Miracle-dz/UniFAR-FTUs huggingface dataset:
The Influence Of IF-Current Blockade On The Parameters Of Action Potential Among 1-Week And 20-Week-Old Rats

Nowadays the currents activated by hyperpolarization are the object of close attention and study among modern physiologists. This type of currents participates in slow diastolic depolarization and is considered the sinus-atrial node current. In literary sources, much is said about the fact that If-currents are present in atypical cardiomyocytes. But the information about the role of currents in functional cardiomyocytes during the generation of action potential is scarce. One of the common current blockers activated by hyperpolarization is ZD7288. The purpose of this work was to reveal the effect of ZD7288 in the concentrations of 10-9 10-5 M on the electrical activity of the heart among 1-week and 20-week-old rats. The experiments were carried out on a microelectrode device using a standard method of action potential shift using a glass microelectrode with the resistance of 40 80 MΩ. In the course of the work, they calculated such parameters of the action potential as the duration of the action potential at the repolarization level of 50%, 90%, and 100%. The results of experiments showed that ZD7288 in concentrations of 10-9 M and 10-8 M did not cause significant changes in action potential parameters. The remaining concentrations cause a significant increase of the action potential duration at the repolarization level of 50%, 90%, and 100%. Consequently, the blockade of currents activated by hyperpolarization causes the increase of the action potential time parameters.
Conversation Extraction from Event Logs

: Event logs are more and more considered for helping IT personnel understand system behaviour or performance. One way to get knowledge from event logs is by extracting conversations (a.k.a. sessions) through the recovering of event correlations. This paper proposes a highly parallel algorithm to retrieve conversations from event logs, without having any knowledge about the used correlation mechanisms. To make the event log exploration effective and efﬁcient, we devised an algorithm that covers an event log and builds the possible conversation sets w.r. t. the data found within the events. To limit the conversation set exploration and quicker recover good candidates, the algorithm is guided by an heuristic based upon the evaluation of invariants and conversation quality attributes. This heuristic also offers ﬂexibility to users, as the quality and invariants can be adapted to the system context. We report experimental results obtained from 6 case studies and show that our algorithm has the capability of recovering the expected conversation sets in reasonable time delays.
Trade openness and horizontal agricultural income inequality in Korea: focusing on sectoral income differences

Abstract In this study, we investigate the impact of agricultural trade openness on horizontal agricultural income inequality in South Korea during the period 1993–2018 using the Kuznets curve hypothesis of the openness-income inequality nexus and autoregressive distributed lag model. We find an inverted-U relationship between agricultural openness and horizontal agricultural income inequality. Further, our results indicate that financial development reduces income inequality within the sub-sectors of agriculture in the country. From these results, the inverted-U relationship between agricultural openness and income inequality should be monitored, and the agricultural sector should be provided with financial support to sustain domestic agricultural production to buffer price volatilities attributed to world agricultural supply shocks.
Optimization of Biomass-to-Bioenergy Logistics Network Design Problem: A Case Study

Abstract Increasing the emissions of greenhouse gases (GHG) due to fossil fuel consumption has led to problems such as global warming, climate change, loss of biodiversity, and urban pollutions. Bioethanol production especially from different biomass such as wheat straw has been specified as one of the sustainable solutions to deal with energy crisis. Bioethanol logistics network optimization will reduce total costs of supply chain management and improves its competency with fossil fuels. In this paper, a mixed-integer linear programming (MILP) model is proposed to integrate and optimize bioethanol logistics network design problem. The proposed model is a multi-period and multi-echelon including feedstock supply centers, collection centers, bio-refineries, and customer centers. The proposed model is applied in a real case in Iran. The results justify the applicability and performance of the model in efficient design of bioethanol logistics network problems.
Performance of Holstein cows subjected to different cooling sessions during subtropical summer

Objective This study aimed to determine the effect of different cooling sessions (CSs) as a water conservation strategy on physiological, and production responses and welfare in Holstein Friesian cows during subtropical summer in Pakistan. Methods Twenty-one cows were subjected to three CS in a completely randomized design. The treatments were: i) eleven hours continuous cooling with sprinklers - control (CNT), ii) four CS, and iii) two CS. The CNT represented the practices of the commercial dairy farms in the area, while the other CSs were used as water reduction strategies. Each CS lasted for 1 h with a 12 min cycle (3 min water on and 9 min off) with a sprinkler flow rate of 1.25 L/min. Results The average temperature humidity index of the shed and the outside open area were 81.9 and 82.5, respectively. The results showed that both physiological responses were highest in the 2CS group followed by the CNT and the 4CS (p = 0.001). The CNT and 4CS groups had similar milk yield (p = 0.040). The 4CS group had more lying and eating times than the CNT and 2CS groups (p = 0.000). The cortisol level in the 2CS group was 2.0 and 2.2 μg/dL more than the CNT and the 4CS groups, respectively (p = 0.000). Conclusion In conclusion, the 4CS was more efficient in cooling the cows and had better welfare, as it yielded similar milk yield, and better physiological responses than the CNT despite using 90% less water.
Introduction of brain computer interface to neurologists

A brain-computer interface (BCI) is a technology that acquires and analyzes electrical signals from the brain to control external devices. BCI technologies can generally be used to control a computer cursor, limb orthosis, or word processing. This technology can also be used as a neurological rehabilitation tool for people with poor motor control. We reviewed historical attempts and methods toward predicting arm movements using brain waves. In addition, representative studies of minimally invasive and noninvasive BCI were summarized.
A Scoring Map Algorithm for Automatically Detecting Structural Similarity of DOM Elements

: Most documents in the WWW are generated from templates that represent user interface (UI) elements, and later filled with contents. In the field of information extraction, many approaches emerged to analyze the documents’ structure, obtain similar features amongst them, and generate wrappers that are used to extract the raw contents from such documents. Therefore, most techniques documented in the literature are optimized to compare full documents, but there are other fields of applicability that require analyzing structural similarity on smaller UI components, like web augmentation or transcoding. In this paper we present two flexible algorithms to measure similarity between DOM Elements by using a mixed approach that considers both elements’ location and inner structure. The proposed algorithms were used in the context of two projects: an approach for automatic usability refactoring, and a web accessibility helper. We also present a wrapper induction technique based on such algorithms. Additionally, we present a precision & recall evaluation of our algorithms as compared with other known approaches, applied to DOM elements of different sizes, but smaller than full scaled documents. The proposed algorithms run in linear time, so they are faster than most approaches that analyze structural similarity.
Association between Serum 25-Hydroxyvitamin D Level and Stroke Risk: An Analysis Based on the National Health and Nutrition Examination Survey

Background To analyze the association between serum 25-hydroxyvitamin D level (25(OH)D) and stroke risk based on the National Health and Nutrition Examination Survey (NHANES). Methods Between 2007 and 2018, the baseline information of participants from NHNES was collected. Univariate analysis was used to identify the covariates. Multivariate logistic regression model was used to analyze the association between serum 25(OH)D level and the stroke risk. Results Of the 8,523 participants, there were 310 participants with stroke and 8,213 participants without stroke. The multivariate logistic analysis showed that serum 25(OH)D deficiency (odds ratio (OR): 1.993, 95% confidence intervals (CI): 1.141-3.481, and P = 0.012) was the significant risk factors for stroke. Subgroup analysis showed that non-Hispanic whites with serum 25(OH)D deficiency (OR: 2.501, 95% CI: 1.094-5.720, and P = 0.001) and insufficiency (OR: 1.853, 95% CI: 1.170-2.934, and P = 0.006) were associated with a higher risk of stroke than those with normal 25(OH)D levels. Conclusions Serum 25(OH)D deficiency may be associated with an increased risk of stroke.
Inverse Design of 2D Airfoils using Conditional Generative Models and Surrogate Log-Likelihoods

This paper shows how to use conditional generative models in 2D airfoil optimization to probabilistically predict good initialization points within the vicinity of the optima given the input boundary conditions, thus warm starting and accelerating further optimization. We accommodate the possibility of multiple optimal designs corresponding to the same input boundary condition and take this inversion ambiguity into account when designing our prediction framework. To this end, we first employ the conditional formulation of our previous work BezierGAN---Conditional BezierGAN (CBGAN)---as a baseline, then introduce its sibling conditional entropic BezierGAN (CEBGAN), which is based on optimal transport regularized with entropy. Compared with CBGAN, CEBGAN overcomes mode collapse plaguing conventional GANs, improves the average lift-drag (C_l/C_d) efficiency of airfoil predictions from 80.8% of the optimal value to 95.8%, and meanwhile accelerates the training process by 30.7%. Furthermore, we investigate the unique ability of CEBGAN to produce a log-likelihood lower bound that may help select generated samples of higher performance (e.g., aerodynamic performance). In addition, we provide insights into the performance differences between these two models with low-dimensional toy problems and visualizations. These results and the probabilistic formulation of this inverse problem justify the extension of our GAN-based inverse design paradigm to other inverse design problems or broader inverse problems.
Viaje al centro de la mitocondria: importación de proteínas, sus alteraciones y enfermedades relacionadas

Las mitocondrias son organelos fascinantes, no solo porque son el sitio en donde se genera la energía de las células, sino por su relevancia en procesos y patologías de interés médico. La gran mayoría de las proteínas que constituyen el proteoma mitocondrial están codificadas en el núcleo y se traducen por ribosomas citosólicos, por lo que deben ser identificadas correctamente para ser distribuidas e insertadas en cada uno de los subcompartimentos mitocondriales. En este artículo realizamos una descripción de las las maquinarias de importación mitocondrial, además de las diferentes respuestas celulares que contrarrestan las alteraciones en los procesos de transporte de las proteínas o cuando existe una disfunción mitocondrial. Finalmente, mencionamos las enfermedades causadas por mutaciones en los complejos transportadores y de distribución de las proteínas de este organelo, que se han identificado hasta la fecha.
Geographical origin traceability of foxtail millet based on the combination of multi-element and chemical composition analysis

ABSTRACT The potential approach of classifying foxtail millet according to geographical origin was investigated using mineral element and chemical composition analysis of samples from various provinces in China. Total 16 mineral elements and five chemical compositions of foxtail millets were analyzed. There were significant differences in 12 elements of millets from different regions. Notable differences were also observed for chemical composition, with Hebei samples showing higher protein content, Henan samples showing higher fat and ash contents and Shandong samples showed higher dietary fiber and amylose contents. Based on the combination of both methods, discriminant analysis provided optimal discrimination among the various geographical origins with a 95.2% classification rate. Our study provides an effective tool to trace the foxtail millet geographic origin through a combination of multi-element and chemical composition analysis.
A NLP Pipeline for the Automatic Extraction of Microorganisms Names from Microbiological Notes

According to the "Istituto Superiore di Sanita'" (ISS), hospital infections are the most frequent and serious complication of health care. This constitutes a real health emergency which requires incisive and joint action at all levels of the local and national health organization. Most of the valuable information related to the presence of a specific microorganism in the blood are written into the notes field of the laboratory exams results. The main objective of this work is to build a Natural Language Processing (NLP) pipeline for the automatic extraction of the names of microorganisms present in the clinical texts. A sample of 499 microbiological notes have been analysed with the developed system and all the microorganisms names have been extracted correctly, according to the labels given by the expert.
The reverse lip design: a design for safe and effective abdominoplasty

Conventional abdominoplasty includes the removal of an ellipse-shaped section of abdominal tissue between the umbilicus and mons pubis. However, this method can result in tension of the undermined flap, especially in the midline. To address this problem, we present reverse lip design as a modified method that also has aesthetic advantages. The reverse lip design entails a longer lower flap edge while preserving the triangular tissue in the vascularly stable pubis area. These markings create an image of a reverse lip shape with a cleft at the bottom of the lower markings. After typical lipoabdominoplasty is performed, redundant waist tissues can easily be pulled inward and downward. The reverse lip design abdominoplasty demonstrated no complications and required no further revisions after the procedure. Patients were generally satisfied with the aesthetic improvements in their body shape. They were also able to return to their routine activities approximately 1 week after the operation while wearing a supporting undergarment. This modified abdominoplasty using the reverse lip design reduces low midline tension of the undermined abdominal flap while enhancing body aesthetics with a slimmer waistline, leading to higher patient satisfaction.
Transcriptome and Metabolome Analyses Reveal Potential Salt Tolerance Mechanisms Contributing to Maintenance of Water Balance by the Halophytic Grass Puccinellia nuttalliana

Elevated soil salinity exacerbated by human activities and global climate change poses serious threats to plant survival. Although halophytes provide many important clues concerning salt tolerance in plants, some unanswered questions remain to be addressed, including the processes of water and solute transport regulation. We performed high-throughput RNA-sequencing in roots and metabolome characterizations in roots and leaves of Puccinellia nuttalliana halophytic grass subjected to 0 (control) and 150 mM NaCl. In RNAseq, a total of 31 Gb clean bases generated were de novo assembled into 941,894 transcripts. The PIP2;2 and HKT1;5 transcript levels increased in response to the NaCl treatment implying their roles in water and ion homeostasis. Several transcription factors, including WRKY39, DEK3, HY5, and ABF2, were also overexpressed in response to NaCl. The metabolomic analysis revealed that proline and dopamine significantly increased due to the upregulation of the pathway genes under salt stress, likely contributing to salt tolerance mechanisms. Several phosphatidylcholines significantly increased in roots suggesting that the alterations of membrane lipid composition may be an important strategy in P. nuttalliana for maintaining cellular homeostasis and membrane integrity under salt stress. In leaves, the TCA cycle was enriched suggesting enhanced energy metabolism to cope with salt stress. Other features contributing to the ability of P. nuttalliana to survive under high salinity conditions include salt secretion by the salt glands and enhanced cell wall lignification of the root cells. While most of the reported transcriptomic, metabolomics, and structural alterations may have consequences to water balance maintenance by plants under salinity stress, the key processes that need to be further addressed include the role of the changes in the aquaporin gene expression profiles in the earlier reported enhancement of the aquaporin-mediated root water transport.
Downscaling Building Energy Consumption Carbon Emissions by Machine Learning

The rapid rate of urbanization is causing increasing annual urban energy usage, drastic energy shortages, and pollution. Building operational energy consumption carbon emissions (BECCE) account for a substantial proportion of greenhouse gas emissions, crucially influencing global warming and the sustainability of urban socioeconomic development. As a foundation of building energy conservation, determination of refined statistics of BECCE is attracting increasing attention. However, reliable and accurate representation of BECCE remains lacking. This study proposed an innovative downscaling method to generate a gridded BECCE intensity benchmark dataset with 1 km2 spatial resolution. First, we calculated BECCE at the provincial level by energy balance table application. Second, on the basis of building climate demarcation, partial least squares regression models were used to establish the BECCE behavior equations for three climate regions. Third, Cubist regression models were built, retrieving down scale at the prefecture level to 1 km2 BECCE, which well-captured the complex relationships between BECCE and multisource covariates (i.e., gross domestic product, population, ground surface temperature, heating degree days, and cooling degree days). The downscaled product was verified using anthropogenic heat flux mapping at the same resolution. In comparison with other published pixel-based datasets of building energy usage, the gridded BECCE intensity map produced in this study showed good agreement and high spatial heterogeneity. This new BECCE intensity dataset could serve as a fundamental database for studies on building energy conservation and forecast carbon emissions, and could support decision makers in developing strategies for realizing the CO2 emission peak and carbon neutralization.
MEDIS: Analysis Methodology for Data with Multiple Complexities

: Hidden and unexpected value can be found in the vast amounts of data generated by IoT devices and industrial sensors. Extracting this knowledge can help on more complex tasks such as predictive maintenance or remaining useful time prediction. Manually inspecting the data is a slow, expensive, and highly subjective task that made automated solutions very popular. However, ﬁnding the value inside Big Data is a difﬁcult task with many complexities. We present a general preprocessing methodology (MEDIS- ME thdology for preprocessing D ata with multiple complexit I e S ) consisting of a set of techniques and approaches which address such complexities.
Hypoglycemic Effect of Zingiber Zerumbet Ethanolic Extracts and Channa Striata Powder in Alloxan-Induced Diabetic Rats

The ethanolic extracts of the Zingiber zerumbet rhizome contains flavonoid and zerumbon compounds, the fish meat powder from Channa striata has an albumin content. The active compounds was contained in both materials had activity that can lower blood sugar levels in diabetic rats. This study aims to determine the hypoglycemic effect of each ingredients and the combination of Z. zerumbet rhizome extract and C. striata powder in alloxan-induced diabetic rats. This research uses pre and post test method with the control group design. The treatment was consisted of 5 groups, the first group was given aquades (as negative control), the second group was given glibenclamide with dose 5 mg/kgBW (as positive control), the third group was given 300 kg/kgBW of Z. zerumbet rhizome extract, the fourth group was given of C. striata fish powder 300 mg/kgBW, and the last group was given a combination materials with doses respectively, 300 mg/kgBW. The ingredient was given for 12 days and blood was taken on days 3, 6, and 12. The results shows the percentage of decrease in blood sugar of combination materials of 70.4% is greater than the single content of Z. zerumbet and C. striata, with the value of successive activity of 62.9% and 34.4%, respectively. The antidiabetic activity of combinations of materials had produced not different significant (p> 0.05) compared with single of C. striata powder as well as the Z. zerumbet rhizome extract.
Consumer willingness to pay for cheese with a social sustainability attribute

ABSTRACT: The social dimension of sustainability is becoming very relevant on the consumer purchasing decision, especially in the food sector. This research analyzed the willingness to pay (WTP) of Chilean consumers for cheese with a social sustainability attribute using a double-bounded dichotomous choice format. Results showed that the WTP for a price premium depends on three variables: age, income and previous knowledge regarding social sustainability. The mean WTP is 7.5% over the average price of cheese; although, combining relevant variables and changing values over their range shows that firms can obtain up to a 12.9% price premium if they focus on younger and high income consumers that have a previous good knowledge about social sustainability practices. Additionally, this study presented a novel market oriented methodological approach for identifying and quantifying specific niche markets based on the WTP.
Stock price prediction via image-based DCNN with CBAM

Stock price prediction has always been an issue full of challenges due to its complex volatile nature. Inspired by the observation that the traders prefer judging the stock trends from the charts, and have concluded useful techniques for predicting stock trends. In this paper, we try to predict stock price trends based on the stock images by using Deep Convolutional Neural Networks (DCNN), and mainly focus on how to generate the stock images and label the image to train the DCNN model. Specifically, on one hand, we transfer the stock time series data to 5 types of stock images, trying to explore effective visual representation for stock data. On the other hand, we label the images in a more meaningful way to train the attention-based DCNN model to build a practical model. Experimental results on the S&P 500 stocks show that the MACD image performs better than other types, and it is hard to predict the stocks’ next day trend. However, the convolutional block attention module (CBAM) can improve the performance of DCNN.