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Ghrelin exerts key effects on islet hormone secretion to regulate blood glucose levels. Here, we sought to determine whether ghrelin's effects on islets extend to the alteration of islet size and [beta] cell mass. We demonstrate that reducing ghrelin - by ghrelin gene knockout (GKO), conditional ghrelin cell ablation, or high-fat diet (HFD) feeding - was associated with increased mean islet size (up to 62%), percentage of large islets (up to 854%), and [beta] cell cross-sectional area (up to 51%). In GKO mice, these effects were more apparent in 10- to 12-week-old mice than in 4-week-old mice. Higher [beta] cell numbers from decreased [beta] cell apoptosis drove the increase in [beta] cell cross-sectional area. Conditional ghrelin cell ablation in adult mice increased the [beta] cell number per islet by 40% within 4 weeks. A negative correlation between islet size and plasma ghrelin in HFD-fed plus chow-fed WT mice, together with even larger islet sizes in HFD-fed GKO mice than in HFD-fed WT mice, suggests that reduced ghrelin was not solely responsible for diet-induced obesity-associated islet enlargement. Single-cell transcriptomics revealed changes in gene expression in several GKO islet cell types, including upregulation of Manf, Dnajc3, and Gnas expression in [beta] cells, which supports decreased [beta] cell apoptosis and/or increased [beta] cell proliferation. These effects of ghrelin reduction on islet morphology might prove useful when designing new therapies for diabetes., Introduction Ghrelin is secreted primarily by gastric ghrelin cells (1). It is mainly via binding to CNS and pituitary growth hormone secretagogue receptors (GHSRs) that ghrelin's actions to increase GH [...]

Counterfeit drugs pose significant health risks due to their variable efficacy and potential harmful ingredients. To combat this issue, a reliable and secure track-and-trace system is essential for pharmaceutical supply chains. This paper proposes an Immutable and Decentralized Pharma (IDP) model, leveraging blockchain technology to ensure the safe and efficient distribution of medications. The IDP model utilizes smart contracts to record transactions between entities onto a blockchain, enabling end-to-end product tracking and provenance. Experimental results on a polygon blockchain test network demonstrate the feasibility and enhanced security of the IDP model in a collaborative environment. Our solution addresses the challenges of data privacy, openness, and authenticity inherent in centralized track-and-trace systems, providing a promising approach to eliminate counterfeits and guarantee product safety in pharmaceutical supply chains. [ABSTRACT FROM AUTHOR]

In clinical research, it is crucial to segment the magnetic resonance (MR) brain image for studying the internal tissues of the brain. To address this challenge in a sustainable manner, a novel approach has been proposed leveraging the power of unsupervised clustering while integrating conditional spatial properties of the image into intuitionistic clustering technique for segmenting MRI images of brain scans. In the proposed technique, an Intuitionistic-based clustering approach incorporates a nuanced understanding of uncertainty inherent in the image data. The measure of uncertainty is achieved through calculation of hesitation degree. The approach introduces a conditional spatial function alongside the intuitionistic membership matrix, enabling the consideration of spatial relationships within the image. Furthermore, by calculating weighted intuitionistic membership matrix, the algorithm gains the ability to adapt its smoothing behavior based on the local context. The main advantages are enhanced robustness with homogenous segments, lower sensitivity to noise, intensity inhomogeneity and accommodation of degree of hesitation or uncertainty that may exist in the real-world datasets. A comparative analysis of synthetic and real datasets of MR brain images proves the efficiency of the suggested approach over different algorithms. The paper investigates how the suggested research methodology performs in medical industry under different circumstances including both qualitative and quantitative parameters such as segmentation accuracy, similarity index, true positive ratio, false positive ratio. The experimental outcomes demonstrate that the suggested algorithm outperforms in retaining image details and achieving segmentation accuracy. [ABSTRACT FROM AUTHOR]

Reducing ghrelin by ghrelin gene knockout (GKO), ghrelin-cell ablation, or high-fat diet feeding increases islet size and β-cell mass in male mice. Here we determined if reducing ghrelin also enlarges islets in females and if pregnancy-associated changes in islet size are related to reduced ghrelin. Islet size and β-cell mass were larger (P =.057 for β-cell mass) in female GKO mice. Pregnancy was associated with reduced ghrelin and increased liver-expressed antimicrobial peptide-2 (LEAP2; a ghrelin receptor antagonist) in wild-type mice. Ghrelin deletion and pregnancy each increased islet size (by ∼19.9-30.2% and ∼34.9-46.4%, respectively), percentage of large islets (>25 µm2×103, by ∼21.8-42% and ∼21.2-41.2%, respectively), and β-cell mass (by ∼15.7-23.8% and ∼65.2-76.8%, respectively). Neither islet cross-sectional area, β-cell cross-sectional area, nor β-cell mass correlated with plasma ghrelin, although all positively correlated with LEAP2 (P =.081 for islet cross-sectional area). In ad lib-fed mice, there was an effect of pregnancy, but not ghrelin deletion, to change (raise) plasma insulin without impacting blood glucose. Similarly, there was an effect of pregnancy, but not ghrelin deletion, to change (lower) blood glucose area under the curve during a glucose tolerance test. Thus, genetic deletion of ghrelin increases islet size and β-cell cross-sectional area in female mice, similar to males. Yet, despite pregnancy-associated reductions in ghrelin, other factors appear to govern islet enlargement and changes to insulin sensitivity and glucose tolerance in the setting of pregnancy. In the case of islet size and β-cell mass, one of those factors may be the pregnancy-associated increase in LEAP2. [ABSTRACT FROM AUTHOR]

The increasing popularity and adoption of the Internet of Things (IoT) ecosystem in various domains has brought attention to the security breaches linked with this paradigm. As the number of IoT devices continues to grow, it is essential to ensure that they are secured to protect against potential threats and attacks. IoT network proliferation of interconnected devices has significantly raised security concerns making them attractive targets for cyber attackers seeking to gain unauthorized access to systems and cause disruptions. As IoT networks collect and transmit sensitive data using centralized architecture, ensuring security and integrity of these networks becomes paramount. Distributed Ledger Technology (DLT) has emerged as a promising solution for enhancing IoT security. Two prominent DLT platforms: Blockchain and Internet of Things Application (IOTA) technologies can provide a more secure and resilient foundation for IoT ecosystems, and also help to mitigate risks associated with central node vulnerabilities. DLT-based IoT systems can also enable the creation of decentralized marketplaces and autonomous agents that can operate without human intervention. The objective of this research is to offer a comprehensive as well as fundamental study of IoT ecosystems and its associated security risks. Moreover, this paper provides a holistic study of the DLT platform and bibliometric inspection using VoS viewer tool on generic DLT platform technologies i.e., Blockchain and IOTA for securing data in IoT ecosystem. By leveraging bibliometric insights resulting from both DLT technologies, this study identities the most promising areas for further investigation and contribute to advancing security in IoT ecosystems. This survey contributes to the ongoing discourse on IoT security by providing a thorough comprehensive comparison of DLT solutions i.e., Blockchain and IOTA technologies on various key metrics, revealing that IOTA technology is projected to offer significant improvements over blockchain in securing sustainable IoT ecosystems. [ABSTRACT FROM AUTHOR]

Gastrointestinal (GI) cancer is leading general tumour in the Gastrointestinal tract, which is fourth significant reason of tumour death in men and women. The common cure for GI cancer is radiation treatment, which contains directing a high-energy X-ray beam onto the tumor while avoiding healthy organs. To provide high dosages of X-rays, a system needs for accurately segmenting the GI tract organs. The study presents a UMobileNetV2 model for semantic segmentation of small and large intestine and stomach in MRI images of the GI tract. The model uses MobileNetV2 as an encoder in the contraction path and UNet layers as a decoder in the expansion path. The UW-Madison database, which contains MRI scans from 85 patients and 38,496 images, is used for evaluation. This automated technology has the capability to enhance the pace of cancer therapy by aiding the radio oncologist in the process of segmenting the organs of the GI tract. The UMobileNetV2 model is compared to three transfer learning models: Xception, ResNet 101, and NASNet mobile, which are used as encoders in UNet architecture. The model is analyzed using three distinct optimizers, i.e., Adam, RMS, and SGD. The UMobileNetV2 model with the combination of Adam optimizer outperforms all other transfer learning models. It obtains a dice coefficient of 0.8984, an IoU of 0.8697, and a validation loss of 0.1310, proving its ability to reliably segment the stomach and intestines in MRI images of gastrointestinal cancer patients. [ABSTRACT FROM AUTHOR]

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making them more intelligent and connected. However, this advancement comes with challenges related to the effectiveness of IoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensure their proper functionality. The success of smart systems relies on their seamless operation and ability to handle faults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore, sensor faults can compromise the system's reliability and undermine the trustworthiness of smart environments. To address these concerns, various techniques and algorithms can be employed to enhance the performance of IoT devices through effective fault detection. This paper conducted a thorough review of the existing literature and conducted a detailed analysis. This analysis effectively links sensor errors with a prominent fault detection technique capable of addressing them. This study is innovative because it paves the way for future researchers to explore errors that have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essential factors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and their corresponding recommended techniques. Additionally, the paper presents a methodical overview of fault detection techniques employed in smart devices, including the metrics used for evaluation. Furthermore, the paper examines the body of academic work related to sensor faults and fault detection techniques within the domain. This reflects the growing inclination and scholarly attention of researchers and academicians toward strategies for fault detection within the realm of the Internet of Things. [ABSTRACT FROM AUTHOR]

An Electric Vehicle (EV) charger or Electric Vehicle Supply Equipment (EVSE) is a piece of equipment that supplies electrical power for charging plug-in electric vehicles. Although batteries can only be charged with Direct Current (DC) power, most electric vehicles have an onboard Alternative Current AC--to--DC converter and most fully electric cars can accept both AC and DC power. The adoption of EVs can bring about significant relief in noise pollution and also environmental pollution if the required electricity is generated using renewable sources. DC charging stations of various levels are commonly equipped with multiple ports of various levels to be able to charge a wide variety of EVs. EVSEs are found at various facilities such as street-side or retail shopping centers, government facilities, and other parking areas. To ensure a sustainable environment by reducing the carbon emissions from vehicles, the use of EVs needs to be promoted. The need for having Electric Vehicle Charging Stations (EVCS) in any region depends upon the demand and cluster density of EVs in that region and is a major factor in the process of promoting the use of EVs and facilitating sustainable tourism using cleaner fuels. The authors of this study have located the various types and numbers of EVSEs throughout all the states and union territories of India, showing the emerging use of EVs so that EV users can conveniently locate charging stations and plan their routes accordingly. Furthermore, other citizens may be encouraged to own and use EVs for better environmental sustainability. [ABSTRACT FROM AUTHOR]

One of the most economical and precious spices ever known is saffron. It has innumerable pharmaceutical applications, and its cultivation is limited to certain topographic regions throughout the world. Although efforts are continuously being made to increase productivity and increase the area under cultivation, there are multiple factors leading to its low production. Corm size is one of the major factors affecting the quality and production of saffron. The more the corm size for a plantation, the better the yield of the crop. This can pose a serious threat to saffron cultivators who are not able to get good quality corms and sort quality corms from infected ones. Additional factors affecting saffron cultivation include attack by fungus leading to corm rots and sclerotineal disease. In this paper, IoT (Internet of Things) has been used to provide an energy-efficient, readyto-use, reliable, and user-friendly solution for corm sorting and treatment before sowing to promote quality, better yield and minimized wastage due to corm rots. After sorting the corms, they are treated with 0.2% of tiabendazole fungicide solution and dried, before sowing and for long-term storage. Firstly, sorting and treatment system for the saffron corms processing as per weight is proposed. First two-layer cleaning mechanism to remove foreign substances, then three-step treatment has been applied to remove microorganisms. An automated conveyor system equipped with IoT devices was used for sorting and treating saffron corms. Different energy efficient LPWA (Low Power Wide Area) and LTE (Long Term Evolution) technologies used in the framework, for communication are NB-IoT (Narrow Band IoT) and LoRa (Long Range Radio). [ABSTRACT FROM AUTHOR]

Background: The 2018 Australian Heart Failure (HF) guidelines strongly recommended commencing sodium‐glucose co‐transporter‐2 inhibitors (SGLT‐2is) in HF patients with type 2 diabetes mellitus (T2DM). The uptake of SGLT‐2is for HF patients with T2DM in our health service is unknown. Aims: To determine the adoption of the 2018 HF guidelines by assessing the temporal trends of SGLT‐2is' usage in HF patients with T2DM at Metro South Health (MSH) hospitals, in South‐East Queensland. Methods: Retrospective analysis of all HF patients (ejection fraction (EF) < 50%) with T2DM who were managed within MSH hospitals between June 2018 and June 2021. Results: A total of 666 patients met the inclusion criteria with 918 HF encounters. Mean age was 72 years and 71% were male (473/666). Mean EF was 30% (SD ± 11%), and mean estimated glomerular filtration rate was 48 mL/min/1.73 m2 (SD ± 25). Fifty‐four per cent (362/666) had contraindications to SGLT‐2is. Among those without contraindications, there was a five‐fold increase in the utility of SGLT‐2is, 7% (2/29) before versus 38% (103/275) after implementation of the HF guidelines (P < 0.001). Patients on SGLT‐2is were younger (64 years vs 69 years, P = 0.002) and had a lower number of HF hospitalisations (1.1 vs 2.1, P = 0.01). Conclusions: During the study period, 54% of our HF patients with T2DM were not on SGLT‐2is due to prescribing guidelines/limitations in the Australian context. We observed a five‐fold significant increase in the uptake of SGLT‐2is before and after implementation of HF guidelines among patients without contraindications to SGLT‐2is. There were significantly fewer HF hospitalisations among patients on SGLT‐2is compared to those without. [ABSTRACT FROM AUTHOR]

Glomeruli are interconnected capillaries in the renal cortex that are responsible for blood filtration. Damage to these glomeruli often signifies the presence of kidney disorders like glomerulonephritis and glomerulosclerosis, which can ultimately lead to chronic kidney disease and kidney failure. The timely detection of such conditions is essential for effective treatment. This paper proposes a modified UNet model to accurately detect glomeruli in whole-slide images of kidney tissue. The UNet model was modified by changing the number of filters and feature map dimensions from the first to the last layer to enhance the model's capacity for feature extraction. Moreover, the depth of the UNet model was also improved by adding one more convolution block to both the encoder and decoder sections. The dataset used in the study comprised 20 large whole-side images. Due to their large size, the images were cropped into 512 × 512-pixel patches, resulting in a dataset comprising 50,486 images. The proposed model performed well, with 95.7% accuracy, 97.2% precision, 96.4% recall, and 96.7% F1-score. These results demonstrate the proposed model's superior performance compared to the original UNet model, the UNet model with EfficientNetb3, and the current state-of-the-art. Based on these experimental findings, it has been determined that the proposed model accurately identifies glomeruli in extracted kidney patches. [ABSTRACT FROM AUTHOR]

Introduction: Paddy leaf diseases have a catastrophic influence on the quality and quantity of paddy grain production. The detection and identification of the intensity of various paddy infections are critical for high-quality crop production. Methods: In this paper, infections in paddy leaves are considered for the identification of illness severity. The dataset contains both primary and secondary data. The four online repositories used for secondary data resources are Mendeley, GitHub, Kaggle and UCI. The size of the dataset is 4,068 images. The dataset is first pre-processed using ImageDataGenerator. Then, a generative adversarial network (GAN) is used to increase the dataset size exponentially. The disease severity calculation for the infected leaf is performed using a number of segmentation methods. To determine paddy infection, a deep learning-based hybrid approach is proposed that combines the capabilities of a convolutional neural network (CNN) and support vector machine (SVM). The severity levels are determined with the assistance of a domain expert. Four degrees of disease severity (mild, moderate, severe, and profound) are considered. Results: Three infections are considered in the categorization of paddy leaf diseases: bacterial blight, blast, and leaf smut. The model predicted the paddy disease type and intensity with a 98.43% correctness rate. The loss rate is 41.25%. Discussion: The findings show that the proposed method is reliable and effective for identifying the four levels of severity of bacterial blight, blast, and leaf smut infections in paddy crops. The proposed model performed better than the existing CNN and SVM classification models. [ABSTRACT FROM AUTHOR]

Pomology, also known as fruticulture, is a significant contributor to the economies of many nations worldwide. While vertical farming methods are not well-suited for fruit cultivation, substrate-based cultivation is commonly practiced. Vertical farming methods use no soil for cultivation of the plants, and the cultivation is done in vertically stacked layers. Therefore, smaller herbs are best suited for such cultivation, whereas, the majority of the fruit trees are big and woody. Therefore, vertical farming methods are not well suited for fruit trees. However, to maximize fruit production, smarter substrate cultivation methods are needed. Utilizing remote sensing techniques, such as Internet of Things (IoT) devices, agriculture sensors, and cloud computing, allows for precision agriculture and smart farming in autonomous systems. Nevertheless, a lack of understanding of fruit nutrient requirements, growing conditions, and soil health conditions can result in reduced fruit production. To address these challenges, this paper proposes an intelligent model based on machine learning that recommends the best fruit to grow based on prevailing soil and climatic conditions. The system is trained on a dataset that includes details on eleven different fruits, such as Nitrogen (N), Phosphorous (P), Potassium (K), temperature, humidity, pH, and rainfall. The model takes into account the soil type and nutrient contents to recommend the most suitable fruit to grow in the prevailing climate. To enhance the model's efficiency, two novel techniques, Gradient-based Side Sampling (GOSS) and Exclusive Feature Bundling (EFB), have been incorporated. The results show that the proposed system has achieved 99% accuracy in recommending the right fruit based on the given environmental conditions. As a result, this system has the potential to significantly improve the profitability of the pomology industry and boost national economies.Article Highlights: This article aims at the creation of an efficient recommendation model for fruit cultivation in soil medium by analyzing the soil nutrient contents and the existing climatic conditions. The most suitable fruit plant corresponding to the existing conditions and soil type is recommended for an enhanced yield of the plant. Three climatic parameters, i.e., temperature, humidity, and rainfall; along with four soil-based parameters, i.e., pH, N content, P content, and K content are considered as the required growing condition for eleven varieties of fruits. To ensure enhanced accuracy, a hundred entries for each fruit type is entered in the dataset. The created dataset is then divided in the proportion of 7:3 as training data: testing data and Light Gradient Boosting Machine (Light GBM) model is applied to the created dataset. The correlation of all the parameters is checked for an efficient recommendation of fruits. Finally, the model is evaluated and its efficiency is checked. [ABSTRACT FROM AUTHOR]

Background The prevention of coronavirus disease 2019 (COVID-19) in vulnerable populations is a global health priority. EVADE was a phase 2/3 multicenter, double-blind, randomized, placebo-controlled trial of adintrevimab, an extended–half-life monoclonal antibody, for postexposure (PEP) and pre-exposure prophylaxis (PrEP) of symptomatic COVID-19. Methods Eligible participants (vaccine-naive, aged ≥12 years) were randomized 1:1 to receive a single 300-mg intramuscular injection of adintrevimab or placebo. Primary efficacy end points were reverse transcription polymerase chain reaction (RT-PCR)–confirmed symptomatic COVID-19 through day 28 in the PEP cohort (RT-PCR-negative at baseline) and through month 3 in the PrEP cohort (RT-PCR-negative and seronegative at baseline) among participants randomized before emergence of the severe acute respiratory syndrome coronavirus 2 Omicron variant (November 30, 2021). Safety was assessed through 6 months. Results Between April 27, 2021, and January 11, 2022, 2582 participants were randomized. In the primary efficacy analysis, RT-PCR-confirmed symptomatic COVID-19 occurred in 3/175 (1.7%) vs 12/176 (6.8%) adintrevimab- and placebo-treated PEP participants, respectively (74.9% relative risk reduction [RRR]; standardized risk difference, −5.0%; 95% CI, −8.87% to −1.08%; P =.0123) and in 12/752 (1.6%) vs 40/728 (5.5%) adintrevimab- and placebo-treated PrEP participants, respectively (71.0% RRR; standardized risk difference, −3.9%; 95% CI, −5.75% to −2.01%; P <.0001). In a prespecified exploratory analysis of 428 PrEP participants randomized after the emergence of Omicron, adintrevimab reduced RT-PCR-confirmed symptomatic COVID-19 by 40.6% (standardized risk difference −8.4%; 95% CI, −15.35% to −1.46%; nominal P =.0177) vs placebo. Adintrevimab was well tolerated, with no serious drug-related adverse events reported. Conclusions A single intramuscular injection of adintrevimab provided prophylactic efficacy against COVID-19 due to susceptible variants without safety concerns. Clinical trial registration. NCT04859517. [ABSTRACT FROM AUTHOR]

Cloud computing plays an important role in every IT sector. Many tech giants such as Google, Microsoft, and Facebook as deploying their data centres around the world to provide computation and storage services. The customers either submit their job directly or they take the help of the brokers for the submission of the jobs to the cloud centres. The preliminary aim is to reduce the overall power consumption which was ignored in the early days of cloud development. This was due to the performance expectations from cloud servers as they were supposed to provide all the services through their services layers IaaS, PaaS, and SaaS. As time passed and researchers came up with new terminologies and algorithmic architecture for the reduction of power consumption and sustainability, other algorithmic anarchies were also introduced, such as statistical oriented learning and bioinspired algorithms. In this paper, an indepth focus has been done on multiple approaches for migration among virtual machines and find out various issues among existing approaches. The proposed work utilizes elastic scheduling inspired by the smart elastic scheduling algorithm (SESA) to develop a more energy-efficient VM allocation and migration algorithm. The proposed work uses cosine similarity and bandwidth utilization as additional utilities to improve the current performance in terms of QoS. The proposed work is evaluated for overall power consumption and service level agreement violation (SLA-V) and is compared with related state of art techniques. A proposed algorithm is also presented in order to solve problems found during the survey. [ABSTRACT FROM AUTHOR]

Introduction: Recurrent episodes of insulin-induced hypoglycemia in patients with diabetes mellitus can result in hypoglycemia-associated autonomic failure (HAAF), which is characterized by a compromised response to hypoglycemia by Counter-regulatory hormones (counter-regulatory response; CRR) and hypoglycemia unawareness. HAAF is a leading cause of morbidity in diabetes and often hinders optimal regulation of blood glucose levels. Yet, the molecular pathways underlying HAAF remain incompletely described. We previously reported that in mice, ghrelin is permissive for the usual CRR to insulin-induced hypoglycemia. Here, we tested the hypothesis that attenuated release of ghrelin both results from HAAF and contributes to HAAF. Methods: C57BL/6N mice, ghrelin-knockout (KO) + control mice, and GhIRKO (ghrelin cell-selective insulin receptor knockout) + control mice were randomized to one of three treatment groups: a "Euglycemia" group was injected with saline and remained euglycemic; a 1X hypoglycemia ("1X Hypo") group underwent a single episode of insulin-induced hypoglycemia; a recurrent hypoglycemia ("Recurrent Hypo") group underwent repeated episodes of insulin-induced hypoglycemia over five successive days. Results: Recurrent hypoglycemia exaggerated the reduction in blood glucose (by -30%) and attenuated the elevations in plasma levels of the CRR hormones glucagon (by 64.5%) and epinephrine (by 52.9%) in C57BL/6N mice compared to a single hypoglycemic episode. Yet, plasma ghrelin was equivalently reduced in "1X Hypo" and "Recurrent Hypo" C57BL/6N mice. Ghrelin-KO mice exhibited neither exaggerated hypoglycemia in response to recurrent hypoglycemia, nor any additional attenuation in CRR hormone levels compared to wild-type littermates. Also, in response to recurrent hypoglycemia, GhIRKO mice exhibited nearly identical blood glucose and plasma CRR hormone levels as littermates with intact insulin receptor expression (floxed-IR mice), despite higher plasma ghrelin in GhIRKO mice. Conclusions: These data suggest that the usual reduction of plasma ghrelin due to insulin-induced hypoglycemia is unaltered by recurrent hypoglycemia and that ghrelin does not impact blood glucose or the blunted CRR hormone responses during recurrent hypoglycemia. [ABSTRACT FROM AUTHOR]

Background Safe and effective treatments are needed to prevent severe outcomes in individuals with coronavirus disease 2019 (COVID-19). We report results from STAMP, a phase 2/3, multicenter, double-blind, randomized, placebo-controlled trial of adintrevimab, an extended half-life monoclonal antibody, for treatment of high-risk ambulatory patients with mild to moderate COVID-19. Methods Nonhospitalized, unvaccinated participants aged ≥12 years with mild to moderate COVID-19 and ≥1 risk factor for disease progression were randomized to receive a single intramuscular injection of 300 mg adintrevimab or placebo. Enrollment was paused due to the global emergence of the Omicron BA.1/BA1.1 variants, against which adintrevimab showed reduced activity in vitro. The primary efficacy endpoint was COVID-19–related hospitalization or all-cause death through day 29 in participants with COVID-19 due to laboratory-confirmed or suspected non-Omicron severe acute respiratory syndrome coronavirus 2 variants. Results Between 8 August 2021 and 11 January 2022, 399 participants were randomized to receive adintrevimab (n = 198) or placebo (n = 201), including 336 with COVID-19 due to non-Omicron variants. COVID-19–related hospitalization or all-cause death through day 29 occurred in 8 of 169 (4.7%) participants in the adintrevimab group and 23 of 167 (13.8%) participants in the placebo group, a 66% relative risk reduction in favor of adintrevimab (standardized risk difference, −8.7% [95% confidence interval, −14.71% to −2.67%]; P =.0047). Incidence of treatment-emergent adverse events (TEAEs) was similar between treatment groups (33.9% for adintrevimab and 39.5% for placebo). No adintrevimab-related serious TEAEs were reported. Conclusions Treatment with a single intramuscular injection of adintrevimab provided protection against severe outcomes in high-risk ambulatory participants with COVID-19 due to susceptible variants, without safety concerns. Clinical Trial Registration. NCT04805671. [ABSTRACT FROM AUTHOR]

Objective: Prader‐Willi syndrome (PWS) is a multisystem genetic disorder. Unfortunately, none of several mouse models carrying PWS mutations emulates the entirety of the human PWS phenotype, including hyperphagia plus obesity. Methods: To determine whether housing at thermoneutrality (TN, 30 °C) permits the development of hyperphagia and obesity in the Snord116del PWS mouse model, the effects of housing three different ages of Snord116del and wild‐type (WT) littermates at TN versus room temperature (RT, 22–24 °C) for 8 weeks were compared. Results: Snord116del mice born and maintained at TN exhibited lower body weight curves, lower percentage fat mass, and lower food intake than WT mice at RT. In 4‐ to 6‐month‐old high‐fat diet–fed female mice, TN raised the Snord116del body weight curve closer to that of RT‐housed WT mice although the TN‐housed Snord116del mice did not gain more adiposity or exhibit greater food intake. In 6‐ to 8‐month‐old high‐fat diet–fed male mice, body weight, adiposity, and food intake of TN‐housed Snord116del mice remained far below levels in RT‐housed WT mice. TN elicited hypotonia in Snord116del adults and exacerbated mortality of Snord116del newborns. Conclusions: In none of three tested TN protocols were greater food intake, body weight, or adiposity induced in Snord116del mice compared with RT‐housed WT mice. [ABSTRACT FROM AUTHOR]

Massive Open Online Courses (MOOCs) have significantly impacted the basic education industry since 2012. Online platforms enable learners to connect with the instructors present worldwide and reduce learning time by approximately 50-60%. Many universities have opted for their survival in the pandemic of COVID-19. During the pandemic, novice learners were not able to enroll in the relevant courses on these platforms, and instructors also faced challenges to satisfy their learners' needs. Each online forum has its own recommender system, and these systems only recommend courses from their own platforms. As a result, these platforms fail to satisfy the learners' educational needs and thereby increase the dropout ratio. The main objective of this study is to create a single platform for learners to search for courses from multiple platforms like Coursera, Udemy, EdX, Udacity, etc., and then recommend courses according to the learning behavior of a learner. A user profile is created in three ways, i.e., by registering, uploading their CV, or through their LinkedIn accounts. The recommender system then uses this user profile as input and recommends the relevant courses for user adaption. In this paper, demographic, content-based and collaboration-based recommender systems are used for recommendations. To validate, multi-model filtering, namely random, userbased collaboration, item-based collaboration, and matrix factorization, is used to obtain the values of the performance metrics such as RMSE, precision, and recall. On the basis of the results, the best result is obtained from user-based collaboration filtering on 6,000 dimensions of the dataset. The value of RMSE in the case of user-based collaboration filtering is 0.101, the value of precision is 0.82, and the value of recall is 0.822. Thus, the learner-centric hybrid filtering-based recommender system for MOOC platforms is implemented to enhance user adaptation. [ABSTRACT FROM AUTHOR]

MANET (Mobile Ad hoc Networks) functionality is determined by routing protocols' ability to adjust to atypical changes in information and communication technologies, topological systems, and connection status. Due to interference, node migration, the growth of several pathways, security, and propagation loss, MANET network configurations are dynamic. The proactive routing protocol enhances the message flow utilized in the neighborhood discovery process by using the multipoint relays (MPR) approach. In order to increase the protocol's effectiveness and efficiency while maintaining the OLSR protocol's reliability, the research presented in this paper proposed an improved OCI-OLSR (Optimized Control Interval-Optimized Link State Routing) that focuses on better control interval management, an advanced MPR selection process, reducing neighbor hold time as well as decreasing flooding. The suggested proposed protocol was examined using the NS3 simulator, and it was compared to the standard OLSR version and AODV(Ad-hoc On-Demand Routing) routing protocol. According to the analysis's findings, the suggested system has a lot of promise in terms of a variety of performance metrics under diverse conditions. Overall, the article makes the case that the OCI-OLSR protocol may enhance the performance of the regular OLSR protocol in wireless ad hoc networks by addressing a number of the protocol's flaws. [ABSTRACT FROM AUTHOR]

Brain tumor diagnosis at an early stage can improve the chances of successful treatment and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures, such as magnetic resonance imaging (MRI), can be used to diagnose brain tumors. Deep learning, a type of artificial intelligence, can analyze MRI images in a matter of seconds, reducing the time it takes for diagnosis and potentially improving patient outcomes. Furthermore, an ensemble model can help increase the accuracy of classification by combining the strengths of multiple models and compensating for their individual weaknesses. Therefore, in this research, a weighted average ensemble deep learning model is proposed for the classification of brain tumors. For the weighted ensemble classification model, three different feature spaces are taken from the transfer learning VGG19 model, Convolution Neural Network (CNN) model without augmentation, and CNN model with augmentation. These three feature spaces are ensembled with the best combination of weights, i.e., weight1, weight2, and weight3 by using grid search. The dataset used for simulation is taken from The Cancer Genome Atlas (TCGA), having a lower-grade glioma collection with 3929 MRI images of 110 patients. The ensemble model helps reduce overfitting by combining multiple models that have learned different aspects of the data. The proposed ensemble model outperforms the three individual models for detecting brain tumors in terms of accuracy, precision, and F1-score. Therefore, the proposed model can act as a second opinion tool for radiologists to diagnose the tumor from MRI images of the brain. [ABSTRACT FROM AUTHOR]

Saffron is a rare and valuable crop that is only cultivated in specific regions with suitable topographical conditions. To improve saffron cultivation, it is crucial to monitor and precisely control the crop's agronomic variables over at least one growth cycle to create a fully automated environment. To this end, agronomic variables in the Punjab region of India were analyzed and set points were calculated using third-order polynomial equations through the application of image processing techniques. The relationship between canopy cover, growth percentage, and agronomic variables was also investigated for optimal yield and quality. The addition of adulterants, such as turmeric and artificial colorants, to saffron is a major concern due to the potential for quality compromise and fraud by supply chain vendors. Hence, there is a need for devising an easy, reliable, and user-friendly mechanism to help in the detection of adulterants added to the saffron stigmas. This paper proposes an automated IoT-based saffron cultivation environment using sensors for determining set points of agronomical variables. In addition, a sensor-based chamber has been proposed to provide quality and adulteration checks of saffron and to eliminate product counterfeiting. The AquaCrop simulator was employed to evaluate the proposed framework's performance. The results of the simulation show improved biomass, yield, and harvest index compared with the existing solutions in precision agriculture. Given the high value and demand for saffron, ensuring its purity and quality is essential to sustain its cultivation and the economic viability of the market. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a platform that manages daily life tasks to establish an interaction between things and humans. One of its applications, the smart office that uses the Internet to monitor electrical appliances and sensor data using an automation system, is presented in this study. Some of the limitations of the existing office automation system are an unfriendly user interface, lack of IoT technology, high cost, or restricted range of wireless transmission. Therefore, this paper presents the design and fabrication of an IoT-based office automation system with a user-friendly smartphone interface. Also, real-time data monitoring is conducted for the predictive maintenance of sensor nodes. This model uses an Arduino Mega 2560 Rev3 microcontroller connected to different appliances and sensors. The data collected from different sensors and appliances are sent to the cloud and accessible to the user on their smartphone despite their location. A sensor fault prediction model based on a machine learning algorithm is proposed in this paper, where the k-nearest neighbors model achieved better performance with 99.63% accuracy, 99.59% F1-score, and 99.67% recall. The performance of both models, i.e., k-nearest neighbors and naive Bayes, was evaluated using different performance metrics such as precision, recall, F1-score, and accuracy. It is a reliable, continuous, and stable automation system that provides safety and convenience to smart office employees and improves their work efficiency while saving resources. [ABSTRACT FROM AUTHOR]