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Mutations in PINK1 kinase are known to be responsible for the early-onset of Parkinson's disease. The protein resides on the outer-membrane of healthy mitochondria, and acts as a quality controller by regulating and maintaining dysfunctional mitochondria also known as mitophagy. Limited knowledge of the mechanism of protein has made it cumbersome to elucidate an effective treatment. In this paper, we screen a set of kinase inhibitors using high-throughput screening for investigating potential activators. We have for the first time shown the plausible binding and selectivity towards the kinase inhibitors in the mutant PINK1 binding site. We also could highlight the ligand bind/unbinding pathway identifying the most stable binding pose utilizing the residues from the P-loop and DFG motif for drugs Tepotinib and Pyrotinib. Interestingly, no direct interaction of mutated residues with the ligand molecules were observed. Furthermore, we highlight that the Ins3 region and SER228 of the N-lobe display distinct conformational states that are inline with crystal structure for substrate binding specificity. Collectively, our findings provide a molecular basis for the PINK1 potential kinase activator, which would further facilitate as a starting point to probe new therapeutics by using the structure based drug designing for treating neurological disorder.

Pancreatic lipase (PL) is a keen target for anti-obesity therapy that reduces dietary fat absorption. Here, we investigated the binding patterns of 220 PL inhibitors having experimental IC50 values, using molecular docking and binding energy calculations. Screening of these compounds illustrated most of them bound at the catalytic site (S1-S2 channel) and a few compounds are at the non-catalytic site (S2-S3 channel/S1-S3 channel) of PL. This binding pattern could be due to structural uniqueness or bias in conformational search. A strong correlation of pIC50 values with SP/XP docking scores, binding energies (ΔGMMGBSA) assured the binding poses are more true positives. Further, understanding of each class and subclasses of polyphenols indicated tannins preferred non-catalytic site wherein binding energies are underestimated due to huge desolvation energy. In contrast, most of the flavonoids and furan-flavonoids have good binding energies due to strong interactions with catalytic residues. While scoring functions limited the understanding of sub-classes of flavonoids. Hence, focused on 55 potent PL inhibitors of IC50 < 5 µM for better in vivo efficacy. The prediction of bioactivity, drug-likeness properties, led to 14 bioactive compounds. The low root mean square deviation (0.1-0.2 nm) of these potent flavonoids and non-flavonoid/non-polyphenols PL-inhibitor complexes during 100 ns molecular dynamics runs (MD) as well as binding energies obtained from both MD and well-tempered metadynamics, support strong binding to catalytic site. Based on the bioactivity, ADMET properties, and binding affinity data of MD and wt-metaD of potent PL-inhibitors suggests Epiafzelechin 3-O-gallate, Sanggenon C, and Sanggenofuran A shall be promising inhibitors at in vivo conditions. Communicated by Ramaswamy H. Sarma Binding aspects of natural compounds reveal the crucial factors of bioactivity towards pancreatic lipase.

Being a structure like a two-dimensional (2D) array of microvalves and cells, Programmable Microfluidic Device PMD biochips have the characteristics of reconfigurability and flexibility unlike conventional flow-based microfluidic biochips. In recent years, several design automation techniques for PMD biochips have been reported. For automated control of the PMD chip implementing a bioprotocol, one of the important tasks is to minimize the number of fluid flows for loading the reactant fluids into specific cells before the bioprotocol is executed. In this work we intensively study the fluid loading problem for PMD chips and we propose a two-phase approach to solve this problem. First, we propose a constraint satisfaction problem (CSP) based method, called loading-aware fluid-to-cell assignment (LAFCA) in order to obtain a suitable fluid-to-cell assignment, which will be beneficial for fluid loading phase. Then we propose an exact method, called CSP-based loading algorithm (CSPLA) and a near-optimal heuristic method, called determining flows from the last (DFL), for determining a sequence of fluid flows required to load different fluids into the cells of a PMD chip. We formulate CSPLA as a single objective optimization problem to minimize the total number of flows. For the output as a sequence of fluid flows we define three loading parameters, the total number of flows (K), the total number of 90° bends in all flow paths (B), and the total flow path length (L). Simulation results confirm that LAFCA combined with CSPLA outperforms (K, B and L reduced by 63.9%, 31.7% and 59.9%, respectively) the state-of-the-art method fluid loading algorithm for PMD (FLAP) [Gupta et al., TODAES, 2019]. Whereas, LAFCA combined with DFL can reduce the loading parameters K, B and L by 61.2%, 20.8% and 57.4%, respectively over using only FLAP. Also from the overall simulation results we can conclude that for many testcases, DFL can find the near-optimal Ks.

Background: Implementing an active system to identify, monitor and manage risk from laboratory errors can enhance patient safety and quality of care. Aims and Objectives: Failure Mode and Effect Analysis (FMEA) technique allows evaluating and measuring the hazards of a process malfunction, to decide where to execute improvement actions, and to measure the outcome of those actions. The aim of this study was to assess pre analytical phase of laboratory testing, mitigate risk and thereby increase patient safety. Materials and Methods: Steps followed in the study were: planning the study, selecting team members, analysis of the processes, risk analysis, and developing a risk reduction protocol by incorporating corrective actions. A Fault Tree Analysis diagram was used to plot the cascade of faults leading to the pre analytical errors. Risk Priority Number (RPN) was assigned. A minimum cut- off 40 RPN was considered for interventions and highest RPN errors were prioritized with corrective actions. Post intervention RPN score was calculated. Results: Eight failure modes had the highest RPN. Corrective actions were prioritized against these errors. RPN scores of test ordering error, sample collection error, transport errors, error in patient identification, site selection, urine samples not received, sample accessioning and sample processing errors decreased, post intervention. Conclusion: With thorough planning, we can use FMEA as a common standard to analyze risk in pre analytical phase of laboratory testing.

A large volcanic eruption can generate large amounts of ash which affect the socio-economic activities of surrounding areas, affecting airline transportation, socio-economics activities, and human health. Accumulated ashfall has devastating impacts on areas surrounding the volcano and in other regions, and eruption scale and weather conditions may escalate ashfall hazards to wider areas. It is crucial to discover places with a high probability of exposure to ashfall deposition. Here, as a reference for ashfall disaster countermeasures, we present a dataset containing the estimated distributions of the ashfall deposit and airborne ash concentration, obtained from a simulation of ash dispersal following a large-scale explosive volcanic eruption. We selected the Taisho (1914) eruption of the Sakurajima volcano, as our case study. This was the strongest eruption in Japan in the last century, and our study provides a baseline for a worst-case scenario. We employed one eruption scenario (OES) approach by replicating the actual event under various extended weather conditions to show how it would affect contemporary Japan. We generated an ash dispersal dataset by simulating the ash transport of the Taisho eruption scenario using a volcanic ash dispersal model and meteorological reanalysis data for 64 years (1958–2021). We explain the dataset production and provide the dataset in multiple formats for broader audiences. We examine the validity of the dataset, its limitations, and its uncertainties. Countermeasure strategies can be derived from this dataset to reduce ashfall risk. The dataset is available at the DesignSafe-CI Data Depot: https://www.designsafe-ci.org/data/browser/public/designsafe.storage.published/PRJ-2848v2 or through the following DOI: https://doi.org/10.17603/ds2-vw5f-t920 by selecting Version 2 (Rahadianto and Tatano, 2020).

Due to the benefits of low power, high bandwidth and complementary metal-oxide-semiconductor (CMOS) compatibility, the design of optical circuits has spurred great attention among researchers in the domain of electronic design automation. With this motivation, all-optical combinational and sequential circuits such as adders, multiplexers, multipliers and flip-flops have been explored in recent times. In this paper, we have explored the designs of all-optical array multiplier and four types of parallel multipliers (carry save adder multiplier, Wallace tree multiplier, Dadda multiplier and reduced area multiplier) using two different design approaches named as Design1 and Design2. In order to design these multipliers, semiconductor optical amplifier (SOA)-based Mach–Zehnder interferometers (MZIs) have been used as the basic optical component. The basic MZI switch, full adder and 2-bit multiplier have been simulated using OptiSystem software to analyze the power loss. Furthermore, an all-optical merged multiplier has been designed, which is often used in digital signal processors. In comparison with other designed multipliers, it is evident from the simulation results that the MZI-based reduced area multiplier of Design1 approach has the highest performance in terms of speed, while the MZI-based carry save adder (CSA) multiplier with Design1 approach has the least optical cost.

The aim of the study was to review in detail, the skeletal, dental and soft-tissue effects of Rapid Maxillary Expansion in young group of patients using Computed Tomographic Scan. The review is conducted through an electronic and manual searches which includes PubMed, Ovid, Cochrane, and Web of Science using the keywords. Searches includes the original studies which were conducted from the January 2000 to May 2020 amongst patients from 6 to 18 years using Hyrax, Haas-type and butterfly-type expanders. Based on the inclusion criteria, 23 relevant articles containing 298 patients were selected to evaluate changes related to skeletal, dental, soft-tissues and airway by assessing Computed Tomography scan. Significant effects have been observed at the mid-palatal suture due to the separation of two hemi-maxillae that led to an increase in the palatal volume, maxillary arch perimeter and correction of posterior crossbite. Changes were also recorded at the circum-maxillary sutures with maximum effects noted in internasal and nasomaxillary sutures. Other observations includes dental changes like buccal tipping of the maxillary posterior anchored teeth, increase in nasal and upper airway dimensions. Few side-effects of Rapid Maxillary Expansion includes recession of alveolar bone and dehiscence in cases of excessive dental tipping, dorsal hump on the nose with flattening of the nasal tip. Interpreting the outcomes with Computed Tomography scan strengthen the orthodontist’s precision in determining the 3-dimension effects with greater specificity. However, more number of Computed Tomography studies on soft-tissue changes and airway effects are required to understand comprehensive mechanics of the procedure.

Reliability of the prepared fluidic samples is a major concern for automated sample preparation using microfluidic biochips, where induced errors in the resultant concentration values severely affect the assay outcome. However, the existing design automation techniques have not thoroughly considered the reliability model to reduce the induced concentration errors during sample preparation. This article proposes a fast reliability-aware sample preparation (RASP) method for determining the optimized sequence of mixing steps (mixing process) with the enhanced reliability. In RASP, a probabilistic concentration prediction model is proposed for analyzing the reliability of a given mixing process. Based on this probabilistic model, a lookup table construction algorithm along with the table query method is proposed to obtain the optimized mixing process. The simulation results show that for any user-specified target concentration, RASP can effectively determine the optimized mixing process, which generates the droplets with target concentration within the error tolerance of 0.1%. Compared with the state-of-the-art sample preparation algorithm, RASP improves the reliability-related accuracy by 91.4% on average via 2048 testcases.

In recent years, silicon photonics (Si-photonics) have received significant attention among researchers due to complementary metal-oxide semiconductor compatibility, and the characteristics of high-speed and low-power dissipation. The integration of electronic and optical circuits on a single chip has opened up new directions of research in the domain of digital logic design and synthesis of photonic integrated circuits (PICs). Several optical switching devices using different technologies have been designed and experimentally demonstrated, which further helps in implementing PICs. In order to efficiently design larger, complex and reliable PICs, the photonic design automation techniques are being explored as electronic design automation techniques have been investigated in case of very large-scale integration circuits. This paper presents an extensive survey of recent work reported in the literature on the domains of logic circuit design, synthesis, and physical design automation for implementing PICs. The aim of this survey is to start with the fundamental optical concepts and then move to the latest research domains of design and synthesis of PICs. Finally, we provide a discussion on the challenges and the future research directions toward practically realizing the Si-photonics and PICs.

With increasingly complex neural network architectures and heterogeneous device characteristics, finding a reasonable graph partitioning and device placement strategy is challenging. There have been prior attempts at learned approaches for solving device placement, these approaches are computationally expensive, unable to handle large graphs consisting over 50000 nodes, and do not generalize well to unseen graphs. To address all these limitations, we propose an efficient single-shot, generalized deep RL method (SGDP) based on a scalable sequential attention mechanism over a graph neural network that is transferable to new graphs. On a diverse set of representative deep learning models, our method on average achieves 20% improvement over human placement and 18% improvement over the prior art with 15× faster convergence. We are the first to demonstrate super human performance on 8-layer recurrent neural network language model and 8-layer GNMT consisting of over 50000 nodes, on 8-GPUs. We provide rationales and sensitivity study on model architecture selections.

Microfluidic technologies enable replacement of time-consuming and complex steps of biochemical laboratory protocols with a tiny chip. Sample preparation (i.e., dilution or mixing of fluids) is one of the primary tasks of any bioprotocol. In real-life applications where several assays need to be executed for different diagnostic purposes, the same sample fluid is often required with different target concentration factors ( CF s). Although several multi-target dilution algorithms have been developed for digital microfluidic biochips, they are not efficient for implementation with continuous-flow-based microfluidic chips, which are preferred in the laboratories. In this article, we present a multi-target dilution algorithm ( MTDA ) for continuous-flow-based microfluidic biochips, which to the best of our knowledge is the first of its kind. We design a flow-based rotary mixer with a suitable number of segments depending on the target- CF profile, error tolerance, and optimization criteria. To schedule several intermediate fluid-mixing tasks, we develop a multi-target scheduling algorithm ( MTSA ) aiming to minimize the usage of storage units while producing dilutions with multiple CF s. Furthermore, we propose a storage architecture for efficiently loading (storing) of intermediate fluids from (to) the storage units.

Predicting glass transition temperature for rubber and rubber composites is immensely important for tire industry for the development of products and fine-tune process conditions. Molecular dynamic...

Silicon photonics (Si-photonics) has been established as a potential technology that integrates both electronic and optical circuits on single integrated circuits (ICs) in order to satisfy the increasing demand for high-speed and low-power in the emerging market of ICs. It has opened up the research directions in the domain of design automation for photonic ICs. On the physical layout of the optical circuits, it is a challenging task to obtain the optimal routing of optical waveguides, while minimizing all the parameters like the number of tracks, total bend loss, worst signal loss, total propagation loss and total crossing loss. In this paper, we proposed two non-Manhattan grid-based methods for reducing the bend loss, worst signal loss and tracks in optical channel routing. First, a 0-1 integer linear programming (ILP) based algorithm called minimizing bend loss (MBL) is proposed, which minimizes the total bend loss (TBL) and the worst signal loss (WSL) while reducing the number of tracks (T ) over the state-of-the-art technique. The execution time of MBL is very high for the large input. Hence next, a scalable heuristic called reducing bend loss (RBL) is presented that provides a better balance between the reduction of the TBL and T over the state-of-the-art and MBL algorithms. Simulation results show that MBL can reduce the TBL and the WSL by an average of 57.9% and 63.1%, respectively, with an average increase of 12% in T over state-of-the-art algorithms. The simulation results show that the RBL reduces the TBL and the WSL by an average of 39.7% and 41.3%, respectively, with an average increase of 23.7% in T over state-of-the-art algorithms.

Medicinal plants are a popular term that everyone can use to describe plants that are useful intreating a variety of conditions that are not treatable by allopathic treatment. Among these, birthcontrol and, at the same time, growing human fertility have both become serious issues in recentyears. This review provides up-to-date information on medicinal plants that have beenscientifically proven to have anti-fertility properties. The botanical name, family, parts used, andchemical elements present in plants are all included in this study. Despite the fast advancementand dissemination of modern medicine and surgery, traditional practices retain their appeal andconfidence. Traditional herbal treatments have been shown to have anti-fertility effects innumerous research.The purpose of this review is to highlight research on plant-based antifertility. This article may aid researchers in identifying therapeutic herbs that have anti-fertilityproperties.

The emergence of pandemic situations originated from severe acute respiratory syndrome (SARS)-CoV-2 and its new variants created worldwide medical emergencies. Due to the non-availability of efficient drugs and vaccines at these emergency hours, repurposing existing drugs can effectively treat patients critically infected by SARS-CoV-2. Finding a suitable repurposing drug with inhibitory efficacy to a host-protein is challenging. A detailed mechanistic understanding of the kinetics, (dis)association pathways, key protein residues facilitating the entry-exit of the drugs with targets are fundamental in selecting these repurposed drugs. Keeping this target as the goal of the paper, the potential repurposing drugs, Nafamostat, Camostat, Silmitasertib, Valproic acid, and Zotatifin with host-proteins HDAC2, CSK22, eIF4E2 are studied to elucidate energetics, kinetics, and dissociation pathways. From an ensemble of independent simulations, we observed the presence of single or multiple dissociation pathways with varying host-proteins-drug systems and quantitatively estimated the probability of unbinding through these specific pathways. We also explored the crucial gateway residues facilitating these dissociation mechanisms. Interestingly, the residues we obtained for HDAC2 and CSK22 are also involved in the catalytic activity. Our results demonstrate how these potential drugs interact with the host machinery and the specific target residues, showing involvement in the mechanism. Most of these drugs are in the preclinical phase, and some are already being used to treat severe COVID-19 patients. Hence, the mechanistic insight presented in this study is envisaged to support further findings of clinical studies and eventually develop efficient inhibitors to treat SARS-CoV-2.

There is sparse literature on the occurrence of acute leukemia in association with Gaucher disease in adults. Earlier, only two cases have been published describing acute leukemia in association with Gaucher disease in the pediatric age group. In this case report, we have described a case of acute myeloid leukemia along with Gaucher disease in an 8-year-old female child who presented with fever with hepatosplenomegaly. Measurement of ?-glucosidase activity was the key modality in diagnosis. The possibility that the reduction of the enzyme in Gaucher disease is related to the development of hematological malignancies needs to be explored.

Background: Pancytopenia is an important entity worldwide but with varying patterns in clinical presentations. Bone marrow aspiration and biopsy are considered as primary investigation to evaluate and diagnose the causes of pancytopenia. But before doing a bone marrow aspiration or biopsy, a note on the newer reticulocyte indices given by automated cell counters other than reticulocyte percent and absolute reticulocyte count helps us to get a picture about the marrow erythropoietic activity which also bypasses the inter-observer variability. These newer reticulocyte indices are the Immature Reticulocyte Fraction (IRF), Reticulocyte Hemoglobin Content (CHr or Ret-He), difference between the reticulocyte and erythrocyte hemoglobin content (Delta-He). IRF was initially introduced to monitor the hematopoietic treatment in cases of childhood pancytopenia due to cancer chemotherapy. We attempted to emphasize the importance of Immature Reticulocyte Fraction (IRF) over other reticulocyte indices in diagnosis of pancytopenia and assessment of marrow response to therapy. Methods: In this study patient’s history were taken. Then EDTA mixed blood examined by Automated Cell Counters (Sysmex XT-4000i) and subsequently bone marrow examination has been done to confirm the etiology. Results were calculated statistically. Results: We found that values of IRF were also significant in the diagnoses of Megaloblastic Anaemia, Aplastic Anaemia, early Marrow Recovery from suppression, Hemolytic Disease and Chronic Diseases. Conclusion: It is hypothesized that IRF is an index of acceleration and the absolute reticulocyte count is a quantitative measurement of the effectiveness of erythropoiesis. So after initiation of treatment, repeated observation of both IRF and Reticulocyte Count may be helpful to observe the effectiveness of therapy.

The emergence of pandemic situations originated from SARS-CoV-2 and its new variants created worldwide medical emergencies. Due to the non-availability of efficient drugs and vaccines, hundreds of thousands of people succumbed to death intoxicated by this virus. At these emergency hours, repurposing existing drugs can effectively treat patients critically infected by SARS-CoV-2. Using a high-throughput screening approach, we validated a list of potential repurposed drugs, like Nafamostat, Camostat, Silmitasertib, Valproic acid, Zotatifin, and essential host target proteins HDAC2, eIF4E2, CSK22, that are essential for viral mechanism. We determined multiple dissociation pathways of repurposed drugs, suggesting the availability of sub pockets within the host target proteins. We showed the preferential residues involved in the (un)binding kinetics of the ligands correlated to the underlying mechanism of the host protein activity. Interestingly, the residues we obtained for HDAC2 and CSK22 target proteins, which we highlighted, are also involved in the catalytic activity. The mechanistic insight presented in this work is envisaged to help use these key host proteins and potential repurposed drugs as a treatment for the SARS-CoV-2 virus.

Microfluidic lab-on-a-chip has emerged as a new technology for implementing biochemical protocols on small-sized portable devices targeting low-cost medical diagnostics. Among various efforts of fabrication of such chips, programmable microfluidic device (PMD) is a relatively new technology for implementation of flow-based lab-on-a-chips. A PMD chip is suitable for automation due to its symmetric nature. In order to implement a bioprotocol on such a reconfigurable device, it is crucial to automate sample preparation on a chip as well. Sample preparation, which is a front-end process to produce the desired target concentrations of the input reagent fluid, plays a pivotal role in every bioassay or bioprotocol. In this paper, first, a method referred as dilution algorithm in two steps (DATS) is proposed, which needs only two diluting operations for any target concentration to achieve. Then, we present another method called as dilution algorithm on a small dilution area (DASDA), which needs less area compared to that by DATS. Finally, we propose the heuristic for efficient dilution of biochemical fluids using a PMD chip referred as dilution algorithm in a restricted dilution area (DARDA) that produces more accurate (with less error) target concentration value on a restricted area of the PMD chip in a shorter mixing time. Simulation results reveal that DARDA outperforms a start-of-the-art dilution algorithm applicable for PMD chips in terms of three performance parameters namely mixing time, mixing area and error in target concentration.

Search-based techniques have been demonstrated effective in solving complex optimization problems that arise in domain-specific compilers for machine learning (ML). Unfortunately, deploying such techniques in production compilers is impeded by two limitations. First, prior works require factorization of a computation graph into smaller subgraphs over which search is applied. This decomposition is not only non-trivial but also significantly limits the scope of optimization. Second, prior works require search to be applied in a single stage in the compilation flow, which does not fit with the multi-stage layered architecture of most production ML compilers. This paper presents Xtat, an autotuner for production ML compilers that can tune both graph-level and subgraph-level optimizations across multiple compilation stages. Xtat applies Xtat-M, a flexible search methodology that defines a search formulation for joint optimizations by accurately modeling the interactions between different compiler passes. Xtat tunes tensor layouts, operator fusion decisions, tile sizes, and code generation parameters in XLA, a production ML compiler, using various search strategies. In an evaluation across 150 ML training and inference models on Tensor Processing Units (TPUs) at Google, Xtat offers up to 2.4x and an average 5% execution time speedup over the heavily-optimized XLA compiler.

In the current work, molecular dynamics simulation is employed to understand the intrinsic growth of carbon dioxide and methane hydrate starting from a seed crystal of methane and carbon dioxide respectively. This comparison was carried out because it has relevance to the recovery of methane gas from natural gas hydrate reservoirs by simultaneously sequestering a greenhouse gas like CO2. The seed crystal of carbon dioxide and methane hydrate was allowed to grow from a super-saturated mixture of carbon dioxide or methane molecules in water respectively. Two different concentrations (1:6 and 1:8.5) of CO2/CH4 molecules per water molecule were chosen based on gas–water composition in hydrate phase. The molecular level growth as a function of time was investigated by all atomistic molecular dynamics simulation under suitable temperature and pressure range which was well above the hydrate stability zone to ensure significantly faster growth kinetics. The concentration of CO2 molecules in water played a significant role in growth kinetics, and it was observed that maximizing the CO2 concentration in the aqueous phase may not result in faster growth of CO2 hydrate. On the contrary, methane hydrate growth was independent of methane molecule concentration in the aqueous phase. We have validated our results by performing experimental work on carbon dioxide hydrate where it was seen that under conditions appropriate for liquid CO2, the growth for carbon dioxide hydrate was very slow in the beginning.

With the evolution of the technology, digital microfluidic (DMF) biochips have become a vital part of biochemical research. Hence, it is required to consider the reliability of the different fluidic operations performed on a biochip. Sample preparation is an important process of any real-life bioprotocol implementation on a DMF biochip. In this process, sequence of mixing and dilution steps are determined to get the desired target concentrations. The mixers used for performing mix-split steps may incorporate some noise during mixing and can result in the erroneous concentrations of the reagents. Thus, the reliability analysis of the resultant target concentration is necessary and methods are required to be developed to reduce these concentration errors. In this paper, the error and reliability models are discussed to compare the reliabilities of the existing mixing algorithms. Simulation results show that for a given target ratio, reliability of common dilution operation sharing (Liu et al. , ICCAD-2013) is higher. We also discuss the mixer assignment techniques and the heuristic approach is found to quickly provide the better order of mixer assignment in order to achieve highly reliable mixture after sample preparation.

In recent years, microfluidic biochips are being dominantly used for implementing a wide range of biochemical laboratory protocols (bioprotocols) on hand-held devices. Accurate preparation of fluid-samples is a fundamental preprocessing step that is needed in many bioprotocols. Oftentimes, for point-of-service microfluidic devices, the number of reservoirs built on-chip may be far less than that of the reactant fluids to be used in an assay. Hence, during the execution of an assay, several fluids are to be unloaded from the reservoirs to make room for loading new fluids stored off-line. Such unload-wash-load steps (switching) may be required several times, and these steps, being manual, significantly impact assay-completion time. In this paper, we address the problem of biochemical mixture preparation and propose Reservoir- and Mixer-constrained Scheduling (RMS) algorithm that executes a given mixing tree aiming to minimize the number of reactant-switching from input reservoirs. We also consider certain constraints on the availability of concurrent mixing modules. The proposed scheduling scheme can not only be applied to a number of mixture preparation algorithms but also to a general class of microfluidic devices such as digital, paper-based, and flow-based biochips. Simulation results over a large number of target ratios show that given the mixing trees obtained by standard mixing algorithms such as MinMix/RMA/CoDOS, RMS reduces switching steps (on average by 40.3%/41.9%/33%) at the cost of increasing mixing time (by only 3.5%/6.2%/4.8%), compared to an existing scheduling scheme invoked with reservoir constraints.

Recently an anti-COVID-19 therapeutic application of the drug 2-deoxy-D- glucose (2-DG) an analogue of glucose has been developed in collaboration between Institute of Nuclear Medicine and Allied Sciences (INMAS), India, Defence Research and Development Organisation (DRDO), India, and Dr Reddy’s Laboratories (DRL), India. As per the reports 2-DG is effective against SARS-COV- 2. Publication of phase 2 and phase 3 clinical trial data is pending. However, it has been shown that 2-DG reduces the supplemental oxygen dependence on covid-19 infected patients and make their recovery faster. The present outbreak of Covid-19 infection due to SARS-CoV-2, a virus from the coronavirus family, has become a major menace to human being. As the understanding of the mechanism of the therapeutic action of 2-DG on SARS-CoV-2 infected hosts is missing, in this work we have studied the possible inhibitory interaction of the drug with two different pathways (a) with non-structured viral proteins involved in translation and replication of SARS-CoV-2 and (b) its inhibition mechanism of the glycolysis pathway. We have used our fully automated novel drug designing platform with state-of-the-art free energy of binding calculator PRinMTML-ESS to evaluate the role of 2-DG as an antiviral and glycolysis pathway inhibitor in SARS-CoV-2 affected humans. Docking, all atom molecular dynamic simulation and enhanced free energy sampling methods used in PRinMTML-ESS have predicted that 2-DG effectively reduced the replication of SARS-CoV-2 in human cell by reducing the glycolytic flux, by competitive inhibition of glucose in binding with the enzyme hexokinase. 2-DG is generally administered in covid patient along with other antivirals and steroid, hence it can be used as a mild clinical therapy which can reduce the viral replication, inflammation when given in the earlier stage of the disease.

Since the onset of global pandemic, the most focused research currently in progress is the development of potential drug candidates and clinical trials of existing FDA approved drugs for other relevant diseases, in order to repurpose them for the COVID-19. At the same time, several high throughput screenings of drugs have been reported to inhibit the viral components during the early course of infection but with little proven efficacies. Here, we investigate the drug repurposing strategies to counteract the coronavirus infection which involves several potential targetable host proteins involved in viral replication and disease progression. We report the high throughput analysis of literature-derived repurposing drug candidates that can be used to target the genetic regulators known to interact with viral proteins based on experimental and interactome studies. In this work we have performed integrated molecular docking followed by molecular dynamics (MD) simulations and free energy calculations through an expedite in silico process where the number of screened candidates reduces sequentially at every step based on physicochemical interactions. We elucidate that in addition to the pre-clinical and FDA approved drugs that targets specific regulatory proteins, a range of chemical compounds (Nafamostat, Chloramphenicol, Ponatinib) binds to the other gene transcription and translation regulatory proteins with higher affinity and may harbour potential for therapeutic uses. There is a rapid growing interest in the development of combination therapy for COVID-19 to target multiple enzymes/pathways. Our in silico approach would be useful in generating leads for experimental screening for rapid drug repurposing against SARS-CoV-2 interacting host proteins. Communicated by Ramaswamy H. Sarma

Silicon photonics (Si-photonics) is a potential technology that integrates the electronic and optical circuits on a single chip, in order to satisfy the increasing demand of highspeed and low-power dissipation in the current very-large-scale integration circuits. It has opened up the research directions in the domain of design automation for photonic integrated circuits. While deciding the physical layout of the optical circuits on the substrate, it is crucial to obtain the optimal routing of optical waveguides. The optical channel routing problem is a multiobjective optimization problem, in which all the parameters like the total bend loss, the total propagation loss, the total crossing loss, and the total number of tracks are required to be minimized. However, in comparison to propagation and crossing loss, the bending loss has a higher impact on the total signal loss. In this paper, a grid-based method has been proposed for optical waveguide channel routing (OWCR) while reducing the total bend loss and the total number of tracks. Simulation results reveals that OWCR can reduce the total bend loss by 25.82 % and 36.57 % over the state-of-the-art technique for h = 1 and h = 1.732, respectively, while increasing the total number of tracks by 18.68 %.

In recent times, computational methods played an important role in the down selection of chemical compounds, which could be a potential drug candidate with a high affinity to target proteins. However, the screening methodologies, including docking, often fails to identify the most effective compound, which could be a ligand for the target protein. To solve that, here we have integrated meta-dynamics, an enhanced sampling molecular simulation method, with all-atom molecular dynamics to determine a specific compound that could target the main protease of novel severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). This combined computational approach uses the enhanced sampling to explore the free energy surface associated with the protein's binding site (including the ligand) in an explicit solvent. We have implemented this method to find new chemical entities that exhibit high specificity of binding to the 3-chymotrypsin-like cysteine protease (3CLpro) present in the SARS-CoV-2 and segregated to the most strongly bound ligands based on free energy and scoring functions (defined and implemented) from a set of 17 ligands which were prescreened for synthesizability and druggability. Additionally, we have compared these 17 ligands' affinities against controls, N3 and 13b α-ketoamide inhibitors, for which experimental crystal structures are available. Based on our results and analysis from the combined molecular simulation approach, we could identify the best compound which could be further taken as a potential candidate for experimental validation.Communicated by Ramaswamy H. Sarma.

Since the onset of global pandemic, the most focused research currently in progress is the development of vaccine candidates and clinical trials of existing FDA approved drugs for other relevant diseases, in order to repurpose them for the COVID-19. Here, we investigate the drug repurposing strategies to counteract the coronavirus infection which involves several potential targetable host proteins involved in viral replication and disease progression. We report the high throughput analysis of literature-derived repurposing drug candidates that can be used to target the genetic regulators known to interact with viral proteins based on experimental and interactome studies. In this work we have performed integrated molecular docking followed by molecular dynamics (MD) simulations and free energy calculations through an expedite insilico process where the number of screened candidates reduces sequentially at every step based on physicochemical information. We elucidate that in addition to the pre-clinical and FDA approved drugs that targets specific regulatory proteins, a range of chemical compounds (Nafamostat, Chloramphenicol, Ponatinib) binds to the other gene transcription and translation regulatory protein with higher affinity and may harbour potential for therapeutic uses.

The estimation of crop area in advance takes us a step closer towards the intelligent farming as it is beneficial in both pre and post harvesting scenarios for better utilization of resources and higher production at a reasonable cost. There are many challenges in the estimation of crop area in freely available low resolution satellite images due to mixed pixels, especially in the boundaries of crop classes. A neural network has the ability to learn from unknown patterns in the satellite images and then take a decision based on their learning. Fuzzy logic is used together with a neural network that can explain partial membership of each class. Hence, in this paper, we integrate these two models and found it useful to perform accurate estimation of area for each crop class. A quantitative analysis is performed with the help of reference data created by drone images and global positioning system field survey. This study indicates that the proposed method improves the accuracy of area estimation for the crop classes.

Earthquakes, floods, tsunami, and other natural disasters are appearing as worldwide threats because of their widespread destruction that results in thousands of human and economic losses. It is vital for first responders to know the root cause of damages in a region so that the emergency response activities can be planned accordingly and more effectively. We proposed a framework for the detection and recognition of natural disasters from satellite images. In this work, satellite images of six different types of disasters are considered, namely earthquake, volcanic eruption, flood, tsunami, and hurricane. The framework relies on the fusion of wavelet image scattering features and local binary pattern features for constructing the final feature vector. We also compared the accuracy of our framework with the existing state-of-the-art hand-crafted and machine learning models. Simulation results confirm that the proposed framework is able to recognize the type of natural disaster from the satellite images with an accuracy of 99.59% (Kappa coefficient 98.54% and F-Score 99.40%). The proposed approach results in less computational cost while achieving better accuracy compared to the deep convolutional neural network. We believe that the proposed model can be integrated with satellite imagery for locating the geographical regions affected by the multiple natural disaster events at the same time or at short intervals.

Microfluidic lab-on-a-chip has emerged as a new technology for implementing biochemical protocols on small-sized portable devices targeting low-cost medical diagnostics. Among various efforts of fabrication of such chips, a relatively new technology is a programmable microfluidic device (PMD) for implementation of flow-based lab-on-a-chip. A PMD chip is suitable for automation due to its symmetric nature. In order to implement a bioprotocol on such a reconfigurable device, it is crucial to automate a sample preparation on-chip as well. In this article, we propose a dilution PMD algorithm (namely DPMD ) and its architectural mapping scheme (namely generalized architectural mapping algorithm ( GAMA )) for addressing fluidic cells of such a device to perform dilution of a reagent fluid on-chip. We used an optimization function that first minimizes the number of mixing steps and then reduces the waste generation and further reagent requirement. Simulation results show that the proposed DPMD scheme is comparative to the existing state-of-the-art dilution algorithm. The proposed design automation using the architectural mapping scheme reduces the required chip area and, hence, minimizes the valve switching that, in turn, increases the life span of the PMD-chip.

Machine Learning (ML) research has primarily focused on improving the accuracy and efficiency of the training algorithms while paying much less attention to the equally important problem of understanding, validating, and monitoring the data fed to ML. Irrespective of the ML algorithms used, data errors can adversely affect the quality of the generated model. This indicates that we need to adopt a data-centric approach to ML that treats data as a first-class citizen, on par with algorithms and infrastructure which are the typical building blocks of ML pipelines. In this demonstration we showcase TensorFlow Data Validation (TFDV), a scalable data analysis and validation system for ML that we have developed at Google and recently open-sourced. This system is deployed in production as an integral part of TFX - an end-to-end machine learning platform at Google. It is used by hundreds of product teams at Google and has received significant attention from the open-source community as well.

Here, we report new chemical entities that exhibit highly specific binding to the 3-chymotrypsin-like cysteine protease (3CLpro) present in the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Because the viral 3CLpro protein controls coronavirus replication, 3CLpro is identified as a target for drug molecules. We implemented an enhanced sampling method in combination with molecular dynamics and docking to reduce the computational screening search space to four molecules that could be synthesized and tested against SARS-CoV-2. Our computational method is much more robust than any other method available for drug screening (e.g., docking) because of sampling of the free energy surface of the binding site of the protein (including the ligand) and use of explicit solvent. We have considered all possible interactions between all the atoms present in the protein, ligands, and water. Using high-performance computing with graphical processing units, we were able to perform a large number of simulations within a month and converge the results to the four most strongly bound ligands (based on free energy and other scores) from a set of 17 ligands with lower docking scores. Additionally, we have considered N3 and 13b α-ketoamide inhibitors as controls for which experimental crystal structures are available. Out of the top four ligands, PI-06 was found to have a higher screening score compared to the controls. Based on our results and analysis, we confidently claim that we have identified four potential ligands, out of which one ligand is the best choice based on free energy and the most promising candidate for further synthesis and testing against SARS-CoV-2.

Microfluidic fully programmable valve array (FPVA) biochips have emerged as general-purpose flow-based microfluidic lab-on-chips (LoCs). An FPVA supports highly re-configurable on-chip components (modules) in the two-dimensional grid-like structure controlled by some software programs, unlike application-specific flow-based LoCs. Fluids can be loaded into or washed from a cell with the help of flows from the inlet to outlet of an FPVA, whereas cell-to-cell transportation of discrete fluid segment(s) is not precisely possible. The simplest mixing module to realize on an FPVA-based LoC is a four-way mixer consisting of a 2 × 2 array of cells working as a ring-like mixer having four valves. In this paper, we propose a design automation method for sample preparation that finds suitable placements of mixing operations of a mixing tree using four-way mixers without requiring any transportation of fluid(s) between modules. We also propose a heuristic that modifies the mixing tree to reduce the sample preparation time. We have performed an extensive simulation and examined several parameters to determine the performance of the proposed solution.

Machine learning has become an essential tool for gleaning knowledge from data and tackling a diverse set of computationally hard tasks. However, the accuracy of a machine learned model is deeply tied to the data that it is trained on. Designing and building robust processes and tools that make it easier to analyze, validate, and transform data that is fed into large-scale machine learning systems poses data management challenges. Drawn from our experience in developing data-centric infrastructure for a production machine learning platform at Google, we summarize some of the interesting research challenges that we encountered, and survey some of the relevant literature from the data management and machine learning communities. Specifically, we explore challenges in three main areas of focus - data understanding, data validation and cleaning, and data preparation. In each of these areas, we try to explore how different constraints are imposed on the solutions depending on where in the lifecycle of a model the problems are encountered and who encounters them.

Recent studies in algorithmic microfluidics have led to the development of several techniques for automated solution preparation using droplet-based digital microfluidic (DMF) biochips. A major challenge in this direction is to produce a mixture of several reactants with a desired ratio while optimizing reactant cost and preparation time. The sequence of mix-split operations that are to be performed on the droplets is usually represented as a mixing tree (or graph). In this article, we present an efficient mixing algorithm, namely, Mixing Tree with Common Subtrees ( MTCS ), for preparing single-target mixtures. MTCS attempts to best utilize intermediate droplets, which were otherwise wasted, and uses morphing based on permutation of leaf nodes to further reduce the graph size. The technique can be generalized to produce multitarget ratios, and we present another algorithm, namely, Multiple Target Ratios ( MTR ). Additionally, in order to enhance the output load, we also propose an algorithm for droplet streaming called Multitarget Multidemand ( MTMD ). Simulation results on a large set of target ratios show that MTCS can reduce the mean values of the total number of mix-split steps ( T ms ) and waste droplets ( W ) by 16% and 29% over Min-Mix (Thies et al. 2008) and by 22% and 34% over RMA (Roy et al. 2015), respectively. Experimental results also suggest that MTR can reduce the average values of T ms and W by 23% and 44% over the repeated version of Min-Mix , by 30% and 49% over the repeated version of RMA , and by 9% and 22% over the repeated-version of MTCS , respectively. It is observed that MTMD can reduce the mean values of T ms and W by 64% and 85%, respectively, over MTR . Thus, the proposed multitarget techniques MTR and MTMD provide efficient solutions to multidemand, multitarget mixture preparationon a DMF platform.

We have constructed a phase diagram from the self-assembled morphologies of linolenic acid sophorolipid in water by performing mesoscale simulations in different concentrations. The dependence of morphologies and its properties of the self-assembled structure as a function of the concentration of bolaamphiphile in water are investigated. Two hydrophilic head groups and one hydrophobic tail group of bolaamphiphile have been mapped to beads for mesoscale simulation. The interaction parameters between different beads are calculated using Flory-Huggins solution theory. Flory-Huggins χ-parameters have been calculated from atomistic simulation and experimental data of pure components constituting linolenic acid sophorolipid. We have obtained different self-assembled morphologies depending upon the concentrations of sophorolipid in water. We have explored the arrangements of hydrophobic and hydrophilic groups of the sophorolipid chains in the different morphologies. The sophorolipid chains found to be present in different structural arrangements. The residence time and flip frequency of these lipids in different orientations have been calculated and discussed in this work.

Clathrate hydrates have a variety of applications ranging from natural gas storage, carbon dioxide sequestration, sea water desalination, gas separation etc. But the applications have still not reached the industrial stage and commercialization, although the concept has been demonstrated. In contrast, scale-up and demonstration of technologies are often done without proper understating of the process at the molecular level. One could always tune the process for better performance if proper knowledge of the process is built by understanding the same at the molecular level. Gas hydrate is essentially a crystallisation phenomenon, and since this is a multiple component and multi-phase system, the mechanism of nucleation and growth of hydrate has to be understood clearly. There are various theories behind the mechanism of hydrate nucleation. The theories talk about the stochastic nature and the steps leading to hydrate formation. Apart from that, there are variety of factors that affect hydrate decomposition. Many of these aspects become challenging to understand using meso and macro scale experiments. This is where Molecular Dynamics simulations plays a significant role. This review provides a molecular understanding of existing theories about nucleation & growth; further, factors affecting decomposition and the analysis techniques that are used to quantify such mechanism are also discussed.

Lichen planus (LP) is a relatively common chronic, mucocutaneous disease of autoimmune origin, involves oral mucosa, skin, scalp, nails, and genital mucosa. The prevalence of oral LP (OLP) varies worldwide, commonly seen in middle-aged and elderly people. It usually presents as symmetrical and bilateral or multiple lesions with burning sensation (BS) sometimes accompanied by pain. Corticosteroids and calcineurin inhibitors have shown promising results in the treatment of OLP, but its chronic course and unpredictable exacerbations/remission continues to result in a high degree of morbidity. The study aimed to evaluate the efficacy of intralesional triamcinolone acetonide (injection TA) combined with topical application of TA orabase and Tacrolimus (TAC) ointment for symptomatic cases of OLP.The prospective study included 52 symptomatic OLP patients to receive (0.5 ml) intralesional injection of TA once a week for the first 4 weeks followed by one injection in the 641 patients (78.8%) had complete remission of disease and 11 (21%) had shown partial improvement. The VAS scores for BS and pain improved significantly. Improvement was also noted with decrease in the average size of active lesions and the number of sites with treatment. The relapse was seen in 17 patients (41%) in the 20TA combined with TAC is a valuable therapeutic option for the treatment of symptomatic OLP. Our findings suggest that patients have shown statistically significant improvement.

We present a mesoscale model of aqueous polyacrylamide in the infinitely dilute concentration regime, by combining an extant coarse-grained (CG) force-field, MARTINI, and the Iterative Boltzmann Inversion protocol (IBI). MARTINI force-field was used to retain the thermodynamics of solvation of the polymer in water, whereas the structural properties and intrapolymer interactions were optimized by IBI. Atomistic molecular dynamics simulations of polymer in water were performed to benchmark the mesoscale simulations. Our results from the CG model show excellent agreement in structure with the atomistic system. We also studied the dynamical behavior of our CG system by computing the shear viscosity and compared it with the standard IBI model. The viscosity trends of our model were similar to the atomistic system, whereas the standard IBI model was highly dissimilar as expected. In summary, our hybrid CG model sufficiently mimics an infinitely dilute system, and is superior to both MARTINI and IBI in representing the structure and thermodynamics of the atomistic system, respectively. Our hybrid coarse-graining strategy promises applicability in large-scale simulations of polymeric/biological systems where the structure needs to be replicated accurately while preserving the thermodynamics of a smoother surrounding.

Perfluoropolyether tetraol (PFPE tetraol) possesses a hydrophobic perfluoropolyether chain in the backbone and two hydroxyl groups at each chain terminal, which facilitates the formation of hydrogen bonds with water molecules resulting in the formation an extended physical network. About 3 wt% water was required for the formation of the microphase separated physical network of PFPE tetraol. The mechanism responsible for the microphase separation of water clusters in the physical network was studied using a combination of techniques such as NMR spectroscopy, molecular dynamics (MD) simulations and DSC. MD simulation studies provided evidence for the formation of clusters in the PFPE tetraol physical network and the size of these clusters increased gradually with an increase in the extent of hydration. Both MD simulations and NMR spectroscopy studies revealed that these clusters position themselves away from the hydrophobic backbone or vice versa. The presence of intra- and inter-chain aggregation possibility among hydrophilic groups was evident. DSC results demonstrated the presence of tightly and loosely bound water molecules to the terminal hydroxyl groups of PFPE tetraol through hydrogen bonding. The data from all the three techniques established the formation of a physical network driven by hydrogen bonding between the hydrophilic end groups of PFPE tetraol and water molecules. The flexible nature of the PFPE tetraol backbone and its low solubility parameter favour clustering of water molecules at the terminal groups and result in the formation of a gel.