Hydrogenotrophic methanogenic granular sludge formation for highly efficient transforming hydrogen to CH4

This paper presents a potential process that can enhance H₂ transformation to CH₄ and simultaneously upgrading biogas by using hydrogenotrophic methanogens. For the first time, anaerobic granules were developed in upflow anaerobic sludge blanket (UASB) reactor feeding H₂/CO₂ syngas as the sole substrate and the granule characterization was thoroughly investigated. The results from experiment revealed that the H₂ consumption rates of UASB reactor increased from 32.2 mmol L^-1·d^-1 at H₂ feeding rate 0.08 g L^-1·d^-1 to 132.0 mmol L^-1·d^-1 at 0.37 g L^-1·d^-1, indicating that the hydrogenotrophic methanogenesis pathway was stimulated by injection of H₂. Abundant cavities and cracks were observed on the surface and cross-section of granules, which greatly facilitated internally transferring H₂/CO₂ synthesis gas and biogas escape. The abundance of hydrogenotrophic Methanobacterium increased, while Methanosaeta, Methanosarcina, and Methanomassiliicoccus decreased with increasing H₂ feeding rate. In general, this paper offers a feasible solution in terms of energy transformation and connecting power to fuel.

Negative symptoms correlate with altered brain structural asymmetry in amygdala and superior temporal region in schizophrenia patients

Negative symptoms play an important role in development and treatment of schizophrenia. However, brain changes relevant to negative symptoms are still unclear. This study examined brain structural abnormalities and their asymmetry in schizophrenia patients and the association with negative symptoms. Fifty-nine schizophrenia patients and 66 healthy controls undertook structural brain scans. Schizophrenia patients were further divided into predominant negative symptoms (PNS, n = 18) and non-PNS (n = 34) subgroups. Negative symptoms were assessed by the Negative Symptom Assessment (NSA). T1-weighted images were preprocessed with FreeSurfer to estimate subcortical volumes, cortical thickness and surface areas, asymmetry Index (AI) was then calculated. MANOVA was performed for group differences while partial correlations in patients were analyzed between altered brain structures and negative symptoms. Compared to healthy controls, schizophrenia patients exhibited thinner cortices in frontal and temporal regions, and decreased leftward asymmetry of superior temporal gyrus (STG) in cortical thickness. Patients with PNS exhibited increased rightward asymmetry of amygdala volumes than non-PNS subgroup. In patients, AI of cortical thickness in the STG was negatively correlated with NSA-Emotion scores (r = -0.30, p = 0.035), while AI of amygdala volume was negatively correlated with NSA-Communication (r = -0.30, p = 0.039) and NSA-Total scores (r = -0.30, p = 0.038). Our findings suggested schizophrenia patients exhibited cortical thinning and altered lateralization of brain structures. Emotion and communication dimensions of negative symptoms also correlated with the structural asymmetry of amygdala and superior temporal regions in schizophrenia patients.

Rapid solvent-free synthesis of nano-sized ZSM-5 with low Si/Al ratio at 90 °C

Nano-sized ZSM-5 zeolites with wide framework Si/Al ratios are highly desired in petrochemical and other fields, whereas the synthesis suffers from cost, efficiency and environmental issues. Herein, a new protocol...

A deep learning-based model for detecting depression in senior population

OBJECTIVES

With the attention paid to the early diagnosis of depression, this study tries to use the biological information of speech, combined with deep learning to build a rapid binary-classification model of depression in the elderly who use Mandarin and test its effectiveness.

METHODS

Demographic information and acoustic data of 56 Mandarin-speaking older adults with major depressive disorder (MDD), diagnosed with the Mini-International Neuropsychiatric Interview (MINI) and the fifth edition of Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and 47 controls was collected. Acoustic data were recorded using different smart phones and analyzed by deep learning model which is developed and tested on independent validation set. The accuracy of the model is shown by the ROC curve.

RESULTS

The quality of the collected speech affected the accuracy of the model. The initial sensitivity and specificity of the model were respectively 82.14% [95%CI, (70.16-90.00)] and 80.85% [95%CI, (67.64-89.58)].

CONCLUSION

This study provides a new method for rapid identification and diagnosis of depression utilizing deep learning technology. Vocal biomarkers extracted from raw speech signals have high potential for the early diagnosis of depression in older adults.

Serum NLRP3 Inflammasome and BDNF: Potential Biomarkers Differentiating Reactive and Endogenous Depression

BACKGROUND

The highly heterogeneous pathogenesis of depression and limited response to current antidepressants call for more objective evidence for depression subtypes. Reactive and endogenous depression are two etiologically distinct subtypes associated with different treatment responses. This study aims to explore the potential biomarkers that differentiate reactive and endogenous depressions.

METHODS

The clinical manifestations and biological indicators of 64 unmedicated mild-to-moderate depression patients (32 reactive depression patients and 32 endogenous depression patients) and 21 healthy subjects were observed. The 24-item Hamilton rating scale for depression (HAMD-24) was used to evaluate the severity of depression. Serum levels of depression-related biological indicators were measured by using the enzyme-linked immunosorbent assay.

RESULTS

The NLRP3 level of reactive depression was significantly lower than those of endogenous depression and healthy controls. There was a significant negative correlation between the BDNF level and the HAMD-24 total scores for patients with reactive depression.

CONCLUSION

Our findings suggested the serum NLRP3 and BDNF levels could be potential biomarkers for detecting and evaluating the severity of reactive depression.

The Chinese Brief Cognitive Test: Normative Data Stratified by Gender, Age and Education

The aim of this study was to develop a brief version of cognitive assessment test for evaluating the efficacy of treatments targeting cognitive impairments in Chinese schizophrenia patients, to examine its reliability, and establish normative data. Stratified according to age, gender, and educational level, healthy adult subjects were recruited from fifteen institutions in seven administrative regions of China and 723 valid samples were obtained, of which 50 were retested. Generalized Linear Models were conducted to analyze the effects of age, sex, and education. There was no significant difference between genders, while significant effects were demonstrated respectively among age and education on the normative data of C-BCT. The Cronbach α of C-BCT is 0.75, and the test-retest reliability (ICC) ranged from 0.62 to 0.76. Normative data of C-BCT were generated by gender, age and education, and the effects of these demographic factors were analyzed. It revealed good internal consistency and test-retest reliability of C-BCT.

Negative symptom dimensions and social functioning in Chinese patients with schizophrenia

OBJECTIVE

Negative symptoms can seriously affect social functioning in patients with schizophrenia. However, the role of various components of negative symptoms in social functioning remains unclear. This study aimed to explore the associations among three different dimensions of negative symptoms (i.e., communication, emotion, and motivation) and social functioning to identify potential therapeutic targets.

METHODS

This cross-sectional study enrolled 202 Chinese participants with schizophrenia. Negative symptoms were evaluated using the Negative Symptom Assessment (NSA). Social functioning was represented by the Personal and Social Performance Scale (PSP) total score and employment status. Correlation analysis was conducted to clarify the relationship between negative symptoms and the PSP total score. Regression analysis was performed to explore the determinants of the PSP total score and employment status, considering negative symptoms and possible confounders, such as demographic features, positive symptoms, cognitive symptoms, depressive symptoms, and extrapyramidal side effects.

RESULTS

The PSP total score was correlated with all three dimensions of negative symptoms (i.e., emotion, motivation, and communication; rs = -0.509, -0.662, and -0.657, respectively). Motivation, instead of emotion or communication, predicted both low PSP total scores and unemployment.

CONCLUSION

Social functioning in patients with schizophrenia was significantly related to motivation. Further studies should focus on motivation and consider it as a therapeutic target to improve patients' social functioning.

Constructing single-crystalline hierarchical plate-like ZSM-5 zeolites with short b-axis length in the synthesis for catalyzing MTO reaction

ZSM-5 zeolite with hierarchical and lamellar structure is highly desired in industrial application. This paper reports an efficient additive, tetramethylguanidine (TMG), modifying crystal growth of the zeolite to this morphology....

Design of highly stable metal/ZSM-5 catalysts for the shape-selective alkylation of toluene with methanol to para-xylene

The co-existence of (Pt + Ni) effectively inhibits the formation of low-carbon olefins during the alkylation of toluene with methanol via the dehydrogenation of methanol to carbon oxides and also the hydrogenation of olefins to paraffins.

Plate-like MFI crystal growth achieved by guanidine compounds

The modifier not only changed the crystals’ morphology, but also enabled more Al or Ti to be incorporated into the MFI framework.

Inflammatory cytokines, cognition, and response to antidepressant treatment in patients with major depressive disorder

BACKGROUND

Inflammation and cognition are related to major depressive disorder (MDD), but the role in treatment response remains unclear. In this study, we investigated correlation between inflammatory cytokines and cognition in MDD patients treated with antidepressant medication.

METHODS

The participants were 149 MDD patients. Cytokines before therapy, cognitive assessments and severity of depression before and after therapy were tested. Logistic regression was used to explore underlying risks treatment response.

RESULTS

There were significant differences in smoking, alcohol drinking, and Stroop Color Test(SCT), Stroop Color-Word Test (SCWT), and Continuous Performance Test(CPT) scores between response group (RG) and non-response group (NRG) at baseline. Performance of patients in RG improved more in Brief Assessment of Cognition in Schizophrenia (BACS), Color Trial Test-I (CTT-I), SCT and SCWT after treatment. Levels of baseline IL-18 were associated with baseline learning and memory, and executive function. Treatment response was associated with drinking, performance of CPT and SCT.

CONCLUSION

MDD patients with different treatment responses have different cognitive defects, especially in speed of processing and executive function. Expression of cytokines is associated with cognition and may influence treatment response. Better speed of processing and executive function, and poorer attention at baseline may respond better to antidepressant treatment.

The application of quality control circle to improve functional exercise execution rate of orthopaedic surgery patients: A SQUIRE-compliant quality-improving study

Since its application in medical institutions in China, quality control circle (QCC) has gained achievements in medical care and thus earned more attention from the administrative department of health. This paper focuses on the application of QCC (bone-strength test circle) as a new management concept and tool circle in improving the implementation rate of functional exercise in patients undergoing orthopedic surgery, which further effectively improve the accuracy and compliance of functional exercise in patients undergoing surgery, and further effectively promote the rehabilitation of patients.From July 2018 to November 2018, the management tools and statistical methods of QCC were used to compare the promotion effect of functional exercise execution rate of orthopedic surgery patients before and after QCC activities.The checklist investigated 6 key causes of functional exercise in patients undergoing orthopedic surgery, the operative rate of orthopaedic surgery in all aspects changed significantly. Among them, the factors of inadequate propaganda and education were compared before and after the improvement, which decreased by 71% after the improvement. The incorrect factors of exercise decreased by 69% after improvement, the effect was more obvious, the difference was statistically significant (P < .05). The improvement rate was 59.4%.Through this activity, functional exercise education process and functional exercise paths, and corresponding standards for various orthopedic diseases were established. At the same time, the measures such as health education for patients were strengthened, and the expected goals were effectively achieved.

Text-Graph Enhanced Knowledge Graph Representation Learning

Knowledge Graphs (KGs) such as Freebase and YAGO have been widely adopted in a variety of NLP tasks. Representation learning of Knowledge Graphs (KGs) aims to map entities and relationships into a continuous low-dimensional vector space. Conventional KG embedding methods (such as TransE and ConvE) utilize only KG triplets and thus suffer from structure sparsity. Some recent works address this issue by incorporating auxiliary texts of entities, typically entity descriptions. However, these methods usually focus only on local consecutive word sequences, but seldom explicitly use global word co-occurrence information in a corpus. In this paper, we propose to model the whole auxiliary text corpus with a graph and present an end-to-end text-graph enhanced KG embedding model, named Teger. Specifically, we model the auxiliary texts with a heterogeneous entity-word graph (called text-graph), which entails both local and global semantic relationships among entities and words. We then apply graph convolutional networks to learn informative entity embeddings that aggregate high-order neighborhood information. These embeddings are further integrated with the KG triplet embeddings via a gating mechanism, thus enriching the KG representations and alleviating the inherent structure sparsity. Experiments on benchmark datasets show that our method significantly outperforms several state-of-the-art methods.

HGAT: Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification

Short text classification has been widely explored in news tagging to provide more efficient search strategies and more effective search results for information retrieval. However, most existing studies, concentrating on long text classification, deliver unsatisfactory performance on short texts due to the sparsity issue and the insufficiency of labeled data. In this article, we propose a novel heterogeneous graph neural network-based method for semi-supervised short text classification, leveraging full advantage of limited labeled data and large unlabeled data through information propagation along the graph. Specifically, we first present a flexible heterogeneous information network (HIN) framework for modeling short texts, which can integrate any type of additional information and meanwhile capture their relations to address the semantic sparsity. Then, we propose Heterogeneous Graph Attention networks (HGAT) to embed the HIN for short text classification based on a dual-level attention mechanism, including node-level and type-level attentions. To efficiently classify new coming texts that do not previously exist in the HIN, we extend our model HGAT for inductive learning, avoiding re-training the model on the evolving HIN. Extensive experiments on single-/multi-label classification demonstrates that our proposed model HGAT significantly outperforms state-of-the-art methods across the benchmark datasets under both transductive and inductive learning.

[Value of the application of enhanced CT radiomics and machine learning in preoperative prediction of microvascular invasion in hepatocellular carcinoma]

Objective: To explore the value of machine learning models in preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on dual-phase contrast-enhanced CT radiomics features. Methods: The data of 148 patients [106 males and 42 females, with an average age of (58±11) years] with HCC confirmed by pathology in the First Affiliated Hospital of Soochow University from January 2015 to May 2020 were retrospectively analyzed, including 88 cases of positive MVI and 60 cases of negative MVI. According to the ratio of 7∶3, the patients were randomly divided into the training and validation sets, respectively. The three-dimensional (3D) radiomics features of HCC in arterial phase (AP) and portal venous phase (PP) were extracted by MaZda software, and the optimal feature subset was obtained by combining three feature selection methods (FPM method) and Lasso regression. Then, six machine learning methods were used to build the prediction models. Receiver operating characteristic (ROC) curves were drawn to evaluate the prediction ability of the aforementioned models, and the area under the curve (AUC), accuracy, sensitivity and specificity were calculated. Results: Radiomics features of HCC in AP and PP were extracted by MaZda software, with 239 in each phase. There were 7 optimal features in AP and 14 optimal features in PP selected by FPM method and Lasso regression, respectively. The AUCs of decision tree, extreme gradient boosting, random forest, support vector machine (SVM), generalized linear model, and neural network based on the 7 optimal features in AP in the validation set were 0.736, 0.910, 0.913, 0.915, 0.897, 0.648, respectively. The SVM had the highest AUC in the validation set, with the accuracy, sensitivity and specificity of 95.35%, 95.83% and 94.74%, respectively. Likewise, the AUCs of machine learning models in prediction of MVI in HCC based on the 14 optimal features in PP in the validation set were 0.873, 0.876, 0.913, 0.859, 0.877, 0.834, respectively, and there were no significant differences (all P>0.05). The random forest had the highest AUC in the validation set, with the accuracy, sensitivity and specificity of 90.70%, 87.50% and 94.74%, respectively. Conclusion: Machine learning models based on dual-phase enhanced CT radiomics features can be used in preoperative prediction of MVI in HCC, particularly the SVM and random forest models have high prediction efficiency.

Neurocognitive profiles of patients with first-episode and recurrent depression: a cross-sectional comparative study from China

BACKGROUND

Accumulating evidence has shown that cognitive deficits are prevalent among patients with major depressive disorder (MDD). However, the pattern and extent of cognitive deficits in patients with first-episode and recurrent depression remain unclear.

METHODS

Objective measures of cognitive function were assessed in 433 first-episode of depression (FED) patients, 206 recurrent major depression (RMD) patients during the acute phase and 287 healthy controls (HCs). Five domains of cognition (speed of processing, attention, verbal learning, visual learning and execution function) were assessed by seven neuropsychological tests. Neuropsychological data were transformed into T scores according to the Chinese norm.

RESULTS

Both FED and RMD patients displayed deficits of mild-to-medium severity across all cognitive domains and their subtests, relative to HCs (Cohen's d = 0.12-0.62). Among MDD patients, RMD patients exhibited poorer verbal learning (Cohen's d = 0.17) and visual learning (Cohen's d = 0.21) than FED patients.

CONCLUSIONS

MDD patients in the acute phase exhibited broad cognitive impairments, and there were significant differences in verbal and visual learning between FED and RMD patients. Our findings indicate that more attention should be paid to the domains of verbal and visual learning in future screening assessments and targeted interventions.

The efficiencies and capacities of carbon conversion in fruit and vegetable waste two-phase anaerobic digestion: Ethanol-path vs. butyrate-path

To rapidly treat and stably utilize great quantities of fruit and vegetable waste (FVW), the strategies in anaerobic digestion pattern have been constantly improved. In this work, the efficiencies and capacities of carbon conversion in different FVW anaerobic digestion systems were studied. Compared to butyrate-path (BD) two-phase and single-phase anaerobic digestion (SD), the ethanol-path two-phase anaerobic digestion (ED) system showed the highest rate of converting insoluble into soluble carbon formation (82.2%) and methane yield conversed from soluble carbon which is 0.14 gCOD_CH4 (gVSS d)^-1. It was also found that the coexistence of Bacillus and Methanococcus in the methanogenic phase maintained fatty acids and methane generation. The advantage of carbon conversion efficiency in ED can be elucidated from the highest acetification rate (704.10 mgCOD (L h)^-1) which means more converted acetate can be smoothly used for methane generation. Compared to methanogenesis converted from butyrate and propionate, the thermodynamic condition of methanogenesis converted from ethanol was more feasible. Also, the highest capacity of max methane production (197773.7 mL) of ED was simulated. ED might be an efficient and advantageous option for FVW methane digestion. Furthermore, comparison of acidogenic product and methane in conversion efficiency revealed that fatty acids should think as ideal anaerobic product rather than methane.