Abnormal Alterations of Regional Spontaneous Neuronal Activity in Inferior Frontal Orbital Gyrus and Corresponding Brain Circuit Alterations: A Resting-State fMRI Study in Somatic Depression

Background: Major depressive disorders often involve somatic symptoms and have been found to have fundamental differences from non-somatic depression (NSD). However, the neural basis of this type of somatic depression (SD) is unclear. The aim of this study is to use the amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) analyses to examine the abnormal, regional, spontaneous, neuronal activity and the corresponding brain circuits in SD patients.Methods: 35 SD patients, 25 NSD patients, and 27 matched healthy controls were selected to complete this study. The ALFF and seed-based FC analyses were employed, and the Pearson correlation was determined to observe possible clinical relevance.Results: Compared with NSD, the SD group showed a significant ALFF increase in the right inferior temporal gyrus; a significant ALFF decrease in left hippocampus, right inferior frontal orbital gyrus and left thalamus; and a significant decrease in the FC value between the right inferior frontal orbital gyrus and the left inferior parietal cortex (p < 0.05, corrected). Within the SD group, the mean ALFF value of the right inferior frontal orbital gyrus was associated with the anxiety factor scores (r = –0.431, p = 0.010, corrected).Conclusions: Our findings suggest that abnormal differences in the regional spontaneous neuronal activity of the right inferior frontal orbital gyrus were associated with dysfunction patterns of the corresponding brain circuits during rest in SD patients, including the limbic-cortical systems and the default mode network. This may be an important aspect of the underlying mechanisms for pathogenesis of SD at the neural level

Thermostat effect on water transport dynamics across CNT membranes

Whereas numerous experimental and simulation studies have been contributed to the investigation of fluid transport across membranes in the past decades, there is a significant discrepancy between experiments and simulations in the magnitude of fluid permeability and the degree of flow rate enhancement. Here, we show that one of the causes of the discrepancy is the variety of thermostating object, via which the temperature of fluid flow in non-equilibrium molecular simulations is controlled. By thermostating either the water system or the membrane material with Langevin method, we examine the temperatures of water flows in two types of membranes, the amounts of absorbed water molecules, fluid velocities, slip lengths and water fluxes. We show that thermostating the CNT membrane brings overall enhanced water flow than directly thermostating the water system. Moreover, increasing the temperature coupling time in the thermostat gives rise to an enhancement of water flux, while weakens the stability of system temperature. In addition to explaining the disparate simulation results on fluid transport in nanopores, this work provides guidelines for diagnosing the setting of NEMD simulations

Complete chloroplast genome sequence of Lirianthe coco (Loureiro) N. H. Xia & C. Y. Wu (Magnoliaceae), a popular ornamental species

Lirianthe coco (Loureiro) N. H. Xia & C. Y. Wu is a popular ornamental species of Magnoliaceae. In the present study, the complete chloroplast genome (cpDNA) of L. coco was sequenced, assembled, and analyzed. The results indicated that the size of chloroplast genome of L. coco is 159,828 bp, which exhibits a typical quadripartite structure including a large single-copy (LSC) region of 87,958 bp and a small single-copy (SSC) region of 18,768 bp separated by a pair of identical inverted repeat regions (IRs) of 26,551 bp each. The genome contained 131 genes (113 unique), including 86 protein-coding genes (80 unique), 37 tRNA genes (29 unique), and 8 rRNA genes (4 unique). Phylogenetic analysis showed that L. coco is affinal to L. odoratissima and forms a nomophyletic group with the latter and L. delavayi

Discrete Element Simulation of the Road Slope Considering Rainfall Infiltration

Rainfall infiltration is the primary factor that induces landslides. In this paper, discrete element software (PFC3D) was used to establish a novel rainfall infiltration model, which integrates water transfer, intensity decay and seepage force into the calculation of the moisture field. By applying this model to the rainfall infiltration analysis of a road slope in Nanping City, Fujian Province, China, the distribution law of water content, the functional relationship between shear strength and water content, and the calculation of permeability at different times can be obtained. The model was verified by comparing simulated results of water content with field monitoring data. The simulation error of water content is lower than 10%. Furthermore, this model application was validated by reproducing the pressure variation of the retaining wall on 12 May 2022. To obtain the accuracy of this model application, it was compared with saturated water content model and seepage force model. The comparison results of the three models showed that the simulation results of this model are best matching with the observation data. Moreover, the verification and validation indicate that our proposed model can be used to effectively analyze the rainfall infiltration of road slope

Discrete Element Simulation of the Road Slope Considering Rainfall Infiltration

Rainfall infiltration is the primary factor that induces landslides. In this paper, discrete element software (PFC3D) was used to establish a novel rainfall infiltration model, which integrates water transfer, intensity decay and seepage force into the calculation of the moisture field. By applying this model to the rainfall infiltration analysis of a road slope in Nanping City, Fujian Province, China, the distribution law of water content, the functional relationship between shear strength and water content, and the calculation of permeability at different times can be obtained. The model was verified by comparing simulated results of water content with field monitoring data. The simulation error of water content is lower than 10%. Furthermore, this model application was validated by reproducing the pressure variation of the retaining wall on 12 May 2022. To obtain the accuracy of this model application, it was compared with saturated water content model and seepage force model. The comparison results of the three models showed that the simulation results of this model are best matching with the observation data. Moreover, the verification and validation indicate that our proposed model can be used to effectively analyze the rainfall infiltration of road slope

Complete chloroplast genome sequence of Michelia champaca var. champaca Linnaeus, an ornamental tree species of Magnoliaceae

Michelia champaca var. champaca is an ornamentally important tree in Magnoliaceae. The paper reported the complete chloroplast genome (cpDNA) of M. champaca var. champaca and its basic annotated information. The size of cpDNA is 160,008 bp, with a typical quadripartite structure of a large single-copy (LSC) region of 88,037 bp and a small single-copy (SSC) region of 18,809 bp separated by a pair identical inverted repeat regions (IRs) of 26,581 bp each. The genome contained 131 genes (113 unique), including 86 protein-coding genes (80 unique), 37 tRNA genes (29 unique), and eight rRNA genes (four unique). Phylogenetic analysis showed that M. champaca var. champaca is affinal to M. baillonii and they form a nomophyletic group with other eight Michelia species. This Michelia clade is sister to the Aromadendron cathcartii clade with high support. All genera mentioned in this analysis are nomophyletic under the system of Magnoliaceae by Sima and Lu

A Multi-Objective Optimization Method of a Mobile Robot Milling System Construction for Large Cabins

Constructing mobile robot milling systems with multiple mounting surfaces for large cabins still has several unsolved issues, such as huge economic and time costs, unpredictable milling accuracy and milling time. Hence, a multi-objective optimization method for constructing a mobile robot milling system of large cabins is proposed in the current paper. Firstly, mathematical models of constructing the system and the optimization objective function are established. Thereafter, a multi-objective optimization method for the mobile robot milling system construction based on NSGA-II (Fast Non-dominated Sorting Genetic Algorithm) is proposed. Finally, feasibility and validity of the proposed method are verified through comparing the optimization result with two practical mobile robot systems. Results show that the proposed method is able to estimate different combinations’ milling accuracy, cost and time consumption

High Performance Drain Engineered InGaN Heterostructure Tunnel Field Effect Transistor

A drain engineered InGaN heterostructure tunnel field effect transistor (TFET) is proposed and investigated by Silvaco Atlas simulation. This structure uses an additional metal on the drain region to modulate the energy band near the drain/channel interface in the drain regions, and increase the tunneling barrier for the flow of holes from the conduction band of the drain to the valence band of the channel region under negative gate bias for n-TFET, which induces the ambipolar current being reduced from 1.93 × 10−8 to 1.46 × 10−11 A/μm. In addition, polar InGaN heterostructure TFET having a polarization effect can adjust the energy band structure and achieve steep interband tunneling. The average subthreshold swing of the polar drain engineered heterostructure TFET (DE-HTFET) is reduced by 53.3% compared to that of the nonpolar DE-HTFET. Furthermore, ION increases 100% from 137 mA/mm of nonpolar DE-HTFET to 274 mA/mm of polar DE-HTFET

Evaluation and machine learning improvement of global hydrological model-based flood simulations

A warmer climate is expected to accelerate global hydrological cycle, causing more intense precipitation and floods. Despite recent progress in global flood risk assessment, the accuracy and improvement of global hydrological models (GHMs)-based flood simulation is insufficient for most applications. Here we compared flood simulations from five GHMs under the Inter-Sectoral Impact Model Intercomparison Project 2a (ISIMIP2a) protocol, against those calculated from 1032 gauging stations in the Global Streamflow Indices and Metadata Archive for the historical period 1971–2010. A machine learning approach, namely the long short-term memory units (LSTM) was adopted to improve the GHMs-based flood simulations within a hybrid physics- machine learning approach (using basin-averaged daily mean air temperature, precipitation, wind speed and the simulated daily discharge from GHMs-CaMa-Flood model chain as the inputs of LSTM, and observed daily discharge as the output value). We found that the GHMs perform reasonably well in terms of amplitude of peak discharge but are relatively poor in terms of their timing. The performance indicated great discrepancy under different climate zones. The large difference in performance between GHMs and observations reflected that those simulations require improvements. The LSTM used in combination with those GHMs was then shown to drastically improve the performance of global flood simulations (especially in terms of amplitude of peak discharge), suggesting that the combination of classical flood simulation and machine learning techniques might be a way forward for more robust and confident flood risk assessment