Miracle-dz/UniFAR-FTUs huggingface dataset:
Prognoza: Parkinson’s Disease Prediction Using Classification Algorithms

Parkinson's Disease (PD) is a persistent neurological condition that has a global impact on a significant number of individuals. The timely detection of PD is imperative for the efficacious treatment and control of the condition. Machine learning (ML) methods have demonstrated significant potential in forecasting Parkinson's disease (PD) based on diverse data sources in recent times. The present research paper outlines a study that employs machine learning [ML]techniques to predict Parkinson's disease. A dataset comprising clinical and demographic characteristics of both patients diagnosed with PD and healthy individuals was taken from Kaggle. The aforementioned dataset was utilized to train and assess multiple machine learning models. The experimental findings indicate that the CatBoost model exhibited superior performance compared to the other models, achieving an accuracy rate of 95.1% and a root mean squared error of of 0.34. In summary, our research showcases the capabilities of machine learning methodologies in forecasting Parkinson's disease and offers valuable insights into the crucial predictors for PD prognosis. The results of our study could potentially contribute to the advancement of diagnostic methods for the timely identification of PD, with increased precision and efficacy.
The Secrets of Secretion: Protein Transport in Cells

Secretion is a fundamental process in which cells release substances to their external environments. Secretion is essential for many body functions, including growth, digestion, and cell communication. To be secreted, proteins must pass through various stations inside cells, which together form what is called the secretory pathway. In this article, I will tell you about the secretory pathway and the stages that proteins go through from their production up until their secretion. Then, I will present the original work that we did in our lab to identify genes related to the secretory pathway in yeast cells. Finally, I will explain the importance of our work to the overall study of the secretory pathway, both in yeast and mammalian cells.
Prescribed herbivory to reduce fuel load in California chaparral

prescribed herbivory to reduce fuel load in california chaparral book by proquest are a good way to achieve details about operating certainproducts. Many products that you buy can be obtained using instruction manuals. These user guides are clearlybuilt to give step-by-step information about how you ought to go ahead in operating certain equipments. Ahandbook is really a user's guide to operating the equipments. Should you loose your best guide or even the productwould not provide an instructions, you can easily obtain one on the net. You can search for the manual of yourchoice online. Here, it is possible to work with google to browse through the available user guide and find the mainone you'll need. On the net, you'll be able to discover the manual that you might want with great ease andsimplicity
Design of solar powered high step up non-isolated converter with low switching stress.

This article proposes a design of solar powered high step up non-isolated converter with low switching stress. The proposed circuit is designed by merging the traditional switched-inductor (SI) boost converter with a boosting cell. The design of new non-isolated boost DC-DC converter is made to control the DC output of the renewable systems particularly for solar. The proposed converter has various benefits such as providing a high efficiency, simplified control, low voltage stress on the active switches and high step-up voltage gain, which makes it suitable for solar energy applications. Additionally, the suggested configuration enables the shared ground connection between supply and load. The performance of the modified non-isolated converter is evaluated under study state conditions. Operation principles of the suggested topology are shown in detail. To confirm the effectiveness, the theoretical analysis of proposed topology is verified by simulation result. The converter achieved 96% efficiency for the proposed design.
Immunomodulatory Activity of Polysaccharides Isolated from Saussurea salicifolia L. and Saussurea frolovii Ledeb

The genus Saussurea has been used in the preparation of therapies for a number of medical problems, yet not much is known about the therapeutic high-molecular-weight compounds present in extracts from these plants. Since polysaccharides are important in immune modulation, we investigated the chemical composition and immunomodulatory activity of Saussurea salicifolia L. and Saussurea frolovii Ledeb polysaccharides. Water-soluble polysaccharides from the aerial parts of these plants were extracted using water at pHs of 2 and 6 and subsequently precipitated in ethanol to obtain fractions SSP2 and SSP6 from S. salicifolia and fractions SSF2 and SSF6 from S. frolovii. The molecular weights of fractions SSP2, SSP6, SFP2, and SFP6 were estimated to be 143.7, 113.2, 75.3, and 64.3 kDa, respectively. The polysaccharides from S. frolovii contained xylose (67.1–71.7%) and glucose (28.3–32.9%), whereas the polysaccharides from S. frolovii contained xylose (63.1–76.7%), glucose (11.8–19.2%), galactose (4.7–8.3%), and rhamnose (6.8–9.4%). Fractions SSP2, SSP6, and SFP2 stimulated nitric oxide (NO) production by murine macrophages, and NO production induced by SSP2, SSP6, and SFP2 was not inhibited by polymyxin B treatment of the fractions, whereaspolymyxin B treatment diminished the effects of SFP6, suggesting that SFP6 could contain lipopolysaccharide (LPS). The LPS-free fractions SSP2, SSP6, and SFP2 had potent immunomodulatory activity, induced NO production, and activated transcription factors NF-κB/AP-1 in human monocytic THP-1 cells and cytokine production by human MonoMac-6 monocytic cells, including interleukin (IL)-1α, IL-1β, IL-6, granulocyte macrophage colony-stimulating factor (GM-CSF), interferon-γ, monocyte chemotactic protein 1 (MCP-1), and tumor necrosis factor (TNF). These data suggest that at least part of the beneficial therapeutic effects reported for water extracts of the Saussurea species are due to the modulation of leukocyte functions by polysaccharides.
COVID-19: Associação da classificação de risco com o Modified Early Warning Score e os desfechos hospitalares

Objetivo: avaliar a associação das categorias de classificação de risco com o Modified Early Warning Score e os desfechos dos pacientes com COVID-19 no serviço de emergência Método: estudo transversal, realizado com 372 pacientes internados com diagnóstico de COVID-19 atendidos no Acolhimento com Classificação de Risco no Pronto-Atendimento. Neste estudo, o Modified Early Warning Score dos pacientes foi categorizado em sem e com deterioração clínica, de 0 a 4 e de 5 a 9, respectivamente. Foram consideradas deteriorações clínicas a insuficiência respiratória aguda, choque e parada cardiorrespiratória. Resultados: o Modified Early Warning Score médio foi de 3,34. Em relação à deterioração clínica dos pacientes, observou-se que em 43% o tempo para deterioração foi menor de 24 horas e que 65,9% delas ocorreu no pronto-socorro. A deterioração mais frequente foi a insuficiência respiratória aguda (69,9%) e o desfecho foi o de alta hospitalar (70,3%). Conclusão: pacientes com COVID-19 que tiveram Modified Early Warning Score 4 foram associados às categorias da classificação de risco urgente, muito urgente e emergente e tiveram mais deterioração clínica, como a insuficiência respiratória e o choque, e evoluíram mais a óbito, o que demonstra que o Protocolo de Classificação de Risco priorizou corretamente os pacientes com risco de vida.
Enhancing the Photocatalytic Performance of BiVO4 for Micropollutant Degradation by Fe and Ag Photomodification

Wider application of BiVO4 (BVO) for photocatalytic water treatment is primarily limited by its modest photocatalytic effectiveness, despite its appropriately narrow band gap for low-cost, sunlight-facilitated water treatment processes. In this study, we have photomodified an isotype BVO, consisting of a tetragonal zircon and monoclinic scheelite phase, with Fe (Fe@BVO) and Ag (Ag@BVO) ionic precursors under UV illumination in an aqueous ethanol solution in order to assess their effect on the opto-electronic properties and effectiveness for the removal of ciprofloxacin (CIP). Fe@BVO failed to demonstrate enhanced effectiveness over pristine BVO, whereas all Ag@BVO achieved improved CIP degradation, especially 1% Ag@BVO. At pH 4 and 6, 1% Ag@BVO demonstrated nearly 24% greater removal of CIP than BVO alone. Photomodification with Fe created surface oxygen vacancies, as confirmed by XPS and Mott–Schottky analysis, which facilitated improved electron mobility, although no distinct Fe-containing phase nor Fe-doping was detected. On the other hand, the introduction of mid-band gap states by oxygen vacancies decreased the reducing power of the photogenerated electrons as the flat band potentials were shifted to more positive values, thus likely negatively impacting superoxide formation. In contrast, Ag-photomodification (Ag@BVO) resulted in the formation of Ag2O/AgO and Ag nanoparticles on the surface of BVO, which, under illumination, generated hot electrons by surface plasmon resonance and enhanced the mobility of photogenerated electrons. Our research underscores the pivotal role of photogenerated electrons for CIP degradation by BiVO4-based materials and emphasizes the importance of appropriate band-edge engineering for optimizing contaminant degradation.
Power Quality Enhancement of Remote Gas Field Generations with Smart Power Converters

Direct power generation near gas fields offers numerous benefits, including optimized economic efficiency and reduced environmental impact. Moreover, building on-site greenhouses emerges as a promising approach to further minimize carbon emissions and residual heat, greatly promoting resource utilization. However, such power plants generally have access to a weak grid due to their remote locations, and they also contain nonlinear local loads, such as the grow lights in the greenhouses. Consequently, the generation system is susceptible to power quality issues, manifested in overvoltage and harmonics. To address these issues, a smart back-to-back converter is employed to interconnect the gas turbine generator and the utility grid. This smart converter not only enhances power quality but also offers potential ancillary services that contribute to the dynamics of the gas generation system, such as damping low-frequency oscillation among parallel-connected generators. In this paper, three control configurations for the back-to-back converter are developed, enabling the coordinated regulation of exported active power, AC voltage, and DC-link voltage in either a grid-following or grid-forming manner. Furthermore, comparative studies are conducted to provide guidelines for selecting an appropriate control strategy that ensures stable operation under various short circuit ratios. A practical gas cogeneration system is chosen to evaluate the performance of the back-to-back converter, and real-time simulations based on RT-LAB are carried out to validate the effectiveness of the methodology.
A model/solution‐adaptive explicit‐implicit time‐marching technique for wave propagation analysis

In this work, an explicit‐implicit time‐marching procedure with model/ solution‐adaptive time integration parameters is proposed for the analysis of hyperbolic models. The two time integrators of the methodology are locally evaluated, enabling their different spatial and temporal distributions. The first parameter defines the explicit/implicit subdomains of the model, and it is defined in a way that stability is always ensured, as well as period elongation errors are reduced; the second parameter controls the dissipative properties of the methodology, allowing spurious high‐frequency modes to be properly eliminated, rendering reduced amplitude decay errors. In addition, the proposed explicit‐implicit approach allows contracted systems of equations to be obtained, reducing the computational effort of the analysis. The main features of the novel methodology can be summarized as follows: (i) it is simple; (ii) it is locally defined; (iii) it has guaranteed stability; (iv) it is an efficient noniterative single‐step procedure; (v) it provides enhanced accuracy; (vi) it enables advanced controllable algorithmic dissipation in the higher modes; (vii) it considers a link between the temporal and the spatial discretization; (viii) it stands as a single‐solve framework based on reduced systems of equations; (ix) it is truly self‐starting; and (x) it is entirely automatic.
PbrChiA: a key chitinase of pear in response to Botryosphaeria dothidea infection by interacting with PbrLYK1b2 and down-regulating ROS accumulation

Abstract Pear ring rot, caused by the pathogenic fungi Botryosphaeria dothidea, seriously affects pear production. While the infection-induced reactive oxygen species (ROS) burst of infected plants limits the proliferation of B. dothidea during the early infection stage, high ROS levels can also contribute to their growth during the later necrotrophic infection stage. Therefore, it is important to understand how plants balance ROS levels and resistance to pathogenic B. dothidea during the later stage. In this study, we identified PbrChiA, a glycosyl hydrolases 18 (GH18) chitinase-encoding gene with high infection-induced expression, through a comparative transcriptome analysis. Artificial substitution, stable overexpression, and virus induced gene silencing (VIGS) experiments demonstrated that PbrChiA can positively regulate pear resistance as a secreted chitinase to break down B. dothidea mycelium in vitro and that overexpression of PbrChiA suppressed infection-induced ROS accumulation. Further analysis revealed that PbrChiA can bind to the ectodomain of PbrLYK1b2, and this interaction suppressed PbrLYK1b2-mediated chitin-induced ROS accumulation. Collectively, we propose that the combination of higher antifungal activity from abundant PbrChiA and lower ROS levels during later necrotrophic infection stage confer resistance of pear against B. dothidea.
What does it mean to follow? A critique of the followership literature and a conceptual model of the emergence of downward following

What does it mean to follow? In this paper, we systematically review the followership literature for the period 2017–2021. Our review shows that the followership literature suffers from three major issues that limit its validity. The followership field is dominated by a role-based approach equating direct reports with followers; empirical studies fail to study actual following behaviors, and there are no studies of downward following, which we define as any behavior or effort aimed at achieving a shared goal, carried out by an individual in a position of formal power who is influenced by one or more individuals in a position of inferior authority. Our manuscript builds on the process approach to study what it means to follow. We argue that the followership field needs to study actual followership behaviors at the micro “interaction episodes” and rely on quantitative behavioral coding. We then propose a conceptual, multi-level model that details antecedents and boundary conditions of the emergence of downward following. We conclude by discussing the organizational implications of our approach and model.
Abstracts from "Coastal Marsh Dieback in the Northern Gulf of Mexico: Extent, Causes, Consequences, and Remedies

Abstract : In the spring of 2000, scientists discovered a new and unprecedented loss of salt marsh vegetation in coastal Louisiana and other areas along the northern coast of the Gulf of Mexico. This dieback of salt marsh vegetation, sometimes called the brown marsh phenomenon', primarily involved the rapid browning and dieback of smooth cordgrass (Spanina alterniflora). Coastal Louisiana has already undergone huge, historical losses of coastal marsh due to both human-induced and natural factors, and the current overall rate of wetland loss (25-35 sq mi 65-91 SQ KM each year) stands to threaten Louisiana's coastal ecosystem, infrastructure, and economy. On January 11-12, 2001, individuals from Federal and State agencies, universities, and the private sector met at the conference 'Coastal Marsh Dieback in the Northern Gulf of Mexico: Extent, Causes, Consequences, and Remedies' to discuss and share information shout the marsh dieback. Presentations discussed trends in the progress of dieback during the summer of 2000 and in environmental conditions occurring at field study sites, possible causes including drought and Mississippi low flow' conditions, changes in soil conditions (salinity, the bioavailability of metals, pathogens, etc.), the potential for wetland loss that could occur if above and below normality occurs and is sustained over an extended period, advanced techniques for tracking the dieback via aerial photography and remote sensing, linkages of marsh hydrology to the dieback, and mechanisms of modeling dieback and recovery. In addition, presentations were made regarding development of a web site to facilitate information sharing and progress in preparation for requests for proposals based on an emergency appropriation by the U.S. Congress. All findings tended to support the idea that the dieback constituted a continuing environmental emergency and research and natural resource management efforts should be expended accordingly.
Novel echocardiographic markers for left ventricular filling pressure prediction in heart failure with preserved ejection fraction (ECHO-PREDICT): a prospective cross-sectional study

Heart failure with preserved ejection fraction is a complex clinical syndrome associated with a high level of morbidity and mortality, constituting 56% of heart failure cases and showing an increasing prevalence. The E/Ea ratio, used for echocardiographic assessment of left ventricular (LV) filling pressure, has been commonly recommended as a noninvasive measure. However, its validity lacks robust prospective validation in patients with preserved LV ejection fraction, and its accuracy has been questioned in comparison to patients with reduced LV ejection fraction. The objective of this study was to evaluate the accuracy of novel echocardiographic markers incorporating peak E velocity, left atrial volume index (LAVi), and pulmonary artery systolic pressure (PAP) for noninvasive estimation of LV end-diastolic pressure (LVEDP) against invasive measurement. In this cross-sectional study conducted at a tertiary care hospital, a sample size of 122 participants was utilized. Statistical analyses including independent samples t-test, χ2 test, and linear regression analysis were employed to explore correlations and predict outcomes. The results indicated that Group 1 (LVEDP <20 mmHg) had a mean age of 59.25 years, while Group 2 (LVEDP >20 mmHg) had a mean age of 56.93 years. Mitral E velocity positively predicted LVEDP, while Mitral E/A ratio showed a negative association. Notably, (E+PAP)/2, (E+LAVi)/2, and Mitral E exhibited good discriminative ability, with respective area under the curve values of 0.840, 0.900, and 0.854. (E+LAVi)/2 demonstrated the highest discriminatory power, with a threshold of 40.100, yielding high sensitivity (0.971) but relatively low specificity (0.302) in predicting LVEDP greater than 20. These findings emphasize the accuracy and utility of combining diastolic variables and peak E velocity as markers for left ventricular filling pressure in patients with a high burden of cardiac disease. Additionally, the study highlights the importance of these parameters in assessing cardiac abnormalities and supports the potential of novel echocardiographic parameters, particularly (E+LAVi)/2, in predicting LVEDP greater than 20. Further research is warranted to validate and explore the prognostic implications of these parameters in larger patient populations, ultimately improving the diagnosis and management of cardiac disease and enhancing clinical outcomes.
Experimental Evaluation of OOP Concepts Learning with a Kit-Build Concept Map: A Case Study

A kit-build is a framework that utilizes a concept map to visualize knowledge and automatically assess the concept map that was created by the learner by recomposing the kit. In this case study, a kit-build will be implemented in object-oriented programming learning to investigate its usefulness in increasing learning gain compared with a traditional summarizing activity. In this study, learners will practically use a kit-build concept map system to facilitate reviewing and reconstruct their knowledge of object-oriented programming learning resources. This study conducted an experiment using two groups: the summarizing group (n = 46) and the kit-build group (n = 35). The variables that were measured were the pre- and post-test scores and learning gain. To evaluate the significance of the difference between the two groups, a non-parametric ANOVA analysis was performed. The experiment result confirms that incorporating the kit-build concept map as a knowledge visualization technique could improve not only the learning performance but also the overall learning gain compared with the group that used the regular summarizing method.
Deep Reinforcement Learning-Based Optimal and Fast Hybrid Equalizer Design Method for High-Bandwidth Memory (HBM) Module

In this article, we propose a deep reinforcement learning (DRL)-based optimal and fast hybrid equalizer (HYEQ) design method for high-bandwidth memory (HBM) module. The HYEQ is a promising key to improving the signal integrity (SI) performance along the broad frequency bands by combining a passive equalizer (PEQ) and an active equalizer (AEQ) for the HBM module. However, the equalizer design process is extremely complex because all of the parameters should be co-optimized by considering the SI characteristics of the target channel. To tackle the design complexity, we utilize the DRL method that trains the policy network to optimize the HYEQ design for maximizing an eye-opening (EO) value. The policy network is configured with a recurrent type of neural network and sequentially determines the optimal values of the HYEQ parameters by considering the relevance between the parameters. Furthermore, the training process for the policy network is implemented in diverse channel dimensions of the silicon interposer. By learning the feature between channel dimensions and equalizer parameters, the policy network directly designs the optimal HYEQ for arbitrary channel dimensions. For verification, the proposed method is compared with the random search (RS) and genetic algorithm (GA) in terms of optimality performance and computational time. The result shows that the proposed method outperforms both RS and GA.