Mapping Materials and Molecules

The visualization of data is indispensable in scientific research, from the early stages when human insight forms to the final step of communicating results. In computational physics, chemistry and materials science, it can be as simple as making a scatter plot or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the “big data” revolution, these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bioactivities. It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural data sets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them. This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large data sets but also reveal hidden patterns that could be easily missed using conventional analyses. The explosion in the amount of computed information in chemistry and materials science has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of data sets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields

Serum Chemistry Values in Wild Black Vultures in Mississippi, USA

Vultures (Cathartidae and Accipitridae) play an important role in ecosystem balance by rapidly disposing animal carcasses and thus preventing the potential spread of pathogens. Blood chemistry values provide a means of assessing the health of wildlife and wild animal populations; however, there are significant differences in chemistries among species and when comparing captive and free-living New and Old World vultures. In 2007, we collected blood serum from 30 female and 14 male wild, healthy black vultures (Coragyps atratus) live-trapped by the U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services from a power substation in Lowndes County, Mississippi, USA. We analyzed the blood serum to provide serum chemistry base values for use in clinical pathology. The chemical analytes we measured included sodium, chloride, potassium, carbon dioxide, anion gap, glucose, creatinine, calcium, phosphorus, total protein, albumin, globulin, and aspartate aminotransferase. In general, blood chemistry values of black vultures were similar to those found in New and Old World vultures and raptor species. Average chemistry values for males were lower than females for sodium, chloride, creatinine, calcium, total protein, albumin, and globulin. The serum chemistry values we describe in this paper can be important indicators of avian health by gender for the black vulture. Our study provided important blood chemistry values from a large sample size, which is rarely available in free-ranging black vultures. These values could be used by scientists, veterinary pathologists, wildlife rehabilitation centers, and other researchers for baseline data for wild and free-ranging birds. Furthermore, the use of such parameters in assessing population health may enable conservationists to further research environmental conditions affecting species reproduction and survival

Mapping Materials and Molecules.

The visualization of data is indispensable in scientific research, from the early stages when human insight forms to the final step of communicating results. In computational physics, chemistry and materials science, it can be as simple as making a scatter plot or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the "big data" revolution, these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bioactivities.It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural data sets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them.This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large data sets but also reveal hidden patterns that could be easily missed using conventional analyses.The explosion in the amount of computed information in chemistry and materials science has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of data sets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields

Inter- and intra-observer agreement of BI-RADS-based subjective visual estimation of amount of fibroglandular breast tissue with magnetic resonance imaging: comparison to automated quantitative assessment.

PURPOSE: To evaluate the inter-/intra-observer agreement of BI-RADS-based subjective visual estimation of the amount of fibroglandular tissue (FGT) with magnetic resonance imaging (MRI), and to investigate whether FGT assessment benefits from an automated, observer-independent, quantitative MRI measurement by comparing both approaches. MATERIALS AND METHODS: Eighty women with no imaging abnormalities (BI-RADS 1 and 2) were included in this institutional review board (IRB)-approved prospective study. All women underwent un-enhanced breast MRI. Four radiologists independently assessed FGT with MRI by subjective visual estimation according to BI-RADS. Automated observer-independent quantitative measurement of FGT with MRI was performed using a previously described measurement system. Inter-/intra-observer agreements of qualitative and quantitative FGT measurements were assessed using Cohen's kappa (k). RESULTS: Inexperienced readers achieved moderate inter-/intra-observer agreement and experienced readers a substantial inter- and perfect intra-observer agreement for subjective visual estimation of FGT. Practice and experience reduced observer-dependency. Automated observer-independent quantitative measurement of FGT was successfully performed and revealed only fair to moderate agreement (k = 0.209-0.497) with subjective visual estimations of FGT. CONCLUSION: Subjective visual estimation of FGT with MRI shows moderate intra-/inter-observer agreement, which can be improved by practice and experience. Automated observer-independent quantitative measurements of FGT are necessary to allow a standardized risk evaluation. KEY POINTS: • Subjective FGT estimation with MRI shows moderate intra-/inter-observer agreement in inexperienced readers. • Inter-observer agreement can be improved by practice and experience. • Automated observer-independent quantitative measurements can provide reliable and standardized assessment of FGT with MRI

Nonlocal Automated Comparative Static Analysis

This paper reviews work on the development of a program Nasa for the automated comparative static analysis of parameterized nonlinear systems over parameter intervals. Nasa incorporates a fast and efficient algorithm Feed for the automatic evaluation of higher-order partial derivatives, as well as an adaptive homotopy continuation algorithm for obtaining all required initial conditions. Applications are envisioned for fields such as economics where models tend to be complex and closed-form solutions are difficult to obtain

Biallelic inherited SCN8A variants, a rare cause of SCN8A‐related developmental and epileptic encephalopathy

ObjectiveMonoallelic de novo gain‐of‐function variants in the voltage‐gated sodium channel SCN8A are one of the recurrent causes of severe developmental and epileptic encephalopathy (DEE). In addition, a small number of de novo or inherited monoallelic loss‐of‐function variants have been found in patients with intellectual disability, autism spectrum disorder, or movement disorders. Inherited monoallelic variants causing either gain or loss‐of‐function are also associated with less severe conditions such as benign familial infantile seizures and isolated movement disorders. In all three categories, the affected individuals are heterozygous for a SCN8A variant in combination with a wild‐type allele. In the present study, we describe two unusual families with severely affected individuals who inherited biallelic variants of SCN8A.MethodsWe identified two families with biallelic SCN8A variants by diagnostic gene panel sequencing. Functional analysis of the variants was performed using voltage clamp recordings from transfected ND7/23 cells.ResultsWe identified three probands from two unrelated families with DEE due to biallelic SCN8A variants. Each parent of an affected individual carried a single heterozygous SCN8A variant and exhibited mild cognitive impairment without seizures. In both families, functional analysis demonstrated segregation of one allele with complete loss‐of‐function, and one allele with altered biophysical properties consistent with partial loss‐of‐function.SignificanceThese studies demonstrate that SCN8A DEE may, in rare cases, result from inheritance of two variants, both of which exhibit reduced channel activity. In these families, heterozygosity for the dominant variants results in less severe disease than biallelic inheritance of two variant alleles. The clinical consequences of variants with partial and complete loss of SCN8A function are variable and likely to be influenced by genetic background.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153117/1/epi16371_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153117/2/epi16371.pd

The ecology of human-caused mortality for a protected large carnivore

Mitigating human-caused mortality for large carnivores is a pressing global challenge for wildlife conservation. However, mortality is almost exclusively studied at local (within-population) scales creating a mismatch between our understanding of risk and the spatial extent most relevant to conservation and management of wide-ranging species. Here, we quantified mortality for 590 radio-collared mountain lions statewide across their distribution in California to identify drivers of human-caused mortality and investigate whether human-caused mortality is additive or compensatory. Human-caused mortality, primarily from conflict management and vehicles, exceeded natural mortality despite mountain lions being protected from hunting. Our data indicate that human-caused mortality is additive to natural mortality as population-level survival decreased as a function of increasing human-caused mortality and natural mortality did not decrease with increased human-caused mortality. Mortality risk increased for mountain lions closer to rural development and decreased in areas with higher proportions of citizens voting to support environmental initiatives. Thus, the presence of human infrastructure and variation in the mindset of humans sharing landscapes with mountain lions appear to be primary drivers of risk. We show that human-caused mortality can reduce population-level survival of large carnivores across large spatial scales, even when they are protected from hunting

Evolutionary paths to macrolide resistance in a Neisseria commensal converge on ribosomal genes through short sequence duplications

Neisseria commensals are an indisputable source of resistance for their pathogenic relatives. However, the evolutionary paths commensal species take to reduced susceptibility in this genus have been relatively underexplored. Here, we leverage in vitro selection as a powerful screen to identify the genetic adaptations that produce azithromycin resistance (� 2 μg/mL) in the Neisseria commensal, N. elongata. Across multiple lineages (n = 7/16), we find mutations that reduce susceptibility to azithromycin converge on the locus encoding the 50S ribosomal L34 protein (rpmH) and the intergenic region proximal to the 30S ribosomal S3 protein (rpsC) through short tandem duplication events. Interestingly, one of the laboratory evolved mutations in rpmH is identical (7LKRTYQ12), and two nearly identical, to those recently reported to contribute to high-level azithromycin resistance in N. gonorrhoeae. Transformations into the ancestral N. elongata lineage confirmed the causality of both rpmH and rpsC mutations. Though most lineages inheriting duplications suffered in vitro fitness costs, one variant showed no growth defect, suggesting the possibility that it may be sustained in natural populations. Ultimately, studies like this will be critical for predicting commensal alleles that could rapidly disseminate into pathogen populations via allelic exchange across recombinogenic microbial genera

The novel sodium channel modulator GS‐458967 (GS967) is an effective treatment in a mouse model of SCN8A encephalopathy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144249/1/epi14196.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144249/2/epi14196-sup-0001-SupInfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144249/3/epi14196_am.pd