Risk Factors for Hypoglycemia Among Neonates: A Prospective Cohort Study Among Pregnant People With Gestational Diabetes Mellitus.

OBJECTIVE: Neonatal hypoglycemia (NH) is the most frequent complication in neonates born to pregnant people with gestational diabetes mellitus (GDM) and an important cause of brain damage and death of neonates. We explored the risk factors for NH in neonates of pregnant people with GDM. METHODS: A prospective cohort study was conducted involving 322 pregnant people with GDM at the Guangzhou Women and Children's Medical Centre. Maternal sociodemographic, clinical, and biochemical data, as well as general characteristics of neonates, were collected to analyze their associations with NH in neonates of pregnant people with GDM. RESULTS: The incidence of NH among neonates of pregnant people with GDM was 19.57% (63/322). After adjustment for confounders, the factors significantly associated with an increased risk of NH were cesarean delivery (relative risk [RR] = 3.44; 95% confidence interval [CI], 1.83-6.45), red blood cell (RBC) count (RR = 2.19; 95% CI, 1.22-3.96), and 1-hour postprandial glucose (RR = 2.35; 95% CI, 1.23-4.46) during pregnancy, whereas later gestational age (RR = 0.58; 95% CI, 0.42-0.80) and multiparity (RR = 0.32; 95% CI, 0.16-0.66) were associated with a reduced risk of NH. CONCLUSION: Cesarean delivery, maternal 1-hour glucose of the oral glucose tolerance test, and increased RBC count of pregnant people with GDM are independent risk factors for NH, while later gestational age and multiparity are protective factors.

Exome sequencing revealed PDE11A as a novel candidate gene for early-onset Alzheimer's disease.

To identify novel risk genes and better understand the molecular pathway underlying Alzheimer's disease (AD), whole-exome sequencing was performed in 215 early-onset AD (EOAD) patients and 255 unrelated healthy controls of Han Chinese ethnicity. Subsequent validation, computational annotation and in vitro functional studies were performed to evaluate the role of candidate variants in EOAD. We identified two rare missense variants in the phosphodiesterase 11A (PDE11A) gene in individuals with EOAD. Both variants are located in evolutionarily highly conserved amino acids, are predicted to alter the protein conformation and are classified as pathogenic. Furthermore, we found significantly decreased protein levels of PDE11A in brain samples of AD patients. Expression of PDE11A variants and knockdown experiments with specific short hairpin RNA (shRNA) for PDE11A both resulted in an increase of AD-associated Tau hyperphosphorylation at multiple epitopes in vitro. PDE11A variants or PDE11A shRNA also caused increased cyclic adenosine monophosphate (cAMP) levels, protein kinase A (PKA) activation and cAMP response element-binding protein phosphorylation. In addition, pretreatment with a PKA inhibitor (H89) suppressed PDE11A variant-induced Tau phosphorylation formation. This study offers insight into the involvement of Tau phosphorylation via the cAMP/PKA pathway in EOAD pathogenesis and provides a potential new target for intervention.

Prediction of Alzheimer's disease using multi-variants from a Chinese genome-wide association study.

Previous genome-wide association studies have identified dozens of susceptibility loci for sporadic Alzheimer's disease, but few of these loci have been validated in longitudinal cohorts. Establishing predictive models of Alzheimer's disease based on these novel variants is clinically important for verifying whether they have pathological functions and provide a useful tool for screening of disease risk. In the current study, we performed a two-stage genome-wide association study of 3913 patients with Alzheimer's disease and 7593 controls and identified four novel variants (rs3777215, rs6859823, rs234434, and rs2255835; Pcombined = 3.07 × 10-19, 2.49 × 10-23, 1.35 × 10-67, and 4.81 × 10-9, respectively) as well as nine variants in the apolipoprotein E region with genome-wide significance (P < 5.0 × 10-8). Literature mining suggested that these novel single nucleotide polymorphisms are related to amyloid precursor protein transport and metabolism, antioxidation, and neurogenesis. Based on their possible roles in the development of Alzheimer's disease, we used different combinations of these variants and the apolipoprotein E status and successively built 11 predictive models. The predictive models include relatively few single nucleotide polymorphisms useful for clinical practice, in which the maximum number was 13 and the minimum was only four. These predictive models were all significant and their peak of area under the curve reached 0.73 both in the first and second stages. Finally, these models were validated using a separate longitudinal cohort of 5474 individuals. The results showed that individuals carrying risk variants included in the models had a shorter latency and higher incidence of Alzheimer's disease, suggesting that our models can predict Alzheimer's disease onset in a population with genetic susceptibility. The effectiveness of the models for predicting Alzheimer's disease onset confirmed the contributions of these identified variants to disease pathogenesis. In conclusion, this is the first study to validate genome-wide association study-based predictive models for evaluating the risk of Alzheimer's disease onset in a large Chinese population. The clinical application of these models will be beneficial for individuals harbouring these risk variants, and particularly for young individuals seeking genetic consultation.

Foliar application of biosynthetic nano-selenium alleviates the toxicity of Cd, Pb, and Hg in Brassica chinensis by inhibiting heavy metal adsorption and improving antioxidant system in plant.

Biosynthetic nano-selenium (bio-SeNP), as a plant growth regulator, has better bioavailability and lower toxicity than selenite and selenate. This study investigated the beneficial role of bio-SeNP in mitigating the adverse effects of multiple heavy metals (HMs, e.g., Cd, Pb, and Hg) on growth and yield of pak choi (Brassica chinensis) grown in slightly or heavily polluted (SP or HP) soil by regulating metabolic and antioxidant systems. The results revealed that foliar application of bio-SeNP (5, 10, 20 mg L-1 Se) at the 6-leaf stage greatly reduced the levels of Cd, Pb, and Hg in shoots and roots of pak choi. Application of 5 mg L-1 bio-SeNP significantly (p < 0.05) decreased the translocation factor (TF) of Cd, Pb, and Hg from root to shoot by 9.83%, 44.21%, and 46.99% for SP soil, 24.17%, 56.00%, and 39.36% for HP soil, respectively. Meanwhile, all bio-SeNP treatments led to a significant improvement in plants growth by enhancing the antioxidant defense system (e.g., AsA-GSH) and promoting chlorophyll synthesis as well as suppressed the lipid peroxidation products contents (MDA) in shoots. Moreover, the enhanced levels of mineral nutrient elements (e.g., Ca, Mg, Fe, or Zn) and organic selenium (e.g., selenocystine, Se-methylselenocysteine, and selenomethionine) in the edible shoots of bio-SeNP-treated pak choi plant under multiple HMs stress indicated the positive impacts of bio-SeNP on the improvement of shoot quality and nutritional values. Collectively, our results indicated that bio-SeNP play an important role in the management of multiple HMs-induced adverse effects on pak choi. Foliar application of bio-SeNP at appropriate concentration (≤ 5 mg L-1 Se) can be considered as a promising agronomic measure for safety leafy vegetable production in multiple HMs polluted soils when bio-SeNP application.

Mediating effects of gut microbiota in the associations of air pollutants exposure with adverse pregnancy outcomes.

BACKGROUND: The aim of this study was to investigate alterations in gut microbiota after exposure to air pollutants during pregnancy and its mediation effect in inducing adverse pregnancy outcomes (APOs). METHODS: Participants (n = 916) were enrolled between 2017 and 2018 from a prospective cohort study of pregnant women in Guangzhou, China. The relative abundance of fecal microbiota was profiled using 16SrRNA V4 region sequencing. Exposure to air pollutants in each trimester of pregnancy was assessed using measurements from the nearest monitoring station. APOs including pre-term birth (PTB), post-term birth (POTB), low birth weight (LBW), macrosomia fetus (MF), birth defects (BDs), pathological cesarean section (PCS) and postpartum hemorrhage (PPH)) were determined by referring to reliable clinical records and diagnostic criteria. Univariate analysis, multivariate analysis and mediation analysis were performed to estimate the association among air pollutants, gut microbiota and APOs. RESULTS: Air pollutants exposure during pregnancy was significantly correlated with the alterations in the gut microbiota, and increased risks of various APOs by 1.07-1.36-fold (P < 0.05). The mediation analyses indicated that alterations in Eggerthella, Phascolarctobacterium and Clostridium partially mediated the effects of air pollutants exposure (PM2.5, PM10, O3, NO2 and SO2) on APOs. The relative abundance of f_Micrococcaceae explained 11.39%, 64.90% and 54.80% of the correlation between SO2, PM2.5, PM10 and POTB, respectively; whereas g_Rothia explained 11.97%, 67.80% and 54.50%, respectively. g_Parabacteroides explained 53.0% of the correlation between PM2.5 and PTB. CONCLUSIONS: Increased air pollutants exposure during pregnancy may induce adverse pregnancy outcomes via alteration of the gut microbiota.

Cognitive profiles in adult-onset neuronal intranuclear inclusion disease: a case series from the memory clinic.

OBJECTIVE: This study aimed to analyze the characteristics of cognitive impairment in adult-onset neuronal intranuclear inclusion disease (NIID). METHODS: Seven patients with adult-onset NIID were collected consecutively from the memory clinic of Xuanwu hospital from February to December 2019. These cases were diagnosed with skin biopsy triggered by DWI high-intensity signals in corticomedullary junction on brain MRI. We used a battery of neuropsychological scales to detect the patient's performance in each cognitive domain, and made a detailed analysis on the characteristics of cognitive impairment. RESULTS: All seven cases had cognitive impairment, and four of them had met the criteria for dementia. The scores of Montreal Cognitive Assessment and Frontal Assessment Battery were abnormal in all patients. The executive dysfunction was confirmed by the abnormal scores of Trail Making Test (5/7, 71%) and Clock Drawing Test (4/7, 57%). Bad performance in Auditory Verbal Learning Test (6/7, 86%) demonstrated that the memory was also a very commonly impaired cognitive domain. The low score on the animal fluency (4/7, 57%), Boston Naming Test (3/7, 43%), and Pentagon and Cube Copying Test (4/7, 57%) indicated that the language and visuospatial skills were also impaired. Fazekas scores were significantly correlated to the global cognition, executive and language functions (r = 0.788-0.906, P < 0.05). CONCLUSIONS: There is obvious impairment in multiple cognitive domains in adult-onset NIID, and both the executive dysfunction and memory deficit are very common. Leukoencephalopathy may be the main course of cognitive impairment in adult-onset NIID.

Improving the fermentable sugar yields of wheat straw by high-temperature pre-hydrolysis with thermophilic enzymes of Malbranchea cinnamomea.

BACKGROUND: Enzymatic hydrolysis is a key step in the conversion of lignocellulosic polysaccharides to fermentable sugars for the production of biofuels and high-value chemicals. However, current enzyme preparations from mesophilic fungi are deficient in their thermostability and biomass-hydrolyzing efficiency at high temperatures. Thermophilic fungi represent promising sources of thermostable and highly active enzymes for improving the biomass-to-sugar conversion process. Here we present a comprehensive study on the lignocellulosic biomass-degrading ability and enzyme system of thermophilic fungus Malbranchea cinnamomea N12 and the application of its enzymes in the synergistic hydrolysis of lignocellulosic biomass. RESULTS: Malbranchea cinnamomea N12 was capable of utilizing untreated wheat straw to produce high levels of xylanases and efficiently degrading lignocellulose under thermophilic conditions. Temporal analysis of the wheat straw-induced secretome revealed that M. cinnamomea N12 successively degraded the lignocellulosic polysaccharides through sequential secretion of enzymes targeting xylan and cellulose. Xylanase-enriched cocktail from M. cinnamomea N12 was more active on native and alkali­pretreated wheat straw than the commercial xylanases from Trichoderma reesei over temperatures ranging from 40 to 75 °C. Integration of M. cinnamomea N12 enzymes with the commercial cellulase preparation increased the glucose and xylose yields of alkali­pretreated wheat straw by 32 and 166%, respectively, with pronounced effects at elevated temperature. CONCLUSIONS: This study demonstrated the remarkable xylanase-producing ability and strategy of sequential lignocellulose breakdown of M. cinnamomea N12. A new process for the hydrolysis of lignocellulosic biomass was proposed, comprising thermophilic enzymolysis by enzymes of M. cinnamomea N12 followed with mesophilic enzymolysis by commercial cellulases. Developing M. cinnamomea N12 as platforms for thermophilic enzyme mixture production will provide new perspectives for improved conversion yields for current biomass saccharification schemes.

PSEN1, PSEN2, and APP mutations in 404 Chinese pedigrees with familial Alzheimer's disease.

INTRODUCTION: The PSENs/APP mutation distribution in Chinese patients with familial Alzheimer's disease (FAD) remains unclear. We aimed to analyze the genetic features of Chinese FAD pedigrees with and without PSENs/APP mutations. METHODS: In total, 1330 patients with Alzheimer's disease (AD) or mild cognitive impairment in 404 pedigrees were enrolled from the Chinese Familial Alzheimer's Disease Network. PSENs/APP mutations and APOE frequencies were determined. RESULTS: In total, 13.12% of pedigrees carried PSENs/APP missense mutations, 3.71% carried PSENs/APP synonymous/untranslated region variants, and 83.17% did not carry PSENs/APP mutations. Eleven missense mutations were first identified. In patients without PSENs/APP mutations, 44.31% carried one APOEε4 allele, and 14.85% two APOEε4 alleles. DISCUSSION: The new PSENs/APP mutations indicate heterogeneity in AD pathogenesis between Chinese and other ethnic groups. The low mutation rate suggests the involvement of other genes/factors in Chinese FAD. APOEε4 might be a major gene for some FAD without PSENs/APP mutations.

The APOE ε4 exerts differential effects on familial and other subtypes of Alzheimer's disease.

INTRODUCTION: The genetic risk effects of apolipoprotein E (APOE) on familial Alzheimer's disease (FAD) with or without gene mutations, sporadic AD (SAD), and normal controls (NC) remain unclear in the Chinese population. METHODS: In total, 15 119 subjects, including 311 FAD patients without PSEN1, PSEN2, APP, TREM2, and SORL1 pathogenic mutations (FAD [unknown]); 126 FAD patients with PSENs/APP mutations (FAD [PSENs/APP]); 7234 SAD patients; and 7448 NC were enrolled. The risk effects of APOE ε4 were analyzed across groups. RESULTS: The prevalence of the APOE ε4 genotype in FAD (unknown), FAD (PSENs/APP), SAD, and NC groups was 56.27%, 26.19%, 36.23%, and 19.54%, respectively. Further, the APOE ε4 positive genotype had predictive power for FAD (unknown) risk (odds ratio: 4.51, 95% confidence interval: 3.57-5.45, P < .001). DISCUSSION: APOE ε4 positive genotype may cause familial aggregation, and the investigation of multiple interventions targeting APOE pathological function to reduce the risk for this disease warrants attention.

The cost of Alzheimer's disease in China and re-estimation of costs worldwide.

INTRODUCTION: The socioeconomic costs of Alzheimer's disease (AD) in China and its impact on global economic burden remain uncertain. METHODS: We collected data from 3098 patients with AD in 81 representative centers across China and estimated AD costs for individual patient and total patients in China in 2015. Based on this data, we re-estimated the worldwide costs of AD. RESULTS: The annual socioeconomic cost per patient was US $19,144.36, and total costs were US $167.74 billion in 2015. The annual total costs are predicted to reach US $507.49 billion in 2030 and US $1.89 trillion in 2050. Based on our results, the global estimates of costs for dementia were US $957.56 billion in 2015, and will be US $2.54 trillion in 2030, and US $9.12 trillion in 2050, much more than the predictions by the World Alzheimer Report 2015. DISCUSSION: China bears a heavy burden of AD costs, which greatly change the estimates of AD cost worldwide.

Distribution of sulfonamides in liquid and solid anaerobic digestates: effects of hydraulic retention time and swine manure to rice straw ratio.

The effects of hydraulic retention time (20 and 15 days) and swine manure to rice straw ratios on distribution of sulfonamides (SAs) in liquid and solid anaerobic digestates were studied using bench-scale completely stirred tank reactors at (37 ± 1) °C. Results showed that anaerobic digestion (AD) treatment exhibited a good removal effect on sulfadiazine (SDZ), sulfadimidine (SM2) and sulfachloropyridazine (SCP), especially at HRT = 20 days and co-digestion with swine manure and rice straw. The removal rates of SDZ and SM2 were more than 90%, but only 72.8% for SCP. The residual SAs were mainly remained in solid digestates, with residual rates ranging from 28.8% to 71.3%, 40.6% to 88.0, and 82.7% to 97.0% for SDZ, SM2 and SCP, respectively. Due to lower pKa and higher log K ow of SCP, its residue in solid digestates was far more than SDZ and SM2. Higher HRT and co-digestion could improve the degradation of SAs, which can also be put down to the occurrence of cometabolism of SAs and COD.

Research on gesture recognition algorithm based on MME-P3D.

A Multiscale-Motion Embedding Pseudo-3D (MME-P3D) gesture recognition algorithm has been proposed to tackle the issues of excessive parameters and high computational complexity encountered by existing gesture recognition algorithms deployed in mobile and embedded devices. The algorithm initially takes into account the characteristics of gesture motion information, integrating the channel attention (CE) mechanism into the pseudo-3D (P3D) module, thereby constructing a P3D-C feature extraction network that can efficiently extract spatio-temporal feature information while reducing the complexity of the algorithmic model. To further enhance the understanding and learning of the global gesture movement's dynamic information, a Multiscale Motion Embedding (MME) mechanism is subsequently designed. The experimental findings reveal that the MME-P3D model achieves recognition accuracies reaching up to 91.12% and 83.06% on the self-constructed conference gesture dataset and the publicly available Chalearn 2013 dataset, respectively. In comparison with the conventional 3D convolutional neural network, the MME-P3D model demonstrates a significant advantage in terms of parameter count and computational requirements, which are reduced by as much as 82% and 83%, respectively. This effectively addresses the limitations of the original algorithms, making them more suitable for deployment on embedded and mobile devices and providing a more effective means for the practical application of hand gesture recognition technology.

Micro-expression recognition based on multi-scale 3D residual convolutional neural network.

In demanding application scenarios such as clinical psychotherapy and criminal interrogation, the accurate recognition of micro-expressions is of utmost importance but poses significant challenges. One of the main difficulties lies in effectively capturing weak and fleeting facial features and improving recognition performance. To address this fundamental issue, this paper proposed a novel architecture based on a multi-scale 3D residual convolutional neural network. The algorithm leveraged a deep 3D-ResNet50 as the skeleton model and utilized the micro-expression optical flow feature map as the input for the network model. Drawing upon the complex spatial and temporal features inherent in micro-expressions, the network incorporated multi-scale convolutional modules of varying sizes to integrate both global and local information. Furthermore, an attention mechanism feature fusion module was introduced to enhance the model's contextual awareness. Finally, to optimize the model's prediction of the optimal solution, a discriminative network structure with multiple output channels was constructed. The algorithm's performance was evaluated using the public datasets SMIC, SAMM, and CASME Ⅱ. The experimental results demonstrated that the proposed algorithm achieves recognition accuracies of 74.6, 84.77 and 91.35% on these datasets, respectively. This substantial improvement in efficiency compared to existing mainstream methods for extracting micro-expression subtle features effectively enhanced micro-expression recognition performance and increased the accuracy of high-precision micro-expression recognition. Consequently, this paper served as an important reference for researchers working on high-precision micro-expression recognition.

Hydrochar drives reduction in bioavailability of heavy metals during composting via promoting humification and microbial community evolution.

This study presented the effects of hydrochar on humification, heavy metals (HMs) bioavailability and bacterial community succession during composting. Results indicated that hydrochar addition led to elevated composting temperature, 7.3% increase in humic acid (HA), and 52.9% increase in ratio of humic acid to fulvic acid. The diethylene triamine pentacetic acid extractable Zn, Cu, Pb, and Ni were reduced by 19.2%, 36.3%, 37.8%, and 27.1%, respectively, in hydrochar-involved composting system. Furthermore, main mechanisms driving the reduced HMs bioavailability by hydrochar addition were revealed. The addition of hydrochar significantly modified the microbial community structure. Correlation analysis and microbial analysis demonstrated that relative abundance of bacterial groups connected with humification and HMs passivation were increased. Consequently, the HA formation was promoted and the HMs bioavailability were reduced through bacterial bioremediation and HA complexation. This study demonstrates the addition of hydrochar as a promising strategy to mitigate the HMs bioavailability during composting.

The bacterial community drive the humification and greenhouse gas emissions during plant residues composting under different aeration rates.

This study investigated the effects of different aeration intensities on organic matter (OM) degradation, greenhouse gas emissions (GHG) as well as humification during plant residue composting. Three intermittent aeration intensities of 0.084 (Tlow), 0.19 (Tmedium) and 0.34 (Thigh) L min-1kg-1 DM with 30 min on/30 min off were conducted on a lab-scale composting experiment. Results showed that OM mineralization in Thigh was more evident than Tlow and Tmedium, resulting in the highest humic acid content. Humic acid content in Tmedium and Thigh was 15.7% and 18.5% higher than that in Tlow. The average O2 concentration was 4.9%, 9.5% and 13.6% for Tlow, Tmedium and Thigh. Compared with Tmedium and Thigh, Tlow reduced CO2 and N2O emissions by 18.3%-39.6% and 72.4%-63.9%, but the CH4 emission was highest in Tlow. But the total GHG emission was the lowest in Thigh. Linear Discriminant Analysis Effect Size analysis showed that the core bacteria within Tlow mainly belonged to Anaerolineaceae, which was significantly negatively correlated to the emission of CH4. Thermostaphylospora, Unclassified_Vicinamibacteraceae and Sulfurifustis were identified as core bacteria in Tmedium and Thigh, and these genus were significantly postively correlated to CO2 and N2O emissions. Redundancy analysis showed that total orgnic carbon, O2 and electrical conductivity were the key factors affecting the evolution of bacterial community.

Sustainable design of photo-Fenton-like oxidation process in actual livestock wastewater through the highly dispersed FeCl3 anchoring on a g-C3N4 substrate.

Photocatalytic technology emerges as a promising solution for the sustainable treatment of contaminated wastewater. However, the practical implementation of designed photocatalysts often faces challenges due to the intricate 'high carbon footprint' process and limited outdoor laboratory investigations. Herein, a simple yet versatile impregnation approach is proposed to anchor highly dispersed FeCl3 on a g-C3N4 substrate (Fe-C3N4) with minimal energy consumption and post-processing. Fe-C3N4 enhances photocatalytic reactivity for antibiotic degradation via a synergistic photo-Fenton-like oxidation technique, efficiently removing antibiotic pollutants from actual livestock wastewater. The Fe-C3N4 catalyst exhibited consistent degradation performance over five cycles in laboratory conditions, maintaining a degradation efficiency exceeding 90 % for tetracycline hydrochloride (TCHCl). Furthermore, we engineered a straightforward Fe-C3N4Na2SiO3 reactor for treating livestock wastewater, achieving an 81.8 % removal of TCHCl in outdoor field tests conducted in the winter and summer in China. The Fe-C3N4 catalyst demonstrated high feasibility in treating antibiotic-contaminated livestock wastewater under year-round climatic conditions, leveraging synergistic effects. The stabilization of Fe-C3N4 for the degradation of antibiotic-containing wastewater under sunlight represents a significant advancement in the practical application of photocatalysts, marking a crucial milestone from experimental conception to implementation. Acute toxicity estimation suggested that intermediates/products generated exhibited lower toxicity compared to TCHCl, indicating their practical applicability. Density functional theory (DFT) analysis successfully predicted significant electron transfer between Fe-C3N4 and TCHCl, indicating efficient interfacial interactions on the TCHCl surface. To ensure the environmental sustainability of Fe-C3N4, a life cycle assessment (LCA) was conducted to compared this photocatalyst with other commonly used emerging photocatalysts. The results demonstrated that Fe-C3N4 exhibits a two orders of magnitude lower CO2 equivalent emission compared to the ZnO photocatalyst, indicating a cost-effective and efficient synergistic photo-Fenton-like catalytic approach. This low-cost photocatalyst, moving from the laboratory to real-world wastewater applications, provides a powerful and more sustainable solution for the efficient treatment of wastewater containing antibiotics from livestock farming.

Renewable hemicellulose-based materials for value-added applications.

The importance of renewable resources and environmentally friendly materials has grown globally in recent time. Hemicellulose is renewable lignocellulosic materials that have been the subject of substantial valorisation research. Due to its distinctive benefits, including its wide availability, low cost, renewability, biodegradability, simplicity of chemical modification, etc., it has attracted increasing interest in a number of value-added fields. In this review, a systematic summarizes of the structure, extraction method, and characterization technique for hemicellulose-based materials was carried out. Also, their most current developments in a variety of value-added adsorbents, biomedical, energy-related, 3D-printed materials, sensors, food packaging applications were discussed. Additionally, the most recent challenges and prospects of hemicellulose-based materials are emphasized and examined in-depth. It is anticipated that in the near future, persistent scientific efforts will enable the renewable hemicellulose-based products to achieve practical applications.

Effects of chronic social defeat stress on behaviour, endoplasmic reticulum proteins and choline acetyltransferase in adolescent mice.

The present study investigated the effects of social defeat stress on the behaviours and expressions of 78-kDa glucose-regulated protein (Grp78), CCAAT/enhancer-binding protein (C/EBP) homologous protein (CHOP) and choline acetyltransferase (Chat) in the brains of adolescent mice. Adolescent male C57BL/6J mice were divided into two groups (susceptible and unsusceptible) after 10 d social defeat stress. In expt 1, behavioural tests were conducted and brains were processed for Western blotting on day 21 after stress. In expt 2, social avoidance tests were conducted and brains were subsequently processed for Western blotting on day 12 after stress. Chronic social defeat stress produced more pronounced depression-like behaviours such as decreased locomotion and social interaction, increased anxiety-like behaviours and immobility, and impaired memory performance in susceptible mice. Moreover, susceptible mice showed greater expression of Grp78 and CHOP in the amygdala (Amyg) on days 12 and 21 compared with the other groups. Susceptible and unsusceptible groups showed significant increases in Grp78 and CHOP expression in the prefrontal cortex (PFC) and hippocampus (Hipp) on day 12 compared with the control group; this persisted until day 21. The levels of Chat measured on days 12 and 21 were significantly lower in the PFC, Amyg and Hipp of all defeated mice compared with controls. The findings of the behavioural tests indicate that chronic social defeat in adolescents produces anxiety-like behaviours, social withdrawal, despair-like behaviours and cognitive impairment. The Grp78, CHOP and Chat results suggest that the selective response of endoplasmic reticulum stress proteins in the Amyg plays an important role in the vulnerability-stress model of depression.

A weakly supervised learning-based segmentation network for dental diseases.

With the development of deep learning, medical image segmentation has become a promising technique for computer-aided medical diagnosis. However, the supervised training of the algorithm relies on a large amount of labeled data, and the private dataset bias generally exists in previous research, which seriously affects the algorithm's performance. In order to alleviate this problem and improve the robustness and generalization of the model, this paper proposes an end-to-end weakly supervised semantic segmentation network to learn and infer mappings. Firstly, an attention compensation mechanism (ACM) aggregating the class activation map (CAM) is designed to learn complementarily. Then the conditional random field (CRF) is introduced to prune the foreground and background regions. Finally, the obtained high-confidence regions are used as pseudo labels for the segmentation branch to train and optimize using a joint loss function. Our model achieves a Mean Intersection over Union (MIoU) score of 62.84% in the segmentation task, which is an effective improvement of 11.18% compared to the previous network for segmenting dental diseases. Moreover, we further verify that our model has higher robustness to dataset bias by improved localization mechanism (CAM). The research shows that our proposed approach improves the accuracy and robustness of dental disease identification.

A global optimization generation method of stitching dental panorama with anti-perspective transformation.

To address the limitation of narrow field-of-view in local oral cavity images that fail to capture large-area targets at once, this paper designs a method for generating natural dental panoramas based on oral endoscopic imaging that consists of two main stages: the anti-perspective transformation feature extraction and the coarse-to-fine global optimization matching. In the first stage, we increase the number of matched pairs and improve the robustness of the algorithm to viewpoint transformation by normalizing the anti-affine transformation region extracted from the Gaussian scale space and using log-polar coordinates to compute the gradient histogram of the octagonal region to obtain the set of perspective transformation resistant feature points. In the second stage, we design a coarse-to-fine global optimization matching strategy. Initially, we incorporate motion smoothing constraints and improve the Fast Library for Approximate Nearest Neighbors (FLANN) algorithm by utilizing neighborhood information for coarse matching. Then, we eliminate mismatches via homography-guided Random Sample Consensus (RANSAC) and further refine the matching using the Levenberg-Marquardt (L-M) algorithm to reduce cumulative errors and achieve global optimization. Finally, multi-band blending is used to eliminate the ghosting due to unalignment and make the image transition more natural. Experiments show that the visual effect of dental panoramas generated by the proposed method is significantly better than that of other methods, addressing the problems of sparse splicing discontinuities caused by sparse keypoints, ghosting due to parallax, and distortion caused by the accumulation of errors in multi-image splicing in oral endoscopic image stitching.