Family Related Factors and Concurrent Heroin Use in Methadone Maintenance Treatment in China.

BackgroundThe use of heroin during Methadone Maintenance Treatment (MMT) is a challenging problem that contributes to poor treatment outcomes. Families may play an important role in addressing concurrent heroin use during MMT, especially in collectivist societies such as China.ObjectivesIn this study, we explored the relationship between family-related factors and concurrent heroin use during MMT in China.MethodsThis study was conducted at 68 MMT clinics in five provinces of China. There were 2,446 MMT clients in the analysis. Demographic information, MMT dosage, family members' heroin use status, family support of MMT, family problem, and self-reported heroin use were collected in a cross-sectional survey. The most recent urinalysis of opiate use was obtained from clinical records.ResultsOf the 2,446 participants, 533 (21.79%) self-reported heroin use in the previous seven days or had a positive urine morphine test result in the clinic record. Participants whose family member[s] used heroin were 1.59 times (95% CI: 1.17, 2.15) more likely to use concurrently during treatment. Those with family members who totally support them on the MMT were less likely to use (AOR: 0.75, 95% CI: 0.60, 0.94). Having more family problems was positively associated with concurrent heroin use (AOR: 2.01, 95% CI: 1.03, 3.93).ConclusionsThe results highlight the importance of the family's role in concurrent heroin use during MMT programs. The study's findings may have implications for family-based interventions that address concurrent heroin use

A systematic approach to detecting transcription factors in response to environmental stresses

[[abstract]]Background Eukaryotic cells have developed mechanisms to respond to external environmental or physiological changes (stresses). In order to increase the activities of stress-protection functions in response to an environmental change, the internal cell mechanisms need to induce certain specific gene expression patterns and pathways by changing the expression levels of specific transcription factors (TFs). The conventional methods to find these specific TFs and their interactivities are slow and laborious. In this study, a novel efficient method is proposed to detect the TFs and their interactivities that regulate yeast genes that respond to any specific environment change. Results For each gene expressed in a specific environmental condition, a dynamic regulatory model is constructed in which the coefficients of the model represent the transcriptional activities and interactivities of the corresponding TFs. The proposed method requires only microarray data and information of all TFs that bind to the gene but it has superior resolution than the current methods. Our method not only can find stress-specific TFs but also can predict their regulatory strengths and interactivities. Moreover, TFs can be ranked, so that we can identify the major TFs to a stress. Similarly, it can rank the interactions between TFs and identify the major cooperative TF pairs. In addition, the cross-talks and interactivities among different stress-induced pathways are specified by the proposed scheme to gain much insight into protective mechanisms of yeast under different environmental stresses. Conclusion In this study, we find significant stress-specific and cell cycle-controlled TFs via constructing a transcriptional dynamic model to regulate the expression profiles of genes under different environmental conditions through microarray data. We have applied this TF activity and interactivity detection method to many stress conditions, including hyper- and hypo- osmotic shock, heat shock, hydrogen peroxide and cell cycle, because the available expression time profiles for these conditions are long enough. Especially, we find significant TFs and cooperative TFs responding to environmental changes. Our method may also be applicable to other stresses if the gene expression profiles have been examined for a sufficiently long time.[[fileno]]2030106030033[[department]]電機工程學

An intervention trial targeting methadone maintenance treatment providers to improve clients' treatment retention in China.

BackgroundService providers including doctors, nurses, and other healthcare professionals play an essential role in methadone maintenance treatment (MMT). This study evaluated the impact of an intervention targeting MMT providers on their clients' treatment retention.MethodsThis study was conducted in 68 MMT clinics in five provinces of China with 36 clients randomly selected from each clinic. The clinics were randomized to intervention or control condition. The MMT CARE intervention started with group sessions to enhance providers' communication skills. The trained providers were encouraged to conduct individual sessions with clients to promote treatment engagement. The outcomes, which include client retention (main outcome) and their reception of provider-delivered individual sessions (process outcome), were measured over a 24-month period.ResultsSignificantly fewer intervention clients dropped out from MMT than control clients during the study period (31% vs. 41%; p < 0.0001). Dropout hazard was significantly lower in the intervention condition compared to the control condition (HR = 0.71, 95% CI: 0.57, 0.89). More intervention clients had individual sessions than control clients (93% vs. 70%; p < 0.0001). Having individual sessions was associated with a significantly lower dropout hazard (HR = 0.30, 95% CI: 0.23, 0.40). The intervention clients had a significantly lower dropout hazard than the control clients if they started the individual sessions during the first six months (HR = 0.68, 95% CI: 0.51, 0.90).ConclusionsThe MMT CARE intervention focusing on provider capacity building has demonstrated efficacy in reducing clients' treatment dropout. This study sheds light on MMT service improvement in China and other global community-based harm reduction programs

On Adaptive Portfolio Management with Dynamic Black-Litterman Approach

This paper presents a novel framework for adaptive portfolio management that combines a dynamic Black-Litterman optimization with the general factor model and Elastic Net regression. This integrated approach allows us to systematically generate investors' views and mitigate potential estimation errors. Our empirical results demonstrate that this combined approach can lead to computational advantages as well as promising trading performances.Comment: 9 pages, 6 figure

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

Graph convolutional network (GCN) has been successfully applied to many graph-based applications; however, training a large-scale GCN remains challenging. Current SGD-based algorithms suffer from either a high computational cost that exponentially grows with number of GCN layers, or a large space requirement for keeping the entire graph and the embedding of each node in memory. In this paper, we propose Cluster-GCN, a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure. Cluster-GCN works as the following: at each step, it samples a block of nodes that associate with a dense subgraph identified by a graph clustering algorithm, and restricts the neighborhood search within this subgraph. This simple but effective strategy leads to significantly improved memory and computational efficiency while being able to achieve comparable test accuracy with previous algorithms. To test the scalability of our algorithm, we create a new Amazon2M data with 2 million nodes and 61 million edges which is more than 5 times larger than the previous largest publicly available dataset (Reddit). For training a 3-layer GCN on this data, Cluster-GCN is faster than the previous state-of-the-art VR-GCN (1523 seconds vs 1961 seconds) and using much less memory (2.2GB vs 11.2GB). Furthermore, for training 4 layer GCN on this data, our algorithm can finish in around 36 minutes while all the existing GCN training algorithms fail to train due to the out-of-memory issue. Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99.36 on the PPI dataset, while the previous best result was 98.71 by [16]. Our codes are publicly available at https://github.com/google-research/google-research/tree/master/cluster_gcn.Comment: In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19

Magnetic Interaction between Surface Engineered Rare-earth Atomic Spins

We report the ab initio study of rare-earth adatoms (Gd) on an insulating surface. This surface is of interest because of previous studies by scanning tunneling microscopy showing spin excitations of transition metal adatoms. The present work is the first study of rare-earth spin-coupled adatoms, as well as the geometry effect of spin coupling, and the underlying mechanism of ferromagnetic coupling. The exchange coupling between Gd atoms on the surface is calculated to be antiferromagnetic in a linear geometry and ferromagnetic in a diagonal geometry, by considering their collinear spins and using the PBE+U exchange correlation. We also find the Gd dimers in these two geometries are similar to the nearest-neighbor (NN) and the next-NN Gd atoms in GdN bulk. We analyze how much direct exchange, superexchange, and RKKY interactions contribute to the exchange coupling for both geometries by additional first-principles calculations of related model systems

DNA Ligase I Is Not Essential for Mammalian Cell Viability

SummaryOf the three DNA ligases present in all vertebrates, DNA ligase I (Lig1) has been considered essential for ligating Okazaki fragments during DNA replication and thereby essential for cell viability. Here, we report the striking finding that a Lig1-null murine B cell line is viable. Surprisingly, the Lig1-null cells exhibit normal proliferation and normal immunoglobulin heavy chain class switch recombination and are not hypersensitive to a wide variety of DNA damaging agents. These findings demonstrate that Lig1 is not absolutely required for cellular DNA replication and repair and that either Lig3 or Lig4 can substitute for the role of Lig1 in joining Okazaki fragments. The establishment of a Lig1-null cell line will greatly facilitate the characterization of DNA ligase function in mammalian cells, but the finding alone profoundly reprioritizes the role of ligase I in DNA replication, repair, and recombination

Determining the physical conditions of extremely young Class 0 circumbinary disk around VLA1623A

We present detailed analysis of high-resolution C18O (2-1), SO (88-77), CO (3-2) and DCO+ (3-2) data obtained by the Atacama Large Millimeter/sub-millimeter Array (ALMA) towards a Class 0 Keplerian circumbinary disk around VLA1623A, which represents one of the most complete analysis towards a Class 0 source. From the dendrogram analysis, we identified several accretion flows feeding the circumbinary disk in a highly anisotropic manner. Stream-like SO emission around the circumbinary disk reveals the complicated shocks caused by the interactions between the disk, accretion flows and outflows. A wall-like structure is discovered south of VLA1623B. The discovery of two outflow cavity walls at the same position traveling at different velocities suggests the two outflows from both VLA1623A and VLA1623B overlays on top of each other in the plane of sky. Our detailed flat and flared disk modeling shows that Cycle 2 C18O J = 2-1 data is inconsistent with the combined binary mass of 0.2 Msun as suggested by early Cycle 0 studies. The combined binary mass for VLA1623A should be modified to 0.3 ~ 0.5 Msun.Comment: 26 pages, 20 figures, accepted by ApJ 2020.2.2

Convex Quadratic Equations and Functions

Three interconnected main results (1)-(3) are presented in closed forms. (1) Regarding the convex quadratic equation (CQE), an analytical equivalent solvability condition and parameterization of all solutions are completely formulated, in a unified framework. The design concept is based on the matrix algebra, while facilitated by a novel equivalence/coordinate transformation. Notably, the parameter-solution bijection is also verified. Two applications are selected as the other two main results. (2) The focus is set on both the infinite and finite-time horizon nonlinear optimal control. By virtue of (1), the CQEs associated with the underlying Hamilton-Jacobi Equation, Hamilton-Jacobi Inequality, and Hamilton-Jacobi-Bellman Equation are algebraically solved, respectively. Each solution set captures the gradient of the associated value function. Moving forward, a preliminary to recover the optimality using the state-dependent (differential) Riccati equation is provided, which can also be used to more efficiently implement the last main result. (3) The nonlinear programming/convex optimization is analyzed via a novel method and perspective. The philosophy is based on the new analysis of CQE in (1), which helps explain the geometric structure of the convex quadratic function (CQF), and the CQE-CQF relation. An impact on the quadratic programming (QP), a basis in the literature, is demonstrated. Specifically, the QPs subject to equality, inequality, equality-and-inequality, and extended constraints are algebraically solved, resp., without knowing a feasible point.Comment: This manuscript is only preliminary and still growing. Therefore, with expectations, we deeply appreciate all kinds of input

A systematic approach to detecting transcription factors in response to environmental stresses

Abstract Background Eukaryotic cells have developed mechanisms to respond to external environmental or physiological changes (stresses). In order to increase the activities of stress-protection functions in response to an environmental change, the internal cell mechanisms need to induce certain specific gene expression patterns and pathways by changing the expression levels of specific transcription factors (TFs). The conventional methods to find these specific TFs and their interactivities are slow and laborious. In this study, a novel efficient method is proposed to detect the TFs and their interactivities that regulate yeast genes that respond to any specific environment change. Results For each gene expressed in a specific environmental condition, a dynamic regulatory model is constructed in which the coefficients of the model represent the transcriptional activities and interactivities of the corresponding TFs. The proposed method requires only microarray data and information of all TFs that bind to the gene but it has superior resolution than the current methods. Our method not only can find stress-specific TFs but also can predict their regulatory strengths and interactivities. Moreover, TFs can be ranked, so that we can identify the major TFs to a stress. Similarly, it can rank the interactions between TFs and identify the major cooperative TF pairs. In addition, the cross-talks and interactivities among different stress-induced pathways are specified by the proposed scheme to gain much insight into protective mechanisms of yeast under different environmental stresses. Conclusion In this study, we find significant stress-specific and cell cycle-controlled TFs via constructing a transcriptional dynamic model to regulate the expression profiles of genes under different environmental conditions through microarray data. We have applied this TF activity and interactivity detection method to many stress conditions, including hyper- and hypo- osmotic shock, heat shock, hydrogen peroxide and cell cycle, because the available expression time profiles for these conditions are long enough. Especially, we find significant TFs and cooperative TFs responding to environmental changes. Our method may also be applicable to other stresses if the gene expression profiles have been examined for a sufficiently long time.</p