VMEO: Vector Modeling Errors and Operands for Approximate adders

Approximate computing techniques are extensively used in computationally intensive applications. Addition architecture being the basic component of computational unit, has received a lot of interest from approximate computing community. Approximate adders are designed with the motivation to reduce area, power and delay of their accurate versions at the cost of bounded loss in accuracy. A major class of approximate adders are implemented using binary logic circuits that operate with a high degree of predictability and speculation. This paper is one of the early attempt to vector model error values that occur in approximate architectures and the inputs fed to them. In this paper, we propose two vectors namely Error Vectors (EVs) and the Input Conditioning Vectors (ICVs) that will form the mathematical foundation of several probabilistic error evaluation methodologies. In other words, the suggested vectors can be used to develop assessment methods to measure the performance of approximate circuits. Our proposed vectors when utilised to analyze approximate circuits, will provide a descriptive idea about (i) chances of error generation and propagation, (ii) the amount of error at specific bit locations and its impact on overall result. This is however not conceivable with existing state-of-the-art methodologies

B2T: The Third Logical Value of a Bit

Modern computing systems predominantly operate on the binary number system that accepts only ‘0’ or ‘1’ as logical values leading to computational homogeneity. But this helps in creating leakage patterns that can be exploited by adversaries to carry out hardware and software-level attacks. Recent research has shown that ternary systems, operating on three logical values (‘0′, ‘1\u27, and ‘z\u27) can surpass binary systems in terms of performance and security. In this paper, we first propose a novel approach that assigns logical values based on the direction of current flow within a conducting element, rather than relying on the voltage scale. Furthermore, we also present the mathematical models for each ternary gate

Size-dependent Failure Behavior of Lithium-Iron Phosphate Battery under Mechanical Abuse

The use of battery electric vehicles is one of the green solutions to reduce environmental pollution and save the Earth. Based on the power, speed, and space constraints, the battery geometries (size and shape) are decided in the battery electric vehicles. However, battery failure assessment and abuse testing are much needed to ensure its safe operation. Herein, four types of lithium-iron phosphate batteries viz. 18650, 22650, 26650, and 32650 are considered to conduct lateral, longitudinal compression, and nail penetration tests. The mechanical failure is characterized by the voltage drop and temperature rise at the onset of the first short-circuit is identified by Aurdino-based voltage sensor module and temperature measurement module. The battery failure load and peak temperature at the onset of internal short-circuit during different mechanical abuse conditions are found to rely on the battery size strongly. The failure due to the onset of internal short circuit is observed to be delayed for small-sized 18650 batteries during lateral compression, unlike longitudinal compression and nail penetration test. At the onset of the short circuit, the LFPBs showed variation in temperature above the ambient value of 28 degree C. Among the LFPBs considered, the lowest variation of temperature rise (considering ambient temperature) is found to be 5.25 degree C for type 26650. The outcome of this work is anticipated to demonstrate the significance of the choice of battery sizes for different desired applications safely.Comment: 15 pages, 6 Figures, 2 tabl

Spectrum of malignant mediastinal masses at a tertiary care centre in Central India

Background: Malignant mediastinal masses can develop from structures that are normally located or pass through the mediastinum during development, as well as from metastases of malignancies that arise elsewhere in the body. Since many tumors that occur in the mediastinum are undifferentiated and have overlapping histologic features, one must consider a broad differential diagnosis and perform a thorough evaluation. This is particularly important since appropriate therapy for various mediastinal tumors differs considerably and may significantly impact survival.Methods: Ours was a retrospective descriptive study of 48 patients who presented or referred to medical oncology department from January 2014 to December 2017 and in whom malignant cause of mediastenal mass was established. All details of the patients pertinent epidemiology, clinical history and pathological including immunohistochemistry details were studied.Results: Out of 48 patients,14 cases (29.2%) were in adolescent and young adult age group (15-29 years). Majority of the patients were symptomatic (91.6%) with most common being cough (87.5%) followed by chest pain (81.5%) and dyspnoea (79.1%). Four of the patients presented with superior vena-caval syndrome. Most of the tumors (64.6%) are in anterior mediastenum region. Histopathological examination revealed non-hodgkins lymphoma in 31.25%, Hodgkins lymphoma in 18.75%, leukaemia in 6.25%, germ cell tumor in 8.33%, thymic neoplasms in 4.16%, neurogenic tumors in 4.16%, lung carcinoma in 10.4% and metastatic carcinoma in 10.4%.Conclusions: Malignant mediastinal masses have a broad range of diagnosis, establishing of which is important. While imaging help in narrowing the differential diagnosis, adequate pathological categorization should be done as many patients responds to specific line of therapy

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Not Available

Not AvailableNearly two decades of revolution in the area of genomics serves as the basis of present-day molecular breeding in major food crops such as rice. Here we report an open source database on two major biotic stresses of rice, named RiceMetaSysB, which provides detailed information about rice blast and bacterial blight (BB) responsive genes (RGs). Meta-analysis of microarray data from different blast- and BB-related experiments across 241 and 186 samples identified 15135 unique genes for blast and 7475 for BB. A total of 9365 and 5375 simple sequence repeats (SSRs) in blast and BB RGs were identified for marker development. Retrieval of candidate genes using different search options like genotypes, tissue, developmental stage of the host, strain, hours/days post-inoculation, physical position and SSR marker information is facilitated in the database. Search options like ‘common genes among varieties’ and ‘strains’ have been enabled to identify robust candidate genes. A 2D representation of the data can be used to compare expression profiles across genes, genotypes and strains. To demonstrate the utility of this database, we queried for blast-responsive WRKY genes (fold change ≥5) using their gene IDs. The structural variations in the 12 WRKY genes so identified and their promoter regions were explored in two rice genotypes contrasting for their reaction to blast infection. Expression analysis of these genes in panicle tissue infected with a virulent and an avirulent strain of Magnaporthe oryzae could identify WRKY7, WRKY58, WRKY62, WRKY64 and WRKY76 as potential candidate genes for resistance to panicle blast, as they showed higher expression only in the resistant genotype against the virulent strain. Thus, we demonstrated that RiceMetaSysB can play an important role in providing robust candidate genes for rice blast and BB.Not Availabl

Not Available

Not AvailableNearly two decades of revolution in the area of genomics serves as the basis of present-day molecular breeding in major food crops such as rice. Here we report an open source database on two major biotic stresses of rice, named RiceMetaSysB, which provides detailed information about rice blast and bacterial blight (BB) responsive genes (RGs). Meta-analysis of microarray data from different blast- and BB-related experiments across 241 and 186 samples identified 15135 unique genes for blast and 7475 for BB. A total of 9365 and 5375 simple sequence repeats (SSRs) in blast and BB RGs were identified for marker development. Retrieval of candidate genes using different search options like genotypes, tissue, developmental stage of the host, strain, hours/days post-inoculation, physical position and SSR marker information is facilitated in the database. Search options like ‘common genes among varieties’ and ‘strains’ have been enabled to identify robust candidate genes. A 2D representation of the data can be used to compare expression profiles across genes, genotypes and strains. To demonstrate the utility of this database, we queried for blast-responsive WRKY genes (fold change ≥5) using their gene IDs. The structural variations in the 12 WRKY genes so identified and their promoter regions were explored in two rice genotypes contrasting for their reaction to blast infection. Expression analysis of these genes in panicle tissue infected with a virulent and an avirulent strain of Magnaporthe oryzae could identify WRKY7, WRKY58, WRKY62, WRKY64 and WRKY76 as potential candidate genes for resistance to panicle blast, as they showed higher expression only in the resistant genotype against the virulent strain. Thus, we demonstrated that RiceMetaSysB can play an important role in providing robust candidate genes for rice blast and BB.Not Availabl

Single nucleotide polymorphism mining and nucleotide sequence analysis of Mx1 gene in exonic regions of Japanese quail

Aim: An attempt has been made to study the Myxovirus resistant (Mx1) gene polymorphism in Japanese quail. Materials and Methods: In the present, investigation four fragments viz. Fragment I of 185 bp (Exon 3 region), Fragment II of 148 bp (Exon 5 region), Fragment III of 161 bp (Exon 7 region), and Fragment IV of 176 bp (Exon 13 region) of Mx1 gene were amplified and screened for polymorphism by polymerase chain reaction-single-strand conformation polymorphism technique in 170 Japanese quail birds. Results: Out of the four fragments, one fragment (Fragment II) was found to be polymorphic. Remaining three fragments (Fragment I, III, and IV) were found to be monomorphic which was confirmed by custom sequencing. Overall nucleotide sequence analysis of Mx1 gene of Japanese quail showed 100% homology with common quail and more than 80% homology with reported sequence of chicken breeds. Conclusion: The Mx1 gene is mostly conserved in Japanese quail. There is an urgent need of comprehensive analysis of other regions of Mx1 gene along with its possible association with the traits of economic importance in Japanese quail