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In perceptual decisions, subjects infer hidden states of the environment based on noisy sensory information. Here we show that both choice and its associated confidence are explained by a Bayesian framework based on partially observable Markov decision processes (POMDPs). We test our model on monkeys performing a direction-discrimination task with post-decision wagering, demonstrating that the model explains objective accuracy and predicts subjective confidence. Further, we show that the model replicates well-known discrepancies of confidence and accuracy, including the hard-easy effect, opposing effects of stimulus variability on confidence and accuracy, dependence of confidence ratings on simultaneous or sequential reports of choice and confidence, apparent difference between choice and confidence sensitivity, and seemingly disproportionate influence of choice-congruent evidence on confidence. These effects may not be signatures of sub-optimal inference or discrepant computational processes for choice and confidence. Rather, they arise in Bayesian inference with incomplete knowledge of the environment., A Bayesian framework based on partially observable Markov decision processes (POMDPs) not only predicts subjects’ confidence in a perceptual decision making task but also explains well-known discrepancies between confidence and choice accuracy as arising from incomplete knowledge of the environment.

Purpose: Neonatal near miss (NNM) allows for the detection of risk factors associated with serious newborn complications and death, the prevention of which could reduce neonatal mortality. This study was conducted with the objective of identifying predictors for NNM in a tertiary hospital in Bangalore city.Methods: This was an unmatched case-control study involving 120 NNM cases and 120 controls. NNM was determined using Pileggi-Castro’s pragmatic and management criteria. Data was collected from in-patient hospital records and interviews of postpartum mothers. Multiple logistic regression of exposure variables was performed to calculate adjusted odds ratio (AOR) with 95% confidence interval (CI).Results: Significant predictors were maternal age ≥30 years (AOR, 5.32; 95% CI, 1.12 to 9.29; P=0.041), inadequate antenatal care (ANC) (AOR, 8.35; 95% CI, 1.98 to 51.12; P=0.032)

A hard X-ray engineering applications beamline (BL-02) was commissioned recently and started operation in March 2019 at the Indian synchrotron source, Indus-2. This bending-magnet-based beamline is capable of operating in various beam modes, viz. white, pink and monochromatic beam. The beamline utilizes the X-ray diffraction technique in energy-dispersive and angle-dispersive modes to carry out experiments mainly focused on engineering problems, viz. stress measurement, texture measurement and determination of elastic constants in a variety of bulk as well as thin-film samples. An open-cradle six-circle diffractometer with ∼12 kg load capacity allows accommodation of a wide variety of engineering samples and qualifies the beamline as a unique facility at Indus-2. The high-resolution mode of this beamline is suitably designed so as to carry out line profile analysis for characterization of micro- and nano-structures. In the present article the beamline is described starting from the beamline design, layout, optics involved, various operational modes and experimental stations. Experiments executed to validate the beamline design parameters and to demonstrate the capabilities of the beamline are also described. The future facilities to be incorporated to enhance the capabilities of the beamline are also discussed.

Antibiotics play a critical role in neonatal sepsis but excessive use is associated with adverse outcomes and the current prescribing rates of antibiotics are unacceptably high.To reduce antibiotic over-use in preterm neonates by implementing an antibiotic stewardship programme using a quality improvement (QI) initiative.This study was conducted at a neonatal intensive care unit in a resource-limited setting. The reasons for antibiotic over-use were analysed and an antibiotic stewardship programme was implemented by using a QI initiative. The duration of the QI was a 1-month baseline phase followed by 3 months of implementation which was undertaken in the form of Plan-Do-Study-Act (P-D-S-A) cycles. The sustainment phase was observed for 2 months. All neonates admitted to the preterm unit were included. The outcome measure was the antibiotic usage expressed as days of therapy (DOT)/1000 patient days.In the baseline phase, DOT/1000 patient days was 1464 which fell to 706, 511, and 442 DOT/1000 patient days, respectively, over 3 months, resulting in a 65% reduction in antibiotic usage. This was achieved by a combination of efforts directed towards defining the conditions for no antibiotics, revising existing antibiotic policy, stopping orders at 48 hours, de-escalation to the narrowest spectrum antibiotic, stopping prophylactic antifungal agents and limited use of broad-spectrum antibiotics. The results were achieved without an increase in culture-positive sepsis or mortality.Implementation of a tailored antibiotic stewardship programme through a QI initiative was effective and safe in reducing antibiotic use in preterm neonates in a resource-limited setting.AIIMS, All India Institute of Medical Sciences; DOT, days of therapy; HIC, high-income countries; ICMR, Indian Council of Medical Research; LMIC, low- to middle-income countries; LOS, late-onset sepsis; NICU, neonatal intensive care unit; NNF, National Neonatology Forum; P-D-S-A, plan-do-study-act; QI, quality improvement; SNCU, Special newborn care unit.

AIM Kangaroo mother care (KMC) can be challenging in multiple births and more so in resource-limited settings. This study aims at increasing the mean duration of KMC with early initiation in twin preterm neonates born at a tertiary care hospital using a quality improvement (QI) initiative. METHODS Barriers for poor KMC practice in twin preterm neonates born at the tertiary care hospital were analysed and baseline data were collected over a period of 4 months using a predesigned proforma. A QI team was formed and suggested solutions were prioritised through focus group discussions in the form of Plan-Do-Study-Act (P-D-S-A) cycles. Each cycle was of one-month duration and three cycles were implemented, followed by the sustenance phase studied at 1-month post-implementation. RESULTS There were a total of 238 twin deliveries in the study period, of which 169 twin pairs were included in the study. At the end of implementation, the average day of initiation of KMC improved from 8th to 3rd day of life and the duration of KMC increased significantly from an average of 2.70 h/infant/day to 7.88 h/infant/day. CONCLUSION This QI project focused on the improvement of KMC practice in twin preterm neonates in a tertiary care hospital where results were achieved with maximal utilisation of available hospital resources and low-cost interventions. This study design is generalizable to other hospitals in resource-limited settings where family participatory care can be strengthened to overcome the challenges of KMC in multiple births.

Wastewater treatment plant (WWTP) reduces environmental load by improving the quality of effluent sewage water. A wastewater treatment plant during its operation, consumes energy for circulation an...

Objective. Recent advances in neural decoding have accelerated the development of brain–computer interfaces aimed at assisting users with everyday tasks such as speaking, walking, and manipulating objects. However, current approaches for training neural decoders commonly require large quantities of labeled data, which can be laborious or infeasible to obtain in real-world settings. Alternatively, self-supervised models that share self-generated pseudo-labels between two data streams have shown exceptional performance on unlabeled audio and video data, but it remains unclear how well they extend to neural decoding. Approach. We learn neural decoders without labels by leveraging multiple simultaneously recorded data streams, including neural, kinematic, and physiological signals. Specifically, we apply cross-modal, self-supervised deep clustering to train decoders that can classify movements from brain recordings. After training, we then isolate the decoders for each input data stream and compare the accuracy of decoders trained using cross-modal deep clustering against supervised and unimodal, self-supervised models. Main results. We find that sharing pseudo-labels between two data streams during training substantially increases decoding performance compared to unimodal, self-supervised models, with accuracies approaching those of supervised decoders trained on labeled data. Next, we extend cross-modal decoder training to three or more modalities, achieving state-of-the-art neural decoding accuracy that matches or slightly exceeds the performance of supervised models. Significance. We demonstrate that cross-modal, self-supervised decoding can be applied to train neural decoders when few or no labels are available and extend the cross-modal framework to share information among three or more data streams, further improving self-supervised training.

BACKGROUND "Kangaroo mother care," a type of newborn care involving skin-to-skin contact with the mother or other caregiver, reduces mortality in infants with low birth weight (

The original predictive coding model of Rao & Ballard [1] focused on spatial prediction to explain spatial receptive fields and contextual effects in the visual cortex. Here, we introduce a new dynamic predictive coding model that achieves spatiotemporal prediction of complex natural image sequences using time-varying transition matrices. We overcome the limitations of static linear transition models (as in, e.g., Kalman filters) using a hypernetwork to adjust the transition matrix dynamically for every time step, allowing the model to predict using a time-varying mixture of possible transition dynamics. We developed a single level model with recurrent modulation of transition weights by a hypernetwork and a two-level hierarchical model with top-down modulation based on a hypernetwork. At each time step, the model predicts the next input and estimates a sparse neural code by minimizing prediction error. When exposed to natural movies, the model learned localized, oriented spatial filters as well as both separable and inseparable (direction-selective) space-time receptive fields at the first level, similar to those found in the primary visual cortex (V1). Longer timescale responses and stability at the second level also emerged naturally from minimizing prediction errors for the first level dynamics. Our results suggest that the multiscale temporal response properties of cortical neurons could be the result of the cortex learning a hierarchical generative model of the visual world with higher order areas predicting the transition dynamics of lower order areas.

Direct cortical stimulation (DCS) has been used extensively as a tool in human neuroscience research and clinically in neurosurgery. Implanted electrocorticography electrodes can deliver electrical stimulation to the cortical surface, eliciting or inhibiting neural activity as an experimental manipulation, or to probe neural tissue function during epilepsy and tumor resection surgeries. Current applications of DCS pave the way for new clinical translation to restore sensation to those who have lost it through spinal-cord injury or stroke. DCS of specific sensory cortical regions in the human brain elicits sensory percepts in specific regions of the body and recent research has shown that DCS can elicit meaningful somatosensory percepts, which may be used for neuroprosthetic feedback and integrated into tasks. In the future, further research using more finely spaced electrodes, tailored patterns of stimulation, and longer term studies will shed light on the intricacies of sensory processing in humans as well as bring these neuroprosthetic applications closer to widespread clinical reality.

Background Recent technological advances in brain recording and machine learning algorithms are enabling the study of neural activity underlying spontaneous human behaviors, beyond the confines of cued, repeated trials. However, analyzing such unstructured data lacking a priori experimental design remains a significant challenge, especially when the data is multi-modal and long-term. New method Here we describe an automated, behavior-first approach for analyzing simultaneously recorded long-term, naturalistic electrocorticography (ECoG) and behavior video data. We identify and characterize spontaneous human upper-limb movements by combining computer vision, discrete latent-variable modeling, and string pattern-matching on the video. Results Our pipeline discovers and annotates over 40,000 instances of naturalistic arm movements in long term (7–9 day) behavioral videos, across 12 subjects. Analysis of the simultaneously recorded brain data reveals neural signatures of movement that corroborate previous findings. Our pipeline produces large training datasets for brain–computer interfacing applications, and we show decoding results from a movement initiation detection task. Comparison with existing methods Spontaneous movements capture real-world neural and behavior variability that is missing from traditional cued tasks. Building beyond window-based movement detection metrics, our unsupervised discretization scheme produces a queryable pose representation, allowing localization of movements with finer temporal resolution. Conclusions Our work addresses the unique analytic challenges of studying naturalistic human behaviors and contributes methods that may generalize to other neural recording modalities beyond ECoG. We publish our curated dataset and believe that it will be a valuable resource for future studies of naturalistic movements.

ObjectiveAdvances in neural decoding have enabled brain-computer interfaces to perform increasingly complex and clinically-relevant tasks. However, such decoders are often tailored to specific participants, days, and recording sites, limiting their practical long-term usage. Therefore, a fundamental challenge is to develop neural decoders that can robustly train on pooled, multi-participant data and generalize to new participants.ApproachWe introduce a new decoder, HTNet, which uses a convolutional neural network with two innovations: (1) a Hilbert transform that computes spectral power at data-driven frequencies and (2) a layer that projects electrode-level data onto predefined brain regions. The projection layer critically enables applications with intracranial electrocorticography (ECoG), where electrode locations are not standardized and vary widely across participants. We trained HTNet to decode arm movements using pooled ECoG data from 11 of 12 participants and tested performance on unseen ECoG or electroencephalography (EEG) participants; these pretrained models were also subsequently fine-tuned to each test participant.Main resultsHTNet outperformed state-of-the-art decoders when tested on unseen participants, even when a different recording modality was used. By fine-tuning these generalized HTNet decoders, we achieved performance approaching the best tailored decoders with as few as 50 ECoG or 20 EEG events. We were also able to interpret HTNet’s trained weights and demonstrate its ability to extract physiologically-relevant features.SignificanceBy generalizing to new participants and recording modalities, robustly handling variations in electrode placement, and allowing participant-specific fine-tuning with minimal data, HTNet is applicable across a broader range of neural decoding applications compared to current state-of-the-art decoders.

Increasingly, neuroimaging researchers are exploring the use of real-time functional magnetic resonance imaging (rt-fMRI) as a way to access a participant’s ongoing brain function throughout a scan. This approach presents novel and exciting experimental applications ranging from monitoring data quality in real time, to delivering neurofeedback from a region of interest, to dynamically controlling experimental flow, or interfacing with remote devices. Yet, for those interested in adopting this method, the existing software options are few and limited in application. This presents a barrier for new users, as well as hinders existing users from refining techniques and methods. Here we introduce a free, open-source rt-fMRI package, the Pyneal toolkit, designed to address this limitation. The Pyneal toolkit is python-based software that offers a flexible and user friendly framework for rt-fMRI, is compatible with all three major scanner manufacturers (GE, Siemens, Phillips), and, critically, allows fully customized analysis pipelines. In this article, we provide a detailed overview of the architecture, describe how to set up and run the Pyneal toolkit during an experimental session, offer tutorials with scan data that demonstrate how data flows through the Pyneal toolkit with example analyses, and highlight the advantages that the Pyneal toolkit offers to the neuroimaging community.

Purpose: To assess the effect of a quality improvement (QI) bundle on improving breast milk output among very low birth weight (VLBW) mothers. Design: Before and after nonrandomized QI project. Sample: Mothers who delivered a VLBW infant in October and November 2015 were the prospective cohort. Those who delivered a VLBW infant in August and September 2015 were the retrospective cohort. The QI bundle consisted of early expression of milk, use of breast pumps, frequent expressions, videos, and regular counseling. This bundle was done for the prospective cohort. Outcomes measures: Quantity of expressed breast milk on day 7. Results: There were 13 mothers in the retrospective cohort and 18 mothers in the prospective one. The mean birth weight (1297.80 and 1207.70 g, p = .19) and gestation (32.5 and 31.5 wk, p = .27) were similar. There was a significant increase in the milk output on day 7 in the prospective group 113.6 ± 45 vs 182 ± 63 mL (p = .001).

To study the impact of a quality improvement (QI) initiative using care bundle approach on Central-line associated bloodstream infections (CLABSI) rates. A QI team for infection control in NICU was formed in a tertiary-care neonatal intensive care unit (NICU) from June 2015 to August 2016. Baseline data were collected over first 3 months followed by the intervention period of 1 year. Measures with respect to strengthening hand hygiene and central line bundle care were implemented during the intervention period. Audits assessing the compliance to hand hygiene and CLABSI bundle protocols were used as process indicators. Multiple PDSA cycles were used to strengthen the practices of proposed interventions, documentation of data and audits of the processes during the study period. The QI initiative achieved a 89% reduction in CLABSI from the baseline rate of 31.7 to 3.5 per 1000 line-days. The blood stream Infections reduced from 7.3 to 2.3 per 1000 patient-days. The overall mortality showed a reduction from 2.9% to 1.7 % during the intervention period. There was a significant improvement in compliance with hand hygiene protocol and compliance with CLABSI protocols. This study demonstrated that simple measures involving hand hygiene and strengthening of the care bundle approach through quality improvement could significantly reduce the blood stream Infections and CLABSI rates.

The first consensus report of the working party of the Asian Pacific Association for the Study of the Liver (APASL) set up in 2004 on acute-on-chronic liver failure (ACLF) was published in 2009. With international groups volunteering to join, the “APASL ACLF Research Consortium (AARC)” was formed in 2012, which continued to collect prospective ACLF patient data. Based on the prospective data analysis of nearly 1400 patients, the AARC consensus was published in 2014. In the past nearly four-and-a-half years, the AARC database has been enriched to about 5200 cases by major hepatology centers across Asia. The data published during the interim period were carefully analyzed and areas of contention and new developments in the field of ACLF were prioritized in a systematic manner. The AARC database was also approached for answering some of the issues where published data were limited, such as liver failure grading, its impact on the ‘Golden Therapeutic Window’, extrahepatic organ dysfunction and failure, development of sepsis, distinctive features of acute decompensation from ACLF and pediatric ACLF and the issues were analyzed. These initiatives concluded in a two-day meeting in October 2018 at New Delhi with finalization of the new AARC consensus. Only those statements, which were based on evidence using the Grade System and were unanimously recommended, were accepted. Finalized statements were again circulated to all the experts and subsequently presented at the AARC investigators meeting at the AASLD in November 2018. The suggestions from the experts were used to revise and finalize the consensus. After detailed deliberations and data analysis, the original definition of ACLF was found to withstand the test of time and be able to identify a homogenous group of patients presenting with liver failure. New management options including the algorithms for the management of coagulation disorders, renal replacement therapy, sepsis, variceal bleed, antivirals and criteria for liver transplantation for ACLF patients were proposed. The final consensus statements along with the relevant background information and areas requiring future studies are presented here., Article Highlights Updated on the basis of AARC data of >3300 cases enrolled into AARC registry prospectivelyACLF is distinct form Acute Decompensation of cirrhosisNewer sections on DILI-ACLF, AIH-ACLF, PVT/HVOTO–ACLFReversibility of Chronic Liver Disease in ACLFPortal and systemic hemodynamics and their relevance in ACLFAcute Portal Hypertension and Variceal progression in ACLFAARC score as a guide for treatment strategies in ACLFACLF in Children-first consensus on pediatric ACLF

Continuous positive airway pressure (CPAP) requires nasopharyngeal suctioning for airway patency, which is painful. Other procedures have used breast milk and 25% dextrose as analgesics. We aimed to compare their analgesic efficacy during nasopharyngeal suctioning in preterm neonates on CPAP. In this blinded randomized controlled trial, babies received 25% dextrose or breast milk orally. Pain before, during and after was assessed using the Premature Infant Pain Profile (PIPP) score. Analysis was done for 40 babies. The mean PIPP score in the 25% dextrose group during the procedure was 11.25 ± 2.73 and 13.2 ± 2.55 (p = 0.02) with the intervention and without. In the breast milk group the PIPP score during the procedure was 11.35 ± 3.05 and 13.45 ± 3.27 (p = 0.04); this difference persisted even after the procedure. There was no significant difference between the interventions. Both interventions significantly reduce pain. The analgesic effect of breast milk was sustained.

The promise of robots assisting humans in everyday tasks has led to a variety of research questions and challenges in human-robot collaboration. Here, we address the question of whether and when a ...

The ability to track odor plumes in dynamic environments is critical for flying insects following attractive odors to localize food or mates. This remarkable tracking behavior requires multimodal i...

The field of brain-computer interfaces is poised to advance from the traditional goal of controlling prosthetic devices using brain signals to combining neural decoding and encoding within a single neuroprosthetic device. Such a device acts as a "co-processor" for the brain, with applications ranging from inducing Hebbian plasticity for rehabilitation after brain injury to reanimating paralyzed limbs and enhancing memory. We review recent progress in simultaneous decoding and encoding for closed-loop control and plasticity induction. To address the challenge of multi-channel decoding and encoding, we introduce a unifying framework for developing brain co-processors based on artificial neural networks and deep learning. These "neural co-processors" can be used to jointly optimize cost functions with the nervous system to achieve desired behaviors ranging from targeted neuro-rehabilitation to augmentation of brain function., Comment: Invited submission to the journal Current Opinion in Neurobiology

Background COVID-19 is disrupting health services for mothers and newborns, particularly in low- and middle-income countries (LMIC). Preterm newborns are particularly vulnerable. We undertook analyses of the benefits of kangaroo mother care (KMC) on survival among neonates weighing ≤2000 g compared with the risk of SARS-CoV-2 acquired from infected mothers/caregivers. Methods We modelled two scenarios over 12 months. Scenario 1 compared the survival benefits of KMC with universal coverage (99%) and mortality risk due to COVID-19. Scenario 2 estimated incremental deaths from reduced coverage and complete disruption of KMC. Projections were based on the most recent data for 127 LMICs (~90% of global births), with results aggregated into five regions. Findings Our worst-case scenario (100% transmission) could result in 1,950 neonatal deaths from COVID-19. Conversely, 125,680 neonatal lives could be saved with universal KMC coverage. Hence, the benefit of KMC is 65-fold higher than the mortality risk of COVID-19. If recent evidence of 10% transmission was applied, the ratio would be 630-fold. We estimated a 50% reduction in KMC coverage could result in 12,570 incremental deaths and full disruption could result in 25,140 incremental deaths, representing a 2·3–4·6% increase in neonatal mortality across the 127 countries. Interpretation The survival benefit of KMC far outweighs the small risk of death due to COVID-19. Preterm newborns are at risk, especially in LMICs where the consequences of disruptions are substantial. Policymakers and healthcare professionals need to protect services and ensure clearer messaging to keep mothers and newborns together, even if the mother is SARS-CoV-2-positive. Funding Eunice Kennedy Shriver National Institute of Child Health & Human Development; Bill & Melinda Gates Foundation; Elma Philanthropies; Wellcome Trust; and Joint Global Health Trials scheme of Department of Health and Social Care, Department for International Development, Medical Research Council, and Wellcome Trust.

IntroductionThe COVID-19 pandemic is disrupting health systems globally. Maternity care disruptions have been surveyed, but not those related to vulnerable small newborns. We aimed to survey reported disruptions to small and sick newborn care worldwide and undertake thematic analysis of healthcare providers’ experiences and proposed mitigation strategies.MethodsUsing a widely disseminated online survey in three languages, we reached out to neonatal healthcare providers. We collected data on COVID-19 preparedness, effects on health personnel and on newborn care services, including kangaroo mother care (KMC), as well as disruptors and solutions.ResultsWe analysed 1120 responses from 62 countries, mainly low and middle-income countries (LMICs). Preparedness for COVID-19 was suboptimal in terms of guidelines and availability of personal protective equipment. One-third reported routine testing of all pregnant women, but 13% had no testing capacity at all. More than 85% of health personnel feared for their own health and 89% had increased stress. Newborn care practices were disrupted both due to reduced care-seeking and a compromised workforce. More than half reported that evidence-based interventions such as KMC were discontinued or discouraged. Separation of the mother–baby dyad was reported for both COVID-positive mothers (50%) and those with unknown status (16%). Follow-up care was disrupted primarily due to families’ fear of visiting hospitals (~73%).ConclusionNewborn care providers are stressed and there is lack clarity and guidelines regarding care of small newborns during the pandemic. There is an urgent need to protect life-saving interventions, such as KMC, threatened by the pandemic, and to be ready to recover and build back better.

Cortical stimulation through electrocorticographic (ECoG) electrodes is a potential method for providing sensory feedback in future prosthetic and rehabilitative applications. Here, we evaluate human subjects’ ability to continuously modulate their motor behavior based on feedback from direct surface stimulation of the somatosensory cortex. Subjects wore a dataglove that measured their hand aperture position and received one of three stimuli over the hand sensory cortex based on their current hand position as compared to a target aperture position. Using cortical stimulation feedback, subjects adjusted their hand aperture to move towards the target aperture region. One subject was able to achieve accuracies and R2 values well above chance (best performance: R2 = 0.93; accuracy = 0.76/1). Performance dropped during the catch trial (same stimulus independent of the position) to below chance levels, suggesting that the subject had been using the varied sensory feedback to modulate their motor behavior. To our knowledge, this study represents one of the first demonstrations of using direct cortical surface stimulation of the human sensory cortex to perform a motor task, and is a first step towards developing closed-loop human sensorimotor brain-computer interfaces.

It is preferable to detect the leakage status at the earliest phase in the manufacturing process, and then, further assembly of hydraulic components with leakage faults can be avoided. Integrating a USB-based microcontroller and a pressure transducer and also with environmental and safety considerations, this study developed a hydraulic leakage detection system by using air in lower-pressure operation ranges, through measuring pressure decay signals captured in real time. Pressure decay data were collected with the developed hydraulic leakage detection system, in which a hydraulic testing tube was installed in tight vs un-tight threading connections to simulate the leakage vs no leakage treatments at two different system pressure levels. Through statistical analysis, it was found that the developed prototype system can capture and visualize the pressure decay curve in real time, and two signals, the pressure drop from the test start to end point and the pressure drop rate in the testing period, can be used as featured characteristics to quickly detect hydraulic leakage components’ status.

ZnO thin films find widespread applications in optoelectronic devices, such as LEDs, UV photodetectors, etc. ZnO is an n-type semiconductor and diffusion of As in ZnO leads to p-type conductivity of ZnO. We report here application of combined x-ray reflectivity (XRR) and grazing incidence x-ray fluorescence (GIXRF) technique for the study of diffusion of As in the ZnO thin film structure, deposited on top of GaAs substrate, with accuracies better than 1 nm. Unlike conventional analysis probes, secondary ion mass spectrometry (SIMS) and depth resolved X-ray photoelectron spectroscopy (XPS), the present method allows element specific analysis of the thin film medium in a non-destructive manner.

Objective: The objective of this paper is to estimate the parameters of a multivariate autoregressive process from noisy multichannel data. Methods: Using a multivariate generalization of the Cadzow method, we propose a new method for estimating autoregressive parameters from noisy data: the nonlinear Cadzow method. Results: We show that our method outperforms existing multivariate methods such as higher order Yule–Walker method and Kalman EM method on simulated data. We apply our method to estimation of Granger causality from noisy data and again obtain superior results compared to previous methods. Finally, when applied to experimental local field potential data from monkey somatosensory and motor cortical areas, our method produces results consistent with cortical physiology. Conclusion: The proposed nonlinear Cadzow method outperforms existing methods in obtaining denoised estimates of multivariate autoregressive parameters. Significance: Since multichannel recordings have become commonplace in biomedical applications ranging from discovering functional connectivity in the brain to speech data analysis and these recordings are inevitably contaminated by measurement noise, we believe our method has the potential for significant impact.

With the advent of tall and complex shaped structures combined with urbanization, the structural designers are posed with a greater challenge to design the structures sustainable to rapid environmental changes. During the design of tall structures, wind loads acting on them are a major factor that needs to be considered. As a pre-requisite, the designer should have the information regarding wind environment of the region, wind forces on the structures and the response of the structure under these forces.

Determining hand grasping forces and kinetic direction and magnitude are important for the development of dexterous neural prosthetics. While many earlier decoding methods have successfully predicted upper-limb kinematic output from cortical signals in the sensorimotor parietal and premotor regions, the full extent of the regions that characterize kinetic behavior is unknown. In this study, we found that neural dynamics based on electrocorticography (ECoG) recorded from the human brain surface can successfully encode structured and unstructured grasping and arm kinetic output. We found a time-averaged linear relationship between gamma band spectral ECoG power and sustained grasping force output with visual feedback. In the kinetic grasping task, we obtained classification accuracy of 47% (25% = chance) using quadratic discriminant analysis. Additionally, we also found a similar linear relationship between spectral power and cued isometric force generation, without concurrent visual feedback, in arm force application; this feature could also classify arm force output categories with an accuracy of 41% (33% = chance). In addition, we applied quadratic discriminant analysis with top 12 principal components to attain approximately 26% accuracy (chance is 16%) in determining arm kinetic force direction. We found that the gamma band spectral power from both experiments in posterior parietal cortex, as well as projections of high gamma variability along the top principal components, can be successfully used to predict sustained kinetic outputs and to explain both structured and unstructured force output variability. Our findings contribute to a deeper understanding of neural dynamics for fine kinetic behavior.

Direct cortical stimulation (DCS) of primary somatosensory cortex (S1) could help restore sensation and provide task-relevant feedback in a neuroprosthesis. However, the psychophysics of S1 DCS is poorly studied, including any comparison to cutaneous haptic stimulation. We compare the response times to DCS of human hand somatosensory cortex through electrocorticographic grids with response times to haptic stimuli delivered to the hand in four subjects. We found that subjects respond significantly slower to S1 DCS than to natural, haptic stimuli for a range of DCS train durations. Median response times for haptic stimulation varied from 198 ms to 313 ms, while median responses to reliably perceived DCS ranged from 254 ms for one subject, all the way to 528 ms for another. We discern no significant impact of learning or habituation through the analysis of blocked trials, and find no significant impact of cortical stimulation train duration on response times. Our results provide a realistic set of expectations for latencies with somatosensory DCS feedback for future neuroprosthetic applications and motivate the study of neural mechanisms underlying human perception of somatosensation via DCS.

We present here a browser-based application for visualizing patterns of connectivity in 3D stacked data matrices with large numbers of pairwise relations. Visualizing a connectivity matrix, looking for trends and patterns, and dynamically manipulating these values is a challenge for scientists from diverse fields, including neuroscience and genomics. In particular, high-dimensional neural data include those acquired via electroencephalography (EEG), electrocorticography (ECoG), magnetoencephalography (MEG), and functional MRI. Neural connectivity data contains multivariate attributes for each edge between different brain regions, which motivated our lightweight, open source, easy-to-use visualization tool for the exploration of these connectivity matrices to highlight connections of interest. Here we present a client-side, mobile-compatible visualization tool written entirely in HTML5/JavaScript that allows in-browser manipulation of user-defined files for exploration of brain connectivity. Visualizations can highlight different aspects of the data simultaneously across different dimensions. Input files are in JSON format, and custom Python scripts have been written to parse MATLAB or Python data files into JSON-loadable format. We demonstrate the analysis of connectivity data acquired via human ECoG recordings as a domain-specific implementation of our application. We envision applications for this interactive tool in fields seeking to visualize pairwise connectivity., 4 pages, IEEE NER 2017

OBJECTIVE To assess the effectiveness of expressed breast milk (EBM) on neonatal pain during screening for retinopathy of prematurity (ROP). METHODS Neonates who were on oral feeds undergoing ROP screening were included. Babies were randomized into intervention group (EBM + Standard practice) and control group. The standard practice is proparacaine, nesting and swaddling. Pain was assessed by PIPP scale, during and at 1 and 5 min after the procedure by the principal investigator who was blinded. RESULTS The groups were similar in baseline characteristics. The group receiving EBM had significantly lower PIPP scores during the procedure 12.7 ± 1.69 compared to the control group 15.5 ± 1.78 (p < 0.05). The beneficial effect persisted at 1 min and 5 min after the procedure 6.20 ± 1.9 vs. 12.4 ± 2.54 (p ≤ 0.05) at 1 min; 3.2 ± 1.5 and 6.85 ± 2.4 (p < 0.05) at 5 min. CONCLUSION Oral EBM significantly reduces pain during and after ROP screening.

Fluid flow studies play a vital role in proper assessing of wind loadson buildings and other civil engineering structures with various shapes. The presence of adjacent structure may alter wind loads on the structure of interest, by altering the fluid dynamics surrounding the structure. This effect is termed as interference effect and it depends on various factors such as terrain category, wind angle, geometry & orientation of the structure, spacing between the structures etc. Thus, proper assessment of interference effect with above factors, can lead to efficient design of structures that can withstand the load variations due to the presence of interfering structures. The existing literature on wind interference effects, focuses more on experimental analysis of the problem, which is both expensive and time consuming. In this regard, computational fluid dynamics (CFD) approach can serve as a quicker and efficient tool. In an attempt to study the effect of geometry &orientation of structures on interference phenomenon, turbulent flows around the buildings, cooling towers and chimneys have been simulated through CFD approach by performing Large Eddy Simulation (LES). In the present study, interference factors of a typical chimney structure has been evaluated by considering the presence of an identical chimney at varying distances, under different wind angles.

Electrocorticography (ECoG) signals can provide high spatio-temporal resolution and high signal to noise ratio recordings of local neural activity from the surface of the brain. Previous studies have shown that broad-band, spatially focal, high-frequency increases in ECoG signals are highly correlated with movement and other cognitive tasks and can be volitionally modulated. However, significant additional information may be present in inter-electrode interactions, but adding additional higher order inter-electrode interactions can be impractical from a computational aspect, if not impossible.In this paper we present a new method of calculating high frequency interactions between electrodes called Short-Time Windowed Covariance (STWC) that builds on mathematical techniques currently used in neural signal analysis, along with an implementation that accelerates the algorithm by orders of magnitude by leveraging commodity, off-the-shelf graphics processing unit (GPU) hardware.Using the hardware-accelerated implementation of STWC, we identify many types of event-related inter-electrode interactions from human ECoG recordings on global and local scales that have not been identified by previous methods. Unique temporal patterns are observed for digit flexion in both low- (10mm spacing) and high-resolution (3mm spacing) electrode arrays.Covariance is a commonly used metric for identifying correlated signals, but the standard covariance calculations do not allow for temporally varying covariance. In contrast STWC allows and identifies event-driven changes in covariance without identifying spurious noise correlations.STWC can be used to identify event-related neural interactions whose high computational load is well suited to GPU capabilities.

Identification of intended movement type and movement phase of hand grasp shaping are critical features for the control of volitional neuroprosthetics. We demonstrate that neural dynamics during visually-guided imagined grasp shaping can encode intended movement. We apply Procrustes analysis and LASSO regression to achieve 72% accuracy (chance = 25%) in distinguishing between visually-guided imagined grasp trajectories. Further, we can predict the stage of grasp shaping in the form of elapsed time from start of trial (R2=0.4). Our approach contributes to more accurate single-trial decoding of higher-level movement goals and the phase of grasping movements in individuals not trained with brain-computer interfaces. We also find that the overall time-varying trajectory structure of imagined movements tend to be consistent within individuals, and that transient trajectory deviations within trials return to the task-dependent trajectory mean. These overall findings may contribute to the further understanding of the cortical dynamics of human motor imagery., Comment: 4 pages, 6 figures, accepted to IEEE NER 2017 (8th International IEEE EMBS Conference on Neural Engineering)

Can the human brain learn to interpret inputs from a virtual world delivered directly through brain stimulation? We answer this question by describing the first demonstration of humans playing a computer game utilizing only direct brain stimulation and no other sensory inputs. The demonstration also provides the first instance of artificial sensory information, in this case depth, being delivered directly to the human brain through noninvasive methods. Our approach utilizes transcranial magnetic stimulation (TMS) of the human visual cortex to convey binary information about obstacles in a virtual maze. At certain intensities, TMS elicits visual percepts known as phosphenes, which transmits information to the subject about their current location within the maze. Using this computer-brain interface (CBI), five subjects successfully navigated an average of 92% of all the steps in a variety of virtual maze worlds. They also became more accurate in solving the task over time. These results suggest that humans can learn to utilize information delivered directly and noninvasively to their brains to solve tasks that cannot be solved using their natural senses, opening the door to human sensory augmentation and novel modes of human-computer interaction.

We propose a new probabilistic framework that allows mobile robots to autonomously learn deep, generative models of their environments that span multiple levels of abstraction. Unlike traditional approaches that combine engineered models for low-level features, geometry, and semantics, our approach leverages recent advances in Sum-Product Networks (SPNs) and deep learning to learn a single, universal model of the robot's spatial environment. Our model is fully probabilistic and generative, and represents a joint distribution over spatial information ranging from low-level geometry to semantic interpretations. Once learned, it is capable of solving a wide range of tasks: from semantic classification of places, uncertainty estimation, and novelty detection, to generation of place appearances based on semantic information and prediction of missing data in partial observations. Experiments on laser-range data from a mobile robot show that the proposed universal model obtains performance superior to state-of-the-art models fine-tuned to one specific task, such as Generative Adversarial Networks (GANs) or SVMs.

Autonomous mobile robots will soon become ubiquitous in human-populated environments. Besides their typical applications in fetching, delivery, or escorting, such robots present the opportunity to assist human users in their daily tasks by gathering and reporting up-to-date knowledge about the environment. In this paper, we explore this use case and present an end-to-end framework that enables a mobile robot to answer natural language questions about the state of a large-scale, dynamic environment asked by the inhabitants of that environment. The system parses the question and estimates an initial viewpoint that is likely to contain information for answering the question based on prior environment knowledge. Then, it autonomously navigates towards the viewpoint while dynamically adapting to changes and new information. The output of the system is an image of the most relevant part of the environment that allows the user to obtain an answer to their question. We additionally demonstrate the benefits of a continuously operating information gathering robot by showing how the system can answer retrospective questions about the past state of the world using incidentally recorded sensory data. We evaluate our approach with a custom mobile robot deployed in a university building, with questions collected from occupants of the building. We demonstrate our system's ability to respond to these questions in different environmental conditions.

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Neural correlates of movement planning onset and direction may be present in human electrocorticography in the signal dynamics of both motor and non-motor cortical regions. We use a three-stage model of jPCA reduced-rank hidden Markov model (jPCA-RR-HMM), regularized shrunken-centroid discriminant analysis (RDA), and LASSO regression to extract direction-sensitive planning information and movement onset in an upper-limb 3D isometric force task in a human subject. This mode achieves a relatively high true positive force-onset prediction rate of 60% within 250ms, and an above-chance 36% accuracy (17% chance) in predicting one of six planned 3D directions of isometric force using pre-movement signals. We also find direction-distinguishing information up to 400ms before force onset in the pre-movement signals, captured by electrodes placed over the limb-ipsilateral dorsal premotor regions. This approach can contribute to more accurate decoding of higher-level movement goals, at earlier timescales, and inform sensor placement. Our results also contribute to further understanding of the spatiotemporal features of human motor planning., 4 pages, 3 figures, accepted to EMBC 2016 (38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society)

Fully automated decoding of human activities and intentions from direct neural recordings is a tantalizing challenge in brain-computer interfacing. Most ongoing efforts have focused on training decoders on specific, stereotyped tasks in laboratory settings. Implementing brain-computer interfaces (BCIs) in natural settings requires adaptive strategies and scalable algorithms that require minimal supervision. Here we propose an unsupervised approach to decoding neural states from human brain recordings acquired in a naturalistic context. We demonstrate our approach on continuous long-term electrocorticographic (ECoG) data recorded over many days from the brain surface of subjects in a hospital room, with simultaneous audio and video recordings. We first discovered clusters in high-dimensional ECoG recordings and then annotated coherent clusters using speech and movement labels extracted automatically from audio and video recordings. To our knowledge, this represents the first time techniques from computer vision and speech processing have been used for natural ECoG decoding. Our results show that our unsupervised approach can discover distinct behaviors from ECoG data, including moving, speaking and resting. We verify the accuracy of our approach by comparing to manual annotations. Projecting the discovered cluster centers back onto the brain, this technique opens the door to automated functional brain mapping in natural settings.

Generative models for vision and pattern recognition have been overshadowed in recent years by powerful non-parametric discriminative models. These discriminative models can learn arbitrary decision boundaries between classes and have proved very effective in classification and detection problems. However, unlike generative models, they do not lend themselves naturally to more general vision tasks such as rendering novel images, de-noising, and in-painting. In this paper we introduce Complementary Kernel Density Estimation (CKDE), a new generative model that adopts many of the features of non-parametric discriminative models: (1) CKDE allows complex decision surfaces and arbitrary class conditional distributions to be learned, (2) it is easy to train because the log likelihood of the model is concave, so it has no local maxima, and (3) one can train its class conditional distributions jointly to share information among the different classes. We first demonstrate that CKDE is more accurate in benchmark classification tasks than a purely discriminative method such as the SVM. We then show that the posterior probability of class labels is more accurately estimated than kernelized logistic regression. Our other results demonstrate that partial images can be accurately classified by marginalizing unobserved pixels from the class conditional distributions, and missing parts of the image can be painted in using the learned generative model.

Non-alcoholic fatty liver disease (NAFLD) is considered an integral part of metabolic syndrome (MS). We aimed to explore the inter-relations of MS and dietary composition in subjects with and without hepatic steatosis and to identify the nutritional risk factors contributing to NAFLD. In all 98 subjects with steatosis and 102 controls were selected for the study after screening 260 consecutive healthy individuals. Anthropometric and nutritional information, biochemical data and clinical profile were analyzed. Prevalence of MS was determined based on the consensus statement for diagnosis of MS for Asian Indians. Multiple logistic regression analysis was done to predict the dietary risk factors in NAFLD. Prevalence of MS was 44.9% among NAFLD cases and 25.5% among controls (P=0.003). Subjects with NAFLD had significantly higher values of body mass index (BMI), waist circumference (WC), percent body fat, total cholesterol, triglycerides and blood pressure than controls. The total calorie intake, percent of carbohydrate and fat intake of NAFLD cases was significantly higher than controls. Multiple logistic regression analysis showed BMI (odds ratio 6.03 (95% confidence interval 3.26–11.14)), WC (5.49 (2.59–11.57)) and percent dietary fat intake (2.51 (1.99–3.31)) as independent nutritional risk factors in NAFLD. In this study, there is a high prevalence of MS among subjects with steatosis and metabolic disorders were closely related to NAFLD. BMI, WC and percent fat intake are independent dietary risk factors in NAFLD. Decreased nutritional intake with restricted fat may constitute an important therapy in subjects with NAFLD.

Probabilistic methods for analyzing sequences are providing new insights into the 4,000-year-old undeciphered script of the Indus civilization.

In this paper, we provide the first demonstration that a humanoid robot can learn to walk directly by imitating a human gait obtained from motion capture (mocap) data without any prior information of its dynamics model. Programming a humanoid robot to perform an action (such as walking) that takes into account the robot's complex dynamics is a challenging problem. Traditional approaches typically require highly accurate prior knowledge of the robot's dynamics and environment in order to devise complex (and often brittle) control algorithms for generating a stable dynamic motion. Training using human mocap is an intuitive and flexible approach to programming a robot, but direct usage of mocap data usually results in dynamically unstable motion. Furthermore, optimization using high-dimensional mocap data in the humanoid full-body joint space is typically intractable. We propose a new approach to tractable imitation-based learning in humanoids without a robot's dynamic model. We represent kinematic informati...

Although no historical information exists about the Indus civilization (flourished ca . 2600–1900 B.C.), archaeologists have uncovered about 3,800 short samples of a script that was used throughout the civilization. The script remains undeciphered, despite a large number of attempts and claimed decipherments over the past 80 years. Here, we propose the use of probabilistic models to analyze the structure of the Indus script. The goal is to reveal, through probabilistic analysis, syntactic patterns that could point the way to eventual decipherment. We illustrate the approach using a simple Markov chain model to capture sequential dependencies between signs in the Indus script. The trained model allows new sample texts to be generated, revealing recurring patterns of signs that could potentially form functional subunits of a possible underlying language. The model also provides a quantitative way of testing whether a particular string belongs to the putative language as captured by the Markov model. Application of this test to Indus seals found in Mesopotamia and other sites in West Asia reveals that the script may have been used to express different content in these regions. Finally, we show how missing, ambiguous, or unreadable signs on damaged objects can be filled in with most likely predictions from the model. Taken together, our results indicate that the Indus script exhibits rich synactic structure and the ability to represent diverse content. both of which are suggestive of a linguistic writing system rather than a nonlinguistic symbol system.

Posttransplant malignancy developing in an allograft is an uncommon complication of organ transplantation. The tumor may represent malignant transformation of donor or recipient cells that were previously normal, metastatic malignancy of recipient origin or malignancy transmitted from organ donor to recipient. Establishing the origin of the malignancy is critical to treatment algorithms. It is generally believed allograft removal and immunosuppression withdrawal will lead to resolution of transmitted malignancies in cases where the renal allograft is the origin. We report a male patient who developed metastatic ovarian malignancy secondary to donor transmission.

We describe a brain-computer interface for controlling a humanoid robot directly using brain signals obtained non-invasively from the scalp through electroencephalography (EEG). EEG has previously been used for tasks such as controlling a cursor and spelling a word, but it has been regarded as an unlikely candidate for more complex forms of control owing to its low signal-to-noise ratio. Here we show that by leveraging advances in robotics, an interface based on EEG can be used to command a partially autonomous humanoid robot to perform complex tasks such as walking to specific locations and picking up desired objects. Visual feedback from the robot's cameras allows the user to select arbitrary objects in the environment for pick-up and transport to chosen locations. Results from a study involving nine users indicate that a command for the robot can be selected from four possible choices in 5 s with 95% accuracy. Our results demonstrate that an EEG-based brain-computer interface can be used for sophisticated robotic interaction with the environment, involving not only navigation as in previous applications but also manipulation and transport of objects.

Electrocorticographic spectral changes during movement show a behavioral inflection in the classic gamma band (30-70 Hz). We quantify this inflection and demonstrate that it limits classification accuracy. We call for the designation of a functionally defined band above it, which we denote the chi-band.

This paper aim to conduct a pilot study in India process industry for sustainable machining process design. A survey questionnaire was framed at five point likert type scale. Respondents were asked to five elements related to sustainable machining process design (SMPD) Fifty responses was collected from various process industries. Simple statistics mean, standard deviation, correlation and t test is applied for the analyse survey responses. It is evident that the Energy efficiency during the process, Vegetable based cutting fluid and machining process utilisation are more emphasised by the companies and the other factors like Waste minimization and cryogenic machining require more focused on it. One sample t test it is evident that Energy efficiency during the process (SMPD5) (t = 6.194) is the most important factor which is highly adopted by the process industry in practice.

We demonstrate the feasibility of real-time cortical mapping from arrays of subdural electrodes using the electrocorticographic signal power in the higher spectral frequencies (76-200 Hz, or "chi-index"). Hand area was mapped offline in eight individuals using brief baseline and hand-movement measurements. In one patient, hand sensorimotor cortex was identified online during a handshake. We propose that this high-frequency component of the electrocorticogram provides a generic, reliable, clinically useful correlate of local cortical function.