AluScan: a method for genome-wide scanning of sequence and structure variations in the human genome

<p>Abstract</p> <p>Background</p> <p>To complement next-generation sequencing technologies, there is a pressing need for efficient pre-sequencing capture methods with reduced costs and DNA requirement. The Alu family of short interspersed nucleotide elements is the most abundant type of transposable elements in the human genome and a recognized source of genome instability. With over one million Alu elements distributed throughout the genome, they are well positioned to facilitate genome-wide sequence amplification and capture of regions likely to harbor genetic variation hotspots of biological relevance.</p> <p>Results</p> <p>Here we report on the use of inter-Alu PCR with an enhanced range of amplicons in conjunction with next-generation sequencing to generate an Alu-anchored scan, or 'AluScan', of DNA sequences between Alu transposons, where Alu consensus sequence-based 'H-type' PCR primers that elongate outward from the head of an Alu element are combined with 'T-type' primers elongating from the poly-A containing tail to achieve huge amplicon range. To illustrate the method, glioma DNA was compared with white blood cell control DNA of the same patient by means of AluScan. The over 10 Mb sequences obtained, derived from more than 8,000 genes spread over all the chromosomes, revealed a highly reproducible capture of genomic sequences enriched in genic sequences and cancer candidate gene regions. Requiring only sub-micrograms of sample DNA, the power of AluScan as a discovery tool for genetic variations was demonstrated by the identification of 357 instances of loss of heterozygosity, 341 somatic indels, 274 somatic SNVs, and seven potential somatic SNV hotspots between control and glioma DNA.</p> <p>Conclusions</p> <p>AluScan, implemented with just a small number of H-type and T-type inter-Alu PCR primers, provides an effective capture of a diversity of genome-wide sequences for analysis. The method, by enabling an examination of gene-enriched regions containing exons, introns, and intergenic sequences with modest capture and sequencing costs, computation workload and DNA sample requirement is particularly well suited for accelerating the discovery of somatic mutations, as well as analysis of disease-predisposing germline polymorphisms, by making possible the comparative genome-wide scanning of DNA sequences from large human cohorts.</p

The interspecific growth–mortality trade-off is not a general framework for tropical forest community structure

Resource allocation within trees is a zero-sum game. Unavoidable trade-offs dictate that allocation to growth-promoting functions curtails other functions, generating a gradient of investment in growth versus survival along which tree species align, known as the interspecific growth–mortality trade-off. This paradigm is widely accepted but not well established. Using demographic data for 1,111 tree species across ten tropical forests, we tested the generality of the growth–mortality trade-off and evaluated its underlying drivers using two species-specific parameters describing resource allocation strategies: tolerance of resource limitation and responsiveness of allocation to resource access. Globally, a canonical growth–mortality trade-off emerged, but the trade-off was strongly observed only in less disturbance-prone forests, which contained diverse resource allocation strategies. Only half of disturbance-prone forests, which lacked tolerant species, exhibited the trade-off. Supported by a theoretical model, our findings raise questions about whether the growth–mortality trade-off is a universally applicable organizing framework for understanding tropical forest community structure

Metronomic chemotherapy prevents therapy-induced stromal activation and induction of tumor-initiating cells

Although traditional chemotherapy kills a fraction of tumor cells, it also activates the stroma and can promote the growth and survival of residual cancer cells to foster tumor recurrence and metastasis. Accordingly, overcoming the host response induced by chemotherapy could substantially improve therapeutic outcome and patient survival. In this study, resistance to treatment and metastasis has been attributed to expansion of stem-like tumor-initiating cells (TICs). Molecular analysis of the tumor stroma in neoadjuvant chemotherapy–treated human desmoplastic cancers and orthotopic tumor xenografts revealed that traditional maximum-tolerated dose chemotherapy, regardless of the agents used, induces persistent STAT-1 and NF-κB activity in carcinoma-associated fibroblasts. This induction results in the expression and secretion of ELR motif–positive (ELR(+)) chemokines, which signal through CXCR-2 on carcinoma cells to trigger their phenotypic conversion into TICs and promote their invasive behaviors, leading to paradoxical tumor aggression after therapy. In contrast, the same overall dose administered as a low-dose metronomic chemotherapy regimen largely prevented therapy-induced stromal ELR(+) chemokine paracrine signaling, thus enhancing treatment response and extending survival of mice carrying desmoplastic cancers. These experiments illustrate the importance of stroma in cancer therapy and how its impact on treatment resistance could be tempered by altering the dosing schedule of systemic chemotherapy

Mortality of emergency abdominal surgery in high-, middle- and low-income countries

Background: Surgical mortality data are collected routinely in high-income countries, yet virtually no low- or middle-income countries have outcome surveillance in place. The aim was prospectively to collect worldwide mortality data following emergency abdominal surgery, comparing findings across countries with a low, middle or high Human Development Index (HDI). Methods: This was a prospective, multicentre, cohort study. Self-selected hospitals performing emergency surgery submitted prespecified data for consecutive patients from at least one 2-week interval during July to December 2014. Postoperative mortality was analysed by hierarchical multivariable logistic regression. Results: Data were obtained for 10 745 patients from 357 centres in 58 countries; 6538 were from high-, 2889 from middle- and 1318 from low-HDI settings. The overall mortality rate was 1⋅6 per cent at 24 h (high 1⋅1 per cent, middle 1⋅9 per cent, low 3⋅4 per cent; P < 0⋅001), increasing to 5⋅4 per cent by 30 days (high 4⋅5 per cent, middle 6⋅0 per cent, low 8⋅6 per cent; P < 0⋅001). Of the 578 patients who died, 404 (69⋅9 per cent) did so between 24 h and 30 days following surgery (high 74⋅2 per cent, middle 68⋅8 per cent, low 60⋅5 per cent). After adjustment, 30-day mortality remained higher in middle-income (odds ratio (OR) 2⋅78, 95 per cent c.i. 1⋅84 to 4⋅20) and low-income (OR 2⋅97, 1⋅84 to 4⋅81) countries. Surgical safety checklist use was less frequent in low- and middle-income countries, but when used was associated with reduced mortality at 30 days. Conclusion: Mortality is three times higher in low- compared with high-HDI countries even when adjusted for prognostic factors. Patient safety factors may have an important role. Registration number: NCT02179112 (http://www.clinicaltrials.gov)

Virtual Metrology in Semiconductor Fabrication Foundry Using Deep Learning Neural Networks

Physical metrology inspections are crucial in semiconductor fabrication foundry to ensure wafers are fabricated within the production specification limits and prevent faulty wafers from being shipped and installed in customers’ devices. However, it is not possible to examine every wafer as such inspection would incur impractical cost on manpower, finances, and production cycle time (CT) of fabrication foundries (fabs). Virtual metrology (VM) presents an alternate approach to perform metrology inspection without incurring high costs by using machine learning (ML) models. By leveraging historical equipment and process data, ML models can be calibrated to estimate the targeted metrology variables to estimate the quality of wafers, thereby performing virtual inspection on wafers. Recently, VM researchers begin introducing deep learning (DL) into VM research works to examine its capability. Specifically, the VM researchers experimented on the convolutional neural network (CNN). The targeted metrologies are metrologies of plasma-based processes in both etching and chemical vapor deposition. Initial success has been reported by the VM researchers. While various CNN-based VM models have been proposed plasma-based fabrication processes, it has yet to be experimented in photolithography process. Motivated by the initial successes of CNN in plasma-based processes, this work is an initiative to experiment CNN’s performance in predicting the overlay errors of photolithography process. Using data from a real fab, this work first establishes a baseline using the methodology of a prior work. Then, the prediction results of the proposed CNN model are compared with the baseline. The results showed that CNN is able to further reduce the prediction errors

A Treatable Encephalopathy in a Peritoneal Dialysis Patient - Cefepime- Induced Encephalopathy

We report a patient with end-stage renal disease on peritoneal dialysis, who developed encephalopathy after receiving a few doses of cefepime. He recovered clinically and electroencephalographically after having discontinued the culprit agent and undergone hemodialysis. This case highlights the importance of promptly recognizing this reversible encephalopathy, which can lead to the avoidance of unnecessary workup, reduce the length of hospital stay, and thereby improve the patients’ outcome

Developing Better Ceramic Membranes for Water and Wastewater Treatment: Where Microstructure Integrates with Chemistry

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