A Method for Assessing the Efficiency in Two-Stage Production Systems in the Presence of Dual-Role Factors

Due to the existence of dual-role factors, it is difficult to evaluate the production efficiency of two-stage systems. Unlike single-stage systems, two-stage systems involve intermediate products that serve as both inputs and outputs. Hence, to overcome existing obstacles, we propose a novel approach called the two-stage enhanced Russell model with dual-role factors (T-ERM-D) to assess the overall efficiency of two-stage production systems. Furthermore, divisional models are developed to evaluate the efficiency of each individual stage. The 0-1 programming is applied to deal with dual-role factors. To handle the non-linearity of these models, the Charnes-Cooper transformation is employed to convert them into linear ones. Using the proposed models, we evaluate efficiency scores of 10 supply chains involving suppliers and producers. By comparing the results obtained from new models with those obtained from models that do not consider dual-role factors, we validate the advantages of the proposed approach

OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection

Compared to typical multi-sensor systems, monocular 3D object detection has attracted much attention due to its simple configuration. However, there is still a significant gap between LiDAR-based and monocular-based methods. In this paper, we find that the ill-posed nature of monocular imagery can lead to depth ambiguity. Specifically, objects with different depths can appear with the same bounding boxes and similar visual features in the 2D image. Unfortunately, the network cannot accurately distinguish different depths from such non-discriminative visual features, resulting in unstable depth training. To facilitate depth learning, we propose a simple yet effective plug-and-play module, \underline{O}ne \underline{B}ounding Box \underline{M}ultiple \underline{O}bjects (OBMO). Concretely, we add a set of suitable pseudo labels by shifting the 3D bounding box along the viewing frustum. To constrain the pseudo-3D labels to be reasonable, we carefully design two label scoring strategies to represent their quality. In contrast to the original hard depth labels, such soft pseudo labels with quality scores allow the network to learn a reasonable depth range, boosting training stability and thus improving final performance. Extensive experiments on KITTI and Waymo benchmarks show that our method significantly improves state-of-the-art monocular 3D detectors by a significant margin (The improvements under the moderate setting on KITTI validation set are $\mathbf{1.82\sim 10.91\%}$ \textbf{mAP in BEV} and $\mathbf{1.18\sim 9.36\%}$ \textbf{mAP in 3D}). Codes have been released at \url{https://github.com/mrsempress/OBMO}.Comment: 10 pages, 7 figure

A Simple Method of Preparation of High Silica Zeolite Y and Its Performance in the Catalytic Cracking of Cumene

A series of high silicon zeolites Y were prepared through direct synthetic method by using silica sol as the silicon source and sodium aluminate as the aluminum source. The effects of alkalinity and crystallization time of the process of synthesis were investigated. To separately reveal the crystalline structure, element content, morphology, and surface areas, the as-synthesized zeolite Y was characterized by powder X-ray diffraction (XRD), X-ray fluorescence (XRF), scanning electron microscopy (SEM), and N2adsorption-desorption isotherms (BET). The results show the as-synthesized zeolite Y with high relative crystallization and uniform morphology; the SiO2/Al2O3ratio was about 4.54~6.46. For an application, the zeolite cracking activity was studied with cumene as the probe molecules.</jats:p

Allosteric Autoregulation of DNA Binding via a DNA-Mimicking Protein Domain: A Biophysical Study of ZNF410-DNA Interaction Using Small Angle X-Ray Scattering

ZNF410 is a highly-conserved transcription factor, remarkable in that it recognizes a 15-base pair DNA element but has just a single responsive target gene in mammalian erythroid cells. ZNF410 includes a tandem array of five zinc-fingers (ZFs), surrounded by uncharacterized N- and C-terminal regions. Unexpectedly, full-length ZNF410 has reduced DNA binding affinity, compared to that of the isolated DNA binding ZF array, both in vitro and in cells. AlphaFold predicts a partially-folded N-terminal subdomain that includes a 30-residue long helix, preceded by a hairpin loop rich in acidic (aspartate/glutamate) and serine/threonine residues. This hairpin loop is predicted by AlphaFold to lie against the DNA binding interface of the ZF array. In solution, ZNF410 is a monomer and binds to DNA with 1:1 stoichiometry. Surprisingly, the single best-fit model for the experimental small angle X-ray scattering profile, in the absence of DNA, is the original AlphaFold model with the N-terminal long-helix and the hairpin loop occupying the ZF DNA binding surface. For DNA binding, the hairpin loop presumably must be displaced. After combining biophysical, biochemical, bioinformatic and artificial intelligence-based AlphaFold analyses, we suggest that the hairpin loop mimics the structure and electrostatics of DNA, and provides an additional mechanism, supplementary to sequence specificity, of regulating ZNF410 DNA binding

Determining the Quantitative Threshold of High-Frequency Oscillation Distribution to Delineate the Epileptogenic Zone by Automated Detection

Objective: We proposed an improved automated high frequency oscillations (HFOs) detector that could not only be applied to various intracranial electrodes, but also automatically remove false HFOs caused by high-pass filtering. We proposed a continuous resection ratio of high order HFO channels and compared this ratio with each patient's post-surgical outcome, to determine the quantitative threshold of HFO distribution to delineate the epileptogenic zone (EZ).Methods: We enrolled a total of 43 patients diagnosed with refractory epilepsy. The patients were used to optimize the parameters for SEEG electrodes, to test the algorithm for identifying false HFOs, and to calculate the continuous resection ratio of high order HFO channels. The ratio can be used to determine a quantitative threshold to locate the epileptogenic zone.Results: Following optimization, the sensitivity, and specificity of our detector were 66.84 and 73.20% (ripples) and 69.76 and 66.13% (fast ripples, FRs), respectively. The sensitivity and specificity of our algorithm for removing false HFOs were 76.82 and 94.54% (ripples) and 72.55 and 94.87% (FRs), respectively. The median of the continuous resection ratio of high order HFO channels in patients with good surgical outcomes, was significantly higher than in patients with poor outcome, for both ripples and FRs (P &lt; 0.05 ripples and P &lt; 0.001 FRs).Conclusions: Our automated detector has the advantage of not only applying to various intracranial electrodes but also removing false HFOs. Based on the continuous resection ratio of high order HFO channels, we can set the quantitative threshold for locating epileptogenic zones

Enhanced Thermoelectric Performance through Tuning Bonding Energy in Cu2Se1–xSx Liquid-like Materials

Thermoelectric materials require an optimal carrier concentration to maximize electrical transport and thus thermoelectric performance. Element doping and composition off-stoichiometry are the two general and effective approaches for optimizing carrier concentrations, which have been successfully applied in almost all semiconductors. In this study, we propose a new strategy called bonding energy variation to tune the carrier concentrations in Cu2Se-based liquid-like thermoelectric compounds. By utilizing the different bond features in Cu2Se and Cu2S, alloying S at the Se sites successfully increases the bonding energy to fix Cu atoms in the crystal lattice to suppress the formation of Cu vacancies, leading to greatly reduced carrier concentrations toward the optimal value. Via a combination of the lowered electrical and lattice thermal conductivities and the relatively good carrier mobility caused by the weak alloy scattering potential, ultrahigh zT values are achieved in slightly S-doped Cu2Se with a maximal value of 2.0 at 1000 K, 30% higher than that in nominally stoichiometric Cu2Se.acceptedVersion© American Chemical Society 2017. This is the authors accepted and refereed manuscript to the article. Locked until 24.7.2018 due to copyright restrictions

Niaoduqing alleviates podocyte injury in high glucose model via regulating multiple targets and AGE/RAGE pathway: Network pharmacology and experimental validation

Purpose: The aim of present study was to explore the pharmacological mechanisms of Niaoduqing granules on the treatment of podocyte injury in diabetic nephropathy (DN) via network pharmacology and experimental validation.Methods: Active ingredients and related targets of Niaoduqing, as well as related genes of podocyte injury, proteinuria and DN, were obtained from public databases. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) network analysis were performed to investigate the potential mechanisms. High glucose (HG) -induced MPC5 cell injury model was treated with the major core active ingredients of Niaoduqing and used to validate the predicted targets and signaling pathways.Results: Totally, 16 potential therapeutic targets were identified by intersecting the targets of Niaoduqing and disease, in which 7 of them were considered as the core targets via PPI network analysis. KEGG enrichment analysis showed that AGE-RAGE signaling pathway was identified as the most crucial signaling pathway. The results of in vitro experiments revealed that the treatment of Niaoduqing active ingredients significantly protected MPC5 cells from HG-induced apoptosis. Moreover, Niaoduqing could significantly attenuate the HG-induced activation of AGE-RAGE signaling pathway, whereas inhibited the over-expression of VEGF-A, ICAM-1, PTGS-2 and ACE in HG-induced MPC5 cells.Conclusion: Niaoduqing might protect against podocyte injury in DN through regulating the activity of AGE/RAGE pathway and expression of multiple genes. Further clinical and animal experimental studies are necessary to confirm present findings

Automated Detection of High-Frequency Oscillations in Epilepsy Based on a Convolutional Neural Network

Epilepsy is one of the most common chronic neurological diseases. High-frequency oscillations (HFOs) have emerged as promising biomarkers for the epileptogenic zone. However, visual marking of HFOs is a time-consuming and laborious process. Several automated techniques have been proposed to detect HFOs, yet these are still far from being suitable for application in a clinical setting. Here, ripples and fast ripples from intracranial electroencephalograms were detected in six patients with intractable epilepsy using a convolutional neural network (CNN) method. This approach proved more accurate than using four other HFO detectors integrated in RIPPLELAB, providing a higher sensitivity (77.04% for ripples and 83.23% for fast ripples) and specificity (72.27% for ripples and 79.36% for fast ripples) for HFO detection. Furthermore, for one patient, the Cohen's kappa coefficients comparing automated detection and visual analysis results were 0.541 for ripples and 0.777 for fast ripples. Hence, our automated detector was capable of reliable estimates of ripples and fast ripples with higher sensitivity and specificity than four other HFO detectors. Our detector may be used to assist clinicians in locating epileptogenic zone in the future

SMYD5 Is a Ribosomal Methyltransferase That Catalyzes RPL40 Lysine Methylation To Enhance Translation Output and Promote Hepatocellular Carcinoma

While lysine methylation is well-known for regulating gene expression transcriptionally, its implications in translation have been largely uncharted. Trimethylation at lysine 22 (K22me3) on RPL40, a core ribosomal protein located in the GTPase activation center, was first reported 27 years ago. Yet, its methyltransferase and role in translation remain unexplored. Here, we report that SMYD5 has robust in vitro activity toward RPL40 K22 and primarily catalyzes RPL40 K22me3 in cells. The loss of SMYD5 and RPL40 K22me3 leads to reduced translation output and disturbed elongation as evidenced by increased ribosome collisions. SMYD5 and RPL40 K22me3 are upregulated in hepatocellular carcinoma (HCC) and negatively correlated with patient prognosis. Depleting SMYD5 renders HCC cells hypersensitive to mTOR inhibition in both 2D and 3D cultures. Additionally, the loss of SMYD5 markedly inhibits HCC development and growth in both genetically engineered mouse and patient-derived xenograft (PDX) models, with the inhibitory effect in the PDX model further enhanced by concurrent mTOR suppression. Our findings reveal a novel role of the SMYD5 and RPL40 K22me3 axis in translation elongation and highlight the therapeutic potential of targeting SMYD5 in HCC, particularly with concurrent mTOR inhibition. This work also conceptually broadens the understanding of lysine methylation, extending its significance from transcriptional regulation to translational control