Migration governance and agrarian and rural development: Comparative lessons from China, Ethiopia, Kyrgyzstan, Moldova, Morocco, Nepal and Thailand

The purpose of this policy brief is to draw together key comparative lessons on different types of migration governance interventions in the AGRUMIG project research regions and examine how they support positive feedback loops between migration and agrarian and rural development. This exploration offers stories of success and omission. Moving beyond the elusive triple-win situation on the benefits of migration for destination and origin countries, migrants themselves and the highly politicized domain of the migration-development nexus, our point of departure is that there are vital prospects for augmenting the positive impacts of migration for societies globally. This brief focuses on how migration governance interventions are potentially useful in maximizing the gains between migration and agrarian development in the sending communities in China, Ethiopia, Kyrgyzstan, Moldova, Morocco, Nepal and Thailand

DeePMD-kit v2: A software package for Deep Potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.Comment: 51 pages, 2 figure

Numerical Simulation and Analysis on Spray Drift Movement of Multirotor Plant Protection Unmanned Aerial Vehicle

In recent years, multirotor unmanned aerial vehicles (UAVs) have become more and more important in the field of plant protection in China. Multirotor unmanned plant protection UAVs have been widely used in vast plains, hills, mountains, and other regions, and become an integral part of China’s agricultural mechanization and modernization. The easy takeoff and landing performances of UAVs are urgently required for timely and effective spraying, especially in dispersed plots and hilly mountains. However, the unclearness of wind field distribution leads to more serious droplet drift problems. The drift and distribution of droplets, which depend on airflow distribution characteristics of UAVs and the droplet size of the nozzle, are directly related to the control effect of pesticide and crop growth in different growth periods. This paper proposes an approach to research the influence of the downwash and windward airflow on the motion distribution of droplet group for the SLK-5 six-rotor plant protection UAV. At first, based on the Navier-Stokes (N-S) equation and SST k–ε turbulence model, the three-dimensional wind field numerical model is established for a six-rotor plant protection UAV under 3 kg load condition. Droplet discrete phase is added to N-S equation, the momentum and energy equations are also corrected for continuous phase to establish a two-phase flow model, and a three-dimensional two-phase flow model is finally established for the six-rotor plant protection UAV. By comparing with the experiment, this paper verifies the feasibility and accuracy of a computational fluid dynamics (CFD) method in the calculation of wind field and spraying two-phase flow field. Analyses are carried out through the combination of computational fluid dynamics and radial basis neural network, and this paper, finally, discusses the influence of windward airflow and droplet size on the movement of droplet groups

Association between Helicobacter pylori Infection and Nonalcoholic Fatty Liver Disease: A Single-Center Clinical Study

Objective. To investigate the association between Helicobacter pylori (H. pylori) infection and nonalcoholic fatty liver disease (NAFLD). Methods. Data from 2051 participants who underwent 13C urea breath test and abdominal ultrasound examinations was collected. Participants were allocated to NAFLD risk group and NAFLD nonrisk group based on definite risk factors for NAFLD. The relationship between H. pylori infection and NAFLD was analyzed. Results. No significant difference was found between rates of H. pylori infection and NAFLD using the chi-square test (P=0.30) or regression analysis (P=0.70). There was no significant difference between rates of H. pylori infection with and without NAFLD (P=0.47) in the NAFLD risk group or in the NAFLD nonrisk group (P=0.59). There was no significant difference between rates of H. pylori infection in men (P=0.69) and in women (P=0.27) or in participants aged 18–40 years (P=0.43), 41–65 years (P=0.14), and ≥66 years (P=0.66) with and without NAFLD in the NAFLD risk group or between the same sex or age groups (P=0.82, P=0.66, P=0.24, P=0.53, and P=1.00, resp.) in the NAFLD nonrisk group. Conclusions. H. pylori infection does not appear to increase the NAFLD prevalence rate or to be associated with, or a risk factor for, NAFLD

Association between Helicobacter pylori Infection and Nonalcoholic Fatty Liver Disease: A Single-Center Clinical Study

Objective. To investigate the association between Helicobacter pylori (H. pylori) infection and nonalcoholic fatty liver disease (NAFLD). Methods. Data from 2051 participants who underwent 13C urea breath test and abdominal ultrasound examinations was collected. Participants were allocated to NAFLD risk group and NAFLD nonrisk group based on definite risk factors for NAFLD. The relationship between H. pylori infection and NAFLD was analyzed. Results. No significant difference was found between rates of H. pylori infection and NAFLD using the chi-square test (P=0.30) or regression analysis (P=0.70). There was no significant difference between rates of H. pylori infection with and without NAFLD (P=0.47) in the NAFLD risk group or in the NAFLD nonrisk group (P=0.59). There was no significant difference between rates of H. pylori infection in men (P=0.69) and in women (P=0.27) or in participants aged 18–40 years (P=0.43), 41–65 years (P=0.14), and ≥66 years (P=0.66) with and without NAFLD in the NAFLD risk group or between the same sex or age groups (P=0.82, P=0.66, P=0.24, P=0.53, and P=1.00, resp.) in the NAFLD nonrisk group. Conclusions. H. pylori infection does not appear to increase the NAFLD prevalence rate or to be associated with, or a risk factor for, NAFLD

ILC2 Cells Promote Th2 Cell Differentiation in AECOPD Through Activated Notch-GATA3 Signaling Pathway

This study is to investigate the capacity of type 2 innate lymphoid cells (ILC2s) in regulating the Th2 type adaptive immune response of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). The study enrolled healthy people, stable chronic obstructive pulmonary disease (COPD) patients, and AECOPD patients. Flow cytometry was used to detect Th2 and ILC2 cells in the peripheral blood. In addition, ILC2s from the peripheral blood of AECOPD patients were stimulated with PBS, IL-33, Jagged1, DAPT, IL-33+Jagged1, IL-33+DAPT, and IL-33+Jagged-1+DAP in vitro. The levels of cytokines in the culture supernatant were detected by ELISA and the culture supernatant was used to culture CD4 + T cells. The mRNA and protein levels of Notch1, hes1, GATA3, RORα, and NF-κB of ILC2s were detected by real-time PCR and Western blot. The proportion of Th2 and ILC2s was significantly increased in the peripheral blood of AECOPD patients, alone with the increased Notch1, hes1, and GATA3 mRNA levels. In vitro results showed that the mRNA and protein levels of Notch1, hes1, GATA3 and NF-κB were significantly increased after stimulation with Notch agonist, meanwhile, the level of type 2 cytokines were increased in the supernatant of cells stimulated with Notch agonist, and significantly promoted differentiation of Th2 cells in vitro. Disruption of Notch pathway weakened GATA3 expression and cytokine production, and ultimately affected the differentiation of Th2 cells. In conclusion, our results suggest that ILC2s can promote Th2 cell differentiation in AECOPD via activated Notch-GATA3 signal pathway

Obstructive sleep apnea is related to alterations in fecal microbiome and impaired intestinal barrier function

Abstract Obstructive Sleep Apnea (OSA) is related to repeated upper airway collapse, intermittent hypoxia, and intestinal barrier dysfunction. The resulting damage to the intestinal barrier may affect or be affected by the intestinal microbiota. A prospective case–control was used, including 48 subjects from Sleep Medicine Center of Nanfang Hospital. Sleep apnea was diagnosed by overnight polysomnography. Fecal samples and blood samples were collected from subjects to detect fecal microbiome composition (by 16S rDNA gene amplification and sequencing) and intestinal barrier biomarkers—intestinal fatty acid-binding protein (I-FABP) and D-lactic acid (D-LA) (by ELISA and colorimetry, respectively). Plasma D-LA and I-FABP were significantly elevated in patients with OSA. The severity of OSA was related to differences in the structure and composition of the fecal microbiome. Enriched Fusobacterium, Megamonas, Lachnospiraceae_UCG_006, and reduced Anaerostipes was found in patients with severe OSA. Enriched Ruminococcus_2, Lachnoclostridium, Lachnospiraceae_UCG_006, and Alloprevotella was found in patients with high intestinal barrier biomarkers. Lachnoclostridium and Lachnospiraceae_UCG_006 were the common dominant bacteria of OSA and intestinal barrier damage. Fusobacterium and Peptoclostridium was independently associated with apnea–hypopnea index (AHI). The dominant genera of severe OSA were also related to glucose, lipid, neutrophils, monocytes and BMI. Network analysis identified links between the fecal microbiome, intestinal barrier biomarkers, and AHI. The study confirms that changes in the intestinal microbiota are associated with intestinal barrier biomarkers among patients in OSA. These changes may play a pathophysiological role in the systemic inflammation and metabolic comorbidities associated with OSA, leading to multi-organ morbidity of OSA