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Pregnancy-associated spontaneous rupture of renal artery aneurysm (PSRRAA) is a rare emergency that may lead to deleterious maternal and fetal outcomes. We present the first report of PSRRAA during early pregnancy and to be associated with trisomy-21 in a six-week pregnant lady. Intraabdominal bleeding suspicion confirmed by contrast-enhanced computed tomography showing aneurysmal rupture which was managed by percutaneous angioembolization. PSRRAA is a challenging diagnosis and a life-threatening emergency. A high index of suspicion to diagnose PSRRAA even in early pregnancy is needed. Management of PSRRAA has a high risk of fetal radiation exposure that may lead to congenital anomalies.

Automatic Target Recognition (ATR) using Correlation Pattern Recognition (CPR) in IoT-based applications encounters limitations like limited memory and inadequate computational resources. One reason is the required quantity of reference templates for each target/object to cover all features of a target/object. To mitigate the issue of reference templates per target/object without accuracy degradation, this paper proposes energy compaction-based CPR autoencoder-training. Additionally, a newly proposed performance metric known as Peak Energy Gain (PEG) estimates the quality of the correlation plane and the feature compression capability CPR methods. The proposed, composite filtering strategy, Eigen Maximum Average Correlation Height (EMACH), and Extended Eigen Maximum Average Correlation Height ( $E^{2}$ MACH) are vigorously validated using publicly available biometric and object image databases. By training a single reference template, the proposed training method achieves 97.97% mean accuracy with the second-best approach of $E^{2}$ MACH that attains 53.04% mean accuracy on the Pose Estimation Database. For bio-metric fingerprint verification, the mean Equal Error Rate (EER) of the proposed approach and the composite strategy is 3% and 29.69%, respectively on the FVC2002DB1A database. Similarly, the mean EER of the proposed approach and the composite strategy is 10.55% and 26.32%, respectively on the FVC2006IA database. For FEI faces dataset, the proposed method achieves 1.41% mean EER, and the composite filtering approach achieves 21.43% mean EER. On the University of Tehran Iris database, the proposed autoencoder-based methodology obtains 19.07%, and 18.07% mean EER on the left and right side iris instances, respectively. The comparative results for each dataset demonstrate superiority of AE-based method over the state-of-the-art CPR methods.

Vehicular Ad-Hoc Networks (VANETs) are a challenging yet active research area. It offers a wide range of applications, including Intelligent Transport System (ITS), effective road traffic monitoring, efficient traffic flow and road safety applications. During real-time data gathering for emergency scenarios, the fixed silent segments cause a problem for smooth communication. Moreover, the critical ITS operations may be delayed due to this problem. This paper proposes a Real-Time Traffic-Aware Data Gathering Protocol (TDG) where the dynamic segmentation switching is adopted to handle the communication limitations. TDG is lightweight and dynamically designed for collecting and forwarding data packets based on current and rapid evolving traffic conditions. The primary objective is to reduce network and data communication overhead to incorporate real-time data collection time constraints. TDG implements a data aggregation scheme for data analysis to fetch information based on location, speed, vehicle id and neighbour count. Moreover, a data extraction scheme is implemented to increase data retrieval and data utilization effectiveness in an intelligent way at the base station. Extensive simulation and evaluation results validate that our proposed solution outperforms existing data gathering protocols in effectiveness, efficiency, delay, communication overhead and data transmission rate.

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.

In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art.

Abstract Background Interferon-gamma (IFN-γ) is an important mediator of type I immune response and has antiviral, immunoregulatory and anti-tumor properties, plays a wide range of roles in inflammation and autoimmune diseases. The aim of this study was to obtain monoclonal antibody (mAb) against caprine IFN-γ by immunizing of BALB/c mice with the purified rIFN-γ. Results Recombinant caprine IFN-γ was expressed in Escherichia coli strain BL21 (DE3) and monoclonal antibodies against caprine IFN-γ were produced by immunizing of BALB/c mice with rIFN-γ. One hybridoma secreting mAb was screened by enzyme-linked immunosorbent assay (ELISA) which was designated as 2C. MAb secreted by this cell line were analyzed through ELISA, western blot and application of the mAb was evaluated by immunofluorescence analysis using goat lip tissues infected with Orf virus. ELISA analysis revealed that mAb 2C can specifically recognize rIFN-γ protein and culture supernatant of goat peripheral blood mononuclear cells (PBMCs) stimulated by concanavalin A (Con A) but cannot recognize the fusion tag protein of pET-32a. Western blot analysis showed that mAb 2C can specifically react with the purified 34.9 kDa rIFN-γ protein but does not react with the fusion tag protein of pET-32a. Immunofluorescence results demonstrated that mAb 2C can detect IFN-γ secreted in histopathological sites of goats infected with Orf virus. Conclusions A caprine IFN-γ-specific mAb was successfully developed in this study. Further analyses showed that the mAb can be used to detect IFN-γ expression level during contagious ecthyma in goats.

Background Type 2 myocardial infarction (T2MI) is common and associated with high cardiovascular event rates. However, the relationship between T2MI and heart failure (HF) is uncertain. Methods and Results We identified patients with T2MI at a large tertiary hospital between October 2017 and May 2018. Patient characteristics, causes of T2MI, and subsequent HF hospitalizations were determined by physician chart review. We identified 359 patients with T2MI over the study period; 184 patients had a history of HF. Among patients with ejection fraction (EF) assessment (N=180), the majority had preserved EF (N=107; 59.4%), followed by reduced EF (N=54; 30.0%), and mid‐range EF (N=19; 10.6%). Acute HF was the most common cause of T2MI (20.9%). Of those whose T2MI was precipitated by HF (N=75), the mean EF was 53.0±16.8% and 16 (21.3%) were de novo diagnoses of HF. Among patients with T2MI who were discharged alive with available follow‐up (N=289), 5.5% were hospitalized with acute HF within 30 days, 17.3% within 180 days, and 22.1% within 1 year. In subgroup analyses, among patients with T2MI with prevalent or new HF (N=161), the rate of HF hospitalization at 1 year was 34.2%, considerably higher than those with T2MI and no HF diagnosis at discharge (7.0%; N=9/128). Conclusions Index presentations of HF or worsening chronic HF represent the most common causes of T2MI. ≈1 in 5 patients with T2MI will be readmitted for HF within 1 year of their event. Strategies to prevent HF events after a T2MI are needed.

Unmanned ground vehicles (UGVs) are becoming the foremost part of rescue teams for protecting human lives from severe disasters and reducing human casualties by informing them about the risks ahead, such as mine detection and clearance. In mine detection, a centralized system is required so that the UGVs can communicate with each other efficiently to disseminate the mine detection messages (MDMs) to incoming vehicles of military and civilians. Therefore, in this piece of research, a novel unmanned ground and aerial vehicle (UGAV)-based mine-detection-vehicle routing (MDVR) protocol has been proposed, mainly for the mine detection and clearance teams using a vehicular ad hoc network (VANET). The protocol disseminates the MDMs using UGVs and unmanned aerial vehicles (UAVs) in combination to overcome the limitations of only inter-UGV communication. The proposed protocol performs cluster-based multicast communication in real time using UAVs so that the dynamic mobility of UGVs cannot affect the performance of MDM dissemination. Hence, the proposed scheme is adaptable because any failure in message delivery can cause a high level of destruction. The proposed cluster-based scheme can adapt to any real-time scenario by introducing the level-based cluster-head election scheme (LBCHE), which works concerning its assigned priority for reducing the delay incurred in MDMs dissemination. The simulation of the proposed protocol in the network simulator (NS) shows that the overhead and delay are reduced in MDMs dissemination. At the same time, the throughput, packet delivery ratio, and stability increased compared to the other competing protocols.

This study aimed to evaluate the expression level of caseins during mastitis of goats. Whole goat caseins were used as primary antigens for mouse immunization. A monoclonal antibody (mAb) named 5B with high specificity to goat αs1-casein was developed. Further results showed that mAb 5B can successfully be applied to western blot and ELISA. In addition, immunofluorescence analysis using this mAb showed increased milk αs1-casein level in mastitis-affected goats. In conclusion, this study has established an effective tool to evaluate the expression level of αs1-casein during mastitis development.

Background: Despite over 4 million cases of novel coronavirus disease 2019 (COVID-19) in the United States, limited data exist including socioeconomic background and post-discharge outcomes for patients hospitalized with this disease. Methods: In this case series, we identified patients with COVID-19 admitted to 3 Partners Healthcare hospitals in Boston, Massachusetts between March 7th, 2020, and March 30th, 2020. Patient characteristics, treatment strategies, and outcomes were determined. Findings: A total of 247 patients hospitalized with COVID-19 were identified; the median age was 61 (interquartile range [IQR]: 50–76 years), 58% were men, 30% of Hispanic ethnicity, 21% enrolled in Medicaid, and 12% dual-enrolled Medicare/Medicaid. The median estimated household income was $66,701 [IQR: $50,336-$86,601]. Most patients were treated with hydroxychloroquine (72%), and statins (76%; newly initiated in 34%). During their admission, 103 patients (42%) required intensive care. At the end of the data collection period (June 24, 2020), 213 patients (86.2%) were discharged alive, 2 patients (0.8%) remain admitted, and 32 patients (13%) have died. Among those discharged alive (n = 213), 70 (32.9%) were discharged to a post-acute facility, 31 (14.6%) newly required supplemental oxygen, 19 (8.9%) newly required tube feeding, and 34 (16%) required new prescriptions for antipsychotics, benzodiazepines, methadone, or opioids. Over a median post-discharge follow-up of 80 days (IQR, 68–84), 22 patients (10.3%) were readmitted. Interpretation: Patients hospitalized with COVID-19 are frequently of vulnerable socioeconomic status and often require intensive care. Patients who survive COVID-19 hospitalization have substantial need for post-acute services.

Background Home‐time, defined as the time spent alive outside of a healthcare institution, has emerged as a patient‐centered health outcome. The discharge locations and distribution of home‐time after a type 2 myocardial infarction are unknown. Methods and Results Patients with a type 2 myocardial infarction between October 2017 and May 2018 at Massachusetts General Hospital were included. Patients discharged to hospice or without follow‐up data were excluded. Our primary outcome was home‐time defined as the number of days lived outside of a hospital, long‐term acute care facility, skilled nursing facility, or rehabilitation facility. We identified 359 patients with type 2 myocardial infarction over the study period. Of those discharged alive (N=321), 62.9% were discharged home, and the remainder went to a facility or hospice. Among those with available follow‐up data (N=289), the median home‐time was 30 (interquartile range [IQR], 16–30) days at 30 days, 171 (IQR, 133–180) days at 180 days, and 347 (IQR, 203–362) days at 365 days. At 1 year, 29 patients (10%) with type 2 myocardial infarction had spent no time at home and only 57 patients (19.7%) spent the entire year alive and at home. At 1 year, postdischarge all‐cause mortality was 23.2%, all‐cause readmission was 69.2%, and major adverse cardiovascular events (composite of all‐cause mortality, recurrent myocardial infarction, or stroke) was 34.9%. Home‐time through 1 year correlated strongly with time‐to‐event all‐cause mortality (τ=0.54, P

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.

Abstract Video visualization (VV) is considered to be an essential part of multimedia visual analytics. Many challenges have arisen from the enormous video content of cameras which can be solved with the help of data analytics and hence gaining importance. However, the rapid advancement of digital technologies has resulted in an explosion of video data, which stimulates the needs for creating computer graphics and visualization from videos. Particularly, in the paradigm of smart cities, video surveillance as a widely applied technology can generate huge amount of videos from 24/7 surveillance. In this paper, a state of the art algorithm has been proposed for 3D conversion from traffic video content to Google Map. Time-stamped glyph-based visualization is used effectively in outdoor surveillance videos and can be used for event-aware detection. This form of traffic visualization can potentially reduce the data complexity, having holistic view from larger collection of videos. The efficacy of the proposed scheme has been shown by acquiring several unprocessed surveillance videos and by testing our algorithm on them without their pertaining field conditions. Experimental results show that the proposed visualization technique produces promising results and found effective in conveying meaningful information while alleviating the need of searching exhaustively colossal amount of video data.

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 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.

Clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (Cas9) is a precise genome manipulating technology that can be programmed to induce double-strand break (DSB) in the genome wherever needed. After nuclease cleavage, DSBs can be repaired by non-homologous end joining (NHEJ) or homology-directed repair (HDR) pathway. For producing targeted gene knock-in or other specific mutations, DSBs should be repaired by the HDR pathway. While NHEJ can cause various length insertions/deletion mutations (indels), which can lead the targeted gene to lose its function by shifting the open reading frame (ORF). Furthermore, HDR has low efficiency compared with the NHEJ pathway. In order to modify the gene precisely, numerous methods arose by inhibiting NHEJ or enhancing HDR, such as chemical modulation, synchronized expression, and overlapping homology arm. Here we focus on the efficiency and other considerations of these methodologies.

It is of significant importance for any classification and recognition system, which claims near or better than human performance to be immune to small perturbations in the dataset. Researchers found out that neural networks are not very robust to small perturbations and can easily be fooled to persistently misclassify by adding a particular class of noise in the test data. This, so-called adversarial noise severely deteriorates the performance of neural networks, which otherwise perform really well on unperturbed dataset. It has been recently proposed that neural networks can be made robust against adversarial noise by training them using the data corrupted with adversarial noise itself. Following this approach, in this paper, we propose a new mechanism to generate a powerful adversarial noise model based on K-support norm to train neural networks. We tested our approach on two benchmark datasets, namely the MNIST and STL-10, using muti-layer perceptron and convolutional neural networks. Experimental results demonstrate that neural networks trained with the proposed technique show significant improvement in robustness as compared to state-of-the-art techniques.

Several power allocation algorithms for cooperative relay networks are presented in the literature. These contributions assume perfect channel knowledge and capacity achieving codes. However in practice, obtaining the channel state information at a relay or at the destination is an estimation problem and can generally not be error free. The investigation of the power allocation mechanism in a wireless network due to channel imperfections is important because it can severely degrade its performance regarding throughput and bit error rate. In this paper, the impact of imperfect channel state information on the power allocation of an adaptive relay network is investigated. Moreover, a framework including Automatic Repeat reQuest (ARQ) mechanism is provided to make the power allocation mechanism robust against these channel imperfections. For this framework, the end-to-end SNR is calculated considering imperfect channel knowledge using ARQ analytically. The goal is to emphasize the impact of imperfect channel knowledge on the power allocation mechanism. In this paper, the simulation results illustrate the impact of channel uncertainties on the average outage probability, throughput, and consumed sum power for different qualities of channel estimation. It is shown that the presented framework with ARQ is extremely robust against the channel imperfections.

In a vehicular ad hoc network (VANET), the vehicles communicate with each other to develop an intelligent transport system (ITS) which provides safety and convenience while driving. The major challenge of VANET is that the topology changes dynamically due to the high speed and unpredictable mobility of vehicles resulting in an inefficient real time message dissemination, especially in emergency scenarios such as in the accident event where it can cause high level of destruction. To the best of our knowledge, there is no such mechanism in existing literature which can handle real time multicast communication in VANET for both urban and highway scenarios. In this paper, we propose a novel real time vehicular communication (RTVC) framework which consists of a VANET cluster scheme (VCS) and VANET multicast routing (VMR) to achieve efficient vehicle communication within both urban and highway scenarios. The RTVC framework develops stable communication links and achieves high throughput with low overhead despite high mobility by combining the multicast routing with a unique cluster based scheme. In VCS, the cluster head (CH) is elected upon cluster threshold value (CTV) to disseminate the messages within the cluster members (CMs) and to other cluster heads by intercluster communication, which reduces the network overhead. In addition, the vehicles cluster head election (VCHE) procedure is proposed to reduce the number of CHs and CMs switches which results in lower overhead of maintaining the clusters. Moreover, another novelty of the framework is that the CTV of VCHE can be adjusted by speed adjustment factor (SAF) to achieve the desired cluster stability depending upon the required VANET application. The simulation results illustrate that the proposed framework has achieved the goal of stable, efficient, and real time communication despite highly dynamic environment of VANET.

Vehicular Ad Hoc Networks (VANETs) are autonomous and self-configurable wireless ad hoc networks and considered as a subset of Mobile Ad Hoc Networks (MANETs). MANET is composed of self-organizing mobile nodes which communicate through a wireless link without any network infrastructure. A VANET uses vehicles as mobile nodes for creating a network within a range of 100 to 1000 meters. VANET is developed for improving road safety and for providing the latest services of intelligent transport system (ITS). The development and designing of efficient, self-organizing, and reliable VANET are a challenge because the node's mobility is highly dynamic which results in frequent network disconnections and partitioning. VANET protocols reduce the power consumption, transmission overhead, and network partitioning successfully by using multicast routing schemes. In multicasting, the messages are sent to multiple specified nodes from a single source. The novel aspect of this paper is that it categorizes all VANET multicast routing protocols into geocast and cluster-based routing. Moreover, the performance of all protocols is analyzed by comparing their routing techniques and approaches.