Colorimetric determination of muscle glycogen in slaughter animals

This project aimed to refine and validate an optical remote sensing method to predict the ultimate pH of slaughter beef animals. An existing commercial method converts muscle glycogen in a known mass of muscle sample into glucose that is determined by a diabetic’s personal meter. The method is expensive in terms of consumables and results are fraught with inadequate operator skill levels. Pilot studies showed that it may be possible to measure the mass of the muscle sample and the concentration of glucose by colorimetry. Redness was a measure of muscle mass in acetate-buffered slurry, and after addition of Fehlings solution and heating, yellowness was a measure of glucose. This was the starting point for the study. Phase 1 determined the value of individual Hunter colour a*, b* and L* values for predicting mass of meat samples by linear equations. Hunter a* was a useful predictor of meat mass, but only within animals, probably due to the different muscular origins of the meat cuts selected. However, it was proposed that if samples were taken from a single muscular site, as in the existing commercial method, among animal variability might be much reduced. In Phase 1, a digital camera was also used to extract colour data, but it proved much less useful than the Hunter meter. Its use was thus discontinued. Phase 2 showed that different concentrations of glucose did not affect the colour due to meat mass, which was a necessary condition for using colour as a predictor of meat mass. Phase 3 explored the broad relationship between glucose concentration and meat mass on colour change due to the Fehlings reaction induced in a microwave oven. As expected from prior research, the concentration of glucose strongly affected the heat-induced colour, but meat mass also affected colour presumably through the Maillard reaction which would compete with the Fehlings reaction for the available glucose. However, if the mass of meat were known, colour values could be adjusted for this effect. In Phase 4, randomly chosen but defined masses of meat, and similarly randomly chosen, defined concentrations of glucose were used with the Fehlings reaction to test the predictive value of equations relating concentration of glucose/mass of meat to various Hunter colour values. The ratio was well predicted by Hunter b* and L*, unexpectedly implying that information about meat mass and glucose could be simultaneously extracted from the same colour data. This result suggests that there may be no need to measure meat mass, gravimetrically or by colour, to get useful results. In a limited way, Phase 5 extended the Phase 4 work by using the ratio of colour values before heating (no Fehlings added) colour values after heating (Fehlings added) to see if this would improve the predictive values established in Phase 4. It did not. The results are discussed with a focus on future work required to confirm the results in Phase 4, and also describe the steps required in a hypothetical semi-automated application of the technology

Providing flow based performance guarantees for buffered crossbar switches

Buffered crossbar switches are a special type of com-bined input-output queued switches with each crosspoint of the crossbar having small on-chip buffers. The introduc-tion of crosspoint buffers greatly simplifies the scheduling process of buffered crossbar switches, and furthermore en-ables buffered crossbar switches with speedup of two to eas-ily provide port based performance guarantees. However, recent research results have indicated that, in order to pro-vide flow based performance guarantees, buffered crossbar switches have to either increase the speedup of the cross-bar to three or greatly increase the total number of cross-point buffers, both adding significant hardware complexity. In this paper, we present scheduling algorithms for buffered crossbar switches to achieve flow based performance guar-antees with speedup of two and with only one or two buffers at each crosspoint. When there is no crosspoint blocking in a specific time slot, only the simple and distributed in-put scheduling and output scheduling are necessary. Other-wise, the special urgent matching is introduced to guarantee the on-time delivery of crosspoint blocked cells. With the proposed algorithms, buffered crossbar switches can pro-vide flow based performance guarantees by emulating push-in-first-out output queued switches, and we use the counting method to formally prove the perfect emulation. For the special urgent matching, we present sequential and paral-lel matching algorithms. Both algorithms converge with N iterations in the worst case, and the latter needs less itera-tions in the average case. Finally, we discuss an alternative backup-buffer implementation scheme to the bypass path, and compare our algorithms with existing algorithms in the literature

Structured electrode additive manufacturing for lithium-ion batteries

A thick electrode with high areal capacity has been developed as a strategy for high-energy-density lithium-ion batteries, but thick electrodes have difficulties in manufacturing and limitations in ion transport. Here, we reported a new manufacturing approach for ultra-thick electrode with aligned structure, called structure electrode additive manufacturing or SEAM, which aligns active materials to the through-thicknesses direction of electrodes using shear flow and a designed printing path. The ultra-thick electrodes with high loading of active materials, low tortuous structure, and good structure stability resulting from a simple and scalable SEAM lead to rapid ion transport and fast electrolyte infusion, delivering a higher areal capacity than slurry-casted thick electrodes. SEAM shows strengths in design flexibility and scalability, which allows the production of practical high energy/power density structure electrodes

Machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome: Findings from the China Suboptimal Health Cohort

Background: Metabolic syndrome (MetS) has been proposed as a clinically identifiable high-risk state for the prediction and prevention of cardiovascular diseases and type 2 diabetes mellitus. As a promising “omics” technology, metabolomics provides an innovative strategy to gain a deeper understanding of the pathophysiology of MetS. The study aimed to systematically investigate the metabolic alterations in MetS and identify biomarker panels for the identification of MetS using machine learning methods. Methods: Nuclear magnetic resonance-based untargeted metabolomics analysis was performed on 1011 plasma samples (205 MetS patients and 806 healthy controls). Univariate and multivariate analyses were applied to identify metabolic biomarkers for MetS. Metabolic pathway enrichment analysis was performed to reveal the disturbed metabolic pathways related to MetS. Four machine learning algorithms, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression were used to build diagnostic models for MetS. Results: Thirteen significantly differential metabolites were identified and pathway enrichment revealed that arginine, proline, and glutathione metabolism are disturbed metabolic pathways related to MetS. The protein-metabolite-disease interaction network identified 38 proteins and 23 diseases are associated with 10 MetS-related metabolites. The areas under the receiver operating characteristic curve of the SVM, RF, KNN, and logistic regression models based on metabolic biomarkers were 0.887, 0.993, 0.914, and 0.755, respectively. Conclusions: The plasma metabolome provides a promising resource of biomarkers for the predictive diagnosis and targeted prevention of MetS. Alterations in amino acid metabolism play significant roles in the pathophysiology of MetS. The biomarker panels and metabolic pathways could be used as preventive targets in dealing with cardiometabolic diseases related to MetS

Highly dispersed and ultrafine Co3O4@N-doped carbon catalyst derived from metal-organic framework for efficient oxygen reduction reaction

932-937Electrocatalysts are composed of transition metal/metal oxide and N-doped carbon can overcome the sluggish kinetics of oxygen reduction reactio. Herein, the Co3O4/ketjen black (KB)@MOF-derived with uniformly dispersed and ultrafine Co3O4 nanoparticles (1-5 nm) are synthesized by a facile in-situ method and subsequent mild pyrolysis process. It exhibits enhanced activity with onset potential of 0.96 V (vs. RHE) and a half-wave potential of 0.86 V (vs. RHE) in 0.1 M KOH solution, the excellent durability with E1/2 a small negative shift of 10 mV after 5000 continuous cycles and good methanol-tolerance property. The ultrahigh catalytic performance of Co3O4/KB@MOF-derived can be ascribed to the small particle size range of 1-5 nm of Co3O4, as well as the strong interaction between the in-situ formed N-Co3O4 active sites and substrate under the mild calcination temperature. Above all, these indicate that the as-prepared Co3O4/KB@MOF-derived may be a good alternative to commercial Pt-based catalysts

FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images

In this paper, we propose an end-to-end deep neural network for solving the problem of imbalanced large and small organ segmentation in head and neck (HaN) CT images. To conduct radiotherapy planning for nasopharyngeal cancer, more than 10 organs-at-risk (normal organs) need to be precisely segmented in advance. However, the size ratio between large and small organs in the head could reach hundreds. Directly using such imbalanced organ annotations to train deep neural networks generally leads to inaccurate small-organ label maps. We propose a novel end-to-end deep neural network to solve this challenging problem by automatically locating, ROI-pooling, and segmenting small organs with specifically designed small-organ sub-networks while maintaining the accuracy of large organ segmentation. A strong main network with densely connected atrous spatial pyramid pooling and squeeze-and-excitation modules is used for segmenting large organs, where large organs' label maps are directly output. For small organs, their probabilistic locations instead of label maps are estimated by the main network. High-resolution and multi-scale feature volumes for each small organ are ROI-pooled according to their locations and are fed into small-organ networks for accurate segmenting small organs. Our proposed network is extensively tested on both collected real data and the \emph{MICCAI Head and Neck Auto Segmentation Challenge 2015} dataset, and shows superior performance compared with state-of-the-art segmentation methods.Comment: MICCAI 201

Crystal Structure of the C-Terminal Cytoplasmic Domain of Non-Structural Protein 4 from Mouse Hepatitis Virus A59

BACKGROUND:The replication of coronaviruses takes place on cytoplasmic double membrane vesicles (DMVs) originating in the endoplasmic reticulum (ER). Three trans-membrane non-structural proteins, nsp3, nsp4 and nsp6, are understood to be membrane anchors of the coronavirus replication complex. Nsp4 is localized to the ER membrane when expressed alone but is recruited into the replication complex in infected cells. It is revealed to contain four trans-membrane regions and its N- and C-termini are exposed to the cytosol. METHODOLOGY/PRINCIPAL FINDINGS:We have determined the crystal structures of the C-terminal hydrophilic domain of nsp4 (nsp4C) from MHV strain A59 and a C425S site-directed mutant. The highly conserved 89 amino acid region from T408 to Q496 is shown to possess a new fold. The wild-type (WT) structure features two monomers linked by a Cys425-Cys425 disulfide bond in one asymmetric unit. The monomers are arranged with their N- and C-termini in opposite orientations to form an "open" conformation. Mutation of Cys425 to Ser did not affect the monomer structure, although the mutant dimer adopts strikingly different conformations by crystal packing, with the cross-linked C-termini and parallel N-termini of two monomers forming a "closed" conformation. The WT nsp4C exists as a dimer in solution and can dissociate easily into monomers in a reducing environment. CONCLUSIONS/SIGNIFICANCE:As nsp4C is exposed in the reducing cytosol, the monomer of nsp4C should be physiological. This structure may serve as a basis for further functional studies of nsp4