Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach.
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ABSTRACT:
SUBMITTER: Karasuyama M
PROVIDER: S-EPMC6197263 | biostudies-literature | 2018 Oct
REPOSITORIES: biostudies-literature
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Project description:Fragility is an empirical property that describes how abruptly a glass-forming material solidifies upon supercooling. The degree of fragility carries important implications for the functionality and processability of a material, as well as for our fundamental understanding of the glass transition. However, the microstructural properties underlying fragility still remain poorly understood. Here, we explain the microstructure-fragility link in vitrimeric networks, a novel type of high-performance polymers with unique bond-swapping functionality and unusual glass-forming behavior. Our results are gained from coarse-grained computer simulations and first-principles mode-coupling theory (MCT) of star-polymer vitrimers. We first demonstrate that the vitrimer fragility can be tuned over an unprecedentedly broad range, from fragile to strong and even superstrong behavior, by decreasing the bulk density. Remarkably, this entire phenomenology can be reproduced by microscopic MCT, thus challenging the conventional belief that existing first-principles theories cannot account for nonfragile behaviors. Our MCT analysis allows us to rationally identify the microstructural origin of the fragile-to-superstrong crossover, which is rooted in the sensitivity of the static structure factor to temperature variations. On the molecular scale, this behavior stems from a change in dominant length scales, switching from repulsive excluded-volume interactions to intrachain attractions as the vitrimer density decreases. Finally, we develop a simplified schematic MCT model which corroborates our microscopically founded conclusions and which unites our findings with earlier MCT studies. Our work sheds additional light on the elusive structure-fragility link in glass-forming matter and provides a first-principles-based platform for designing amorphous materials with an on-demand dynamic response.
| S-EPMC6911242 | biostudies-literature
Project description:ImportanceHospital readmissions are associated with patient harm and expense. Ways to prevent hospital readmissions have focused on identifying patients at greatest risk using prediction scores.ObjectiveTo identify the type of score that best predicts hospital readmissions.Design, setting, and participantsThis prognostic study included 14?062 consecutive adult hospital patients with 16?649 discharges from a tertiary care center, suburban community hospital, and urban critical access hospital in Maryland from September 1, 2016, through December 31, 2016. Patients not included as eligible discharges by the Centers for Medicare & Medicaid Services or the Chesapeake Regional Information System for Our Patients were excluded. A machine learning rank score, the Baltimore score (B score) developed using a machine learning technique, for each individual hospital using data from the 2 years before September 1, 2016, was compared with standard readmission risk assessment scores to predict 30-day unplanned readmissions.Main outcomes and measuresThe 30-day readmission rate evaluated using various readmission scores: B score, HOSPITAL score, modified LACE score, and Maxim/RightCare score.ResultsOf the 10?732 patients (5605 [52.2%] male; mean [SD] age, 54.56 [22.42] years) deemed to be eligible for the study, 1422 were readmitted. The area under the receiver operating characteristic curve (AUROC) for individual rules was 0.63 (95% CI, 0.61-0.65) for the HOSPITAL score, which was significantly lower than the 0.66 for modified LACE score (95% CI, 0.64-0.68; P?<?.001). The B score machine learning score was significantly better than all other scores; 48 hours after admission, the AUROC of the B score was 0.72 (95% CI, 0.70-0.73), which increased to 0.78 (95% CI, 0.77-0.79) at discharge (all P?<?.001). At the hospital using Maxim/RightCare score, the AUROC was 0.63 (95% CI, 0.59-0.69) for HOSPITAL, 0.64 (95% CI, 0.61-0.68) for Maxim/RightCare, and 0.66 (95% CI, 0.62-0.69) for modified LACE score. The B score was 0.72 (95% CI, 0.69-0.75) 48 hours after admission and 0.81 (95% CI, 0.79-0.84) at discharge. In directly comparing the B score with the sensitivity at cutoff values for modified LACE, HOSPITAL, and Maxim/RightCare scores, the B score was able to identify the same number of readmitted patients while flagging 25.5% to 54.9% fewer patients.Conclusions and relevanceAmong 3 hospitals in different settings, an automated machine learning score better predicted readmissions than commonly used readmission scores. More efficiently targeting patients at higher risk of readmission may be the first step toward potentially preventing readmissions.
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Project description:A new family of cobalt complexes with the general formula [CoII(OTf)2(Y,XPyMetacn)] (1R , Y,XPyMetacn = 1-[(4-X-3,5-Y-2-pyridyl)methyl]-4,7-dimethyl-1,4,7-triazacyclononane, (X = CN (1CN ), CO2Et (1CO2Et ), Cl (1Cl ), H (1H ), NMe2 (1NMe2 )) where (Y = H, and X = OMe when Y = Me (1DMM )) is reported. We found that the electronic tuning of the Y,XPyMetacn ligand not only has an impact on the electronic and structural properties of the metal center, but also allows for a systematic water-reduction-catalytic control. In particular, the increase of the electron-withdrawing character of the pyridine moiety promotes a 20-fold enhancement of the catalytic outcome. By UV-Vis spectroscopy, luminescence quenching studies and Transient Absorption Spectroscopy (TAS), we have studied the direct reaction of the photogenerated [IrIII(ppy)2(bpy?-)] (PSIr ) species to form the elusive CoI intermediates. In particular, our attention is focused on the effect of the ligand architecture in this elemental step of the catalytic mechanism. Finally, kinetic isotopic experiments together with DFT calculations provide complementary information about the rate-determining step of the catalytic cycle.
| S-EPMC5892349 | biostudies-literature
Project description:The sensitivity of animal photoreceptors to different wavelengths of light strongly influence the perceived visual contrast of objects in the environment. Outside of the human visual wavelength range, ultraviolet sensitivity in many species provides important and behaviourally relevant visual contrast between objects. However, at the opposite end of the spectrum, the potential advantage of red sensitivity remains unclear. We investigated the potential benefit of long wavelength sensitivity by modelling the visual contrast of a wide range of jewel beetle colours against flowers and leaves of their host plants to hypothetical insect visual systems. We find that the presence of a long wavelength sensitive photoreceptor increases estimated colour contrast, particularly of beetles against leaves. Moreover, under our model parameters, a trichromatic visual system with ultraviolet (λmax = 355 nm), short (λmax = 445 nm) and long (λmax = 600 nm) wavelength photoreceptors performed as well as a tetrachromatic visual system, which had an additional medium wavelength photoreceptor (λmax = 530 nm). When we varied λmax for the long wavelength sensitive receptor in a tetrachromatic system, contrast values between beetles, flowers and leaves were all enhanced with increasing λmax from 580 nm to at least 640 nm. These results suggest a potential advantage of red sensitivity in visual discrimination of insect colours against vegetation and highlight the potential adaptive value of long wavelength sensitivity in insects.
| S-EPMC8770459 | biostudies-literature
Project description:We describe a simple technique to alter the shape of silver nanoparticles (AgNPs) by rolling a glass tube over them to mechanically compress them. The resulting shape change in turn induces a red-shift in the localized surface plasmon resonance (LSPR) scattering spectrum and exposes new surface area. The flattened particles were characterized by optical and electron microscopy, single nanoparticle scattering spectroscopy, and surface enhanced Raman spectroscopy (SERS). AFM and SEM images show that the AgNPs deform into discs; increasing the applied load from 0 to 100 N increases the AgNP diameter and decreases the height. This deformation caused a dramatic red shift in the nanoparticle scattering spectrum and also generated new surface area to which thiolated molecules could attach as evident from SERS measurements. The simple technique employed here requires no lithographic templates and has potential for rapid, reproducible, inexpensive and scalable tuning of nanoparticle shape, surface area, and resonance while preserving particle volume.
| S-EPMC5325716 | biostudies-literature
Project description:In this work, we integrated finite element (FE) method and machine learning (ML) method to predict the stent expansion in a calcified coronary artery. The stenting procedure was captured in a patient-specific artery model, reconstructed based on optical coherence tomography images. Following FE simulation, eight geometrical features in each of 120 cross sections in the pre-stenting artery model, as well as the corresponding post-stenting lumen area, were extracted for training and testing the ML models. A linear regression model and a support vector regression (SVR) model with three different kernels (linear, polynomial, and radial basis function kernels) were adopted in this work. Two subgroups of the eight features, i.e., stretch features and calcification features, were further assessed for the prediction capacity. The influence of the neighboring cross sections on the prediction accuracy was also investigated by averaging each feature over eight neighboring cross sections. Results showed that the SVR models provided better predictions than the linear regression model in terms of bias. In addition, the inclusion of stretch features based on mechanistic understanding could provide a better prediction, compared with the calcification features only. However, there were no statistically significant differences between neighboring cross sections and individual ones in terms of the prediction bias and range of error. The simulation-driven machine learning framework in this work could enhance the mechanistic understanding of stenting in calcified coronary artery lesions, and also pave the way toward precise prediction of stent expansion.
| S-EPMC9328568 | biostudies-literature
Project description:It is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes. We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors found by the model, mutations in gyrA residues Ser83 and Asp87, a mutation in parC residue Ser80 and presence of the qnrS1 gene, have been experimentally validated before. Using only these four predictors in a linear regression model, 65% and 93% of the test samples' MIC were correctly predicted within a two- and a four-fold dilution range, respectively. The presented work does not treat machine learning as a black box model concept, but also identifies the genomic features that determine susceptibility. The recent progress in WGS technology in combination with machine learning analysis approaches indicates that in the near future WGS of bacteria might become cheaper and faster than a MIC measurement.
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Project description:We measure the orientation tuning of red-green colour and luminance vision at low (0.375?c/deg) and mid (1.5?c/deg) spatial frequencies using the low-contrast psychophysical method of subthreshold summation. Orientation bandwidths of the underlying neural detectors are found using a model involving Minkowski summation of the rectified outputs of a bank of oriented filters. At 1.5?c/deg, we find orientation-tuned detectors with similar bandwidths for chromatic and achromatic contrast. At 0.375?c/deg, orientation tuning is preserved with no change in bandwidth for achromatic stimuli, however, for chromatic stimuli orientation tuning becomes extremely broad, compatible with detection by non-oriented colour detectors. A non-oriented colour detector, previously reported in single cells in primate V1 but not psychophysically in humans, can transmit crucial information about the color of larger areas or surfaces whereas orientation-tuned detectors are required to detect the colour or luminance edges that delineate an object's shape.
| S-EPMC3942698 | biostudies-literature
Project description:INTRODUCTION:Adverse event (AE) under-reporting has been a recurrent issue raised during health authorities Good Clinical Practices (GCP) inspections and audits. Moreover, safety under-reporting poses a risk to patient safety and data integrity. The current clinical Quality Assurance (QA) practices used to detect AE under-reporting rely heavily on investigator site and study audits. Yet several sponsors and institutions have had repeated findings related to safety reporting, and this has led to delays in regulatory submissions. Recent developments in data management and IT systems allow data scientists to apply techniques such as machine learning to detect AE under-reporting in an automated fashion. OBJECTIVE:In this project, we developed a predictive model that enables Roche/Genentech Quality Program Leads oversight of AE reporting at the program, study, site, and patient level. This project was part of a broader effort at Roche/Genentech Product Development Quality to apply advanced analytics to augment and complement traditional clinical QA approaches. METHOD:We used a curated data set from 104 completed Roche/Genentech sponsored clinical studies to train a machine learning model to predict the expected number of AEs. Our final model used 54 features built on patient (e.g., demographics, vitals) and study attributes (e.g., molecule class, disease area). RESULTS:In order to evaluate model performance, we tested how well it would detect simulated test cases based on data not used for model training. For relevant simulation scenarios of 25%, 50%, and 75% under-reporting on the site level, our model scored an area under the curve (AUC) of the receiver operating characteristic (ROC) curve of 0.62, 0.79, and 0.92, respectively. CONCLUSION:The model has been deployed to evaluate safety reporting performance in a set of ongoing studies in the form of a QA/dashboard cockpit available to Roche Quality Program Leads. Applicability and production performance will be assessed over the next 12-24 months in which we will develop a validation strategy to fully integrate our model into Roche QA processes.
| S-EPMC6689279 | biostudies-other
Project description:BackgroundMachine learning has been attracting increasing attention for use in healthcare applications, including neonatal medicine. One application for this tool is in understanding and predicting neurodevelopmental outcomes in preterm infants. In this study, we have carried out a systematic review to identify findings and challenges to date.MethodsThis systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Four databases were searched in February 2022, with articles then screened in a non-blinded manner by two authors.ResultsThe literature search returned 278 studies, with 11 meeting the eligibility criteria for inclusion. Convolutional neural networks were the most common machine learning approach, with most studies seeking to predict neurodevelopmental outcomes from images and connectomes describing brain structure and function. Studies to date also sought to identify features predictive of outcomes; however, results varied greatly.ConclusionsInitial studies in this field have achieved promising results; however, many machine learning techniques remain to be explored, and the consensus is yet to be reached on which clinical and brain features are most predictive of neurodevelopmental outcomes.ImpactThis systematic review looks at the question of whether machine learning can be used to predict and understand neurodevelopmental outcomes in preterm infants. Our review finds that promising initial works have been conducted in this field, but many challenges and opportunities remain. Quality assessment of relevant articles is conducted using the Newcastle-Ottawa Scale. This work identifies challenges that remain and suggests several key directions for future research. To the best of the authors' knowledge, this is the first systematic review to explore this topic.
| S-EPMC9153218 | biostudies-literature