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\"This volume brings together definitive writings on career and technical education by leading figures involved in the history, philosophy, practice and theories of the field\"--Provided by publisher.

The Cancer Genome Atlas (TCGA) provides a genetic characterization of more than ten thousand tumors, enabling the discovery of novel driver mutations, molecular subtypes, and enticing drug targets across many histologies. Here we investigated why some mutations are common in particular cancer types but absent in others. As an example, we observed that the gene CCDC168 has no mutations in the stomach adenocarcinoma (STAD) cohort despite its common presence in other tumor types. Surprisingly, we found that the lack of called mutations was due to a systematic insufficiency in the number of sequencing reads in the STAD and other cohorts, as opposed to differential driver biology. Using strict filtering criteria, we found similar behavior in four other genes across TCGA cohorts, with each gene exhibiting systematic sequencing depth issues affecting the ability to call mutations. We identified the culprit as the choice of exome capture kit, as kit choice was highly associated with the set of genes that have insufficient reads to call a mutation. Overall, we found that thousands of samples across all cohorts are subject to some capture kit problems. For example, for the 6353 samples using the Broad Institute's Custom capture kit there are undercalling biases for at least 4833 genes. False negative mutation calls at these genes may obscure biological similarities between tumor types and other important cancer driver effects in TCGA datasets.

The function of B cells in osteoblast (OB) dysfunction in rheumatoid arthritis (RA) has not been well-studied. Here we show that B cells are enriched in the subchondral and endosteal bone marrow (BM) areas adjacent to osteocalcin + OBs in two murine RA models: collagen-induced arthritis and the TNF-transgenic mice. Subchondral BM B cells in RA mice express high levels of OB inhibitors, CCL3 and TNF, and inhibit OB differentiation by activating ERK and NF-κB signaling pathways. The inhibitory effect of RA B cells on OB differentiation is blocked by CCL3 and TNF neutralization, and deletion of CCL3 and TNF in RA B cells completely rescues OB function in vivo, while B cell depletion attenuates bone erosion and OB inhibition in RA mice. Lastly, B cells from RA patients express CCL3 and TNF and inhibit OB differentiation, with these effects ameliorated by CCL3 and TNF neutralization. Thus, B cells inhibit bone formation in RA by producing multiple OB inhibitors. B cells contribute to rheumatoid arthritis pathogenesis and bone erosion, but the underlying mechanisms are still unclear. Here the authors show, using mouse models and patient tissues, that B cells directly inhibit osteoblast differentiation by producing CCL3 and TNF, thereby providing a potentially new direction for arthritis therapy.

The biosynthesis of the redox shuttle, phenazines, in Pseudomonas aeruginosa, an ubiquitous microorganism in wastewater microflora, is regulated by the 2-heptyl-3,4-dihydroxyquinoline (PQS) quorum-sensing system. However, PQS inhibits anaerobic growth of P. aeruginosa. We constructed a P. aeruginosa strain that produces higher concentrations of phenazines under anaerobic conditions by over-expressing the PqsE effector in a PQS negative ΔpqsC mutant. The engineered strain exhibited an improved electrical performance in microbial fuel cells (MFCs) and potentiostat-controlled electrochemical cells with an approximate five-fold increase of maximum current density relative to the parent strain. Electrochemical analysis showed that the current increase correlates with an over-synthesis of phenazines. These results therefore demonstrate that targeting microbial cell-to-cell communication by genetic engineering is a suitable technique to improve power output of bioelectrochemical systems.

Proliferative vitreoretinopathy (PVR) is characterized by the growth and contraction of cellular membranes within the vitreous cavity and on both surfaces of the retina, resulting in recurrent retinal detachments and poor visual outcomes. Proinflammatory cytokines like tumor necrosis factor alpha (TNFα) have been associated with PVR and the epithelial-mesenchymal transition (EMT) of retinal pigment epithelial (RPE) cells. Cigarette smoke is the only known modifiable risk factor for PVR, but the mechanisms are unclear. The purpose of this study was to examine the impact of cigarette smoke on the proinflammatory TNFα/NF-κB/Snail pathway in RPE cells to better understand the mechanisms through which cigarette smoke increases the risk of PVR. Human ARPE-19 cells were exposed to cigarette smoke extract (CSE), for 4 to 24-hours and TNFα, Snail, IL-6, IL-8, and α-SMA levels were analyzed by qPCR and/or Western blot. The severity of PVR formation was assessed in a murine model of PVR after intravitreal injection of ARPE-19 cells pre-treated with CSE or not. Fundus imaging, OCT imaging, and histologic analysis 4 weeks after injection were used to examine PVR severity. ARPE-19 cells exposed to CSE expressed higher levels of TNFα , SNAIL , IL6 and IL8 mRNA as well as SNAIL, Vimentin and α-SMA protein. Inhibition of TNFα and NF-κB pathways blocked the effect of CSE. In vivo , intravitreal injection of ARPE-19 cells treated with CSE resulted in more severe PVR compared to mice injected with untreated RPE cells. These studies suggest that the TNFα pathway is involved in the mechanism whereby cigarette smoke increases PVR. Further investigation into the role of TNFα/NF-κB/Snail in driving PVR and pharmacological targeting of these pathways in disease are warranted.

The somatosensory nervous system is critical for the organism's ability to respond to mechanical, thermal, and nociceptive stimuli. Somatosensory neurons are functionally and anatomically diverse but their molecular profiles are not well-defined. Here, we used transcriptional profiling to analyze the detailed molecular signatures of dorsal root ganglion (DRG) sensory neurons. We used two mouse reporter lines and surface IB4 labeling to purify three major non-overlapping classes of neurons: 1) IB4+SNS-Cre/TdTomato+, 2) IB4−SNS-Cre/TdTomato+, and 3) Parv-Cre/TdTomato+ cells, encompassing the majority of nociceptive, pruriceptive, and proprioceptive neurons. These neurons displayed distinct expression patterns of ion channels, transcription factors, and GPCRs. Highly parallel qRT-PCR analysis of 334 single neurons selected by membership of the three populations demonstrated further diversity, with unbiased clustering analysis identifying six distinct subgroups. These data significantly increase our knowledge of the molecular identities of known DRG populations and uncover potentially novel subsets, revealing the complexity and diversity of those neurons underlying somatosensation. In the nervous system, a network of specialized neurons—known as the somatosensory system—carries information about sensations including touch, muscle position, temperature and pain. Distinct sets of somatosensory neurons are thought to carry information about the different types of sensations. In young animals, the precise switching on, or ‘expression’, of genes controls the formation of the network of neurons. However, it is not known exactly which genes are expressed in what types of neurons, where, or when. Here, Chiu et al. used a technique called flow cytometry using different fluorescent markers to isolate a group of cells called Dorsal Root Ganglion (DRG) neurons in mice. These neurons have long thread-like fibers that extend from the spinal cord to the skin, muscles and joints all over the body. These fibers carry sensory information to the spinal cord, where it can be relayed to the brain and processed. The experiments compared three distinct types of DRG neuron and found that they differed in their ability to send information to other cells. Chiu et al. analyzed the expression of all the genes in the three types of DRG neurons. Each type of neuron had distinct groups of genes that were being expressed. Also, several genes that are known to be important for sensation were expressed at different levels in the different types of cells. Next, large numbers of single cells were analyzed to find out the finer details about the three types of neuron. These findings made it possible to further divide the DRG neurons into six distinct subsets that matched previously known groups of somatosensory neurons, and also identified new ones. Chiu et al.'s findings reveal the complexity and diversity of the neurons involved in carrying information about sensations towards the brain. This is an important step in classifying the nervous system, and uncovers many genes previously not linked to sensation. The next challenges lie in understanding how the expression of these genes in each type of neuron relates to their unique roles.

Effective assessment of flood vulnerability and risk is essential for communities to manage flood hazards. This paper presents an empirical modeling methodology to predict flood vulnerability and risk, considering factors of hazard distribution, property exposure, built environment, and socio‐demographic and economic characteristics of a community. Vulnerability is empirically modeled as the expected fraction of property loss that is uninsured within a community (i.e., census tract) given water depth. Risk is derived as the expected annual uninsured property loss and loss ratio. The proposed framework is applied to the state of North Carolina in the United States. For model calibration, modeled flood loss data from Hurricanes Matthew in 2016 and Florence in 2018 and insurance claims data from the Federal Insurance and Mitigation Administration's National Flood Insurance Program are used. The Federal Emergency Management Agency's National Flood Hazard Layer is adopted, along with empirical probability distribution of water depth given flood event, to characterize hazard distribution. Results demonstrate how the presented methodology can be used to predict annual loss in terms of currency and to highlight hotspots of flood vulnerability and risk. Future work is needed to reduce uncertainty associated with limited hazard information available to the public.

This study addresses the question of whether or not active learning can be taught online. There are many definitions of learning: It is the process and the sum total of acquiring knowledge, skills, attitudes, values, beliefs, and emotions. There is, however, a nuanced definition of active online learning, defined as methods by which learners actively participate in the learning process (e.g., online discussion groups, problem-solving, experimentation, and the like). Theoretical presuppositions such as informal learning, contiguity, reinforcement, repetition, social-cultural principles and andragogy not only guide the assumption that active learning can take place online but also reinforce that active learning may lead to the creation of new knowledge and the skills needed by learners in this current century. This research reveals that technology, used effectively, enhances active learning benefitting the instructor as well as the learner.

The perpetual pursuit of pain elimination has been constant throughout human history and pervades human cultures. In some ways it is as old as medicine itself. Cultures throughout history have practiced the art of pain management through remedies such as oral ingestion of herbs or techniques believed to have special properties. In fact, even Hippocrates wrote about the practice of trepanation, the cutting of holes in the body to release pain. Current therapies for management of pain include the pervasive utilization of opioids, which have an extensive history, spanning centuries. There is general agreement about the appropriateness of opioids for the treatment of acute and cancer pain, but the long-term use of these drugs for treatment of chronic non-malignant pain remains controversial. The pros and cons regarding these issues are beyond the scope of this review. Instead, the purpose of this review will be directed towards the pharmacology of commonly prescribed opioids in the treatment of various chronic pain syndromes. Opium, derived from the Greek word for “juice,” is extracted from the latex sap of the opium poppy (Papaverum somniferum). The juice of the poppy is the source of some 20 different alkaloids of opium. These alkaloids of opioids can be divided into 2 chemical classes: phenanthrenes (morphine, codeine, and thebaine) and benzylisoquinolines (agents that do not interact with opioid receptors). Key words: Opioid metabolism, opioid interactions, morphine, codeine, hydrocodone, oxycodone, hydromorphone, methadone, intractable pain, endorphins, enkephalins, dynorphins, narcotics, pharmacology, propoxyphene, fentanyl, oxymorphone, tramadol

Background Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities. Methods We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR). Results Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification. Conclusion Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research. Plain language summary Determining the volume and location of lesions caused by acute ischemic strokes - in which blood flow is restricted to part of the brain - is crucial to guide treatment and patient prognosis. However, this process is time-consuming and labor-intensive for clinicians. Here, using brain imaging datasets from patients with ischemic strokes, we create an artificial intelligence-based tool to quickly and accurately determine the volume and location of stroke lesions. Our tool outperforms some similar existing approaches, it is fast, publicly available, accessible to non-experts, and it runs on normal computers with minimal computational requirements. As such, it may be useful both for clinicians treating patients and researchers studying ischemic stroke. Liu et al. develop a deep learning-based tool to detect and segment diffusion abnormalities seen on magnetic resonance imaging (MRI) in acute ischemic stroke. The tool is tested in two clinical MRI datasets and outperforms existing algorithms in the detection of small lesions, potentially allowing clinicians and clinical researchers to more quickly and accurately diagnose and assess ischemic strokes.