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Phosphorus, in its orthophosphate form (P i ), is one of the most limiting macronutrients in soils for plant growth and development. However, the whole-genome molecular mechanisms contributing to plant acclimation to P i deficiency remain largely unknown. White lupin (Lupinus albus) has evolved unique adaptations for growth in P i -deficient soils, including the development of cluster roots to increase root surface area. In this study, we utilized RNA-Seq technology to assess global gene expression in white lupin cluster roots, normal roots, and leaves in response to P i supply. We de novo assembled 277,224,180 Illumina reads from 12 complementary DNA libraries to build what is to our knowledge the first white lupin gene index (LAGI 1.0). This index contains 125,821 unique sequences with an average length of 1,155 bp. Of these sequences, 50,734 were transcriptionally active (reads per kilobase per million reads ≥ 3), representing approximately 7.8% of the white lupin genome, using the predicted genome size of Lupinus angustifolius as a reference. We identified a total of 2,128 sequences differentially expressed in response to P i deficiency with a 2-fold or greater change and P ≤ 0.05. Twelve sequences were consistently differentially expressed due to P i deficiency stress in three species, Arabidopsis (Arabidopsis thaliam), potato (Solanum tuberosum), and white lupin, making them ideal candidates monitor the P i status of plants. Additionally, classic physiological experiments were coupled with RNA-Seq data to examine the role of cytokinin and gibberellic acid in P i deficiency-induced cluster root development. This global gene expression analysis provides new insights into the biochemical and molecular mechanisms involved in the acclimation to P i deficiency.

Frame-projected independent-fiber photometry (FIP) enables the concurrent monitoring and manipulation of neural activity at multiple sites in the brains of freely behaving mice. Real-time activity measurements from multiple specific cell populations and projections are likely to be important for understanding the brain as a dynamical system. Here we developed frame-projected independent-fiber photometry (FIP), which we used to record fluorescence activity signals from many brain regions simultaneously in freely behaving mice. We explored the versatility of the FIP microscope by quantifying real-time activity relationships among many brain regions during social behavior, simultaneously recording activity along multiple axonal pathways during sensory experience, performing simultaneous two-color activity recording, and applying optical perturbation tuned to elicit dynamics that match naturally occurring patterns observed during behavior.

Background Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality. Results We present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which were synthetically defocused to one of 11 absolute defocus levels during training. The trained model can generalize on previously unseen real Hoechst stain images, identifying the absolute image focus to within one defocus level (approximately 3 pixel blur diameter difference) with 95% accuracy. On a simpler binary in/out-of-focus classification task, the trained model outperforms previous approaches on both Hoechst and Phalloidin (actin) stain images (F-scores of 0.89 and 0.86, respectively over 0.84 and 0.83), despite only having been presented Hoechst stain images during training. Lastly, we observe qualitatively that the model generalizes to two additional stains, Hoechst and Tubulin, of an unseen cell type (Human MCF-7) acquired on a different instrument. Conclusions Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetically defocused images precludes the need for a manually annotated training dataset. The model also generalizes to different image and cell types. The framework for model training and image prediction is available as a free software library and the pre-trained model is available for immediate use in Fiji (ImageJ) and CellProfiler.

Amid the ever-increasing number of cyberthreats, cybersecurity degree programs represent a potential growth area for business schools. This study examines undergraduate cybersecurity programs offered by AACSB-accredited business schools in the US. It surveyed 503 AACSB-accredited schools and identified 72 cybersecurity programs. Using the IS2020 and CAE-CD standards, this study assessed these programs' core curricula and found that the top three core courses are Cybersecurity Foundations, Application Development and Programming, and IT Infrastructure. A cybersecurity curriculum model is developed based on the survey results. The results are compared with those of a 2017 study to gain insights into the evolution of cybersecurity curricula in business schools.

The quest to identify materials with tailored properties is increasingly expanding into high-order composition spaces, with a corresponding combinatorial explosion in the number of candidate materials. A key challenge is to discover regions in composition space where materials have novel properties. Traditional predictive models for material properties are not accurate enough to guide the search. Herein, we use high-throughput measurements of optical properties to identify novel regions in three-cation metal oxide composition spaces by identifying compositions whose optical trends cannot be explained by simple phase mixtures. We screen 376,752 distinct compositions from 108 three-cation oxide systems based on the cation elements Mg, Fe, Co, Ni, Cu, Y, In, Sn, Ce, and Ta. Data models for candidate phase diagrams and threecation compositions with emergent optical properties guide the discovery of materials with complex phase-dependent properties, as demonstrated by the discovery of a Co-Ta-Sn substitutional alloy oxide with tunable transparency, catalytic activity, and stability in strong acid electrolytes. These results required close coupling of data validation to experiment design to generate a reliable end-to-end high-throughput workflow for accelerating scientific discovery.

Stroke is a multiphasic process in which initial cerebral ischemia is followed by secondary injury from immune responses to ischemic brain components. Here we demonstrate that peripheral CD11b + CD45 + myeloid cells magnify stroke injury via activation of triggering receptor expressed on myeloid cells 1 (TREM1), an amplifier of proinflammatory innate immune responses. TREM1 was induced within hours after stroke peripherally in CD11b + CD45 + cells trafficking to ischemic brain. TREM1 inhibition genetically or pharmacologically improved outcome via protective antioxidant and anti-inflammatory mechanisms. Positron electron tomography imaging using radiolabeled antibody recognizing TREM1 revealed elevated TREM1 expression in spleen and, unexpectedly, in intestine. In the lamina propria, noradrenergic-dependent increases in gut permeability induced TREM1 on inflammatory Ly6C + MHCII + macrophages, further increasing epithelial permeability and facilitating bacterial translocation across the gut barrier. Thus, following stroke, peripheral TREM1 induction amplifies proinflammatory responses to both brain-derived and intestinal-derived immunogenic components. Critically, targeting this specific innate immune pathway reduces cerebral injury. Cerebral ischemia activates innate immune responses in injured brain lesions. Andreasson and colleagues show TREM1 is upregulated after stroke and amplifies these proinflammatory responses by peripheral CD11b + myeloid cells in both the ischemic brain and distally in the intestine.

This paper investigates the core curricula of Information Systems (IS) master's programs. It examines all 532 AACSB-accredited business schools in the United States and identifies 74 IS master's programs. MSIS 2016 and other curricular models and studies are used in a research framework to survey core courses. The top three required courses are Data, Information, and Content Management, Systems Development and Deployment, and Project and Change Management. One unexpected result is that Business Intelligence/Analytics/Data Mining is now the fourth most popular core course, while Business Continuity and Information Assurance is the fifth. The results are compared to those of a 2012 study to examine IS master curricula' change over the last decade. Based on actual data on core courses being offered, a new IS master's curriculum model is developed.

Molecular diagnostics are revolutionising the clinical practice of infectious disease. Their effects will be significant in acute-care settings where timely and accurate diagnostic tools are critical for patient treatment decisions and outcomes. PCR is the most well-developed molecular technique up to now, and has a wide range of already fulfilled, and potential, clinical applications, including specific or broad-spectrum pathogen detection, evaluation of emerging novel infections, surveillance, early detection of biothreat agents, and antimicrobial resistance profiling. PCR-based methods may also be cost effective relative to traditional testing procedures. Further advancement of technology is needed to improve automation, optimise detection sensitivity and specificity, and expand the capacity to detect multiple targets simultaneously (multiplexing). This review provides an up-to-date look at the general principles, diagnostic value, and limitations of the most current PCR-based platforms as they evolve from bench to bedside.

Thousands of pathogens are known to infect humans, but only a fraction are readily identifiable using current diagnostic methods. Microbial cell-free DNA sequencing offers the potential to non-invasively identify a wide range of infections throughout the body, but the challenges of clinical-grade metagenomic testing must be addressed. Here we describe the analytical and clinical validation of a next-generation sequencing test that identifies and quantifies microbial cell-free DNA in plasma from 1,250 clinically relevant bacteria, DNA viruses, fungi and eukaryotic parasites. Test accuracy, precision, bias and robustness to a number of metagenomics-specific challenges were determined using a panel of 13 microorganisms that model key determinants of performance in 358 contrived plasma samples, as well as 2,625 infections simulated in silico and 580 clinical study samples. The test showed 93.7% agreement with blood culture in a cohort of 350 patients with a sepsis alert and identified an independently adjudicated cause of the sepsis alert more often than all of the microbiological testing combined (169 aetiological determinations versus 132). Among the 166 samples adjudicated to have no sepsis aetiology identified by any of the tested methods, sequencing identified microbial cell-free DNA in 62, likely derived from commensal organisms and incidental findings unrelated to the sepsis alert. Analysis of the first 2,000 patient samples tested in the CLIA laboratory showed that more than 85% of results were delivered the day after sample receipt, with 53.7% of reports identifying one or more microorganisms. Assessment of metagenomic sequencing of cell-free DNA directly from patient samples as a diagnostic for infections.

MicroRNAs (miRNAs) play a pivotal role in post-transcriptional regulation of gene expression in plants. Information on miRNAs in legumes is as yet scarce. This work investigates miRNAs in an agronomically important legume, common bean (Phaseolus vulgaris). A hybridization approach employing miRNA macroarrays - printed with oligonucleotides complementary to 68 known miRNAs - was used to detect miRNAs in the leaves, roots and nodules of control and nutrient-stressed (phosphorus, nitrogen, or iron deficiency; acidic pH; and manganese toxicity) common bean plants. Thirty-three miRNAs were expressed in control plants and another five were only expressed under stress conditions. The miRNA expression ratios (stress:control) were evaluated using principal component and hierarchical cluster analyses. A group of miRNAs responded to nearly all stresses in the three organs analyzed. Other miRNAs showed organ-specific responses. Most of the nodule-responsive miRNAs showed up-regulation. miRNA blot expression analysis confirmed the macroarray results. Novel miRNA target genes were proposed for common bean and the expression of selected targets was evaluated by quantitative reverse transcriptase-polymerase chain reaction. In addition to the detection of previously reported stress-responsive miRNAs, we discovered novel common bean stress-responsive miRNAs, for manganese toxicity. Our data provide a foundation for evaluating the individual roles of miRNAs in common bean.