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Inherited thrombophilic disorders are well‐established predisposing factors for venous thromboembolism, but their role in arterial ischaemic stroke is uncertain. The exact mechanism of arterial thrombosis in thrombophilias remains elusive. Herein, we report a case of a 30-year-old woman who was admitted to our facility with sudden-onset right-sided ptosis and ophthalmoplegia. Detailed clinical features, neuroimaging and laboratory evaluation clinched the diagnosis of ischaemic stroke in midbrain due to microvascular obstruction associated with isolated protein S deficiency. She was treated with oral anticoagulant (warfarin) and physiotherapy; without any improvement of her symptoms at 2 months of follow-up. A high index of clinical suspicion is needed in any case of young ischaemic stroke in absence of common cardiac and vascular risk factors, to recognise the presence of inherited thrombophilia.

Background Neonates at risk of hypoglycemia are often roomed in with mothers, but there is paucity of literature on the occurrence of hypoglycemia in these exclusively breastfed high-risk neonates. The primary objective was to estimate the incidence of hypoglycaemia in high-risk neonates on exclusive breastfeeding. The secondary objectives were to study the time of presentation, symptoms of hypoglycaemia, and the various maternal and neonatal risk factors. Methods This prospective observational study was carried out in a tertiary care teaching hospital of eastern India between January 2017 and June 2018. All neonates roomed in with mothers with high-risk factors such as low birth weight, preterm, small for gestational age, large for gestational age and infants of diabetic mothers were included. All included neonates were exclusive breastfed and underwent blood glucose monitoring at 2, 6, 12, 24, 48 and 72 h of life using glucometer strips and also whenever clinical features suggested hypoglycaemia. Hypoglycemia was defined as the blood glucose level ≤46 mg/dL. Results Of a total of 250 neonates studied, 52 (20.8%) developed hypoglycaemia in first 72 h. Hypoglycaemia was detected in most at 2 h with the second peak at 48 h of age. Only 8 (3.2%) neonates had symptomatic hypoglycaemia with jitteriness being the commonest symptom, followed by lethargy and poor feeding. Conclusion There is a need to closely monitor the blood glucose levels for at least first 48 h in high-risk neonates roomed in with mothers on exclusive breastfeeding.

Background Tribal people of India still use ancient traditional health care practices, using available plants in their surroundings. Traditional knowledge of medicine is vanishing due to urbanization and outmigration thus its urgent need to preserve this valuable knowledge. Methods This recent study was carried out in Pakyong subdivision of East Sikkim during 2016–17, and information was collected interviewing 71 informants of the Lepcha, Bhotia, and Nepali tribes. Results Present study documenting 60 useful plant species belonging to 38 families, used for local health care needs including ten veterinary useful species. Herbaceous plants were used in high proportion (32 species, 54%) followed by trees (20, 33%), shrubs (20, 8%) and climbers (3, 5%). Leaves were the most frequently used plant parts and decoction was the common drug formulation among the inhabitants. The Informant consensus factor (ICF) ranged between 0.88 and 0.97, and fidelity levels in the study region were varied from 11.53% to 100%. Conclusion Documentation of local plant uses by tribal people will be helpful in sustainable use of indigenous practices of medicinal plants, and further information may be used for pharmacological investigation and novel phytoconstituents isolation.

Modified split-ring resonators (SRR) are used develop a high gain UWB antenna for microwave imaging applications. This paper analyzes two different ground designs for the proposed patch radiator with modified SRR; defected ground structure and the second one is rectangular slotted ground. Two SRRs are placed on the circular radiator patch element, each in the shape of alphabet letters ‘S’ and ‘V’. The results of the first design with DGS demonstrate that the reflection coefficient is well below −10 dB in the frequency range from 1.9 GHZ −11.9 GHz with the fractional bandwidth of 110%. Further modification in the resonant modes of UWB is obtained by introducing single rectangular slot on the ground plane. This helps in eliminating the lower band from 1.9 GHz to 6 GHz. This band rejection helps in alleviating the interferences caused by other unlicensed wireless applications lying in sub 6GHZ band.

Applications hosted in commercial clouds are typically multi-tier and comprise multiple tightly coupled virtual machines (VMs). Service providers (SPs) cater to the users using VM instances with different configurations and pricing depending on the location of the data center (DC) hosting the VMs. However, selecting VMs to host multi-tier applications is challenging due to the trade-off between cost and quality of service (QoS) depending on the placement of VMs. This paper proposes a multi-cloud broker model called CoMCLOUD to select a sub-optimal VM coalition for multi-tier applications from an SP with minimum coalition pricing and maximum QoS. To strike a trade-off between the cost and QoS, we use an ant-colony-based optimization technique. The overall service selection game is modeled as a first-price sealed-bid auction aimed at maximizing the overall revenue of SPs. Further, as the hosted VMs often face demand spikes, we present a parallel migration strategy to migrate VMs with minimum disruption time. Detailed experiments show that our approach can improve the federation profit up to 23% at the expense of increased latency of approximately 15%, compared to the baselines.

Agriculture plays a major role in developing countries like India, however the food security still remains a vital issue. Most of the crops get wasted due to lack of storage facility, transportation, and plant diseases. More than 15% of the crops get wasted in India due to diseases and hence it has become one of the major concern to be resolved. There is a need of automatic system that can identify these diseases and help farmers to take appropriate steps to get rid of crop loss. Farmers have followed the conventional method of identifying the plant disease with their naked eyes, and it not possible for all the farmers to identify these diseases the same way. With the advance in Artificial Intelligence, there is a need to incorporate the facilities of the computer vision in the field of agriculture. Deep Learning rich libraries and user as well as developer friendly environment to work with, all these qualities make Deep Learning as the favorable method to get started with this problem. In this paper we have used Deep Learning because of the advantages it offers to work with images especially in image classification to get improvised results. The methodology includes taking leaves of infected crops and label them as per the disease pattern. The images of infected leaves are processed pixel based operations are applied to improve the information from the image. As a next step feature extraction is done followed by image segmentation and at the last classification of crop diseases based on the patterns extracted from the diseased leaves. The CNN (Convolutional Neural Network) is used for the classification of diseases, for the demonstration purpose the public dataset is used consisting of around 87 K images (RGB type images) including healthy as well as diseased leaves.

The outbreak of Coronavirus Disease 2019 (COVID-19) occurred at the end of 2019, and it has continued to be a source of misery for millions of people and companies well into 2020. There is a surge of concern among all persons, especially those who wish to resume in person activities, as the globe recovers from the epidemic and intends to return to a level of normalcy. Wearing a face mask greatly decreases the likelihood of viral transmission and gives a sense of security, according to studies. However, manually tracking the execution of this regulation is not possible. The key to this is technology. We present a Deep Learning-based system that can detect instances of improper use of face masks. A dual-stage Convolutional Neural Network (CNN)architecture is used in our system to recognie masked and unmasked faces. This will aid in the tracking of safety breaches, the promotion of face mask use, and the maintenance of a safe working environment. This paper will automate the tasks of mask detection in public places when incorporated with CCTV cameras and will alert the system manager when a person without mask or wearing incorrect mask tries to enter. This paper includes multi face detection model which has the potential to target and identify a group of people whether they are wearing masks or not. We tried to collect various facial pictures and tried to identify the face Region of Interest (ROI), and then we separated it. Applying facial milestones, to permit the restriction the eyes, nose, mouth, and so. face was then completed and we tried to detect the presence of mask. To prepare a custom face cover locator, breaking our venture into two unmistakable stages was required, each with its own separate sub-steps. 1. Preparing: Here, stacking our face veil discovery dataset from plate, preparing a model on this dataset, and afterward serializing the face cover locator to circle was the focus. 2. Sending: Once the face veil identifier is prepared, the accompanying advance of stacking the cover finder, performing face recognition, and afterward characterizing each face as with veil or without veil, can be executed.

s The perforated stiffened panel is generally found as a sub-component of sophisticated structures. The fundamental purpose of this panel is to withstand against buckling under complicated loading and environmental conditions. Hence, an accurate knowledge of critical buckling behaviour of stiffened panels is very much essential for a reliable and lightweight structural design. In this paper, the focus is on quasi-laminated panels with different cutout shapes of various sizes and their responses to hygrothermal environments under nonlinearly varying edge loads and is compared with the locally stiffened panels. Towards this, the modelling of the panel and stiffener is done by adopting nine-noded heterosis plate elements and three noded beam elements respectively. The stiffener formulation is suitably modified in order to take the torsional effect also into consideration along with the effect of shear deformation. Initially, the plate and the stiffener elements are treated separately, and then the displacement compatibility is maintained between them by using the transformation matrix. For a given loading and geometric discontinuity, the stress distribution within the perforated panel is highly non-uniform in nature and hence a dynamic approach has been used to calculate buckling loads by adopting two sets of boundary conditions, one set for pre-buckling stress analysis and the second set for buckling analysis. Four different quasi-isotropic stacking sequences are deliberated in this work by varying different ply-orientation in each scheme. The study also addresses the effect of various parameters such as nonlinear loads, hygro-thermal loads, cutout size and shapes, position of cutout, stiffener parameters, stacking sequences, thickness of plate and boundary conditions.

Low-rank plus sparse matrix decomposition (LSD) is an important problem in computer vision and machine learning. It has been solved using convex relaxations of the matrix rank and l0-pseudo-norm, which are the nuclear norm and l1-norm, respectively. Convex approximations are known to result in biased estimates, to overcome which, nonconvex regularizers such as weighted nuclear-norm minimization and weighted Schatten p-norm minimization have been proposed. However, works employing these regularizers have used heuristic weight-selection strategies. We propose weighted minimax-concave penalty (WMCP) as the nonconvex regularizer and show that it admits an equivalent representation that enables weight adaptation. Similarly, an equivalent representation to the weighted matrix gamma norm (WMGN) enables weight adaptation for the low-rank part. The optimization algorithms are based on the alternating direction method of multipliers technique. We show that the optimization frameworks relying on the two penalties, WMCP and WMGN, coupled with a novel iterative weight update strategy, result in accurate low-rank plus sparse matrix decomposition. The algorithms are also shown to satisfy descent properties and convergence guarantees. On the applications front, we consider the problem of foreground-background separation in video sequences. Simulation experiments and validations on standard datasets, namely, I2R, CDnet 2012, and BMC 2012 show that the proposed techniques outperform the benchmark techniques.

Traffic calming techniques are extensively used as safety tools to reduce the speed of motorised vehicles along any type of paved road. The objective of this study is to evaluate the efficacy of isolated speed humps on urban roads in India and to suggest an optimised geometry of speed humps for the desired level of speed reduction. For this, five different urban road sections were identified in Delhi and video graphic data were collected at each study section. The median speed of vehicles at each grid is estimated and speed profiles were developed for each category of vehicle approaching the speed hump. The speed behaviour of the vehicles approaching a circular or parabolic shape of isolated speed hump are examined and compared. This study developed speed profile models using regression techniques for an individual vehicle and also for the combined data. The observed speed of the vehicle was considered as a response variable and the physical properties of the speed humps were considered as explanatory variables. Further, the combined linear equation was considered for determining the optimum height and entry radius of the speed hump for the desired speed by using the simplex method.

Internet of Vehicles (IoV) is considered to be a backbone of smart transportation system where vehicles are expected to exchange traffic information and other data using vehicle-to-vehicle (V2V) communications. Furthermore, V2V communications is regarded as a medium to send upcoming traffic information to vehicles/drivers or passengers using wireless technology in a timely manner. This will help reduce traffic accidents, traffic jams, fuel consumption caused by traffic jams, loss of hours caused by delayed commuting and carbon emission caused by vehicles. Because of the critical nature of IoV for smart transportation systems, secure wireless communication is one of the critical and essential components to offer trustworthy services through IoV. Like in other wireless systems, wireless communication is open in air, which makes attackers' job easy to launch the malicious actions in IoV. In this paper, we investigate the impact of mobility of vehicles for secure communications in IoV with joint jamming and eavesdropping attacks. We investigate different scenarios, when vehicles (legitimate ones and attackers) travel in same and opposite directions with low speed (school zone speed) limit to highway speed limit. Furthermore, we study the impact of association time and encryption time not only for establishing the connection between vehicles for V2V communications but also for exchanging messages for vehicles after successful association between vehicles and encryption of the message. In addition, we also study the joint impact of jamming and eavesdropping attacks in IoV for different speed limits, transmission ranges and wireless bandwidths. We investigate the extra cost in terms of transmit power and bandwidth incurred by jamming and eavesdropping attacks while maintaining the same secrecy rate as it could have been without those attacks. Numerical results are used to support the analysis.

Background Breastfeeding has several benefits for both the infants and mothers. This study assessed breastfeeding knowledge, attitudes, and practices (KAP) among mothers of infants with Retinopathy of Prematurity (ROP). Methods A cross-sectional study was conducted among mothers of infants with ROP visiting the ROP clinic in a selected tertiary care centre. A total of 100 mothers were selected through the convenience sampling method and interviewed using a structured questionnaire. Result A significant number of mothers had average to poor knowledge (81%) with a mean score of 8.32 ± 2.7. More than half of the mothers had poor practices related to breastfeeding with a mean score of 7.6 ± 2.4. The attitudes of the mothers towards breastfeeding was either neutral or positive towards formula feeding. Conclusion The KAPs of mothers regarding breastfeeding were inadequate. Intervention measures are recommended for promoting breastfeeding practices among the mothers of infants with ROP.

The Internet of Things (IoT) has been growing over the past few years due to its flexibility and ease of use in real-time applications. The IoT’s foremost task is ensuring that there is proper communication between different types of applications and devices, and that the application layer protocols fulfill this necessity. However, as the number of applications grows, it is necessary to modify or enhance the application layer protocols according to specific IoT applications, allowing specific issues to be addressed, such as dynamic adaption to network conditions and interoperability. Recently, several IoT application layer protocols have been enhanced and modified according to application requirements. However, no existing survey articles have focused on these protocols. In this article, we survey traditional and recent advances in IoT application layer protocols, as well as relevant real-time applications and their adapted application layer protocols for improving performance. As changing the nature of protocols for each application is unrealistic, machine learning offers means of making protocols intelligent and able to adapt dynamically. In this context, we focus on providing open challenges to drive IoT application layer protocols in such a direction.

Chyle leak after living donor nephrectomy is a rare complication and is associated with a significant postoperative burden. To the best of our knowledge, only 1 case of chyle leak after robotic living donor nephrectomy has been reported in the literature. In this study, we present our experience with 2 cases of chyle leak: 1 after and 1 during robotic donor nephrectomy. We discuss previously published studies and man - agement options of chyle leak in kidney donors.

Introduction: The goal of this study was to see if the Chronic Obstructive Pulmonary Disease (COPD) Assessment Test (CAT) score was beneficial in determining the severity of COPD patients. It produces a score that reflects the impact of the condition on their health status. GOLD guideline (2013) recommends CAT above other respiratory questionnaires since it is regarded to be a better assessment tool. Methods: Cross-sectional study conducted in Karnali Academy of Health Sciences, Jumla (Nepal) who presented with COPD exacerbation. A validated CAT questionnaire in the local language (Nepali) was employed and completed by the patient, and then the total CAT score was calculated and grouped into four impact levels. Data was evaluated using descriptive statistics (such as percentage and mean) and Pearson’s correlation tests to compare means. Results: Out of 55 patients, 52.7% were females; all 55 had firewood exposure with current smoking history more prevalent in the high-impact group 0.5%. At 3 months, the median number of COPD exacerbations was 2 (range: 0–90) with median CAT score 30 (range: 17–37). Patients were divided into severity groups based on their CAT scores. 47.3% were in the high impact group, whereas none were in the low impact group. The “very high impact” group had a considerably higher median number of exacerbations than the other high impact and medium groups (5 vs. 2 and 0) respectively with p

The case provides an opportunity for undergraduate, graduate, and doctoral students to demonstrate a practical application of supply chain modeling in the area of livestock management for a group of rural communities in India. An animal husbandry department needs to ensure timely replenishment of liquid nitrogen to several livestock locations to preserve semen straws, which are frozen for artificial insemination. The department wishes to explore a possible route redesign for cost reduction. The description in the case, as well as the presented data, is based on a real project undertaken by the authors. The case enables students to conceptualize and design sample routes manually to meet the cost objective. However, the main objective is to make students understand and implement solution heuristics for large-scale vehicle routing problems. It also allows students to compare various designs trying not only possible vehicle capacities but also three solution heuristics. Nevertheless, students can also realize the importance of the spreadsheet as a decision support tool for evaluating several options by integrating the chosen heuristic. An important milestone of the case project was the implementation of our recommendations by the animal husbandry department.

Significant growth and successful integration of mobile devices to the education landscape have garnered the introduction of mobile learning (mLearning) as a new technology-enabled educational tool. Mobile learning has become a new higher educational paradigm which makes learning more flexible and accessible. However, student readiness and their perceptions about educational use of tablet devices are still a concern particularly in the developing countries like the Pacific island countries (PICs). This article investigates these two important attributes of tablet devices in a first year blended course from a higher education institute in the Pacific with a sample of 43 students. The results revealed that the participants perceived that tablet devices were effective and innovative learning tools. The participants supported the idea of using tablet devices and were ready to incorporate them for learning provided there was training in place. This article finally outlines recommendations for the education sectors in the Pacific.

Nowadays, people use electricity in all aspects of their lives so that electricity consumption increases gradually. There can be wastage of electricity due to various reasons, such as human negligence, daylighting, etc. Hence, conservation of energy is the need of the day. This paper deals with the fabrication of an "Automated Power Conservation System (APCS)" that has multiple benefits like saving on power consumption there by saving on electricity bills of the organization, eliminating human involvement and manpower which is often required to manually toggle the lights and electrical devices on/off, and last but most importantly conserve the precious natural resources by reducing electrical energy consumption. Two IR sensors are used in this project and these two sensors are used for detecting the presence of a person in the classroom. When the existence of the person is detected by the APCS it automatically turns on the fans and lights in that classroom and during the absence they will be automatically turned off, thus paving the easiest way to conserve power. This hardware is integrated with the Android app, where the user can get data on his smartphone regarding the number of fans and lights that are turned on at a particular instance of time. The user can also switch on/off the fans and lights from anywhere in the world by using the Android App., 8 Pages

Aiming at delivering power to sensitive loads with an enhanced level of reliability and quality, a compact multilevel battery-based electric spring (ES2) topology founded on the Packed E-Cell (PEC) inverter and an artificial neural network (ANN) based control strategy are introduced. This multilevel ES2 overcomes the limitations of the two-level ES2s and offers key features in the area of electric spring that have not been considered before. From the reliability point of view, the PEC-based ES2 (PEC-ES2) has the capability of instant nine to five-level operation under its bidirectional switch faulty condition. Regarding the power quality, in comparison with the half or full bridge ES2 topologies, PEC-ES2 has switches with halved voltage rating, lower harmonic content in its output current and voltage, considerably lower switching frequency, higher power applications, etc. The proposed intelligent ANN-based controller can also tune and stabilize both the grid voltage and responsive load setup power factor independently with improved dynamic performance. The operation and viability of the proposed ES2 configuration and controller, have been also tested extensively.

As valuable assets of corporations, governments and not-for-profit organizations, brands have attracted considerable research attention. We know a lot about how brands create knowledge in their target consumers’ minds, which leads to an attitude towards or relationship with the brand that translates into a number of favorable outcomes. The resultant brand equity is often associated with improved performance of the organization in reaching its objectives such as increasing market value. We know less about the dynamic nature of brand equity and, in particular, how it may interact with turbulence in the external environment in which the brand competes, both positively and negatively. We examine three key dimensions of brand equity—brands’ access to their target markets, perceived differentiation, and level of brand engagement with their target consumers—that influence the effect environmental turbulence has on diminishing equity or providing future opportunities for brand equity growth. Borrowing from the strategy literature, we suggest ways in which agile and resilient firms can use brand equity to resist environmental turbulence, recover from any damage that may result from it, and reinvent themselves to leverage opportunities created by a radically altered external environment. We close with a set of propositions intended to guide managers in anticipating and responding to environmental turbulence and inform and shape future research in this area.

BACKGROUND: Smokers manifest varied phenotypes of pulmonary impairment. RESEARCH QUESTION: Which pulmonary phenotypes are associated with coronary artery disease (CAD) in smokers? STUDY DESIGN AND METHODS: We analyzed data from the University of Pittsburgh COPD Specialized Center for Clinically Oriented Research (SCCOR) cohort (n = 481) and the Genetic Epidemiology of COPD (COPDGene) cohort (n = 2,580). Participants were current and former smokers with > 10 pack-years of tobacco exposure. Data from the two cohorts were analyzed separately because of methodologic differences. Lung hyperinflation was assessed by plethysmography in the SCCOR cohort and by inspiratory and expiratory CT scan lung volumes in the COPDGene cohort. Subclinical CAD was assessed as the coronary artery calcium score, whereas clinical CAD was defined as a self-reported history of CAD or myocardial infarction (MI). Analyses were performed in all smokers and then repeated in those with airflow obstruction (FEV(1) to FVC ratio, < 0.70). RESULTS: Pulmonary phenotypes, including airflow limitation, emphysema, lung hyperinflation, diffusion capacity, and radiographic measures of airway remodeling, showed weak to moderate correlations (r < 0.7) with each other. In multivariate models adjusted for pulmonary phenotypes and CAD risk factors, lung hyperinflation was the only phenotype associated with calcium score, history of clinical CAD, or history of MI (per 0.2 higher expiratory and inspiratory CT scan lung volume; coronary calcium: OR, 1.2; 95% CI, 1.1-1.5; P = .02; clinical CAD: OR, 1.6; 95% CI, 1.1-2.3; P = .01; and MI in COPDGene: OR, 1.7; 95% CI, 1.0-2.8; P = .05). FEV(1) and emphysema were associated with increased risk of CAD (P < .05) in models adjusted for CAD risk factors; however, these associations were attenuated on adjusting for lung hyperinflation. Results were the same in those with airflow obstruction and were present in both cohorts. INTERPRETATION: Lung hyperinflation is associated strongly with clinical and subclinical CAD in smokers, including those with airflow obstruction. After lung hyperinflation was accounted for, FEV(1) and emphysema no longer were associated with CAD. Subsequent studies should consider measuring lung hyperinflation and examining its mechanistic role in CAD in current and former smokers.

The COVID-19 pandemic is affecting food and nutrition security through economic and social systems shocks, food system disruptions and gaps in coverage of essential health and nutrition services. Food systems in low- and middle-income groups must adapt and strengthen food and nutrition security in the wake of COVID-19. Smallholder farmers are a crucial part of the food value chain in India, as well as a critical element of the global food system. The COVID-19 pandemic has brought new risks that threaten livelihoods as well as food security. Post the rabi harvest in April, farmers prepare for the next (kharif) season in May. However, the COVID-19 induced disruptions have reduced production capacity for farm inputs and have led to an increase in price, making these resources inaccessible to smallholder and marginal farmers in the country. The corona-virus pandemic has caused a global reduction in economic activity and although this is major cause for concern, the ramping down of human activity appears to have had a positive impact on the environment. The COVID-19 lockdown has several social and economic effects. Additionally, COVID-19 has caused several impacts on global migration. Carbon emissions have dropped, and the COVID-19 lockdown has led to an improvement in air quality and a reduction in water pollution in many cities around the globe. We found that the COVID-19 lockdown in India has primarily impacted farmers’ ability to sell their crops and livestock products and decreased daily wages and dietary diversity. In this context, we aim to synthesize the early evidence of the COVID-19 impact on the Indian agricultural system viz., production, marketing and consumption followed by a set of potential strategies to recover and prosper post-pandemic. Findings indicate that the pandemic has affected production and marketing through labour and logistical constraints, while the negative income shock restricted access to markets and increased prices of food commodities affecting the consumption pattern.

Background: Inflammation of the cornea is known as keratitis. Microbial keratitis is a great challenge for the physicians due to its varied presentation, overlapping symptoms and rapid progression. Though bacterial keratitis is the most prevalent in developing countries but recent increasing trend of fungal keratitis carries a significant risk factors and one of the leading causes of vision loss. Early diagnosis and treatment are the cornerstone for its effective control. Purpose: To determine the outcome and efficacy of treatment with topical natamycin and topical voriconazole in different groups. Materials and Methods: It was a randomised, prospective, comparative, experimental study. The study populations were selected according to inclusion and exclusion criteria after proper evaluation. The study populations were divided into Group A (treated with 1% topical natamycin) and group B (treated 5% topical voriconazole. The patients were followed up subsequently. Data were collected, tabulated in Excel sheet and analyzed in percentage, proportion, t-test and chi square (2) test. The statistically significant was considered if p value

This case report has described the patient having the central pontine myelinolysis (CPM) associated with hypokalemia correction with asymptomatic history of gastroesophageal reflux disease (GERD). It was suspected that due to GERD and vomiting there might be loss of electrolytes resulting to hypokalemia. The hypokalemia was corrected and patient was discharged from the tertiary neurological center in Nepal.Central Pontine Myelinolysis in Association with Correction of Hypokalemia with Asymptomatic Case of Gastroesophageal Reflux Disease

Capturing of infrared images is an easy task but perceptual visualization is difficult due to environmental conditions such as light rain, partly cloudy, mostly cloudy, haze, poor lightening conditions, noise generated by the sensors, geographical distance and appearances of the objects. To improve the human perception and quality of the infrared images for further processing like image analysis, image enhancement is an essential process. This paper provides a detailed review of various image enhancement techniques from contrast stretching to optimization methods used in infrared images. It also discusses the existing infrared image enhancement techniques as group such as histogram based methods, filter based methods, transform domain based methods, morphological based methods, saliency extraction methods, fuzzy based methods, learning methods, optimization methods and its popular algorithms also address the countless issues. Some of the existing image enhancement methods (Histogram Equlization, Max-median filter, Top-Hat transform) and infrared image enhancement methods (multi-scale top-hat transform, adaptive infrared image enhancement) are implemented along with the adaptive fuzzy based infrared image enhancement method and its obtained results evaluation is done on subjective and objective ways. From the results observed that the fuzzy based method works well for both subjective and objective evaluation. The paper aims to provide a complete study on image enhancement techniques and how they specially utilized while dealing with infrared images. In addition, the paper helps the researchers to select the suitable infrared image enhancement techniques for their infrared image application needs.

Objectives Current treatments are effective only in 30% of lupus nephritis patients emphasizing the need for novel therapeutic strategies. To develop mechanistic hypotheses and explore novel biomarkers, we analyzed the longitudinal urinary proteomic profiles in patients with lupus nephritis undergoing treatment. Methods We quantified 1,000 urinary proteins in 30 patients with lupus nephritis at the time of the diagnostic renal biopsy and after 3, 6, and 12 months. The proteins and molecular pathways detected in the urine proteome were then analyzed with respect to baseline clinical features and longitudinal trajectories. The intrarenal expression of candidate biomarkers was evaluated using single cell transcriptomics of renal biopsies from lupus nephritis patients. Results Our analysis revealed multiple biological pathways including chemotaxis, neutrophil activation, platelet degranulation, and extracellular matrix organization that could be noninvasively quantified and monitored in the urine. We identified 237 urinary biomarkers associated with lupus nephritis as compared to controls without SLE. IL-16, CD163, and TGF-β mirrored intrarenal nephritis activity. Response to treatment was paralleled by a reduction of urinary IL-16, a CD4 ligand with proinflammatory and chemotactic properties. Single cell RNA sequencing independently demonstrated that IL16 is the second most expressed cytokine by most infiltrating immune cells in lupus nephritis kidneys. IL-16 producing cells were found at key sites of kidney injury. Conclusion Urine proteomics may profoundly change the diagnosis and management of lupus nephritis by noninvasively monitor active intrarenal biological pathways. These findings implicate IL-16 in lupus nephritis pathogenesis designating it as a potentially treatable target and biomarker.

With rising temperatures and events contributing climate change, the world is facing extreme weather patterns. Recently, Australia was hit hard by bushfires, the most devastating fires ever faced by the country. The economic damage reported was nearly one billion Australian dollars and an estimated 3 billion native animals killed or adversely affected. Given the extent and intensity of this damage, researchers are seeking effective solutions to enable the prediction of fire before it starts to increase the time available for firefighters to protect lives and assets and prepare to mitigate the fires. This motivated us to investigate an approach to address this critical problem. In this paper, we propose a Machine Learning (ML)–based approach that detects anomalies in spatiotemporal measurements of environmental parameters (e.g., temperature, relative humidity, etc.). In the proposed approach, an ML–based model learns the normal spatiotemporal behaviour of the environmental data (collected over a period of one year). This is carried out during a one-time training phase. Then, during the detection phase, any spatiotemporal pattern in the real–time data (received from the field sensors) that is different than the normal pattern will be identified by the model as anomaly which indicates a possible bushfire situation. Following this, we propose a supplementary classification model based on Moran’s I index to ensure that the detected anomalies are not due to either a sensor failure or a security attack (which are common in IoTs). We developed three different ML models for performance evaluation and comparison and used the Forest Fire dataset to train them. The results of our experiments confirm the effectiveness of the proposed approach in the early detection of fire symptoms.