Stigma experienced by primary care givers of persons with Epilepsy

Stigma is society's negative evaluation of particular features or behaviour. Cultural beliefs that define certain conditions negatively may create tainted and discounted identities for affected individuals and their families. The present study was intended to assess the presence and degree of stigma experienced by primary care givers of persons with epilepsy (PCG) and to find out the causal attribution for epilepsy among the PCG. The study was a cross sectional hospital based study. By using purposive sampling techniques 100 Caregivers of persons diagnosed with epilepsy were taken from the OPD (Epilepsy Clinic) of CIP. Family Interview Schedule to assess both stigma and causal attribution (Sartarius et al., 1996) were administered. Median split technique was used to divide respondents into two stigma groups, low and high. It can be seen that the socio-demographic variables (of persons with epilepsy) religion emerged as statistically significant. Hindus in our sample seemed to have higher stigma than both Muslims and Christians. It was found more stigma when persons with epilepsy were younger. There were a significantly high number of care givers of high stigma group attributive influence of depression/unhappiness as a cause of epilepsy in their family member. Although not statistically significant but it was found that stigma tended to be more when care giver’s age was young. It was also found that, those care givers who had attributed no cause it just happened or don't know for epilepsy in their family member experienced more stigma which have important implication in psycho-educational programs and intervention to dispel stigma. Keywords: Stigma, care givers, Epileps

ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis

Profile hidden Markov models (pHMMs) are widely employed in various bioinformatics applications to identify similarities between biological sequences, such as DNA or protein sequences. In pHMMs, sequences are represented as graph structures. These probabilities are subsequently used to compute the similarity score between a sequence and a pHMM graph. The Baum-Welch algorithm, a prevalent and highly accurate method, utilizes these probabilities to optimize and compute similarity scores. However, the Baum-Welch algorithm is computationally intensive, and existing solutions offer either software-only or hardware-only approaches with fixed pHMM designs. We identify an urgent need for a flexible, high-performance, and energy-efficient HW/SW co-design to address the major inefficiencies in the Baum-Welch algorithm for pHMMs. We introduce ApHMM, the first flexible acceleration framework designed to significantly reduce both computational and energy overheads associated with the Baum-Welch algorithm for pHMMs. ApHMM tackles the major inefficiencies in the Baum-Welch algorithm by 1) designing flexible hardware to accommodate various pHMM designs, 2) exploiting predictable data dependency patterns through on-chip memory with memoization techniques, 3) rapidly filtering out negligible computations using a hardware-based filter, and 4) minimizing redundant computations. ApHMM achieves substantial speedups of 15.55x - 260.03x, 1.83x - 5.34x, and 27.97x when compared to CPU, GPU, and FPGA implementations of the Baum-Welch algorithm, respectively. ApHMM outperforms state-of-the-art CPU implementations in three key bioinformatics applications: 1) error correction, 2) protein family search, and 3) multiple sequence alignment, by 1.29x - 59.94x, 1.03x - 1.75x, and 1.03x - 1.95x, respectively, while improving their energy efficiency by 64.24x - 115.46x, 1.75x, 1.96x.Comment: Accepted to ACM TAC

Thecamoebians (Testate Amoebae) Straddling the Permian-Triassic Boundary in the Guryul Ravine Section, India: Evolutionary and Palaeoecological Implications

Exceptionally well-preserved organic remains of thecamoebians (testate amoebae) were preserved in marine sediments that straddle the greatest extinction event in the Phanerozoic: the Permian-Triassic Boundary. Outcrops from the Late Permian Zewan Formation and the Early Triassic Khunamuh Formation are represented by a complete sedimentary sequence at the Guryul Ravine Section in Kashmir, India, which is an archetypal Permian-Triassic boundary sequence. Previous biostratigraphic analysis provides chronological control for the section, and a perspective of faunal turnover in the brachiopods, ammonoids, bivalves, conodonts, gastropods and foraminifera. Thecamoebians were concentrated from bulk sediments using palynological procedures, which isolated the organic constituents of preserved thecamoebian tests. The recovered individuals demonstrate exceptional similarity to the modern thecamoebian families Centropyxidae, Arcellidae, Hyalospheniidae and Trigonopyxidae, however, the vast majority belong to the Centropyxidae. This study further confirms the morphologic stability of the thecamoebian lineages through the Phanerozoic, and most importantly, their apparent little response to an infamous biological crisis in Earth's history

Early Prediction of DNN Activation Using Hierarchical Computations

Deep Neural Networks (DNNs) have set state-of-the-art performance numbers in diverse fields of electronics (computer vision, voice recognition), biology, bioinformatics, etc. However, the process of learning (training) from the data and application of the learnt information (inference) process requires huge computational resources. Approximate computing is a common method to reduce computation cost, but it introduces loss in task accuracy, which limits their application. Using an inherent property of Rectified Linear Unit (ReLU), a popular activation function, we propose a mathematical model to perform MAC operation using reduced precision for predicting negative values early. We also propose a method to perform hierarchical computation to achieve the same results as IEEE754 full precision compute. Applying this method on ResNet50 and VGG16 shows that up to 80% of ReLU zeros (which is 50% of all ReLU outputs) can be predicted and detected early by using just 3 out of 23 mantissa bits. This method is equally applicable to other floating-point representations

Early Prediction of DNN Activation Using Hierarchical Computations

Deep Neural Networks (DNNs) have set state-of-the-art performance numbers in diverse fields of electronics (computer vision, voice recognition), biology, bioinformatics, etc. However, the process of learning (training) from the data and application of the learnt information (inference) process requires huge computational resources. Approximate computing is a common method to reduce computation cost, but it introduces loss in task accuracy, which limits their application. Using an inherent property of Rectified Linear Unit (ReLU), a popular activation function, we propose a mathematical model to perform MAC operation using reduced precision for predicting negative values early. We also propose a method to perform hierarchical computation to achieve the same results as IEEE754 full precision compute. Applying this method on ResNet50 and VGG16 shows that up to 80% of ReLU zeros (which is 50% of all ReLU outputs) can be predicted and detected early by using just 3 out of 23 mantissa bits. This method is equally applicable to other floating-point representations