Dynamic Landscape Fragmentation and the Driving Forces on Haitan Island, China

Island ecosystems have distinct and unique vulnerabilities that place them at risk from threats to their ecology and socioeconomics. Spatially exhibiting the fragmentation process of island landscapes and identifying their driving factors are the fundamental prerequisites for the maintenance of island ecosystems and the rational utilization of islands. Haitan Island was chosen as a case study for understanding landscape fragmentation on urbanizing Islands. Based on remote sensing technology, three Landsat images from 2000 to 2020, landscape pattern index, transect gradient analysis, and moving window method were used in this study. The results showed that from 2000 to 2020, impervious land increased by 462.57%. In 2000, the predominant landscape was cropland (46.34%), which shifted to impervious land (35.20%) and forest (32.90%) in 2020. Combining the moving window method and Semivariogram, 1050 m was considered to be the best scale to reflect the landscape fragmentation of Haitan Island. Under this scale, it was found that the landscape fragmentation of Haitan Island generally increased with time and had obvious spatial heterogeneity. We set up sampling bands along the coastline and found that the degree of landscape fragmentation, advancing from the coast inland, was decreasing. Transects analysis showed the fragmentation intensity of the coastal zone: the north-western and southern wooded zones decreased, while the concentration of urban farmland in the north-central and southern areas increased. The implementation of a comprehensive experimental area plan on Haitan Island has disturbed the landscape considerably. In 2000, landscape fragmentation was mainly influenced by topography and agricultural production. The critical infrastructure construction, reclamation and development of landscape resources have greatly contributed to the urbanisation and tourism of Haitan Island, and landscape fragmentation in 2013 was at its highest. Due to China’s “Grain for Green Project” and the Comprehensive Territorial Spatial Planning policy (especially the protection of ecological control lines), the fragmentation of Haitan Island was slowing. This study investigated the optimal spatial scale for analyzing spatiotemporal changes in landscape fragmentation on Haitan Island from 2000 to 2020, and the essential influencing factors in urban islands from the perspective of natural environment and social development, which could provide a basis for land use management and ecological planning on the island.

Antidepressant Treatment-Induced State-Dependent Reconfiguration of Emotion Regulation Networks in Major Depressive Disorder

Deficits in emotion regulation are the main clinical features, common risk factors, and treatment-related targets for major depressive disorder (MDD). The neural bases of emotion regulation are moving beyond specific functions and emphasizing instead the integrative functions of spatially distributed brain areas that work together as large-scale brain networks, but it is still unclear whether the dynamic interactions among these emotion networks would be the target of clinical intervention for MDD. Data were collected from 70 MDD patients and 43 sex- and age-matched healthy controls. The dynamic functional connectivity (dFC) between emotion regions was estimated via a sliding-window method based on resting-state functional magnetic resonance imaging (R-fMRI). A k-means clustering method was applied to classify all time windows across all participants into several dFC states reflecting recurring functional interaction patterns among emotion regions over time. The results showed that four dFC states were identified in the emotion networks. Their alterations of state-related occurrence proportion were found in MDD and subsequently normalized following 12-week antidepressant treatment. Baseline strong dFC could predict the reduction rate of Hamilton Depression Rating Scale (HAMD) scores. These findings highlighted the state-dependent reconfiguration of emotion regulation networks in MDD patients owing to antidepressant treatment.

Mononuclear subsets and cytokine profile of venous and capillary blood in patients with psoriasis and healthy people

Psoriasis is considered an autoimmune disease with a predominantly cellular mechanism for the development of disorder. Studies in immune pathogenesis of psoriasis were performed either in animal model, which is not just similar to humans, or the data were obtained in patients by means of skin window method, which is traumatic, or by examining venous blood. However, it is difficult to discern parameters of the local immune response in venous blood samples. We have attempted to find an adequate method which would be convenient both for the patient and for the researcher, in order to assess local immune processes occurring in the skin affected by psoriasis. We examined 20 patients with a verified diagnosis of psoriasis, the average age was 44.3 years. The control group included 15 healthy adults, with average age of 46.6 years. Capillary blood was taken by fingerprick, whereas, in psoriatic patients, the samples were taken near the psoriatic lesion at a final volume of 400 μL in two microvettes. Venous blood (3 mL) was taken from the cubital vein into a vacuum tube with EDTA. Clinical analysis of venous and capillary blood was performed in automated hematological analyzer. Immunophenotyping was performed by four-color staining of whole capillary and venous blood followed by lysis of erythrocytes. Cytofluorometry was performed using techniques and reagents from BD Biosciences (USA). Plasma cytokines were determined by multiplex approach (MagPix, BioRad, USA). Upon clinical analysis of blood, the difference between capillary and venous blood was not found, either in healthy group, or among patients with psoriasis. In healthy people, the subsets of mononuclear cells, did not differ between venous and capillary blood. The samples of capillary and venous blood in the patients with psoriasis showed significantly increased levels of double-positive lymphocytes (CD45RA+/CD45R0+), B lymphocytes and NKT lymphocytes (both for relative and and absolute values). A significant increase in the percentage of naive T lymphocytes, activated helpers (Thact) and Treg, as well as B1 cells and Breg, and a significant decrease in B2 lymphocytes was registered in capillary blood of the patients with psoriasis. In venous blood samples from psoriatic patients, only a significant increase in Thact, Treg, and Breg was revealed. In the capillary blood of patients with psoriasis, we found a significant increase in the levels of non-classical M2 monocytes and inflammatory Minfl monocytes, and a decrease in classical M1 monocyte levels; in venous blood of psoriatic patients, only an increase in inflammatory Minfl monocytes was revealed. In capillary blood, all the studied cytokines in psoriasis patients significantly exceeded the levels of corresponding cytokines in healthy controls, except of IL-10. The levels of this cytokine did not differ from healthy group. In venous blood, the levels of most studied cytokines in the group of patients with psoriasis did not differ from the group of healthy ones. Approximately two-fold increase was revealed for IL-4, IL-21, IL-23 and TNF. First, the subsets of mononuclear cells and the cytokine profile of capillary and venous blood of healthy people did not differ significantly. Secondly, our proposed method for determining the subsets of mononuclear cells and capillary blood cytokines profile from the area of psoriatic lesions may be used to monitor local immunity in the patients with psoriasis. This approach is significantly less traumatic than the skin window method and more informative than the studies of venous blood.

Implementation and Application of Fall Detection on Mobile Perception Robot

With the rapid increase in the number of people living in the elderly population, reducing and dealing with the problem of falls in the elderly has become the focus of research for decades. It is impossible to completely eliminate falls in daily life and activities. Detecting a fall in time can protect the elderly from injury as much as possible. This article uses the Turtlebot robot and the ROS robot operating system, combined with simultaneous positioning and map construction technology, Monte Carlo positioning, A* path planning, dynamic window method, and indoor map navigation. The YOLO network is trained using the stance and fall data sets, and the YOLOv4 target detection algorithm is combined with the robot perception algorithm to finally achieve fall detection on the turtlebot robot, and use the average precision, precision, recall and other indicators to measure.

Deep Learning Technology in Pathological Image Analysis of Breast Tissue

To explore the application value of the multilevel pyramid convolutional neural network (MPCNN) model based on convolutional neural network (CNN) in breast histopathology image analysis, in this study, based on CNN algorithm and softmax classifier (SMC), a sparse autoencoder (SAE) is introduced to optimize it. The sliding window method is used to identify cells, and the CNN + SMC pathological image cell detection method is established. Furthermore, the local region active contour (LRAC) is introduced to optimize it and the LRAC fine segmentation model driven by local Gaussian distribution is established. On this basis, the sparse automatic encoder is further introduced to optimize it and the MPCNN model is established. The proposed algorithm is evaluated on the pathological image data set. The results showed that the Acc value, F value, and Re value of pathological cell detection of CNN + SMC algorithm were significantly higher than those of the other two algorithms ( P < 0.05). The Dice, OL, Sen, and Spe values of pathological image regional segmentation of CNN algorithm were significantly higher than those of the other two algorithms, and the difference was statistically significant ( P < 0.05). The accuracy, recall, and F-measure of the optimized CNN algorithm for detecting breast histopathological images were 85.25%, 89.27%, and 80.09%, respectively. In the two databases with segmentation standards, the segmentation accuracy of MPCNN is 55%, 73.1%, 78.8%, and 82.1%. In the deep convolution network model, the training time of the MPCNN algorithm is about 80 min. It shows that when the feature dimension is low, the feature map extracted by MPCNN is more effective than the traditional feature extraction method.

Altered Dynamic Amplitude of Low-frequency Fluctuations in Patients with Postpartum Depression

Background Postpartum depression (PPD) is a common mood disorder with increasing incidence year by year. However, the dynamic changes in local neural activity remain unclear. In this study, we utilized the dynamic amplitude of low-frequency (dALFF) to investigate the abnormal temporal variability of local neural activity. Methods Twenty-four patients with PPD and nineteen healthy postpartum women controls (HCs) matched for age, education level and body mass index were examined by resting-state functional magnetic resonance imaging (rs-fMRI). A sliding-window method was used to assess the dALFF, and a k-means clustering method was used to identify dALFF states. Two-sample t-test was used to compare the differences of dALFF variability and state metrics between PPD and HCs. Pearson correlation analysis was used to analyze the relationship between dALFF variability, states metrics and clinical severity. Results (1) Patients with PPD had lower variance of dALFF than HCs in the cognitive control network, cerebellar network, and sensorimotor network. (2) Four dALFF states were identified, and the number of transitions between the four dALFF states increased in the patients compared with that in HCs. (3) Multiple dALFF states were found to be correlated with the severity of depression. The variance of dALFF in the right middle frontal gyrus was negatively correlated with the Edinburgh postnatal depression scale score. Conclusion This study provides new insights into the brain dysfunction of PPD from the perspective of dynamic local brain activity, and highlights its important role in understanding the neurophysiological mechanisms of PPD.

Altered static and dynamic functional network connectivity in post-traumatic headache

Background Post-traumatic headache (PTH) is a very common symptom following mild traumatic brain injury (mTBI), yet much remains unknown about the underlying pathophysiological mechanisms of PTH. Neuroimaging studies suggest that aberrant functional network connectivity (FNC) may be an important factor in pain disorders. The present study aimed to investigate the functional characteristics of static FNC (sFNC) and dynamic FNC (dFNC) in mTBI patients with PTH. Methods With Institutional Review Board (IRB) approval, we prospectively recruited 50 mTBI patients with PTH, who were diagnosed with ICHD-3 beta diagnostic criteria and 39 mTBI without PTH who were well matched for age, gender and education. Resting-state functional magnetic resonance imaging (fMRI) scanning (3.0 T, Philips Medical Systems, Netherlands), Montreal Cognitive Assessment (MoCA) and headache symptom measurement (headache frequency and headache intensity) were performed. The resting-state fMRI sequence took 8 min and 10 s. Independent component analysis and sliding window method were applied to examine the FNC on the basis of nine resting-state networks, namely, default mode network (DMN), sensorimotor network (SMN), executive control network (ECN), auditory network (AuN), attention network (AN), salience network (SN), visual network (VN), and cerebellum network (CN). The differences in sFNC and dFNC were determined and correlated with clinical variables using Pearson rank correlation. Results For sFNC, compared with mTBI patients without PTH, mTB with PTH group showed four altered interactions, including decreased interactions in SN-SMN and VN-DMN pairs, increased sFNC in SN-ECN and SMN-DMN pairs. For dFNC, significant group differences were found in State 2, including increased connectivity alteration in the DMN with CN, DMN with SMN, and AuN with CN. Significant reduced connectivity changes in the DMN with VN was found in State 4. Furthermore, the number of transitions (r=0.394, p=0.005) between states was positively associated with headache frequency. Additionally, dwell time (r=-0.320, p=0.025) in State 1 was negatively correlated with MoCA score. Conclusions MTBI patients with PTH are characterized with altered sFNC and dFNC, which could provide new perspective to understand the neuropathological mechanism underlying the PTH to determine more appropriate management, and may be a useful imaging biomarker for identifying and predicting mTBI with PTH.