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Piezo1 mechanosensitive ion channel (MSC) plays a significant role in human physiology. Despite several research on the function and expression of Piezo1 in the nervous system, its electrophysiological properties in neuroinflammatory astrocytes remain unknown. We tested whether astrocytic neuroinflammatory state regulates Piezo1 using electrical recordings, calcium imaging, and wound healing assays on cultured astrocytes. In this study, we determined whether neuroinflammatory condition regulates astrocytic Piezo1 currents in astrocytes. First, we performed electrophysiological recordings on the mouse cerebellum astrocytes (C8-S) under lipopolysaccharide (LPS)-induced neuroinflammatory condition. We found that LPS treatment significantly increased MSC currents in C8-S. The half-maximal pressure of LPS treated MSC currents was left-shifted but the slope sensitivity was not altered by LPS treatment. LPS-induced increase of MSC currents were further augmented by Piezo1 agonist, Yoda1 but were normalized by Piezo1 inhibitor, GsMTx4. Furthermore, silencing Piezo1 in LPS treated C8-S normalized not only MSC currents but also calcium influx and cell migration velocity. Together, our results show that LPS sensitized Piezo1 channel in C8-S astrocytes. These findings will suggest that astrocytic Piezo1 is a determinant of neuroinflammation pathogenesis and may in turn become the foundation of further research into curing several neuronal illnesses and injury related inflammation of neuronal cells.

ObjectiveTo determine the effect of SARS-CoV-2 variants on non-respiratory features of COVID-19 in vaccinated and not fully vaccinated patients using a University of California database.MethodsA longitudinal retrospective review of medical records (n = 63,454) from 1/1/2020-4/26/2022 using the UCCORDS database was performed to compare non-respiratory features, vaccination status, and mortality between variants. Chi-square tests were used to study the relationship between categorical variables using a contingency matrix.ResultsFever was the most common feature across all variants. Fever was significantly higher in not fully vaccinated during the Delta and Omicron waves (p = 0.001; p = 0.001). Cardiac features were statistically higher in not fully vaccinated during Omicron; tachycardia was only a feature of not fully vaccinated during Delta and Omicron; diabetes and GI reflux were features of all variants regardless of vaccine status. Odds of death were significantly increased among those not fully vaccinated in the Delta and Omicron variants (Delta OR: 1.64, p = 0.052; Omicron OR: 1.96, p

BackgroundAcute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems. However, most previous work has focused on healthy subjects in controlled environments, with limited attention to real-world postoperative pain scenarios. This gap necessitates the development of robust, multimodal approaches capable of addressing the unique challenges associated with assessing pain in clinical settings, where factors like motion artifacts, imbalanced label distribution, and sparse data further complicate pain monitoring. ObjectiveThis study aimed to develop and evaluate a multimodal machine learning–based framework for the objective assessment of pain in postoperative patients in real clinical settings using biosignals such as electrocardiogram, electromyogram, electrodermal activity, and respiration rate (RR) signals. MethodsThe iHurt study was conducted on 25 postoperative patients at the University of California, Irvine Medical Center. The study captured multimodal biosignals during light physical activities, with concurrent self-reported pain levels using the Numerical Rating Scale. Data preprocessing involved noise filtering, feature extraction, and combining handcrafted and automatic features through convolutional and long-short-term memory autoencoders. Machine learning classifiers, including support vector machine, random forest, adaptive boosting, and k-nearest neighbors, were trained using weak supervision and minority oversampling to handle sparse and imbalanced pain labels. Pain levels were categorized into baseline and 3 levels of pain intensity (1-3). ResultsThe multimodal pain recognition models achieved an average balanced accuracy of over 80% across the different pain levels. RR models consistently outperformed other single modalities, particularly for lower pain intensities, while facial muscle activity (electromyogram) was most effective for distinguishing higher pain intensities. Although single-modality models, especially RR, generally provided higher performance compared to multimodal approaches, our multimodal framework still delivered results that surpassed most previous works in terms of overall accuracy. ConclusionsThis study presents a novel, multimodal machine learning framework for objective pain recognition in postoperative patients. The results highlight the potential of integrating multiple biosignal modalities for more accurate pain assessment, with particular value in real-world clinical settings.

SARS-CoV-2 (COVID-19) has caused over 80 million infections 973,000 deaths in the United States, and mutations are linked to increased transmissibility. This study aimed to determine the effect of SARS-CoV-2 variants on respiratory features, mortality, and to determine the effect of vaccination status. A retrospective review of medical records (n = 55,406 unique patients) using the University of California Health COvid Research Data Set (UC CORDS) was performed to identify respiratory features, vaccination status, and mortality from 01/01/2020 to 04/26/2022. Variants were identified using the CDC data tracker. Increased odds of death were observed amongst unvaccinated individuals and fully vaccinated, partially vaccinated, or individuals who received any vaccination during multiple waves of the pandemic. Vaccination status was associated with survival and a decreased frequency of many respiratory features. More recent SARS-CoV-2 variants show a reduction in lower respiratory tract features with an increase in upper respiratory tract features. Being fully vaccinated results in fewer respiratory features and higher odds of survival, supporting vaccination in preventing morbidity and mortality from COVID-19.

The integration of renewable energy sources, such as wind and solar, into co-located hybrid power plants (HPPs) has gained significant attention as an innovative solution to address the intermittency and variability inherent in renewable systems among plant developers because of advancements in technology, economies of scale, and government policies. However, it is essential to examine different challenges and aspects during the development of a major work on large-scale hybrid plants. This includes the need for optimization, sizing, energy management, and a control strategy. Hence, this research offers a thorough examination of the present state of co-located utility-scale wind–solar-based HPPs, with a specific emphasis on the problems related to their sizing, optimization, and energy management and control strategies. The authors developed a review approach that includes compiling a database of articles, formulating inclusion and exclusion criteria, and conducting comprehensive analyses. This review highlights the limited number of peer-reviewed studies on utility-scale HPPs, indicating the need for further research, particularly in comparative studies. The integration of machine learning, artificial intelligence, and advanced optimization algorithms for real-time decision-making is highlighted as a potential avenue for addressing complex energy management challenges. The insights provided in this manuscript will be valuable for researchers aiming to further explore HPPs, contributing to the development of a cleaner, economically viable, efficient, and reliable power system.

Background: An outbreak of fever and cough was notified to public health authorities by the management of school X among their students, Kozhikode district, Kerala, on Jan 1, 2020. We conducted an outbreak investigation to confirm the diagnosis, describe the case patients by time, place, and person, and propose appropriate recommendations. Materials and Methods: We defined a probable case of influenza as any student or staff of school X, Kozhikode district with a fever and cough between Dec 30, 2019, and Jan 14, 2020. We conducted an active case search and contact tracing in the school. We described the cases by date of symptom onset using an Epicurve, plotted cases by their classroom, and calculated the attack rate by age, and gender. Results: We identified 270 cases of influenza; among them, 264 (98%) were students, and 6 (2%) were the staff. The overall attack rate was 36%. The attack rate was higher among the students (39%) than the staff (12%, 6/49, P < 0.0003). The attack rate was higher among students of class 10E (90%, 37/41) and class 10A (80%, 33/41), where the index case had contact with the students during the symptomatic phase. Conclusion: An outbreak of influenza A (H1N1) occurred among students and staff, predominantly affecting the 10th division. School and health authorities implemented several interventions to limit the outbreak, including training students on personal hygiene. We recommended conducting surveillance of influenza, maintaining adequate spacing of benches, self-hygiene practices, and classroom ventilation.

Structural and chemical order impact magnetic properties of solids, which are governed by spin-orbit coupling and exchange interaction. The ordered L10 phase of FePt is a key material to heat-assisted magnetic recording; to enable high storage density, a solid understanding is needed of structural and chemical disorder at small length scales, as well as associated modifications of the electronic band structure. Here, we investigate the effect of boron and copper additions (≲6 mol% Cu) on structural and magnetic properties of L10 FePt granular media. Two copper-driven mechanisms, although competing, can lead to improvements in both structural and magnetic properties. In particular, the Cu substitution on the Fe-site leads to a degradation of magnetic properties due to the delocalized electron orbitals originating from a larger Cu d-orbital occupancy. At the same time, Cu substitution leads to an enhanced crystallographic order and consequently magneto-crystalline anisotropy, which offsets the former effect to a large extent. Our study is based on magnetometry, x-ray absorption spectroscopy, ab-initio calculations and a phenomenological theory of disordered FePt granular media. We do not observe a sizable modification to Fe moments and electronic configuration; Cu reveals two different resonances associated with the presence and absence of Cu-B bonds that vary with total Cu concentration.

To identify the association of seed borne fungi in wheat seed samples (20 wheat genotypes collected from 11 different districts of Baghlan province), the standard blotter method was used. Thirteen seed borne fungi, viz. Alternaria alternata, Bipolaris spp, Gibberella zeae, Curvularia lunata, Rhizopus stolonifer, Aspergillus flavus, A. niger, Penicillium spp, Cladosporium cladosporioides, Acremonium spp, Ulocladium spp, Arthrobotrys oligospora and Tilletia indica were observed from the seeds. Nine wheat varieties collected from Zonal Agricultural Research Station, Baghlan, Afghanistan were analyzed for the association of seed borne fungi. Ten fungi, viz. A. alternata, Acremonium spp, Cladosporium cladosporioides, Ulocladium spp, Curvularia lunata, Bipolaris spp, Gibberella zeae, A. flavus, A. niger and Penicillium spp. were recorded. Among them A. alternata and Bipolaris spp. were predominant in most of the seed samples. Minimum per cent infection of 1.33% was recorded with treatment having Ridomil @2.5 g/kg seed whereas maximum seeds infection of 52% was recoded in control. Present study revealed the occurrence of seed borne fungi in different wheat genotypes in Baghlan province of Afghanistan. Studies helped in identifying suitable fungicides and bioagents for managing seed borne fungi in wheat.

We study the effect of carbon segregants on the spin and orbital moments of L10 FePt granular media using x-ray magnetic circular dichroism (XMCD) spectroscopy and report an effective decoupling of the structural film properties from the magnetic parameters of the grains. The carbon concentration reduces the grain size from (200 ± 160) nm2 down to (50 ± 20) nm2 for 40 mol. %C and improves sphericity and the order of grains, while preserving the crystalline order, spin and orbital moments, and perpendicular magnetocrystalline anisotropy. We identify the primary cause of enhanced saturation and coercive fields as the reduced demagnetization fields of individual grains. The ability to shrink grains without impairing their magnetic properties is a critical requirement for the commercialization of Heat-Assisted Magnetic Recording.

FePt granular films with grain size smaller than 10 nm are promising candidates for storage media used in the next generation heat-assisted magnetic recording technology. However, FePt films show degraded magnetic properties when the grain size is reduced to this scale, which cannot be explained solely by the finite size theory. In this study, we explored the structural cause of property degradation by employing advanced electron microscopy and atomistic modeling. Structural features unique to the nanostructured FePt granular films at significantly reduced grain sizes of 2∼8 nm were studied by high-resolution scanning transmission electron microscopy with geometric aberrations corrected up to the third order. Two critical structural parameters, the threshold grain size corresponding to the upper size limit of the FePt grains with zero chemical ordering and the sub-nanometer thin interfacial impurity at grain boundaries, were identified. A new atomistic model was developed to correlate these structural characteristics with key magnetic properties such as Curie temperature, saturation magnetization, magnetocrystalline anisotropy, and their grain-to-grain variation. The model shows good agreement with the experimental magnetic data and explains the gap in magnetic properties between the bulk and nanostructured FePt.

This study aims to analyze the function and realization of the meaning contained in the Poda ni Namonangon Ribut as an ancient script text contained in the Ulu Pungkut sub-district, Mandailing Natal district. This study uses a qualitative method with the documentation method in which the data is sourced from the Medan Archeology Agency. Data were analyzed using Systemic Functional Linguistics theory on Ideational Functions and to find metaphors of ideational meaning. The results of this study indicate the distribution of the relational process dominates from Participant, Cirmuntance. The realization of the meaning of the ideational function can be seen in the first, metaphor as a description of the community who want to have offspring, where the aim of Poda is ido napajadi boru "what makes a daughter". In the second, there is a metaphor that explains the word Na sundat realized with the words "Heavy Rain", "Divorce", "Birds perch", "ravine". In the third, shows that Poda has methafora like when the expectation is reached, it is the services of the writer "Shaman”. then when the writer has explained what the ingredients are. Social context of text that has a narrative and the author's conclusion about the solution to the problem of everyone who wants offspring in his family. The tradition of writing with the mandailing script (sumurat) became quite a tradition so that there are ancient texts in the traditional house became the animist beliefs and ideologies which can be seen in Bagas Godang.

Background: The hypothesis of “cross-addiction” has never been validated, and numerous aspects speak against it. Objectives: To compare the differences between sleeve gastrectomy (SG) and gastric bypass (GB) procedures concerning cross-addiction. Setting: Center for maximum care in Germany. Methods: We performed a prospective analysis of patients undergoing SG or GB as the first surgical treatment for severe obesity. All patients completed validated questionnaires to evaluate food intake (Yale Food Addiction Scale, YFAS), alcohol intake (Alcohol Use Disorders Identification Test), nicotine use (Fagerstrom Test for Nicotine Dependence), exercise (Exercise Addiction Inventory), drug addiction (20-item Drug Abuse Screening Test), and Internet use disorder (Internet Addiction Test) before the operation (T0) and 6 (T6) and 24 (T24) months postoperatively (ClinicalTrials.gov identifier: NCT02757716). Results: One hundred thirteen patients underwent SG (n = 68) or GB (n = 45). At the follow-up, 61% completed the questionnaires at T6 and 44% at T24. In the YFAS, the percentage of patients diagnosed with food addiction decreased from 69 to 10%, and the mean symptom count decreased from 3.52 ± 1.95 to 1.26 ± 0.99 at T24 (p < 0.0001); these values did not differ between the surgical groups (p = 0.784). No significant evidence of cross-addiction was observed for use of alcohol, nicotine, drugs, the Internet, or exercise in either surgical group. The percentage of patients with moderate nicotine dependence increased in the SG group (+8.9%) at T24, but this was not significant. Conclusion: In this single-center cohort study, surgery for obesity caused significant addiction remission regarding food but without inducing cross-addiction after 2 years. Importantly, no significant differences were seen between the SG and GB procedures.

Bronze metal matrix composites (MMCs) are futuristic materials that may find applications in automobile, aviation, and marine industries, specifically for propellers in submarines, bearings, and bushings for defence purposes. The present investigation studied the effect of Si3N4 (5, 10, 15 wt%) ceramic particles on the physical, metallurgical, and tribological behaviour of Cu-10Sn/Si3N4 MMCs. Cast rods of three composites and a base alloy were fabricated using the liquid metallurgy route. The microstructural characterisation for the cast samples was conducted using FESEM (Field Emission Scanning Electron Microscope), EDS (Energy Dispersive Spectroscopy), XRD (X-ray diffraction), and TEM (Transmission Electron Microscope), which revealed that the Cu-10Sn alloy reinforced with 5 wt% of Si3N4 had homogeneous distribution and perfect bonding of the Si3N4 with the bronze MMC. The dry sliding wear test was performed by varying parameters such as the applied load (10, 20, 30 N) and sliding velocity (1, 2, 3 m/s). The specific wear rate (SWR) increased against an increased load. However, the SWR and coefficient of friction decreased and then increased against an increasing sliding velocity due to tribolayer formation. The primary wear mechanism observed at low and high loads was severe delamination. In contrast, the wear mechanism was adhesion wear at high and low velocities. Amongst the researched samples, Cu-10Sn/5 wt% Si3N4 composites revealed the least SWR at a load of 10 N and sliding velocity of 2 m/s and hence can be recommended for manufacturing bearings and bushings in the automobile and defence industry.

The integration of renewable energy sources, such as wind and solar, into co-located hybrid power plants (HPPs) has gained significant attention as an innovative solution to address the intermittency and variability inherent in renewable systems among plant developers because of advancements in technology, economies of scale, and government policies. However, it is essential to examine different challenges and aspects during the development of a major work on large-scale hybrid plants. This includes the need for optimization, sizing, energy management, and a control strategy. Hence, this research offers a thorough examination of the present state of co-located utility-scale wind–solar-based HPPs, with a specific emphasis on the problems related to their sizing, optimization, and energy management and control strategies. The authors developed a review approach that includes compiling a database of articles, formulating inclusion and exclusion criteria, and conducting comprehensive analyses. This review highlights the limited number of peer-reviewed studies on utility-scale HPPs, indicating the need for further research, particularly in comparative studies. The integration of machine learning, artificial intelligence, and advanced optimization algorithms for real-time decision-making is highlighted as a potential avenue for addressing complex energy management challenges. The insights provided in this manuscript will be valuable for researchers aiming to further explore HPPs, contributing to the development of a cleaner, economically viable, efficient, and reliable power system. [ABSTRACT FROM AUTHOR]