Computer-Aided Assembly Sequence Planning for High-Mix Low-Volume Products in the Electronic Appliances Industry

Electronic appliance manufacturers are facing the challenge of frequent product orders. Based on each product order, the assembly process and workstations need to be planned. An essential part of the assembly planning is defining the assembly sequence, considering the mechanical product’s design, and handling of the product’s components. The assembly sequence determines the order of processes for each workstation, the overall layout, and thereby time and cost. Currently, the assembly sequence is decided by industrial engineers through a manual approach that is time-consuming, complex, and requires technical expertise. To reduce the industrial engineers’ manual effort, a Computer-Aided Assembly Sequence Planning (CAASP) system is proposed in this paper. It compromises the components for a comprehensive system that aims to be applied practically. The system uses Computer-Aided Design (CAD) files to derive Liaison and Interference Matrices that represent a mathematical relationship between parts. Subsequently, an adapted Ant Colony Optimization Algorithm generates an optimized assembly sequence based on these relationships. Through a web browser-based application, the user can upload files and interact with the system. The system is conceptualized and validated using the CAD file of an electric motor example product. The results are discussed, and future work is outlined

Overcoming Drug Resistance in Colon Cancer by Aptamer-Mediated Targeted Co-Delivery of Drug and siRNA Using Grapefruit-Derived Nanovectors

Background/Aims: Multidrug resistance (MDR) is the most common cause of chemotherapy failure. Upregulation of P-glycoprotein (P-gp) is one of the main mechanisms underlying MDR. Methods: In this study, we developed a targeted drug and small interfering (si)RNA co-delivery system based on specific aptamer-conjugated grapefruit-derived nanovectors (GNVs) that we tested in MDR LoVo colon cancer cells. The internalization of nanovectors in cancer cells was tested by fluorescence microscopy and flow cytometry. The anti-cancer activity in vitro was determined by colony formation and cell apoptosis assays. The biodistribution of nanovectors was analyzed by live imaging and the anti-cancer activity in vivo was observed. Results: GNVs loaded with aptamer increased doxorubicin (Dox) accumulation in MDR LoVo cells, an effect that was abolished by pretreatment with DNase. The LA1 aptamer effectively promoted nanovector internalization into cells at 4°C and increased the targeted delivery of Dox to tumors. Constructs harboring Dox, LA1, and P-gp siRNA more effectively inhibited proliferation and enhanced apoptosis in cultured MDR LoVo cells while exhibiting more potent anti-tumor activity in vivo than free Dox or GNVs loaded with Dox alone or in conjunction with LA1, an effect that was associated with downregulation of P-gp expression. Conclusion: This GNV-based system may be an effective strategy for overcoming MDR in clinical settings

Molecular Basis of NDM-1, a New Antibiotic Resistance Determinant

The New Delhi Metallo-β-lactamase (NDM-1) was first reported in 2009 in a Swedish patient. A recent study reported that Klebsiella pneumonia NDM-1 positive strain or Escherichia coli NDM-1 positive strain was highly resistant to all antibiotics tested except tigecycline and colistin. These can no longer be relied on to treat infections and therefore, NDM-1 now becomes potentially a major global health threat

MEIS2C and MEIS2D promote tumor progression via Wnt/β-catenin and hippo/YAP signaling in hepatocellular carcinoma

Abstract Background MEIS2 has been identified as one of the key transcription factors in the gene regulatory network in the development and pathogenesis of human cancers. Our study aims to identify the regulatory mechanisms of MEIS2 in hepatocellular carcinoma (HCC), which could be targeted to develop new therapeutic strategies. Methods The variation of MEIS2 levels were assayed in a cohort of HCC patients. The proliferation, clone-formation, migration, and invasion abilities of HCC cells were measured to analyze the effects of MEIS2C and MEIS2D (MEIS2C/D) knockdown with small hairpin RNAs in vitro and in vivo. Chromatin immunoprecipitation (ChIP) was performed to identify MEIS2 binding site. Immunoprecipitation and immunofluorescence assays were employed to detect proteins regulated by MEIS2. Results The expression of MEIS2C/D was increased in the HCC specimens when compared with the adjacent noncancerous liver (ANL) tissues. Moreover, MEIS2C/D expression negatively correlated with the prognosis of HCC patients. On the other hand, knockdown of MEIS2C/D could inhibit proliferation and diminish migration and invasion of hepatoma cells in vitro and in vivo. Mechanistically, MESI2C activated Wnt/β-catenin pathway in cooperation with Parafibromin (CDC73), while MEIS2D suppressed Hippo pathway by promoting YAP nuclear translocation via miR-1307-3p/LATS1 axis. Notably, CDC73 could directly either interact with MEIS2C/β-catenin or MEIS2D/YAP complex, depending on its tyrosine-phosphorylation status. Conclusions Our studies indicate that MEISC/D promote HCC development via Wnt/β-catenin and Hippo/YAP signaling pathways, highlighting the complex molecular network of MEIS2C/D in HCC pathogenesis. These results suggest that MEISC/D may serve as a potential novel therapeutic target for HCC.https://deepblue.lib.umich.edu/bitstream/2027.42/152244/1/13046_2019_Article_1417.pd

(+)-Rutamarin as a Dual Inducer of Both GLUT4 Translocation and Expression Efficiently Ameliorates Glucose Homeostasis in Insulin-Resistant Mice

Glucose transporter 4 (GLUT4) is a principal glucose transporter in response to insulin, and impaired translocation or decreased expression of GLUT4 is believed to be one of the major pathological features of type 2 diabetes mellitus (T2DM). Therefore, induction of GLUT4 translocation or/and expression is a promising strategy for anti-T2DM drug discovery. Here we report that the natural product (+)-Rutamarin (Rut) functions as an efficient dual inducer on both insulin-induced GLUT4 translocation and expression. Rut-treated 3T3-L1 adipocytes exhibit efficiently enhanced insulin-induced glucose uptake, while diet-induced obese (DIO) mice based assays further confirm the Rut-induced improvement of glucose homeostasis and insulin sensitivity in vivo. Subsequent investigation of Rut acting targets indicates that as a specific protein tyrosine phosphatase 1B (PTP1B) inhibitor Rut induces basal GLUT4 translocation to some extent and largely enhances insulin-induced GLUT4 translocation through PI3 kinase-AKT/PKB pathway, while as an agonist of retinoid X receptor α (RXRα), Rut potently increases GLUT4 expression. Furthermore, by using molecular modeling and crystallographic approaches, the possible binding modes of Rut to these two targets have been also determined at atomic levels. All our results have thus highlighted the potential of Rut as both a valuable lead compound for anti-T2DM drug discovery and a promising chemical probe for GLUT4 associated pathways exploration

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Dynamic Modeling and Flow Distribution of Complex Micron Scale Pipe Network

A fluid simulation calculation method of the microfluidic network is proposed as a means to achieve the flow distribution of the microfluidic network. This paper quantitatively analyzes the influence of flow distribution in microfluidic devices impacted by pressure variation in the pressure source and channel length. The flow distribution in microfluidic devices with three types of channel lengths under three different pressure conditions is studied and shows that the results obtained by the simulation calculation method on the basis of the fluid network are close to those given by the calculation method of the conventional electrical method. The simulation calculation method on the basis of the fluid network studied in this paper has computational reliability and can respond to the influence of microfluidic network length changes to the fluid system, which plays an active role in Lab-on-a-chip design and microchannel flow prediction

Performance Assessment of Multi-GNSS PPP Ambiguity Resolution with LEO-Augmentation

The fast motion of low Earth orbit (LEO) satellites provides rapid geometric changes in a short time, which can accelerate the initialization of precise point positioning (PPP). The rapid convergence of ambiguity parameters is conducive to the rapid success of ambiguity fixing. This paper presents the performance of single- and four-system combined PPP Ambiguity Resolution (AR), enhanced with an ambiguity-float solution LEO. Two LEO constellations were designed: L was a typical polar orbit constellation, with a higher number of visible satellites at high latitudes than at low and middle latitudes; and M was designed to compensate for the lack of visible satellites at low and middle latitudes. The ground observation data of the LEO satellites at the MGEX stations were simulated. Because the global navigation satellite systems (GNSSs) were fully operational, the GNSS data were real observation data from the MGEX stations. Based on the daily observation datasets collected at 258 stations in the global MGEX observation network over three days (from 1 January to 3 January 2022), in addition to the LEO simulation data, we evaluated the positioning performance of LEO ambiguity-float solution-enhanced PPP ambiguity resolution and compared it with LEO-enhanced PPP. The L+M mixed constellation was able to reduce the time to first fix (TTFF) of the four-system combined PPP-AR to 5 min, and four LEO satellites were sufficient to achieve this. L+M mixed constellation was able to reduce the convergence time of the four-system combined PPP to 2 min. Unlike PPP-AR, PPP required more LEO satellites for augmentation to saturate

Genome-Wide Identification of WRKY Family Genes and the Expression Profiles in Response to Nitrogen Deficiency in Poplar

The fast-growing arbor poplar is widely distributed across the world and is susceptible to nitrogen availability. The WRKY transcription factor is an important regulatory node of stress tolerance as well as nutrient utilization. However, the potential response mechanism of WRKY genes toward nitrogen is poorly understood. Therefore, the identification of WRKY genes on the Populus trichocarpa genome was performed, and 98 PtWRKYs (i.e., PtWRKY1 to PtWRKY98) were identified. Phylogenetic analysis and the promoter cis-acting element detection revealed that PtWRKYs have multiple functions, including phosphorus and nitrogen homeostasis. By constructing multilayer-hierarchical gene regulatory networks (ML-hGRNs), it was predicted that many WRKY transcription factors were involved in the nitrogen response, such as PtWRKY33 and PtWRKY95. They mainly regulated the expression of primary nitrogen-responsive genes (NRGs), such as PtNRT2.5A, PtNR2 and PtGLT2. The integrative analysis of transcriptome and RT-qPCR results show that the expression levels of 6 and 15 PtWRKYs were regulated by nitrogen availability in roots and leaves, respectively, and those were also found in ML-hGRN. Our study demonstrates that PtWRKYs respond to nitrogen by regulating NRGs, which enriches the nitrate-responsive transcription factor network and helps to uncover the hub of nitrate and its related signaling regulation

Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework

Background. An estimated 425 million people globally have diabetes, accounting for 12% of the world’s health expenditures, and the number continues to grow, placing a huge burden on the healthcare system, especially in those remote, underserved areas. Methods. A total of 584,168 adult subjects who have participated in the national physical examination were enrolled in this study. The risk factors for type II diabetes mellitus (T2DM) were identified by p values and odds ratio, using logistic regression (LR) based on variables of physical measurement and a questionnaire. Combined with the risk factors selected by LR, we used a decision tree, a random forest, AdaBoost with a decision tree (AdaBoost), and an extreme gradient boosting decision tree (XGBoost) to identify individuals with T2DM, compared the performance of the four machine learning classifiers, and used the best-performing classifier to output the degree of variables’ importance scores of T2DM. Results. The results indicated that XGBoost had the best performance (accuracy=0.906, precision=0.910, recall=0.902, F‐1=0.906, and AUC=0.968). The degree of variables’ importance scores in XGBoost showed that BMI was the most significant feature, followed by age, waist circumference, systolic pressure, ethnicity, smoking amount, fatty liver, hypertension, physical activity, drinking status, dietary ratio (meat to vegetables), drink amount, smoking status, and diet habit (oil loving). Conclusions. We proposed a classifier based on LR-XGBoost which used fourteen variables of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of T2DM. The classifier can accurately screen the risk of diabetes in the early phrase, and the degree of variables’ importance scores gives a clue to prevent diabetes occurrence