Cyclin-G-associated kinase GAK/dAux regulates autophagy initiation via ULK1/Atg1 in glia.

Autophagy is a major means for the elimination of protein inclusions in neurons in neurodegenerative diseases such as Parkinson's disease (PD). Yet, the mechanism of autophagy in the other brain cell type, glia, is less well characterized and remains largely unknown. Here, we present evidence that the PD risk factor, Cyclin-G-associated kinase (GAK)/Drosophila homolog Auxilin (dAux), is a component in glial autophagy. The lack of GAK/dAux increases the autophagosome number and size in adult fly glia and mouse microglia, and generally up-regulates levels of components in the initiation and PI3K class III complexes. GAK/dAux interacts with the master initiation regulator UNC-51like autophagy activating kinase 1/Atg1 via its uncoating domain and regulates the trafficking of Atg1 and Atg9 to autophagosomes, hence controlling the onset of glial autophagy. On the other hand, lack of GAK/dAux impairs the autophagic flux and blocks substrate degradation, suggesting that GAK/dAux might play additional roles. Importantly, dAux contributes to PD-like symptoms including dopaminergic neurodegeneration and locomotor function in flies. Our findings identify an autophagy factor in glia; considering the pivotal role of glia under pathological conditions, targeting glial autophagy is potentially a therapeutic strategy for PD.

Intracellular Protein Adsorption Behavior and Biological Effects of Polystyrene Nanoplastics in THP-1 Cells.

Micro(nano)plastics (MNPs) are emerging pollutants that can adsorb pollutants in the environment and biological molecules and ultimately affect human health. However, the aspects of adsorption of intracellular proteins onto MNPs and its biological effects in cells have not been investigated to date. The present study revealed that 100 nm polystyrene nanoplastics (NPs) could be internalized by THP-1 cells and specifically adsorbed intracellular proteins. In total, 773 proteins adsorbed onto NPs with high reliability were identified using the proteomics approach and analyzed via bioinformatics to predict the route and distribution of NPs following cellular internalization. The representative proteins identified via the Kyoto Encyclopedia of Genes and Genomes pathway analysis were further investigated to characterize protein adsorption onto NPs and its biological effects. The analysis revealed that NPs affect glycolysis through pyruvate kinase M (PKM) adsorption, trigger the unfolded protein response through the adsorption of ribophorin 1 (RPN1) and heat shock 70 protein 8 (HSPA8), and are chiefly internalized into cells through clathrin-mediated endocytosis with concomitant clathrin heavy chain (CLTC) adsorption. Therefore, this work provides new insights and research strategies for the study of the biological effects caused by NPs.

Ethylene-responsive SbWRKY50 suppresses leaf senescence by inhibition of chlorophyll degradation in sorghum.

The onset of leaf de-greening and senescence is governed by a complex regulatory network including environmental cues and internal factors such as transcription factors (TFs) and phytohormones, in which ethylene (ET) is one key inducer. However, the detailed mechanism of ET signalling for senescence regulation is still largely unknown. Here, we found that the WRKY TF SbWRKY50 from Sorghum bicolor L., a direct target of the key component ETHYLENE INSENSITIVE 3 in ET signalling, functioned for leaf senescence repression. The clustered regularly interspaced short palindromic repeats/CRISPR-associated protein9-edited SbWRKY50 mutant (SbWRKY5O-KO) of sorghum displayed precocious senescent phenotypes, while SbWRKY50 overexpression delayed age-dependent and dark-induced senescence in sorghum. SbWRKY50 negatively regulated chlorophyll degradation through direct binding to the promoters of several chlorophyll catabolic genes. In addition, SbWRKY50 recruited the Polycomb repressive complex 1 through direct interaction with SbBMI1A, to induce histone 2A mono-ubiquitination accumulation on the chlorophyll catabolic genes for epigenetic silencing and thus delayed leaf senescence. Especially, SbWRKY50 can suppress early steps of chlorophyll catabolic pathway via directly repressing SbNYC1 (NON-YELLOW COLORING 1). Other senescence-related hormones could also influence leaf senescence through repression of SbWRKY50. Hence, our work shows that SbWRKY50 is an essential regulator downstream of ET and SbWRKY50 also responds to other phytohormones for senescence regulation in sorghum.

Ultrastrong coupling in Super Yellow polymer microcavities and development of highly efficient polariton light-emitting diodes and light-emitting transistors.

We present detailed studies on exciton-photon coupling and polariton emission based on a poly(1,4-phenylenevinylene) copolymer, Super Yellow (SY), in a series of optical microcavities and optoelectronic devices, including light-emitting diode (LED) and light-emitting transistor (LET). We show that sufficiently thick SY microcavities can generate ultrastrong coupling with Rabi splitting energies exceeding 1 eV and exhibit spectrally narrow, nearly angle-independent photoluminescence following lower polariton (LP) mode dispersion. When the microcavity is designed with matched LP low-energy state and exciton emission peak for radiative pumping, the conversion efficiency from exciton to polariton emission can reach up to 80%. By introducing appropriate injection layers in a SY microcavity and optimizing the cavity design, we further demonstrate a high-performance ultrastrongly coupled SY LED with weakly dispersive electroluminescence along LP mode and a maximum external quantum efficiency (EQE) of 2.8%. Finally, we realize an ultrastrongly coupled LET based on vertical integration of a high-mobility ZnO transistor and a SY LED in a microcavity, which enables a large switching ratio, uniform emission in the ZnO pattern, and LP mode emission with a maximum EQE of 2.4%. This vertical LET addresses the difficulties of achieving high emission performance and precisely defining the emission area in typical planar LETs, and opens up the possibility of applying various strongly coupled emitters for advanced polariton devices and high-resolution applications.

Swin Unet3D: a three-dimensional medical image segmentation network combining vision transformer and convolution.

BACKGROUND: Semantic segmentation of brain tumors plays a critical role in clinical treatment, especially for three-dimensional (3D) magnetic resonance imaging, which is often used in clinical practice. Automatic segmentation of the 3D structure of brain tumors can quickly help physicians understand the properties of tumors, such as the shape and size, thus improving the efficiency of preoperative planning and the odds of successful surgery. In past decades, 3D convolutional neural networks (CNNs) have dominated automatic segmentation methods for 3D medical images, and these network structures have achieved good results. However, to reduce the number of neural network parameters, practitioners ensure that the size of convolutional kernels in 3D convolutional operations generally does not exceed [Formula: see text], which also leads to CNNs showing limitations in learning long-distance dependent information. Vision Transformer (ViT) is very good at learning long-distance dependent information in images, but it suffers from the problems of many parameters. What's worse, the ViT cannot learn local dependency information in the previous layers under the condition of insufficient data. However, in the image segmentation task, being able to learn this local dependency information in the previous layers makes a big impact on the performance of the model. METHODS: This paper proposes the Swin Unet3D model, which represents voxel segmentation on medical images as a sequence-to-sequence prediction. The feature extraction sub-module in the model is designed as a parallel structure of Convolution and ViT so that all layers of the model are able to adequately learn both global and local dependency information in the image. RESULTS: On the validation dataset of Brats2021, our proposed model achieves dice coefficients of 0.840, 0.874, and 0.911 on the ET channel, TC channel, and WT channel, respectively. On the validation dataset of Brats2018, our model achieves dice coefficients of 0.716, 0.761, and 0.874 on the corresponding channels, respectively. CONCLUSION: We propose a new segmentation model that combines the advantages of Vision Transformer and Convolution and achieves a better balance between the number of model parameters and segmentation accuracy. The code can be found at https://github.com/1152545264/SwinUnet3D .

SbNAC9 Improves Drought Tolerance by Enhancing Scavenging Ability of Reactive Oxygen Species and Activating Stress-Responsive Genes of Sorghum.

Drought stress severely threatens the yield of cereal crops. Therefore, understanding the molecular mechanism of drought stress response of plants is crucial for developing drought-tolerant cultivars. NAC transcription factors (TFs) play important roles in abiotic stress of plants, but the functions of NAC TFs in sorghum are largely unknown. Here, we characterized a sorghum NAC gene, SbNAC9, and found that SbNAC9 can be highly induced by polyethylene glycol (PEG)-simulated dehydration treatments. We therefore investigated the function of SbNAC9 in drought stress response. Sorghum seedlings overexpressing SbNAC9 showed enhanced drought-stress tolerance with higher chlorophyll content and photochemical efficiency of PSII, stronger root systems, and higher reactive oxygen species (ROS) scavenging capability than wild-type. In contrast, sorghum seedlings with silenced SbNAC9 by virus-induced gene silencing (VIGS) showed weakened drought stress tolerance. Furthermore, SbNAC9 can directly activate a putative peroxidase gene SbC5YQ75 and a putative ABA biosynthesis gene SbNCED3. Silencing SbC5YQ75 and SbNCED3 led to compromised drought tolerance and reduced ABA content of sorghum seedlings, respectively. Therefore, our findings revealed the important role of SbNAC9 in response to drought stress in sorghum and may shed light on genetic improvement of other crop species under drought-stress conditions.

Status and clinical influencing factors of involuntary admission in chinese patients with schizophrenia.

BACKGROUND: Though controversial for its various disadvantages, involuntary admission (IA) is necessary in providing mental health care for patients suffering from schizophrenia in China. This article examines the IA rate in a representative sample, and under which circumstances are these patients more likely to be admitted involuntarily. METHODS: Adult patients consecutively admitted to two typical hospitals in Shanghai between 2013 and 2014 with a diagnosis of ICD-10 schizophrenia were included. 2167 patients were included in this study. Sociodemographic and clinical data, as well as personal information of psychiatrists who made risk assessment, were collected. The whole sample was divided into voluntary and involuntary admission groups. Group comparisons were performed with SPSS 17.0, using one-way ANOVA, Wilcoxon rank sum test, Chi-squares and Logistic regression. RESULTS: Among 2167 inpatients, the majority (2003, 92.4%) were involuntarily admitted. Clinical features, including age of patients (p < 0.001, OR = 1.037), lacking of insight (p < 0.001, OR = 3.691), were statistically significant for IA. Psychiatrist's age (p < 0.001, OR = 1.042) was independently associated with IA. However, risk behaviors had dramatically affected patients' admission status, of which the strongest predictor of IA was noncompliance with treatment (p < 0.001, OR = 3.597). The areas under the curve of the ROC and accuracy for the regression model were 0.815 and 0.927, respectively. CONCLUSION: IA patients account for a major proportion of all those hospitalized with schizophrenia in China. Insights and risk behaviors contributed the most reasons for admission status of patients. This research shed light on necessity of further qualitative studies learning detailed evaluation processes of IA and high-quality interventional studies aiming to limit the performance of IA among patients with schizophrenia.

An imConvNet-based deep learning model for Chinese medical named entity recognition.

BACKGROUND: With the development of current medical technology, information management becomes perfect in the medical field. Medical big data analysis is based on a large amount of medical and health data stored in the electronic medical system, such as electronic medical records and medical reports. How to fully exploit the resources of information included in these medical data has always been the subject of research by many scholars. The basis for text mining is named entity recognition (NER), which has its particularities in the medical field, where issues such as inadequate text resources and a large number of professional domain terms continue to face significant challenges in medical NER. METHODS: We improved the convolutional neural network model (imConvNet) to obtain additional text features. Concurrently, we continue to use the classical Bert pre-training model and BiLSTM model for named entity recognition. We use imConvNet model to extract additional word vector features and improve named entity recognition accuracy. The proposed model, named BERT-imConvNet-BiLSTM-CRF, is composed of four layers: BERT embedding layer-getting word embedding vector; imConvNet layer-capturing the context feature of each character; BiLSTM (Bidirectional Long Short-Term Memory) layer-capturing the long-distance dependencies; CRF (Conditional Random Field) layer-labeling characters based on their features and transfer rules. RESULTS: The average F1 score on the public medical data set yidu-s4k reached 91.38% when combined with the classical model; when real electronic medical record text in impacted wisdom teeth is used as the experimental object, the model's F1 score is 93.89%. They all show better results than classical models. CONCLUSIONS: The suggested novel model (imConvNet) significantly improves the recognition accuracy of Chinese medical named entities and applies to various medical corpora.

Parallel Subgradient Algorithm with Block Dual Decomposition for Large-scale Optimization.

This paper studies computational approaches for solving large-scale optimization problems using a Lagrangian dual reformulation, solved by parallel sub-gradient methods. Since there are many possible reformulations for a given problem, an important question is: Which reformulation leads to the fastest solution time? One approach is to detect a block diagonal structure in the constraint matrix, and reformulate the problem by dualizing the constraints outside of the blocks; the approach is defined herein as block dual decomposition. Main advantage of such a reformulation is that the Lagrangian relaxation has a block diagonal constraint matrix, thus decomposable into smaller sub-problems that can solved in parallel. We show that the block decomposition can critically affect convergence rate of the sub-gradient method. We propose various decomposition methods that use domain knowledge or apply algorithms using knowledge about the structure in the constraint matrix or the dependence in the decision variables, towards reducing the computational effort to solve large-scale optimization problems. In particular, we introduce a block decomposition approach that reduces the number of dualized constraints by utilizing a community detection algorithm. We present empirical experiments on an extensive set of problem instances including a real application. We illustrate that if the number of the dualized constraints in the decomposition increases, the computational effort within each iteration of the sub-gradient method decreases while the number of iterations required for convergence increases. The key message is that it is crucial to employ prior knowledge about the structure of the problem when solving large scale optimization problems using dual decomposition.

Significantly Red-Shifted Emissions of Nonconventional AIE Polymers Containing Zwitterionic Components.

Nonconventional luminescence polymers without any aromatic structures have attracted great interest from researchers due to their special structure and excellent biocompatibility. However, these materials mostly emit in the blue or green region, in which preparation of materials with long-wavelength (especially near-infrared) emission is still a great challenge. In this work, it is found that 2-(dimethyl amino) ethyl methacrylate (DMA) and itaconic anhydride (ITA) undergo a ring-opening reaction at room temperature, and subsequently generate zwitterionic compound (IDMA). Based on the clustering-triggered emission (CTE) mechanism, ionic bond can effectively promote the isolated electron-rich chromophores to form new emissive clusters with extended electron delocalization. Herein, two oligomers (P1 and P2) with different fluorescence emissions by controlling the concentration of zwitterionic monomers before polymerization are synthesized. It is worth noting that the maximum emission wavelength of P2 at high concentration is up to 712 nm, which is very rare in previous reports. In addition, the resulting oligomer (P2) shows typical aggregation-enhanced emission (AEE), excitation-dependent fluorescence, temperature-sensitive emission, and solvatochromism. The cytotoxicity assay demonstrates that P2 was low toxic to Huh7 and LM3 cells, and suitable for cell imaging. This research provides the possibility for rational molecular design and the feasibility of luminescence regulation.

Increased plasma asprosin levels in patients with drug-naive anorexia nervosa.

PURPOSE: Asprosin is a centrally acting appetite-promoting hormone and promotes glucose production in the liver. This study is the first to investigate the difference in asprosin in the plasma between anorexia nervosa (AN) and healthy controls, and to explore the relationship between asprosin changes and plasma glucose levels and AN symptoms. METHODS: Plasma asprosin and glucose concentrations were detected in AN patients (n = 46) and healthy control subjects (n = 47). Eating Disorder Inventory-2 (EDI-2) was used to assess subjects' eating disorder symptoms and related personality traits. The patient's concomitant levels of depression and anxiety were also measured using the beck depression inventory and beck anxiety inventory, respectively. RESULTS: Results indicate that AN patients had a higher asprosin concentration in their plasma compared to healthy controls (p = 0.033). Among AN patients, plasma asprosin levels correlated positively with EDI-2 interoceptive awareness subscale score (p = 0.030) and negatively with duration of illness (p = 0.036). Multiple linear regression analyses showed that increases in asprosin levels (p = 0.029), glucose levels (p = 0.024) and body mass index (p = 0.003) were associated with an increase of the score of EDI-2 bulimia subscale. CONCLUSIONS: Our findings suggest that the increase in plasma asprosin concentration in patients with AN may be a compensation for the body's energy shortage, and asprosin may be involved in the development of bulimia and lack of interoceptive awareness in AN patients. LEVEL OF EVIDENCE: Level III, case-control analytic study.

Immediate psychological distress in quarantined patients with COVID-19 and its association with peripheral inflammation: A mixed-method study.

Since the end of 2019, Corona Virus Disease 2019 (COVID-19) has been the cause of a worldwide pandemic. The mental status of patients with COVID-19 who have been quarantined and the interactions between their psychological distress and physiological levels of inflammation have yet to be analyzed. Using a mixed-method triangulation design (QUAN + QUAL), this study investigated and compared the mental status and inflammatory markers of 103 patients who, while hospitalized with mild symptoms, tested positive with COVID-19 and 103 matched controls that were COVID-19 negative. The severity of depression, anxiety, and post-traumatic stress symptoms (PTSS) was measured via an on-line survey. Using a convenience sampling technique, qualitative data were collected until the point of data saturation. In addition, a semi-structured interview was conducted among five patients with COVID-19. Peripheral inflammatory markers were also collected in patients, both at baseline and within ± three days of completing the on-line survey. Results revealed that COVID-19 patients, when compared to non-COVID controls, manifested higher levels of depression (P < 0.001), anxiety (P < 0.001), and post-traumatic stress symptoms (P < 0.001). A gender effect was observed in the score of "Perceived Helplessness", the subscale of PSS-10, with female patients showing higher scores compared to male patients (Z = 2.56, P = 0.010), female (Z = 2.37, P = 0.018) and male controls (Z = 2.87, P = 0.004). Levels of CRP, a peripheral inflammatory indicator, correlated positively with the PHQ-9 total score (R = 0.37, P = 0.003, Spearman's correlation) of patients who presented symptoms of depression. Moreover, the change of CRP level from baseline inversely correlated with the PHQ-9 total score (R = -0.31, P = 0.002), indicative of improvement of depression symptoms. Qualitative analysis revealed similar results with respect to patient reports of negative feelings, including fear, guilt, and helplessness. Stigma and uncertainty of viral disease progression were two main concerns expressed by COVID-19 patients. Our results indicate that significant psychological distress was experienced by hospitalized COVID-19 patients and that levels of depressive features may be related to the inflammation markers in these patients. Thus, we recommend that necessary measures should be provided to address depression and other psychiatric symptoms for COVID-19 patients and attention should be paid to patient perceived stigma and coping strategies when delivering psychological interventions.

The classification of eating disorders in China: A categorical model or a dimensional model.

OBJECTIVE: According to the ICD-10 and DSM-5, eating disorders (EDs) are classified using a categorical model that assumes the subtypes are qualitatively different from one another. However, it is still intensely debated that a dimensional model is more suitable. The aim of this study is to examine whether EDs have a categorical or dimensional latent structure using a sample of Chinese ED patients. METHOD: The sample included 322 patients, diagnosed with an ED from 2010 to 2017 in the Shanghai Mental Health Center, and comparison participants (N = 850), recruited from undergraduate students in one university in Shanghai. Participants were evaluated with the Eating Disorder Inventory-2 (EDI-2) questionnaire and another questionnaire developed by the researchers. Three taxometric procedures (MAXimum EIGenvalue [MAXEIG], latent-mode factor analysis [L-Mode], and Mean Above Minus Below A Cut [MAMBAC]) were applied, respectively, to analyze the patients' clinical symptoms data. RESULTS: Patients were divided into three groups according to their clinical diagnosis. The plots of the three taxometric analysis procedures supported the categorical construct in anorexia nervosa, binge-eating/purging group, and bulimia nervosa group. The Comparison Curve Fit Indices of the MAXEIG, L-Mode, and MAMBAC procedures were 0.694, 0.709, 0.704 in the AN-BP group and 0.727, 0.67, 0.62 in the BN group, respectively, which also support the categorical construct. DISCUSSION: The results support two distinct classes of ED subtypes among Chinese sample. Further work on applying hybrid model in analysis has been discussed.

On designing of a low leakage patient-centric provider network.

BACKGROUND: When a patient in a provider network seeks services outside of their community, the community experiences a leakage. Leakage is undesirable as it typically leads to higher out-of-network cost for patient and increases barrier for care coordination, which is particularly problematic for Accountable Care Organization (ACO) as the in-network providers are financially responsible for quality of care and outcome. We aim to design a data-driven method to identify naturally occurring provider networks driven by diabetic patient choices, and understand the relationship among provider composition, patient composition, and service leakage pattern. By doing so, we learn the features of low service leakage provider networks that can be generalized to different patient population. METHODS: Data used for this study include de-identified healthcare insurance administrative data acquired from Capital District Physicians' Health Plan (CDPHP) for diabetic patients who resided in four New York state counties (Albany, Rensselaer, Saratoga, and Schenectady) in 2014. We construct a healthcare provider network based on patients' historical medical insurance claims. A community detection algorithm is used to identify naturally occurring communities of collaborating providers. For each detected community, a profile is built using several new key measures to elucidate stakeholders of our findings. Finally, import-export analysis is conducted to benchmark their leakage pattern and identify further leakage reduction opportunity. RESULTS: The design yields six major provider communities with diverse profiles. Some communities are geographically concentrated, while others tend to draw patients with certain diabetic co-morbidities. Providers from the same healthcare institution are likely to be assigned to the same community. While most communities have high within-community utilization and spending, at 85% and 86% respectively, leakage still persists. Hence, we utilize a metric from import-export analysis to detect leakage, gaining insight on how to minimize leakage. CONCLUSIONS: We identify patient-driven provider organization by surfacing providers who share a large number of patients. By analyzing the import-export behavior of each identified community using a novel approach and profiling community patient and provider composition we understand the key features of having a balanced number of PCP and specialists and provider heterogeneity.

Early energy performance analysis of smart buildings by consolidated artificial neural network paradigms.

The assessment of energy performance in smart buildings has emerged as a prominent area of research driven by the increasing energy consumption trends worldwide. Analyzing the attributes of buildings using optimized machine learning models has been a highly effective approach for estimating the cooling load (CL) and heating load (HL) of the buildings. In this study, an artificial neural network (ANN) is used as the basic predictor that undergoes optimization using five metaheuristic algorithms, namely coati optimization algorithm (COA), gazelle optimization algorithm (GOA), incomprehensible but intelligible-in-time logics (IbIL), osprey optimization algorithm (OOA), and sooty tern optimization algorithm (STOA) to predict the CL and HL of a residential building. The models are trained and tested via an Energy Efficiency dataset (downloaded from UCI Repository). A score-based ranking system is built upon three accuracy evaluators including mean absolute percentage error (MAPE), root mean square error (RMSE), and percentage-Pearson correlation coefficient (PPCC) to compare the prediction accuracy of the models. Referring to the results, all models demonstrated high accuracy (e.g., PPCCs >89%) for predicting both CL and HL. However, the calculated final scores of the models (43, 20, 39, 38, and 10 in HL prediction and 36, 20, 42, 42, and 10 in CL prediction for the STOA, OOA, IbIL, GOA, and COA, respectively) indicated that the GOA, IbIL, and STOA perform better than COA and OOA. Moreover, a comparison with various algorithms used in earlier literature showed that the GOA, IbIL, and STOA provide a more accurate solution. Therefore, the use of ANN optimized by these three algorithms is recommended for practical early forecast of energy performance in buildings and optimizing the design of energy systems.

Taxonomic notes on two antlion genera Holzezus Krivokhatsky, 1992 and Subgulina Krivokhatsky, 1996 (Neuroptera: Myrmeleontidae: Myrmecaelurini) from China.

A little-known antlion genus, i.e., Holzezus Krivokhatsky, 1992, with its type species H. compactus Krivokhatsky, 1992, is first recorded from China based on newly collected specimens from Turpan-Hami region of Xinjiang. In this region, another rare genus Subgulina Krivokhatsky, 1996, with its type species S. kerzhneri Krivokhatsky, 1996, in which the males possess a sac-like structure from the gula, is rediscovered from China. In addition, we briefly discuss the complicated phylogenetic relationships among the genera of Myrmecaelurini and other related lineages with similar habitat preference to the arid areas.

The Asian rock-dwelling antlions Gatzara Navás, 1915 and Nepsalus Navás, 1914 (Neuroptera: Myrmeleontidae): new advancements in systematics, biogeography and life history.

The antlion genera Gatzara and Nepsalus (Myrmeleontidae: Dendroleontinae) inhabit mountain forests and are characterised by camouflaging larvae. Both genera remain poorly known despite recent findings on systematics and distribution. We report the discovery of new specimens and the previously unknown larvae of the rare species Gatzara jubilaea Navás, 1915, Nepsalus insolitus (Walker, 1860) and N. decorosus (Yang, 1988). These provide new evidence regarding the affinities of these species, and updated knowledge of the distribution, larval morphology and biology. Moreover, a new species of Nepsalus , N. maclachlani Badano, Zheng & Liu, sp. nov. is described from Sri Lanka based on historical museum collections. The discovery of the immature stages of Gatzara shows that the larvae of this genus share the same specialised ecological characteristics and habits as those of Nepsalus but are less morphologically derived. We also reconstruct a molecular phylogeny of this lineage, estimating the divergence time and biogeographical history by adding the new samples. The evolution of the Gatzara + Nepsalus lineage is associated with two major mountain ranges on the southern Tibetan Plateau, i.e. the Himalayas and the Hengduan Mountains. ZooBank: urn:lsid:zoobank.org:pub:68E68211-DFC1-4D98-997B-8A23BA8F9B69.

Structural and functional abnormalities across clinical stages of psychosis: A multimodal neuroimaging investigation.

BACKGROUND: Structural and functional neurobiological abnormalities have been observed in schizophrenia. Previous studies have concentrated on specific illness stages, obscuring relationships between functional/structural changes and disorder progression. The present study aimed to quantify structural and functional abnormalities across different clinical stages using functional near-infrared spectroscopy (fNIRS) and structural magnetic resonance imaging (sMRI). METHODS: Fifty-four participants with first-episode schizophrenia (FES), 120 with clinically high risk of psychosis (CHR), and 111 healthy controls (HCs) underwent functional near-infrared spectroscopy (fNIRS) to measure oxyhemoglobin (Oxy-Hb) during the verbal fluency task. Among them, 28FES, 64CHR and 55HC also finished sMRI. Oxy-Hb and gray matter volume (GMV) were compared among the three groups while controlling for covariates, including age, sex, years of education, and task performance. Mediation analysis was utilized to determine the mediating effect of GMV on Oxy-Hb and cognition. RESULTS: Compared with the HC group, CHR and FES groups showed significantly reduced brain activity. However, there were no significant differences between the FES and CHR. Pronounced GMV increase in the right frontal pole area (F = 4.234, p = 0.016) was identified in the CHR and FES groups. Mediation analysis showed a significant mediation effect of the right frontal pole GMV between Channel 31 Oxy-Hb and processing speed (z = 2.105, p = 0.035) and attention/vigilance (z = 1.992, p = 0.046). CONCLUSIONS: Brain activation and anatomical deficits were observed in different brain regions, suggesting that anatomical and functional abnormalities are dissociated in the early stages of psychosis. The relationship between neural activity and anatomy may reflect a specific pathophysiology related to cognitive deterioration in schizophrenia.

Capture of regulatory factors via CRISPR-dCas9 for mechanistic analysis of fine-tuned SERRATE expression in Arabidopsis.

SERRATE (SE) plays an important role in many biological processes and under biotic stress resistance. However, little about the control of SE has been clarified. Here we present a method named native chromatin-associated proteome affinity by CRISPR-dCas9 (CASPA-dCas9) to holistically capture native regulators of the SE locus. Several key regulatory factors including PHYTOCHROME RAPIDLY REGULATED 2 (PAR2), WRKY DNA-binding protein 19 (WRKY19) and the MYB-family protein MYB27 of SE are identified. MYB27 recruits the long non-coding RNA-PRC2 (SEAIR-PRC2) complex for H3K27me3 deposition on exon 1 of SE and subsequently represses SE expression, while PAR2-MYB27 interaction inhibits both the binding of MYB27 on the SE promoter and the recruitment of SEAIR-PRC2 by MYB27. The interaction between PAR2 and MYB27 fine-tunes the SE expression level at different developmental stages. In addition, PAR2 and WRKY19 synergistically promote SE expression for pathogen resistance. Collectively, our results demonstrate an efficient method to capture key regulators of target genes and uncover the precise regulatory mechanism for SE.