A Miniature Fibre-Optic Raman Probe Fabricated by Ultrafast Laser-Assisted Etching

Optical biopsy describes a range of medical procedures in which light is used to investigate disease in the body, often in hard-to-reach regions via optical fibres. Optical biopsies can reveal a multitude of diagnostic information to aid therapeutic diagnosis and treatment with higher specificity and shorter delay than traditional surgical techniques. One specific type of optical biopsy relies on Raman spectroscopy to differentiate tissue types at the molecular level and has been used successfully to stage cancer. However, complex micro-optical systems are usually needed at the distal end to optimise the signal-to-noise properties of the Raman signal collected. Manufacturing these devices, particularly in a way suitable for large scale adoption, remains a critical challenge. In this paper, we describe a novel fibre-fed micro-optic system designed for efficient signal delivery and collection during a Raman spectroscopy-based optical biopsy. Crucially, we fabricate the device using a direct-laser-writing technique known as ultrafast laser-assisted etching which is scalable and allows components to be aligned passively. The Raman probe has a sub-millimetre diameter and offers confocal signal collection with 71.3% ± 1.5% collection efficiency over a 0.8 numerical aperture. Proof of concept spectral measurements were performed on mouse intestinal tissue and compared with results obtained using a commercial Raman microscope

A Detailed Observational Analysis of V1324 Sco, the Most Gamma-Ray Luminous Classical Nova to Date

It has recently been discovered that some, if not all, classical novae emit GeV gamma rays during outburst, but the mechanisms involved in the production of the gamma rays are still not well understood. We present here a comprehensive multi-wavelength dataset---from radio to X-rays---for the most gamma-ray luminous classical nova to-date, V1324 Sco. Using this dataset, we show that V1324 Sco is a canonical dusty Fe-II type nova, with a maximum ejecta velocity of 2600 km s$^{-1}$ and an ejecta mass of few $\times 10^{-5}$ M$_{\odot}$. There is also evidence for complex shock interactions, including a double-peaked radio light curve which shows high brightness temperatures at early times. To explore why V1324~Sco was so gamma-ray luminous, we present a model of the nova ejecta featuring strong internal shocks, and find that higher gamma-ray luminosities result from higher ejecta velocities and/or mass-loss rates. Comparison of V1324~Sco with other gamma-ray detected novae does not show clear signatures of either, and we conclude that a larger sample of similarly well-observed novae is needed to understand the origin and variation of gamma rays in novae.Comment: 26 pages, 13 figure

Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality

Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiograms contain hidden risk markers that can help prognosticate post-operative mortality. Methods. In a derivation cohort of 45,969 pre-operative patients (age 59+- 19 years, 55 percent women), a deep learning algorithm was developed to leverage waveform signals from pre-operative ECGs to discriminate post-operative mortality. Model performance was assessed in a holdout internal test dataset and in two external hospital cohorts and compared with the Revised Cardiac Risk Index (RCRI) score. Results. In the derivation cohort, there were 1,452 deaths. The algorithm discriminates mortality with an AUC of 0.83 (95% CI 0.79-0.87) surpassing the discrimination of the RCRI score with an AUC of 0.67 (CI 0.61-0.72) in the held out test cohort. Patients determined to be high risk by the deep learning model's risk prediction had an unadjusted odds ratio (OR) of 8.83 (5.57-13.20) for post-operative mortality as compared to an unadjusted OR of 2.08 (CI 0.77-3.50) for post-operative mortality for RCRI greater than 2. The deep learning algorithm performed similarly for patients undergoing cardiac surgery with an AUC of 0.85 (CI 0.77-0.92), non-cardiac surgery with an AUC of 0.83 (0.79-0.88), and catherization or endoscopy suite procedures with an AUC of 0.76 (0.72-0.81). The algorithm similarly discriminated risk for mortality in two separate external validation cohorts from independent healthcare systems with AUCs of 0.79 (0.75-0.83) and 0.75 (0.74-0.76) respectively. Conclusion. The findings demonstrate how a novel deep learning algorithm, applied to pre-operative ECGs, can improve discrimination of post-operative mortality

Two novel human cytomegalovirus NK cell evasion functions target MICA for lysosomal degradation

NKG2D plays a major role in controlling immune responses through the regulation of natural killer (NK) cells, αβ and γδ T-cell function. This activating receptor recognizes eight distinct ligands (the MHC Class I polypeptide-related sequences (MIC) A andB, and UL16-binding proteins (ULBP)1–6) induced by cellular stress to promote recognition cells perturbed by malignant transformation or microbial infection. Studies into human cytomegalovirus (HCMV) have aided both the identification and characterization of NKG2D ligands (NKG2DLs). HCMV immediate early (IE) gene up regulates NKGDLs, and we now describe the differential activation of ULBP2 and MICA/B by IE1 and IE2 respectively. Despite activation by IE functions, HCMV effectively suppressed cell surface expression of NKGDLs through both the early and late phases of infection. The immune evasion functions UL16, UL142, and microRNA(miR)-UL112 are known to target NKG2DLs. While infection with a UL16 deletion mutant caused the expected increase in MICB and ULBP2 cell surface expression, deletion of UL142 did not have a similar impact on its target, MICA. We therefore performed a systematic screen of the viral genome to search of addition functions that targeted MICA. US18 and US20 were identified as novel NK cell evasion functions capable of acting independently to promote MICA degradation by lysosomal degradation. The most dramatic effect on MICA expression was achieved when US18 and US20 acted in concert. US18 and US20 are the first members of the US12 gene family to have been assigned a function. The US12 family has 10 members encoded sequentially through US12–US21; a genetic arrangement, which is suggestive of an ‘accordion’ expansion of an ancestral gene in response to a selective pressure. This expansion must have be an ancient event as the whole family is conserved across simian cytomegaloviruses from old world monkeys. The evolutionary benefit bestowed by the combinatorial effect of US18 and US20 on MICA may have contributed to sustaining the US12 gene family

3D Imaging Analysis on an Organoid-Based Platform Guides Personalized Treatment in Pancreatic Ductal Adenocarcinoma

BACKGROUNDPancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies, with unpredictable responses to chemotherapy. Approaches to assay patient tumors before treatment and identify effective treatment regimens based on tumor sensitivities are lacking. We developed an organoid-based platform (OBP) to visually quantify patient-derived organoid (PDO) responses to drug treatments and associated tumor-stroma modulation for personalized PDAC therapy.METHODSWe retrospectively quantified apoptotic responses and tumor-stroma cell proportions in PDOs via 3D immunofluorescence imaging through annexin A5, α-smooth muscle actin (α-SMA), and cytokeratin 19 (CK-19) levels. Simultaneously, an ex vivo organoid drug sensitivity assay (ODSA) was used to measure responses to standard-of-care regimens. Differences between ODSA results and patient tumor responses were assessed by exact McNemar\u27s test.RESULTSImmunofluorescence signals, organoid growth curves, and Ki-67 levels were measured and authenticated through the OBP for up to 14 days. ODSA drug responses were not different from patient tumor responses, as reflected by CA19-9 reductions following neoadjuvant chemotherapy (P = 0.99). PDOs demonstrated unique apoptotic and tumor-stroma modulation profiles (P \u3c 0.0001). α-SMA/CK-19 ratio levels of more than 1.0 were associated with improved outcomes (P = 0.0179) and longer parental patient survival by Kaplan-Meier analysis (P = 0.0046).CONCLUSIONHeterogenous apoptotic drug responses and tumor-stroma modulation are present in PDOs after standard-of-care chemotherapy. Ratios of α-SMA and CK-19 levels in PDOs are associated with patient survival, and the OBP could aid in the selection of personalized therapies to improve the efficacy of systemic therapy in patients with PDAC.FUNDINGNIH/National Cancer Institute grants (K08CA218690, P01 CA117969, R50 CA243707-01A1, U54CA224065), the Skip Viragh Foundation, the Bettie Willerson Driver Cancer Research Fund, and a Cancer Center Support Grant for the Flow Cytometry and Cellular Imaging Core Facility (P30CA16672)

Systematic identification of functional modules and cis-regulatory elements in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>Several large-scale gene co-expression networks have been constructed successfully for predicting gene functional modules and cis-regulatory elements in Arabidopsis (<it>Arabidopsis thaliana</it>)<it>.</it> However, these networks are usually constructed and analyzed in an <it>ad hoc</it> manner. In this study, we propose a completely parameter-free and systematic method for constructing gene co-expression networks and predicting functional modules as well as cis-regulatory elements.</p> <p>Results</p> <p>Our novel method consists of an automated network construction algorithm, a parameter-free procedure to predict functional modules, and a strategy for finding known cis-regulatory elements that is suitable for consensus scanning without prior knowledge of the allowed extent of degeneracy of the motif. We apply the method to study a large collection of gene expression microarray data in Arabidopsis. We estimate that our co-expression network has ~94% of accuracy, and has topological properties similar to other biological networks, such as being scale-free and having a high clustering coefficient. Remarkably, among the ~300 predicted modules whose sizes are at least 20, 88% have at least one significantly enriched functions, including a few extremely significant ones (ribosome, <it>p</it> < 1E-300, photosynthetic membrane, <it>p</it> < 1.3E-137, proteasome complex, <it>p</it> < 5.9E-126). In addition, we are able to predict cis-regulatory elements for 66.7% of the modules, and the association between the enriched cis-regulatory elements and the enriched functional terms can often be confirmed by the literature. Overall, our results are much more significant than those reported by several previous studies on similar data sets. Finally, we utilize the co-expression network to dissect the promoters of 19 Arabidopsis genes involved in the metabolism and signaling of the important plant hormone gibberellin, and achieved promising results that reveal interesting insight into the biosynthesis and signaling of gibberellin.</p> <p>Conclusions</p> <p>The results show that our method is highly effective in finding functional modules from real microarray data. Our application on Arabidopsis leads to the discovery of the largest number of annotated Arabidopsis functional modules in the literature. Given the high statistical significance of functional enrichment and the agreement between cis-regulatory and functional annotations, we believe our Arabidopsis gene modules can be used to predict the functions of unknown genes in Arabidopsis, and to understand the regulatory mechanisms of many genes.</p

Study of FoxA Pioneer Factor at Silent Genes Reveals Rfx-Repressed Enhancer at Cdx2 and a Potential Indicator of Esophageal Adenocarcinoma Development

Understanding how silent genes can be competent for activation provides insight into development as well as cellular reprogramming and pathogenesis. We performed genomic location analysis of the pioneer transcription factor FoxA in the adult mouse liver and found that about one-third of the FoxA bound sites are near silent genes, including genes without detectable RNA polymerase II. Virtually all of the FoxA-bound silent sites are within conserved sequences, suggesting possible function. Such sites are enriched in motifs for transcriptional repressors, including for Rfx1 and type II nuclear hormone receptors. We found one such target site at a cryptic “shadow” enhancer 7 kilobases (kb) downstream of the Cdx2 gene, where Rfx1 restricts transcriptional activation by FoxA. The Cdx2 shadow enhancer exhibits a subset of regulatory properties of the upstream Cdx2 promoter region. While Cdx2 is ectopically induced in the early metaplastic condition of Barrett's esophagus, its expression is not necessarily present in progressive Barrett's with dysplasia or adenocarcinoma. By contrast, we find that Rfx1 expression in the esophageal epithelium becomes gradually extinguished during progression to cancer, i.e, expression of Rfx1 decreased markedly in dysplasia and adenocarcinoma. We propose that this decreased expression of Rfx1 could be an indicator of progression from Barrett's esophagus to adenocarcinoma and that similar analyses of other transcription factors bound to silent genes can reveal unanticipated regulatory insights into oncogenic progression and cellular reprogramming

Madagascar’s extraordinary biodiversity: Threats and opportunities

Madagascar's unique biota is heavily affected by human activity and is under intense threat. Here, we review the current state of knowledge on the conservation status of Madagascar's terrestrial and freshwater biodiversity by presenting data and analyses on documented and predicted species-level conservation statuses, the most prevalent and relevant threats, ex situ collections and programs, and the coverage and comprehensiveness of protected areas. The existing terrestrial protected area network in Madagascar covers 10.4% of its land area and includes at least part of the range of the majority of described native species of vertebrates with known distributions (97.1% of freshwater fishes, amphibians, reptiles, birds, and mammals combined) and plants (67.7%). The overall figures are higher for threatened species (97.7% of threatened vertebrates and 79.6% of threatened plants occurring within at least one protected area). International Union for Conservation of Nature (IUCN) Red List assessments and Bayesian neural network analyses for plants identify overexploitation of biological resources and unsustainable agriculture as themost prominent threats to biodiversity. We highlight five opportunities for action at multiple levels to ensure that conservation and ecological restoration objectives, programs, and activities take account of complex underlying and interacting factors and produce tangible benefits for the biodiversity and people of Madagascar