Explore the vast range of titles available.

Major depressive disorder (MDD) is a debilitating psychiatric illness. However, there is currently no objective laboratory-based diagnostic tests for this disorder. Although, perturbations in multiple neurotransmitter systems have been implicated in MDD, the biochemical changes underlying the disorder remain unclear, and a comprehensive global evaluation of neurotransmitters in MDD has not yet been performed. Here, using a GC-MS coupled with LC-MS/MS-based targeted metabolomics approach, we simultaneously quantified the levels of 19 plasma metabolites involved in GABAergic, catecholaminergic, and serotonergic neurotransmitter systems in 50 first-episode, antidepressant drug-naïve MDD subjects and 50 healthy controls to identify potential metabolite biomarkers for MDD (training set). Moreover, an independent sample cohort comprising 49 MDD patients, 30 bipolar disorder (BD) patients and 40 healthy controls (testing set) was further used to validate diagnostic generalizability and specificity of these candidate biomarkers. Among the 19 plasma neurotransmitter metabolites examined, nine were significantly changed in MDD subjects. These metabolites were mainly involved in GABAergic, catecholaminergic and serotonergic systems. The GABAergic and catecholaminergic had better diagnostic value than serotonergic pathway. A panel of four candidate plasma metabolite biomarkers (GABA, dopamine, tyramine, kynurenine) could distinguish MDD subjects from health controls with an AUC of 0.968 and 0.953 in the training and testing set, respectively. Furthermore, this panel distinguished MDD subjects from BD subjects with high accuracy. This study is the first to globally evaluate multiple neurotransmitters in MDD plasma. The altered plasma neurotransmitter metabolite profile has potential differential diagnostic value for MDD.

The scientific evaluation of property perceived service quality (PPSQ) needs multi-stage, multi-source and large-group perceived information, which is deemed to be the decision problem for dynamic, heterogeneous and large-scale data processing. Aiming at the problem, we propose a general multi-attribute multi-scale (MAMS) method based on the dynamic linear programming technique for multi-dimensional analysis of preference (LINMAP). In the dynamic LINMAP model, the classic MAMS matrix is introduced and extended into a general form. The dynamic LINMAP model is constructed by defining dynamic consistency and dynamic inconsistency. The time series weight is determined by Orness method. The new method adapts to the requirements of modern PPSQ. Finally, we verify the feasibility and effectiveness of dynamic LINMAP method by analyzing a PPSQ evaluation example. The new method improves the traditional PPSQ evaluation, and provides a perspective for large-scale data processing by the classic decision method. First published online 23 June 2020

Depression is a complex psychiatric disorder. Various depressive rodent models are usually constructed based on different pathogenesis hypotheses. Herein, using our previously established naturally occurring depressive (NOD) model in a non-human primate (cynomolgus monkey, ), we performed metabolomics analysis of cerebrospinal fluid (CSF) from NOD female macaques (N=10) and age-and gender-matched healthy controls (HCs) (N=12). Multivariate statistical analysis was used to identify the differentially expressed metabolites between the two groups. Ingenuity Pathways Analysis and MetaboAnalyst were applied for predicted pathways and biological functions analysis. Totally, 37 metabolites responsible for discriminating the two groups were identified. The NOD macaques were mainly characterized by perturbations of fatty acid biosynthesis, ABC transport system, and amino acid metabolism (eg, aspartate, glycine, serine, and threonine metabolism). Interestingly, we found that eight altered CSF metabolites belonging to short-chain fatty acids and amino acids were also observed in the serum of NOD macaques (N=13 per group). Our findings suggest that peripheral and central short-chain fatty acids and amino acids are implicated in the onset of depression.

In this paper, we introduce several improved Shapley values for cooperative transferable utility (TU) games depending on the players’ least square contributions instead of their marginal contributions. This work is mainly enlightened by the Shapley value and the player’s productivity-based excess (usually called the excess of the player). Based on the player’s contribution-based excess, two quadratic programming models for obtaining the players’ (weighted) least square contributions are constructed. The efficient weighted Shapley-like value proposed in this paper as an extension of the Shapley value can be characterized by four independent axioms such as the symmetry, the efficiency, the additivity, and the quasi-null player, which are almost similar to the Shapley value’s four axioms except that the anonymity is replaced with the quasi-null player. The four axioms are proven to uniquely determine the efficient weighted Shapley-like value. Finally, the advantages of the proposed values are illustrated with a real case about the collaborative profit sharing of the rural e-commerce.

In this paper, we investigate the equal surplus division value for cooperative games with a level structure, which is a sequence of coalition structures becoming coarser and coarser. We propose three axiomatizations of the value. Among them, the first two use different variations of the recent population solidarity axiom, and the third one invokes a special reduced game consistency axiom. Due to the existence of a level structure, our axioms impose special restrictions on the players we focus. We show that our value can be characterized with these axioms and other variations of well-known axioms, such as efficiency, standardness, and quotient game property. Besides characterizing the value, we also connect it to the recent field of ordered tree cooperative games, wherein we find that the iterative value can be viewed as a special case of our value.

To evaluate the comprehensive impacts of tourism in Hainan, this paper proposes an approach integrating input-output (IO) model with multi-criteria decision making (MCDM) methods. Four steps are taken: (1) constructing an extended IO model that can separate out domestic and imported effects from the traditional IO system; (2) constituting a 3-level evaluation criteria hierarchy based on various tourism multipliers obtained from the extended IO model; (3) calculating the weights of criteria using the entropy theory; (4) giving an evaluation of comprehensive impacts of tourism based on four MCDM methods – WSM, TOPSIS, ELECTRE, and PROMETHEE. Using Hainan 2002, 2007, and 2012 IO tables as database, the proposed approach is implemented in the empirical study of Hainan. The results show economic dimension is the most important consideration and the overall performance of tourism in Hainan shows a trend of first increasing and then decreasing from 2002 to 2012. Two types sensitivity analysis of weights show single criterion weight change has little influence on the results. But it is different when four dimensions weights change. Especially, when the environmental dimension is valued, the overall performance gets worse annually. These findings can offer insightful policies for the development of Hainan’s tourism. First published online 23 June 2020

Big data and intelligent decision methods in economy, innovation and sustainable development. Technological and Economic Development of Economy, 26(5), pp. 970-973.

Ultra-high performance concrete (UHPC) and ultra-high toughness cementitious composite (UHTCC) offer superior mechanical properties compared to normal concrete, with UHPC excelling in compressive strength and UHTCC in tensile ductility and crack resistance. This study focuses on UHPC/UHTCC-encased steel tubular (UEST) columns, establishing finite element (FE) models to simulate the axial behavior of UEST columns, conducting parametric studies on stud number, encasement thickness, steel yield strength, and width-to-thickness ratio, and developing a theoretical model considering thin-walled steel buckling to calculate the axial resistance of UEST columns. The proposed theoretical model predicts axial resistance with an average error of 3.4%, providing a reliable design method for engineering applications.

In proof-of-work (PoW)-based blockchain network, the blockchain miners publish blocks by contributing computing power to solve crypto-puzzles. Due to the weak computing power of single miner, miners tend to join a mining pool and share the profits from the mining pool according to the contribution proportions of the miners. However, some miners may initiate block withholding attack which may result in wasting computing power, even threatening the efficiency of the blockchain network. To address this problem, in this paper, we use the biform game model to optimize the miners’ strategy choices. We firstly formulate the mining process as a non-cooperative–cooperative biform game model. We use the model to exhibit miners’ strategy choices (non-cooperation stage) and the cooperation mining process (cooperation stage). Then we set the conditions to maintain the voluntary honest behavior of miners. After that, we employ the semi-CIS (semi-the center of imputation set value) value to compute the solutions of the cooperative games in the cooperation stage, and optimize miners’ strategy choices to prevent the block withholding attack. Hence we can ensure the blockchain network is secure. Finally, the validity and applicability of the proposed model and method are verified by a numerical example.

A high Mandard score may indicate the tumor is insensitive to chemotherapy. We analyzed tumor regression and lymph node response under different Mandard scores to assess the impact of Mandard score on prognosis. Methods. Mandard scores and ypN stage of postoperative pathological reports were recorded. The results were reviewed by a professional pathologist. The radiologist compared the tumor regression before and after chemotherapy by computed tomography (CT). The survival of all patients was obtained by telephone follow-up. Multivariate Cox regression was used to assess the relationship between overall risk of death and Mandard score, imaging evaluation, and ypN stage. Results. In the Mandard score (4-5) group, the median survival time for PR and ypN0 patients was 68.5 and 76.7 months. While in the Mandard score (1-2) group, the median survival time for PD and ypN3a patients was 15.6 and 14.5 months. Imaging evaluation of tumor regression (PR 68.5 months, SD 27.8 months, and PD 10.2 months) and lymph node remission (ypN0 76.7 months, ypN1 61.6 months, ypN2 18.0 months, ypN3a 18.7 months, and ypN3b 18.3 months) showed improved survival. Mandard score, imaging evaluation, and ypN stage are important prognostic factors affecting prognosis. Conclusion. A high Mandard score does not mean neoadjuvant chemotherapy is ineffective in gastric cancer. Patients with imaging evaluation of tumor regression and ypN stage reduction may benefit from neoadjuvant chemotherapy.