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The newly discovered vein-type Shirengou gold deposit on the northern edge of the North China Craton contains at least 8.69 tons of Au at an average grade of 9.80 g/t. Six auriferous quartz veins have been identified within Upper Jurassic rhyolitic breccia-bearing crystal-lithic tuff, and they follow NW-trending secondary faults. Gold mineralization was closely related to silicification, with pyrite as the predominant ore mineral. Four mineralization stages can be discerned, involving Stage I milky quartz, Stage II quartz-pyrite, Stage III gray quartz-polymetallic sulfide, and Stage IV quartz-calcite ± pyrite. Zircon U Pb dating of a pre-ore porphyryritic rhyolite dike sets an upper age limit on the gold mineralization of ~131.1 Ma. Two-phase and single-phase aqueous inclusions were observed in quartz related to all four mineralization stages. From Stage I to Stage IV, the fluid inclusion homogenization temperatures decrease from respectively 257–340 °C to 230–277 °C, 201–250 °C and 161–200 °C, with corresponding salinities of 3.39–9.21, 2.24–8.00, 1.22–7.59, and 0.18–3.55 wt% NaCl equivalent, respectively. Thus, the ore-forming fluid can be described as moderate-high temperature, moderate-low salinity H 2 O-NaCl fluids. The δ34S values of sulfides vary from 0.0 to 5.3 ‰, indicating that the S was derived primarily from magmatic source(s), with some contribution from the metamorphic rocks of the surrounding Jianping Group. In-situ LA-ICP-MS trace element analysis of sulfides, together with our new age data, suggest that the ore-forming fluid was initially of Early Cretaceous magmatic-hydrothermal origin, and subsequently modified by interaction with the wall rocks of the Jianping Group, extracting the ore-forming metals from these rocks into the fluids. • Zircon U Pb geochronology confine the timing of gold mineralization after 131.1 Ma. • The ore-forming fluid formed in a H 2 O-NaCl hydrothermal system. • S isotope imply that S was derived from magmatic source with some from wall rock. • The ore-forming fluid was of magmatic-hydrothermal origin and modified by wall rock. • The Shirengou gold deposit was related to the Early Cretaceous magmatism. [ABSTRACT FROM AUTHOR]

Aldehydes are the main inhibitors generated during the pretreatment of lignocellulosic biomass, which can inhibit cell growth and disturb subsequent fermentation. Saccharomyces cerevisiae has the intrinsic ability to in situ detoxify aldehydes to their less toxic or nontoxic alcohols by numerous aldehyde dehydrogenases/reductases during the lag phase. Herein, we report that an uncharacterized open reading frame YMR152W from S. cerevisiae encodes a novel aldehyde reductase with catalytic functions for reduction of at least six aldehydes, including two furan aldehydes (furfural and 5-hydroxymethylfurfural), three aliphatic aldehydes (acetaldehyde, glycolaldehyde, and 3-methylbutanal), and an aromatic aldehyde (benzaldehyde) with NADH or NADPH as the co-factor. Particularly, Ymr152wp displayed the highest specific activity (190.86 U/mg), and the best catalytic rate constant (K cat), catalytic efficiency (K cat /K m), and affinity (K m) when acetaldehyde was used as the substrate with NADH as the co-factor. The optimum pH of Ymr152wp is acidic (pH 5.0–6.0), but this enzyme is more stable in alkaline conditions (pH 8.0). Metal ions, chemical protective additives, salts, and substrates could stimulate or inhibit enzyme activities of Ymr152wp in varying degrees. Ymr152wp was classified into the quinone oxidoreductase (QOR) subfamily of the medium-chain dehydrogenase/reductase (MDR) family based on the results of amino acid sequence analysis and phylogenetic analysis. Although Ymr152wp was grouped into the QOR family, no quinone reductase activity was observed using typical quinones (9,10-phenanthrenequinone, 1,2-naphthoquinone, and p -benzoquinone) as the substrates. This study provides guidelines for exploring more uncharacterized aldehyde reductases in S. cerevisiae for in situ detoxification of aldehyde inhibitors derived from lignocellulosic hydrolysis. • YMR152W from Saccharomyces cerevisiae encodes an aldehyde reductase. • Ymr152wp showed much better specific activity for reduction of acetaldehyde. • Overexpression of YMR152W conferred resistance to aldehyde inhibitors. • Ymr152wp is classified into the QOR subfamily of the MDR family. [ABSTRACT FROM AUTHOR]

Image restoration is the process of restoring latent clear images from degraded images, and it has received substantial attention in various restoration scenarios recently. Contrary to the considerable progress of image restoration algorithms, the quality evaluation for image restoration falls behind, which may hinder the further development of advanced image restoration techniques. So far, only several objective quality metrics have been proposed to evaluate the quality of restored images for specific restoration scenarios, and little work has been dedicated to the applications of image restoration quality metrics. Besides, the performance of these metrics remains an open problem, and an obvious disadvantage of these metrics is that their robustness is quite weak. To bridge this gap, we present this survey paper. First, the difference between the traditional image quality assessment and quality assessment for image restoration was analyzed profoundly. Then, for different image restoration scenarios, this paper provides (i) a comprehensive description of the existing subjective quality databases, (ii) a thorough insight into existing objective quality metrics and (iii) two applications of image restoration quality metrics, namely, parameter selection and benchmarking image restoration algorithms. After that, the experimental results and a detailed performance analysis on (i) quality assessment of restored images, (ii) benchmarking image restoration algorithms and (iii) time complexity are given based on public restored image databases. Finally, the paper outlines the challenges and future trends of subjective and objective quality assessment for image restoration in various aspects. • The difference between the traditional image quality assessment and quality assessment for image restoration was analyzed profoundly. • For different image restoration scenarios, this paper provides a comprehensive description of the existing i) subjective quality databases, ii) objective quality metrics and iii) two applications of image restoration quality metrics. • A detailed performance analysis on i) quality assessment of restored images, ii) benchmarking image restoration algorithms and iii) time complexity are given based on public restored image databases. • The paper outlines the challenges and future trends of subjective and objective quality assessment for image restoration in various aspects. [ABSTRACT FROM AUTHOR]

Highlights • We propose a no-reference quality metric for DIBR-synthesized videos. • A two-stage flickering region detection method is proposed. • The experiments have demonstrated the best performance of the proposed method. Depth-image-based-rendering (DIBR) is the most popular view synthesis method. The rendering process in DIBR introduces artifacts to synthesized views. Temporal inconsistency of these artifacts causes flickering, which is one of the dominant distortions in DIBR-synthesized videos. We propose a no-reference quality index for DIBR-synthesized videos. A two-stage flickering region detection method is first proposed by calculating the gradient distance between the matching blocks in adjacent frames followed by a refinement operation. Subsequently, the distortion intensity of flickering regions is measured in singular value decomposition domain. The average flickering intensity is computed as the quality score. Experimental results show that our method outperforms the state-of-the-arts on the IRCCyN/IVC database and ranks the second on the SIAT database, which is only slightly worse than the best-performing one. Furthermore, the presented method is applied to improve the performances of existing metrics and benchmark DIBR algorithms, which achieves very promising results for both tasks. [ABSTRACT FROM AUTHOR]

Image quality assessment (IQA) is a fundamental problem in image processing. While in practice almost all images are represented in the color format, most of the current IQA metrics are designed in gray-scale domain. Color influences the perception of image quality, especially in the case where images are subject to color distortions. With this consideration, this paper presents a novel color image quality index based on Sparse Representation and Reconstruction Residual (SRRR). An overcomplete color dictionary is first trained using natural color images. Then both reference and distorted images are represented using the color dictionary, based on which two feature maps are constructed to measure structure and color distortions in a holistic manner. With the consideration that the feature maps are insensitive to image contrast change, the reconstruction residuals are computed and used as a complementary feature. Additionally, luminance similarity is also incorporated to produce the overall quality score for color images. Experiments on public databases demonstrate that the proposed method achieves promising performance in evaluating traditional distortions, and it outperforms the existing metrics when used for quality evaluation of color-distorted images. [ABSTRACT FROM AUTHOR]

Acupuncture, taking the advantage of modality-specific neural pathways, has shown promising results in the treatment of brain disorders that affect different modalities such as pain and vision. However, the precise underlying mechanisms of within-modality neuromodulation of acupoints on human high-order cognition remain largely unknown. In the present study, we used a non-invasive and easy-operating method, focused ultrasound, to stimulate two language-relevant acupoints, namely GB39 (Xuanzhong) and SJ8 (Sanyangluo), of thirty healthy adults. The effect of focused ultrasound stimulation (FUS) on brain activation was examined by functional magnetic resonance imaging (fMRI). We found that stimulating GB39 and SJ8 by FUS evoked overlapping but distinct brain activation patterns. Our findings provide a major step toward within-modality (in this case, language) acupoint-brain (acubrain) mapping and shed light on to the potential use of FUS as a personalized treatment option for brain disorders that affect high-level cognitive functions. [ABSTRACT FROM AUTHOR]

An increasing number of recent brain imaging studies are dedicated to understanding the neuro mechanism of cognitive impairment in type 2 diabetes mellitus (T2DM) individuals. In contrast to efforts to date that are limited to static functional connectivity, here we investigate abnormal connectivity in T2DM individuals by characterizing the time-varying properties of brain functional networks. Using group independent component analysis (GICA), sliding-window analysis, and k-means clustering, we extracted thirty-one intrinsic connectivity networks (ICNs) and estimated four recurring brain states. We observed significant group differences in fraction time (FT) and mean dwell time (MDT), and significant negative correlation between the Montreal Cognitive Assessment (MoCA) scores and FT/MDT. We found that in the T2DM group the inter- and intra-network connectivity decreases and increases respectively for the default mode network (DMN) and task-positive network (TPN). We also found alteration in the precuneus network (PCUN) and enhanced connectivity between the salience network (SN) and the TPN. Our study provides evidence of alterations of large-scale resting networks in T2DM individuals and shed light on the fundamental mechanisms of neurocognitive deficits in T2DM., Competing Interests: Competing InterestThe authors declare that they have no competing financial interests., (© The Author(s) 2022.)

Brain structural MRI has been widely used for assessing future progression of cognitive impairment (CI) based on learning-based methods. Previous studies generally suffer from the limited number of labeled training data, while there exists a huge amount of MRIs in large-scale public databases. Even without task-specific label information, brain anatomical structures provided by these MRIs can be used to boost learning performance intuitively. Unfortunately, existing research seldom takes advantage of such brain anatomy prior. To this end, this paper proposes a brain anatomy-guided representation (BAR) learning framework for assessing the clinical progression of cognitive impairment with T1-weighted MRIs. The BAR consists of a pretext model and a downstream model , with a shared brain anatomy-guided encoder for MRI feature extraction. The pretext model also contains a decoder for brain tissue segmentation, while the downstream model relies on a predictor for classification. We first train the pretext model through a brain tissue segmentation task on 9,544 auxiliary T1-weighted MRIs, yielding a generalizable encoder. The downstream model with the learned encoder is further fine-tuned on target MRIs for prediction tasks. We validate the proposed BAR on two CI-related studies with a total of 391 subjects with T1-weighted MRIs. Experimental results suggest that the BAR outperforms several state-of-the-art (SOTA) methods. The source code and pre-trained models are available at https://github.com/goodaycoder/BAR.

Magnetic resonance imaging (MRI) and positron emission tomography (PET) are increasingly used to forecast progression trajectories of cognitive decline caused by preclinical and prodromal Alzheimer's disease (AD). Many existing studies have explored the potential of these two distinct modalities with diverse machine and deep learning approaches. But successfully fusing MRI and PET can be complex due to their unique characteristics and missing modalities. To this end, we develop a hybrid multimodality fusion (HMF) framework with cross-domain knowledge transfer for joint MRI and PET representation learning, feature fusion, and cognitive decline progression forecasting. Our HMF consists of three modules: 1) a module to impute missing PET images, 2) a module to extract multimodality features from MRI and PET images, and 3) a module to fuse the extracted multimodality features. To address the issue of small sample sizes, we employ a cross-domain knowledge transfer strategy from the ADNI dataset, which includes 795 subjects, to independent small-scale AD-related cohorts, in order to leverage the rich knowledge present within the ADNI. The proposed HMF is extensively evaluated in three AD-related studies with 272 subjects across multiple disease stages, such as subjective cognitive decline and mild cognitive impairment. Experimental results demonstrate the superiority of our method over several state-of-the-art approaches in forecasting progression trajectories of AD-related cognitive decline.

Image reconstruction from partial observations has attracted increasing attention. Conventional image reconstruction methods with hand-crafted priors often fail to recover fine image details due to the poor representation capability of the hand-crafted priors. Deep learning methods attack this problem by directly learning mapping functions between the observations and the targeted images can achieve much better results. However, most powerful deep networks lack transparency and are nontrivial to design heuristically. This paper proposes a novel image reconstruction method based on the Maximum a Posterior (MAP) estimation framework using learned Gaussian Scale Mixture (GSM) prior. Unlike existing unfolding methods that only estimate the image means (i.e., the denoising prior) but neglected the variances, we propose characterizing images by the GSM models with learned means and variances through a deep network. Furthermore, to learn the long-range dependencies of images, we develop an enhanced variant based on the Swin Transformer for learning GSM models. All parameters of the MAP estimator and the deep network are jointly optimized through end-to-end training. Extensive simulation and real data experimental results on spectral compressive imaging and image super-resolution demonstrate that the proposed method outperforms existing state-of-the-art methods.

Both network pruning and neural architecture search (NAS) can be interpreted as techniques to automate the design and optimization of artificial neural networks. In this paper, we challenge the conventional wisdom of training before pruning by proposing a joint search-and-training approach to learn a compact network directly from scratch. Using pruning as a search strategy, we advocate three new insights for network engineering: 1) to formulate adaptive search as a cold start strategy to find a compact subnetwork on the coarse scale; and 2) to automatically learn the threshold for network pruning; 3) to offer flexibility to choose between efficiency and robustness. More specifically, we propose an adaptive search algorithm in the cold start by exploiting the randomness and flexibility of filter pruning. The weights associated with the network filters will be updated by ThreshNet, a flexible coarse-to-fine pruning method inspired by reinforcement learning. In addition, we introduce a robust pruning strategy leveraging the technique of knowledge distillation through a teacher-student network. Extensive experiments on ResNet and VGGNet have shown that our proposed method can achieve a better balance in terms of efficiency and accuracy and notable advantages over current state-of-the-art pruning methods in several popular datasets, including CIFAR10, CIFAR100, and ImageNet. The code associate with this paper is available at: https://see.xidian.edu.cn/faculty/wsdong/Projects/AST-NP.htm.

Lignocellulosic biomass is still considered a feasible source of bioethanol production. Saccharomyces cerevisiae can adapt to detoxify lignocellulose-derived inhibitors, including furfural. Tolerance of strain performance has been measured by the extent of the lag phase for cell proliferation following the furfural inhibitor challenge. The purpose of this work was to obtain a tolerant yeast strain against furfural through overexpression of YPR015C using the in vivo homologous recombination method. The physiological observation of the overexpressing yeast strain showed that it was more resistant to furfural than its parental strain. Fluorescence microscopy revealed improved enzyme reductase activity and accumulation of oxygen reactive species due to the harmful effects of furfural inhibitor in contrast to its parental strain. Comparative transcriptomic analysis revealed 79 genes potentially involved in amino acid biosynthesis, oxidative stress, cell wall response, heat shock protein, and mitochondrial-associated protein for the YPR015C overexpressing strain associated with stress responses to furfural at the late stage of lag phase growth. Both up- and down-regulated genes involved in diversified functional categories were accountable for tolerance in yeast to survive and adapt to the furfural stress in a time course study during the lag phase growth. This study enlarges our perceptions comprehensively about the physiological and molecular mechanisms implicated in the YPR015C overexpressing strain's tolerance under furfural stress. Construction illustration of the recombinant plasmid. a) pUG6-TEF1p-YPR015C, b) integration diagram of the recombinant plasmid pUG6-TEF1p-YPR into the chromosomal DNA of Saccharomyces cerevisiae ., (© 2023 Getachew Tafere Abrha et al., published by Sciendo.)

Purpose: The white matter (WM) of the brain of type 2 diabetes mellitus (T2DM) patients is susceptible to neurodegenerative processes, but the specific types and positions of microstructural lesions along the fiber tracts remain unclear., Methods: In this study 61 T2DM patients and 61 healthy controls were recruited and underwent diffusion spectrum imaging (DSI). The results were reconstructed with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). WM microstructural abnormalities were identified using tract-based spatial statistics (TBSS). Pointwise WM tract differences were detected through automatic fiber quantification (AFQ). The relationships between WM tract abnormalities and clinical characteristics were explored with partial correlation analysis., Results: TBSS revealed widespread WM lesions in T2DM patients with decreased fractional anisotropy and axial diffusivity and an increased orientation dispersion index (ODI). The AFQ results showed microstructural abnormalities in T2DM patients in specific portions of the right superior longitudinal fasciculus (SLF), right arcuate fasciculus (ARC), left anterior thalamic radiation (ATR), and forceps major (FMA). In the right ARC of T2DM patients, an aberrant ODI was positively correlated with fasting insulin and insulin resistance, and an abnormal intracellular volume fraction was negatively correlated with fasting blood glucose. Additionally, negative associations were found between blood pressure and microstructural abnormalities in the right ARC, left ATR, and FMA in T2DM patients., Conclusion: Using AFQ, together with DTI and NODDI, various kinds of microstructural alterations in the right SLF, right ARC, left ATR, and FMA can be accurately identified and may be associated with insulin and glucose status and blood pressure in T2DM patients., (© 2022. The Author(s).)

This paper reports the background and results of the Surface Defect Detection Competition with Bio-inspired Vision Sensor, as well as summarizes the champion solutions, current challenges and future directions., (© The Author(s) 2023. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd.)

Type 2 diabetes mellitus (T2DM) is closely linked to cognitive decline and alterations in brain structure and function. Resting-state functional magnetic resonance imaging (rs-fMRI) is used to diagnose neurodegenerative diseases, such as cognitive impairment (CI), Alzheimer's disease (AD), and vascular dementia (VaD). However, whether the functional connectivity (FC) of patients with T2DM and mild cognitive impairment (T2DM-MCI) is conducive to early diagnosis remains unclear. To answer this question, we analyzed the rs-fMRI data of 37 patients with T2DM and mild cognitive impairment (T2DM-MCI), 93 patients with T2DM but no cognitive impairment (T2DM-NCI), and 69 normal controls (NC). We achieved an accuracy of 87.91% in T2DM-MCI versus T2DM-NCI classification and 80% in T2DM-NCI versus NC classification using the XGBoost model. The thalamus, angular, caudate nucleus, and paracentral lobule contributed most to the classification outcome. Our findings provide valuable knowledge to classify and predict T2DM-related CI, can help with early clinical diagnosis of T2DM-MCI, and provide a basis for future studies., (© 2023. The Author(s).)

Unlike the success of neural architecture search (NAS) in high-level vision tasks, it remains challenging to find computationally efficient and memory-efficient solutions to low-level vision problems such as image restoration through NAS. One of the fundamental barriers to differential NAS-based image restoration is the optimization gap between the super-network and the sub-architectures, causing instability during the searching process. In this paper, we present a novel approach to fill this gap in image denoising application by connecting model-guided design (MoD) with NAS (MoD-NAS). Specifically, we propose to construct a new search space under a model-guided framework and develop more stable and efficient differential search strategies. MoD-NAS employs a highly reusable width search strategy and a densely connected search block to automatically select the operations of each layer as well as network width and depth via gradient descent. During the search process, the proposed MoD-NAS remains stable because of the smoother search space designed under the model-guided framework. Experimental results on several popular datasets show that our MoD-NAS method has achieved at least comparable even better PSNR performance than current state-of-the-art methods with fewer parameters, fewer flops, and less testing time.

Purpose: This study aimed to investigate the changes in brain structure and function in middle-aged patients with type 2 diabetes mellitus (T2DM) using morphometry and blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI)., Methods: A total of 44 middle-aged patients with T2DM and 45 matched healthy controls (HCs) were recruited. Surface-based morphometry (SBM) was used to evaluate the changes in brain morphology. Degree centrality (DC) and functional connectivity (FC) were used to evaluate the changes in brain function., Results: Compared with HCs, middle-aged patients with T2DM exhibited cortical thickness reductions in the left pars opercularis, left transverse temporal, and right superior temporal gyri. Decreased DC values were observed in the cuneus and precuneus in T2DM. Hub-based FC analysis of these regions revealed lower connectivity in the bilateral hippocampus and parahippocampal gyrus, left precuneus, as well as left frontal sup., Conclusion: Cortical thickness, degree centrality, as well as functional connectivity were found to have significant changes in middle-aged patients with T2DM. Our observations provide potential evidence from neuroimaging for analysis to examine diabetes-related brain damage., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Kang, Chen, Wu, Liang, Rao, Yue, Lyu, Li, Tan, Huang and Qiu.)

Purpose: Cognitive impairment is generally found in individuals with type 2 diabetes mellitus (T2DM). Although they may not have visible symptoms of cognitive impairment in the early stages of the disorder, they are considered to be at high risk. Therefore, the classification of these patients is important for preventing the progression of cognitive impairment., Methods: In this study, a convolutional neural network was used to construct a model for classifying 107 T2DM patients with and without cognitive impairment based on T1-weighted structural MRI. The Montreal cognitive assessment score served as an index of the cognitive status of the patients., Results: The classifier could identify T2DM-related cognitive decline with a classification accuracy of 84.85% and achieved an area under the curve of 92.65%., Conclusions: The model can help clinicians analyze and predict cognitive impairment in patients and enable early treatment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer FL declared a past co-authorship with the author SQ to the handling editor., (Copyright © 2022 Tan, Wu, Ma, Kang, Yue, Rao, Li, Huang, Chen, Lyu, Qin, Li, Feng, Liang and Qiu.)

In epidemiological studies, type 2 diabetes mellitus (T2DM) has been associated with cognitive impairment and dementia, but studies about functional network connectivity in T2DM without cognitive impairment are limited. This study aimed to explore network connectivity alterations that may help enhance our understanding of damage-associated processes in T2DM. MRI data were analyzed from 82 patients with T2DM and 66 normal controls. Clinical, biochemical, and neuropsychological assessments were conducted in parallel with resting-state functional magnetic resonance imaging, and the cognitive status of the patients was assessed using the Montreal Cognitive Assessment-B (MoCA-B) score. Independent component analysis revealed a positive correlation between the salience network and the visual network and a negative connection between the left executive control network and the default mode network in patients with T2DM. The differences in dynamic brain network connectivity were observed in the precuneus, visual, and executive control networks. Internal network connectivity was primarily affected in the thalamus, inferior parietal lobe, and left precuneus. Hemoglobin A1c level, body mass index, MoCA-B score, and grooved pegboard (R) assessments indicated significant differences between the two groups ( p < 0.05). Our findings show that key changes in functional connectivity in diabetes occur in the precuneus and executive control networks that evolve before patients develop cognitive deficits. In addition, the current study provides useful information about the role of the thalamus, inferior parietal lobe, and precuneus, which might be potential biomarkers for predicting the clinical progression, assessing the cognitive function, and further understanding the neuropathology of T2DM., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wu, Kang, Su, Liu, Fan, Ma, Tan, Huang, Feng, Chen, Lyu, Zeng, Qiu and Hu.)

Background: Alzheimer disease (AD) is the most common type of dementia with cognitive decline as one of the core symptoms in older adults. Numerous studies have suggested the value of psychosocial interventions to improve cognition in this population, but which one should be preferred are still matters of controversy. Consequently, we aim to compare and rank different psychosocial interventions in the management of mild to moderate AD with cognitive symptoms., Methods: We did a network meta-analysis to identify both direct and indirect evidence in relevant studies. We searched MEDLINE, EMBASE, PsycINFO through the OVID database, CENTRAL through the Cochrane Library for clinical randomized controlled trials investigating psychosocial interventions of cognitive symptoms in patients with Alzheimer disease, published up to August 31, 2017. We included trials of home-based exercise(HE), group exercise(GE), walking program(WP), reminiscence therapy(RT), art therapy(AT) or the combination of psychosocial interventions and acetylcholinesterase inhibitor (ChEIs). We extracted the relevant information from these trials with a predefined data extraction sheet and assessed the risk of bias with the Cochrane risk of bias tool. The outcomes investigated were Mini-Mental State Examination (MMSE) and compliance. We did a pair-wise meta-analysis using the fixed-effects model and then did a random-effects network meta-analysis within a Bayesian framework., Results: We deemed 10 trials eligible, including 682 patients and 11 treatments. The quality of included study was rated as low in most comparison with Cochrane tools. Treatment effects from the network meta-analysis showed WP was better than control (SMD 4.89, 95% CI -0.07 to 10.00) while cognitive training and acetylcholinesterase inhibitor (CT + ChEIs) was significantly better than the other treatments, when compared with simple ChEIs treatment, assessed by MMSE. In terms of compliance, the pair-wise meta-analysis indicated that WP and HE are better than GE and AT, while CT + ChEIs, CST + ChEIs are better than other combined interventions., Conclusion: Our study confirmed the effectiveness of psychosocial interventions for improving cognition or slowing down the progression of cognitive impairment in AD patients and recommended several interventions for clinical practice.

Strategies to control biofouling without using antimicrobial chemicals are needed to prevent the spread of antibiotic resistance genes and disruption of microbial activities in biological treatment. This study developed an environmentally friendly biofouling resistant membrane by incorporating d-tyrosine onto a commercial nanofiltration membrane using FAU type zeolite nanoparticles that covalently bound to the membrane surface as carriers for slow release. The d-tyrosine loaded membrane had similar water permeability as the unmodified membrane, but greatly reduced initial cell attachment and strongly inhibited subsequent biofilm formation without inactivating the bacteria. The membrane slowly released d-tyrosine in the time course of over 5 days, and retained its anti-biofouling capability in repeated 24 h efficacy tests for as long as 6 days. In nanofiltration operation, the d-tyrosine incorporated zeolite coating completely inhibited cell adhesion on the membrane surface and significantly alleviated membrane flux decline., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

With more than a billion people lacking accessible drinking water, there is a critical need to convert nonpotable sources such as seawater to water suitable for human use. However, energy requirements of desalination plants account for half their operating costs, so alternative, lower energy approaches are equally critical. Membrane distillation (MD) has shown potential due to its low operating temperature and pressure requirements, but the requirement of heating the input water makes it energy intensive. Here, we demonstrate nanophotonics-enabled solar membrane distillation (NESMD), where highly localized photothermal heating induced by solar illumination alone drives the distillation process, entirely eliminating the requirement of heating the input water. Unlike MD, NESMD can be scaled to larger systems and shows increased efficiencies with decreased input flow velocities. Along with its increased efficiency at higher ambient temperatures, these properties all point to NESMD as a promising solution for household- or community-scale desalination., Competing Interests: Conflict of interest statement: A patent was filed on the NESMD process. This patent is pending.