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Background: Nanoscale connectomics, which aims to map the fine connections between neurons with synaptic-level detail, has attracted increasing attention in recent years. Currently, the automated reconstruction algorithms in electron microscope volumes are in great demand. Most existing reconstruction methodologies for cellular and subcellular structures are independent, and exploring the inter-relationships between structures will contribute to image analysis. The primary goal of this research is to construct a joint optimization framework to improve the accuracy and efficiency of neural structure reconstruction algorithms. Results: In this investigation, we introduce the concept of connectivity consensus between cellular and subcellular structures based on biological domain knowledge for neural structure agglomeration problems. We propose a joint graph partitioning model for solving ultrastructural and neuronal connections to overcome the limitations of connectivity cues at different levels. The advantage of the optimization model is the simultaneous reconstruction of multiple structures in one optimization step. The experimental results on several public datasets demonstrate that the joint optimization model outperforms existing hierarchical agglomeration algorithms. Conclusions: We present a joint optimization model by connectivity consensus to solve the neural structure agglomeration problem and demonstrate its superiority to existing methods. The intention of introducing connectivity consensus between different structures is to build a suitable optimization model that makes the reconstruction goals more consistent with biological plausible and domain knowledge. This idea can inspire other researchers to optimize existing reconstruction algorithms and other areas of biological data analysis. [ABSTRACT FROM AUTHOR]

Neuron reconstruction algorithms used in electron microscope volumes have received increasing attention in recent years. Most current methods are highly reliant on neuron membrane boundary evidence without considering biological plausibility. In this investigation, we present a novel neuron reconstruction framework via the fusion of a global optimization goal and biologically inspired priors. We encode the 3D instances of synapses and mitochondria as two types of constraints to allow for the direct inclusion of non-local connectivity information in the neuron segmentation. Moreover, a flexible decision procedure is designed to retain high-confidence priors to deal with the possible influence of the upstream ultrastructure error. We construct the constrained graph partitioning model and adapt two greedy algorithms with the polynomial time complexity to solve the proposed model. We perform comparative studies on several public datasets and demonstrate that the decision of ultrastructural connectivity constraints contributes to significant improvements over existing hierarchical agglomeration algorithms. The ablation studies of ultrastructures from different recognition accuracy suggest the generality and applicability of the proposed method. • Neuron reconstruction under electron microscopy images is a challenging task. • A novel graph optimization model with connectivity constraints is presented. • A flexible decision procedure is designed to retain high-confidence and reduce error. • Inclusion of ultrastructural connectivity constraints improve the algorithms significantly. [ABSTRACT FROM AUTHOR]

Background: As an extension of electron tomography (ET), serial section electron tomography (serial section ET) aims to align the tomographic images of multiple thick tissue sections together, to break through the volume limitation of the single section and preserve the sub-nanoscale voxel size. It could be applied to reconstruct the intact synapse, which expands about one micrometer and contains nanoscale vesicles. However, there are several drawbacks of the existing serial section ET methods. First, locating and imaging regions of interest (ROIs) in serial sections during the shooting process is time-consuming. Second, the alignment of ET volumes is difficult due to the missing information caused by section cutting and imaging. Here we report a workflow to simplify the acquisition of ROIs in serial sections, automatically align the volume of serial section ET, and semi-automatically reconstruct the target synaptic structure. Results: We propose an intelligent workflow to reconstruct the intact synapse with sub-nanometer voxel size. Our workflow includes rapid localization of ROIs in serial sections, automatic alignment, restoration, assembly of serial ET volumes, and semi-automatic target structure segmentation. For the localization and acquisition of ROIs in serial sections, we use affine transformations to calculate their approximate position based on their relative location in orderly placed sections. For the alignment of consecutive ET volumes with significantly distinct appearances, we use multi-scale image feature matching and the elastic with belief propagation (BP-Elastic) algorithm to align them from coarse to fine. For the restoration of the missing information in ET, we first estimate the number of lost images based on the pixel changes of adjacent volumes after alignment. Then, we present a missing information generation network that is appropriate for small-sample of ET volume using pre-training interpolation network and distillation learning. And we use it to generate the missing information to achieve the whole volume reconstruction. For the reconstruction of synaptic ultrastructures, we use a 3D neural network to obtain them quickly. In summary, our workflow can quickly locate and acquire ROIs in serial sections, automatically align, restore, assemble serial sections, and obtain the complete segmentation result of the target structure with minimal manual manipulation. Multiple intact synapses in wild-type rat were reconstructed at a voxel size of 0.664 nm/voxel to demonstrate the effectiveness of our workflow. Conclusions: Our workflow contributes to obtaining intact synaptic structures at the sub-nanometer scale through serial section ET, which contains rapid ROI locating, automatic alignment, volume reconstruction, and semi-automatic synapse reconstruction. We have open-sourced the relevant code in our workflow, so it is easy to apply it to other labs and obtain complete 3D ultrastructures which size is similar to intact synapses with sub-nanometer voxel size. [ABSTRACT FROM AUTHOR]

• We are the first one to utilize deep residual contextual network with sub-pixel strategy to recover the details of neuronal boundaries in EM image stacks, which significantly improves the accuracy of the segmentation. • On the ISBI EM segmentation challenge, the propose method comes out at the top among the leader board and yields Rand score of 0.98788, which is close to the human accuracy values. • The proposed method contributes to the development of connectomics, which provides neurologists with an effective approach to obtain the segmentation and reconstruction of neurons and helps them reduce the burden of manual neurite labeling and validation. Background and Objective: The goal of micro-connectomics research is to reconstruct the connectome and elucidate the mechanisms and functions of the nervous system via electron microscopy (EM). Due to the enormous variety of neuronal structures, neuron segmentation is among most difficult tasks in connectome reconstruction, and neuroanatomists desperately need a reliable neuronal structure segmentation method to reduce the burden of manual labeling and validation. Methods: In this article, we proposed an effective deep learning method based on a deep residual contextual and subpixel convolution network to obtain the neuronal structure segmentation in anisotropic EM image stacks. Furthermore, lifted multicut is used for post-processing to optimize the prediction and obtain the reconstruction results. Results: On the ISBI EM segmentation challenge, the proposed method ranks among the top of the leader board and yields a Rand score of 0.98788. On the public data set of mouse piriform cortex, it achieves a Rand score of 0.9562 and 0.9318 in the different testing stacks. The evaluation scores of our method are significantly improved when compared with those of state-of-the-art methods. Conclusions: The proposed automatic method contributes to the development of micro-connectomics, which improves the accuracy of neuronal structure segmentation and provides neuroanatomists with an effective approach to obtain the segmentation and reconstruction of neurons. [ABSTRACT FROM AUTHOR]

Tamiflu , the ethyl ester form of oseltamivir carboxylic acid (OC), is the first orally available anti-influenza drug for the front-line therapeutic option. In this study, the OC-hydroxamates, OC-sulfonamides and their guanidino congeners (GOC) were synthesized. Among them, an OC-hydroxamate 7d bearing an O -(2-indolyl)propyl substituent showed potent NA inhibition (IC 50 = 6.4 nM) and good anti-influenza activity (EC 50 = 60.1 nM) against the wild-type H1N1 virus. Two GOC-hydroxamates ( 9b and 9d ) and one GOC-sulfonamide ( 12a ) were active to the tamiflu-resistant H275Y virus (EC 50 = 2.3–6.9 μM). [ABSTRACT FROM AUTHOR]

Examines the involvement of focal adhesion kinase (FAK), a non-receptor protein tyrosine kinase, in mechanosensing during fibroblast migration. Induction of focal adhesions by pulling forces; Migration speed and directional persistence of FAK-null and FAK-expressing cells; Movements of cells on substrates with a rigidity gradient.

Investigates whether substrate flexibility regulates growth and apoptosis of normal but not transformed cells. Determination of cell cycle rate through the measurement of the rate of apoptosis; Influence of substrate flexibility on DNA synthesis of normal and H-ras-transformed cells; Correlation between the degree of cell spreading and the rate of cell growth.

Demonstrates that responses to substrate rigidity play a major role in distinguishing the growth behavior of normal cells from that of transformed cells. Effects of substrate flexibility on cell growth and apoptosis; Effects of substrate flexibility on cell spreading and traction forces.

Background: Internet is gradually reshaping adolescents' behaviors in China. It is important to identify the prevalence and risk factors to predict Internet addiction among adolescent psychiatric patients. Methods: The survey was conducted among inpatient or outpatient adolescent patients with mental disorders. All participants were interviewed face-to-face and completed Young Internet Addiction Test and other relevant assessments. Binary logistic regression analysis was used to exam between-group differences of internet addiction. Result: The prevalence for internet addiction (mild to severe) in adolescent psychiatric patients was 80.2%, where the prevalence for "medium and severe internet addiction was 25.5%. Logistic regression analysis identified two independent predictors for "medium and severe internet addiction" including the total score of Adolescent Non-suicidal-self-injury (NSSI) Behavior Function Assessment Scale (ANBFAS) and the diagnosis of generalized anxiety disorder (GAD) (R2 =0.27, p =.02 for total ANBFAS score, p =.01 for GAD) in psychiatric adolescent patients. Conclusion: The prevalence of internet addiction is notably high among adolescent psychiatric patients in China. Coping for Internet addiction in adolescent psychiatric patients should conclude the coping of NSSI and GAD. [ABSTRACT FROM AUTHOR]

Background: Mental health literacy (MHL) is rarely reported in the Chinese elderly. This study explored the pattern of MHL in the Chinese elderly in relation to depression, anxiety and poor sleep quality. Methods: A cross‐sectional study was conducted among older adults in Guangzhou, south China. Participants were investigated face‐to‐face using the Chinese National Mental Health Literacy Scale, the Patient Health Questionnaire‐9 item (PHQ‐9), the 7‐item Generalized Anxiety Disorder (GAD‐7) and the Pittsburgh Sleep Quality Index (PSQI). Mental Health Literacy Scale contains three dimensions: mental health knowledge, mental health skills (such as social support, cognitive reappraisal and attentional distraction) and mental health awareness. Multivariate logistic regression was used for examining the association between MHL and mental health. Results: A total of 506 older adults were recruited. The percentage of depression, anxiety, and poor sleep quality were 16.6%, 7.9% and 40.9%, respectively. MHL dimensions independently associated with depression included cognitive reappraisal (OR = 1.95, p <.001), attentional distraction (OR = 0.61, p = 0.044) and awareness (OR = 0.56, p = 0.027). MHL dimensions independently associated with anxiety symptoms included cognitive reappraisal (OR = 1.90, p = 0.011) and attentional distraction (OR = 0.44, p = 0.016). MHL dimensions independently associated with poor sleep quality included social support (OR = 0.75, p = 0.022), cognitive reappraisal (OR = 1.55, p = 0.003) and attentional distraction (OR = 0.65, p = 0.016). Conclusion: Given the low MHL and its association with poor mental health in the Chinese elderly, policymakers and health professionals should improve the older adults' MHL, which could be conducive to the prevention and control of their mental health problems. [ABSTRACT FROM AUTHOR]

Current methods of evaluating the degree of diabetic retinopathy are highly subjective and have no quantitative standard. To objectively evaluate the slight changes in tissue structure during the early stage of retinal diseases, a subjective interpretation and qualitative analysis of the pathological sections of retinal HE in diabetic animals is required for screening and evaluating the degree of diabetic retinopathy and drug efficacy. To develop an innovative method for screening and evaluating the degree of diabetic retinopathy and drug treatment based on artificial intelligence algorithms. Based on the change law of the early nerve fiber layer and the ganglion cells, we get disparate characteristics of the microscopic image of diabetes animal retina HE slices. Using image recognition and deep learning methods on these HE slices, we can identify the changes in the ganglion cells and nerve fiber layer for diagnosing early retinopathy and evaluated the therapeutic effect of the potential drugs. We conduct quantitative calculation per unit length of the nerve fiber layer and total area of the nerve fiber layer to identify biology significance of edema. Additionally, we also perform quantitative calculation with the number of unit area ganglion cells to identify the section in biology cell hyperplasia. Finally, we get the significance of quantitative calculation on the unit cell area to identify ganglion cell shriveling in biology. In addition to the evaluation of the disease degree and changes, we also obtained retinal HE sections after different drug interventions and evaluated the therapeutic effect of the drugs. This study presents a novel quantitative method for screening and evaluating of diabetic retinopathy and drug efficacy. [ABSTRACT FROM AUTHOR]