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Background: Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory disorder often treated with inhaled corticosteroids (ICS). While ICS are effective in managing COPD, prolonged use may have psychological side effects, including anxiety and depression. Objective: This study explores the impact of the duration of inhaled steroid use on psychological symptoms, specifically anxiety and depression, in COPD patients. Methods: A cross-sectional study was conducted with 160 COPD patients from outpatient respiratory clinics. Anxiety and depression were assessed using the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) scales, respectively. The duration of ICS use was categorized into three groups: <5 years, 6-10 years, and 11-15 years. Spearman's rank correlation coefficient was used to analyze the relationship between ICS duration and psychological symptoms. Results: The prevalence of anxiety increased with longer ICS use, though not statistically significant (Spearman's rho = 0.152, P = 0.056). Depression showed a significant positive correlation with ICS duration (Spearman's rho = 0.243, P < 0.01). Conclusion: Prolonged use of inhaled corticosteroids in COPD patients is associated with increased prevalence of depression. Regular monitoring and timely intervention for psychological symptoms are recommended for COPD patients undergoing long-term ICS therapy. [ABSTRACT FROM AUTHOR]

Certain applications requires a scalable cost effective storage and execution system with facility to store data and have feature to analyze data to its finest granularity level in future. This increase the quality and accuracy of result analysis. Wireless sensor Network (WSN) nodes deployed for certain data intensive applications such as surveillance, war zone monitoring etc. generates a massive amount of raw data. There is an essential requirement of storing this data in its native format for analytics purpose in anticipation of future requirements. In present work, a data lake implemented on Amazon AWS is presented for storage of data in original version for future reference. Data Lake implementation service is utilized for storing the data generated in big volumes, high speed and in variety. The data in Data Lake is stored in three zones i.e. raw, reformed and curated. This paper proposes an efficient method of storing structured, unstructured and semi-structured, data in to Data Lake for future retrieval and analytics purpose. The results are comprehensively presented highlighting the advantages of using Data Lake in place of data warehouses.

The promise of university-public school partnerships as contexts for mutually beneficial learning, or "simultaneous renewal," has been well established (Goodlad, 1994, 1999). However, difficulties in creating and sustaining these kinds of collaborative contexts for teacher education are also well known, including practical challenges such as time and distance, as well as the nuanced and layered tensions between institutional missions, cultures, and practices (Mantle-Bromley, 2002; Teitel, 1997). Contemporary efforts to create and sustain university-public school partnerships are further complicated by dramatically increased accountability pressures arising from No Child Left Behind (NCLB). Discretionary time for public educators is a scarce and dwindling resource. As high stakes testing and related accountability pressures increase on institutions of higher education, chronic shortages of resources to support collaborative work with public school partners are a widely acknowledged fact of life in partnership work. In the context of these kinds of institutional pressures it is clear that collaborative partnership work must be carefully designed to yield visible and valued benefits for both university and public school-based educators (Yendol-Hoppey, League, Gregory, Ohlson, & Jackson, 2006). In this article, the authors describe a design strategy aimed at creating shared opportunities for teacher learning and development, including the learning of university faculty, that may be embedded in practical activities related to the analysis of P-12 student work. The authors offer three illustrations of how they have used this strategy to design shared contexts for learning that are relevant to the (differing) needs of teacher candidates, public school colleagues and university faculty. (Contains 1 table and 1 figure.)

Objective: Given fibromyalgia (FM) frequently co‐occurs with autoimmune disease, this study was initiated to objectively evaluate FM in a multiracial/ethnic cohort of patients with systemic lupus erythematosus (SLE). Methods: Patients with SLE were screened for FM using the 2016 FM classification criteria during an in‐person rheumatologist visit. We evaluated hybrid Safety of Estrogens in Lupus National Assessment (SELENA)‐SLE Disease Activity Index (SLEDAI) scores, SLE classification criteria, and Systemic Lupus International Collaborating Clinics damage index. We compared patients with and without FM and if differences were present, compared patients with FM with patients with non‐FM related chronic pain. Results: 316 patients with SLE completed the FM questionnaire. 55 (17.4%) met criteria for FM. The racial composition of patients with FM differed from those without FM (P = 0.023), driven by fewer Asian patients having FM. There was no difference in SLE disease duration, SELENA‐SLEDAI score, or active serologies. There was more active arthritis in the FM group (16.4%) versus the non‐FM group (1.9%) (P < 0.001). The Widespread Pain Index and Symptom Severity Score did not correlate with degree of SLE activity (r = −0.016; 0.107) among patients with FM or non‐FM chronic pain (r = 0.009; −0.024). Regarding criteria, patients with FM had less nephritis and more malar rash. Systemic Lupus International Collaborating Clinics damage index did not differ between groups. Conclusion: Except for arthritis, patients with SLE with FM are not otherwise clinically or serologically distinguishable from those without FM, and Widespread Pain Index and Symptom Severity Score indices do not correlate with SLEDAI. These observations support the importance of further understanding the underlying biology of FM in SLE. [ABSTRACT FROM AUTHOR]

The gate-all-around (GAA) silicon-on-insulator (SOI) Schottky barrier (SB) nano-wire (NW) MOSFET was recently proposed for low-power and high-frequency analog and radio frequency (RF) circuits. But their use in low-power and high-frequency analog/RF circuits is still under investigation. In this work, basic analog signal processing circuits using gate-all-around SOI-SB-NW MOSFETs are designed for low-power and high-frequency applications. These basic and necessary analog processing circuits are designed for ± 0.3 V and ± 0.25 V power supplies to work on frequencies up to 10 GHz. The analog/RF characteristics of the GAA SOI-SB MOSFET are compared with those of the GAA SB NW MOSFET. The SOI-SB NW MOSFET shows improvements in early voltage, output resistance, and transconductance generation factor. The Silvaco TCAD simulator is used to obtain the results and perform numerical simulations. Simulation results show good analog/RF performance of the analog processing circuits. [ABSTRACT FROM AUTHOR]

Background: Neonatal sepsis significantly contributes to neonatal mortality and morbidity in developing countries. The delay in diagnosis due to the time-consuming gold standard blood culture necessitates rapid, accessible, and cost-effective diagnostic markers. Aims: This study aimed to evaluate the sensitivity and specificity of Micro-ESR for early neonatal sepsis diagnosis and compare its effectiveness with other standard markers. Methodology: Conducted at D.Y. Patil Hospital's Department of Pediatrics in Navi Mumbai, this prospective observational study included 200 neonates suspected of sepsis. Blood samples were analyzed for CBC, CRP, Micro-ESR, and bacterial culture before antibiotic initiation. Micro-ESR's diagnostic performance was assessed against other markers based on confirmed sepsis cases via blood culture. Results: Among 200 neonates, 16 had a positive blood culture. Micro-ESR showed a sensitivity of 87.5% and specificity of 28.80%. In comparison, Total Leucocyte Count (TLC) and Absolute Neutrophil Count (ANC) demonstrated higher specificity (91.25% and 70% respectively) but lower sensitivity. Platelet count and CRP indicated high specificity (94.56%) and sensitivity (100%) respectively, albeit with low predictive values. Conclusions: While CRP exhibited the highest sensitivity, TLC and platelet count showed superior specificity. Despite its lower specificity, Micro-ESR's high sensitivity suggests its utility as a supportive early diagnostic marker alongside CBC, CRP, and ANC. Blood culture remains the definitive diagnostic standard. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a chronic neurodegenerative disease that causes brain cells to degenerate, resulting in decreased physical and mental abilities and, in severe cases, permanent memory loss. It is considered as the most common and fatal form of dementia. Although mild cognitive impairment (MCI) precedes Alzheimer's disease (AD), it does not necessarily show the obvious symptoms of AD. As a result, it becomes challenging to distinguish between mild cognitive impairment and cognitively normal. In this paper, we propose an ensemble of deep learners based on convolutional neural networks for the early diagnosis of Alzheimer's disease. The proposed approach utilises simple averaging ensemble and weighted averaging ensemble methods. The ensemble-based transfer learning model demonstrates enhanced generalization and performance for AD diagnosis compared to traditional transfer learning methods. Extensive experiments on the OASIS-3 dataset validate the effectiveness of the proposed model, showcasing its superiority over state-of-the-art transfer learning approaches in terms of accuracy, robustness, and efficiency. [ABSTRACT FROM AUTHOR]

Introduction: Dexmedetomidine is one of the anesthetics of choice for drug induced sleep endoscopy (DISE), with advantages including limited respiratory depression, analgesia, and decreased incidence of emergence delirium. However, challenges with determining sedation levels and prolonged recovery have limited its usage. An improved understanding of the effect of dexmedetomidine on the level of sedation and the corresponding electroencephalographic (EEG) changes could help overcome these barriers. Methods: Fifty-one patients received dexmedetomidine sedation with Richmond Agitation-Sedation Scale (RASS) score assessment and continuous EEG monitoring via SedLine for DISE. We constructed a pharmacokinetic model to determine continuous dexmedetomidine blood concentration. From the SedLine, we extracted the patient state index (PSI), and from the EEG we calculated the spectral edge frequency 95% (SEF95) and the correlation dimension (CD), a type of fractal dimension used to assess the complexity of a system. These metrics were subsequently compared against one another and with the dexmedetomidine concentration. Results: Our pharmacokinetic model yielded a two-compartment model with volumes of 51.8 L and 106.2 L, with clearances of 69.5 and 168.9 L/h, respectively, and a time to effect of 9 min, similar to prior studies. Based on this model, decreasing RASS score, SEF95, CD, and PSI were all significantly associated with increasing dexmedetomidine concentration (p < 0.001, p = 0.006, p < 0.001 respectively). The CD, SEF95, and PSI better captured the effects of increasing dexmedetomidine concentration as compared to the RASS score. Simulating dexmedetomidine concentration based on titration to target levels derived from CD and PSI confirmed commonly used dexmedetomidine infusion dosages. Conclusion: Dexmedetomidine use for DISE confirmed previous pharmacokinetic models seen with dexmedetomidine. Complex EEG metrics such as PSI and CD, as compared to RASS score and SEF95, better captured changes in brain state from dexmedetomidine and have potential to improve the monitoring of dexmedetomidine sedation. [ABSTRACT FROM AUTHOR]

Conservation strategies for imperiled species are frequently based on identifying and addressing the probable causes of population decline, an approach known as the declining population paradigm. Causes, however, are frequently linked to demographic outcomes by multiple mechanisms, and failing to target the primary mechanisms can reduce the effectiveness and efficiency of conservation actions. Increasingly, conservation strategies also need to consider emerging threats, such as climate change. Here, we use boreal caribou (Rangifer tarandus caribou), a threatened ecotype of woodland caribou, as a case study to illustrate how landscape disturbance and climate change can each exert negative demographic effects on caribou through multiple and complex mechanisms. We reviewed the extensive literature focused on woodland caribou to identify and assess the relative importance of each putative mechanism. While disturbance‐mediated apparent competition, the expansion of novel predators, and altered predator behavior appear to be primary mechanisms dictating past and current declines of caribou, climate change has increasing potential to exert strong direct and indirect effects now and in the future. Predicted climate effects may prevent some populations from regaining self‐sustaining status, despite local conservation actions. Our review revealed several knowledge gaps, notably a lack of clarity on the spatial extent of undisturbed habitat required for caribou populations to be stable. We used outcomes from our review to demonstrate how a mechanistic understanding of population decline can inform habitat‐based conservation strategies for caribou. For populations residing within highly disturbed ranges, habitat restoration is a key recommendation of current conservation strategies, yet the large spatial extent of disturbances will require prioritization of areas for restoration. Maximizing the conservation return‐on‐investment for caribou will require a mechanistically informed prioritization process that targets conservation actions toward the primary mechanisms underlying population decline. [ABSTRACT FROM AUTHOR]

rGO/Fe2O3 nanocomposites were synthesized using facile and inexpensive hydrothermal route. For the first time, these nanocomposites were incorporated in aerosol forming composites (AFCs), characterized and investigated for ultra-fast fire extinguishing applications. The morphological and structural characteristics were examined using scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transmission infrared spectroscopy (FTIR), Raman spectroscopy and energy dispersive X-ray spectroscopy (EDX). AFCs were also characterized for burn rate, combustion efficiency, minimum fire extinguishing concentration, fire extinguishing time, combustion temperature and activation energy. Characterization revealed successful synthesis of rGO/Fe2O3 nanocomposites having Fe2O3 nanoparticles (25–50 nm) over and in-between the graphene oxide nanosheets. Increase in combustion efficiency (9%) and burn rate (35%) and reduction in onset temperature (17.16%), combustion temperature (13.46%) and activation energy (37.71%) were observed on addition of 3% rGO/5.0Fe2O3 nanocomposite to base AFC. Increase in burn rate by 35% led to ultra-fast aerosol generation and faster (50% reduction in fire extinguishing time) fire extinguishment. AFCs found promising for incorporation in condensed aerosol-based fire extinguishing systems to comply futuristic quicker fire extinguishment requirements. [ABSTRACT FROM AUTHOR]

Determining the optimal feature set is a challenging problem, especially in an unsupervised domain. To mitigate the same, this paper presents a new unsupervised feature selection method, termed as densest feature graph augmentation with disjoint feature clusters. The proposed method works in two phases. The first phase focuses on finding the maximally non-redundant feature subset and disjoint features are added to the feature set in the second phase. To experimentally validate, the efficiency of the proposed method has been compared against five existing unsupervised feature selection methods on five UCI datasets in terms of three performance criteria, namely clustering accuracy, normalized mutual information, and classification accuracy. The experimental analyses have shown that the proposed method outperforms the considered methods. [ABSTRACT FROM AUTHOR]

We describe here the paradoxical development of spontaneous experimental autoimmune encephalomyelitis (EAE) in transgenic mice expressing a myelin oligodendrocyte glycoprotein (MOG)-specific T cell antigen receptor (TCR) in the absence of MOG. We report that in Mog-deficient mice ([Mog.sup.-/-]), the autoimmune response by transgenic T cells is redirected to a neuronal cytoskeletal self antigen, neurofilament-M (NF-M). Although components of radically different protein classes, the cross-reacting major histocompatibility complex [I-A.sup.b]--restricted epitope sequences of [MOG.sub.35-55] and [NF-M.sub.18-30] share essential TCR contact positions. This pattern of cross-reaction is not specific to the transgenic TCR but is also commonly seen in [MOG.sub.35-55]--[I- A.sup.b]--reactive T cells. We propose that in the C57BL/6 mouse, MOG and NF-M response components add up to overcome the general resistance of this strain to experimental induction of autoimmunity. Similar cumulative responses against more than one autoantigen may have a role in spontaneously developing human autoimmune diseases., Organ-specific autoimmune disease is a key group of inflammatory disorders that includes rheumatoid arthritis, type 1 diabetes mellitus, thyroiditis and multiple sclerosis. The prevailing thinking is that the pathogenic changes [...]

Contact angle and surface tension are the two most widely used surface analysis approaches for reservoir fluid characterization in petroleum industries. The pendant drop method has among the most widely used techniques for the estimation of surface tension. The present work utilizes a python-based computer program to automatically determine interfacial tension (IFT) and contact angle from the pendant drop image acquired from a typical pendant drop apparatus. The proposed program uses python-based image processing libraries for the analysis of the pendant drop image. Also, the program is tested on images acquired from the standard solutions for the IFT and contact angle calculation showing promising results with a standard deviation of less than 1.7 mN/m. [ABSTRACT FROM AUTHOR]

Objective: Enrollment of patients of Black African ancestry with systemic lupus erythematosus (SLE) in phase II and phase III of the belimumab trials was not reflective of the racial distribution observed in the lupus population. This study was undertaken to assess the efficacy and safety of intravenous (IV) belimumab plus standard therapy in patients of self‐identified Black race. Methods: EMBRACE (GSK Study BEL115471; ClinicalTrials.gov identifier: NCT01632241) was a 52‐week multicenter, double‐blind, placebo‐controlled trial in adults of self‐identified Black race with active SLE who received monthly belimumab 10 mg/kg IV, or placebo, plus standard therapy. The optional 26‐week open‐label extension phase included patients who completed the double‐blind phase. The primary end point of the study was SLE Responder Index (SRI) response rate at week 52 with modified proteinuria scoring adapted from the SLE Disease Activity Index 2000 (SLEDAI‐2K) (SRI–SLEDAI‐2K). Key secondary end points included SRI response rate at week 52, time to first severe SLE flare, and reductions in prednisone dose. Results: The modified intent‐to‐treat population comprised 448 patients, of whom 96.9% were women and the mean ± SD age was 38.8 ± 11.42 years. The primary end point (improvement in the SRI–SLEDAI‐2K response rate at week 52) was not achieved (belimumab 48.7%, placebo 41.6%; odds ratio 1.40 [95% confidence interval 0.93, 2.11], P = 0.1068); however, numerical improvements favoring belimumab were observed, in which the SRI–SLEDAI‐2K response rates were higher in those who received belimumab compared with those who received placebo, especially in patients with SLE who had high disease activity or renal manifestations at baseline. The safety profile of belimumab was generally consistent with that observed in previous SLE trials. Adverse events were the primary reasons for double‐blind phase withdrawals (belimumab 5.4%, placebo 6.7%). Conclusion: The primary end point of this study was not achieved, but improvement with belimumab versus placebo was observed, suggesting that belimumab remains a suitable treatment option for SLE management in patients of Black African ancestry. [ABSTRACT FROM AUTHOR]

A new online multi-class learning algorithm is proposed with three main characteristics. First, in order to make the feature pool fitter for the pattern pool, the adaptive feature pool is proposed to dynamically combine the three general features, Haar-like, Histogram of Oriented Gradient (HOG), and Local Binary Patterns (LBP). Second, the external model is integrated into the proposed model without re-training to enhance the efficacy of the model. Third, a new multi-class learning and updating mechanism are proposed that help to find unsuitable decisions and adjust them automatically. The performance of the proposed model is validated with multi-class detection and online learning system. The proposed model achieves a better score than other non-deep learning algorithms used in public pedestrian and multi-class databases. The multi-class databases contain data for pedestrians, faces, vehicles, motorcycles, bicycles, and aircraft. [ABSTRACT FROM AUTHOR]