Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance

A rapid blood-based diagnostic modality to detect pancreatic ductal adenocarcinoma (PDAC) with high accuracy is an unmet medical need. The study aimed to validate a unique diagnosis system using Probe Electrospray Ionization Mass Spectrometry (PESI-MS) and Machine Learning to the diagnosis of PDAC. Peripheral blood samples were collected from a total of 322 consecutive PDAC patients and 265 controls with a family history of PDAC. Five µl of serum samples were analyzed using PESI-MS system. The mass spectra from each specimen were then fed into machine learning algorithms to discriminate between control and cancer cases. A total of 587 serum samples were analyzed. The sensitivity of the machine learning algorithm using PESI-MS profiles to identify PDAC is 90.8% with specificity of 91.7% (95% CI 83.9%-97.4% and 82.8%-97.7% respectively). Combined PESI-MS profiles with age and CA19-9 as predictors, the accuracy for stage 1 or 2 of PDAC is 92.9% and for stage 3 or 4 is 93% (95% CI 86.3-98.2; 87.9-97.4 respectively). The accuracy and simplicity of the PESI-MS profiles combined with machine learning provide an opportunity to detect PDAC at an early stage and must be applicable to the examination of at-risk populations. [Abstract copyright: AJTR Copyright © 2020.

Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information

The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30-60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods. © 2013 Elsevier B.V

Space-efficient Feature Maps for String Alignment Kernels

String kernels are attractive data analysis tools for analyzing string data. Among them, alignment kernels are known for their high prediction accuracies in string classifications when tested in combination with SVM in various applications. However, alignment kernels have a crucial drawback in that they scale poorly due to their quadratic computation complexity in the number of input strings, which limits large-scale applications in practice. We address this need by presenting the first approximation for string alignment kernels, which we call space-efficient feature maps for edit distance with moves (SFMEDM), by leveraging a metric embedding named edit sensitive parsing (ESP) and feature maps (FMs) of random Fourier features (RFFs) for large-scale string analyses. The original FMs for RFFs consume a huge amount of memory proportional to the dimension d of input vectors and the dimension D of output vectors, which prohibits its large-scale applications. We present novel space-efficient feature maps (SFMs) of RFFs for a space reduction from O(dD) of the original FMs to O(d) of SFMs with a theoretical guarantee with respect to concentration bounds. We experimentally test SFMEDM on its ability to learn SVM for large-scale string classifications with various massive string data, and we demonstrate the superior performance of SFMEDM with respect to prediction accuracy, scalability and computation efficiency.Comment: Full version for ICDM'19 pape

Control moment gyro optimization study

Control moment gyro optimizatio

Visualizing the microscopic coexistence of spin density wave and superconductivity in underdoped NaFe1-xCoxAs

Although the origin of high temperature superconductivity in the iron pnictides is still under debate, it is widely believed that magnetic interactions or fluctuations play an important role in triggering Cooper pairing. Because of the relevance of magnetism to pairing, the question of whether long range spin magnetic order can coexist with superconductivity microscopically has attracted strong interests. The available experimental methods used to answer this question are either bulk probes or local ones without control of probing position, thus the answers range from mutual exclusion to homogeneous coexistence. To definitively answer this question, here we use scanning tunneling microscopy to investigate the local electronic structure of an underdoped NaFe1-xCoxAs near the spin density wave (SDW) and superconducting (SC) phase boundary. Spatially resolved spectroscopy directly reveal both the SDW and SC gap features at the same atomic location, providing compelling evidence for the microscopic coexistence of the two phases. The strengths of the SDW and SC features are shown to anti correlate with each other, indicating the competition of the two orders. The microscopic coexistence clearly indicates that Cooper pairing occurs when portions of the Fermi surface (FS) are already gapped by the SDW order. The regime TC < T < TSDW thus show a strong resemblance to the pseudogap phase of the cuprates where growing experimental evidences suggest a FS reconstruction due to certain density wave order. In this phase of the pnictides, the residual FS has a favorable topology for magnetically mediated pairing when the ordering moment of the SDW is small.Comment: 18 pages, 4 figure

Human Clostridium difficile infection caused by a livestock-associated PCR ribotype 237 strain in Western Australia

Introduction: Clostridium difficile infection (CDI) is a significant gastrointestinal disease in the developed world and increasingly recognised as a zoonotic infection. In North America and Europe, the PCR ribotype (RT) 078 strain of C. difficile is commonly found in production animals and as a cause of disease in humans although proof of transmission from animals is lacking. This strain is absent in Australian livestock. We report a case of human CDI caused by a strain of C. difficile belonging to known Australian livestock-associated RT 237. Case presentation: A young male was admitted for multiple trauma following a motor vehicle accident and placed on piperacillin/tazobactam for pneumonia. After 4 days of treatment, he developed symptoms of CDI, which was confirmed in the laboratory. His symptoms resolved after 6 days of intravenous metronidazole. The strain of C. difficile isolated was identified as RT 237, an unusual RT previously found in with several Western Australia piggeries. Conclusion: This case of CDI caused by an unusual livestock-associated C. difficile RT 237 supports the hypothesis of zoonotic transmission. The case highlights the potential of livestock to act as reservoir for C. difficile and the need for continued surveillance of CDI in both human and animal populations

Dry semi-continuous anaerobic digestion of food waste in the mesophilic and thermophilic modes: New aspects of sustainable management and energy recovery in South Korea

© 2016 Elsevier Ltd In this study, parallel, bench-scale, mesophilic and thermophilic, dry, semi-continuous anaerobic digestion (DScAD) of Korea food waste (FW, containing 22% total solids (TS) and 20% volatile solids (VS)) was investigated thoroughly under varying operational conditions, including hydraulic retention times (HRTs) and organic loading rates (OLRs). The aim was to evaluate the start-up, stability, overall removal efficiency, and inhibitory effects of toxic compounds on process performance over a long-term operation lasting 100 days. The results from both digesters indicate that the simultaneous reduction of VS and the production of gas improved as the HRT decreased or the OLR increased. The highest average rates of VS reduction (79.67%) and biogas production (162.14 m3biogas/ton of FW, 61.89% CH4), at an OLR of 8.62 ± 0.34 kg VS/m3day (25 days of HRT), were achieved under thermophilic DScAD. In addition, the average rates of reduction of VS and the production of biogas in thermophilic DScAD were higher by 6.88% and 16.4%, respectively, than were those in mesophilic DScAD. The inhibitory effects of ammonia, H2S, and volatile fatty acids (VFAs) on methane production was not clear from either of the digesters, although, apparently, their concentrations did fluctuate. This fluctuation could be attributed to the self-adaptation of the microbial well. However, digestion that was more stable and faster was observed under thermophilic conditions compared with that under mesophilic conditions. Based on our results, the optimum operational parameters to improve FW treatment and achieve higher energy yields could be determined, expanding the application of DScAD in treating organic wastes

Visualisation of peripheral retinal degenerations and anomalies with ocular imaging

Purpose: Certain peripheral retinal degenerations pose a significant risk to vision and require prompt detection and management. Other historically “benign” peripheral lesions are being recognised as clinically significant due to their associations with ocular and systemic disorders. Assessment and documentation of these entities however can be difficult due to challenges in visualisation of the peripheral retina. This review addresses this by providing a series of clinical examples of these entities visualised with a variety of ocular imaging technologies. Methods: A literature search was performed in Embase, Medline, and Google Scholar. We identified and analysed all papers referring to peripheral retinal degenerations and the peripheral retina, as well as reference lists of retrieved articles until August 2019. Results: Using ocular imaging technologies including ultra-widefield imaging and peripheral optical coherence tomography, we comprehensively describe current evidence and knowledge of a number of peripheral retinal degenerations and anomalies including microcystoid, pavingstone, lattice, snail track, snowflake and reticular pigmentary degenerations, peripheral drusen, white without pressure, retinal holes and vitreoretinal tufts. A summary of these entities is also provided as a short and easily interpretable chairside guide to facilitate the translation of this evidence base into clinical practice. Conclusion: While ocular technologies are useful in visualising peripheral retinal degenerations, the current evidence is fragmented throughout the literature and there is a paucity of information on imaging of “benign” peripheral lesions. This review facilitates a multimodal imaging approach to evaluating peripheral lesions

A hybrid constructed wetland for organic-material and nutrient removal from sewage: Process performance and multi-kinetic models

© 2018 Elsevier Ltd A pilot-scale hybrid constructed wetland with vertical flow and horizontal flow in series was constructed and used to investigate organic material and nutrient removal rate constants for wastewater treatment and establish a practical predictive model for use. For this purpose, the performance of multiple parameters was statistically evaluated during the process and predictive models were suggested. The measurement of the kinetic rate constant was based on the use of the first-order derivation and Monod kinetic derivation (Monod) paired with a plug flow reactor (PFR) and a continuously stirred tank reactor (CSTR). Both the Lindeman, Merenda, and Gold (LMG) analysis and Bayesian model averaging (BMA) method were employed for identifying the relative importance of variables and their optimal multiple regression (MR). The results showed that the first-order–PFR (M2) model did not fit the data (P > 0.05, and R2 0.5). The pollutant removal rates in the case of M1 were 0.19 m/d (CODCr) and those for M3 were 25.2 g/m2∙d for CODCr and 2.63 g/m2∙d for NH4-N. By applying a multi-variable linear regression method, the optimal empirical models were established for predicting the final effluent concentration of five days' biochemical oxygen demand (BOD5) and NH4-N. In general, the hydraulic loading rate was considered an important variable having a high value of relative importance, which appeared in all the optimal predictive models