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Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia.
To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers.
We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites.
We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19–85.74%; sensitivity, 75.31–89.29% and area under the receiver operating characteristic curve, 0.797–0.909.
These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.
Schizophrenia is a severe and complex psychiatric disorder that needs treatment based on extensive experience. Antipsychotic drugs have already become the cornerstone of the treatment for schizophrenia; however, the therapeutic effect is of significant variability among patients, and only around a third of patients with schizophrenia show good efficacy. Meanwhile, drug-induced metabolic syndrome and other side-effects significantly affect treatment adherence and prognosis. Therefore, strategies for drug selection are desperately needed. In this study, we will perform pharmacogenomics research and set up an individualised preferred treatment prediction model.
We aim to create a standard clinical cohort, with multidimensional index assessment of antipsychotic treatment for patients with schizophrenia.
This trial is designed as a randomised clinical trial comparing treatment with different kinds of antipsychotics. A total sample of 2000 patients with schizophrenia will be recruited from in-patient units from five clinical research centres. Using a computer-generated program, the participants will be randomly assigned to four treatment groups: aripiprazole, olanzapine, quetiapine and risperidone. The primary outcomes will be measured as changes in the Positive and Negative Syndrome Scale of schizophrenia, which reflects the efficacy. Secondary outcomes include the measure of side-effects, such as metabolic syndromes. The efficacy evaluation and side-effects assessment will be performed at baseline, 2 weeks, 6 weeks and 3 months.
This trial will assess the efficacy and side effects of antipsychotics and create a standard clinical cohort with a multi-dimensional index assessment of antipsychotic treatment for schizophrenia patients.
This study aims to set up an individualized preferred treatment prediction model through the genetic analysis of patients using different kinds of antipsychotics.
Barnyardgrass [Echinochloa crus-galli (L.) P. Beauv.] is a noxious grass weed that infests rice fields and causes huge crop yield losses. In this study, we collected 12 E. crus-galli populations from rice ﬁelds of Ningxia Province in China and investigated the resistance levels to the acetolactate synthase (ALS) inhibitor penoxsulam and the acetyl-CoA carboxylase (ACCase) inhibitor cyhalofop-butyl. The results showed that eight populations exhibited resistance to penoxsulam and four populations evolved resistance to cyhalofop-butyl. Moreover, all four cyhalofop-butyl–resistant populations (NX3, NX4, NX6, and NX7) displayed multiple herbicide resistance to both penoxsulam and cyhalofop-butyl. The alternative herbicides bispyribac-sodium, metamifop, and fenoxaprop-p-ethyl cannot effectively control the multiple herbicide–resistant (MHR) plants. To characterize the molecular mechanisms of resistance, we ampliﬁed and sequenced the target site–encoding genes in resistant and susceptible populations. Partial sequences of three ALS genes and six ACCase genes were examined. A Trp-574-Leu mutation was detected in EcALS1 and EcALS3 in two high-level (65.84- and 59.30-fold) penoxsulam-resistant populations, NX2 and NX10, respectively. In addition, one copy (EcACC4) of ACCase genes encodes a truncated aberrant protein due to a frameshift mutation in E. crus-galli populations. None of the amino acid substitutions that are known to confer herbicide resistance were detected in ALS and ACCase genes of MHR populations. Our study reveals the wide spread of MHR E. crus-galli populations in Ningxia Province that exhibit resistance to several ALS and ACCase inhibitors. Non–target site based mechanisms are likely to be involved in E. crus-galli resistance to the herbicides, at least in four MHR populations.
This paper presents an investigation of the precise point positioning (PPP) performance of a combined solution from BDS-2 and BDS-3 satellites. To simultaneously process different BDS signal observations, i.e., B1/B1C, B2/B2a and B3C, undifferenced and uncombined observations with ionosphere delay constrained by the deterministic plus stochastic ionosphere model are used in the basic model. Special attention is paid to code bias and receiver clock parameters in the derivation of the observation model. The analysis is carried out using more than one-month data for BDS-2 and BDS-3 collected at the CANB, DWIN, KNDY and PETH stations in the Asia-Pacific region. The results suggest that compared with BDS-2 alone, the BDS-2 and BDS-3 solution provides significantly more accurate PPP, with increases of 28%, 21% and 5% in the up, north and east directions, respectively. In addition, the average root mean square error decreases to 0·21, 0·13 and 0·16 m for the three directions. Furthermore, the PPP convergence time for BDS-2 and BDS-3 is about 1·5 h and less than 1 h for the horizontal and vertical components, respectively, whereas that for BDS-2 alone is about 2·3 h for both directions.
Schizophrenia is a complex mental disorder with high heritability and polygenic inheritance. Multimodal neuroimaging studies have also indicated that abnormalities of brain structure and function are a plausible neurobiological characterisation of schizophrenia. However, the polygenic effects of schizophrenia on these imaging endophenotypes have not yet been fully elucidated.
To investigate the effects of polygenic risk for schizophrenia on the brain grey matter volume and functional connectivity, which are disrupted in schizophrenia.
Genomic and neuroimaging data from a large sample of Han Chinese patients with schizophrenia (N = 509) and healthy controls (N = 502) were included in this study. We examined grey matter volume and functional connectivity via structural and functional magnetic resonance imaging, respectively. Using the data from a recent meta-analysis of a genome-wide association study that comprised a large number of Chinese people, we calculated a polygenic risk score (PGRS) for each participant.
The imaging genetic analysis revealed that the individual PGRS showed a significantly negative correlation with the hippocampal grey matter volume and hippocampus–medial prefrontal cortex functional connectivity, both of which were lower in the people with schizophrenia than in the controls. We also found that the observed neuroimaging measures showed weak but similar changes in unaffected first-degree relatives of patients with schizophrenia.
These findings suggested that genetically influenced brain grey matter volume and functional connectivity may provide important clues for understanding the pathological mechanisms of schizophrenia and for the early diagnosis of schizophrenia.
Additive nanofabrication by two-photon polymerization (TPP) has recently drawn increased attention due to its sub-100 nm resolution and truly three-dimensional (3D) structuring capability. However, besides additive processes, subtractive process is also demanded for many 3D fabrications. Method possessing both additive and subtractive fabrication capabilities was rarely reported. In this study, we developed a complementary 3D micro/nano-fabrication process by integrating both additive two-photon polymerization (TPP) and subtractive multi-photon ablation (MPA) into a single platform of femtosecond-laser direct writing process. Functional device structures were successfully fabricated including: polymer fiber Bragg gratings containing periodic holes of 500-nm diameter and 3D micro-fluidic systems containing arrays of channels of 1-µm diameter. The integration of TPP and MPA processes enhances the nanofabrication efficiency and enables the fabrication of complex 3D micro/nano-structures that are impractical to produce by either TPP or MPA alone, which is promising for a wide range of applications including integrated optics, metamaterials, MEMS, and micro-fluidics.
This paper describes a novel ultracapacitor made from carbon nano-onions. Characterization of the material was performed including measurement of the impedance spectra and cyclic voltammetry. A new ultracapacitor model composed of an LRC circuit and a constant phase element was developed along with a parameter extraction procedure. The model was validated using experimental data and simulation.
The electron emission yield γ induced by Ne2+ and O2+ impacting on a clean tungsten surface has been measured. The range of projectile energy is from 3 keV/u to 14 keV/u. The total electron yield gradually increases with the projectile velocity. It is found simultaneously that the total electron yield for O2+ is larger than the total electron yield for Ne2+, which is opposite to the results for higher projectile velocity. After considering the contribution from recoiling atoms to the energy distribution and electron emission yield, we find that recoiling atoms are of crucial importance in electron emission in our energy range. Thus, the unexpected results in our experiment can be explained successfully.
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