Miracle-dz/UniFAR-FTUs huggingface dataset:
Fetal Membranes Contribute to Drug Transport across the Feto-Maternal Interface Utilizing the Breast Cancer Resistance Protein (BCRP)

During pregnancy, the placenta is established as a primary organ for drug transport at the maternal-fetal interface. The fetal membranes (FM) also form an interface with maternal tissues; however, their role in drug transport has not been previously investigated. Knowledge of drug transport across this feto-maternal interface along with the placenta can improve new drug development and testing for use during pregnancy. We also hypothesize that extracellular vesicles (exosomes 30–160 nm) released from the FM and placental cells may also contain drug transport proteins and might impact drug trafficking across the feto-maternal interfaces. The objectives were to (1) localize the breast cancer resistance protein (BCRP) in human FM; (2) determine the drug transport function of BCRP in chorion trophoblast cells (CTCs) of the FM; and (3) investigate the presence of BCRP in FM cell-derived exosomes, as a paracrine modifier of the tissue environment for transport functions. The gene and protein expressions of ABCG2/BCRP in FMs were determined by quantitative real-time PCR (qRT-PCR) and western blotting (WB) and were localized by immunohistochemistry (IHC). The surface expression of BCRP in FM cells was determined by flow cytometry. The functional role of BCRP was assessed by an EFFLUX dye multidrug resistance assay. The presence of BCRP in exosomes derived from CTCs and BeWo cells was examined using ExoView®. Data derived from CTCs are compared with placental trophoblast cells (BeWo). BCRP is expressed and localized in the fetal membrane, primarily in the chorion trophoblast cell layer and scarcely in the amnion epithelial layer (AEC), and primarily localized on both AEC and CTC cell surfaces. Efflux assay data showed that FM cells have similar drug resistance activity as BeWo cells, suggesting that FM also have drug transportation capabilities. BeWo- and CTC-derived exosomes expressed limited BCRP protein on the surface, so it was predominantly contained in the exosomal lumen. As far as we are aware, this is the first study to report BCRP expression in fetal membrane cells and as cargo in fetal membrane-derived exosomes. We report that fetal membrane cells are capable of drug transportation. Based on these results, investigational drug trials should include the FM and its exosomes as possible drug transportation routes in pregnancy.
Changing Patterns of Marriage in Rana-Tharu Communities

Marriage is an institution of the society which leads to the establishment of family. The Rana-Tharus have their own pattern, culture, rituals, norms, and values regarding marriage. The main aim of the study was to analyse the changing patterns in the Rana-Tharu marriage system. The study employed the situational analysis design of the qualitative research. Semi-structured interview and observation were used as the tools for collecting the data. This study employed the situational analysis design of the qualitative research. Semi-structured interview and observation were used as the tools for collecting the data. For conducting this research, ten informants from Rana Tharu of Shukhalaphanta Municipality-11, Kalwapur villages, Kanchanpur were selected. Thematic analysis was used for the analysis of the data. From the analysed data, it was found that the cultures, rituals, norms that are practised in Rana-Tharu traditional marriage are changed in the present context of marriage. Only those rituals, norms are followed which reflect their cultural identity and some rituals are practised in modified forms. Some of the rituals which seem not so important and are highly costly and time consuming in present context have been abandoned.
Quantitative Real-Time Analysis of Differentially Expressed Genes in Peripheral Blood Samples of Hypertension Patients

Hypertension (HTN) is considered one of the most important and well-established reasons for cardiovascular abnormalities, strokes, and premature mortality globally. This study was designed to explore possible differentially expressed genes (DEGs) that contribute to the pathophysiology of hypertension. To identify the DEGs of HTN, we investigated 22 publicly available cDNA Affymetrix datasets using an integrated system-level framework. Gene Ontology (GO), pathway enrichment, and transcriptional factors were analyzed to reveal biological information. From 50 DEGs, we ranked 7 hypertension-related genes (p-value < 0.05): ADM, ANGPTL4, USP8, EDN, NFIL3, MSR1, and CEBPD. The enriched terms revealed significant functional roles of HIF-1-α transcription; endothelin; GPCR-binding ligand; and signaling pathways of EGF, PIk3, and ARF6. SP1 (66.7%), KLF7 (33.3%), and STAT1 (16.7%) are transcriptional factors associated with the regulatory mechanism. The expression profiles of these DEGs as verified by qPCR showed 3-times higher fold changes (2−ΔΔCt) in ADM, ANGPTL4, USP8, and EDN1 genes compared to control, while CEBPD, MSR1 and NFIL3 were downregulated. The aberrant expression of these genes is associated with the pathophysiological development and cardiovascular abnormalities. This study will help to modulate the therapeutic strategies of hypertension.
Using the Conservation Standards Framework to Address the Effects of Climate Change on Biodiversity and Ecosystem Services

Climate change has challenged biodiversity conservation practitioners and planners. In this paper, we provide scalable guidance on integrating climate change into conservation planning and adaptive management that results in the most appropriate conservation strategies. This integrated “Climate-Smart Conservation Practice” focuses on analyzing the potential impact of climate change on species, ecosystems, and ecosystem services, combined with “conventional” (non-climate) threats, and incorporating this knowledge into projects. The guidance is based on the already widely-used “Open Standards for the Practice of Conservation”, an application of systems thinking and adaptive management, which has been successfully applied to thousands of conservation projects. Our framework emphasizes a methodical analysis of climate change impacts for projects to support more productive goals and strategy development. We provide two case studies showing the applicability and flexibility of this framework. An initial key element is developing “situation models” that document both current and future threats affecting biodiversity while showing the interactions between climate and conventional threats. Guidance is also provided on how to design integrated, climate-smart goals and strategies, and detailed theories of change for selected strategies. The information and suggestions presented are intended to break down the steps to make the process more approachable, provide guidance to teams using climate change information within a systematic conservation planning process, and demonstrate how climate scientists can provide appropriate information to conservation planners.
Flash flood susceptibility mapping in urban area using genetic algorithm and ensemble method

Abstract Flooding is the main recurring natural disaster in Sungai Pinang catchment, Malaysia. Flash flood susceptibility mapping (FFSM) explains a key component of flood risk analysis and enables efficient estimation of the spatial extent of flood characteristics. The current study applied four machine learning models (i.e. Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)) ensembled with the Statistical Index (SI) to develop flash flood susceptibility mapping (FFSM). 110 flash flood locations in the Sungai Pinang catchment were used in this study. Genetic algorithm (GA) was combined with Fuzzy Unordered Rules Induction Algorithm (FURIA), Rotation Forest, and Random Subspace for the feature selection method (FSM). The results showed that GA-FURIA outperformed the other two models in terms of accuracy based on the FSM. Twelve flash flood variables were selected by GA-FURIA. The FFSM results showed that the SI-RF model has the highest area under the receiver operating characteristics (AUROC) curve of success rate (0.978), whereas the SI-XGB has the best AUROC in terms of validation rate (0.997). The findings suggest that the twelve ideal conditioning variables may be used to optimize FFSM development.
Amylolytic Enzyme in Rehydrated Corn and Sorghum Grain Silage in the Diet of Ears

Aimed of this work was to evaluate rehydrated corn and sorghum grains silages, with and without α-AMYLASE, on fermentation profile, nutritional value, digestion and metabolism on diets for sheep. Two experiments were conducted. In the first experiment 28 silos were divided into: 1- RSGS (rehydrated sorghum grain silage); 2- RSGS+A (rehydrated sorghum grain silage + amylase,); 3- RCGS (rehydrated corn grain silage); 4 RCGS+A (rehydrated corn kernel silage + amylase). In experiment II, 12 lambs were distributed in: RSGS; RSGS+A; RCGS; RCGS+A. In experiment I, there was an effect of grain x enzyme interaction for GL MN. The enzyme reduced the NFC content. In experiment II intake of DM kg/day was not affected by the starch content in the diet, with an average of 1.2 kg/day meaning that the type of grain and the enzyme had no influence on the intake of animals. There was an interaction for the intake of starch (kg/day), blood urea and N-NH 3. Lambs fed with RCGS+A had a higher concentration of ruminal ammonia. The use of enzymes improved the fermentation profile of the silages. RSGS can replace RCGS in sheep feed without modifying nutrient intake and digestibility.
Learning to Classify Open Intent via Soft Labeling and Manifold Mixup

Open intent classification is a practical yet challenging task in dialogue systems. Its objective is to accurately classify samples of known intents while at the same time detecting those of open (unknown) intents. Existing methods usually use outlier detection algorithms combined with K-class classifier to detect open intents, where K represents the class number of known intents. Different from them, in this paper, we consider another way without using outlier detection algorithms. Specifically, we directly train a (K+1)-class classifier for open intent classification, where the (K+1)-th class represents open intents. To address the challenge that training a (K+1)-class classifier with training samples of only K classes, we propose a deep model based on Soft Labeling and Manifold Mixup (SLMM). In our method, soft labeling is used to reshape the label distribution of the known intent samples, aiming at reducing model’s overconfident on known intents. Manifold mixup is used to generate pseudo samples for open intents, aiming at well optimizing the decision boundary of open intents. Experiments on four benchmark datasets demonstrate that our method outperforms previous methods and achieves state-of-the-art performance. All the code and data of this work can be obtained at.1
The Influence of Personality and Demographic Characteristics on Aggressive Driving Behaviors in Eastern Chinese Drivers

Purpose Road safety research is important due to the large number of road traffic fatalities globally. This study investigated the influences of age, driving experience and other covariates on aggressive driving behavior. Methods A cross-sectional survey was conducted in Yixing City, Wuxi City, Jiangsu Province, China. Regression analysis was applied to explore the influences of age and driving experience and their interactions with other covariates on aggressive driving behavior. Two analyses methodologies were used to assess the simple effect of the interactions. Firstly, the Jamovi automatic analysis classification program was used to calculate the simple slope test. Second, the SPSS macro program was also used to calculate the simple slope test also. Results A total of 570 drivers (247 males, 282 females) participated in the survey. A negative correlation was found between age and aggressive driving behaviors, and a positive correlation was found between neuroticism and aggressive driving behaviors in the multiple regression analysis. Significant associations were also found between age, driving experience, and depression, as well as age, driving experience, and neuroticism. Simple slope tests showed that depressive symptoms could increase aggressive behaviors in the elderly and experienced drivers. When experiencing neuroticism, individuals with higher driving experience were more aggressive in driving than shorter experienced drivers. Conclusion Age and neuroticism influenced aggressive driving behaviors. Veteran drivers could be aggressive drivers when experiencing depressive symptoms or neuroticism. Mobile intervention could be sent to the potentially risky drivers, which would be safe and broadly feasible to prevent aggressive driving behavior in the background of COVID-19.
A Hardware-in-the-Loop Test Platform for the Real-Time State Estimation of Active Distribution Networks using Phasor Measurement Units

The paper describes the development and the performance assessment of a Hardware-in-the-Loop (HIL) test platform built as a proof-of-concept of a sub-second State Estimator (SE) of Active Distribution Networks (ADNs). The SE relies on the availability of data coming from Phasor Measurement Units (PMUs). The paper firstly illustrates the architecture of the experimental setup, then, by using a SE process developed by the Authors, based on the use of Iterated Kalman Filter (IKF), presents the experimental assessment of the time latencies of the whole process together with the SE accuracy assessment.
Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network. The ability of the IoT to collect, analyze and mine data into information and knowledge motivates the integration of IoT with grid and cloud computing. New job scheduling techniques are crucial for the effective integration and management of IoT with grid computing as they provide optimal computational solutions. The computational grid is a modern technology that enables distributed computing to take advantage of a organization’s resources in order to handle complex computational problems. However, the scheduling process is considered an NP-hard problem due to the heterogeneity of resources and management systems in the IoT grid. This paper proposed a Greedy Firefly Algorithm (GFA) for jobs scheduling in the grid environment. In the proposed greedy firefly algorithm, a greedy method is utilized as a local search mechanism to enhance the rate of convergence and efficiency of schedules produced by the standard firefly algorithm. Several experiments were conducted using the GridSim toolkit to evaluate the proposed greedy firefly algorithm’s performance. The study measured several sizes of real grid computing workload traces, starting with lightweight traces with only 500 jobs, then typical with 3000 to 7000 jobs, and finally heavy load containing 8000 to 10,000 jobs. The experiment results revealed that the greedy firefly algorithm could insignificantly reduce the makespan makespan and execution times of the IoT grid scheduling process as compared to other evaluated scheduling methods. Furthermore, the proposed greedy firefly algorithm converges on large search spacefaster , making it suitable for large-scale IoT grid environments.
Solar Wind—Magnetosphere Coupling Functions: Pitfalls, Limitations, and Applications

Solar wind‐magnetosphere coupling functions have now been in use for almost 50 years. In that time, a very large number of formulations have been proposed. As they become increasingly subsumed into systems analysis and machine‐learning studies of the magnetosphere, it is timely to establish best practice in their derivation and study their limitations. This paper reports on a number of studies carried out to establish some key points. Particular attention is paid to the best metric used to evaluate their performance and how it depends on the application for which the coupling function is intended.
Energy density balance during shock wave implosion in water

Analytical modeling of the evolution of cylindrical and spherical shock waves (shocks) during an implosion in water is presented for an intermediate range of convergence radii. Up to now this range is determined only in experiments observations as a range of implosion radii which are already far from the piston influence, but yet not described by well-known self-similar solutions. The model is based on an analysis of the change in pressure and kinetic energy density, as well as on the corresponding fluxes of internal and kinetic energy densities behind the shock front. It shows that the spatial evolution of the shock velocity strongly depends on the initial compression, the adiabatic index of water, and the geometry of convergence. The model also explains the transition to a rapid like self-similar increase in the shock velocity at only a certain radius of the shock that is observed in experiments. The dependence of the threshold radius, where the shock implosion follows the power law (quasi self-similarity), on the initial compression is determined. It is stated that in the entire range of the shock radii the internal and kinetic energy density fluxes are equal, which is in agreement with known experimental data.
MDR Tuberculosis Treatment

Multidrug-resistant (MDR) tuberculosis (TB), resistant to isoniazid and rifampicin, continues to be one of the most important threats to controlling the TB epidemic. Over the last few years, there have been promising pharmacological advances in the paradigm of MDR TB treatment: new and repurposed drugs have shown excellent bactericidal and sterilizing activity against Mycobacterium tuberculosis and several all-oral short regimens to treat MDR TB have shown promising results. The purpose of this comprehensive review is to summarize the most important drugs currently used to treat MDR TB, the recommended regimens to treat MDR TB, and we also summarize new insights into the treatment of patients with MDR TB.
Stretching Method-Based Damage Detection Using Neural Networks

We present in this paper a framework for damage detection and localization using neural networks. The data we use to train the network are m×d pixel images consisting of measurements of the relative variations of m natural frequencies of the structure under monitoring over a period of d-days. To measure the relative variations of the natural frequencies, we use the stretching method, which allows us to obtain reliable measurements amidst fluctuations induced by environmental factors such as temperature variations. We show that even by monitoring a single natural frequency over a few days, accurate damage detection can be achieved. The accuracy for damage detection significantly improves when a small number of natural frequencies is monitored instead of a single one. More importantly, monitoring multiple natural frequencies allows for damage localization provided that the network can be trained for both healthy and damaged scenarios. This is feasible under the assumption that damage occurs at a finite number of damage-prone locations. Several results obtained with numerically simulated data illustrate the effectiveness of the proposed approach.
Comparative Assessment of Phytochemical Compounds and Antioxidant Properties of Kernel Oil from Eight Sour Cherry (Prunus cerasus L.) Cultivars

New plant oils as a potential natural source of nutraceutical compounds are still being sought. The main components of eight cultivars (‘Koral’, ‘Lucyna’, ‘Montmorency’, ‘Naumburger’, ‘Wanda’, ‘Wigor’, ‘Wołyńska’, and ‘Wróble’) of sour cherry (Prunus cerasus L.) grown in Poland, including crude fat, protein, and oil content, were evaluated. The extracted oils were analysed for chemical and biological activity. The oils had an average peroxide value of 1.49 mEq O2/kg, acid value of 1.20 mg KOH/g, a saponification value of 184 mg of KOH/g, and iodine value of 120 g I2/100 g of oil. The sour cherry oil contained linoleic (39.1–46.2%) and oleic (25.4–41.0%) acids as the major components with smaller concentrations of α-eleostearic acid (8.00–15.62%), palmitic acid (5.45–7.41%), and stearic acid (2.49–3.17%). The content of sterols and squalene varied significantly in all the studied cultivars and ranged between 336–973 mg/100 g and 66–102 mg/100 g of oil. The contents of total tocochromanols, polyphenols, and carotenoids were 119–164, 19.6–29.5, and 0.56–1.61 mg/100 g oil, respectively. The cultivar providing the highest amounts of oil and characterised by the highest content of PUFA (including linoleic acid), plant sterols, α-and β-tocopherol, as well as the highest total polyphenol and total carotenoids content was been found to be ‘Naumburger’. The antioxidant capacity of sour cherry kernel oils, measured using the DPPH• and ABTS•+ methods, ranged from 57.7 to 63.5 and from 38.2 to 43.2 mg trolox/100 g oil, respectively. The results of the present study provide important information about potential possibilities of application of Prunus cerasus kernel oils in cosmetic products and pharmaceuticals offering health benefits.
FORMULASI SNEDDS (SELF-NANOEMULSIFYING DRUG DELIVERY SYSTEM) UNTUK GAMAVUTON-0 DENGAN MENGGUNAKAN MINYAK NABATI

Gamavuton-0 (GVT-0) merupakan senyawa analog kurkumin dengan rumus molekul 1,5-bis(4'-hidroksi-3'-metoksifenil)-1,4-pentadien-3-on. GVT-0 memiliki aktivitas biologis sebagai antiinflamasi dan antioksidan dengan kestabilan dan potensi yang lebih baik daripada kurkumin. Kelemahan GVT-0 adalah kelarutannya yang rendah. Kelarutan yang rendah berakibat pada rendahnya ketersediaan hayati GVT-0. Salah satu cara meningkatkan ketersediaan hayati GVT-0 adalah formulasi GVT-0 dalam bentuk nanopartikel. Salah satu tipe nanopartikel yang dapat diaplikasikan untuk formulasi GVT-0 adalah nanoemulsi. Metode yang dipilih untuk menghasilkan nanoemulsi pada penelitian ini yaitu Self-Nanoemulsifying Drug Delivery System (SNEDDS). Metode ini dipilih karena mampu menjaga kestabilan nanoemulsi yang terbentuk. SNEDDS merupakan sistem penghantaran obat dengan memformulasikan obat, minyak, surfaktan, dan ko-surfaktan menjadi campuran isotropik yang mampu membawa obat lipofil dan mengemulsikannya ke dalam air sebagai tetesan berukuran kurang dari 100 nm. Nanoemulsi GVT-0 dikarakterisasi melalui pengamatan ukuran, potensial zeta, morfologi tetesan nanoemulsi, emulsification time, dan stabilitas nanoemulsi dalam akuades, artificial gastric fluid (AGF), dan dalam artificial intestinal fluid (AIF). Nanoemulsi GVT-0 diperoleh melalui formula SNEDDS yang terdiri dari 10% minyak zaitun, 67,5% tween 80, 22,5% PEG 400, dan 20 mg GVT-0/mL SNEDDS. Tetesan nanoemulsi GVT-0 memiliki ukuran rerata sebesar 30,37 Â± 4,10 nm, potensial zeta sebesar -23,24 mV. Emulsification time pada suhu 25oC selama 26,63 Â± 0,93 detik dalam akuades, 22,33 Â± 1,15 detik dalam AGF, dan 22,43 Â± 0,55 detik dalam AIF. Emulsification time pada suhu 37oC selama 8,67 Â± 0,12 detik dalam akuades, 9,53 Â± 0,31 detik dalam AGF, dan10,50 Â± 0,17 detik dalam AIF. Uji stabilitas menunjukkan nanoemulsi GVT-0 tetap stabil hingga 4 jam pada media akuades, AGF, dan AIF pada suhu 25oC dan 37oC.