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The aim of this study was to ascertain the safety of high-intensity focused ultrasound (HIFU) for high-grade cervical intraepithelial neoplasia grade 2/3 (CIN 2/3) in patients with fertility requirements. This was a prospective one-arm study. Consecutive CIN 2/3 patients diagnosed with histopathology were screened, enrolled and treated from September 2019 to September 2020 in the Affiliated Hospital of North Sichuan Medical College. All patients were treated with a combination of HIFU and antiviral treatment with REBACIN. The scheduled follow-up visits were 1 week, 1 mo, 3 mo, 6 mo and 12 mo after surgery. The primary outcomes included cure and human papillomavirus clearance rates. We screened 287 consecutive CIN 2/3 patients in our hospital, 29 of whom were enrolled and treated in this study. The cure rate reached 82.8% at 7 mo after treatment and 96.6% within 1 y. The HPV-negative rate reached 72.4% (21/29) around 6 mo after treatment, with mild side effects during and after the procedure. Our study suggests that in CIN 2/3 study participants with fertility requirements, HIFU + REBACIN therapy is a safe and effective therapeutic option with a high cure rate, HPV clearance and few side effects.

Cervical cancer is a malignant tumor that threatens the life and health of women. Circular RNA (circRNA) is a research hotspot in human diseases including cervical cancer. However, the research of circRNA viral protein R-binding protein (circ_VPRBP) in cervical cancer is blank.Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression of target genes in cervical cancer tissues and cells. The expression of related proteins was detected by western blot. The localization of circ_VPRBP was detected by nuclear cytoplasmic separation, and the stability of circ_VPRBP was verified by actinomycin D. After transfection with oligonucleotides and/or plasmids, cell proliferation, migration, invasion and apoptosis were detected by 3-(4, 5-dimethylthiazol-2-yl) -2, 5-diphenyl-2-H-tetrazolium bromide (MTT), colony formation, 5-ethynyl-2'-deoxyuridine (EdU), transwell, or flow cytometry assays. Mechanistically, the interaction between microRNA-93-5p (miR-93-5p) and circ_VPRBP/FERM domain containing 6 (FRMD6) was verified by dual luciferase reporter assay. Animal experiment was conducted to investigate the role of circ_VPRBP in vivo.Circ_VPRBP was down-regulated in cervical cancer tissues and cells, and overexpression of circ_VPRBP inhibited proliferation and promoted apoptosis of Caski and C33A cells. MiR-93-5p was a target of circ_VPRBP, and miR-93-5p mimic reversed the effect of circ_VPRBP on cell behavior. FRMD6 was a downstream target of miR-93-5p, and down-regulated FRMD6 reversed the cell viability, migration and invasion of cervical cancer cells inhibited by anti-miR-93-5p. Circ_VPRBP inhibited tumor growth by regulating miR-93-5p and FRMD6 in vivo.Circ_VPRBP inhibited cell proliferation, migration and invasion and promoted cell apoptosis of cervical cancer cells by regulating miR-93-5p/FRMD6 axis.

We present a preliminary design for a microbial-enhanced permeable reactive barrier technology for the treatment of nitrate contaminated groundwater.

The study aimed to explore the performance of deep learning-based hysteroscopy intelligent examination combined with ultrasound examination in the diagnosis of endometrial carcinoma (EC). Specifically, 80 EC patients, diagnosed by hysteroscopic cervical tissue biopsy were selected as the research subjects, and they were divided into the experimental group, and the control group. The Dense-Pyramid-Attention U-Net (DPA-UNet) algorithm image processing method based on deep learning was applied to diagnose patients in the experimental group. Then, different diagnosis methods were compared for the accuracy rates of the preoperative staging and the diagnosis results. The results showed that compared with U-Net and Dense-Net models, the image clarity processed by the DPA-UNet model was improved and the lesion site was clearer, and its Dice similarity coefficient (DSC), precision, and recall were 80.4 ± 18%, 80.1 ± 15%, 87.6 ± 11%, respectively, higher than those of U-Net and Dense-Net model, and the difference wax statistically significant (P

Fe as an important redox-active transition metal plays a key role in the carbon cycle of ecosystems. To date, the mechanisms by which Fe affects organic carbon (soil respiration rate [Rs] and dissolved organic carbon [DOC] structure) remain unclear, because most studies only focused on the effect of Fe on soil organic carbon content. To understand these effects, a 30-day laboratory incubation experiment was conducted using forest swamp soils from northeastern China amended with different concentrations of exogenous Fe (no exogenous Fe added [L0], add exogenous Fe at 1 time the soil background value [L1], add exogenous Fe at 2 times the soil background value [L2]). Our results showed that exogenous Fe addition reduced the soil respiration rate by 54.8% during the incubation time. The DOC concentration decreased by 40.5% with exogenous Fe addition during the incubation time. The dissolved organic matter (DOM) characteristic parameters showed apparent variations (p 0.05), including significant increases in the fluorescence and biological index and significant decreases in the humification index, which indicate that exogenous Fe addition reduced humification, which may lead to the increased fixation of dissolved organic carbon. In addition significant increases in tryptophan-like DOM was observed when exogenous Fe addition resulted in a soil Fe concentration of twice the background value (p 0.05). These findings indicate that exogenous Fe addition promotes the production of endogenous soil DOC by microorganisms. Overall, Our study uses three-dimensional fluorescence spectroscopy techniques combined with the parallel factor analysis (PARAFAC) to characterize the dissolved organic matter components in soil samples under exogenous Fe addition conditions, with a view to exploring the differences in the effects of Fe on the DOC concentration and structure of wetland soils, providing a theoretical basis for the mechanisms of soil carbon fixation and soil organic matter transformation in wetland soils.

Located in semi-arid regions of Hulun Buir League in China Inner Mongolia Autonomous Region, Hailar Basin is a region with less precipitation, where groundwater is the most important source for water supply. It is very important to study groundwater characteristics and hydrogeochemical processes for better management of the groundwater resource.The current status of fluorine (F), arsenic (As), and uranium (U) co-contamination exists in groundwater of the Hailar Basin, China. To understand the concentration mechanism of F, As, and U in groundwater in the study area, groundwater samples were collected for detection and analysis. The results showed that the main hydrochemical types were Cl-Na, HCO3-Na, and HCO3-Ca in the study area. The average values of F, As, and U were 3.94 mg/L, 0.04 mg/L, and 0.07 mg/L, respectively, which all exceeded the World Health Organization(WHO) guidelines. Through the Gibbs diagram and the end member diagram, it can be seen that the groundwater in the study area is mainly affected by the hydrogeochemical effects of evaporative crystallization, rock weathering mechanism, and the dissolution of silicate rock and evaporative salt rock minerals. Mineral dissolution, cation exchange, and weakly alkaline environment are important factors affecting F concentration; Low NO3ˉ and SO42- will cause a reducing environment and the competitive adsorption of HCO3ˉ will promote As pollution; The concentration of Ca2+,Mg2+, SO42-,and NO3ˉ have a great influence on the concentration of U.

Background. Sleep is an essential physiological activity for human beings, while sleep deprivation (SD) has become a public health concern and causes damage to multiple organs. Circadian rhythms generated by endogenous circadian clocks play an important role in human activities and have become one of the hot spots of research in life sciences. However, there is still a lack of studies on the role of circadian genes and liver function after SD. Here we aimed to investigate the changes and relationship of circadian genes and liver function after SD, providing strategies for preventing liver injury caused by SD.Methods. Datasets GSE92913 from Gene expression omnibus (GEO) were used to explore differentially expressed genes (DEGs). Subsequently, Gene oncology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) were performed to explore the function of DEGs and differentially expressed circadian genes. Protein-protein interaction (PPI) analysis was employed to identify important circadian genes, with their relationship determined by the Pearson correlation analysis. The transcript level and protein level of circadian genes in liver were verified. Besides, after SD, the expression of circadian genes was investigated. Moreover, CIBERSORT was performed in R software to analyze the immune environment in the liver after SD.Results. The quality of datasets GSE92913 met the criteria for further analysis. Functional analysis on DEGs showed that metabolic process and circadian rhythms in liver were significantly suppressed after SD. MCODE analysis obtained 16 most important circadian genes, most of which shared a high correlation. Seven kinds of immune cells changed after SD, such as CD4 naïve T cells and M2 macrophages. Six circadian genes exhibited a relationship with immune cells. Finally, DEGs based on Cry1, one of the most important circadian genes, showed an association with metabolic function and circadian rhythms.Conclusions. Liver metabolism and circadian rhythms are impaired after SD. Disorder of circadian genes is a factor in suppressing metabolism after SD. Also, six circadian genes play crucial roles in the immune environment. These findings provide potential strategies for the treatment of liver injury after SD.

Transvaginal ultrasound (TVUS) is widely used in infertility treatment. The size and shape of the ovary and follicles must be measured manually for assessing their physiological status by sonographers. However, this process is extremely time-consuming and operator-dependent. In this study, we propose a novel composite network, namely CR-Unet, to simultaneously segment the ovary and follicles in TVUS. The CR-Unet incorporates the spatial recurrent neural network (RNN) into a plain U-Net. It can effectively learn multi-scale and long-range spatial contexts to combat the challenges of this task, such as the poor image quality, low contrast, boundary ambiguity, and complex anatomy shapes. We further adopt deep supervision strategy to make model training more effective and efficient. In addition, self-supervision is employed to iteratively refine the segmentation results. Experiments on 3204 TVUS images from 219 patients demonstrate the proposed method achieved the best segmentation performance compared to other state-of-the-art methods for both the ovary and follicles, with a Dice Similarity Coefficient (DSC) of 0.912 and 0.858, respectively.

Quality control/assessment of ultrasound (US) images is an essential step in clinical diagnosis. This process is usually done manually, suffering from some drawbacks, such as dependence on operator's experience and extensive labors, as well as high inter- and intra-observer variation. Automatic quality assessment of US images is therefore highly desirable. Fetal US cardiac four-chamber plane (CFP) is one of the most commonly used cardiac views, which was used in the diagnosis of heart anomalies in the early 1980s. In this paper, we propose a generic deep learning framework for automatic quality control of fetal US CFPs. The proposed framework consists of three networks: (1) a basic CNN (B-CNN), roughly classifying four-chamber views from the raw data; (2) a deeper CNN (D-CNN), determining the gain and zoom of the target images in a multi-task learning manner; and (3) the aggregated residual visual block net (ARVBNet), detecting the key anatomical structures on a plane. Based on the output of the three networks, overall quantitative score of each CFP is obtained, so as to achieve fully automatic quality control. Experiments on a fetal US dataset demonstrated our proposed method achieved a highest mean average precision (mAP) of 93.52% at a fast speed of 101 frames per second (FPS). In order to demonstrate the adaptability and generalization capacity, the proposed detection network (i.e., ARVBNet) has also been validated on the PASCAL VOC dataset, obtaining a highest mAP of 81.2% when input size is approximately 300 × 300.

The ultrasound (US) screening of the infant hip is vital for the early diagnosis of developmental dysplasia of the hip (DDH). The US diagnosis of DDH refers to measuring alpha and beta angles that quantify hip joint development. These two angles are calculated from key anatomical landmarks and structures of the hip. However, this measurement process is not trivial for sonographers and usually requires a thorough understanding of complex anatomical structures. In this study, we propose a multi-task framework to learn the relationships among landmarks and structures jointly and automatically evaluate DDH. Our multi-task networks are equipped with three novel modules. Firstly, we adopt Mask R-CNN as the basic framework to detect and segment key anatomical structures and add one landmark detection branch to form a new multi-task framework. Secondly, we propose a novel shape similarity loss to refine the incomplete anatomical structure prediction robustly and accurately. Thirdly, we further incorporate the landmark-structure consistent prior to ensure the consistency of the bony rim estimated from the segmented structure and the detected landmark. In our experiments, 1,231 US images of the infant hip from 632 patients are collected, of which 247 images from 126 patients are tested. The average errors in alpha and beta angles are 2.221 degrees and 2.899 degrees. About 93% and 85% estimates of alpha and beta angles have errors less than 5 degrees, respectively. Experimental results demonstrate that the proposed method can accurately and robustly realize the automatic evaluation of DDH, showing great potential for clinical application., Comment: Accepted by IEEE Journal of Biomedical and Health Informatics. 14 pages, 10 figures and 10 tables

Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variability. From the perspective of image analysis, it is essential to develop advanced automatic US image analysis methods to assist in US diagnosis and/or to make such assessment more objective and accurate. Deep learning has recently emerged as the leading machine learning tool in various research fields, and especially in general imaging analysis and computer vision. Deep learning also shows huge potential for various automatic US image analysis tasks. This review first briefly introduces several popular deep learning architectures, and then summarizes and thoroughly discusses their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation. Finally, the open challenges and potential trends of the future application of deep learning in medical US image analysis are discussed. Keywords: Deep learning, Medical ultrasound analysis, Classification, Segmentation, Detection

Increasing evidence suggests that cortical folding patterns of human cerebral cortex manifest overt structural and functional differences. However, for interpretability, few studies leverage advanced techniques (e.g., deep learning) to investigate the difference among cortical folds, resulting in more differences yet to be extensively explored. To this end, we proposed an effective topology-preserving transfer learning framework to differentiate cortical fMRI time series extracted from cortical folds. Our framework consists of three main parts: (1) Neural architecture search (NAS), which is used to devise a well-performing network structure based on an initialized hand-designed super-graph in an image dataset; (2) Topology-preserving transfer, which takes the model searched by NAS as the source network, keeping the topological connectivity in the network unchanged, while transforming all 2D operations including convolution and pooling into 1D, therefore resulting in a topology-preserving network, named TPNAS-Net; (3) Classification and correlation analysis, which involves using the TPNAS-Net to classify 1D cortical fMRI time series for each individual brain, and performing a group difference analysis between autism spectrum disorder (ASD) and healthy control (HC) and correlation analysis with clinical information (i.e., age). Extensive experiments on two ASD datasets obtain consistent results, demonstrating that the TPNAS-Net not only discriminates cortical folding patterns at high classification accuracy, but also captures subtle differences between ASD and HC (p-value = 0.042). In addition, we discover that there is a positive correlation between the classification accuracy and age in ASD (r = 0.39, p-value = 0.04). These findings together suggest that structural and functional differences in cortical folding patterns between ASD and HC may provide a potentially useful biomarker for the diagnosis of ASD.

Deep Neural Networks (DNNs) suffer from the performance degradation when image appearance shift occurs, especially in ultrasound (US) image segmentation. In this paper, we propose a novel and intuitive framework to remove the appearance shift, and hence improve the generalization ability of DNNs. Our work has three highlights. First, we follow the spirit of universal style transfer to remove appearance shifts, which was not explored before for US images. Without sacrificing image structure details, it enables the arbitrary style-content transfer. Second, accelerated with Adaptive Instance Normalization block, our framework achieved real-time speed required in the clinical US scanning. Third, an efficient and effective style image selection strategy is proposed to ensure the target-style US image and testing content US image properly match each other. Experiments on two large US datasets demonstrate that our methods are superior to state-of-the-art methods on making DNNs robust against various appearance shifts., IEEE International Symposium on Biomedical Imaging (IEEE ISBI 2020)

3D ultrasound (US) can facilitate detailed prenatal examinations for fetal growth monitoring. To analyze a 3D US volume, it is fundamental to identify anatomical landmarks of the evaluated organs accurately. Typical deep learning methods usually regress the coordinates directly or involve heatmap-matching. However, these methods struggle to deal with volumes with large sizes and the highly-varying positions and orientations of fetuses. In this work, we exploit an object detection framework to detect landmarks in 3D fetal facial US volumes. By regressing multiple parameters of the landmark-centered bounding box (B-box) with a strict criteria, the proposed model is able to pinpoint the exact location of the targeted landmarks. Specifically, the model uses a 3D region proposal network (RPN) to generate 3D candidate regions, followed by several 3D classification branches to select the best candidate. It also adopts an IoU-balance loss to improve communications between branches that benefits the learning process. Furthermore, it leverages a distance-based graph prior to regularize the training and helps to reduce false positive predictions. The performance of the proposed framework is evaluated on a 3D US dataset to detect five key fetal facial landmarks. Results showed the proposed method outperforms some of the state-of-the-art methods in efficacy and efficiency., Comment: IEEE International Symposium on Biomedical Imaging (IEEE ISBI 2020)

Background In the past decades, substantial effort has been made to explore the genetic influence on brain structural/functional abnormalities in schizophrenia, as well as cognitive impairments. In this work, we aimed to extend previous studies to explore the internal mediation pathway among genetic factor, brain features and cognitive scores in a large Chinese dataset. Methods Gray matter (GM) volume, fractional amplitude of low-frequency fluctuations (fALFF), and 4522 schizophrenia-susceptible single nucleotide polymorphisms (SNP) from 905 Chinese subjects were jointly analyzed, to investigate the multimodal association. Based on the identified imaging-genetic pattern, correlations with cognition and mediation analysis were then conducted to reveal the potential mediation pathways. Findings One linked imaging-genetic pattern was identified to be group discriminative, which was also associated with working memory performance. Particularly, GM reduction in thalamus, putamen and bilateral temporal gyrus in schizophrenia was associated with fALFF decrease in medial prefrontal cortex, both were also associated with genetic factors enriched in neuron development, synapse organization and axon pathways, highlighting genes including CSMD1, CNTNAP2, DCC, GABBR2 etc. This linked pattern was also replicated in an independent cohort (166 subjects), which although showed certain age and clinical differences with the discovery cohort. A further mediation analysis suggested that GM alterations significantly mediated the association from SNP to fALFF, while fALFF mediated the association from SNP and GM to working memory performance. Interpretation This study has not only verified the impaired imaging-genetic association in schizophrenia, but also initially revealed a potential genetic-brain-cognition mediation pathway, indicating that polygenic risk factors could exert impact on phenotypic measures from brain structure to function, thus could further affect cognition in schizophrenia.

There is compelling evidence that epigenetic factors contribute to the manifestation of depression, in which microRNA132 (miR-132) is suggested to play a pivotal role in the pathogenesis and neuronal mechanisms underlying the symptoms of depression. Additionally, several depression-associated genes [MECP2, ARHGAP32 (p250GAP), CREB, and period genes] were experimentally validated as miR-132 targets. However, most studies regarding miR-132 in major depressive disorder are based on post-mortem, animal models or genetic comparisons. This work will be the first attempt to investigate how miR-132 dysregulation may impact covariation of multimodal brain imaging data in 81 unmedicated major depressive patients and 123 demographically-matched healthy controls, as well as in a medication-naïve subset of major depressive patients. MiR-132 values in blood (patients > controls) was used as a prior reference to guide fusion of three MRI features: fractional amplitude of low frequency fluctuations, grey matter volume, and fractional anisotropy. The multimodal components correlated with miR-132 also show significant group difference in loadings. Results indicate that (i) higher miR-132 levels in major depressive disorder are associated with both lower fractional amplitude of low frequency fluctuations and lower grey matter volume in fronto-limbic network; and (ii) the identified brain regions linked with increased miR-132 levels were also associated with poorer cognitive performance in attention and executive function. Using a data-driven, supervised-learning method, we determined that miR-132 dysregulation in major depressive disorder is associated with multi-facets of brain function and structure in fronto-limbic network (the key network for emotional regulation and memory), which deepens our understanding of how miR-132 dysregulation in major depressive disorders contribute to the loss of specific brain areas and is linked to relevant cognitive impairments.

Objectives: To investigate the safety, efficacy, and practicability of minimally invasive percutaneous nephrolithotomy (MPCNL) with the aid of a patented irrigation clearance system in treating renal staghorn calculi. Methods: From August 2009 to July 2014, 4 hospitals had executed a prospective multicenter study with a total of 912 cases. The patients were randomly divided into 3 groups: suctioning MPCNL, standard percutaneous nephrolithotomy (PCNL), and traditional MPCNL groups. Multiple operative and perioperative parameters were compared. Results: Blood loss and intrapelvic pressure in the suctioning MPCNL group were significantly less than those in the standard PCNL group. The average operation time, intrapelvic pressure, and amount of bleeding in the suctioning MPCNL group were better than those in the traditional MPCNL group. The suctioning MPCNL used one tract more frequently and 2 or 3 tracts less frequently than the standard MPCNL and traditional MPCNL groups. The stone-free rate by one surgery in the suctioning MPCNL group was significantly higher than that in standard PCNL and traditional MPNCL groups. Conclusions: Suctioning MPCNL using our patented system shows several advantages in treating renal staghorn calculi, including minimal invasion, shorter operation time, lower intrapelvic pressure, less bleeding and the need for a smaller number of ­percutaneous tracts, and higher stone clearance rate by one ­surgery.

Flexible ureteroscopic lithotripsy (URS) is rapidly becoming a first-line therapy for patients with renal and ureteral calculi. Most current medical infusion devices can only monitor infusion flow and pressure, but not renal pelvic pressure (RPP).We designed a patented intelligent system to facilitate flexible URS that included an irrigation and suctioning platform and a ureteral access sheath (UAS) with a pressure-sensitive tip, enabling regulation of the infusion flow precisely and control of the vacuum suctioning by computerized real-time recording and monitoring of RPP. A stable RPP was ensured by pressure feedback technology. Ninety-three patients with renal or ureteral calculi participated in the study and received flexible URS. Gravel particles were sucked out automatically during the flexible URS. Patients were evaluated on postoperative days 1 and 30 by X-ray of kidneys, ureters, and bladder to assess stone-free status.In 81 of the 93 patients, only one surgery was needed to remove the stone. There were nine cases who failed the first surgery due to difficulty in placing the UAS, but flexible URS was performed in these patients after indwelling a Double-J stent to the ureter with the calculus for 2 weeks. Three cases were converted to percutaneous nephrolithotomy due to significant ureteral stenosis. For the 90 patients who underwent flexible URS, the actual RPP was controlled under 20 mmHg with clear operative visualization. The stone-free rates at postoperative days 1 and 30 were 90.0% (81/90) and 95.6% (86/90), respectively. Clavien I complications were noted in 13 cases, while Clavien II complications were noted in two cases. No major complications (Clavien III-V) were noted.Our patented system is technically feasible, safe, and efficient for treating upper urinary calculi. The advantages include breaking stones effectively and low complication rates because of its automatic control of RPP.

MicroRNAs (miRNAs) play important roles in epithelial-to-mesenchymal transition (EMT). Moreover, hyperglycaemia induces damage to renal tubular epithelial cells, which may lead to EMT in diabetic nephropathy. However, the effects of miRNAs on EMT in diabetic nephropathy are poorly understood. In the present study, we found that the level of microRNA-23b (miR-23b) was significantly decreased in high glucose (HG)-induced human kidney proximal tubular epithelial cells (HK2) and in kidney tissues of db/db mice. Overexpression of miR-23b attenuated HG-induced EMT, whereas knockdown of miR-23b induced normal glucose (NG)-mediated EMT in HK2 cells. Mechanistically, miR-23b suppressed EMT in diabetic nephropathy by targeting high mobility group A2 (HMGA2), thereby repressing PI3K-AKT signalling pathway activation. Additionally, HMGA2 knockdown or inhibition of the PI3K-AKT signalling pathway with LY294002 mimicked the effects of miR-23b overexpression on HG-mediated EMT, whereas HMGA2 overexpression or activation of the PI3K-AKT signalling pathway with BpV prevented the effects of miR-23b on HG-mediated EMT. We also confirmed that overexpression of miR-23b alleviated EMT, decreased the expression levels of EMT-related genes, ameliorated renal morphology, glycogen accumulation, fibrotic responses and improved renal functions in db/db mice. Taken together, we showed for the first time that miR-23b acts as a suppressor of EMT in diabetic nephropathy through repressing PI3K-AKT signalling pathway activation by targeting HMGA2, which maybe a potential therapeutic target for diabetes-induced renal dysfunction.

To explore the clinical significance of pre-diagnostic curettage under hysteroscopy in the diagnosis of endometrial carcinoma, evaluate the clinical staging, and optimize the function of hysteroscopy computer information system after artificial intelligence algorithm optimization, 200 patients who were diagnosed as endometrial carcinoma in our hospital between June 2015 and July 2019 were included and divided into a hysteroscopy segmental curettage group (101 patients) and a segmented curettage group (99 patients). For patients in both groups, the histopathological examination was performed, and the abdominal lavage fluid was collected. The artificial intelligence algorithm was utilized to optimize the hysteroscopy computer information system. The results showed that in the hysteroscopy segmented curettage group, the coincidence rate of postoperative pathological diagnosis was 97%. In the segmented curettage group, the coincidence rate of pathological histological types diagnosed before and after the surgery was 83.8%. There was a statistically significant difference between the two groups (P 0.05). The positive rate of patients in the early stage of surgery-pathology reached 16.7%, which was different from the surgery-pathology staging, with statistical significance (P 0.05). The type of surgery and the amount of blood loss during surgery were positively correlated with nerve injury in the two groups of patients, with statistical significance (P

Multimodal fusion has been regarded as a promising tool to discover covarying patterns of multiple imaging types impaired in brain diseases, such as schizophrenia (SZ). In this article, we aim to investigate the covarying abnormalities underlying SZ in a large Chinese Han population (307 SZs, 298 healthy controls [HCs]). Four types of magnetic resonance imaging (MRI) features, including regional homogeneity (ReHo) from resting-state functional MRI, gray matter volume (GM) from structural MRI, fractional anisotropy (FA) from diffusion MRI, and functional network connectivity (FNC) resulted from group independent component analysis, were jointly analyzed by a data-driven multivariate fusion method. Results suggest that a widely distributed network disruption appears in SZ patients, with synchronous changes in both functional and structural regions, especially the basal ganglia network, salience network (SAN), and the frontoparietal network. Such a multimodal coalteration was also replicated in another independent Chinese sample (40 SZs, 66 HCs). Our results on auditory verbal hallucination (AVH) also provide evidence for the hypothesis that prefrontal hypoactivation and temporal hyperactivation in SZ may lead to failure of executive control and inhibition, which is relevant to AVH. In addition, impaired working memory performance was found associated with GM reduction and FA decrease in SZ in prefrontal and superior temporal area, in both discovery and replication datasets. In summary, by leveraging multiple imaging and clinical information into one framework to observe brain in multiple views, we can integrate multiple inferences about SZ from large-scale population and offer unique perspectives regarding the missing links between the brain function and structure that may not be achieved by separate unimodal analyses.

We report an efficient, general methodology for producing high-surface area metal oxide nanomaterials for a vast range of metal oxides, including at least one metal oxide nanomaterial from nearly every transition metal and semi-metal group in the periodic table (groups 3–4 and 6–15) as well as several from the lanthanide group (see Table 1). The method requires only 2–3 simple steps; a hydrated metal salt (usually a nitrate or chloride salt) is ground with bicarbonate (usually NH4HCO3) for 10–30 minutes to form a precursor that is then either untreated or rinsed before being calcined at relatively low temperatures (220–550 °C) for 1–3 hours. The method is thus similar to surfactant-free aqueous methods such as co-precipitation but is unique in that no solvents are added. The resulting “solvent-deficient” environment has interesting and unique consequences, including increased crystallinity of the products over other aqueous methods and a mesoporous nature in the inevitable agglomerates. The products are chemically pure and phase pure with crystallites generally 3–30 nm in average size that aggregate into high surface area, mesoporous agglomerates 50–300 nm in size that would be useful for catalyst and gas sensing applications. The versatility of products and efficiency of the method lend its unique potential for improving the industrial viability of a broad family of useful metal oxide nanomaterials. In this paper, we outline the methodology of the solvent-deficient method using our understanding of its mechanism, and we describe the range and quality of nanomaterials it has produced thus far.

Deep learning has gained considerable attention in the scientific community, breaking benchmark records in many fields such as speech and visual recognition [1]. Motivated by extending advancement of deep learning approaches to brain imaging classification, we propose a framework, called “deep neural network (DNN)+ layer-wise relevance propagation (LRP)”, to distinguish schizophrenia patients (SZ) from healthy controls (HCs) using functional network connectivity (FNC). 1100 Chinese subjects of 7 sites are included, each with a 50∗50 FNC matrix resulted from group ICA on resting-state fMRI data. The proposed DNN+LRP not only improves classification accuracy significantly compare to four state-of-the-art classification methods (84% vs. less than 79%, 10 folds cross validation) but also enables identification of the most contributing FNC patterns related to SZ classification, which cannot be easily traced back by general DNN models. By conducting LRP, we identified the FNC patterns that exhibit the highest discriminative power in SZ classification. More importantly, when using leave-one-site-out cross validation (using 6 sites for training, 1 site for testing, 7 times in total), the cross-site classification accuracy reached 82%, suggesting high robustness and generalization performance of the proposed method, promising a wide utility in the community and great potentials for biomarker identification of brain disorders.

Background Calculus pyonephrosis is difficult to manage. The aim of this study is to explore the value of a patented suctioning sheath assisted minimally invasive percutaneous nephrolithotomy (MPCNL) in the treatment of calculus pyonephrosis. Methods One hundred and eighty two patients with calculus pyonephrosis were randomizely divided into observation group (n = 91) and control group (n = 91). The control group was treated with MPCNL traditionally using peel-away sheath while the observation group was treated with MPCNL using the patented suctioning sheath. Results All the patients in the observation group underwent one stage surgical treatment, 14 patients in the control group underwent first-stage surgery with the rest of the group underwent one stage surgery. The complication rate was 12.1% in the observation group, significantly lower than the rate in the control group which was 51.6%; One surgery stone clearance in the observation group was 96.7% while it was 73.6% in the control group; operative time in the observation group was (54.5 ± 14.5) min, compared to (70.2 ± 11.7) min in the control group; the bleeding amount in the observation group was (126.4 ± 47.2) ml, compared to (321.6 ± 82.5) ml in the control group; the hospitalization duration for the observation group was (6.4 ± 2.3) days, compared to (10.6 ± 3.7) days in the control group. Comparison of the above indicators, the observation group was better than the control group with significant difference (p

The specific heats of highly hydrated 7 nm nanoparticles and lightly hydrated 200 nm particles of rutile and anatase show a small, broad maximum below 5 K. The excess heat capacity can be modeled w...

At high temperature, coarse-grained (bulk) rutile is well established as the stable phase of TiO2, and nanophase anatase, thermodynamically stable relative to nanophase rutile, transforms irreversibly to rutile as it coarsens. The lack of experimental heat-capacity data for bulk anatase below 52 K lends uncertainty to its standard entropy and leaves open a slight possibility that anatase may have a thermodynamic stability field at low temperature, as suggested by some theoretical calculations. In the present study, the molar heat capacities of rutile and anatase were measured from 0.5 K to about 380 K. These data were combined with previously measured high-temperature heat capacities, and fits of the resulting data set were used to generate C P ,m°, ΔT S m°, ΔT H m°, and ΔT G m ° values at smoothed temperatures between 0.5 and 1300 K for anatase and 0.5 and 1800 K for rutile. Using these new data and the enthalpy of transformation between anatase and rutile at 298 K, the change in Gibbs free energy for the transition between anatase and rutile from 0 to 1300 K was calculated. These calculations reveal that the transformation from bulk anatase to bulk rutile is thermodynamically favorable at all temperatures between 0 and 1300 K, confirming that bulk anatase does not have a thermodynamic stability field. Implications for the natural occurrence of these two minerals in terrestrial, lunar, and planetary settings are discussed. In particular, anatase requires low-temperature aqueous conditions for its formation and may be a reliable indicator of such conditions in both terrestrial and extraterrestrial settings.

The heat capacity of water in the form of hexagonal ice was measured between T = 0.5 K and T = 38 K using a semi-adiabatic calorimetric method. Since heat capacity data below T = 2 K have never been measured for water, this study presents the lowest measured values of the specific heat of water to date. Fits of the data were used to generate thermodynamic functions of water at smoothed temperatures between 0.5 K and 38 K. Both our experimental heat capacities and calculated enthalpy increments agree well with previously published values and thus supplement other studies well.

The title compounds were synthesized and characterized by structural measurements and electronic structure calculations. Single-crystal X-ray diffraction analyses established that they all have the orthorhombic inverse -PbCl 2 -type structure ( Pnma , Z =4, a =8.108(2), 8.124(2), 8.421(2), 8.509(2) A; b =4.944(1), 4.949(1), 5.168(1), 5.189(1) A; c =9.170(2), 9.184(2), 9.685(2), 9.740(2) A, respectively). The tetrel ( Tt ) atoms are situated in tricapped trigonal prisms of ordered Sr and Ca atoms in which the smaller Ca atoms play a distinctive role. The structure is distinguishable from the Co 2 Si type by its more nearly ideal 6+3 (TCTP) environment about Tt rather than a higher coordination by cations. Other representations of the two structural types are also considered. Electronic band structure calculations suggest that the compounds are semiconductors, in agreement with literature data on their Ae 2 Tt analogues.

The title compounds have been synthesized and characterized structurally and through property measurements and electronic structure calculations. Single-crystal X-ray diffraction analyses reveal that the two compounds crystallize in an orthorhombic system, MgCuAl(2) type (Cmcm, Z = 4, a = 4.486(2), 4.491(3) A, b = 10.991(5), 10.990(6) A, c = 8.154(1), 8.140(4) A for SrPdTl(2), and SrPtTl(2), respectively). The structure can be directly derived from that of hexagonal SrTl(2) (CaIn(2) type) in which four-bonded thallium atoms in shared puckered hexagons generate tunnels. The Pd or Pt is encapsulated (with symmetry reduction) on the side of each tunnel within a distorted trigonal prism. Band structure calculations (EHTB) on both SrTl(2) and SrPdTl(2) demonstrate the effects of the conversion, with strong Pd-Tl bonding and appreciable electron transfer from Tl to Pd. Property measurements show that SrPdTl(2) is metallic, as expected.

Nano-scale rare earth and transition metal oxides, obtained via a sol-gel process, were used as precursors to synthesize the intermetallic compound Nd2Fe17 with the reduction-diffusion method. The product obtained is a single Nd2Fe17 phase. The particles of the produced alloy, which can be controlled by the particle size of the precursors, are narrowly distributed with a typical size of a few micrometers.

The present work aimed to optimize the culture conditions to produce extracellular alginate-lyase by Pseudoalteromonas sp. LJ1.A bacterial alginate-lyase producing strain LJ1 was isolated from Laminaria japonica by enrichment culture technique. The strain was identified based on phenotypic characters, fatty acid compositions and 16S rRNA gene sequencing. Culture conditions were optimized to produce the extracellular alginate-lyase by the single factor and orthogonal tests.Strain LJ1 was identified as Pseudoalteromonas sp.. The optimal medium components were: sodium alginate 3 g/L, (NH4)2SO4 3 g/L, NaCl 20 g/L, KH2PO4 0.1 g/L, CaCl2 0.1 g/L; The optimal culture conditions were: 25 ml medium in 250 mL Erlenmeyer flask, inoculum's volume 3%, shaking speed of 150 r/min, initial pH 7.5, at 28 degrees C for 24 h. The enzyme exhibited maximal activity at pH 7.6, 40 degrees C and NaCl 0.3 mol/L. The enzyme activity was improved by Mg2+, and inhibited by Co2+ and Zn2+ at 1 mol/L.Strain LJ1 was a novel alginate-lyase producing bacterium of Pseudoalteromonas.