Hypodopaminergic state of the nigrostriatal pathway drives compulsive alcohol use

The neurobiological mechanisms underlying compulsive alcohol use, a cardinal feature of alcohol use disorder, remain elusive even though they have often been suggested to involve dopamine (DA). Here, we found that rats expressing compulsive alcohol-related behavior, operationalized as punishment-resistant self-administration, showed a decrease in DA levels restricted to the dorsolateral territories of the striatum, the main output structure of the nigrostriatal DA pathway. We then causally demonstrated that a chemogenetic-induced selective hypodopaminergia of this pathway results in compulsive alcohol self-administration in rats otherwise resilient, accompanied by the emergence of alcohol withdrawal-like motivational impairments. These results demonstrate a major implication of tonic nigrostriatal hypodopaminergic state in alcohol addiction and provide new insights into our understanding of the neurobiological mechanisms underlying compulsive alcohol use.

The value chain of traded products of medicinal plants in Tanzania: the emerging role of formulators

PurposeProducts of medicinal plants play significant roles in management of diseases. Their accessibility through trade plays a key role in health, economic and livelihood improvement. However, the traceability of the production process from their source in Tanzania is lacking. This study aims to depicture the production process of formulated products of medicinal plants.Design/methodology/approachThe study applied the value chain theory using qualitative data from literature review and survey to practitioners of traditional medicine. Survey data were collected through 15 in-depth interviews and ten focus group discussions in five regions of Tanzania.FindingsInput to output structure is performed through a five actors' value chain. The raw material is provided by harvesters who collected medicinal plants from wild. The processing is conducted by wholesalers and formulators. The wholesalers add value by drying, milling and bulk packaging of individual medicinal plants. Formulators mix different medicinal plants to create readymade products for specified diseases. Distribution is done by retailers and healers. There were six regulating and two supporting organizations. Private supporters were millers and transporters. Governance structure was deduced to be relational. Relational governance was a result of lack official standards along the value chain.Originality/valueThe described value chain can be used to guide investments in production of products of medicinal plants by improving formulation technology.

Inter-Industry Structural Convergence of Bulgaria’s Economy to the Eurozone

The article presents an analysis of the inter-industry structural convergence process of Bulgaria’s economy to the Eurozone for the 2000–2018 period. In order to study the specific characteristics of the process, the article explores the dynamics of the relative shares of separate industries (economic activities) in gross value added and compares them to the reference economy, along with possible explanations and implications. The divergence index is employed as a quantitative measure of the degree of structural similarity, and comparisons to other EU economies with similar characteristics are made as well. Results indicate that the output structure of Bulgaria’s economy is slowly converging towards the Eurozone throughout the period despite some variation in developments before and after the 2009 recession.

The driving force effect of trade embodied carbon emissions and embodied SO2 emissions transferred in resources-rich areas: A case study of Shanxi province

SDA (Structural Decomposition Analysis) model was applied to analyze the driving factors of embodied carbon and SO2 emissions transferred in Shanxi during 2007–2012 based on the input-output model from the perspectives of region and industry. The results showed that the change of embodied carbon emissions and embodied SO2 emissions of Shanxi and other regions were hindered by the carbon (sulfur) emissions strength effect, but promoted by the intermediate (final) demand scale effect, the intermediate (final) structure effect and the input-output structure effect. The carbon emissions strength effect had a significant contribution to reducing the embodied carbon emissions transferred from industries in Shanxi to other regions. The intermediate (final) demand scale effect was the driving factor to increase the embodied carbon emissions transferred from industries in Shanxi to other regions. The sulfur emissions strength effect was the only factor that reduced the embodied SO2 emissions transferred from Shanxi to other industries. The change of embodied carbon emissions from industries in other regions to Shanxi was hindered by the carbon emissions strength effect, but the input-output structure effect and final demand scale effect both increased the embodied carbon emissions from industries in other regions to Shanxi. The change of the embodied SO2 emissions transferred from industries in other regions to Shanxi was inhibited by the sulfur emissions strength effect, but the input-output structure effect, the intermediate demand structure effect and the final demand scale effect were both the driving force effect of increasing the embodied SO2 emissions transferred from industries in other regions to Shanxi. The corresponding suggestions and measures were put forward.

A new approach to modelling the input–output structure of regional economies using non-survey methods

This paper proposes a new approach to the regionalization of national input–output tables where suitable regional data are scarce and analysts are considering using location quotients (LQs). We focus on the FLQ formula, which frequently yields the best results of the pure LQ-based methods, and develop an enhanced way of implementing this approach. We use a modified cross-entropy (MCE) method, along with a regression model, to estimate values of the unknown parameter δ in the FLQ formula, specific to both region and country. An analysis of survey-based data for 16 South Korean regions reveals that the proposed FLQ+ approach yields more accurate estimates of both input coefficients and sectoral output multipliers than those from simpler LQ-based methods or the MCE approach alone. Sectoral outputs (or employment) are the only regional data required. The MCE method also clearly outperforms GRAS.

Learning U-Net Based Multi-Scale Features in Encoding-Decoding for MR Image Brain Tissue Segmentation

Accurate brain tissue segmentation of MRI is vital to diagnosis aiding, treatment planning, and neurologic condition monitoring. As an excellent convolutional neural network (CNN), U-Net is widely used in MR image segmentation as it usually generates high-precision features. However, the performance of U-Net is considerably restricted due to the variable shapes of the segmented targets in MRI and the information loss of down-sampling and up-sampling operations. Therefore, we propose a novel network by introducing spatial and channel dimensions-based multi-scale feature information extractors into its encoding-decoding framework, which is helpful in extracting rich multi-scale features while highlighting the details of higher-level features in the encoding part, and recovering the corresponding localization to a higher resolution layer in the decoding part. Concretely, we propose two information extractors, multi-branch pooling, called MP, in the encoding part, and multi-branch dense prediction, called MDP, in the decoding part, to extract multi-scale features. Additionally, we designed a new multi-branch output structure with MDP in the decoding part to form more accurate edge-preserving predicting maps by integrating the dense adjacent prediction features at different scales. Finally, the proposed method is tested on datasets MRbrainS13, IBSR18, and ISeg2017. We find that the proposed network performs higher accuracy in segmenting MRI brain tissues and it is better than the leading method of 2018 at the segmentation of GM and CSF. Therefore, it can be a useful tool for diagnostic applications, such as brain MRI segmentation and diagnosing.