Potential Difference Research Articles

Algebra discourses in mathematics and physics textbooks: comparing the use of algebraic symbols

It is agreed that algebra has an important role in physics, particularly through handling symbols. A lot of previous research has focused on how mathematics is used in physics from perspectives where mathematics is taken for granted, and not addressing potential differences of mathematics in the physics classroom and in the mathematics classroom. Studies addressing differences between both subjects have been based on researchers’ own experiences of mathematics in both subjects. Thus, more focused empirical research is needed. The purpose of this study is to clarify similarities and differences between mathematics and physics concerning the use of algebraic symbols. Analyses were based on comparisons between upper secondary textbooks in mathematics and in physics from a discourse perspective. Statistical methods were used to decide if there were any significant differences between the subjects. Results showed an overlap in the algebra discourse in both subjects, but also several differences concerning core aspects of algebra. For example, a higher number of different algebraic symbols in equations in physics than in mathematics, and algebraic symbols are more seldom referred to by words in mathematics than in physics. This can make it difficult for students to identify similarities in the algebraic discourses in both subjects.

String-level compact modeling of erase operations in the body-floated vertical channel of 3D charge trapping flash memory

In this study, we examined the use of string-level compact modeling as an effective framework for circuit simulation focusing on erase operation in 3D charge trapping flash (CTF) memory devices. We analyzed the behaviors of the accumulated hole from p-type bulk (p-well) and the corresponding difference of channel electrostatic potential in the CTF cell string, which is attributed to the variation in hole barrier height in the channel of the ground select line (GSL) transistor during erase operation. We derived a formula for the hole current delivering positive potential from the p-well to the channel region and established a modeling procedure. Technology computer-aided design (TCAD) simulation results were used to extract model parameters and analyze channel electrostatic potential during the erase operation. Additionally, experimental data for erase speed were verified using simulation program with integrated circuit emphasis (SPICE) results. Because the erase efficiency is strongly related to hole behaviors based on the conditions of the GSL transistor, the proposed compact modeling is an effective tool for circuit designers and system architects to achieve better performance in erase execution and optimizing the design of 3D CTF memory devices.

Corticosteroid therapy for nephrotic syndrome in children.

In nephrotic syndrome protein leaks from the blood to the urine through the glomeruli resulting in hypoproteinaemia and generalised oedema. Children with untreated nephrotic syndrome frequently die from infections. The majority of children with nephrotic syndrome respond to corticosteroids. However about 70% of children experience a relapsing course with recurrent episodes of oedema and proteinuria. Corticosteroid usage has reduced the mortality rate in childhood nephrotic syndrome to around 3%, with infection remaining the most important cause of death. However corticosteroids have known adverse effects such as obesity, poor growth, hypertension, diabetes mellitus, osteoporosis and adrenal suppression. The original treatment schedules for childhood nephrotic syndrome were developed in an ad hoc manner. The optimal doses and durations of corticosteroid therapy that are most beneficial and least harmful have not been clarified. The aim of this review was to determine the benefits and harms of different corticosteroid regimens in preventing relapse in children with steroid sensitive nephrotic syndrome (SSNS). We searched the Cochrane Controlled Trials Register (Cochrane Library, Issue 2, 2002), Cochrane Renal Group Specialised Register (July 2002), MEDLINE (1966 - July 2002) and EMBASE (1980 - July 2002) without language restriction, reference lists of articles, abstracts from conference proceedings and contact with known investigators in the area. Randomised controlled trials were included if they were carried out in children (aged three months to 18 years) in their initial or subsequent episode of SSNS, if they compared different durations, total doses or other dose strategies using prednisone or other corticosteroid agent and if they had outcome data at six months or more. Two reviewers independently reviewed all eligible studies for inclusion, assessed study quality and extracted data. The principle outcome measure was the number of children with and without relapse after six and 12-24 months. Secondary outcomes sought included the number of children who developed frequently relapsing nephrotic syndrome and adverse events. A random effects model was used to estimate summary effect measures (relative risk (RR), risk difference (RD)) after testing for heterogeneity. Meta-regression was used to explore potential between-study differences due to the baseline risk of relapse, study quality and types of interventions used. Nineteen trials have been included in this review. A meta-analysis of six trials, which compared two months of prednisone with three months or more in the first episode, showed that the longer duration significantly reduced the risk of relapse at 12 - 24 months (RR 0.70; 95% CI 0.58 to 0.84) without an increase in adverse events. There was an inverse linear relationship between the duration of treatment and risk of relapse (RR = 1.26 - 0.112 duration; r(2) = 0.56; p = 0.03). The number of children who became frequent relapsers and the mean relapse rate/patient/year were also significantly reduced without increase in serious adverse events. In children with frequently relapsing nephrotic syndrome, deflazacort was significantly more effective in maintaining remission than prednisone (RR 0.44; 95% CI 0.25 to 0.78). Children in their first episode of SSNS should be treated for at least three months with an increase in benefit being demonstrated for up to seven months of treatment. In a population with a baseline risk for relapse following the first episode of 60% with two months of prednisone, daily prednisone for four weeks followed by alternate day therapy for six months would be expected to reduce the number of children experiencing a relapse by about 33%. In children who relapse frequently, deflazacort deserves further study.

Cyto- and genotoxicity evaluation of water samples collected from two rivers in the Kosovo.

River water in Kosovo is exposed to various discharges from industrial and agricultural activities as well as to urban wastewater. Rivers Sitnica and Drenica are among the most affected ones and water samples drawn from these rivers show the presence of various toxic substances. Genotoxic effects are seen in fish living in these rivers indicating a cytotoxic and mutagenic potential of the river water. Aiming at substantiating these observations, we assessed the cyto- and genotoxic effects of water samples collected at different locations from the Drenica and Sitnica rivers. Samples drawn from Lake Badovc served for comparison. To address seasonal effects, samples were collected at different seasons/time points during the period of summer 2016-spring 2018. The water samples were analyzed employing primary rat hepatocytes as a reliable in vitro cell model for the assessment of cytotoxic effects (mitotic arrest and cell death) and DNA damage/genotoxicity (micronucleus assay and Comet assay). The results do not account for significant effects associated with specific locations but demonstrate seasonal differences of the genotoxic potential of the water samples collected along both rivers, which are accompanied by a limited cytotoxic potential. Our data provide substantial support to earlier observations and strongly warrant the need for continuous chemical as well as biological monitoring of the river water in Kosovo, focusing on improved toxicant profiling of the river water and investigations addressing the observed seasonal variations.

The sodium correction factor for dogs undergoing treatment for a hyperglycemic crisis is a 1.6-mEq/L decrease in sodium per 100-mg/dL increase in glucose.

To determine the sodium correction factor for clinical use in hyperglycemic diabetic dogs. Retrospective analysis of 76 hospitalization episodes from 67 different dogs presenting to the University of Georgia Veterinary Teaching Hospital between January 1, 2015, and January 1, 2023. For each hospitalization episode, paired blood sodium and glucose concentration measurements were recorded from the time of presentation until glucose concentration was ≤ 201 mg/dL. Therapies administered, primary diagnosis, and concurrent diseases were also recorded for each episode. A linear mixed model was used to determine the sodium correction factor per 100-mg/dL increase in glucose. Piecewise linear mixed models were also constructed for blood glucose measurements ≤ 400 mg/dL and > 400 mg/dL to explore potential correction factor differences between low and high glucose concentrations. A sodium correction factor of a 1.6-mEq/L (95% CI, 1.3 to 1.9 mEq/L) decrease in sodium concentration per 100-mg/dL increase in blood glucose concentration was calculated. Differences in the correction factor between conditions of low and high glucose concentrations could not be determined due to a small sample size of blood glucose values > 400 mg/dL. Most dogs received similar treatments throughout the study period, including balanced isotonic crystalloids (97% [74/76]), electrolyte supplementation (84% [64/76]), and regular insulin (97% [74/76]). Almost all patients (93% [71/76]) had 1 or more concurrent diseases. A sodium correction factor of 1.6 mEq/L (decrease in sodium per 100-mg/dL increase in glucose) is recommended for clinical use in hyperglycemic diabetic dogs.

Sex differences in discrimination behavior and orbitofrontal engagement during context-gated reward prediction.

Animals, including humans, rely on contextual information to interpret ambiguous stimuli. Impaired context processing is a hallmark of several neuropsychiatric disorders, including schizophrenia, autism spectrum disorders, post-traumatic stress disorder, and addiction. While sex differences in the prevalence and manifestations of these disorders are well established, potential sex differences in context processing remain uncertain. Here, we examined sex differences in the contextual control over cue-evoked reward seeking and its neural correlates, in rats. Male and female rats were trained in a bidirectional occasion-setting preparation in which the validity of two auditory reward-predictive cues was informed by the presence, or absence, of a visual contextual feature (LIGHT: X+/DARK: X-/LIGHT: Y-/DARK: Y+). Females were significantly slower to acquire contextual control over cue-evoked reward seeking. However, once established, the contextual control over behavior was more robust in female rats; it showed less within-session variability (less influence of prior reward) and greater resistance to acute stress. This superior contextual control achieved by females was accompanied by an increased activation of the orbitofrontal cortex (OFC) compared to males. Critically, these behavioral and neural sex differences were specific to the contextual modulation process and not observed in simple, context-independent, reward prediction tasks. These results indicate a sex-biased trade-off between the speed of acquisition and the robustness of performance in the contextual modulation of cued reward seeking. The different distribution of sexes along the fast learning ↔ steady performance continuum might reflect different levels of engagement of the OFC, and might have implications for our understanding of sex differences in psychiatric disorders.

Social well-being and problematic school absence among Danish adolescents: A nationwide cross-sectional study.

Adolescents' well-being and school absence are important factors for public health. The aim of this study was to examine the association between social well-being and problematic school absence among Danish adolescents in the ninth grade, as well as to examine potential sex differences, using a large cohort of adolescents. In this cross-sectional study, information on social well-being was obtained from the yearly Danish National Well-being Questionnaire mandatory in compulsory school. Data on school absence was obtained from the Ministry of Children and Education. The study population comprised 203,570 adolescents in the school years 2014/2015 to 2019/2020. The association between social well-being and problematic school-absence was analysed using logistic regression. A stratified analysis was made to investigate potential sex differences. A total of 17,555 (9.16%) adolescents had problematic school absence, defined as >10% illegal absence and/or sickness absence in the ninth grade. Adolescents with low social well-being had higher odds for having problematic school absence compared with adolescents with high social well-being, adjusted odds ratio=2.22 (95% confidence interval 2.10-2.34). When stratifying for sex, the association was strongest for girls. The results remained after adjustment for parents' educational level and family structure. Associations between adolescents' social well-being and problematic school absence were found, where girls had the strongest association. These findings might provide knowledge about social well-being as an important factor for problematic school absence, while underlining the importance of early focus and prevention, which is of importance for both the adolescents and society.

The Deep-Match Framework: R-Peak Detection in Ear-ECG.

The Ear-ECG provides a continuous Lead I like electrocardiogram (ECG) by measuring the potential difference related to heart activity by electrodes which are embedded within earphones. However, the significant increase in wearability and comfort enabled by Ear-ECG is often accompanied by a degradation in signal quality - an obstacle that is shared by the majority of wearable technologies. We aim to resolve this issue by introducing a Deep Matched Filter (Deep-MF) for the highly accurate detection of R-peaks in wearable ECG, thus enhancing the utility of Ear-ECG in real-world scenarios. The Deep-MF consists of an encoder stage, partially initialised with an ECG template, and an R-peak classifier stage. Through its operation as a Matched Filter, the encoder searches for matches with an ECG template in the input signal, prior to filtering these matches with the subsequent convolutional layers and selecting peaks corresponding to the ground-truth ECG. The latent representation of R-peak information is then fed into a R-peak classifier, of which the output provides precise R-peak locations. The proposed Deep Matched Filter is evaluated using leave-one-subject-out cross-validation over 36 subjects with an age range of 18-75, with the Deep-MF outperforming existing algorithms for R-peak detection in noisy ECG. The Deep-MF achieves a median R-peak recall of 94.9% and a median precision of 91.2% across subjects when evaluated with leave-one-subject-out cross validation. Overall, this Deep-Match framework serves as a valuable step forward for the real-world functionality of Ear-ECG and, through its interpretable operation, the acceptance of deep learning models in e-Health.

Anatomy-Guided Spatio-Temporal Graph Convolutional Networks (AG-STGCNs) for Modeling Functional Connectivity Between Gyri and Sulci Across Multiple Task Domains.

The cerebral cortex is folded as gyri and sulci, which provide the foundation to unveil anatomo-functional relationship of brain. Previous studies have extensively demonstrated that gyri and sulci exhibit intrinsic functional difference, which is further supported by morphological, genetic, and structural evidences. Therefore, systematically investigating the gyro-sulcal (G-S) functional difference can help deeply understand the functional mechanism of brain. By integrating functional magnetic resonance imaging (fMRI) with advanced deep learning models, recent studies have unveiled the temporal difference in functional activity between gyri and sulci. However, the potential difference of functional connectivity, which represents functional dependency between gyri and sulci, is much unknown. Moreover, the regularity and variability of the G-S functional connectivity difference across multiple task domains remains to be explored. To address the two concerns, this study developed new anatomy-guided spatio-temporal graph convolutional networks (AG-STGCNs) to investigate the regularity and variability of functional connectivity differences between gyri and sulci across multiple task domains. Based on 830 subjects with seven different task-based and one resting state fMRI (rs-fMRI) datasets from the public Human Connectome Project (HCP), we consistently found that there are significant differences of functional connectivity between gyral and sulcal regions within task domains compared with resting state (RS). Furthermore, there is considerable variability of such functional connectivity and information flow between gyri and sulci across different task domains, which are correlated with individual cognitive behaviors. Our study helps better understand the functional segregation of gyri and sulci within task domains as well as the anatomo-functional-behavioral relationship of the human brain.

Synthesis of Consequent Pole Vernier Permanent Magnet Machine Based on Oscillating Magnetic Potential Difference Model

Synthesis of Consequent Pole Vernier Permanent Magnet Machine Based on Oscillating Magnetic Potential Difference Model

Application of artificial intelligence in predicting major arrhythmic events based on clinical and left ventricular electro-anatomical mapping data

Abstract Introduction Artificial intelligence (AI) is a ground-breaking frontier in the analysis of intricate and extensive datasets, also within the medical domain. AI can be used to meticulously and systematically analyse data derived from electro-anatomical mapping, scrutinizing the numerical data extracted from the mapping engineering files. Purpose The primary objective of the project is to utilize machine learning methods to identify potential correlations between clinical, procedural, and substrate-mapping variables with major arrhythmic cardiac events (MaCE). Methods Machine learning algorithms were implemented using a database of 220 patients who underwent endocardial left ventricular electro-anatomical mapping (EAM). A total of 223 clinical-procedural variables were collected (analysis conducted for the 61 most interesting variables) and substrate mapping engineering files/data were extrapolated for analysis. Combinations of 4 and 5 variables are explored, resulting in 487,635 and 5,461,512 combinations, respectively. All the results obtained from all the combinations and different methods were evaluated by calculating the AUC (parameter indicating the probability that the prediction is in the correct order). For each patient, the numerical data extracted from the EAM files were assessed, specifically focusing on: 1. The late potentials extension and dispersion (%LatArea). 2. Point-to-point difference of the bipolar potential compared to the unipolar potential, with identification of regions where multiple points with high differences are clustered (%UniBip) 3. Areas of deceleration, characterized by very early and very late potentials within a short range, indicating a substantial difference in the activation timing (GradVal). Results Analyzing the best six 4-variable logistic regression models, we note that the variables that are most frequently correlated with a MaCE during follow-up are "NYHA class", "PAPs" and "GradVal" (AUC of the best models was: 0,96; 0,94; 0,91; 0,90; 0,90; 0,90). Analyzing the best six 5-variable logistic regression models emerges that the variables most frequently correlated with a MaCE during follow-up are "NYHA class", "PAPs", "GradVal", "Arrhythmic Storm", "TAPSE" and "%UniBip" (AUC of the best models was: 0,97; 0,93; 0,92; 0,92; 0,92; 0,92). EAM data added to clinical data increases the number of regression variables and the model complexity; "GradVal" and "%UniBip" seem to have greater relevance. Conclusion The study successfully employs machine learning algorithms to identify key variables influencing the prediction of MaCE. The analysis of EAM numerical data through AI can allow for the stratification of MaCE risk. The application of artificial intelligence allows to analyse a large amount of computer data that would not be analysable by human capabilities alone. Ongoing refinement and expansion of the dataset can enhance the models' predictive capabilities for clinical applications.

Relationship between ICD implantation volume and treatment parameters of patients receiving an ICD with remote monitoring.

Both highly specialized heart centres and less specialized hospitals care for patients with implantable ICDs/CRT-Ds with remote monitoring. To investigate potential differences in patient treatment according to centre's ICD implantation volume. Based on their 2012 ICD/CRT-D implantation volume, centres enrolled in the NORDIC ICD trial in Germany were assigned to one of three groups: high- (HV, n= 345), medium- (MV, n= 340) or low-volume (LV, n= 189). The HV-centres had a significant higher CRT-D proportion (41.7%; LV: 36.5%; MV: 23.2%; P𝑔𝑙𝑜𝑏𝑎𝑙< 0.001), significant shorter median procedure duration (49 min; MV: 58 min; LV: 60 min; P𝑔𝑙𝑜𝑏𝑎𝑙< 0.001) but significant longer median hospital stay (4 days; MV and LV: 3 days; P𝑔𝑙𝑜𝑏𝑎𝑙< 0.001) compared to MV- and LV-centres. The X-ray exposure was shorter in MV/HV-centres (MV: 3.4 min; HV: 3.6 min; LV: 5.5 min; P𝑔𝑙𝑜𝑏𝑎𝑙< 0.001). Only 3.5% (LV: 2.6%; HV: 3.5%; MV: 4.1%) patients received at least one delivered inappropriate shock and 2.5% (HV: 2.0%; LV: 2.6%; MV: 2.9%) patients had withheld inappropriate ICD shocks without subsequent inappropriate shock delivery within 24.5 months of median follow-up. Implantation volume-dependent differences were observed in the device selection, procedure duration and x-ray exposure duration. Remote monitoring in combination with adequate response pattern prevented imminent inappropriate shocks in all three groups.

Regional Evaluation Study of VFTO Interference to Secondary Side Cables Based on Cloud Model and MARCOS.

With the advent of the data era, most power secondary side equipment tends to be digitized. The power system needs more accurate numerical results to further improve its operating efficiency. Therefore, it is important to study the electromagnetic interferences of very fast transient overvoltage (VFTO) generated by gas-insulated switchgear (GIS). To protect the secondary side cable from interferences, the secondary side cable is wrapped with an outer shield and the shield is grounded. When the interference of VFTO comes, it will couple the interference current and interference voltage on the shield of the cable. By grounding, the interference is greatly discharged. However, due to the grounding resistance, there will be a potential difference between the grounding points at the two ends of the shield of the cable. This causes a corresponding interference current to flow through the shield, which will affect the transmission of signals inside the cable. In the actual substation, the resistivity of the soil, the ambient temperature and humidity of the area, and so on will have impacts on the grounding resistance. In addition, the irregularity of the cable arrangement and the time of the use of the cable will have impacts on the signal transmission of the cable. Based on the abovementioned issues, this article proposed a comprehensive assessment method based on the combination of the cloud model and measurement of alternatives and ranking according to compromise solution (MARCOS). The method brings the cloud model into MARCOS by the algorithm of the contribution of the cloud droplets. It overcomes the difficulty of cloud model quantification. By comparing the results of the proposed method with the actual conditions at the substation and the results of the common MARCOS assessment method, the validity of the method is verified, and a reference scheme is provided for substation optimization.

Characterization of chitosan nanoparticles extracted from maggot shell and paddy snail waste using the ionic gelation method

The aim this study was to characterize of chitosan nanoparticles extracted from maggot shell waste and paddy snail waste using the ionic gelation method. The Scanning Electron Microscope of nanochitosan extracted from maggot shell showed heterogeneity in particle size ranging from 218.60 to 279.04 nm by 73.5%. SEM results of nanochitosan extracted from paddy snail waste showed ranging from 193.48 to 356.20 nm by 60.4% and 740.89 to 945.74 nm by 14.35%. The zeta potential of nanochitosan particles of maggot shell waste and paddy snail waste were found to be 9.7 mV and -28.9 mV, respectively. The source of chitosan nanoparticles might produce the difference of particle size and zeta potential of chitosan nanoparticles.

Impact of Prolonged Sport Stoppage on Knee Injuries in High School Athletes: An Ecological Study.

In March 2020, public health concerns resulted in school closure throughout the United States. The prolonged sport cessation may affect knee injury risk in high school athletes. The purpose of this study was to describe and compare risk of knee injuries in high school athletes during 2019-2020 and 2020-2021 academic years, and stratify by gender, severity, mechanism of injury, injury type, and knee anatomic region. Historical-prospective cohort study. This historical-prospective cohort study included 176 schools in 6 states matched by sport participation in control and COVID years from July 1, 2019 to June 30, 2021. Injury rates per 1000 athletes per year were calculated with 95% confidence intervals. A negative binomial regression was performed to assess potential differences in knee injuries between academic years. 94,847 and 72,521 high school athletes participated in the 2019-2020 (19-20) and 2020-2021 (20-21) seasons. Knee injury risk was higher in the 20-21 season (19-20: 28.89% [27.82-29.96]; 20-21: 33.82% [32.50-35.14]). Risk increased for male athletes from 2019-2020 to 2020-2021 (19-20: 29.42% [28.01-30.83]; 20-21: 40.32% [38.89-41.75]). Female knee injury risk was similar between years (19-20: 25.78% [24.29-27.27]; 20-21: 26.03% [24.31-27.75]). Knee injuries increased by a ratio of 1.2 ([95% CI, 1.1-1.3], P < .001) during 2020-2021. Knee injury risk and relative risk increased among males in 2020-2021. Results indicate changes in knee injury risk following return from COVID shelter in place among high school athletes and implicate potential negative downstream effects of interrupted sports training and participation on high school injury risk.

Magnetic field based finite element method for magneto-static problems with discontinuous electric potential distributions

We introduce two finite element formulations to approximate magneto-static problems with discontinuous electric potential based respectively on the electrical scalar potential and the magnetic field. This work is motivated by our interest in Liquid Metal Batteries (LMBs), a promising technology for storing intermittent renewable sources of energy in large scale energy storage devices. LMBs consist of three liquid layers stably stratified and immiscible, with a light liquid metal on top (negative electrode), a molten salt in the middle (electrolyte) and a heavier liquid metal on bottom (positive electrode). Energy is stored in electrical potential differences that can be modeled as jumps at each electrode-electrolyte interface. This paper focuses on introducing new finite element methods for computing current and potential distributions, which account for internal voltage jumps in liquid metal batteries. Two different formulations that use as primary unknowns the electrical potential and magnetic field, respectively, are presented. We validate them using various manufactured test cases, and discuss their applications for simulating the current distribution during the discharge phase in a liquid metal battery.

A Study on Yield and Value Sustainability in Groundnut (Arachis hypogea) Through Cluster Frontline Demonstrations Approach in Cuddalore District of Tamil Nadu

Background: Groundnut is a prominent oilseed crop in India but its productivity is far below the potential yield due to lack of knowledge and adoption of improved varieties and technologies. Technology gap is a major problem in groundnut production in North Eastern zone of Tamil Nadu State in which Cuddalore district falls. A scientific and systematic effort was made to study the impact on yield by assessing technological gap existing in various components of groundnut cultivation through cluster frontline demonstrations (CFLDs) with scientific technologies. Methods: Cluster Frontline Demonstrations was implemented in groundnut to improve the production potential of improved varieties and new technologies through Krishi Vigyan Kendra (KVK). The study with one of its objective to assess yield and technology gap in groundnut was conducted with 175 demonstrations during the period from 2020-21 to 2022-23 covering seven blocks in an area of 70 hectares in Cuddalore district following cluster random sampling method. Two main varieties VRI 8 and VRI 10 along with improved technologies were demonstrated in farmer’s field by providing necessary critical inputs seed pods, bio control agents along with seed drill sowing, Result: There resulted 62.13 per cent increase in yield as observed in demonstration plots over farmers’ practices in groundnut. The study revealed that in groundnut, the average extension gap (difference of demo and farmer plot yield) is 16.26 q/ha, average technology gap (difference of potential and demo plot yield) is 12.21 q/ha and average technology index of demonstrations is 22.21 per cent. The higher average net returns (Rs. 149758/ha) were recorded in demonstration plot (BCR 2.3) compared to farmers’ plot. The Sustainability Yield Index (SYI) and Sustainability Value Index (SVI) in demo plot are higher consistently than farmers plot mainly due to the effect of cluster frontline demonstrations with proper application of inputs/technologies viz., VRI 8 and VRI 10 varieties, seed treatment with bio fertilizers, bio control agents, soil test based nutrient management, application of TNAU crop booster groundnut rich and gypsum application.

WITHDRAWN: Transdifferentiation of Human Neonatal Foreskin Fibroblasts into Keratinocyte-like Cells via Regulating the AKT/P53/WNT/LEF1 Axis

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Knowledge, Attitudes, and Practices Regarding Tropical Diseases among Nursing Students: A Comparative Cross-Sectional Study.

This study aimed to compare bachelor nursing students' knowledge, attitudes, and practices (KAP) regarding tropical diseases. This cross-sectional study was conducted with 128 participants who were recruited using stratified random sampling and completed a sociodemographics questionnaire and KAP questionnaire concerning tropical diseases. Descriptive statistics were used to present the data, and inferential statistics, including one-way analysis of variance and Mann-Whitney U tests, were calculated to identify potential differences between variables. The study found nursing students who took the tropical nursing courses had better knowledge and attitudes regarding preventing tropical diseases than those who did not. Participants who took the tropical nursing courses had a significantly higher median knowledge score of 22 with an interquartile range (IQR) of 2 (P <0.001) and a median attitude score of 31 (IQR: 6, P = 0.02) compared with those who did not take it. However, there were no significant differences between the two groups in practice regarding tropical diseases (P = 0.20). In conclusion, students who take tropical nursing courses may improve their knowledge and attitudes toward tropical diseases, but the two groups in this study did not significantly differ in practice, possibly because of self-reported practices rather than direct observation. This study highlights the potential of accurate digital information to provide credible resources in enhancing nursing students' KAP concerning tropical diseases.

Quantifying Exhaled Particles in Healthy Humans During Various Respiratory Activities Under Realistic Conditions.

Background: Quantitatively collecting and characterizing exhaled aerosols is vital for infection risk assessment, but the entire droplet size spectrum has often been neglected. We analyzed particle number and size distribution of healthy participants in various respiratory activities, considering inter-individual variability, and deployed a simplified far-field model to inform on infection risks. Methods: Participants repeated the same respiratory activities on two visits. Particles were collected using an airtight extraction helmet supplied with High Efficiency Particulate Air (HEPA) filtered air. The sampling volume flow was transported to two particle counters covering the small and large particle spectrum. The applied simple mass balance model included respiratory activity, viral load, room size, and air exchange rates. Results: Thirty participants completed the study. The major fraction of the number-based size distribution was <5 μm in all respiratory activities. In contrast, the major fraction of the volume-based size distribution was 2-12 μm in tidal breathing, but >60 μm in all other activities. Aerosol volume flow was lowest in tidal breathing, 10-fold higher in quiet/normal speaking, deep breathing, coughing, and 100-fold higher in loud speaking/singing. Intra-individual reproducibility was high. Between participants, aerosol volume flow varied by two orders of magnitude in droplets <80 μm, and three orders of magnitude in droplets >80 μm. Simple model calculations not accounting for potential particle size-dependent differences in viral load and infection-related differences were used to model airborne pathogen concentrations. Conclusions: Quantitative analysis of exhaled aerosols for the entire droplet size spectrum as well as the variability in aerosol emission between individuals provides information that can support infection research. Clinical Trial Registration number: NCT04771585.