The spin measurement of MAXI J1348-630 using the Insight-HXMT data

We report the results of fitting Insight-HXMT data to the black hole X-ray binary MAXI J1348-430, which was discovered on January 26th, 2019, with the Gas Slit Camera (GSC) on-board MAXI. Several observations at the beginning of the first burst were selected, with a total of 10 spectra. From the residuals of fits using disk plus power law models, X-ray reflection signatures were clearly visible in some of these observations. We use the state-of the-art relxill series reflection model to fit six spectra with distinct reflection signatures and a joint fit to these spectra. In particular, we focus on the results for the black hole spin values. Assuming Rin = RISCO, the spin parameter is constrained to be 0.82+0.04-0.03 with 90% confidence level (statistical only).Comment: Revisions to MNRAS are submitted, and comments are welcome

Revealing spatiotemporal transmission patterns and stages of COVID-19 in China using individual patients' trajectory data.

Gauging viral transmission through human mobility in order to contain the COVID-19 pandemic has been a hot topic in academic studies and evidence-based policy-making. Although it is widely accepted that there is a strong positive correlation between the transmission of the coronavirus and the mobility of the general public, there are limitations to existing studies on this topic. For example, using digital proxies of mobile devices/apps may only partially reflect the movement of individuals; using the mobility of the general public and not COVID-19 patients in particular, or only using places where patients were diagnosed to study the spread of the virus may not be accurate; existing studies have focused on either the regional or national spread of COVID-19, and not the spread at the city level; and there are no systematic approaches for understanding the stages of transmission to facilitate the policy-making to contain the spread. To address these issues, we have developed a new methodological framework for COVID-19 transmission analysis based upon individual patients' trajectory data. By using innovative space-time analytics, this framework reveals the spatiotemporal patterns of patients' mobility and the transmission stages of COVID-19 from Wuhan to the rest of China at finer spatial and temporal scales. It can improve our understanding of the interaction of mobility and transmission, identifying the risk of spreading in small and medium-sized cities that have been neglected in existing studies. This demonstrates the effectiveness of the proposed framework and its policy implications to contain the COVID-19 pandemic

NudC L279P Mutation Destabilizes Filamin A by Inhibiting the Hsp90 Chaperoning Pathway and Suppresses Cell Migration

Filamin A, the first discovered non-muscle actin filament cross-linking protein, plays a crucial role in regulating cell migration that participates in diverse cellular and developmental processes. However, the regulatory mechanism of filamin A stability remains unclear. Here, we find that nuclear distribution gene C (NudC), a cochaperone of heat shock protein 90 (Hsp90), is required to stabilize filamin A in mammalian cells. Immunoprecipitation-mass spectrometry and western blotting analyses reveal that NudC interacts with filamin A. Overexpression of human NudC-L279P (an evolutionarily conserved mutation in NudC that impairs its chaperone activity) not only decreases the protein level of filamin A but also results in actin disorganization and the suppression of cell migration. Ectopic expression of filamin A is able to reverse these defects induced by the overexpression of NudC-L279P. Furthermore, Hsp90 forms a complex with filamin A. The inhibition of Hsp90 ATPase activity by either geldanamycin or radicicol decreases the protein stability of filamin A. In addition, ectopic expression of Hsp90 efficiently restores NudC-L279P overexpression-induced protein stability and functional defects of filamin A. Taken together, these data suggest NudC L279P mutation destabilizes filamin A by inhibiting the Hsp90 chaperoning pathway and suppresses cell migration

Pioglitazone Improves Mitochondrial Function in the Remnant Kidney and Protects against Renal Fibrosis in 5/6 Nephrectomized Rats

Pioglitazone is a type of peroxisome proliferator-activated receptor γ (PPARγ) agonist and has been demonstrated to be effective in chronic kidney diseases (CKD) treatment. However, the underlying mechanism involved in the renoprotection of pioglitazone has not been fully revealed. In the present study, the renoprotective mechanism of pioglitazone was investigated in 5/6 nephrectomized (Nx) rats and TGF-β1-exposed HK-2 cells. Pioglitazone attenuated renal injury and improved renal function, as examined by 24 h urinary protein, blood urea nitrogen and plasma creatinine in Nx rats. Renal fibrosis and enhanced expressions of profibrotic proteins TGF-β1, fibronectin and collagen I caused by Nx were significantly alleviated by pioglitazone. In addition, pioglitazone protected mitochondrial functions by stabilizing the mitochondrial membrane potential, inhibiting ROS generation, maintaining ATP production and the activities of complexes I and III, and preventing cytochrome C leakage from mitochondria. Pioglitazone also upregulated the expression levels of ATP synthase β, COX I and NDUFB8, which were downregulated in the kidney of Nx rats and TGF-β1-exposed HK-2 cells. Furthermore, pioglitazone increased fusion proteins Opa-1 and Mfn2 expressions and decreased fission protein Drp1 expression. The results imply that pioglitazone may exert the renoprotective effects through modulating mitochondrial electron transport chain and mitochondrial dynamics in CKD. Finally, these recoveries were completely or partly inhibited by GW9662, which suggests that these effects at least partly PPARγ dependent. This study provides evidence for the pharmacological mechanism of pioglitazone in the treatment of CKD

Haplotype Information and Linkage Disequilibrium Mapping for Single Nucleotide Polymorphisms

Single nucleotide polymorphisms in the human genome have become an increasingly popular topic in that their analyses promise to be a key step toward personalized medicine. We investigate two related questions, how much the haplotype information contributes to linkage disequilibrium (LD) mapping and whether an in silico haplotype construction preceding the LD analysis can help. For disease gene mapping, using both simulated and real data sets on cystic fibrosis and the Alzheimer disease, we reached the following conclusions: (1) for simple Mendelian diseases, in which case a tractable full statistical model can be developed, the loss of haplotype information for either control or disease data do not have a great impact on LD fine mapping, and haplotype inference should be carried out jointly with LD mapping; (2) for complex diseases, inferring haplotype phases for individuals prior to LD mapping helps achieve a better accuracy. An improved version of the linkage disequilibrium mapping program, BLADE v2, is available at http://www.fas.harvard.edu/junliu/TechRept/03folder/bladev2.tgz

An Improved Elephant Herding Optimization for Energy-Saving Assembly Job Shop Scheduling Problem with Transportation Times

The energy-saving scheduling problem (ESSP) has gained increasing attention of researchers in the manufacturing field. However, there is a lack of studies on ESSPs in the assembly job shop environment. In contrast with traditional scheduling problems, the assembly job shop scheduling problem (AJSP) adds the additional consideration of hierarchical precedence constraints between different jobs of each final product. This paper focuses on developing a methodology for an energy-saving assembly job shop scheduling problem with job transportation times. Firstly, a mathematical model is constructed with the objective of minimizing total energy consumption. Secondly, an improved elephant herding optimization (IEHO) is proposed by considering the problem’s characteristics. Finally, thirty-two different instances are designed to verify the performance of the proposed algorithm. Computational results and statistical data demonstrate that the IEHO has advantages over other algorithms in terms of the solving accuracy for the considered problem

An Improved Elephant Herding Optimization for Energy-Saving Assembly Job Shop Scheduling Problem with Transportation Times

The energy-saving scheduling problem (ESSP) has gained increasing attention of researchers in the manufacturing field. However, there is a lack of studies on ESSPs in the assembly job shop environment. In contrast with traditional scheduling problems, the assembly job shop scheduling problem (AJSP) adds the additional consideration of hierarchical precedence constraints between different jobs of each final product. This paper focuses on developing a methodology for an energy-saving assembly job shop scheduling problem with job transportation times. Firstly, a mathematical model is constructed with the objective of minimizing total energy consumption. Secondly, an improved elephant herding optimization (IEHO) is proposed by considering the problem’s characteristics. Finally, thirty-two different instances are designed to verify the performance of the proposed algorithm. Computational results and statistical data demonstrate that the IEHO has advantages over other algorithms in terms of the solving accuracy for the considered problem

A New Interior Search Algorithm for Energy-Saving Flexible Job Shop Scheduling with Overlapping Operations and Transportation Times

Energy-saving scheduling has been pointed out as an interesting research issue in the manufacturing field, by which energy consumption can be effectively reduced through production scheduling from the operational management perspective. In recent years, energy-saving scheduling problems in flexible job shops (ESFJSPs) have attracted considerable attention from scholars. However, the majority of existing work on ESFJSPs assumed that the processing of any two consecutive operations in a job cannot be overlapped. In order to be close to real production, the processing overlapping of consecutive operations is allowed in this paper, while the job transportation tasks are also involved between different machines. To formulate the problem, a mathematical model is set up to minimize total energy consumption. Due to the NP-hard nature, a new interior search algorithm (NISA) is elaborately proposed following the feature of the problem. A number of experiments are conducted to verify the effectiveness of the NISA algorithm. The experimental results demonstrate that the NISA provides promising results for the considered problem. In addition, the computational results indicate that the increasing transportation time and sub-lot number will increase the transportation energy consumption, which is largely responsible for the increase in total energy consumption

A New Interior Search Algorithm for Energy-Saving Flexible Job Shop Scheduling with Overlapping Operations and Transportation Times

Energy-saving scheduling has been pointed out as an interesting research issue in the manufacturing field, by which energy consumption can be effectively reduced through production scheduling from the operational management perspective. In recent years, energy-saving scheduling problems in flexible job shops (ESFJSPs) have attracted considerable attention from scholars. However, the majority of existing work on ESFJSPs assumed that the processing of any two consecutive operations in a job cannot be overlapped. In order to be close to real production, the processing overlapping of consecutive operations is allowed in this paper, while the job transportation tasks are also involved between different machines. To formulate the problem, a mathematical model is set up to minimize total energy consumption. Due to the NP-hard nature, a new interior search algorithm (NISA) is elaborately proposed following the feature of the problem. A number of experiments are conducted to verify the effectiveness of the NISA algorithm. The experimental results demonstrate that the NISA provides promising results for the considered problem. In addition, the computational results indicate that the increasing transportation time and sub-lot number will increase the transportation energy consumption, which is largely responsible for the increase in total energy consumption