PATHFINDER: Designing Stimuli for Neuromodulation through data-driven inverse estimation of non-linear functions

There has been tremendous interest in designing stimuli (e.g. electrical currents) that produce desired neural responses, e.g., for inducing therapeutic effects for treatments. Traditionally, the design of such stimuli has been model-driven. Due to challenges inherent in modeling neural responses accurately, data-driven approaches offer an attractive alternative. The problem of data-driven stimulus design can be thought of as estimating an inverse of a non-linear ``forward" mapping, which takes in as inputs the stimulus parameters and outputs the corresponding neural responses. In most cases of interest, the forward mapping is many-to-one, and hence difficult to invert using traditional methods. Existing methods estimate the inverse by using conditional density estimation methods or numerically inverting an estimated forward mapping, but both approaches tend to perform poorly at small sample sizes. In this work, we develop a new optimization framework called PATHFINDER, which allows us to use regression methods for estimating an inverse mapping. We use toy examples to illustrate key aspects of PATHFINDER, and show, on computational models of biological neurons, that PATHFINDER can outperform existing methods at small sample sizes. The data-efficiency of PATHFINDER is especially valuable in stimulus design as collecting data is expensive in this domain

Simultaneous estimation of Amino acids by using HPLC

Various methods for the individual as well as simultaneous estimation of amino acids using various techniques like HPLC and other way outs like electrophoresis have been described in this review paper. The amino acid determination by using HPLC can either be done by using pre-column or post column derivatization. The amino acid is first derivatized into a particular derivative and then is analysed into the column in the case of pre-column derivatization, whereas in the case of post column derivatization, the amino acid is first passed through the column for the sake of separation and then the separated amino acids are derivatized into their such derivatives which can be detected by fluorescence detector. Out of the above two mentioned techniques, pre-column derivatization is used more oftenly than the post column derivatization. Few of the most commonly used derivatization agents are phenylisothiocyanate, o-phthalaldehyde+2-mercaptoethanol, dansyl chloride, phenylthiohydantoin etc

Computing Unique Information for Poisson and Multinomial Systems

Bivariate Partial Information Decomposition (PID) describes how the mutual information between a random variable M and two random variables Y and Z is decomposed into unique, redundant, and synergistic terms. Recently, PID has shown promise as an emerging tool to understand biological systems and biases in machine learning. However, computing PID is a challenging problem as it typically involves optimizing over distributions. In this work, we study the problem of computing PID in two systems: the Poisson system inspired by the 'ideal Poisson channel' and the multinomial system inspired by multinomial thinning, for a scalar M. We provide sufficient conditions for both systems under which closed-form expressions for many operationally-motivated PID can be obtained, thereby allowing us to easily compute PID for these systems. Our proof consists of showing that one of the unique information terms is zero, which allows the remaining unique, redundant, and synergistic terms to be easily computed using only the marginal and the joint mutual information

Complete Bilateral Calcified Psoas Abscess- Rare Sequelae of Untreated Pott’s Spine

Although rare in the western world; psoas abscess is a frequent finding in Indian sub continent associated with Pott’s spine. Untreated Pott’s spine may lead to various sequelae like destruction of vertebra, kyphosis, paraplageia etc which in modern world is amenable to anti-tubercular drugs and surgical management. We report a case of untreated Pott’s spine with bilateral calcified psoas abscess with kyphosis. To the best of our knowledge no such case of complete bilateral calcified psoas abscesses has been reported earlier. We want to discuss this case with relevant literature review and its influence on treatment plan. 
 Keywords: aminoglycoside; antistaphylococcal; psoas

Complete Bilateral Calcified Psoas Abscess- Rare Sequelae of Untreated Pott’s Spine

Although rare in the western world; psoas abscess is a frequent finding in Indian sub continent associated with Pott’s spine. Untreated Pott’s spine may lead to various sequelae like destruction of vertebra, kyphosis, paraplageia etc which in modern world is amenable to anti-tubercular drugs and surgical management. We report a case of untreated Pott’s spine with bilateral calcified psoas abscess with kyphosis. To the best of our knowledge no such case of complete bilateral calcified psoas abscesses has been reported earlier. We want to discuss this case with relevant literature review and its influence on treatment plan. Keywords: aminoglycoside; antistaphylococcal; psoas

Improved Positioning Precision using a Multi-rate Multi-sensor in Industrial Motion Control Systems

Industrial motion control systems, e. pick-andplace tasks in semiconductor manufacturing equipment, require precise positioning for achiering high machine throughput. Linear encoders are the standard industrial sensors used for position feedback due to their relatively low cost, high resolution, and high operating frequency. The challenge is that the linear encoders measure the positions at the points-of-control of the equipment, eg motors, and not at the points-of-interest, e.g. pick-and-place positions The coupling between a point-of-control and the point-of-interest is affected by external disturbances such as mechanical misalignment of the product, friction, and warping of the material, and linear encoders fail to sense these disturbances. Vision-based sensing is a potential alternative to achieve robust sensing and high-precision control. However, vision processing has a long computational delay and affects the machine throughput. In this paper, we propose a multi-rate multi-sensor fusion approach to improve the positioning accuracy of industrial motion control systems with different points-of-control and pointsof-interest. We present a multi-rate Kalman filter with bias correction to fuse accurate but slow and delayed vision sensor data with fast but less accurate linear encoder data for highprecision position control. We validate the proposed method in an evaluation framework by considering an industrial case study of a semiconductor die-bonding machine. A design-space exploration is done to evaluate the performance of the proposed solution with respect to various relevant design parameters. The effectiveness of the proposed solution depends on the type of disturbances and vision processing delay. For the parameter range under consideration, we achieve a positioning accuracy of 1μm

An Evaluation Framework for Vision-in-the-Loop Motion Control Systems

Industrial applications and processes such as quality inspections, pick and place operations, and semiconductor manufacturing require accurate positioning control for achieving the high throughput of the assembly machines. Vision-based sensing is considered to be a potential means to achieve robust positioning control which is referred to as a vision-in-the-loop (VIL) system. In such motion systems, the point-of-control and the point-of-interest are often different due to several physical factors. In this case, validation of a system is done only when a machine prototype is available. A physical prototype is often expensive and infeasible in real-life. This paper proposes an evaluation framework for VIL systems targeting a predictable multi-core embedded platform. The presented framework offers model-in-the-loop (MIL), software-in-the-loop (SIL), and processor-in-the-loop (PIL) simulation features for evaluating the closed-loop performance of industrial motion control systems. As a deployment platform, we consider a predictable embedded platform CompSOC. The predictable nature of the CompSOC platform guarantees periodic and deterministic execution of the control applications and allows verification of the timing properties and performance of the VIL system. Additionally, the framework offers automatic code generation feature targeting the CompSOC platform. Closed-loop simulation setup models the system dynamics and camera position in the CoppeliaSim physics simulation engine and simulates the system software in C and MATLAB. CoppeliaSim runs as a server and MATLAB as a client in synchronous mode. We show the effectiveness of our framework using a vision-based motion control example

Response of wastewater-based epidemiology predictor for the second wave of COVID-19 in Ahmedabad, India: A long-term data Perspective

In this work, we present an eight-month longitudinal study of wastewater-based epidemiology (WBE) in Ahmedabad, India, where wastewater surveillance was introduced in September 2020 after the successful containment of the first wave of COVID-19 to predict the resurge of the infection during the second wave of the pandemic. The study aims to elucidate the weekly resolution of the SARS-CoV-2 RNA data for eight months in wastewater samples to predict the COVID-19 situation and identify hotspots in Ahmedabad. A total of 287 samples were analyzed for SARS-CoV-2 RNA using RT-PCR, and Spearman's rank correlation was applied to depict the early warning potential of WBE. During September 2020 to April 2021, the increasing number of positive wastewater influent samples correlated with the growing number of confirmed clinical cases. It also showed clear evidence of early detection of the second wave of COVID-19 in Ahmedabad (March 2021). 258 out of a total 287 samples were detected positive with at least two out of three SARS-CoV-2 genes (N, ORF- 1 ab, and S). Monthly variation represented a significant decline in all three gene copies in October compared to September 2020, followed by an abrupt increase in November 2020. A similar increment in the gene copies was observed in March and April 2021, which would be an indicator of the second wave of COVID-19. A lead time of 1-2 weeks was observed in the change of gene concentrations compared with clinically confirmed cases. Measured wastewater ORF- 1 ab gene copies ranged from 6.1 x 102 (October 2020) to 1.4 x 104 (November 2020) copies/mL, and wastewater gene levels typically lead to confirmed cases by one to two weeks. The study highlights the value of WBE as a monitoring tool to predict waves within a pandemic, identify local disease hotspots within a city, and guide rapid management interventions.This work is funded by UNICEF, Gujarat, India and Science and Engineering Research Board, New Delhi (CVD/2022/000033). We acknowledge the help received from UPES SEED Grant, Gujarat Pollution Control Board and Ahmedabad Municipal Corporation. Funding for DWG was provided by an EPSRC Impact Acceleration Award (EP/R511584/1) and a NERC award (NE/V004883/1) in the COVID-19 Urgency Programme.Peer reviewe

Response of wastewater-based epidemiology predictor for the second wave of COVID-19 in Ahmedabad, India: A long-term data Perspective

In this work, we present an eight-month longitudinal study of wastewater-based epidemiology (WBE) in Ahmedabad, India, where wastewater surveillance was introduced in September 2020 after the successful containment of the first wave of COVID-19 to predict the resurge of the infection during the second wave of the pandemic. The study aims to elucidate the weekly resolution of the SARS-CoV-2 RNA data for eight months in wastewater samples to predict the COVID-19 situation and identify hotspots in Ahmedabad. A total of 287 samples were analyzed for SARS-CoV-2 RNA using RT-PCR, and Spearman\u27s rank correlation was applied to depict the early warning potential of WBE. During September 2020 to April 2021, the increasing number of positive wastewater influent samples correlated with the growing number of confirmed clinical cases. It also showed clear evidence of early detection of the second wave of COVID-19 in Ahmedabad (March 2021). 258 out of a total 287 samples were detected positive with at least two out of three SARS-CoV-2 genes (N, ORF- 1 ab, and S). Monthly variation represented a significant decline in all three gene copies in October compared to September 2020, followed by an abrupt increase in November 2020. A similar increment in the gene copies was observed in March and April 2021, which would be an indicator of the second wave of COVID-19. A lead time of 1–2 weeks was observed in the change of gene concentrations compared with clinically confirmed cases. Measured wastewater ORF- 1 ab gene copies ranged from 6.1 x 102 (October 2020) to 1.4 x 104 (November 2020) copies/mL, and wastewater gene levels typically lead to confirmed cases by one to two weeks. The study highlights the value of WBE as a monitoring tool to predict waves within a pandemic, identify local disease hotspots within a city, and guide rapid management interventions