Establishment of a viable cell detection system for microorganisms in wine based on ethidium monoazide and quantitative PCR

Fermentability and contamination level of wine can be assessed through the detection of viable fermentation-related and spoilage-related microorganisms. Ethidium monoazide in combination with quantitative PCR (EMA-qPCR) has been considered as a promising method to enumerate viable cells. Milling for 80 s by O 500-mu m glass beads is demonstrated to be optimal for DNA extraction from yeasts, lactic acid bacteria (LAB) and acetic acid bacteria (AAB) in wine to be used as a template for PCR. EMA-qPCR results from experiments using DNA extracted by this method correlate well with the results of a plating assay (R-2 > 0.99), and a PCR efficiency between 96% and 105% was obtained. Moreover, for all of these microorganisms, EMA treatment of pure cultures at a low concentration (10 mu g/mL) for 20 min photoactivation resulted in effective differentiation between viable and non-viable cells and had no effect on viable cells. Due to sublethal injury to some cells, underestimation of cell counts was found in most of the wine samples tested using the EMA-qPCR method, and a 40-min incubation in recovery medium could completely offset this error. Our results suggest an optimal glass-bead DNA extraction method and EMA treatment suitable for all of the main microorganisms in wine. The EMA-qPCR method was successfully applied to quantify yeasts. Saccharomyces cerevisiae (S. cerevisiae), LAB, non-Oenococcus oeni LAB (non-O. oeni LAB) and AAB in wine samples. (C) 2012 Elsevier Ltd. All rights reserved

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

商工電子科儀器設備水準與學生學業成就之相關研究

[[abstract]]壹、本研究基於師資、課程、設備為影響工職教育之三大要素，而師資及課程都有 先進學者作過專論研究，惟獨設備欠缺一方，故本人欲對此一方向，盡一已之力， 期拋磚引玉，共同改進工職教育之缺失。 貳、本研究採取因變動有電子科儀器設備水準及智力成績，依變數有學生之學業成 就測驗成績，而採取變異數分析法，分析設備水準學生之學業成就之關連性。 參、本研究樣本取自台灣及台北市含有電子科之綜合中學或高工計廿校，公、私立 各十所，內計公立學生人數四一七人，私立計四二六人，合計八四三人，全部為應 屆高三學生。 肆、本研究採用之研究工具有（一）自編高工電子科成就測驗卷（二）借自師大附 中之歐迪斯智力測驗量表（三）高工電子科設備調查表。 伍、研究結論： (1)高工電子科設備水準與學生之學業成就有很大之關連性。 (2)公立高工，因在各客觀因素較整齊下，設備水準對學生之學業成就，達到顯著 之相關。 (3)私立學校在學業成就上比不上公立學校。 (4)私立學校設備水準對學生之學業成就，沒有顯著影響，但是從原始資料看出， 私校設備水準偏低，各方面教學因素需要輔導。 #2810822

Application of YOLOv4 Algorithm for Foreign Object Detection on a Belt Conveyor in a Low-Illumination Environment

The most common failures of belt conveyors are runout, coal piles and longitudinal tears. The detection methods for longitudinal tearing are currently not particularly effective. A key study area for minimizing longitudinal belt tears with the advancement of machine learning is how to use machine vision technology to detect foreign items on the belt. In this study, the real-time detection of foreign items on belt conveyors is accomplished using a machine vision method. Firstly, the KinD++ low-light image enhancement algorithm is used to improve the quality of the captured low-quality images through feature processing. Then, the GridMask method partially masks the foreign objects in the training images, thus extending the data set. Finally, the YOLOv4 algorithm with optimized anchor boxes is combined to achieve efficient detection of foreign objects in belt conveyors, and the method is verified as effective.Transport Engineering and Logistic

The reinforcement effects of PVA, PE, and steel fibers on AAS material

This paper employs PVA, PE, steel fibers, as well as the hybrids of two of the three fibers to reinforce alkali-activated slag (AAS) material, aiming to prepare strain-hardening and clinker-free composites. The flexural strength, compressive strength, uniaxial tensile performance of the composites and bond behavior between fibers and the matrix were tested to clarify the reinforcement effects of different fibers on the matrix. Strain-hardening AAS materials are obtained with compressive strengths of 116 MPa − 137 MPa (with fibers contributions of 17%−38%) and strain capacities over 0.8% at 60 d. The results indicate that there are several kinds of reinforcement effects of fibers on the matrix, namely bridging effect, lapping effect (for steel fibers), synergetic effect (for hybrid fibers) and static effect (for flexible fibers). Deterioration of PVA and PE fibers are found, indicating that these two fibers have poor adaptability in AAS material with a high alkalinity. This paper specially distinguishes the difference of the crack numbers during the strain-hardening stage only with the ones during the whole period including the following strain-softening stage. A new relationship is established between the crack numbers and the strain-stress curves, which provides a more reasonable way to characterize the strain-hardening property of fiber-reinforced composites.Materials and Environmen

3-D Instance Segmentation of MVS Buildings

We present a novel 3-D instance segmentation framework for multiview stereo (MVS) buildings in urban scenes. Unlike existing works focusing on semantic segmentation of urban scenes, the emphasis of this work lies in detecting and segmenting 3-D building instances even if they are attached and embedded in a large and imprecise 3-D surface model. Multiview red green blue (RGB) images are first enhanced to RGB height (RGBH) images by adding a heightmap and are segmented to obtain all roof instances using a fine-tuned 2-D instance segmentation neural network. Instance masks from different multiview images are then clustered into global masks. Our mask clustering accounts for spatial occlusion and overlapping, which can eliminate segmentation ambiguities among multiview images. Based on these global masks, 3-D roof instances are segmented out by mask back-projections and extended to the entire building instances through a Markov random field optimization. A new dataset that contains instance-level annotation for both 3-D urban scenes (roofs and buildings) and drone images (roofs) is provided. To the best of our knowledge, it is the first outdoor dataset dedicated to 3-D instance segmentation with much more annotations of attached 3-D buildings than existing datasets.1 Quantitative evaluations and ablation studies have shown the effectiveness of all major steps and the advantages of our multiview framework over the orthophoto-based method.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Urban Data Scienc

Carbonation and related behaviors of hardened cement pastes under different hydration degrees

This paper develops a kind of molded disc samples to investigate the carbonation and related behaviors of hardened cement pastes under different previous hydration degrees. Weight and length changes of cement pastes over time are monitored during a multistep process including carbonation, drying, rewetting, and redrying. The combination of X-ray diffraction (XRD) and thermogravimetric analysis (TGA) is used to identify and quantify the mineral compositions of carbonated cement pastes. An exponential function between CO2 uptake capacity and hydration time of cement pastes is established, which shows that the CO2 uptake capacity of cement pastes decreases dramatically at the very beginning days of hydration and then remaining relatively stable as hydration time is prolonged. Two reasons for this finding are revealed: i) the equilibrium between the carbonation and the post-carbonation reaction of carbonation product, i.e., silica-alumina gel; ii) refining of pore structures by hydration products which hinders carbonation. A clearer zonation of carbonation areas is proposed, and the spatial distribution equations of CO2 absorption are initially established. By monitoring carbonation and drying behavior of cement pastes with different hydration ages, it is revealed that carbonation reduces drying shrinkage of cement pastes especially for early-age samples, whereas drying increases carbonation shrinkage. By investigating the water changes during the multistep process, it is found that water is little released during the carbonation of C–S–H gels. New insight into mechanism of carbonation shrinkage is provided by a newly proposed model.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care. Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Materials and Environmen

3D concrete printing: Lattice modeling of structural failure considering damage and deformed geometry

This research studies the impact of localized damage and deformed printing geometry on the structural failure of plastic collapse for 3D concrete printing (3DCP) using the lattice model. Two different approaches are utilized for buildability quantification: the (previously developed) load-unload method, which updates and relaxes the printing system after each analysis step and repeatedly applies the gravitational loading to the undeformed structure; and the incremental method, which keeps the load after each analysis step and applies the incremental loading to the deformed printing system. The former can consider the material yielding but cannot capture accurately the structural deformation during printing process. Compared to the load-unload method, the incremental method can not only consider deformed printing geometry but can also simulate the non-proportional loading conditions and disequilibrium force occurring during 3D printing. In this study, computational uniaxial compression tests are first conducted to compare two algorithms. The numerical results indicate the consideration of nonequilibrium force and deformed geometry affects the peak load and crack information for fracture analysis. Subsequently, the incremental method is incorporated into the lattice model to quantify buildability of 3DCP. The predictions are compared with previously published numerical results obtained using the load-unload method. The lattice model based on incremental method reproduces correct failure mode; better quantitative agreement about critical printing height also can be obtained. These numerical analyses demonstrate that the incremental solution is an approximate method for buildability quantification since it can account for the nonequilibrium force induced by the deformed printing geometry and localized damage.Materials and Environmen

Automatic enhancement of vascular configuration for self-healing concrete through reinforcement learning approach

Vascular self-healing concrete (SHC) has great potential to mitigate the environmental impact of the construction industry by increasing the durability of structures. Designing concrete with high initial mechanical properties by searching a specific arrangement of vascular structure is of great importance. Herein, an automatic optimization method is proposed to arrange vascular configuration for minimizing the adverse influence of vascular system through a reinforcement learning (RL) approach. A case study is carried out to optimize a concrete beam with 3 pores (representing a vascular network) positioned in the beam midspan within a design space of 40 possibilities. The optimization is performed by the interaction between RL agent and Abaqus simulation environment with the change of target properties as a reward signal. The results illustrates that the RL approach is able to automatically enhance the vascular arrangement of SHC given the fact that the 3-pore structures that have the maximum target mechanical property (i.e., peak load or fracture energy) are accessed for all of the independent runs. The RL optimization method is capable of identifying the structure with high fracture energy in the new optimization task for 4-pore concrete structure.Materials and Environmen

More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks

Graph Neural Networks (GNNs) are a class of deep learning-based methods for processing graph domain information. GNNs have recently become a widely used graph analysis method due to their superior ability to learn representations for complex graph data. Due to privacy concerns and regulation restrictions, centralized GNNs can be difficult to apply to data-sensitive scenarios. Federated learning (FL) is an emerging technology developed for privacy-preserving settings when several parties need to train a shared global model collaboratively. Although several research works have applied FL to train GNNs (Federated GNNs), there is no research on their robustness to backdoor attacks.This paper bridges this gap by conducting two types of backdoor attacks in Federated GNNs: centralized backdoor attacks (CBA) and distributed backdoor attacks (DBA). Our experiments show that the DBA attack success rate is higher than CBA in almost all cases. For CBA, the attack success rate of all local triggers is similar to the global trigger, even if the training set of the adversarial party is embedded with the global trigger. To explore the properties of two backdoor attacks in Federated GNNs, we evaluate the attack performance for a different number of clients, trigger sizes, poisoning intensities, and trigger densities. Finally, we explore the robustness of DBA and CBA against two state-of-the-art defenses. We find that both attacks are robust against the investigated defenses, necessitating the need to consider backdoor attacks in Federated GNNs as a novel threat that requires custom defenses.Cyber Securit