Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in the Expression of MIF and Other Key Enzymes of Energy Metabolism in the Myocardia of Broiler Chickens with Ascites Syndrome

Simple Summary Ascites syndrome (AS), a nutritional, metabolic disease in broiler chickens, exerts serious effects on the economic efficiency of the broiler industry. To date, the pathogenesis of broiler ascites syndrome is still not well understood. In this study, we explored the metabolic function of the right ventricles of clinical ascitic broilers and healthy broilers (control) from the same flock. Our study showed the myocardial energy supply in ascitic broiler chickens occurs mainly through glycolysis and fatty acid metabolism, and the oxidation of the TCA cycle is blunted. These findings suggest that there is insufficient energy metabolism in the right hearts of broilers with ascites syndrome, causing a state of functional failure. These results may provide useful information for elucidating the pathogenesis of broiler ascites syndrome and may also provide a reference for future research on similar diseases (including pulmonary hypertension syndrome and heart failure) in humans and domestic animals. Abstract Ascites syndrome (AS) is a metabolic disease observed mainly in fast-growing broilers. The heart is one of the most important target organs of the disease. The goal of this study was to evaluate the metabolic function of the right ventricles in clinical ascitic broilers. HE staining was performed to observe histopathological changes in the right ventricle of the heart, while Western blotting was used to detect the protein expression levels of macrophage migration inhibitory factor (MIF) and phosphorylated AMP-activated protein kinase (p-AMPK), as well as other key enzymes of energy metabolic pathways (i.e., glycolytic pathway: HK2, PFK1, PFK2, and PKM2; the tricarboxylic acid cycle (TCA cycle) pathway: OGDH, IDH2, and CS; and the fatty acid oxidation pathway: CPT-1A and ACC) in myocardial tissue. The histopathological examination of the myocardia of ascitic broilers revealed disoriented myocardial cells in the myofibril structure and a large number of blood cells deposited in the intermyofibrillar vessels, suggesting right heart failure in ascitic broilers. The Western blotting analysis demonstrated significantly increased levels of MIF and p-AMPK in the myocardia of ascitic broilers compared to those of the control group (p < 0.05). Additionally, the protein expression of key enzymes was dramatically increased in the glycolytic and fatty acid oxidation pathways, while the protein expression of key enzymes in the TCA cycle pathway was decreased in the ascitic broiler group. These findings suggest enhanced glycolysis and fatty acid oxidation metabolism, and a diminished TCA cycle, in the myocardia of broiler chickens with ascites syndrome.
Addressing Treatment Outcomes and Substance Use Among Formerly Incarcerated Individuals Living with HIV

Incarcerated individuals, who are overrepresented among those infected with HIV, experience multiple barriers to optimal antiretroviral therapy (ART) adherence after incarceration. Substance use is highly prevalent among legal system-involved individuals and is commonly associated with suboptimal medication adherence. This manuscript describes the development and pilot test results of two interventions designed to improve ART adherence among formerly incarcerated individuals living with HIV (PLWH). Thirty participants were randomized and completed a multi-session ART adherence and risk reduction intervention or a brief ART adherence intervention. Participants were interviewed at baseline, 1-month, and 3-month follow-up. While past 30-day ART adherence improved in both groups, participants in the brief intervention group took a higher proportion of their ART medication. Multi-session intervention group participants experienced greater reductions in substance use and problematic experiences associated with drug use. ART medication adherence interventions can potentially improve treatment outcomes among PLWH who experience incarceration.
The Low Dose of Saccharomyces cerevisiae Is Beneficial for Rumen Fermentation (Both In Vivo and In Vitro) and the Growth Performance of Heat-Stressed Goats

This study aimed to investigate the effects of Saccharomyces cerevisiae on rumen fermentation and the growth performance of heat-stressed goats. The fermentation experiment was conducted using Saccharomyces cerevisiae added at 0‰ (HS1), 0.30‰ (SC1), 0.60‰ (SC2), and 1.20‰ (SC3) of the dry matter (DM) weight of the basal diet. The results showed that supplementing with 0.60‰ (SC2) could increase the pH, acetic acid to propionic acid ratio, the concentrations of ammonia nitrogen, total volatile fatty acids, acetic acid, propionic acid, butyric acid, and the degradability of DM, neutral detergent fiber, and acid detergent fiber in rumen fluids of heat-stressed goats. In the feeding experiment, twelve heat-stressed goats were assigned to a 4 × 4 Latin square experimental design, and the Saccharomyces cerevisiae supplement levels are similar to the fermentation experiment above. Similar effects on rumen fermentation and digestibility parameters were obtained with a supplement with 0.60‰ of Saccharomyces cerevisiae (SC2A) compared to the fermentation trial. Moreover, in the SC2A group, the DM intake and average daily gain also increased significantly compared with other groups. These results suggested that a low dose of Saccharomyces cerevisiae can still effectively improve the rumen fermentation and growth performance of heat-stressed goats.
Postpartum Depression after Maternal Isolation during the COVID-19 Pandemic: The MUMI-19 Study (Mothers Undergoing Mental Impact of COVID-19 Pandemic)

Background: The COVID-19 pandemic has shaken the world by imposing unprecedented health measures, including in the postpartum period. Objectives: We aim to assess the impact of maternal isolation in the immediate postpartum period on the rate of postpartum depression (PPD) in a tertiary center. Study Design: We conducted a prospective cohort study, between 22 April and 29 October 2020, using anonymous questionnaires on 265 participants (129 during lockdown and 135 outside). The Edinburgh Postnatal Depression Scale (EPDS) was used as screening for PPD. We used a univariate logistic regression model to analyze the association between risk factors and PPD. Results: There was no difference between the two groups for PPD assessed by an EPDS score >10.5 on day 30 and/or day 60 (23.1% vs. 29.3%, p = 0.661) but on day 3 it was higher (31% vs. 17.8%, p = 0.015) during the lockdown period and partners were more impacted psychologically (48.3% vs. 10.5%, p < 0.001). Parity ≥1 was a protective factor for PPD (OR = 0.2, 95% CI [0.1–0.6], p = 0.003). Risk factors of PPD were: history of psychological abuses (OR = 6.4, CI 95% [1.1–37.6], p = 0.04), stressful life event (OR = 4.5, CI 95% [1.6–12.6], p = 0.004), and bad birth experience (OR = 5.1, CI 95% [1.4–17.8], p = 0.012). Conclusion: Maternal isolation in the immediate postpartum period is associated with an increased rate of moderate to severe symptoms of postpartum blues. The well-known long-term consequences of PPD must be balanced against the expected benefits of partner’s restrictive access to maternity ward.
A Natural Explanation for Magnetars

Neutron stars possess some of the strongest magnetic fields k nown in the universe. The surface magnetic fields of radio pulsars are estimated to be in the range 10 8 to 10 13 Gauss, with 10 12 Gauss being the typical value. Magnetars, a class of neutron stars with even stronger magnetic fields, ∼ 10 15 Gauss, are believed to be “magnetically powered” stars, deriving most of their radiative luminosity at the cost of their magnetic fields. The origin of the strong magnetic fields of neutron stars, in p articular those of magnetars, has essentially been an open question for decades. In this paper we explore the possibility that a magnetar may owe its strong field to a magnetized core which, as indicated by certain equations of state, may form due to phase transitions at high density mediated by strong interaction within a suffi ciently massive neutron star. We argue that the field derived from such a core could explain several inferred evolutionary behaviors of magnetars.
Post‐fire restoration of Sphagnum bogs in the Tasmanian Wilderness World Heritage Area, Australia

Sphagnum bogs in Australia are small, with a limited distribution, but of high conservation value. They are restricted to cool, wet environments that are typically fire free and are poorly adapted to recover from fire disturbance, unlike most Australian flora. Increased fire activity due to anthropogenic climate change is threatening Sphagnum bogs. This increased threat has stimulated interest in their restoration. Compared with the northern hemisphere, there have been few studies of the ecology of Sphagnum restoration in the southern hemisphere. Here, we report on a field experiment in Tasmania, in an area burned by an extensive fire in 2016. We investigated the role of shade, fertilizer and transplants, factors demonstrated to be important in the restoration of burnt bogs on the Australian mainland. Treatments commenced three to 4 years after the fire. Overall, we found that fire‐damaged Sphagnum recovers very slowly, and that there was no recovery in severely burned areas. The addition of shade increased recovery of damaged Sphagnum, but fertilizer was harmful, even to healthy Sphagnum. Transplants in fire‐killed Sphagnum grew poorly in both moderately and severely burnt Sphagnum areas. Our findings support the use of shading in post‐fire Sphagnum recovery projects, although further work is required to determine the optimal approach and duration of providing shade.
Monads and extensive quantities

If T is a commutative monad on a cartesian closed category, then there exists a natural T-bilinear pairing from T(X) times the space of T(1)-valued functions on X ("integration"), as well as a natural T-bilinear action on T(X) by the space of these functions. These data together make the endofunctors T and "functions into T(1)" into a system of extensive/intensive quantities, in the sense of Lawvere. A natural monad map from T to a certain monad of distributions (in the sense of functional analysis (Schwartz)) arises from this integration.
Assessment of Nurses’ Knowledge and Confidence Regarding Tracheostomy Care in a Pediatric Long Term Care Hospital in Saudi Arabia

Background The incidence of tracheostomy insertion in pediatric patients has increased over the last few decades. Tracheostomized pediatric patients need daily, meticulous care by qualified nurses to minimize severe, avoidable complications. Adequately trained nurses facilitate patients’ stability, accelerate weaning from the ventilator, and reduce potential tracheostomy dislodgement. Methods A cross-sectional, retrospective cohort survey was conducted in September 2021, using an electronic version of a self-questionnaire, to assess nurses’ knowledge and comfort level regarding tracheostomy care of pediatric patients at the International Extended Care Center in Jeddah, Saudi Arabia. Statistical analysis of the accrued data was performed using the SPSS 21.0 software package and a P-value <0.05 calculated by t-Test, was considered significant. Results Among 43 nurses included in the study, 14 (32.6%) were very comfortable taking care of tracheostomized patients, 13 (30.2%) were comfortable, and 16 (37.2%) were uncomfortable. Regarding knowledge, three main aspects of tracheostomy care were correctly answered (%) by all the nurses: knowledge of routine tracheal care (55%), tracheal care skills (11.6%), and tracheal emergency care (2.3%). The study showed a significant positive correlation between nurses’ comfort level with tracheal care and academic degree, duration of pediatric experience, completion of more than one life support course, and attendance at the annual local tracheostomy care competency learning program (TCCLP; all P <0.05). Conclusion Deficits exist in nurses’ knowledge of tracheostomy care. Improved knowledge garnered through repetitive participation in tracheostomy competency programs and life support courses correlate with greater comfort and more than 5 years of pediatric experience. Nurses’ deficits in emergency care knowledge and skills should be addressed through a structured educational program and a simulation, hands-on based TCCLP course, irrespective of comfort level with tracheostomy care.
Hernia Causing Ureteral Obstruction With Hydronephrosis and Subsequent Urinary Tract Infection and Sepsis

Inguinal hernias are common anatomic defects, especially among men. Complications of inguinal hernias include incarceration, but incarcerated hernias rarely cause other disturbances. An 86-year-old man with a history of chronic kidney disease (stage IIIb) presented with recurrent urinary tract infections and acute kidney injury with sepsis. Physical examination revealed a right inguinal hernia, and non-contrast abdominal computed tomography revealed right ureteral obstruction and distal entrapment by the inguinal hernia, with hydronephrosis. The patient underwent right retrograde pyelography and ureteral stent placement, followed by laparoscopic inguinal herniorrhaphy with mesh, which restored renal function. Our case is unique among obstructive uropathies due to inguinal hernias because the distal ureter was entrapped within the bowel mesentery communicating between the peritoneal cavity and retroperitoneum. Both inguinal hernias and obstructive uropathy are common among elderly men. While the latter is often secondary to prostate malfunction, other causes of obstructive uropathy or hydronephrosis should be considered, especially if unilateral.
HPLC–MS2 Analysis of Chlorophylls in Green Teas Establishes Differences among Varieties

Green teas are nonfermented teas, the quality of which is measured by the green color. However, this category encompasses a high number of tea varieties that differ in cultivation and processing. For example, leaf or stem/bubble tea, plants cultivated under a light or shadow regime, powdered or unpowdered tea, etc. These variables determine the different qualities among green teas (Matcha, Sencha, Gyokuro, etc.) and consequently their different values on the market. Our purpose is to determine if these variables can exert an influence on the chlorophyll profile and to establish a characteristic profile for specific green teas. With such an aim, we analyzed the chlorophyll profiles of 6 different green tea varieties via HPLC-hr ESI/APCI–MS2 and identified up to 17 different chlorophyll compounds. For the first time, 132-hydroxy-chlorophylls, 132-hydroxy-pheophytins, and 151-hydroxy-lactone-pheophytins have been identified in green teas. Shadow teas (Matcha and Sencha) and light-regimen green teas can be statistically differentiated by the total chlorophyll content and the a/b ratio. However, only Matcha tea contains a higher proportion of chlorophylls a and b among the green tea varieties analyzed, justifying the higher quality and price of this variety. Other chlorophyll metabolites (pheophytins, pyropheophytins, and oxidized chlorophylls) are indicative of the various processing and storage conditions.
A new approach to short-term wind speed prediction: the prophet model

ABSTRACT With the increasing integration of wind power systems into conventional power systems, an accurate wind speed (WS) forecasting technique is essential for the reliable and stable operation of the power grid. The availability of wind energy increases with the correct WS estimation technique. Linear statistical methods such as the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model have been popular in recent times for short-term and very short-term forecasting of WS. However, studies are continuing the effect of forecast errors resulting from different disaggregated time series on the resulting WS forecast error. In this paper, we present a state-of-the-art machine learning model, the Prophet model, which has not been used in short-term wind forecasting before in the literature. The performance reliability and accuracy of the developed Prophet model’s results were compared to those of the SARIMA model. The success of forecasting time-series data was evaluated using Mean Absolute Error (MAE) and Mean Square Error (RMSE) results. The models’ reliability, accuracy, fit, and performance were evaluated using standardized residuals, autocorrelation function (ACF), and partial autocorrelation (PACF). The 7-day performance metrics of the Prophet model are 1.9, 2.5, 4.1, 3.8, 4.0, 8.4, and 2.1 for MAE, and 2.3, 2.9, 4.7, 5.0, 4.4, 8.6, and 2.5 for RMSE, respectively. The performance measures of the SARIMA model are 3.3, 4.1, 5.1, 4.8, 5.1, 1.8, and 5.4 for the MAE, and 4.2, 5.1, 6.2, 6.2, 5.7, 2.6, and 6.2 for the RMSE, respectively. The Prophet model gave higher accuracy than SARIMA for all other days except 1 day (06.01.2018). As a result, the Prophet model outperformed the SARIMA model in predicting WS measurements 24 h ahead using quarterly training data.
Enhancement of flux pinning and critical currents in YBa2Cu3O7−δ films by nanoscale iridium pretreatment of substrate surfaces

We have acquired positive results in a controlled study to investigate the effects of substrate surface modification on the growth-induced flux-pinning nanostructures in YBa2Cu3O7−δ (YBCO) films. Nanoscale iridium (Ir) particles were applied to single-crystal SrTiO3 substrate surfaces using dc-magnetron sputtering. Superconducting properties of YBCO films grown on the Ir-modified substrates, measured by transport and magneto-optical imaging, have shown substantial improvement in the critical current densities (Jc) at 77 K over those on untreated, control substrates. Results also show a nearly uniform enhancement of Jc over all orientations of magnetic field. Present results are found to be consistent with cross-sectional transmission electron microscopy investigations. Ultimately, the objective of this approach is to produce enhancements in the properties of coated conductors by a simple pretreatment of the substrate surface.
Efficient Estimation of Integrated Volatility Incorporating Trading Information

We consider a setting where market microstructure noise is a parametric function of trading information, possibly with a remaining noise component. Assuming that the remaining noise is Op(1/n), allowing irregular times and jumps, we show that we can estimate the parameters at rate n, and propose a volatility estimator which enjoys n convergence rate. Simulation studies show that our method performs well even with model misspecification and rounding. Empirical studies demonstrate the practical relevance and advantages of our method. Furthermore, we find that a simple model can account for a high percentage of the total variation in microstructure noise.
Different Strategies for Optimization Using the Quantum Adiabatic Algorithm

We present the results of a numerical study, with 20 qubits, of the performance of the Quantum Adiabatic Algorithm on randomly generated instances of MAX 2-SAT with a unique assignment that maximizes the number of satisfied clauses. The probability of obtaining this assignment at the end of the quantum evolution measures the success of the algorithm. Here we report three strategies which consistently increase the success probability for the hardest instances in our ensemble: decreasing the overall evolution time, initializing the system in excited states, and adding a random local Hamiltonian to the middle of the evolution.
Dissipativity Analysis of Delayed Recurrent Neural Networks via a Flexible Negative-Definiteness Determination Method

This brief is concerned with the issue of dissipativity analysis for delayed recurrent neural networks. First, a flexible negative-definiteness determination method is presented, which brings more flexibility and can further reduce the conservatism of some existing methods. Second, by employing the flexible negative-definiteness determination method and some integral inequalities, a tight upper bound of the Lyapunov-Krasovkii functional derivative can be derived. Then, a less conservative delay-dependent criterion is derived to guarantee delayed recurrent neural networks strictly dissipative. Finally, simulations are provided to confirm the superiority of our result.
Forecasting stock price movement direction by machine learning algorithm

Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous. The continuous development of powerful algorithms features in machine learning and artificial intelligence has opened a promising new direction. This study compares the predictive ability of three forecasting models, including support vector machine (SVM), artificial neural networks (ANN), and logistic regression. The data used is those of the stocks in the VN30 basket with a holding period of one day. With the rolling window method, this study got a highly predictive SVM with an average accuracy of 92.48%.
Advancing Reproducibility and Accountability of Unsupervised Machine Learning in Text Mining: Importance of Transparency in Reporting Preprocessing and Algorithm Selection

Machine learning (ML) enables the analysis of large datasets for pattern discovery. ML methods and the standards for their use have recently attracted increasing attention in organizational research; recent accounts have raised awareness of the importance of transparent ML reporting practices, especially considering the influence of preprocessing and algorithm choice on analytical results. However, efforts made thus far to advance the quality of ML research have failed to consider the special methodological requirements of unsupervised machine learning (UML) separate from the more common supervised machine learning (SML). We confronted these issues by studying a common organizational research dataset of unstructured text and discovered interpretability and representativeness trade-offs between combinations of preprocessing and UML algorithm choices that jeopardize research reproducibility, accountability, and transparency. We highlight the need for contextual justifications to address such issues and offer principles for assessing the contextual suitability of UML choices in research settings.
Collaborative Relay Radio Network Using Reconfigurable Intelligent Surface

In this paper, we have studied a model of a relay radio network system using Reconfigurable Intelligent Surface (RIS). Specifically, we used a relay network that uses RIS when there is an extra direct link from the Source (S) to the Destination (D). Next, an approximate closed-form expressions of the Outage Probability (OP) and Ergodic Capacity (EC) are considered. Based on the simulation results of OP and EC, the results show that our proposed system is more optimal than the system using supported RIS without direct link and the system without using RIS. In addition, changing the number of the RIS reflecting elements and the RIS’s location near (S) or (D) has a significant impact on the performance of the system. The analytical expression match the simulation results through the Monte Carlo simulation method. Furthermore, the simulation results of energy efficiency (EE) also show that when the target spectral efficiency (SE), Rth, is high (more than 5.45 b/s/Hz), the system using supported RIS with direct link will help reduce the transmit power and optimize the most energy compared to the other two systems.