinal variation of the ionospheric Pi2 pulsations in both Bx and Bz components is the consequence of the spatial mode structure in the north‐south direction of trapped fast mode waves inside the plasmasphere.
Study on the flame retardancy of nano-Sb2O3/BPS-PBT composites

To improve the flame retardancy of polybutylene terephthalate (PBT), PBT-based flame retardant composites containing antimony trioxide nanoparticles (nano-Sb2O3) and brominated polystyrene (BPS) were investigated. Nano-Sb2O3, BPS, and PBT were dispersed by ball milling method to obtain composite powders, and the nano-Sb2O3/BPS-PBT samples were prepared by melt blending and injection molding methods. The flame retardancy of nano-Sb2O3/BPS-PBT composites was investigated. The results showed that nano-Sbs2O3 can obviously improve the flame retardancy of PBT-based composites. When the nano-Sb2O3/BPS-PBT composite contains nano-Sb2O3 with 5 wt% of mass fraction and BPS with 10 wt% of mass fraction, the nano-Sb2O3/BPS-PBT composite has excellent flame retardancy, in which the UL94 degree of flame retardancy achieves V-0 grade and the limit oxygen index is 28.3%.
Values and attitude certainty: The case for attitude clarity and correctness

Three studies examined how the perception that one’s attitudes are based in values affects attitude clarity and correctness. Specifically, perceiving that one’s attitude is based in important values increases attitude clarity (the subjective sense that one knows one’s attitude) but not attitude correctness (the subjective sense that the attitude is correct). To test this, participants read a counterattitudinal message and were given feedback about the basis of their attitude. Relative to participants who learned that their attitudes were weakly based in values, participants who were told that their attitudes were strongly based in values reported greater attitude clarity than correctness (Study 1). Similarly, increases in attitude clarity from having an attitude based in values increased the perception that participants effortfully processed the message (Studies 2 and 3), the belief that participants more successfully resisted the message, and participants’ intentions to act on the attitude.
Vitamin E concentration in breast milk in different periods of lactation: Meta-analysis

Objective This study systematized information about vitamin E concentration in healthy breast milk during different stages of lactation in order to support the strategies of protecting postpartum women and infants. Methods Studies published before April 30th, 2021, which detected vitamin E concentration in breast milk of healthy women by High Performance Liquid Chromatography (HPLC) or Ultra High Performance Liquid Chromatographic (UHPLC), were evaluated. The databases of CNKI (Chinese), WanFang Data (Chinese), VIP (Chinese), PubMed, Cochrane Library, Web of Science and Embase were searched. The random effect models were used to conduct meta-analysis by the statistical software package Stata 14.0. Results In all 4,791 searched publications, 53 with full text were selected, which included 46 descriptive studies, 1 case-control study, 1 non-randomized controlled trial, and 5 randomized controlled trials. The pooled mean of vitamin E concentration was 10.57 mg α-TE/L (95%CI 8.94–12.20) in colostrum, 4.03 mg α-TE/L (95%CI 3.29–4.77) in transitional milk and 3.29 mg α-TE/L (95%CI 2.95–3.64) in mature milk. Subgroup analysis showed that vitamin E concentration of colostrum in Asian countries was lower than that in Western countries in colostrum and transitional milk. Conclusions Vitamin E concentration in breast milk decreased during lactation until the mature milk was produced. The vitamin E concentration of colostrum in Asian countries was evidently lower than that in Western countries. The vitamin E concentration in mature milk is similar in different regions. The concentration of vitamin E in breast milk started to be stable from about 2 to 3 weeks postpartum until 4 or 6 months postpartum, but it needs additional evidence to support.
Snow Consolidation Properties by using Mechanical Press Machine

Authors develop the high speed snow compressor (HSSC) to assist the snow disposal at aging and depopulation marginal society by reducing the disposal volume. The snow consolidation density by using square cross section pressure vessel and mechanical press machine was investigated for obtaining newly fundamental data. And a back pressure on inner lateral wall of the pressure vessel was investigated to confirm an occurrence of ununiform deformation of the snow or not. Some tests for different geometry conditions of H/W = 0.5, 1.1 and 1.7 were performed to investigate the geometry effects on the snow consolidation properties, respectively. Here parameter H and W show height and width of the pressure vessel, respectively. By the result, it was found that the snow was easy consolidated by lower axial formation pressure compared with cases by using hydraulic press machine. And the snow consolidation properties by using mechanical press machine were almost same for H/W = 0.5, 1.1 and 1.7. And occurrence of large impulsive force at bottom dead center was observed, but the excessive large force is not related to increase the snow compaction density was found quantitatively.
The effects of genre on the syntactic complexity of argumentative and expository writing by Chinese EFL learners

Genre researchers have found that writing in different genres involves different cognitive task loads and requires different linguistic demands. Generally speaking, narratives involve the description of events with a focus on people and their actions within a specific time frame, whereas non-narrative genres focus on making an argument or discussing ideas or beliefs in a logical fashion, thus resulting in distinct language features. However, the vast majority of genre-based studies have either focused on one single genre or made comparisons between narrative and non-narrative writing (mostly argumentative) in academic contexts without examining how EFL writers perform across non-narrative genres. Moreover, the measures used in quantifying the syntactic complexity of writing are varied, leading to inconsistent findings. This study investigated the effects of genre on the syntactic complexity of writing through comparing argumentative and expository compositions written by Chinese English-as-a-foreign-language (EFL) learners over one academic year. Fifty-two participants were asked to write eight compositions (with two genres alternated), four argumentative and four expository, which were parsed via the Syntactic Complexity Analyzer. The results with time as the within-subjects variable showed a significant development of syntactic complexity in both argumentative and expository compositions over one academic year. Meanwhile, the paired-sample t-test with genre as the within-subjects variable exhibited a higher syntactic complexity in argumentative compositions than in expository ones on most of the 14 measures examined at four time points over the year. Additionally, a two-way repeated measures analysis of variance with genre and time as independent variables ascertained an interactional effect of time and genre on some of the 14 measures. The present study tested and verified the impact genre exerts on the syntactic complexity of writing, providing implications for teachers to be more informed in teaching and assessing EFL writing and for students to be more conscious of genre difference in EFL writing.
How does corporate management affect green innovation via business environmental strategies?

Environmental issues are getting greater attention now that experts and authorities are paying attention to global warming. The industrial segment is mostly to blame for these environmental hitches, according to past research. The industrial sector is actively addressing the issues brought on by climate change. This study’s primary focus is on business environmental strategies in green innovation, which takes into account the company’s goals for sustainable development. This study also takes into account the importance of corporate management (CEO, ownership concentration, and gender diversity) for green innovation. This study evaluated fact by natural resource theory, resource dependency theory, agency theory and Porter hypothesis. Results from practical generalized least squares and generalized moments approaches provide various conclusions. The findings of this study demonstrated that companies with business environmental strategies as, environmental regulation, proactive environmental plans, corporate social responsibility, and board sustainable committees were more likely to implement green innovation practices. Additionally, corporate management (CEO, ownership concentration, and gender diversity) supports businesses’ efforts to innovate in the green sector. Importantly, our research showed that the importance of corporate management (CEO, ownership concentration, and gender diversity) in business environmental policies cannot be overstated (environmental regulations, proactive environmental strategies, corporate social responsibility, board sustainable committee, and green innovation). green innovation, as well. These findings significantly expand the scant amount of knowledge on corporate environmental initiatives and green innovation. In order to encourage green innovation for higher profitability while minimizing negative industrial consequences, this study also provides a number of suggestions and recommendations for stakeholders, including regulators, owners, and governments.
From conceptual model to conceptual framework: A sustainable business framework for community water supply businesses

Businesses must do more than safeguard their financial interests to survive in today’s market. Sustainability, or long-term viability, requires consideration of society, the environment, and the global community. However, community-based businesses cannot use some of the current components of the sustainable business framework because they were created for large corporations. Both quantitative and qualitative approaches are used in this study. First, quantitative methods were used to develop a conceptual model for the organization’s current needs using the PPT, the TOE, and an Expectation Confirmation Theory (ECT). A simple random sampling method was used to gather the data, with a sample size of 14 villages. Using a five-point Likert scale, the researcher gathered data from 2,584 households and collected 627 valid responses. After that, descriptive statistics were used to describe the data (frequency distributions, percentages, averages, medians, and standard deviations), and PLS-SEM was used to investigate the interactions between variables and launch the conceptual model using partial least squares (PLS) path modeling. First and foremost, qualitative through Interactive Qualitative Analysis (IQA). There are two ways to create a congested SID and an uncluttered SID: the Affinity Relationship Table (ART), the mapping of the Inter-Relationship Diagram (IRD), and the system influence diagram. According to the study, sustainable community water supply businesses are the primary driver. The conceptual framework presented in this paper is consistent with the results of the combination of quantitative and qualitative methods and the current constraints placed on community water supply businesses to thrive.
Moderate ethanol exposure during early ontogeny of the rat alters respiratory plasticity, ultrasonic distress vocalizations, increases brain catalase activity, and acetaldehyde-mediated ethanol intake

Early ontogeny of the rat (late gestation and postnatal first week) is a sensitive period to ethanol’s positive reinforcing effects and its detrimental effects on respiratory plasticity. Recent studies show that acetaldehyde, the first ethanol metabolite, plays a key role in the modulation of ethanol motivational effects. Ethanol brain metabolization into acetaldehyde via the catalase system appears critical in modulating ethanol positive reinforcing consequences. Catalase system activity peak levels occur early in the ontogeny. Yet, the role of ethanol-derived acetaldehyde during the late gestational period on respiration response, ultrasonic vocalizations (USVs), and ethanol intake during the first week of the rat remains poorly explored. In the present study, pregnant rats were given a subcutaneous injection of an acetaldehyde-sequestering agent (D-penicillamine, 50 mg/kg) or saline (0.9% NaCl), 30 min prior to an intragastric administration of ethanol (2.0 g/kg) or water (vehicle) on gestational days 17–20. Respiration rates (breaths/min) and apneic episodes in a whole-body plethysmograph were registered on postnatal days (PDs) 2 and 4, while simultaneously pups received milk or ethanol infusions for 40-min in an artificial lactation test. Each intake test was followed by a 5-min long USVs emission record. On PD 8, immediately after pups completed a 15-min ethanol intake test, brain samples were collected and kept frozen for catalase activity determination. Results indicated that a moderate experience with ethanol during the late gestational period disrupted breathing plasticity, increased ethanol intake, as well brain catalase activity. Animals postnatally exposed to ethanol increased their ethanol intake and exerted differential affective reactions on USVs and apneic episodes depending on whether the experience with ethanol occur prenatal or postnatally. Under the present experimental conditions, we failed to observe, a clear role of acetaldehyde mediating ethanol’s effects on respiratory plasticity or affective states, nevertheless gestational acetaldehyde was of crucial importance in determining subsequent ethanol intake affinity. As a whole, results emphasize the importance of considering the participation of acetaldehyde in fetal programming processes derived from a brief moderate ethanol experience early in development, which in turn, argues against “safe or harmless” ethanol levels of exposure.
Impacts of non-native fishes under a seasonal temperature gradient are forecasted using functional responses and abundances

Developing predictive methods to forecast the impacts of existing and emerging invasive species is of critical importance to biodiversity conservation. However, invader impacts are context-dependent, making reliable and robust predictions challenging. In particular, it is unclear how temporal variabilities in relation to temperature regime shifts influence invader ecological impacts. In the present study, we quantify the functional responses of three coexisting freshwater fishes: the native freshwater River Goby Glossogobiuscallidus, and the non-native Mozambique Tilapia Oreochromismossambicus and Western Mosquitofish Gambusiaaffinis, under two temperature treatments using chironomid larvae as prey. This was used along with fish abundance data to determine temporal differences in ecological impacts of each fish species between seasons (i.e. at two corresponding temperatures). All three fish species exhibited potentially population-destabilizing Type II functional responses. Their maximum feeding rates were consistently higher in the warm temperature treatment, whereas attack rates tended to be reduced. Non-native Mozambique Tilapia had the highest maximum feeding rate under both temperature treatments (18 °C and 25 °C), followed by the non-native Western Mosquitofish and lastly the native River Goby, suggesting greater per capita impacts on native prey by non-native fishes. The predatory fish abundances differed significantly according to season, with native River Goby and non-native Mozambique Tilapia generally more abundant than non-native Western Mosquitofish. By multiplying functional response maximum feeding rates with abundances of each fish species across the seasonal gradient, the relative impact potential of non-native Mozambique Tilapia was consistently higher compared to that of native gobies. Western Mosquitofish impacts were less apparent, owing to their low abundances. We demonstrate how seasonal temperature fluctuations affect the relative impact capacities of introduced species and the utility of consumer functional response and the relative impact potential metric in impact forecasting.
River Water Salinity Impact on Drinking Water Treatment Plant Performance Using Artificial neural network

The river water salinity is a major concern in many countries, and salinity can be expressed as total dissolved solids. So, the water salinity impact of the river is one of the major factors effects of water quality. Tigris river water salinity increase with streamline and time due to the decrease in the river flow and dam construction from neighboring countries. The major objective of this research to developed salinity model to study the change of salinity and its impact on the Al-Karkh, Sharq Dijla, Al-Karama, Al-Wathba, Al-Dora, and Al-Wihda water treatment plant along Tigris River in Baghdad city using artificial neural network model (ANN). The parameter used in a model built is (Turbidity, Ec, T.s, S.s, and TDS in) to predict the salinity TDSout. Results showed that the effectiveness of the artificial neural network model to predicting the salinity is a good agreement between observed and the predicted value of the TDS, through the determination coefficient of the model is (0.998, 0.966, 0.997, 0.998, 0.996, and 0.996) for Al. Karkh, Sharq Dijla, Al.Karama, Al.Wathba, Al.Dora and Al.Wihda respectively. From this value can be shown that ANN is a successful tool for predicting the nonlinear equation of the salinity under different and complicated environmental case along the river.
Supervised and Unsupervised Learning in Data Mining for Employment Prediction of Fresh Graduate Students

Data mining techniques are widely used in engineering, medicine, industry, agriculture and even used in education to predict a future situation. In this paper, the used of data mining techniques applied in features selection and determine the best model that can be used to predict the employment status of fresh graduate Public Institutions either employed or unemployed, six months after graduation. In CRISP-DM methodology, six phases were adopted. The algorithm in supervised and unsupervised learning; K-Nearest Neighbor, Naive Bayes, Decision Tree, Neural Network, Logistic Regression and Support Vector Machines were compared using the training data set from Tracer Study to determine the highest accuracy in turn is used as a predictive model. Rapid Miner as a data mining tool was used for data analysis algorithm