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This study summarizes the importance of melatonin in different plants and fungi. In this review, we discussed the biosynthetic pathway of melatonin, its metabolites, and its oxidative reduction. Melatonin is a molecule derived from tryptophan, with pleiotropic activity. It is present in nearly every organism. Its synthetic course depends on the organism in which it resides. The tryptophan to the melatonin pathway, for example, varies in plants and animals. It is thought that the synthetic mechanism for melatonin was inherited in eukaryotes from bacteria caused by endosymbiosis. Nevertheless, the synthetic pathways of melatonin in microorganisms are unknown. The metabolism of melatonin is exceptionally complex with these enzymatic processes developed out of cytochrome C. As well as the enzymatic degradation, melatonin is metabolized by interactive pseudoenzymes and free radicals processes.

DNA methylation is a well-known epigenetic modification that is essential for gene regulation and genome stability. Anomalies in plant development can result from aberrant DNA methylation patterns. DNA methylation is much more important in plants with more complicated genomes when it comes to growth and abiotic stress tolerance. Dynamic regulation via de novo methylation, maintenance of methylation, and active demethylation, which are catalysed by diverse enzymes that are targeted by different regulatory mechanisms, results in a unique DNA methylation state. We explain DNA methylation in plants, including methylating and demethylating enzymes and regulatory changes, as well as the coordination of methylation and demethylation activities by a mechanism known as the methylstat. We also explain the roles of DNA methylation in regulating transposon silencing, gene expression, and chromosome interactions, as well as the intervention of DNA methylation in plant responses to biotic and abiotic stresses.

Crop plant architecture is an important agronomic trait that contributes greatly to crop yield. Tiller angle is one of the most critical components that determine crop plant architecture, which in turn substantially affects grain yield mainly owing to its large influence on plant density. Gravity is a fundamental physical force that acts on all organisms on earth. Plant organs sense gravity to control their growth orientation, including tiller angle in rice (Oryza sativa). This review summarizes recent research advances made using rice tiller angle as a research model, providing insights into domestication of rice tiller angle, genetic regulation of rice tiller angle, and shoot gravitropism. Finally, we propose that current discoveries in rice can shed light on shoot gravitropism and improvement of plant tiller angle in other species, thereby contributing to agricultural production in the future.

Over the last few years, the use of clustered regularly interspaced short palindromic repeats (CRISPR) for genetic manipulation has transformed life science. CRISPR was first found in bacteria and archaea as an adaptable immune system, and later modified to create specific DNA breaks in living cells and creatures. Various DNA alterations can occur throughout the cellular DNA repair process. Since the first demonstration of CRISPR in plant genome editing in 2013, there has been much progress in fundamental crop research and plant improvement. Plants can use the CRISPR toolset to do programmable genome editing, epigenome editing, and transcriptome regulation. However, the difficulties of plant genome editing must be properly understood and answers sought. With an emphasis on achievements and prospective utility in plant biology, this review aims to provide an instructive assessment of the current advancements and discoveries in CRISPR technology. CRISPR will, in the end, not only make fundamental research easier, but it will also speed up plant breeding and germplasm development. In the light of global climate change, as well as present agricultural, environmental, and ecological concerns, the use of CRISPR to improve germplasm is extremely significant.

The breeding of crops is dependent on the potential to interrupt or maintain genetic links between characteristics, and the availability of genetic variability. CRISPR-Cas is a new genome-editing technique that has made it possible for breeders to introduce regulated and site-specific genetic diversity while simultaneously improving qualities with high efficacy. The existence of genomic linkage is a barrier in transferring desirable features among domesticated species from their wild counterparts. One way to address this issue is to create mutants with deficiencies in the meiotic recombination machinery, thereby enhancing global crossover frequencies between homologous parental chromosomes. Although this seemed to be a promising approach at first, thus far, no crossover frequencies could be enhanced in recombination-cold regions of the genome. Consequently, attempts have been made to induce site-specific DSBs in both somatic and meiotic plant cells by utilizing CRISPR–Cas techniques to achieve preset crossovers among homologs. Nonetheless, this method has not yielded significant heritable homologous crossings which were recombination-based. Lately, CRISPR–Cas has been used to achieve hereditary chromosomal rearrangements (CRs), including translocations and inversions, in plants. This method allows for the development of megabase CRs by DSB repair through non-homologous end-joining after insertion of DSBs in somatic plant cells. This technique may potentially make it possible to restructure genomes on a more global scale, culminating in the creation not just of synthetic plant chromosomes, but also that of new plant species.

CRISPR–Cas genome editing technology developed from prokaryotes has transformed the molecular biology of plants past all assumptions. CRISPR–Cas, which is distinguished by its resilience, relatively high specificity, and easy implementation, enables specific genetic modification of crops, allowing for the creation of germplasms with favorable characters and the development of innovative, highly efficient agricultural systems. Moreover, many new biotechnologies in the framework of CRISPR–Cas platforms have bolstered basic research as well as synthetic biology toolkit of plants. In this article, initially, we provide a brief overview of CRISPR–Cas gene editing, emphasis on the modern, most specific gene-editing techniques, such as prime and base editing. Following that, the major role of CRISPR–Cas in plants in enhancing pesticide and disease resistance, quality, yield, breeding, and faster domestication are next discussed. In this review, we discuss the current advancements in plant biotechnology linked to CRISPR–Cas, such as CRISPR–Cas gene control, reagent conveyance, multiplexed gene editing, directed evolution, and mutagenesis. In the end, we talk about how this innovative technology may be used in the future.

The comparison of several associated species and plant genome sequencing efforts has increased in recent years. The inflated level of the genomic variety leads to the discovery that the single reference genomes may not reflect the variability in a species, resulting in the evolution of a pan-genome idea. Pan-genomes exhibit a species' genetic variability and contain mutant genes lacking in some individuals and essential genes present in all individuals. Mutant gene classifications often reveal cross-species parallels, including genes for abiotic and biotic stresses generally concentrated within mutant gene groupings. Here we discuss the history of pan-genomics in plants, investigate the causes of gene variation, deletion, and existence and demonstrate why pan-genomes might assist crop genetics and breeding research.

Abiotic stress is one of the major environmental stresses that decrease crop growth and yield even in irrigated soils worldwide. An important plant hormone abscisic acid (ABA) plays a vital role in addressing various stresses, such as thermal or heat stress, high salinity level, heavy metal stress, low temperature, drought, and stress on radiation. Its role is well explained in different processes for development, including germination of seed, stomata closure, and dormancy. Abscisic acid works through alteration of the gene expression levels and subsequently analyzing the cis and trans-regulatory components for receptive promoters. It is considered to have an interaction with the signaling elements of processes taking part in stress response and seed development. In general, a plant can be vulnerable or tolerant to stress when the correlated actions of different stress-reacting genes are considered. Many transcription factors are required for the regulation of expression of abscisic acid-responsive genes through interacting with their specific cis-acting components. Therefore, the mechanism behind it should be understood to make the plants stress-tolerant. This review explains the significance and function of ABA signaling concerning specific stress, the management of abscisic acid biosynthesis, and transcription factors (TFs) associated with stress tolerance.

Human Action Recognition (HAR) is a current research topic in the field of computer vision that is based on an important application known as video surveillance. Researchers in computer vision have introduced various intelligent methods based on deep learning and machine learning, but they still face many challenges such as similarity in various actions and redundant features. We proposed a framework for accurate human action recognition (HAR) based on deep learning and an improved features optimization algorithm in this paper. From deep learning feature extraction to feature classification, the proposed framework includes several critical steps. Before training fine-tuned deep learning models – MobileNet-V2 and Darknet53 – the original video frames are normalized. For feature extraction, pre-trained deep models are used, which are fused using the canonical correlation approach. Following that, an improved particle swarm optimization (IPSO)-based algorithm is used to select the best features. Following that, the selected features were used to classify actions using various classifiers. The experimental process was performed on six publicly available datasets such as KTH, UT-Interaction, UCF Sports, Hollywood, IXMAS, and UCF YouTube, which attained an accuracy of 98.3%, 98.9%, 99.8%, 99.6%, 98.6%, and 100%, respectively. In comparison with existing techniques, it is observed that the proposed framework achieved improved accuracy.

INTRODUCTION: Choosing a medical specialty is one of the most crucial and difficult decisions made during medical school. Given that specialty exposure is among the most important factors in decision-making, the Careers in Medicine (CiM) multi-specialty elective was designed to provide clerkship students an avenue to explore three or more specialties of interest during a single elective. METHODS: A cross-sectional study was conducted at Vanderbilt University School of Medicine using anonymous surveys and de-identified written reflections submitted by students enrolled in the CiM course between August 2015 and June 2018. Data were analyzed using a mixed-methods approach. RESULTS: The majority of students reported the elective guided them in ruling out (80%) and ruling in (65%) specialties. About half (51%) of students decided between the procedural versus critical-thinking dichotomy. Finally, 80% of students reported that they would take the course again rather than a focused elective. Major themes identified from student reflections included course attributes, specialty impacts, and student values. DISCUSSION: Implementation of a multi-specialty elective during the clerkship year was an effective way to help students understand their career values, gain early exposure to specialties not featured in core clinical curriculums, and determine future fields of interest. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40670-021-01311-0.

The automatic classification of bone texture into healthy or osteoporotic cases presents a major challenge since there is no visual difference between the two cases. This classification requires an inspection of the fine granularity in the bone radiographs which is usually difficult with a naked eye. We have proposed a novel method in this paper, that can be used for the classification of bone radiographs into healthy or osteoporotic cases. We mimic the observations of the physicians by preprocessing the bone radiographs with Gabor filters bearing a high frequency. Later, we design and utilize a convolutional neural network wherein filtered images are fed as input to the system which classifies the images into their respective classes. The proposed algorithm has been validated on a bone radiograph challenge dataset. Our results depict that the method proposed in this research exhibits very good results in terms of classification. A comparison of the proposed and the contemporary research methods has also been shown in this paper. The experimental results show that by exploiting high frequency Gabor filters and employing the convolutional neural network architecture, good results in performing the classification of bone radiographs are achieved.

Convolutional Neural Networks (CNNs) in the Internet-of-Things (IoT)-based applications face stringent constraints, like limited memory capacity and energy resources due to many computations in convolution layers. In order to reduce the computational workload in these layers, this paper proposes a hybrid convolution method in conjunction with a Particle of Swarm Convolution Layer Optimization (PSCLO) algorithm. The hybrid convolution is an approximation that exploits the inherent symmetry of filter termed as symmetry approximation and Winograd algorithm structure termed as tile quantization approximation. PSCLO optimizes the balance between workload reduction and accuracy degradation for each convolution layer by selecting fine-tuned thresholds to control each approximation’s intensity. The proposed methods have been evaluated on ImageNet, MNIST, Fashion-MNIST, SVHN, and CIFAR-10 datasets. The proposed techniques achieved $\sim 5.28\text{x}$ multiplicative workload reduction without significant accuracy degradation ( $\sim 1.08\text{x}$ less multiplicative workload as compared to state-of-the-art Winograd CNN pruning. For LeNet, $\sim 3.87\text{x}$ and $\sim 3.93\text{x}$ was the multiplicative workload reduction for MNIST and Fashion-MNIST datasets. The additive workload reduction was $\sim 2.5\text{x}$ and $\sim 2.56\text{x}$ for the respective datasets. There is no significant accuracy loss for MNIST and Fashion-MNIST dataset.

Globally, the wind power capacities are growing every passing year, which is an indicative of social and commercial acceptance of this technology by a larger section of the populations. In Indian perspective, the wind power capacities are also increasing with annual additions of new capacities and most of the development work is taking place in the southern part and that too in Tamil Nadu state. Research work in the area of accurate wind power assessment is being conducted to optimize the utilization of wind power and at the same time efforts are being exerted to enhance the operation and maintenance capabilities of the local skilled and semi-skilled work force. This study utilizes 38 years of hourly mean wind speed data from seven locations for providing the accurate wind power assessment and understanding the longitudinal behavior of its characteristics. The wind speed is found to be increasing with decreasing latitudes and having lesser variation in wind direction fluctuations, simply means conversing wind direction to narrower bands. Kanyakumari is identified as the most probable wind power deployment site with annual energy yield of 227.55 MWh and capacity factor of 34% followed by Vedaranyam, and Thoothukudi, as second and third priority sites with respective annual yields of 223.36 MWh and 218.73 MWh.

Background and Objectives: Missed appointments represent a significant challenge to the efficient and effective provision of care in the outpatient setting. High no-show rates result in ineffective use of human resources and contribute to loss of follow-up. Shade Tree Clinic (STC) is a student-run, comprehensive primary care clinic that serves more than 350 Middle Tennessee residents. This study aimed to use available data to predict no-shows to improve clinic efficiency and service quality. Methods: Data were pulled from clinic scheduling software for all appointments at STC between January 1, 2010 and December 31, 2015. Weather data were added for each appointment date using an online database. Multivariable logistic regression was used to create models from these historical data. Results: A total of 13,499 appointments were included with an overall show rate of 69.2%. The final model contained previous show rate (OR 1.063; P

Aims Closure of fascial defect (CFD) during laparoscopic incisional/ventral hernia repair (LIVHR) remains a controversial issue which requires further investigations to reach a solid conclusion. The objective of this study is to present a systematic review comparing the outcomes of randomized controlled trials evaluating the defect closure versus no-defect closure in patients undergoing LIVHR. Methods A systematic review of randomized, controlled trials reporting the fascial defect closure in patients undergoing LIVHR until January 2021 published in Embase, Medline, PubMed, PubMed Central and Cochrane databases was performed using the principles of meta-analysis. Results A total of four RCTs involving 443 patients were included. In the random effects model analysis, using the statistical software Review Manager, defect closure during LIVHR showed no difference in hernia recurrence (risk ratio (RR), 0.89; 95% CI, 0.31, 2.57; z = 0.21; P = 0.84). In addition, the post-operative complications (RR, 0.69; 95% CI, 0.41, 1.16; z = 1.41; P = 0.16), duration of operation (Standardized mean difference (SMD), -0.04; 95% CI, -0.52, 0.43; z = 0.18; P = 0.86) and hospital stay (SMD, 0.27; 95% CI, -0.02, 0.56; z = 1.80; P = 0.07) were also statistically similar in both groups. CFD was associated with an increased post-operative pain score (SMD, 1.82; 95% CI, 0.61, 3.03; z = 2.95; P = 0.003). Conclusion Fascial defect closure in patients undergoing LIVHR does not demonstrate any superiority over no-defect closure in terms of recurrence, post-operative morbidity, post-operative pain duration of operation and length of hospital stay.

Aims The primary aim of this study was to audit the experience of core surgical trainees in operating theatres with a view to devise some interventions to improve the quality of theatre experience. Methods This study was a prospective audit that involved filling out a set proforma by all core surgical trainees working in a busy surgical department of a tertiary care university hospital. The proforma included a breakdown of questions to signpost indicators of quality experience and check compliance with the Joint Committee on Surgical Training (JCST) guidelines. It was completed with particular consideration given to the experience while trainees were on their CEPOD week to facilitate accuracy and relevance of feedback. Results 8 core surgical trainees filled out the proforma. 75% of trainees had the opportunity to reflect the case with the senior surgeon. Lowest compliance was shown for pre-operative discussion of crucial learning points with the senior surgeon. Only 50% of trainees had a chance to do a briefing pre-operatively which is one of the JCST quality indicator for core surgical trainees. Conclusion This audit demonstrates the potential for improvement in the operating theatre experience of junior surgical trainees considering JCST Quality Improvement indicators. A checklist may be introduced to achieve maximum utilisation of the finite training opportunities available to current junior surgical trainees. A loop closing audit after the checklist will be able to assess the change in practice and theatre experience.

Aims Proctological procedures such as haemorrhoidectomy have been reported with significant post-operative pain affecting quality of life as well as capacity to perform daily activities. The objective of this article is to explore the role of conventionally used antibiotic metronidazole as a proctological analgesic. Methods A systematic review of the randomized, controlled trials reporting the use of oral metronidazole as post-operative proctological analgesic agent in patients undergoing haemorrhoidectomy published on Embase, Medline, PubMed, PubMed Central and Cochrane databases was performed using the principles of meta-analysis. Results A total of eight randomized, controlled trials on 447 patients were included in this study. In the random effects model analysis using the statistical software Review Manager, the use of oral metronidazole as a post-operative proctological analgesic agent was significantly associated with the reduced pain score on day 1 (Standardized mean difference (SMD), -0.56; 95% CI, -1.04, -0.07; z = 2.26; P = 0.02), day 3 (SMD, -0.82; 95% CI, -1.33, -0.31; z = 3.15; P = 0.002) and day 7 (SMD, -1.48; 95% CI, -2.51, -0.45; z = 2.82; P = 0.005). There was significant heterogeneity (Tau2 = 0.39, chi2 = 38.38, df = 7, [p = 0.00001]; I2 = 82 %) among included studies. Conclusion The use of oral metronidazole as a post-operative proctological analgesic agent following haemorrhoidectomy seems to have proven clinical advantages and may routinely be used.

Background The learning curve of new surgical procedures has implications for the education, evaluation and subsequent adoption. There is currently no standardised surgical training for those willing to make their first attempts at minimally invasive pancreatic surgery. This study aims to ascertain the learning curve in minimally invasive pancreatic surgery. Methods A systematic search of PubMed, Embase and Web of Science was performed up to March 2021. Studies investigating the number of cases needed to achieve author-declared competency in minimally invasive pancreatic surgery were included. Results In total, 31 original studies fulfilled the inclusion criteria with 2682 patient outcomes being analysed. From these studies, the median learning curve for distal pancreatectomy was reported to have been achieved in 17 cases (10–30) and 23.5 cases (7–40) for laparoscopic and robotic approach respectively. The median learning curve for pancreaticoduodenectomy was reported to have been achieved at 30 cases (4–60) and 36.5 cases (20–80) for a laparoscopic and robotic approach respectively. Mean operative times and estimated blood loss improved in all four surgical procedural groups. Heterogeneity was demonstrated when factoring in the level of surgeon’s experience and patient’s demographic. Conclusions There is currently no gold standard in the evaluation of a learning curve. As a result, derivations are difficult to utilise clinically. Existing literature can serve as a guide for current trainees. More work needs to be done to standardise learning curve assessment in a patient-centred manner.

This paper proposes the design of an accident detection system for motorcycles that notifies the emergency contact of the injured motorcycle driver about their precise location so that necessary medical help can be provided timely. The proposed system is based on a tilt sensor that calculates the inclination of the motorcycle and then transmits notification to the concerned people through SMS and GPRS via an online server using a GSM module. The main contribution of this paper is that the developed system has extensively been tested in real time scenario and data has been collected from ten different bikes to determine an optimum tilt angle. Moreover, crash tests have also been performed. The system has a detection rate of 97.33%.

Stone accumulation in kidney is a typical disease/ sickness in most countries all around the world. Its frequency rate is continually expanding. It has been observed that, the classification of renal stones prompts an imperative decrease of the re-occurrence. The classification of stones based on particular texture, surface highlights and lab examinations are a standout amongst the most utilized strategies. In this paper we use dataset of explicitly intended for top captured pictures of 454 expelled kidney stones which extracted through urinary or surgical procedure for classification purpose. In this paper different techniques have been learned and applied the specialist’s defined framework to arrange them into defined classes then perform classification process. In this paper we use feature fusion technique to collect as much as possible features. We select VGG16, InceptionV3 and ALEX features for fusion using serial feature fusion method. We choose C-SVM and F-KNN classifier to get improved accuracy of same dataset and predict better correctness’s with the possibilities of expansion of the dataset measure. In initial testing classification accuracy recorded at 83.43%, FNR 19.25%, Precision Rate 88.48% and Sensitivity of 86.51% on CSVM, later on the best testing classification accuracy recorded at 99.5%, FNR 0.1%, Precision Rate 99.90% and Sensitivity of 99.96% on F-KNN.

Preventive healthcare requires continuous monitoring of the blood pressure (BP) of patients, which is not feasible using conventional methods. Photoplethysmogram (PPG) signals can be effectively used for this purpose as there is a physiological relation between the pulse width and BP and can be easily acquired using a wearable PPG sensor. However, developing real-time algorithms for wearable technology is a significant challenge due to various conflicting requirements such as high accuracy, computationally constrained devices, and limited power supply. In this paper, we propose a novel feature set for continuous, real-time identification of abnormal BP. This feature set is obtained by identifying the peaks and valleys in a PPG signal (using a peak detection algorithm), followed by the calculation of rising time, falling time and peak-to-peak distance. The histograms of these times are calculated to form a feature set that can be used for classification of PPG signals into one of the two classes: normal or abnormal BP. No public dataset is available for such study and therefore a prototype is developed to collect PPG signals alongside BP measurements. The proposed feature set shows very good performance with an overall accuracy of approximately 95%. Although the proposed feature set is effective, the significance of individual features varies greatly (validated using significance testing) which led us to perform weighted voting of features for classification by performing autoregressive modeling. Our experiments show that the simplest linear classifiers produce very good results indicating the strength of the proposed feature set. The weighted voting improves the results significantly, producing an overall accuracy of about 98%. Conclusively, the PPG signals can be effectively used to identify BP, and the proposed feature set is efficient and computationally feasible for implementation on standalone devices.

Video cameras are among the most commonly used devices throughout the world which results in imaging technology being one of the most important areas for research and development. Imaging technology requires constant research as it is used in crucial applications such as video conferencing and surveillance. In the field of image processing, motion detection and estimation are fundamental steps in extracting information on objects segmented from their backgrounds. In this paper, a cohesive approach is presented that uses two algorithms for motion estimation and detection. The proposed method is able to detect moving objects using maximum average correlation height (MACH) filter. Upon obtaining the accurate coordinates of an object of interest from the MACH filter, the next part of the algorithm starts tracking the object. For tracking, a particle filter is used to estimate the motion of the object using a Markov chain. To enhance the accuracy of particle filter, an approximate proximal gradient algorithm is employed for unconstrained minimization of the particles which restricts the tracking process to target templates (most essential information) only. Finally, a comparison between the proposed algorithm and recent similar algorithms is made that demonstrates the minimization of tracking errors using the proposed technique.

The end-to-end performance metric for a conventional relay network with chase combining is the total Signal-to-Noise Ratio (SNR) delivered at the destination. However, the accumulated mutual information at the destination is the most suitable metric for a relay network which performs code combining (incremental redundancy) instead of chase combining at the destination. This paper investigates the accumulated mutual information acquired at the destination in an Amplify-and-Forward (AF), Decode-and-Forward (DF), and Coded Cooperation (CC) relay network. So far, the analytical comparison of the accumulated mutual information for the different relaying protocols is not reported in the literature. In this paper, it is proved analytically that the mutual information of a relay network with coded cooperation is always greater than or equal to the mutual information of decode-and-forward and amplify-and-forward for the case when all the relays can decode successfully. Moreover, it is also shown that the mutual information of a network with coded cooperation is always greater than or equal to that of a decode-and-forward relay network.

Introduction: Type 2 myocardial infarction (T2MI) is common and associated with recurrent cardiovascular events. How often T2MI patients are evaluated by a cardiologist and the association between these evaluations and diagnostic testing and treatments are unknown. Hypothesis: T2MI patients evaluated by a cardiologist are more likely to undergo cardiovascular testing and be placed on therapies for ischemic heart disease (IHD). Methods: We identified adjudicated patients with T2MI at Massachusetts General Hospital between October 2017 and May 2018. We examined baseline characteristics, diagnostic testing performed, and discharge medications, stratified by cardiologist evaluation during their admission. Results: We identified 359 patients with T2MI. During admission, 207 patients (57.7%) were evaluated by a cardiologist; 120 (33.4%) received a cardiology consultation and 87 (24.2%) were admitted to a cardiology service. Patients evaluated by a cardiologist were more likely to have hyperlipidemia (67.1% vs 52%, p=0.005), known CAD (58.9% vs. 38.8%, p Conclusions: Fewer than 60% of patients with T2MI were evaluated by a cardiologist during their admission and those that did were more likely to undergo further cardiovascular testing and to be discharged on therapies for IHD. Most T2MI patients did not have an outpatient cardiology follow-up visit after their event.

Ensuring the actual presence of a genuine legitimate trait as opposed to a fake self-manufactured synthetic is a major problem in bio-metric authentication. The proposed system's objective is to improve the reliability of bio metric recognition systems through the use of image quality evaluation. The proposed technique uses general image quality features derived from a single image to distinguish between legitimate and impostor samples, making it optimal for applications with a very low degree of complexity. In the proposed method, we are using publicly available ATVSFir_ DB dataset of iris which makes it highly competitive. We have also tested the algorithm on self-generated dataset for authenticity and rigorous testing purposes. The results acquired from the experimental phase were satisfying and authentic. The proposed method is able to achieve an averaged accuracy of 99.1% for the ATVS-Fir_DB dataset and 99.9% for the self-generated dataset.

The early prediction of Alzheimer’s disease (AD) can be vital for the endurance of patients and establishes as an accommodating and facilitative factor for specialists. The proposed work presents a robotized predictive structure, dependent on machine learning (ML) methods for the forecast of AD. Neuropsychological measures (NM) and magnetic resonance imaging (MRI) biomarkers are deduced and passed on to a recurrent neural network (RNN). In the RNN, we have used long short-term memory (LSTM), and the proposed model will predict the biomarkers (feature vectors) of patients after 6, 12, 21 18, 24, and 36 months. These predicted biomarkers will go through fully connected neural network layers. The NN layers will then predict whether these RNN-predicted biomarkers belong to an AD patient or a patient with a mild cognitive impairment (MCI). The developed methodology has been tried on an openly available informational dataset (ADNI) and accomplished an accuracy of 88.24%, which is superior to the next-best available algorithms.

Internet of Medical Things (IOMT) is playing vital role in healthcare industry to increase the accuracy, reliability and productivity of electronic devices. Researchers are contributing towards a digitized healthcare system by interconnecting the available medical resources and healthcare services. As IOT converge various domains but our focus is related to research contribution of IOT in healthcare domain. This paper presents the peoples contribution of IOT in healthcare domain, application and future challenges of IOT in term of medical services in healthcare. We do hope that this work will be useful for researchers and practitioners in the field, helping them to understand the huge potential of IoT in medical domain and identification of major challenges in IOMT. This work will also help the researchers to understand applications of IOT in healthcare domain. This contribution will help the researchers to understand the previous contribution of IOT in healthcare industry.

Detection of a moving human is challenging for real-time systems. Misdetection in high alert security areas may lead to heavy losses. This paper presents an optimized approach to avoid this misdetection in sensitive areas. Rotation invariant optimized correlation filters are used for detection of humans. Some pre-processing algorithms such as background subtraction and color space conversion have been linked to the correlation filters to minimize processing time and maximize the accuracy of target detection. The experimental tests of the proposed methodology validate that better accuracy can be achieved if the proposed optimized approach is utilized for moving human detection in real-time systems. In future work, the proposed approach will be extended to detect human activity at night and thermal imagery.

The demands of high throughput in networking radio communication system require the use of wideband networking waveform in software defined radio (SDR). The design of physical layer of any SDR waveform involves a crucial stage of burst detection as part of timing synchronization. In this paper, an efficient burst detection algorithm based on novel usage of Zadoff-chu sequence is proposed. The burst detection algorithm is Data Aided (DA) and is based on proposed time domain repetitive training sequence designed by using Zadoff-Chu sequence. The proposed technique reduces re-transmission overhead and thus increases throughput of the overall system. It is shown through simulation results that the proposed algorithm outperforms the existing Golay sequence based burst detection by approximately 1.5 dB. This improvement in performance is achieved without increase in the computational complexity.

Correlation-based pattern recognition filtering methods such as the eigenextended maximum average correlation height (EEMACH) filter is considered an effective tool in object recognition applications. However, these approaches require exclusive training for all possible distortions including in-plane as well as out-of-plane rotation, scale and illumination variations. The overall training process is exhaustive and requires training of filter banks to handle specific types of distortion separately. To overcome the aforementioned limitations, the authors propose a new difference of Gaussian (DoG)-based logarithmically preprocessed EEMACH filter which can manage multiple distortions in a single training instance while ensuring inherent control over illumination variations. The DoG-based logarithmic treatment exploits inherent capabilities of logarithmic preprocessing to manage scale and in-plane rotations. By reducing the number of classifier instances to one third, it not only reduces the computation complexity of the process to 33%, but also enhances the object recognition performance. The cumulative improvement is up to 2.73% in case of rotations and 10.8% in case of scaling by incorporating reinforced edges due to DoG operation. The resultant filter displays significantly enhanced recognition performance leading to a higher percentage of correct machine decisions, especially when an input scene contains multiple distortions.

Internet of Things (IoT) is helping to create a smart world by connecting sensors in a seamless fashion. With the forthcoming fifth generation (5G) wireless communication systems, IoT is becoming increasingly important since 5G will be an important enabler for the IoT. Sensor networks for IoT are increasingly used in diverse areas, e.g., in situational and location awareness, leading to proliferation of sensors at the edge of physical world. There exist several variable step-size strategies in literature to improve the performance of diffusion-based Least Mean Square (LMS) algorithm for estimation in wireless sensor networks. However, a major drawback is the complexity in the theoretical analysis of the resultant algorithms. Researchers use several assumptions to find closed-form analytical solutions. This work presents a unified analytical framework for distributed variable step-size LMS algorithms. This analysis is then extended to the case of diffusion based wireless sensor networks for estimating a compressible system and steady state analysis is carried out. The approach is applied to several variable step-size strategies for compressible systems. Theoretical and simulation results are presented and compared with the existing algorithms to show the superiority of proposed work.

Background Mucoepidermoid carcinoma of the trachea is a rare pediatric malignancy that presents unique challenges in diagnosis, operative management, and surveillance. Methods and results We present a 17-year-old girl with primary tracheal mucoepidermoid carcinoma presenting in acute respiratory distress due to near-total occlusion of the tracheal airway. An algorithmic approach to preoperative planning was developed to evaluate and remove the tumor endoscopically without compromising oxygenation. After initial palliative resection, endobronchial ultrasound was uniquely applied to evaluate depth of tumor invasion, and subsequent tracheal resection with primary anastomosis was performed as curative treatment. Conclusion Removal of distal tracheal masses can be performed safely with the implementation of an algorithmic approach to tumor visualization and resection. Endobronchial ultrasound can be used to evaluate the extent of tumor invasion and plan for definitive resection.

The negative impact of clutters on correlation output still remains the problem not handled in correlation pattern recognition (CPR) paradigm. Some of the known impacts caused by the presence of clutters include pronounced side-lobe generation and reduction in height of correlation peak. The occurrence of the side-lobes results in the distortion of actual correlation output while making the class decision a difficult task. Whereas, reduction in correlation height may cause class decisions to go wrong. The authors propose a novel clutter defiance strategy based on object segmentation through active contours approach thus evading a negative effect on the correlation output. Instead of allowing clutters to participate in CPR-based classification decision they recommend blocking irrelevant details to avoid distortion in the correlation process. An appropriate tweaking in the optimal logarithmic maximum average correlation height filter design yields a performance gain up to 80% in the correlation peak to side-lobe ratio, thus making it well pronounced to classify the true class objects correctly. The improvement remains consistent throughout the range of in-plane angular distortions against synthetically cluttered challenging images.

Although the concept of myocardial infarction (MI) occurring in the absence of coronary thrombus was first identified in 1939, it was 2007 before the term type 2 MI , referring to infarctions related to supply–demand imbalance, was first introduced into clinical practice [(1)][1]. Since then, our

Motion pattern analysis can be performed automatically on the basis of object trajectories by means of tracking videos; an effective approach to analyse and to model the traffic behaviour; is important to describe motion by taking the whole trajectory whereas it’s more essential to identify and evaluate object behaviour online. In this paper, pattern detection approach is presented which takes spatio-temporal characteristic of vehicle trajectories. A real time system is built to infer and track the object behaviour quickly by online performing trajectory analysis. Every independent vehicle in the video frame is tracked over time. As the anomaly behaviour occurs, glyph is generated to show it occurrences. Vehicle counting is done by estimating the trajectories and compared with Hungarian tracker. Several surveillance videos are taken into account for the performance checking of system. Experimental results demonstrated that proposed method in comparison with the state of the art algorithms, provides robust vehicle density estimation and event information i.e., lane change information.

Advanced correlation filters are an effective tool for target detection within a particular class. Most correlation filters are derived from a complex filter equation leading to a closed form filter solution. The response of the correlation filter depends upon the selected values of the optimal trade-off (OT) parameters. In this paper, the OT parameters are optimized using particle swarm optimization with respect to two different cost functions. The optimization has been made generic and is applied to each target separately in order to achieve the best possible result for each scenario. The filters obtained using standard particle swarm optimization (PSO) and hierarchal particle swarm optimization (HPSO) algorithms have been compared for various test images with the filter solutions available in the literature. It has been shown that optimization improves the performance of the filters significantly.

Data and image segmentation plays pivotal role in the application of machine learning. k-means, as a tool for unsupervised clustering, is a widely used algorithm for segmentation due to its inherent simplicity and efficiency. k-means partitions datasets into subsets based on their fitness value. As such k-means is a well suited algorithm for implementation on hardware platform such as Field Programmable Gate Array (FPGA) but requires high computation time. Hardware accelerators can help in reducing the computation complexity of the algorithm. In this paper, we present a simplified multicore based scalable hardware architecture for implementation of k-means. Mean and fitness modules in proposed architecture are further unfolded to further enhance the speed of k-means clustering algorithm. The unfolding factor has to be selected by keeping the area of the target device in check. In the proposed architecture, the cores are further connected through Network on Chip (NoC) interconnect network which allows for higher scalability while elevating the bottleneck of message passing. The performance of our MPSoC architecture has been evaluated with respect to Average Speedup, Average Throughput and Area consumption with and without use of NoC interconnect. Finally, we compare the use of different NoC interconnect models with respect to maximum Operating Frequency, average Throughput and Area overhead.

There is a growing problem of waste management faced by developing countries. Karachi is the biggest city of Pakistan having a population of approximately 24 million. Statistics indicate that on a daily basis, about 12,000 tons of solid waste is generated in Karachi alone, of which forty percent can be found on the city streets. Improper management of solid waste is causing the spread of infectious diseases and environmental pollution. This study examines how the solid waste management process is implemented in Karachi and the challenges faced by the responsible authorities. Solid waste management combines all activities performed in order to keep the city clean on a regular basis and handle waste collection and disposal, sewerages, water treatment, recycling, and health and hygiene issues. This study is qualitative in nature and the mediums of observations and interviews were used to collect data. A total of twenty respondents were interviewed using both structured and unstructured questions. These included residents of selected areas of Karachi as well as municipal officials who are responsible for solid waste management in the city. Results revealed that the citizens are dissatisfied with the current strategies of solid waste management implemented by the municipalities. The public thinks that the municipalities are ill-equipped to handle the developing situation of substantial waste in the city due to which they have to confront many problems. The municipalities, on the other hand, were found to be underfunded and ineffective in the department of solid waste management and therefore, they lack the proper instruments to ensure their efficiency. Residents of Karachi were also a contributing element in the growing waste problem by means of their participation in activities such as illegal dumping and violation of laws. With the ratio of solid waste, increasing per day in the urban center, there is an immediate need to implement major steps to improve the current situation. A more consolidated strategy for solid waste management needs to be designed and implemented which streamlines the processes of waste collection and disposal. Citizens of Karachi also need to be educated on how they can play their part in reducing the quantity of solid waste through the promotion of habits such as recycling.

The Global Registry of Acute Coronary Events (GRACE) and Thrombolysis In Myocardial Infarction (TIMI) risk scores are validated to predict risk of adverse outcomes among patients with type 1 myocardial infarction (MI). Type 2 MI (T2MI) is common and is associated with significant mortality, yet the

A spatial domain optimal trade-off Maximum Average Correlation Height (SPOT-MACH) filter has been shown to have advantages over frequency domain implementations of the Optimal Trade-Off Maximum Average Correlation Height (OR-MACH) filter as it can be made locally adaptive to spatial variations in the input image background clutter and normalized for local intensity changes. This enables the spatial domain implementation to be resistant to illumination changes. The Affine Scale Invariant Feature Transform (ASIFT) is an extension of previous feature transform algorithms; its features are invariant to six affine parameters which are translation (2 parameters), zoom, rotation and two camera axis orientations. This results in it accurately matching increased numbers of key points which can then be used for matching between different images of the object being tested. In this paper a novel approach will be adopted for enhancing the performance of the spatial correlation filter (SPOT MACH filter) using ASIFT in a pre-processing stage enabling fully invariant object detection and recognition in images with geometric distortions. An optimization criterion is also be developed to overcome the temporal overhead of the spatial domain approach. In order to evaluate effectiveness of algorithm, experiments were conducted on two different data sets. Several test cases were created based on illumination, rotational and scale changes in the target object. The performance of correlation algorithms was also tested against composite images as references and it was found that this results in a well-trained filter with better detection ability even when the target object has gone through large rotational changes.

Correlation filters have been implemented in software and have proven very effective for automatic target detection, biometric verification and security applications. In this paper, these filters are implemented in hardware keeping in view their importance in real time applications. Hardware implementations are compared with results generated through software. These vary by as little as 10-4 which is demonstrated in the experimental section of the paper. The hardware design of these filters is implemented in LabView which can be subsequently employed in real-time security applications. This design may be expanded for other advanced variants of correlation filters in future work.

Text summarization has become a sophisticated approach for the quick searching, automatic sorting, abstract generating etc., to the large amount of data. The involvement of complete study of passage and extra time is needed to generate the essence of any content. Subsequently, Natural Language Processing is an information extraction approach to automatically extract the artifacts from the textual descriptions. Moreover, NLP is often applied to generate the various element of concerns like essential terms, class models, test cases from the initial Textual descriptions. However, it is usually required to study complete passage to extract relevant information from textual content that makes this process time consuming. This research article proposed a novel and fully automatic NLP methodology to generate crux from content. As a part of research, a tool Efficient Text Summary from Text (ETST) is developed. Research authentication is achieved through the implementation of two state-of-the-art case studies. The experimental outcome proved that our suggested Natural Language Processing methodology is novel and fully automatic and is also useful for the future researchers of this domain.