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The project’s goal is to provide a facility for managing medicine stock. It is intended for all items found within the pharmacy. This project illustrates the design and implementation of that pharmacy, which today emphasizes management as the most crucial component. It simplifies stock management while also providing sophistication to the user. It enriched pharmaceutical sector management in India because pharmaceutical management is very important in our country for patient safety through pharmacy. The pharmaceutical supply system, for example, incorporates order placement, receiving, and storing of pharmaceutical products via inventory management software that is accessible via smartphones. The pharmacy's entire data set is analyzed, including medicine, pharmaceutical instruments, and inventory management software. We must consider a practical method for effectively managing your medical store, such as an inventory system. A well-managed chemist can provide significant benefits to your company. Proper drug storage is critical for keeping track of your inventory and avoiding shortages. As a result, you can ensure that your pharmaceutical business is long-term efficient and profitable.

Cancer patients are less likely to undergo percutaneous coronary intervention (PCI) after cardiac arrest, although they demonstrate improved mortality benefit from the procedure. We produced the largest nationally representative analysis of mortality of cardiac arrest and PCI for patients with cancer versus non-cancer.Propensity score adjusted multivariable regression for mortality was performed in this case-control study of the United States' largest all-payer hospitalized dataset, the 2016 National Inpatient Sample. Regression models of mortality and PCI weighted by the complex survey design were fully adjusted for age, race, income, cancer metastases, NIS-calculated mortality risk by Diagnosis Related Group (DRG), acute coronary syndrome, and likelihood of undergoing PCI.Of the 30,195,722 hospitalized adult patients, 15.43% had cancer, and 0.79% of the whole sample presented with cardiac arrest (of whom 20.57% underwent PCI). In fully adjusted regression analysis among patients with cardiac arrest, PCI significantly reduced mortality (OR 0.15, 95 %CI 0.13-0.19; p 0.001) among patients with cancer greater than those without it (OR 0.21, 95 %CI 0.20-0.23; p 0.001).This nationally representative study suggests that post-cardiac arrest PCI is underutilized among patients with cancer despite its significant mortality reduction for such patients (independent of clinical acuity).

Introduction: Cardiovascular comorbidities may predispose to adverse outcomes in hospitalized patients with coronavirus disease 2019 (COVID-19). However, across the USA, the burden of cardiovascular comorbidities varies significantly. Whether clinical outcomes of hospitalized patients with COVID-19 differ between regions has not yet been studied systematically. Here, we report differences in underlying cardiovascular comorbidities and clinical outcomes of patients hospitalized with COVID-19 in Texas and in New York state. Methods: We established a multicenter retrospective registry including patients hospitalized with COVID-19 between March 15 and July 12, 2020. Demographic and clinical data were manually retrieved from electronic medical records. We focused on the following outcomes: mortality, need for pharmacologic circulatory support, need for mechanical ventilation, and need for hemodialysis. Univariate and multivariate logistic regression analyses were performed. Results: Patients in the Texas cohort (n = 296) were younger (57 vs. 63 years, p value 2 vs. 28.5 kg/m2, p = 0.015), and they had higher rates of diabetes mellitus (41 vs. 30%; p = 0.014). In contrast, patients in the New York state cohort (n = 218) had higher rates of coronary artery disease (19 vs. 10%, p = 0.005) and atrial fibrillation (11 vs. 5%, p = 0.012). Pharmacologic circulatory support, mechanical ventilation, and hemodialysis were more frequent in the Texas cohort (21 vs. 13%, p = 0.020; 30 vs. 12%, p < 0.001; and 11 vs. 5%, p = 0.009, respectively). In-hospital mortality was similar between the 2 cohorts (16 vs. 18%, p = 0.469). After adjusting for differences in underlying comorbidities, only the use of mechanical ventilation remained significantly higher in the participating Texas hospitals (odds ratios [95% CI]: 3.88 [1.23, 12.24]). Median time to pharmacologic circulatory support was 8 days (interquartile range: 2, 13.8) in the Texas cohort compared to 1 day (0, 3) in the New York state cohort, while median time to in-hospital mortality was 16 days (10, 25.5) and 7 days (4, 14), respectively (both p < 0.001). In-hospital mortality was higher in the late versus the early study phase in the New York state cohort (24 vs. 14%, p = 0.050), while it was similar between the 2 phases in the Texas cohort (16 vs. 15%, p = 0.741). Conclusions: Geographical differences, including practice pattern variations and the impact of disease burden on provision of health care, are important for the evaluation of COVID-19 outcomes. Unadjusted data may cause bias affecting future regulatory policies and proper allocation of resources.

Supplementary information is available for this paper at https://doi.org/10.1038/s41587-020-0446-y, Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organisms metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Geneproteinreaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality., We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjánsdóttir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503)., info:eu-repo/semantics/publishedVersion

Salmonella enterica Typhimurium is a serious pathogen that is involved in human nontyphoidal infections. Tackling Typhimurium infections is difficult due to the species’ dynamic adaptation to its environment, which is dictated by a complex transcriptional regulatory network (TRN). While traditional biomolecular methods provide characterizations of specific regulators, it is laborious to construct the global TRN structure from this bottom-up approach. Here, we used a machine learning technique to understand the transcriptional signatures of S. enterica Typhimurium from the top down, as a whole and in individual strains. Furthermore, we conducted cross-strain comparison of 6 strains in serovar Typhimurium to investigate similarities and differences in their TRNs with pan-genomic analysis. By decomposing all the publicly available RNA-Seq data of Typhimurium with independent component analysis (ICA), we obtained over 400 independently modulated sets of genes, called iModulons. Through analysis of these iModulons, we 1) discover three transport iModulons linked to antibiotic resistance, 2) describe concerted responses to cationic antimicrobial peptides (CAMPs), 3) uncover evidence towards new regulons, and 4) identify two iModulons linked to bile responses in strain ST4/74. We extend this analysis across the pan-genome to show that strain-specific iModulons 5) reveal different genetic signatures in pathogenicity islands that explain phenotypes and 6) capture the activity of different phages in the studied strains. Using all high-quality publicly-available RNA-Seq data to date, we present a comprehensive, data-driven Typhimurium TRN. It is conceivable that with more high-quality datasets from more strains, the approach used in this study will continue to guide our investigation in understanding the pan-transcriptome of Typhimurium. Interactive dashboards for all gene modules in this project are available at https://imodulondb.org/ under the “Salmonella Typhimurium” page to enable browsing for interested researchers.

The transcriptional regulatory network in prokaryotes controls global gene expression mostly through transcription factors (TFs), which are DNA-binding proteins. Chromatin immunoprecipitation (ChIP) with DNA sequencing methods can identify TF binding sites across the genome, providing a bottom-up, mechanistic understanding of how gene expression is regulated. ChIP provides indispensable evidence toward the goal of acquiring a comprehensive understanding of cellular adaptation and regulation, including condition-specificity. ChIP-derived data's importance and labor-intensiveness motivate its broad dissemination and reuse, which is currently an unmet need in the prokaryotic domain. To fill this gap, we present proChIPdb (prochipdb.org), an information-rich, interactive web database. This website collects public ChIP-seq/-exo data across several prokaryotes and presents them in dashboards that include curated binding sites, nucleotide-resolution genome viewers, and summary plots such as motif enrichment sequence logos. Users can search for TFs of interest or their target genes, download all data, dashboards, and visuals, and follow external links to understand regulons through biological databases and the literature. This initial release of proChIPdb covers diverse organisms, including most major TFs of Escherichia coli, and can be expanded to support regulon discovery across the prokaryotic domain.

Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, and some studies have performed system-level analysis, few have been able to provide a network-based model of the TRN that also provides the relative shifts in transcriptional regulator activity triggered by changing environments. Here, we compiled a compendium of nearly 650 publicly available, high quality M. tuberculosis RNA-sequencing data sets and applied an unsupervised machine learning method to obtain a quantitative, top-down TRN. It consists of 80 independently modulated gene sets known as “iModulons,” 41 of which correspond to known regulons. These iModulons explain 61% of the variance in the organism’s transcriptional response. We show that iModulons (i) reveal the function of poorly characterized regulons, (ii) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and infection events, and (iii) identify intrinsic clusters of regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date. IMPORTANCE Mycobacterium tuberculosis H37Rv is one of the world's most impactful pathogens, and a large part of the success of the organism relies on the differential expression of its genes to adapt to its environment. The expression of the organism's genes is driven primarily by its transcriptional regulatory network, and most research on the TRN focuses on identifying and quantifying clusters of coregulated genes known as regulons. While previous studies have relied on molecular measurements, in the manuscript we utilized an alternative technique that performs machine learning to a large data set of transcriptomic data. This approach is less reliant on hypotheses about the role of specific regulatory systems and allows for the discovery of new biological findings for already collected data. A better understanding of the structure of the M. tuberculosis TRN will have important implications in the design of improved therapeutic approaches.

Introduction: We sought to produce the first nationally representative analysis of racial and income inequities by mortality and cost for patients with chronic total occlusion (CTO) of coronary arteries by the presence or absence of active cancer. Methods: Propensity score adjusted and backward propagation neural network machine learning augmented multivariable regression among patients with CTO and stratified by race and income was conducted for the above outcomes in this case-control study of the United States’ largest and most current all-payer hospitalized dataset, the 2016 National Inpatient Sample. Regression models were fully adjusted for age, geographic region, anemia, thrombocytopenia, cancer metastases, NIS-calculated mortality risk by Diagnosis Related Group (DRG), and the likelihood of undergoing PCI (in addition to length of stay [LOS] for cost). To produce nationally representative estimates, analyses were also adjusted for the complex survey design. Results: Of the 30,195,722 adult hospitalized patients, 1,159,994 (3.84%) underwent PCI (of whom 30,624 [2.64%] were in patients with active cancer), and 120,815 (0.40%) had CTO (of whom 3,068 [2.54%] had active cancer). In multivariable regression among patients with CTO, mortality was comparable across races in both PCI and cancer, but significantly increased among the lowest income quartile for patients with cancer (OR 4.09; 95%CI 1.30-12.80; p=0.016). PCI in Hispanic cancer patients unlike other races had significantly increased costs ($207,218.80; 95%CI 76,799.21-337,738.30; p=0.002) as did the lowest income quartile ($26,327.08; 95%CI 21,123.93-31,530.23; p=0.042) unlike other income groups significantly increased costs. Conclusions: This large nationally representative study suggests PCI can safely done in hospitalized patients with CTO and active cancer though its prevalence is far less than in those without cancer despite comparable risk profiles; there also appears to be significant racial and income inequities in both mortality and cost by both race and income.

Dynamic cellular responses to environmental constraints are coordinated by the transcriptional regulatory network (TRN), which modulates gene expression. This network controls most fundamental cellular responses, including metabolism, motility, and stress responses. Here, we apply independent component analysis, an unsupervised machine learning approach, to 95 high-quality Sulfolobus acidocaldarius RNA-seq datasets and extract 45 independently modulated gene sets, or iModulons. Together, these iModulons contain 755 genes (32% of the genes identified on the genome) and explain over 70% of the variance in the expression compendium. We show that 5 modules represent the effects of known transcriptional regulators, and hypothesize that most of the remaining modules represent the effects of uncharacterized regulators. Further analysis of these gene sets results in: (1) the prediction of a DNA export system composed of 5 uncharacterized genes, (2) expansion of the LysM regulon, and (3) evidence for an as-yet-undiscovered global regulon. Our approach allows for a mechanistic, systems-level elucidation of an extremophile’s responses to biological perturbations, which could inform research on gene-regulator interactions and facilitate regulator discovery in S. acidocaldarius. We also provide the first global TRN for S. acidocaldarius. Collectively, these results provide a roadmap towards regulatory network discovery in archaea.

We are firmly in the era of biological big data. Millions of omics datasets are publicly accessible and can be employed to support scientific research or build a holistic view of an organism. Here, we introduce a workflow that converts all public gene expression data for a microbe into a dynamic representation of the organism’s transcriptional regulatory network. This five-step process walks researchers through the mining, processing, curation, analysis, and characterization of all available expression data, using Bacillus subtilis as an example. The resulting reconstruction of the B. subtilis regulatory network can be leveraged to predict new regulons and analyze datasets in the context of all published data. The results are hosted at https://imodulondb.org/, and additional analyses can be performed using the PyModulon Python package. As the number of publicly available datasets increases, this pipeline will be applicable to a wide range of microbial pathogens and cell factories.

Aluminium has always been a key material in manufacturing industries. These days, automobile and space industries are looking for better and better materials for the majority of their manufacturing system, and aluminium being light in weight along with good specific strength, good castability and lower price is the perfect alternative. Due to the fascinating attractions and improved hardness, aluminium-based hybrid metal matrix composites (HMMC) are getting fame at the present time. The non-homogenous mixture made due to the presence of reinforcement particles leads to improvement in microhardness, mechanical and microstructural properties. Now among several methods available, stir casting and friction stir processing are widely used to fabricate AMMCs. In this paper, the changes and the improvements observed in properties of aluminium after addition of reinforcement in bulk(stir casting) and on the surface(friction stir processing) are reviewed and discussed.

Introduction: Although primarily a respiratory illness, COVID-19 frequently involves the cardiovascular system. Hypothesis: Accordingly, we assessed whether abnormal electrocardiographic findings, including cardiac dysrhythmias, ST/T wave changes, and QTc interval prolongation, predict adverse outcomes in patients with COVID-19. Methods: A multi-center retrospective study of hospitalized patients with COVID-19 was performed across 4 hospitals in Texas and 1 in New York. Initial and subsequent ECG findings among patients with uncomplicated disease course, patients with adverse clinical outcomes (need for vasopressors, mechanical ventilation or renal replacement therapy) and patients who died were analyzed. Univariable and multivariable Cox analyses were performed. Results: We identified 297 consecutive patients with COVID-19. Of these patients, 91% had available ECGs. Median heart rate on initial ECG was 92 bpm (IQR: 27), and median QTc interval was 442 msec (IQR: 40). Longer QTc interval on initial ECG was associated with adverse clinical outcomes (443 msec, IQR: 43) and death (457 msec, IQR: 52, vs 441.5 msec, IQR: 36; p=0.033). Cardiac dysrhythmias or ST/T wave changes were noted in subsequent ECGs of 46% of patients and were associated with adverse clinical outcomes (58%) or death (68% vs 33%; HR: 2.2, 95% CI: 1.3-3.6; p=0.002). Sixty-four patients (23%) developed a QTc interval >500msec during their hospitalization. In multivariable Cox analysis, history of coronary artery disease was independently associated with development of QTc>500msec (HR 2.1, 95% CI 1.0-4.4; p=0.047) while other baseline comorbidities were not. Patients with QTc>500msec were more commonly treated with azithromycin (91% vs 77%; p=0.048); no other differences in COVID-19 treatment were identified. In-hospital mortality among patients with QTc>500msec was 37% versus 13% among patients with shorter QTc intervals (HR: 2.6, 95% CI 1.5-4.5; p=0.001). Conclusions: Prolonged QTc interval on initial ECG of COVID-19 patients predicted adverse outcomes and death. Azithromycin was associated with development of QTc>500msec. Patients with a QTc interval >500msec had a 2.6 times higher risk for in-hospital mortality compared to patients with shorter QTc intervals.

Introduction: In a time span of 6 months, COVID-19 has infected more than 7 million and cost the lives of more than 400,000 people worldwide. Cardiovascular involvement is associated with adverse clinical outcomes. Hypothesis: Increased plasma troponin and brain natriuretic peptide (BNP) or NT-proB-type natriuretic peptide (NT-proBNP) levels are associated with the presence of underlying cardiovascular disease and predict increased in-hospital mortality. Methods: Here we present a multicenter observational study of patients diagnosed with COVID-19, who were hospitalized in 5 hospitals (4 in Texas and 1 in New York) between March 16 th and May 22 nd , 2020. Demographic and clinical characteristics were abstracted after patient’s discharge or death. To address the non-standard troponin and BNP/NT-proBNP assays across institutions we used the upper 99th percentile as the cut-off for abnormal on each test. Results: We identified 297 consecutive patients hospitalized with COVID-19. Troponin levels were available in 84% and BNP/NT-proBNP levels in 47% of patients. Troponin levels were increased in 14% and BNP/NT-proBNP in 46% of patients. In multivariate Cox analysis, history of coronary artery disease was independently associated with elevated troponin levels (Hazard Ratio [HR]: 5.24; 95% confidence interval [CI]: 1.37-19.97; p=0.015), while history of congestive heart failure was independently associated with elevated BNP/NT-proBNP levels (HR: 2.58; 95% CI: 1.05 6.31; p=0.038). Older age, history of hypertension, dyslipidemia, or stroke, were associated with elevated troponin or BNP/NT-proBNP levels only in the univariate analysis. Other demographic and clinical characteristics were not significantly associated with cardiac biomarker elevation. Collectively, elevated troponin or BNP/NT-proBNP were significantly associated with in-hospital mortality (HR: 3.47 95% CI: 1.90-6.35 and HR: 3.56; 95% CI: 1.44-8.77, respectively; both p Conclusions: Increased levels of cardiac biomarkers were associated with a 3-fold increase of in-hospital mortality among patients with COVID-19. Troponin or BNP/pro-BNP testing has a prognostic value and may guide treatment.

Hydralazine-induced lupus syndrome (HILS) is a rare clinical entity with variable manifestations. Pericardial involvement is an uncommon but serious manifestation of the condition. In this report, we present a case of large symptomatic pericardial effusion secondary to HILS. We highlight the important considerations in the evaluation and management of this rare syndrome. HILS should be considered in the differential diagnosis for cardiac tamponade of otherwise unclear etiology in patients taking 100 mg daily or more of hydralazine for longer than three months. A temporal association between the offending drug and presenting symptoms, resolution of symptoms upon discontinuation, and a positive anti-histone antibody test can all support the diagnosis of this syndrome.

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Since the optimal management of coronary artery disease (CAD) and breast cancer is unknown, we sought to conduct the first known nationally representative study of mortality and cost effectiveness for breast cancer and percutaneous coronary interventions (PCI). Backward propagation neural network

There are no nationally representative studies of mortality and cost effectiveness for percutaneous coronary interventions (PCI) and colon cancer despite the growing prevalence of cardiovascular disease (CVD) and this cancer. Propensity score-matched analysis and backward propagation neural network

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.Here, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the model’s performance parameters, which supports informed model development and facilitates error detection.Memote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

OBJECTIVES Evaluation of plethysmography as a reliable triage tool to access collateral circulation in the hand prior to transradial access. BACKGROUND Hand plethysmographic testing has been used for access site triage prior to cardiac catheterization despite a lack of data supporting its result's stability. METHODS Cohort of consecutive outpatients presenting for evaluation prior to cardiac catheterization underwent hand plethysmographic testing at the beginning of the office visit and before discharge. Demographics and clinical data was recorded. RESULTS Hands (N = 887) in 445 patients were evaluated using the Barbeau plethysmography technique. Barbeau Test (BT) patterns A, B, C, and D were observed in 58%, 30%, 8% and 4% on initial evaluation. On preliminary evaluation, 780 (88%) hands had a "low-risk" pattern (A or B) and 107 (12%) hands had a "high-risk" pattern (C or D). Repeat evaluation performed 50 ± 26 min later revealed a 'low-risk' pattern in 809 (91%) hands with 78 (9%) of hands showing a persistent "high risk" pattern. In 29 (27%) high-risk hands, spontaneous conversion to pattern B within

We report cloud point phenomenon in binary water-anionic surfactant (sodium linear alkylbenzene sulfonate, NaLAS) after incorporation of organic salts, such as tetrabutyl- and benzyltributyl- ammonium halides. The effect of alkyl/aromatic hydrophobic substituents of such reagents on the cloud point phenomenon was also investigated. It was observed that the aqueous solution clouding behavior is dependent upon surfactant concentration, type of hydrophobic substituent of the salt, and operating temperature. At a given temperature, with increasing surfactant concentration, a higher amount of salt is required for the onset of clouding. Besides, at a given surfactant concentration, the amount of salt required for the cloud point is inversely proportional to the temperature. With addition of an oil phase to the binary water(salt)-surfactant system, a three-phase microemulsion could be generated. Phase diagrams for these ternary systems were determined, and the formation of the microemulsion phase behavior was established from phase visualization and oil—water interfacial tension measurements.

In recent years, automatic visual inspection system has become useful tool in industrial process. The citrus fruit sorting and grading process has now been completely automated with the exception of the final stage of classification concerned with visual inspection. Due to the growing demand of quality citrus fruit, an automated and reliable identification and classification mechanism in order to handle the bulk of data is implemented. Human vision sometimes leads to mismatch between the fruits. Using machine vision system, it will be simple to reduce human effort and achieve a better output. The objective of this project is to assess the fruit by color grading. In this paper we describe an integrated system developed for the sorting on citrus fruit and for the color grading of the defected fruits. The result suggests that the performance is adequate to provide the basis for a viable commercial visual inspection system. The following project aims at implementing such an automated system using image processing with images of citrus fruit as a basis of classification depending on its color. The goal of the inspection is not to give a statistical analysis of the production but to classify every citrus fruit into quality constant batches.

A lattice-type Monte Carlo based mesoscale model and simulation of the lithography process has been described previously [1]. The model includes the spin coating, post apply bake, exposure, post exposure bake and development steps. This simulation has been adapted to study the insoluble particle generation that arises from statistically improbable events. These events occur when there is a connected pathway of soluble material that envelops a volume of insoluble material due to fluctuations in the deprotection profile that occur during the post exposure bake [2]. Development erodes the insoluble material into the developer stream as an insoluble particle. This process may produce a cavity on the line edge that can be far larger than a single polymer molecule. The insoluble particles generated may coalesce in developer to form large aggregates of insoluble material that ultimately deposit on the wafer surface and the tooling. The recent modifications made in mesoscale models for the PEB and dissolution steps, which have enabled this study are briefly described. An algorithm that was used for particle detection in the current study is also discussed. The effect of the resist formulation and the different lithographic steps, namely, exposure, post exposure bake and development, on the extent of particle generation is analyzed. These simulations can be used to set process variables to minimize the extent of particle generation.

Current minimum feature si zes in the microelectronics industry dictate that molecular interactions affect process fidelity and produce stochastic excursions like line edge roughness (LER). The composition of future resists is still unknown at this point, and so simulation of various resist platforms should provide useful information about resist design that minimizes LER. In the past, researchers developed a mesoscale model 1, 2 for exploring representative 248 nm resist systems through dynamic Monte Carlo methods and adaptation of critical ionization theory 3 . This molecular modeling uses fundamental interaction energies combined with a Me tropolis algorithm to model the full lithographic process (spin coat, PAB, exposure, PEB, and development). Application of this model to 193 nm platforms allows for comparison between 248 and 193 nm resist systems based on molecular interactions. This paper discusses the fundamental modifications involved in adapting the mesoscale model to a 193 nm platform and investigates how this new model predicts well-understood lithographic phenomena including the relationship between LER and aerial image, the relationship between LER and resist components, and the impact of non-uniform PAG distribution in the resist film. Limited comparisons between the 193 nm system and an analogous 248 nm platform will be discussed. Keywords: modeling, LWR, PAG segregation, mesoscale, acid propagation front

The current scale of features size in the microelectronics industry has reached the point where molecular level interactions affect process fidelity and produce excursions from the continuum world like line edge roughness (LER). Here we present a 3D molecular level model based on the adaptation of the critical ionization (CI) theory using a fundamental interaction energy approach. The model asserts that it is the favorable interaction between the ionized part of the polymer and the developer solution which renders the polymer soluble. Dynamic Monte Carlo methods were used in the current model to study the polymer dissolution phenomenon. The surface ionization was captured by employing an electric double layer at the interface, and polymer motion was simulated using the Metropolis algorithm. The approximated interaction parameters, for different species in the system, were obtained experimentally and used to calibrate the simulated dissolution rate response to polymer molecular weight and developer concentration. The predicted response is in good agreement with experimental dissolution rate data. The simulation results support the premise of the CI theory and provide an insight into the CI model from a new prospective. This model may provide a means to study the contribution of development to LER and other related defects based on molecular level interactions between distinct components in the polymer and the developer.

We present a summary of various methods for inverting top and bottom critical dimension (CD) data to extract dose and focus information. We explain analytical, numerical, and library inversion techniques in detail, and explore their relative merits for the purposes of online and offline focus monitoring use models. We also detail the modeling requirements associated with each inversion technique, and -- for cases where the model form is flexible -- present a cross-validation methodology for optimizing the response model to fit experimental data. We present modeling and inversion results from seven exemplary photolithography processes, and study the results from each methodology in detail. While each method has its own set of advantages and disadvantages, we show that the library method represents the optimum choice to satisfy a variety of use models while minimizing cost.

c Lithoguru.com, 1605 Watchhill Road, Austin, Texas 78703 Abstract. A lattice-type Monte Carlo-based mesoscale model and simulation of the lithography process have been adapted to study the insoluble particle generation that arises from statistically improbable events. These events occur when there is a connected pathway of soluble material that envelops a volume of insoluble material due to fluctuations in the deprotection profile. The simulation shows that development erodes the insoluble material into the developer stream and produces a cavity on the line edge that can be far larger than a single polymer molecule. The insoluble particles can coalesce to form aggregates that deposit on the wafer surface. The effect of the resist formulation, exposure, postexposure bake, and development variables on particle gen- eration was analyzed in both low- and high-frequency domains. It is suggested that different mechanisms are dominant for the formation of line-edge roughness (LER) at different frequencies. The simulations were used to assess the commonly proposed measures to reduce LER such as the use of low molecular weight polymers, addition of quenchers, varying acid diffusion length, etc. The simulation can be used to help set process var- iables to minimize the extent of particle generation and LER. © 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)

We present a theory of the pinning of an air-liquid interface at the feature edge of a template during the imprint step of step and flash imprint lithography (SFIL). Pinning occurs when the pressure at the air-liquid interface reaches the pressure of the bulk liquid. At this condition, there is no pressure gradient or driving force to move the liquid and fill the feature. We examined several parameters, including residual layer thickness, wettability of the feature edge, applied force on the template, and the radius of curvature of the feature edge, to study their effect on pinning. An optimal SFIL process window and template modifications are proposed that minimize the pinning at the feature edge while still preventing any extrusion along the mesa (pattern containing area on the template) edge.

The authors analyze the feature filling phenomena for step and flash imprint lithography (SFIL) via diffusion of a gas, entrapped in the features, through liquid imprint resist. The model and simulation for the dynamics of feature filling including gas dissolution during the SFIL imprint step is presented. Several factors including the gas concentration profile across the liquid resist drop, the filling progression, and the total filling time for different pattern configurations are investigated to quantify feature filling. Simulation results show that initial filling is pressure controlled and very rapid [∼O(1ms)]. The rest of the feature filling is diffusion controlled, but fast enough [∼O(1s)] to conclude that diffusion of entrapped gas is not a cause for nonfilling of features. Also, the result that the smaller size features fill faster than the larger ones (with the same depth) makes SFIL the prime contender for high resolution and high throughput next generation lithography.