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Classification of Alzheimer’s disease (AD) is one of the most challenging issues for neurologists. Manual methods are time consuming and may not be accurate all the time. Since, brain is the most affected region in AD, a proper classification framework using brain images may provide more accurate results. Deep Learning (DL) is a popular representation of machine learning techniques, that emulate the functionalities of a human brain to process information and creates patterns that help in making complex decisions. The ability to absorb information, even from the unstructured and unlabeled data, makes DL one of the first choices by researchers. In this paper, some of the most popular DL models are discussed along with their implementation results for AD classification. All brain Magnetic Resonance (MR) images are acquired from the online data-set, “Alzheimer’s Disease Neuroimaging Initiative (ADNI)”. From the performance comparison amongst all the discussed models, it is observed that the DenseNet-121 model achieves a convincing result with an average performance rate of 88.78%. But one limitation of the DenseNet model is that it uses lots of convolutional operations that make the model computationally slower than many of the discussed models. Depth-wise convolution is a popular way to make a convolutional operation faster and better. Hence, to improve the execution time, we have proposed replacing the convolution layers in the original DenseNet-121 architecture with depth-wise convolution layers. The new architecture also improved the performance of the model with an average rate of 90.22%.

Deep Learning and its applications have cascaded impactful research and development with a diverse range of modalities present in the real-world data. More recently, this has enhanced research interests in the intersection of the Vision and Language arena with its numerous applications and fast-paced growth. In this paper, we present a detailed overview of the latest trends in research pertaining to visual and language modalities. We look at its applications in their task formulations and how to solve various problems related to semantic perception and content generation. We also address task-specific trends, along with their evaluation strategies and upcoming challenges. Moreover, we shed some light on multi-disciplinary patterns and insights that have emerged in the recent past, directing this field toward more modular and transparent intelligent systems. This survey identifies key trends gravitating recent literature in VisLang research and attempts to unearth directions that the field is heading toward.

Due to the lack of a comprehensive multi-objective Solid-State-Transformer (SST) design framework, SST designs are mostly obtained through trial/experience. In this paper, a machine learning (ML) aided optimal SST design framework is proposed, which involves the objectives of maximizing efficiency (η) and power density (ρ). The challenges of computationally expensive magnetics design, coupled with the correlation between magnetics design and performance of semiconductor devices, are tackled by developing a hybrid local optimization algorithm. This local optimization is subsequently learnt through ML techniques, using a limited number of optimal design datasets, and thus, assists in genesis of optimal SST design limits for several combinations of semiconductor devices and switching frequencies. The proposed framework is implemented for a Cascaded Matrix-based Dual-Active-Bridge (CMB-DAB) SST comprising of SiC MOSFETs to demonstrate the optimization routine. The optimization results exhibit low-error fits of the selected ML models and the η-ρ limits in different categories of optimal SST designs. The SiC based SST designs are also observed to offer better η-ρ optimal designs compared to Si based SSTs. A laboratory scale CMB-DAB prototype with experimental measurements is also presented to validate the proposed design optimization framework at a scaled-down level.

Usually the real-world (RW) datasets are imbalanced in nature, i.e., there is a significant difference between the number of negative and positive class samples in the datasets. Because of this, most of the conventional classifiers do not perform well on RW data classification problems. To handle the class imbalanced problems in RW datasets, this paper presents a novel density-weighted twin support vector machine (DWTWSVM) for binary class imbalance learning (CIL). Further, to boost the computational speed of DWTWSVM, a density-weighted least squares twin support vector machine (DWLSTSVM) is also proposed for solving the CIL problem, where, the optimization problem is solved by simply considering the equality constraints and by considering the 2-norm of slack variables. The key ideas behind the models are that during the model training phase, the training data points are given weights based on their importance, i.e., the majority class samples are given more importance compared to the minority class samples. Simulations are carried on a synthetic imbalanced and some real-world imbalanced datasets. The model performance in terms of F1-score, G-mean, recall and precision of the proposed DWTWSVM and DWLSTSVM are compared with support vector machine (SVM), twin SVM (TWSVM), least squares TWSVM (LSTWSVM), fuzzy TWSVM (FTWSVM), improved fuzzy least squares TWSVM (IFLSTWSVM) and density-weighted SVM for binary CIL. Finally, a statistical study is carried out based on F1-score and G-mean on RW datasets to verify the efficacy and usability of the suggested models.

The embedding systems play an inevitable role in healthcare informatics and medical diagnosis. This article presents the design of a microcontroller ( $\mu C$ )-based online parameter detection and diagnosis system using real-time nerve signals. In developing the system, a low cost and low power battery-based voltage controlled neurostimulator is designed to stimulate the targeted sites of underlying nerve. In the second phase, a signal conditioning circuit is designed to accurately process and obtain the recorded signals for parameter extraction. In offline mode, an optimum neural network structure is determined from MATLAB simulation using prediagnosed nerve signal dataset through cross-validation technique which is then implemented in programmable interface controller (PIC) $\mu C$ (PIC18F45K22) for online estimation of parameter and diagnosis. Afterwards, nerve signals are collected from various subjects in laboratory following the guidelines of standard nerve conduction study (NCS) which are processed through embedded programmed $\mu C$ . Experimental results and subsequent comparison with the state-of-the-art designs indicate that the proposed system is promising and reliable for online detection of the NCS parameters and subsequent diagnosis.

Rainfall is the key climatic variable, on which water availability, food security and livelihood depend, especially in an agrarian society like the northeast region of India. It is an ecologically sensitive zone, harbouring world’s three biodiversity hotspots and the world’s highest rainfall zone. Therefore, the assessment of variability and trend in the rainfall regime in this region is imperative. The present study focuses on testing the homogeneity status and prevalent trends in the long-term rainfall data at five different locations in North-East India, namely Cherrapunji, Dibrugarh, Guwahati, Kailashahar and Tulihal. The impact of rainfall variability on groundwater level was further investigated. The estimation of precipitation concentration index and standard precipitation anomaly was also carried out to investigate the intra-annual variability and drought conditions in this region. The results indicate the homogeneous nature of rainfall time series at 99% significance level at all the sites with low coefficient of variance (CV, %) at Dibrugarh in monsoon and annual rainfall series. The precipitation concentration index values show high intra-annual variability in the rainfall data. Decadal and annual standard precipitation anomaly values indicate the presence of extreme and severe drought conditions in the last two decades. The trend analysis results display the presence of significant negative trends in monsoonal and annual rainfall at Dibrugarh. During pre-monsoon season, all the sites exhibiting positive drift with only Guwahati and Tulihal have significant trends (at 90% significance level). Monthly rainfall trend analysis results revealed strong and significant (95%) negative trends during peak monsoon months of July at Dibrugarh and Guwahati and with 85% significance level at Cherrapunji (which was the world’s highest rainfall zone in recent past). The impact assessment results indicate a direct association between rainfall and groundwater at lag2, i.e., the impact of any change in the rainfall amount on groundwater in a given area may be evident after two seasons. The results revealed an enhancement of 1.0 ± 0.1, 1.1 ± 0.2, 0.9 ± 0.2 mm in the groundwater level for each mm increase in the rainfall at DBR, GHY and KSH, respectively. At CHR, the relation was evident only after 10 seasons because of excessive rainfall and runoff. The results had significant bearing for the policy makers and farming community in the region for better planning of crop cultivation in order to adapt to the changing climate in this region.

Evidence concerning prescription audits conducted in developing countries like India is scarce, especially from the rural parts of the country. Therefore, the present prescription audit was undertaken in a rural tertiary care hospital to investigate prescriptions for their completeness, in format of prescription, legibility of writing and it was assessed against the World Health Organization (WHO) recommendation of core indicators for prescription writing in order to investigate the rational usage of drugs. A total of 200 prescriptions were randomly selected, irrespective of clinical departments, patient characteristics and diagnosis over a period of six months. All the prescriptions were prospectively analyzed and conferred to an assessment of the quality of prescribing practice, general details, medical components, WHO core drug use indicators and legibility. Amongst the 200 prescriptions precisely monitored, we found that 100% prescriptions had general details of the patients such as name, age, gender, OPD/IPD registration number, hospital name & address and consulting unit/department. While evaluating the handwriting of the doctors, 83.5% (177/200) of the prescriptions had legible handwriting, wherein the degree of legibility showed 68.5% (137/200) prescriptions with easy legibility, 20% (40/200) difficult legibility while 11.5% (23/200) were illegible. Along with the different types of drugs obtained from the selected prescriptions, we found that antibiotics were prescribed in 51.5% (103/200) of the prescriptions. A prescription audit is a good tool to systemically review the day to day work, maintenance of records and assessment of accuracy of the diagnosis given by doctors and also the outcome of the treatment received.

Nanofiltration membranes (NF) are a well-recognized membrane technology used for various applications. NF is not only used for the applications like wastewater treatment, water desalination, biotechnological and pharmaceutical but also used in food industries, dairy and vegetable oil processing industries, fruit juice, plant extracts, and beverage industries. The present review is a wide look at the current advances of NF membranes in plant extracts, food, and dairy industries along with the future potentials in the production of high-quality food and dairy products. In this review article, we discussed the effect of different physicochemical parameters on the performance of NF membrane in the form of flux, rejection, and product quality. Different fabrication methods along with modification of NF membranes in food industry applications are also discussed in this review. All the parameters are briefly assessed and strategically compared with relevant literature. The focus is also given to find out the solution of fouling issues that are drawbacks against NF competitory with particular reference to food processing industries. The review article ends with numerous commendations on the future prospective.

Silk fibroin (SF) protein was reported as a versatile platform for selective detection of picric acid (PA) as well as for UV photodetection. SF in aqueous media undergoes about 74% fluorescence quenching in the presence of 24 nM concentration of PA in aqueous media with a limit of detection of about 0.203 nM. The quenching was found to due to the combined effect of Forster resonance energy transfer, hydrogen-bonding, and electrostatic interaction. The method is also applicable for real environmental samples. Electrical measurement-based sensing of PA was also performed by fabricating flexible sensor devices from SF and polyvinyl alcohol on polyethylene terephthalate substrates. The sensors show about twofold enhancement in current conduction and about 33% higher ionic conduction in the presence of the analyte. Interestingly, the combination of SF and PA acts as a photoactive material for UV light and generates about 7.15 µA photocurrent under 360 nm light-emitting diodes. The devices exhibit excellent stability in repetitive bending of the devices and also air-stability at varying relative humidity conditions (40–80%). Additionally, an electronic circuit was designed for precise electrical parameters required during optoelectronic measurements. Therefore, it can be stated that, SF can be used as a versatile platform for dual mode PA sensor as well as UV photodetector.

PurposeDelay in the clinical information system (CIS) restoration overseeing critical health-care operations after an unexpected data loss can be fatal for patients under care. Investment in information technology (IT) capabilities and synergy between various computerized systems has been argued as the resilient information system's enablers. The purpose of this study is to empirically quantify the influence of IT investment, integration and interoperability in recovering the CIS from a data disaster.Design/methodology/approachAn archival dataset sourced from a European Commission-sponsored survey of 773 hospitals across 30 countries in Europe is utilized to study the relationships. The study adopts a quasi-experimental research design approach where sample observations are weighted based on their propensity to be selected in treatment groups. The artificial weighing allows attaining a pseudo-random sample to counter the effects of selection bias.FindingsThe study finds that hospitals with more than 5% of the budget dedicated to IT have 100% higher odds of recovering immediately from a critical data loss in comparison to those that have less than 1% investment in IT. The greater extent of IT integration significantly reduces the time to recover the CIS, while interoperability problems at the organizational level lessen the odds of immediate recovery by 19%. Interoperability problems at the technical and semantic levels do not significantly impact recovery times of the CIS.Originality/valueThe study proposes several empirically quantified and scientifically tested recommendations for health-care providers for faster restoration of critical CIS operations post data loss. The differential impact of the interoperability problems at the technical, semantic and organizational levels has also been highlighted.

We address the problem of recognizing emotion cause in conversations, define two novel sub-tasks of this problem, and provide a corresponding dialogue-level dataset, along with strong Transformer-based baselines. The dataset is available at https://github.com/declare-lab/RECCON. Introduction: Recognizing the cause behind emotions in text is a fundamental yet under-explored area of research in NLP. Advances in this area hold the potential to improve interpretability and performance in affect-based models. Identifying emotion causes at the utterance level in conversations is particularly challenging due to the intermingling dynamics among the interlocutors. Method: We introduce the task of Recognizing Emotion Cause in CONversations with an accompanying dataset named RECCON, containing over 1,000 dialogues and 10,000 utterance cause-effect pairs. Furthermore, we define different cause types based on the source of the causes, and establish strong Transformer-based baselines to address two different sub-tasks on this dataset: causal span extraction and causal emotion entailment. Result: Our Transformer-based baselines, which leverage contextual pre-trained embeddings, such as RoBERTa, outperform the state-of-the-art emotion cause extraction approaches Conclusion: We introduce a new task highly relevant for (explainable) emotion-aware artificial intelligence: recognizing emotion cause in conversations, provide a new highly challenging publicly available dialogue-level dataset for this task, and give strong baseline results on this dataset., https://github.com/declare-lab/RECCON, Accepted at Cognitive Computation

Background: Insects have formed a part of the human diet since pre-historic times. Entomophagy has garnered recent attention due to its increased role in food security as nutrient-rich alternatives of conventional livestock. Methods: We have made an effort in this paper to study the edible insect composition of Manas National Park, a UNESCO Natural World Heritage Site and Biosphere Reserve, located in the Indo- Burma biodiversity hotspot of Southern Asia. Edible insect variety was determined through a field reconnaissance while the nutrient composition (macronutrient and micronutrient) of each edible insect species was computed through biochemical analyses with a view to explore the possibility of promoting them as food/feed or as a base for nutritive products. We conducted nutrient profiling using the Nutrient Value Score (NVS) model and UK Ofcom nutrient profiling (NP) model. Results: Hymenoptera and Orthoptera were found to be the dominant orders with 4 species each, followed by Hemiptera with 3 species, Lepidoptera with 2 species, Mantodea, Blattodea and Coleoptera with 1 species each. The nutrient analysis shows that insects have a rich nutrient composition that is comparable with typical livestock. Conclusion: As realised during COVID-19, insects may prove to be an alternative, sustainable, and cheaper sources of nutrients necessary for ensuring global food security.

The emergence of Cross-Border E-commerce (CBeC) has brought substantial changes to both businesses and consumers. Although CBeC businesses have existed for less than a decade, many academic researchers addressed important issues in this context. It is essential to evaluate what has been studied through a structured review of the literature and derive meaningful insights given that research on this topic is new and largely fragmented. Therefore, this study conducts a review of CBeC literature to find the current gaps and fragmentation to provide guidelines for future research. The review shows that research in this domain needs more attention and enforcement to address the current research gaps. Addressing the current gaps helps academia build a rigorous body of knowledge and enables practitioners to solve challenging business problems.

The present study was conducted with an objective of assessing the knowledge gained by trainees about various aspects of scientific pig farming organised by Krishi Vigyan Kendra, Lakhimpur. A total of 180 numbers of trainees participated in trainings on scientific pig farming over a period during 2019 and 2020 on random selection. The data were collected on pre and post completion of the training with the help of questionnaire which were distributed to the trainees before training. The questionnaire consisted of 13 different aspects on knowledge on Indian and exotic Pig Breeds, knowledge on selection of piglets, castration age of piglets, attainment of puberty in pigs, oestrus period of a sow, heat detection in gilt, gestation period of sow, care of young piglets, deworming in pigs, marketing age of pigs, feeding of lactating sow, knowledge on Vaccination of pigs and common diseases of pigs. A score of one and zero score was assigned for each correct and incorrect response, respectively for analysis of knowledge gain. It was found that majority of the trainees were youths (52.22%), followed by Middle aged (32.22%) and Old age (15.55%).Majority of the trainees were from ST category (33.88%) followed by OBC (32.77%), General (21.11%) and SC (12.22%). Among those who attended training 32.22% had education till middle school level followed by primary 22.77, 16.11 % were found illiterate whereas 15% completed secondary, 10.55% higher secondary and 3.33 % completed education upto graduate level. The overall knowledge of trainees on pre training evaluation was found to be 19.17% which ended up on a high note with 93.15% on post training evaluation, which is indicative of the positive impact of training on knowledge gain. Evaluation of knowledge gain on various aspects of scientific pig farming showed that maximum knowledge was gained on vaccination in pigs (88.89%, Rank I) and minimum gain was in knowledge of gestation period of sow (54.44%, Rank XIII).

BACKGROUND Central neuraxial blocks, mainly spinal anaesthesia is the preferred anaesthesia over general anaesthesia in fracture of femur surgeries in elderly patients because of various side effects associated with general anaesthesia. However, the majority of the patients during positioning for spinal anaesthesia are associated with severe pain for which positioning becomes difficult. We wanted to assess the effectiveness of ultrasound (USG) guided femoral nerve block (FNB) to reduce pain during the positioning for spinal anaesthesia in fracture femur patients. METHODS It was a randomized control trial conducted on 80 elderly ASA I, II and III patients (45 - 70 years) posted for various femur surgeries under spinal anaesthesia having visual analogue scale (VAS) ≥ 4 during positioning. Patients in group FNB (N = 40) received USG guided femoral nerve block with 0.2 % ropivacaine and in control group (N = 40) didn’t receive any interventions preoperatively. All patients received injection fentanyl during positioning if VAS ≥ 4. Parameters recorded included VAS at baseline, 15 mins and during positioning, anaesthesiologist’s satisfaction (yes or no), total fentanyl consumption and patient’s satisfaction (Likert scale). RESULTS Demographic variables were comparable to each group. VAS in FNB at 15 mins and during positioning was 2.3 ± 1.042 and 3.025 ± 0.831 in comparison to 6.125 ± 0.607 and 7.2 ± 0.822 in control group respectively. Only 5 patients required injection fentanyl and total consumption was 142 ± 3.21 µg in FNB group while all patients in control group required injection fentanyl and total consumption was 1162 ± 4.43 µg. Anaesthesiologists and patients satisfaction were much higher in FNB group. CONCLUSIONS USG - guided FNB is very effective in controlling pain during positioning for spinal anaesthesia in fracture femur patients. KEY WORDS Ultrasound, Femoral Nerve Block, Spinal Anaesthesia, Ropivacaine, Fracture Femur

Breast cancer (BrC) is a lethal form of cancer which causes numerous deaths in women across the world. Generally, mammograms and histopathology biopsy images are recommended for early detection of BrC as they enable a more reliable prediction than just using mammograms. However, research indicates that even the most experienced dermatologists can detect BrC in early stage with an average accuracy of less than 80%. Over the years, researchers have made significant progress in the development of automated tools and techniques to assist radiologists or medical practitioners in BrC detection. Various machine learning and deep learning based architectures are extensively experimented on different publicly available datasets to improve the performance measures. There is further scope of improvements by extracting better representative features with deep architectural variants or ensembles techniques to minimize the misclassifications. Learnt parameters of any pretrained deep models may provide a better starting point for any other architectures using transfer learning technique. In this work, we propose computer-aided transfer learning based deep model as a binary classifier for breast cancer detection. Generally, deep learning architectures are sequential, following only a single channel for features’ extraction and further classification. However, fused features extracted from multiple channels may better represent features qualitatively. The novelty of our approach is the use of multi-channel merging techniques for devising a dual-architecture ensemble. The models are trained and tested on the BreakHis dataset and an improvement in comparison with the state-of-the-arts is observed in various performance metrics. Among several combinations for ensemble architectures by utilizing various pretrained models, the Xception + InceptionV3 combination achieved an average accuracy of 97.5% for multi-channelled architecture, setting benchmarking results for further research in this direction.

It is important to evaluate the media's health coverage of Indigenous communities both because these communities have been hit very hard by health inequities, and because misinformation can negatively affect the future health of Indian Country. This study takes the unique angle of examining both Indigenous and non-Indigenous ("mainstream") news publications to evaluate information gaps in health, health policy, and health efficacy coverage. The Indigenous media examined, which covered 14 times more health stories than mainstream media, highlighted health issues in Indian Country through the lens of resilience by using framing to emphasize unequal power, while at the same time providing depth and specificity. Instead of putting Indigenous health stories into historical and cultural context, mainstream media focused on the lack of resources and the chronic struggle of Indigenous communities. Mainstream media often only covered the topic once per outlet; however, those outlets with connections to Indigenous communities did provide more balanced coverage. Instead of promoting change, most mainstream media stories blamed Indigenous people for their situation and offered a doom trajectory for tribes hit hardest by health disparities. This study reveals how embedded framing and mediatization direct non-Native readers' attention away from the systemic deprivation of support to U.S. Indigenous tribes that was guaranteed to them by the U.S. government in tribal agreements. The implications for journalism and policy are discussed.

An online survey was performed among the different categories of farm households in Nagaon district, Assam. Out of 300 cropped based agricultural farm households, of farmers were selected randomly and were grouped in accordance to their operational holdings viz. marginal (4.01 ha). The necessary information was collected through their Whatsapps, messages and telephonic conversation from different 43 revenue villages. With a view to estimate the loss of crops cultivated during Lockdown Period (24th March, 2020 to 18th April, 2020) due to COVID 19 and determines the loss percentage of different crop enterprises for each group of farmers with district as a whole. After analyzing the received information it was found that large farms suffered maximum (71.69%) due to small tea garden and betel vine and medium farms incurred loss of about 61.33%. the overall loss of crops was 68.96% . The loss was found to be chiefly due to acute shortage of field labourers following COVID 19 restrictions during Lock Down period 1. As a result of which plucking, harvesting, marketing etc. all are at halt.

What role infectious agents play in the causation of psychotic disorders? To investigate this area, we have aimed to investigate the relationship between Toxoplasma gondii and psychotic disorders. A hospital-based cross-sectional study is designed. IgM and IgG antibodies to T. gondii in patients with psychotic disorders will be measured and presented in result. Seropositivity rates will be compared with first-degree relatives and healthy volunteers. Also, types of psychotic disorders and seropositivity rate will be compared. Here, we are presenting the study protocol with implications toward a brain-based diagnostic system and novel treatment approaches.

The current study aimed to characterize patients from a rheumatology referral center in north India, who satisfied the definition of interstitial pneumonia with autoimmune features (IPAF) as given by the American Thoracic Society and European Respiratory Society (ATS/ERS) consensus committee in 2015. Thirty-five adult patients aged 18 years and above, fulfilling the 2015 ATS/ERS criteria for IPAF were included in the study. The clinical and immunological profile, and radiologic findings on high-resolution computerized tomography thorax were noted. Antinuclear antibody (ANA) by indirect immunofluorescence at 1:320 titer and myositis-specific antibody (MSA) assays were performed. Non-parametric tests were used to compare variables between groups. The study cohort included predominantly female patients with a mean age of 50.6 ± 13 years and mean duration of disease of 38.8 ± 28.4 months. Majority of patients (49%) fulfilled the morphologic and serologic domains as per the IPAF consensus criteria and 31% patients had features in all three domains. Non-specific interstitial pneumonia was the most common pattern observed in 77% patients. Raynaud’s phenomenon and inflammatory arthritis were the predominant autoimmune features. Pulmonary arterial hypertension was documented in 60% of patients on echocardiography. Positive ANA at 1:320 dilution was present in all 26 patients tested, whereas extractable nuclear antigen and MSA assays detected autoantibodies in 49% and 51% of patients respectively. IPAF predominantly affected females in the age group of 50 years and above, with varied autoimmune manifestations and autoantibody profile. Supplementary Information The online version contains supplementary material available at 10.1007/s00296-021-04883-7.

The spatial evolution of an elliptical Gaussian beam in fluoride glass (Kerr medium) is studied using nonlinear Schrodinger equation solved by split-step beam propagation method. Solutions obtained by numerical simulation are analysed to study the effect of variation of input beam ellipticity on the process of multiple filamentation in elliptical Gaussian beam, where emphasis is on the properties of the self-guided Gaussian laser beam by externally induced input beam astigmatism. Controlling the properties of self-guided laser beam is a necessity in generation of tailored filaments.

Aim and objective We report a case of synovial sarcoma of the thyroid gland and review the literature of few such reported cases. Background Synovial sarcoma of the thyroid gland is exceedingly rare with aggressive features and very poor prognosis. Case description We report a case of 65-year-old man from Iraq who presented initially with a neck mass and was operated at the local hospital for total thyroidectomy, and a separate neck mass extending from the thyroid gland to the upper mediastinum was found. A re-excision of the mass was done with HPE showing malignant peripheral nerve sheath tumor. He was referred to our institute with relapse of the neck mass with compressive symptoms. The mass was excised with the HPE and IHC staining showing grade 2 biphasic synovial sarcoma. Within twenty days of surgery, he presented with severe respiratory distress and tumor with intraluminal tracheal growth. He died of compressive symptoms two weeks after the relapse. Conclusion The early diagnosis and margin-free resection give the best chance of survival for the patient. How to cite this article Lal RK, Hazarika B, Julaha M. Primary Synovial Sarcoma of Thyroid Gland: A Case Report. Int J Otorhinolaryngol Clin 2020;12(2):27–30.

The risk of meningococcal transmission is increased with crowding and prolonged close proximity between people. There have been numerous invasive meningococcal disease (IMD) outbreaks associated with mass gatherings and other overcrowded situations, including cramped accommodation, such as student and military housing, and refugee camps. In these conditions, IMD outbreaks predominantly affect adolescents and young adults. In this narrative review, we examine the situation in India, where the burden of IMD-related complications is significant but the reported background incidence of IMD is low. However, active surveillance for meningococcal disease is suboptimal and laboratory confirmation of meningococcal strain is near absent, especially in non-outbreak periods. IMD risk factors are prevalent, including frequent mass gatherings and overcrowding combined with a demographically young population. Since overcrowded situations are generally unavoidable, the way forward relies on preventive measures. More widespread meningococcal vaccination and strengthened disease surveillance are likely to be key to this approach.

Lymphatic system imaging has been a challenging field in Radiology. We here describe a case of a 26-year-old male who presented with complaints of left-sided inguinoscrotal swelling and was subsequently diagnosed with abdomino-thoracic cystic lymphangiomatosis. Detailed imaging evaluation across the modalities of ultrasonography, computed tomography scan and magnetic resonance imaging evaluation revealed extensive involvement and diagnostic imaging appearance of the unique lymphatic system abnormality. Imaging findings were confirmed on histopathology.

Humanity currently faces one of its biggest challenges, created by a tiny quasi-life form, the severe acute respiratory syndrome coronavirus 2. The rapidity of spread and the enormous burden placed on public health infrastructure by the coronavirus disease 2019 (COVID-19) pandemic has forced researchers to look for quick answers for therapy. Drug repurposing is probably the quickest way to develop an effective therapy in a very short time. With additional input from artificial intelligence (AI), drug repurposing may emerge as one of the major techniques by which humanity can overcome this as well as future challenges. The field of rheumatology has been one of the biggest benefactors of drug repurposing. This article reviews the various ways drugs used in rheumatological disorders are being repurposed for possible COVID-19 treatment. An overview of other nonantiviral drugs being repurposed is also undertaken, and the role of AI in drug repurposing is touched upon.

Alzheimer’s disease (AD) is one of the most serious neurological disorders for elderly people. AD affected patient experiences severe memory loss. One of the main reasons for memory loss in AD patients is atrophy in the hippocampus, amygdala, etc. Due to the enormous growth of AD patients and the paucity of proper diagnostic tools, detection and classification of AD are considered as a challenging research area. Before a Cognitively normal (CN) person develops symptoms of AD, he may pass through an intermediate stage, commonly known as Mild Cognitive Impairment (MCI). MCI is having two stages, namely StableMCI (SMCI) and Progressive MCI (PMCI). In SMCI, a patient remains stable, whereas, in the case of PMCI, a person gradually develops few symptoms of AD. Several research works are in progress on the detection and classification of AD based on changes in the brain. In this paper, we have analyzed few existing state-of-art works for AD detection and classification, based on different feature extraction approaches. We have summarized the existing research articles with detailed observations. We have also compared the performance and research issues in each of the feature extraction mechanisms and observed that the AD classification using the wavelet transform-based feature extraction approaches might achieve convincing results.

The presence of more than one autoimmune disease in a patient can complicate not only the diagnosis but also pose a therapeutic challenge to the treating physician. Myasthenia gravis (MG) is a rare disorder which may overlap with a number of other autoimmune conditions. It is important to recognize its co-presence since the management of the accompanying autoimmune condition may need to be tailored accordingly. In this article, we describe two cases with MG overlap, one with primary Sjogren's syndrome and the other with Takayasu arteritis. Herein, we review the literature and clinical approach and management of such patients.

Recognizing emotions in conversations is a challenging task due to the presence of contextual dependencies governed by self- and inter-personal influences. Recent approaches have focused on modeling these dependencies primarily via supervised learning. However, purely supervised strategies demand large amounts of annotated data, which is lacking in most of the available corpora in this task. To tackle this challenge, we look at transfer learning approaches as a viable alternative. Given the large amount of available conversational data, we investigate whether generative conversational models can be leveraged to transfer affective knowledge for detecting emotions in context. We propose an approach, TL-ERC, where we pre-train a hierarchical dialogue model on multi-turn conversations (source) and then transfer its parameters to a conversational emotion classifier (target). In addition to the popular practice of using pre-trained sentence encoders, our approach also incorporates recurrent parameters that model inter-sentential context across the whole conversation. Based on this idea, we perform several experiments across multiple datasets and find improvement in performance and robustness against limited training data. TL-ERC also achieves better validation performances in significantly fewer epochs. Overall, we infer that knowledge acquired from dialogue generators can indeed help recognize emotions in conversations., Information Fusion

Covid-19 and lockdown as its preventive measure hit Indian Economy at a worst time. The trade performances prior to the pandemic was not satisfactory and held a decreasing trend in terms of the previous periods. In this paper an honest attempt has been made to analyse international trade as an Engine of Economic Growth, that is, its importance or significance in Economic Growth processes as well as to assess the comparative trend in international trade before and after the pandemic situation. This paper is based on secondary data. The paper is essentially a descriptive and analytical in nature. Statistical tools like graph, table etc are used for better understanding. The foreign trade is found to be diminishing in its share while analysing the data of last 10 years prior to the pandemic and nationwide lockdown. The merchandise trade surplus in June 2020 was estimated at USD 0.79 billion as against deficit of USD 15.28 billion in June, 2019. The trade balance for service is estimated at USD 6.83 on 15th July,2020. India recorded first trade surplus since January, 2002.

Purpose: The people of Northeast India comprise mostly of Mongoloid descent with characteristic craniofacial features. The purpose of this study was to evaluate the normal orbital structures with MRI and determine normative data in the Northeast Indian pediatric population. Methods: MRI images of the 302 orbits of 151 pediatric patients below 16 years of age were retrospectively evaluated to measure the various orbital structures. Both axial and coronal MRI images were utilized for various orbital measurements. An independent sample t-test was done to compare various orbital data according to the sex and side. Linear regression was also done. Results: The mean age of the pediatric population was 9.64 ± 1.47 years with a male: Female ratio of 1.13:1. The mean interzygomatic line was 88.4 ± 9.09 mm and the interorbital line was 22.18 ± 3.62 mm. The mean thicknesses of medial, lateral, superior, and inferior recti muscles were 2.58 ± 0.46, 2.34 ± 0.42, 2.16 ± 0.40, and 2.53 ± 0.49 mm in males and 2.41 ± 0.41, 2.08 ± 0.34, 2.08 ± 0.46, and 2.46 ± 0.49 mm in females. The mean horizontal orbital, vertical orbital diameters, orbital index, mean volume of eyeball, and orbital cavity were 30.27 ± 2.97,3 7.06 ± 3.57, 122.58 ± 7.39, 4.63 ± 0.84, and 15.29 ± 3.52 in males, while 29.16 ± 3.23, 34.96 ± 3.99,119.96 ± 7.31, 4.49 ± 0.87, and 14.65 ± 3.47 in females, respectively. With an increase in age, the interzygomatic line (r = 0.883, r2 = 0.780; P < 0.0005), anterior medial interorbital line (r = 0.808, r2 = 0.652; P < 0.0005), mean volume of eyeball (r = 0.915, r2 = 0.838; P < 0.0005), orbital cavity (r = 0.924, r2 = 0.854; P < 0.0005), and distance between the optic nerve entry site (r = 0.829, r2 = 0.687; P < 0.0005) were increased. Conclusion: This study provides normative data of various orbital structures in a pediatric population and these data likely to be useful for diagnosing various pediatric orbital disorders and helps in the planning of various surgical procedures of orbits.

Background The orbit is an anatomically complex structure comprising the globe, extraocular muscles, fat, vascular, nervous, glandular, and connective tissues. A wide variety of neoplasms can arise from different orbital structures, which can create a diagnostic challenge to the pathologists. No formal study has been conducted in this regard in North East India. Aim and Objectives This article aims to document the pattern and prevalence of orbital tumors in our institute and assess the utility of histopathological examination (HPE) and immunohistochemistry (IHC) in the precise diagnosis of these neoplasms. Materials and Methods A retrospective analysis of orbital tumors was performed over a period of 5 years from 2013 to 2018 in the department of pathology at a tertiary cancer center of North East India following all the guidelines of the institutional ethics committee. Results A total of 35 cases of orbital neoplasms, evaluated by HPE and IHC, were found, all of them being malignant tumors. The age range was 4 months to 85 years. Male to female ratio was 1.5:1. The most common tumor found was lymphoma, accounting for 10 cases (28.6%), all of which were non-Hodgkin lymphoma (NHL). All these cases except one occurred in adults, thus making it the most common tumor in adults in this study. Diffuse large B cell lymphoma, not otherwise specified, was the most common NHL, followed by follicular lymphoma, mature T cell NHL, extranodal marginal zone lymphoma, and B cell lymphoblastic lymphoma. Rhabdomyosarcoma and poorly differentiated/undifferentiated carcinoma jointly were the second most common tumors, totaling seven cases (21.21%) each. This was followed by melanoma (three cases), myeloid sarcoma (three cases), Ewing sarcoma/peripheral neuroectodermal tumor (PNET) (three cases), neuroblastoma (one case), and angiosarcoma (one case). Among these, rhabdomyosarcoma, granulocytic sarcoma, Ewing sarcoma/PNET, and neuroblastoma exclusively troubled the children. IHC markers including the lymphoma panel, and soft tissue ones were crucial in the precise diagnosis of the neoplasms encountered. Conclusion A variety of malignant orbital tumors may be seen in clinical practice. Management of these tumors requires a multidisciplinary approach. HPE in conjunction with IHC evaluation is of utmost importance in the veracious recognition of orbital tumors for their proper management.

Background & Objective:: This paper presents a wireless sensor network for monitoring field parameters inside a low-cost polyhouse. The micro climate inside a polyhouse differs from that on the outside, which provides a favorable condition for unseasonal crops. Methods: The physical parameters associated with the polyhouse’s microclimate were monitored by a reliable low-cost wireless sensor network, which in turn helps to take decisions for enhancing yield quality and quantity. Sensor network development, signal conditioning, calibration of the soil temperature measurement system and field experience of the installed system are discussed in this paper. The field parameters for the growing period of cucumber (Cucumis sativus) inside the polyhouse are provided in the paper. Results & Conclusion: It showed significant variations in temperature, relative humidity and wind speed inside the polyhouse to that of the outside. It was also observed that soil temperature, soil moisture in mulched soil differed from that of the open condition. Enhancement of the crop yield was found for mulched soil.

The work reported in this article aims at developing a simple sensor-based system that detects and identifies faults of a train. Four different trains are considered for analysis as suggested by the Northeast Frontier Railway (NFR) authorities. For this purpose, an embedded system containing an ADXL335 sensor is used to capture the vibration of a railway track during the movement of a train over the track. The embedded system transfers the captured signals from ADXL335 to a laptop. These signals are processed using linear time–frequency transform (wavelet transform) in conjunction with quadratic time–frequency transform (Wigner–Ville transform) to find out whether there are any faults and thereby quantify the quality of the moving trains. Wheel-flat fault is detected for one train with the wheel position and the bogie number. This is a low-cost technique as the method involves only one ADXL335 sensor and an Arduino development board, and the software used for the analysis is python that is an open-source and platform-independent software.

Olfaction is one of the primary senses of a living organism and has been used as first aid for inspecting freshness, edibility, and the quality of food. This article addresses a technique where the biological olfaction process has been mimicked by electronic nose (E-Nose) to detect a pathogen named Citrus Tristeza Virus (CTV) in Khasi Mandarin Orange plants. The proposed technique may be used as an alternative to currently used traditional serological or molecular tests, which requires costly and elaborate laboratory infrastructures. In this article, leaves from 62 plants were collected and examined for viral infection using the gold-standard polymerase chain reaction test. A commercial E-Nose system, Alpha MOS Fox 3000, was used for hypothesis testing, resulting in an accuracy of 95.30% using random forest classifier. These results were used to identify the most prominent sensors and their target gases responding to the CTV infection and were considered as a base for sensor selection for custom developing a prototype. The prototype was designed based on simulations for optimum airflow in the sensor chamber. Bootstrap ensemble of $k$ -nearest neighbors and adaptive boost ensemble of decision tree classifier distinguished the data generated from the prototype with an accuracy of 99.36% and 97.58%, respectively. The performance of the classifiers was visualized graphically using dimensionality reduction techniques. These results indicated that the E-Nose technique employed could differentiate the healthy and infected samples and the prototype could serve as an effective first-hand diagnosing tool for large-scale orchards.

Purpose This study aims to investigate patterns of social media posting occurring during the initial post-disaster recovery period. In addition, the study investigates the antecedents of user engagement with Twitter posts or “tweets.” Design/methodology/approach This study uses Hurricane Harvey as the research focus because of its $125bn in damage and the recovery effort associated with it. Analyzing nearly 38,000 tweets related to 2017’s Hurricane Harvey and specifically focuses on rescuers who became to be known as the “Cajun Navy.” Findings The popularity of the tweet significantly influences social media engagement; disaster relief organizations first should seek out alliances and partnerships with those who already are well-known or influential in social media. In addition, by regularly monitoring and participating in social media, such as encouraging retweets and mentions even when not responding to a disaster, agencies will gain social media followers and influence when these are most needed. Research limitations/implications By analyzing patterns of social media posts during and after Hurricane Harvey, this study attempts to quantify social media’s effectiveness during this disaster, in particular with a focus on what characteristics of Harvey-related Twitter posts most influenced user engagement. Practical implications Findings underscore the need for agencies to strengthen their social media presence and use it in all three phases of disaster conceptualization. Beyond managing communication with other agencies and the community, which alone is critical for an agency’s impact, social media offers the world as a potential audience for agencies that can deftly leverage it. Originality/value Social media has revolutionized not only communication but also inter-connectivity as people have normalized its use. Its ubiquity and efficiency have seen its incorporation into the critical area of emergency management, both during and after disasters. The main objective of this paper is to investigate and evaluate the usefulness of social media in disaster recovery efforts, focusing on its advantages in relation to a recent disaster event.

The performance of ultra-thin Au–Ag–Au tri-layer film deposited thermally over a flexible substrate is investigated using structural, optical, mechanical and electrical-transport measurements. The optimum total thickness of the tri-layer for high transparency and conductivity is determined to be around 8 nm using a theoretical model. The Au–Ag–Au tri-layer shows maximum transmittance (≅ 62%) at wavelength 500 nm. XRD pattern shows peak corresponding to (111) plane of Au and/or Ag. Sheet resistance (≅ 10.42 Ω/□) measured at 300 K using four probe technique is stable up to 150 °C. Hall effect measurements show high conductivity (1.34 × 105 (Ω cm)−1), carrier concentration (2.48 × 1023/cm3), and mobility (3.4 cm2/Vs). Scotch tape test confirms good adhesion of the film onto PET substrate. Bending-twisting tests using an indigenous apparatus indicate high resistance-stability even after 50,000 cycles. These results imply the viability of Au–Ag–Au tri-layer film as a transparent conducting electrode worth exploring for optoelectronic applications.

Purpose To describe the morphological patterns of keratic precipitates (KPs) in vitreoretinal lymphoma (VRL) using in vivo confocal microscopy (IVCM). Methods Six eyes of three biopsy-proven VRL patients were included. KPs were identified and analyzed on IVCM. Results On examination, pigmented KPs in four eyes, white central KPs in two eyes and anterior chamber cells with flare in six eyes and pseudo hypopyon in one eye were identified. A typical floral pattern of KPs on IVCM was noted in all eyes. Three eyes each showed the complete and incomplete floral patterns, respectively. Resolution of KPs on IVCM was noted after chemotherapy. Conclusion In addition to the routinely used clinical and imaging markers like the visual acuity, presence of lymphomatous cells in the vitreous and optical coherence tomography findings, the presence and appearance of KPs on IVCM can also be considered as a useful, diagnostic and treatment monitoring marker in VRL.

BACKGROUND Neonatal mortality is one of the most daunting health challenges faced by India. Newborn care is sub-optimal in the slums and the newborns here are victims of various malpractices endangering their health and survival. We wanted to study the existing newborn care practices in slums of Guwahati city and evaluate the various factors influencing them. METHODS A community-based cross-sectional study was conducted among 300 mothers having children below 12 months of age in the slums of Guwahati city, Assam from August 2018 to July 2019. RESULTS Out of 300 mothers interviewed, majority (63 %) had institutional deliveries and only 37 % of the mothers delivered at home. Out of 111 home deliveries, new blade was used in majority (82.88 %) for cutting the cord. Some material was applied to the cord stump like mustard oil, ash, vermilion, talcum powder etc. as customary practice in 68 % of home deliveries. Majority of the newborns (89 %) were wrapped with clean cloth immediately after birth. However, 59.33 % of the newborns were given first bath within the first day of birth. Majority of mothers (80.67 %) initiated breastfeeding within one hour of birth and 71.33 % of the newborns were given colostrum. However, pre lacteal feed was highly practised (59.33 %) in the study area. CONCLUSIONS All the domains in newborn care were not appropriately practised. These issues need to be addressed effectively through health education and counselling.

Near-infrared (NIR) spectroscopy was investigated to relate the intrinsic properties of rice to its extrinsic properties, and thereby, to provide a better solution at the consumer’s level for identification of rice characteristics. Spectral data in the wavelength range of 740–1070 nm are collected with the help of a portable NIR sensor and processed with machine learning techniques were used to develop a rapid predictive model for on-site evaluation of rice quality. Rice properties like glycemic index (GI), amylose content (AC) and viscogram, obtained from laboratory measurements, were mapped to the spectral data employing the machine learning techniques like principal component analysis, linear discriminant analysis, random forest classifier and partial least square (PLS). The regression coefficient and root mean squared error of the PLS model for AC estimation are 0.715 and 1.736; however, a lower value for regression coefficient was obtained for the GI model. Similarly, a confusion matrix of 100% true value prediction was obtained at lower AC values, 83% at high AC values; however, at intermediate range of AC confusion matrix yielded 60% true value prediction. A comparison of classification of rice for parboiling, based on the viscogram and NIR spectral data, revealed that the NIR data produce better clusters with Euclidean distance of 5.46 units between the centroid of the closest clusters, viz., open parboiled and pressure parboiled. The developed model was used to develop a smartphone-based applet for the estimation of AC in rice.

Introduction Minimal access surgery has altered the field of surgery with its revolutionary advancements with respect to laparoscopy wherein the latter has been elevated to a safer procedure than ever before. However, along with its benefits, minimally invasive surgical procedures have detrimental environmental implications as well. Further, the overall bio-economics of carbon emissions during the surgery is another important factor. Aim The present article makes an effort to discuss and analyse the carbon footprint of minimal access surgery and to understand the co-benefits and co-costs in terms of environmental safety and bio-economics. Results The findings indicate that carbon footprint in these surgical procedures are rarely studied which otherwise bear significant negative relations with respect to the environment. Conclusion The study concludes that work on improving the design of these technologies is to be done so that apart from reducing the costs improvement of safety, comfort and better impact on future generation can also be achieved.

Background Isolated posterior spinal (element) tuberculosis (TB) is uncommon compared to classical anterior spinal or para-discal TB. Here, we report magnetic resonance imaging (MRI) findings of posterior spinal TB in 19 patients without involvement of the vertebral body and intervertebral disc. Purpose To evaluate the MRI findings in isolated posterior spinal (element) TB. Material and Methods Clinical and MRI data of 19 patients of isolated posterior spinal TB were retrospectively evaluated. Results Of the 19 patients, group A comprised 4 (21%) patients with rapid onset lower limb weakness and pyramidal signs while group B comprised 15 (79%) patients without any neurological deficit. Lumbar vertebrae commonly affected 9 (47.4%) patients followed by dorsal vertebrae in 8 (42.1%) patients and cervical vertebrae in 2 (10.5%) patients. The pedicle was most commonly involved in 12 (63.2%) patients followed by the lamina in 11 (58%) patients, and spinous process and facet joint in 6 (31.6%) patients each. Extra-spinal inflammation/pyomyositis/paraspinal abscess was found in 13 (68.4%) patients followed by epidural abscess 3 (15.8%) patients and both extra spinal inflammation and epidural abscess in 3 (15.8%) patients (15.8%). Compressive cord myelopathy was observed in 4 (21%) patients, where three patients underwent emergency decompression laminectomy and the remaining 16 patients were treated conservatively with anti-tubercular therapy. Conclusion Initial diagnosis of isolated posterior element TB is challenging and requires a high index of suspicion. Early diagnosis of isolated posterior spinal TB is important as early treatment may be beneficial and decreases patient morbidity.

Acute renal infection or acute pyelonephritis (AP) denotes the process of inflammation of the renal parenchyma and its collecting system and the urothelium following infection. Uncomplicated AP commonly affects otherwise healthy, young women without structural or functional urinary tract abnormalities and without relevant comorbidities. More severe and complicated AP occurs in patients with a structurally or functionally abnormal genitourinary tract, or in persons with a predisposing medical condition like immune compromised state and diabetes. Complicated AP is characterized by a broader spectrum of clinical presentations, a wider variety of infecting organisms, and a greater risk of progression to a complication, such as intrarenal or perinephric abscess or emphysematous pyelonephritis and has the capacity to damage the organ and at times maybe life threatening. Role of imaging in renal infection is secondary, and in most situations, imaging is done to confirm the clinical diagnosis, map progression of disease in immune-compromised group of patients, or to evaluate for potential complications and therapeutic interventions. This article attempts to discuss the pathophysiology of AP from the standpoint of medical imaging and also brings out illustrative examples of various manifestations of AP and its complications. It provides imaging insight into various stages of inflammation, development of complication, and a roadmap for understanding AP through cross-sectional imaging.

Nanoscale semiconductors show remarkably tunable properties. For metal halide perovskite (MHP) nanocrystals (NCs), surface energy and lattice strain stabilize desirable MHP compositions and crystal...