Extinction risk in extant marine species integrating palaeontological and biodistributional data

Extinction risk assessments of marine invertebrate species remain scarce, which hinders effective management of marine biodiversity in the face of anthropogenic impacts. To help close this information gap, in this paper we provide a metric of relative extinction risk that combines palaeontological data, in the form of extinction rates calculated from the fossil record, with two known correlates of risk in the modern day: geographical range size and realized thermal niche. We test the performance of this metric-Palaeontological Extinction Risk In Lineages (PERIL)-using survivorship analyses of Pliocene bivalve faunas from California and New Zealand, and then use it to identify present-day hotspots of extinction vulnerability for extant shallow-marine Bivalvia. Areas of the ocean where concentrations of bivalve species with higher PERIL scores overlap with high levels of climatic or anthropogenic stressors should be considered of most immediate concern for both conservation and management.

Toward Fast Neural Computing using All-Photonic Phase Change Spiking Neurons

The rapid growth of brain-inspired computing coupled with the inefficiencies in the CMOS implementations of neuromrphic systems has led to intense exploration of efficient hardware implementations of the functional units of the brain, namely, neurons and synapses. However, efforts have largely been invested in implementations in the electrical domain with potential limitations of switching speed, packing density of large integrated systems and interconnect losses. As an alternative, neuromorphic engineering in the photonic domain has recently gained attention. In this work, we propose a purely photonic operation of an Integrate-and-Fire Spiking neuron, based on the phase change dynamics of Ge₂Sb₂Te₅ (GST) embedded on top of a microring resonator, which alleviates the energy constraints of PCMs in electrical domain. We also show that such a neuron can be potentially integrated with on-chip synapses into an all-Photonic Spiking Neural network inferencing framework which promises to be ultrafast and can potentially offer a large operating bandwidth.

SpiLinC: Spiking Liquid-Ensemble Computing for Unsupervised Speech and Image Recognition

In this work, we propose a Spiking Neural Network (SNN) consisting of input neurons sparsely connected by plastic synapses to a randomly interlinked liquid, referred to as Liquid-SNN, for unsupervised speech and image recognition. We adapt the strength of the synapses interconnecting the input and liquid using Spike Timing Dependent Plasticity (STDP), which enables the neurons to self-learn a general representation of unique classes of input patterns. The presented unsupervised learning methodology makes it possible to infer the class of a test input directly using the liquid neuronal spiking activity. This is in contrast to standard Liquid State Machines (LSMs) that have fixed synaptic connections between the input and liquid followed by a readout layer (trained in a supervised manner) to extract the liquid states and infer the class of the input patterns. Moreover, the utility of LSMs has primarily been demonstrated for speech recognition. We find that training such LSMs is challenging for complex pattern recognition tasks because of the information loss incurred by using fixed input to liquid synaptic connections. We show that our Liquid-SNN is capable of efficiently recognizing both speech and image patterns by learning the rich temporal information contained in the respective input patterns. However, the need to enlarge the liquid for improving the accuracy introduces scalability challenges and training inefficiencies. We propose SpiLinC that is composed of an ensemble of multiple liquids operating in parallel. We use a “divide and learn” strategy for SpiLinC, where each liquid is trained on a unique segment of the input patterns that causes the neurons to self-learn distinctive input features. SpiLinC effectively recognizes a test pattern by combining the spiking activity of the constituent liquids, each of which identifies characteristic input features. As a result, SpiLinC offers competitive classification accuracy compared to the Liquid-SNN with added sparsity in synaptic connectivity and faster training convergence, both of which lead to improved energy efficiency in neuromorphic hardware implementations. We validate the efficacy of the proposed Liquid-SNN and SpiLinC on the entire digit subset of the TI46 speech corpus and handwritten digits from the MNIST dataset.

Training Deep Spiking Convolutional Neural Networks With STDP-Based Unsupervised Pre-training Followed by Supervised Fine-Tuning

Spiking Neural Networks (SNNs) are fast becoming a promising candidate for brain-inspired neuromorphic computing because of their inherent power efficiency and impressive inference accuracy across several cognitive tasks such as image classification and speech recognition. The recent efforts in SNNs have been focused on implementing deeper networks with multiple hidden layers to incorporate exponentially more difficult functional representations. In this paper, we propose a pre-training scheme using biologically plausible unsupervised learning, namely Spike-Timing-Dependent-Plasticity (STDP), in order to better initialize the parameters in multi-layer systems prior to supervised optimization. The multi-layer SNN is comprised of alternating convolutional and pooling layers followed by fully-connected layers, which are populated with leaky integrate-and-fire spiking neurons. We train the deep SNNs in two phases wherein, first, convolutional kernels are pre-trained in a layer-wise manner with unsupervised learning followed by fine-tuning the synaptic weights with spike-based supervised gradient descent backpropagation. Our experiments on digit recognition demonstrate that the STDP-based pre-training with gradient-based optimization provides improved robustness, faster (~2.5 ×) training time and better generalization compared with purely gradient-based training without pre-training.

Delayed presenting traumatic Extradural Haematoma- whether surgery always necessary? – An experience in a tertiary care hospital

Background: Extradural haematoma (EDH), considered being the most serious preventable complication of head injury, requiring immediate diagnosis and surgical intervention. Though surgical evacuation constitutes the definitive treatment of this condition but many patients who presented late to emergency can be saved from craniotomy with watchful repeated neurological assessments.Aims and Objectives: To evaluate the role of surgical and non surgical management of delayed presenting traumatic EDH at a tertiary care Hospital.Materials and Methods: This study was conducted from December 2015 to February 2017 at Nil Ratan Sircar medical College, Kolkata. A total 100 cases of traumatic Extradural Haematoma were admitted with history of prior head injury of greater than 8 hours duration. All the patients were assessed clinically on admission and by NECT brain either prior to or immediately after admission. All patients with traumatic EDH were evaluated by dedicated trauma team and by Neurosurgeons, patients who came to hospital facility with more than 8 hours history of incident with haematoma <30 cm3, no associated midline shift and no signs of focal neurodeficits or papillary asymmetry with GCS 13-15, were initially managed conservatively, those who failed any of the chosen criteria treated by operative interventions.Results: Of all patients more 50% cases were associated with vehicular accident. Eighty percent cases were referred from primary or secondary care level hospitals and the remaining directly from accident site or scene of injury. Fifteen patients had post injury seizures, most of the cases were associated with additional intradural lesions like contusions or intracerebral haematoma. Approximately one forth patients presented with GCS >13, all these patients experiences positive outcomes. In this series of EDH location was temporoparietal region constitute 45% of the total, in 36 % of cases there were associated skull fracture. 55% of the patients in this series underwent operative intervention and 45% treated non operatively. Overall, 78% patients had good recovery, whereas 12% patients remained moderate to severe disabled at 6 weeks follow up period.Conclusion: Although surgical management is the treatment of choice in EDH, in selected delayed presenting EDH patients can be managed non-operatively with good outcome.Asian Journal of Medical Sciences Vol.9(4) 2018 41-45

Handwritten Indic Script Identification in Multi-Script Document Images: A Survey

Script identification is crucial for automating optical character recognition (OCR) in multi-script documents since OCRs are script-dependent. In this paper, we present a comprehensive survey of the techniques developed for handwritten Indic script identification. Different pre-processing and feature extraction techniques, including classifiers used for script identification, are categorized and their merits and demerits are discussed. We also provide information about some handwritten Indic script datasets. Finally, we highlight the extensions and/or future scope of works together with challenges.

Content Independent Writer Identification on Bangla Script: A Document Level Approach

Offline writer identification is one of the major fields of study in behavioral biometric. It is a process of matching a questioned document with other documents of known writers to find the appropriate writer. In this paper, local handwriting-based attributes are used as features, and multi-layer perceptron and simple logistic classifiers are used for decision making. The method is tested on an unconstrained handwritten Bangla database of 1383 documents with variable number of datasets from 190 writers. Experimental results show the effectiveness of our system, since it outperforms the state-of-the-art methods by approximately 3% (top-3 and top-4 choices). Further, our method is approximately 27 times faster than conventional segmentation-based methods.

Application of Structural and Topological Features to Recognize Online Handwritten Bangla Characters

This article presents a set of novel features for robust online Bangla handwritten character recognition. Two feature extraction methods are presented here. The first describes the transition from background to foreground pixels and vice versa. The second uses a combination of topological features and centre-of-gravity- (CG) based circular features where global information, local information, and Circular Quadrant Mass Distribution information have been extracted. The impact of each along with their combination have also been analyzed. A total of 15,000 isolated online Bangla character samples have been collected and used for the evaluation. A Support Vector Machine classifier records the best recognition rate when the transition count feature, CG-based circular features, and topological features are combined.

Analog Approach to Constraint Satisfaction Enabled by Spin Orbit Torque Magnetic Tunnel Junctions

Boolean satisfiability (k-SAT) is an NP-complete (k ≥ 3) problem that constitute one of the hardest classes of constraint satisfaction problems. In this work, we provide a proof of concept hardware based analog k-SAT solver, that is built using Magnetic Tunnel Junctions (MTJs). The inherent physics of MTJs, enhanced by device level modifications, is harnessed here to emulate the intricate dynamics of an analog satisfiability (SAT) solver. In the presence of thermal noise, the MTJ based system can successfully solve Boolean satisfiability problems. Most importantly, our results exhibit that, the proposed MTJ based hardware SAT solver is capable of finding a solution to a significant fraction (at least 85%) of hard 3-SAT problems, within a time that has a polynomial relationship with the number of variables(<50).

Greener chemicals for the future: QSAR modelling of the PBT index using ETA descriptors

Persistent, bioaccumulative and toxic (PBT) chemicals symbolize a group of substances that are not easily degraded; instead, they accumulate in different organisms and exhibit an acute or chronic toxicity. The limited empirical data on PBT chemicals, the high cost of testing together with the regulatory constraints and the international push for reduced animal testing motivate a greater reliance on predictive computational methods like quantitative structure-activity relationship (QSAR) models in PBT assessment. Papa and Gramatica have recently proposed a PBT index that could be computed directly from structural features. In the current study, we have modelled the experimentally derived PBT index data using an extended topological atom (ETA) along with constitutional descriptors to show the usefulness of the ETA indices in modelling the endpoint. The models developed through a double cross-validation (DCV) method gave the best results in terms of both internal and external validation metrics. The developed models were comparable in predictive quality to those previously reported. The current models were further used for consensus predictions of PBT behaviour for a set of pharmaceuticals and a set of synthetic drug-like compounds. The developed models can be used in PBT hazard screening for identification and prioritization of chemicals from the structural information alone.

S, Dasgupta S. Sellar and Suprasellar Tuberculoma

Tuberculosis is responsible for 20% of the intracranial space-occupying lesions in India, and tuberculomas of the sellar and suprasellar region comprise only 1% of all intracranial tuberculomas. The clinical and radiological features of these lesions mimic a typical pituitary adenoma. Surgery is not usually indicated, except for obtaining biopsies to confirm diagnosis, as these lesions tend to resolve with appropriate antitubercular therapy. There is no consensus regarding the type of antitubercular regimen and duration of the treatment as the experience with tuberculomas of pituitary is limited. We report a rare case of hypophyseal tuberculosis in a patient as a sequela of tuberculous meningitis, which is a rare complication of tubercular meningitis.

Learning to Generate Sequences with Combination of Hebbian and Non-hebbian Plasticity in Recurrent Spiking Neural Networks

Synaptic Plasticity, the foundation for learning and memory formation in the human brain, manifests in various forms. Here, we combine the standard spike timing correlation based Hebbian plasticity with a non-Hebbian synaptic decay mechanism for training a recurrent spiking neural model to generate sequences. We show that inclusion of the adaptive decay of synaptic weights with standard STDP helps learn stable contextual dependencies between temporal sequences, while reducing the strong attractor states that emerge in recurrent models due to feedback loops. Furthermore, we show that the combined learning scheme suppresses the chaotic activity in the recurrent model substantially, thereby enhancing its' ability to generate sequences consistently even in the presence of perturbations.

Molecular and epidemiological evidence of patient-to-patient hepatitis C virus transmission in a Scottish emergency department

BACKGROUND

Transmission of hepatitis C virus (HCV) in the healthcare setting is rare. Routine infection prevention and control measures mean that this should be a preventable 'never event'.

AIM

To investigate the diagnosis of acute healthcare-associated HCV infection.

METHODS

Epidemiological and molecular investigation of a case of acute HCV infection associated with nosocomial exposure.

FINDINGS

Detailed investigation of the treatment history of a patient with acute HCV infection identified transmission from a co-attending patient in an emergency department as the likely source; this possibility was confirmed by virus sequence analysis. The precise route of transmission was not identified, though both patient and source had minimally invasive healthcare interventions. Review of infection, prevention and control identified potentially contributory factors in the causal pathway including hand hygiene, inappropriate use of personal protective equipment, and blood contamination of the surface of the departmental blood gas analyser.

CONCLUSION

We provide molecular and epidemiological evidence of HCV transmission between patients in an emergency department that was made possible by environmental contamination. Patients with HCV infection are higher users of emergency care than the general population and a significant proportion of those affected remain unknown and/or infectious. Equipment, departmental design, staff behaviour, and patient risk require regular review to minimize the risk of nosocomial HCV transmission.

Unusual Clinical and Imaging Presentation of Chronic Subdural Hematoma

Various clinical and radiologic presentations of chronic subdural hematoma (SDH) are reported in the literature. Therefore, sometimes the presentation of a patient with chronic SDH often creates confusion regarding decision making. Here, the authors present three cases of chronic SDH, in which the clinical presentation, radiology, and operative findings were unusual. In the first case, the patient presented with acute extradural hematoma like clinical as well as radiologic presentation but intraoperatively found to have chronic calcified SDH, whereas another case with history of bilateral ventriculoperitoneal (VP) shunting at childhood presented with large head with discharging sinus at the forehead. Radiologic and operative findings were very much unusual. Intraoperatively, the bilateral subdural collection was found to have fungus-like projections with subdural space communicating with the forehead sinus. In another case, a 10-year-old girl with history of VP shunting at age of 6 months presented with left hemiparesis of subacute onset. Computed tomographic (CT) scan revealed biconvex lesion at the right parietal region intraoperatively. The authors found the shell-like lesion with inner and outer membrane calcified, within which the subdural collection was present. In these three cases, they observed the very unusual mode of presentation of chronic SDH, and in the literature such mode of presentation and operative findings of such type are very rare.

A Study Assessing Surgical Management of Traumatic Orbital Fractures with Delayed Presentation at Tertiary Care Institutions: An Experience of 1 Year

Background Orbital and periorbital injuries are significant contributors to traumatic facial injuries. Orbital fractures can occur either alone or in conjugation with other facial bone fractures and cranial and maxillofacial injuries. Objectives The study aims to find out the incidence of various types of fractures occurring in patients, mode of trauma, clinical presentation, and results of delayed surgical repair in cases of orbital fractures.

Materials and Methods This is a “prospective observational study” including 12 patients. Surgical repair of orbital fractures was considered for suspected muscle entrapment in fractures, restricted ocular motility, symptomatic diplopia not improving for over 2-week period, or if enophthalmos greater than 2 mm was present.

Results Regarding age incidence, the maximum number of cases, that is 41.66%, were aged between 21and 30 years. The main modes of trauma in most cases, that is, 50%, were due to road traffic accidents followed by fall from height, that is, 25%. The majority of cases presented to us with complex fractures involved one or more orbital bones, that is 33.33%. Postsurgery outcomes were good and fair in 75% and 25% patients, respectively.

Conclusion Proper orbital fracture stabilization is crucial to bring out good cosmetic as well as ocular outcome.

Staged Inference using Conditional Deep Learning for energy efficient real-time smart diagnosis

Recent progress in biosensor technology and wearable devices has created a formidable opportunity for remote healthcare monitoring systems as well as real-time diagnosis and disease prevention. The use of data mining techniques is indispensable for analysis of the large pool of data generated by the wearable devices. Deep learning is among the promising methods for analyzing such data for healthcare applications and disease diagnosis. However, the conventional deep neural networks are computationally intensive and it is impractical to use them in real-time diagnosis with low-powered on-body devices. We propose Staged Inference using Conditional Deep Learning (SICDL), as an energy efficient approach for creating healthcare monitoring systems. For smart diagnostics, we observe that all diagnoses are not equally challenging. The proposed approach thus decomposes the diagnoses into preliminary analysis (such as healthy vs unhealthy) and detailed analysis (such as identifying the specific type of cardio disease). The preliminary diagnosis is conducted real-time with a low complexity neural network realized on the resource-constrained on-body device. The detailed diagnosis requires a larger network that is implemented remotely in cloud and is conditionally activated only for detailed diagnosis (unhealthy individuals). We evaluated the proposed approach using available physiological sensor data from Physionet databases, and achieved 38% energy reduction in comparison to the conventional deep learning approach.

Magnetic Tunnel Junction as an On-Chip Temperature Sensor

Temperature sensors are becoming an increasingly important component in System-on-Chip (SoC) designs with increasing transistor scaling, power density and associated heating effects. This work explores a compact nanoelectronic temperature sensor based on a Magnetic Tunnel Junction (MTJ) structure. The MTJ switches probabilistically depending on the operating temperature in the presence of thermal noise. Performance evaluation of the proposed MTJ temperature sensor, based on experimentally measured device parameters, reveals that the sensor is able to achieve a conversion rate of 2.5K samples/s with energy consumption of 8.8 nJ per conversion (1–2 orders of magnitude lower than state-of-the-art CMOS sensors) for a linear sensing regime of 200–400 K.

Magnetoelectric oxide based stochastic spin device towards solving combinatorial optimization problems

Solving combinatorial optimization problems is challenging. Mapping onto the ground-state search problem of the Ising Hamiltonian is a promising approach in this field, where the components of the optimization set are modeled as artificial spin units. The search for a suitable physical system to realize these spin units is an active area of research. In this work, we have demonstrated a scheme to model the Ising Hamiltonian with multiferroic oxide/nanomagnet units. Although nanomagnet-based implementation has been shown before, we have utilized the magnetoelectric effect of the multiferroics to make voltagecontrolled spin units with less current flow in the network. Moreover, we have proposed a unique approach of configuring the coupling network of the system directly from the Ising Hamiltonian of a traveling salesman problem (TSP). We have developed a coupled micromagnetic simulation framework and solved TSPs of size 26-city and 15-city with an accuracy of 100% for the latter.