Calibration of probabilistic quantitative precipitation forecasts with an artificial neural network

A feed-forward neural network is configured to calibrate the bias of a high-resolution probabilistic quantitative precipitation forecast (PQPF) produced by a 12-km version of the NCEP Regional Spectral Model (RSM) ensemble forecast system. Twice-daily forecasts during the 2002-2003 cool season (1 November-31 March, inclusive) are run over four U.S. Geological Survey (USGS) hydrologic unit regions of the southwest United States. Calibration is performed via a cross-validation procedure, where four months are used for training and the excluded month is used for testing. The PQPFs before and after the calibration over a hydrological unit region are evaluated by comparing the joint probability distribution of forecasts and observations. Verification is performed on the 4-km stage IV grid, which is used as "truth." The calibration procedure improves the Brier score (BrS), conditional bias (reliability) and forecast skill, such as the Brier skill score (BrSS) and the ranked probability skill score (RPSS), relative to the sample frequency for all geographic regions and most precipitation thresholds. However, the procedure degrades the resolution of the PQPFs by systematically producing more forecasts with low nonzero forecast probabilities that drive the forecast distribution closer to the climatology of the training sample. The problem of degrading the resolution is most severe over the Colorado River basin and the Great Basin for relatively high precipitation thresholds where the sample of observed events is relatively small. © 2007 American Meteorological Society

Short-range probabilistic quantitative precipitation forecasts over the southwest United States by the RSM ensemble system

The National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) is used to produce twice-daily (0000 and 1200 UTC), high-resolution ensemble forecasts to 24 h. The forecasts are performed at an equivalent horizontal grid spacing of 12 km for the period 1 November 2002 to 31 March 2003 over the southwest United States. The performance of 6-h accumulated precipitation is assessed for 32 U.S. Geological Survey hydrologic catchments. Multiple accuracy and skill measures are used to evaluate probabilistic quantitative precipitation forecasts. NCEP stage-IV precipitation analyses are used as "truth," with verification performed on the stage-IV 4-km grid. The RSM ensemble exhibits a ubiquitous wet bias. The bias manifests itself in areal coverage, frequency of occurrence, and total accumulated precipitation over every region and during every 6-h period. The biases become particularly acute starting with the 1800-0000 UTC interval, which leads to a spurious diurnal cycle and the 1200 UTC cycle being more adversely affected than the 0000 UTC cycle. Forecast quality and value exhibit marked variability over different hydrologic regions. The forecasts are highly skillful along coastal California and the windward slopes of the Sierra Nevada Mountains, but they generally lack skill over the Great Basin and the Colorado basin except over mountain peaks. The RSM ensemble is able to discriminate precipitation events and provide useful guidance to a wide range of users over most regions of California, which suggests that mitigation of the conditional biases through statistical postprocessing would produce major improvements in skill. © 2007 American Meteorological Society

Deep Learning Based Vehicle Make-Model Classification

This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN (Convolutional Neural Network) model to train on the database. In the pipeline, we first detect the vehicles by following an algorithm which reduces the time for annotation. Then, we feed them into the CNN model. It is reached approximately 4% better classification accuracy result than using a conventional CNN model. Next, we propose to use the detected vehicles as ground truth bounding box (GTBB) of the images and feed them into an SSD model in another pipeline. At this stage, it is reached reasonable classification accuracy result without using perfectly shaped GTBB. Lastly, an application is implemented in a use case by using our proposed pipelines. It detects the unauthorized vehicles by comparing their license plate numbers and make & models. It is assumed that license plates are readable.Comment: 10 pages, ICANN 2018: Artificial Neural Networks and Machine Learnin

Assembly strategy of liposome and polymer systems for siRNA delivery

In recent years, gene therapy has made tremendous progress in the development of disease treatment. Among them, siRNA offers specificity of gene silencing, ease of synthesis, and short development period, and has been intensively studied worldwide. However, siRNA as the hydrophilic polyanion is easily degraded in vivo and poorly taken up into cells and so, the benefits of its powerful gene silencing ability will not be realized until better carriers are developed that are capable of protecting siRNA and delivering it intact to the cytoplasm of the target cells. Cationic liposomes (CL) and cationic polymers (CP) are the main non-viral siRNA vectors, there have been a lot of reports on the use of these two carriers to deliver siRNA. Whereas, as far as we know, there have been few review articles that provide an in-depth summary of the siRNA loading principle and internal structures of the siRNA delivery system. We summarize the formation principle and assembly structure of the cationic liposome-siRNA and polymer-siRNA complexes, and point out their advantages and characteristics and also show how to perfect their assembly and improve their clinical application in the future. It supports some useful suggestions for siRNA therapy, specifically, safe and efficient delivery

The effects of incisional bupivacaine infusions on postoperative opioid consumption and pain scores after total abdominal hysterectomy

Background: The aim of this study was to determine opioid requirements and pain intensity scores in patients after a total abdominal hysterectomy (TAH) administered with a bupivacaine infusion for a 30-hour period, and then to compare the data with that of a control group. Method: This was a prospective, parallel, single-blinded randomised trial which took place at the Rahima Moosa Mother and Child Hospital, Johannesburg. Thirty-six consenting patients, who underwent a TAH, were randomised to either having a 0.39% bupivacaine infusion in the incisional site or not. Morphine was administered via a patient-controlled analgesia pump (PCA) for rescue analgesia. Dynamic, static and worst pain scores were assessed one, six and 30 hours after surgery by using a visual analogue scale (VAS). Morphine consumption was recorded at set intervals. Results: There were statistically significant differences between the two groups’ dynamic VAS scores in the first hour and at 24 hours and 30 hours; in the static VAS score in the first hour; and in the VAS scores for the worst pain experienced since the patients were last seen in the first hour and six hours after the operation. There was no statistical difference between the two groups’ opioid consumption at all set observation points. Conclusion: The opioid requirements of the two groups were comparable, although participants who had the bupivacaine infusion experienced reduced pain intensity which lasted until six hours postoperatively, and also had reduced pain intensity when moving around 30 hours after the operation.Keywords: incisional bupivacaine infusions, total abdominal hysterectomy, postoperative pain contro

Effective Organizational Improvision in Information Systems Development: insights from Tencent messaging system development

Organizational improvisation has been increasingly observed in information systems development (ISD), which leads to both positive and negative results. Extant studies have examined how to conduct effective improvisation, but findings are mainly based on variancebased models; a process model is missing. Our study fills this gap by conducting an in-depth case study of the Tencent messaging system development. Our study identifies a four-phase process model featuring a continuous iteration between improvisational search and build. Our findings make important contributions to organizational improvisation literature in ISD and general management and provide a step-by-step guide for ISD managers to conduct effective improvisation.National Natural Science Foundation of China & Fundamental Research Funds for Central Universitie

On single and double soft behaviors in NLSM

In this paper, we study the single and double soft behaviors of tree level off-shell currents and on-shell amplitudes in nonlinear sigma model(NLSM). We first propose and prove the leading soft behavior of the tree level currents with a single soft particle. In the on-shell limit, this single soft emission becomes the Adler's zero. Then we establish the leading and sub-leading soft behaviors of tree level currents with two adjacent soft particles. With a careful analysis of the on-shell limit, we obtain the double soft behaviors of on-shell amplitudes where the two soft particles are adjacent to each other. By applying Kleiss-Kuijf (KK) relation, we further obtain the leading and sub-leading behaviors of amplitudes with two nonadjacent soft particles.Comment: 41 pages, 6 tables, 9 figures, minor revised, more content about nonadjacent double soft limit, update the reference

Adverse prognostic and predictive significance of low DNA-dependent protein kinase catalytic subunit (DNA-PKcs) expression in early-stage breast cancers

Background: DNA-dependent protein kinase catalytic subunit (DNA-PKcs), a serine threonine kinase belonging to the PIKK family (phosphoinositide 3-kinase-like-family of protein kinase), is a critical component of the non-homologous end joining (NHEJ) pathway required for the repair of DNA double strand breaks. DNA-PKcs may be involved in breast cancer pathogenesis. Methods: We evaluated clinicopathological significance of DNA-PKcs protein expression in 1161 tumours and DNA-PKcs mRNA expression in 1950 tumours. We correlated DNA-PKcs to other markers of aggressive phenotypes, DNA repair, apoptosis and cell cycle regulation. Results: Low DNA-PKcs protein expression was associated with higher tumour grade, higher mitotic index, tumour de-differentiation and tumour type (ps<0.05). Absence of BRCA1, low XRCC1/SMUG1/APE1/Polβ were also more likely in low DNA-PKcs expressing tumours (ps<0.05). Low DNA-PKcs protein expression was significantly associated with worse breast cancer specific survival (BCCS) in univariate and multivariate analysis (ps<0.01). At the mRNA level, low DNA-PKcs was associated with PAM50.Her2 and PAM50.LumA molecular phenotypes (ps<0.01) and poor BCSS. In patients with ER positive tumours who received endocrine therapy, low DNA-PKcs (protein and mRNA) was associated with poor survival. In ER negative patients, low DNA-PKcs mRNA remains significantly associated with adverse outcome. Conclusions: Our study suggests that low DNA-PKcs expression may have prognostic and predictive significance in breast cancers

Dosimetry-based treatment for Graves' disease.

Objective The aim of this retrospective study was to assess the long-term outcome of a personalized dosimetry approach in Graves' disease aiming to render patients euthyroid from a planned thyroid absorbed dose of 60 Gy.Patients and methods A total of 284 patients with Graves' disease were followed prospectively following administration of radioiodine calculated to deliver an absorbed dose of 60 Gy. Patients with cardiac disease were excluded. Outcomes were analysed at yearly intervals for up to 10 years with a median follow-up of 37.5 months.Results A single radioiodine administration was sufficient to render a patient either euthyroid or hypothyroid in 175 (62%) patients, the remainder requiring further radioiodine. The median radioactivity required to deliver 60 Gy was 77 MBq. Less than 2% patients required 400-600 MBq, the standard activity administered in many centres. In the cohort receiving a single administration, 38, 32 and 26% were euthyroid on no specific thyroid medication at 3, 5 and 10 years, respectively. Larger thyroid volumes were associated with the need for further therapy. The presence of nodules on ultrasonography did not adversely affect treatment outcome.Conclusion A personalized dosimetric approach delayed the long-term onset of hypothyroidism in 26% of patients. This was achieved using much lower administered activities than currently recommended. Future studies will aim to identify those patients who would benefit most from this approach

High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines.

Hundreds of genetically characterized cell lines are available for the discovery of genotype-specific cancer vulnerabilities. However, screening large numbers of compounds against large numbers of cell lines is currently impractical, and such experiments are often difficult to control. Here we report a method called PRISM that allows pooled screening of mixtures of cancer cell lines by labeling each cell line with 24-nucleotide barcodes. PRISM revealed the expected patterns of cell killing seen in conventional (unpooled) assays. In a screen of 102 cell lines across 8,400 compounds, PRISM led to the identification of BRD-7880 as a potent and highly specific inhibitor of aurora kinases B and C. Cell line pools also efficiently formed tumors as xenografts, and PRISM recapitulated the expected pattern of erlotinib sensitivity in vivo