Emerging roles of hydrogel in promoting periodontal tissue regeneration and repairing bone defect.

Periodontal disease is the most common type of oral disease. Periodontal bone defect is the clinical outcome of advanced periodontal disease, which seriously affects the quality of life of patients. Promoting periodontal tissue regeneration and repairing periodontal bone defects is the ultimate treatment goal for periodontal disease, but the means and methods are very limited. Hydrogels are a class of highly hydrophilic polymer networks, and their good biocompatibility has made them a popular research material in the field of oral medicine in recent years. This paper reviews the current mainstream types and characteristics of hydrogels, and summarizes the relevant basic research on hydrogels in promoting periodontal tissue regeneration and bone defect repair in recent years. The possible mechanisms of action and efficacy evaluation are discussed in depth, and the application prospects are also discussed.

Prognostic implications of premature ventricular contractions and non-sustained ventricular tachycardia in light-chain cardiac amyloidosis.

AIMS: Premature ventricular contractions (PVC) and non-sustained ventricular tachycardia (NSVT) are commonly observed in light chain cardiac amyloidosis (AL-CA), but their association with prognosis is still unclear. We aimed to evaluate the prognostic value of PVCs and NSVT in patients with moderate-to-advanced AL-CA. METHODS AND RESULTS: We retrospectively included patients with AL-CA at modified 2004 Mayo stages II-IIIb between February 2014 and December 2020. Twenty-four-hour Holter recordings were assessed on admission. The outcomes included (i) new onset of adverse ventricular arrhythmia (VA) or sudden cardiac death (SCD) and (ii) cardiac death during follow-up. Of the 143 patients studied (60.41 ± 11.06 years, male 64.34%), 132 (92.31%) had presence of PVC, and 50 (34.97%) had NSVT on Holter. Twelve (8.4%) patients died in hospital and 131 patients were followed up (median 24.4 months), among whom 71 patients had cardiac death, and 15 underwent adverse VA/SCD. NSVT [hazard ratio (HR): 13.57, 95% confidence interval (CI): 3.06-60.18, P < 0.001], log-transformed PVC counts (HR: 1.46, 95%CI: 1.15-1.86, P = 0.002) and PVC burden (HR: 1.43 95%CI:1.14-1.80, P = 0.002) were predictive of new onset of adverse VA/SCD. The highest tertile of PVC counts (HR: 2.33, 95%CI: 1.27-4.28, P = 0.006) and PVC burden (HR: 2.58, 95%CI: 1.42-4.69, P = 0.002), rather than NSVT (HR: 1.16, 95%CI: 0.67-1.98, P = 0.603), was associated with cardiac death. Higher PVC counts/burden provided incremental value on modified 2004 Mayo stage in predicting cardiac death, with C index increasing from 0.681 to 0.712 and 0.717, respectively (P values <0.05). CONCLUSION: PVC count, burden, and NSVT significantly correlated with adverse VA/SCD during follow-up in patients with AL-CA. Higher PVC counts/burdens added incremental value for predicting cardiac death.

An alfalfa MYB-like transcriptional factor MsMYBH positively regulates alfalfa seedling drought resistance and undergoes MsWAV3-mediated degradation.

Drought is a major threat to alfalfa (Medicago sativa L.) production. The discovery of important alfalfa genes regulating drought response will facilitate breeding for drought-resistant alfalfa cultivars. Here, we report a genome-wide association study of drought resistance in alfalfa. We identified and functionally characterized an MYB-like transcription factor gene (MsMYBH), which increases the drought resistance in alfalfa. Compared with the wild-types, the biomass and forage quality were enhanced in MsMYBH overexpressed plants. Combined RNA-seq, proteomics and chromatin immunoprecipitation analysis showed that MsMYBH can directly bind to the promoters of MsMCP1, MsMCP2, MsPRX1A and MsCARCAB to improve their expression. The outcomes of such interactions include better water balance, high photosynthetic efficiency and scavenge excess H2O2 in response to drought. Furthermore, an E3 ubiquitin ligase (MsWAV3) was found to induce MsMYBH degradation under long-term drought, via the 26S proteasome pathway. Furthermore, variable-number tandem repeats in MsMYBH promoter were characterized among a collection of germplasms, and the variation is associated with promoter activity. Collectively, our findings shed light on the functions of MsMYBH and provide a pivotal gene that could be leveraged for breeding drought-resistant alfalfa. This discovery also offers new insights into the mechanisms of drought resistance in alfalfa.

The chromosome-level assembly of the wild diploid alfalfa genome provides insights into the full landscape of genomic variations between cultivated and wild alfalfa.

Alfalfa (Medicago sativa L.) is one of the most important forage legumes in the world, including autotetraploid (M. sativa ssp. sativa) and diploid alfalfa (M. sativa ssp. caerulea, progenitor of autotetraploid alfalfa). Here, we reported a high-quality genome of ZW0012 (diploid alfalfa, 769 Mb, contig N50 = 5.5 Mb), which was grouped into the Northern group in population structure analysis, suggesting that our genome assembly filled a major gap among the members of M. sativa complex. During polyploidization, large phenotypic differences occurred between diploids and tetraploids, and the genetic information underlying its massive phenotypic variations remains largely unexplored. Extensive structural variations (SVs) were identified between ZW0012 and XinJiangDaYe (an autotetraploid alfalfa with released genome). We identified 71 ZW0012-specific PAV genes and 1296 XinJiangDaYe-specific PAV genes, mainly involved in defence response, cell growth, and photosynthesis. We have verified the positive roles of MsNCR1 (a XinJiangDaYe-specific PAV gene) in nodulation using an Agrobacterium rhizobia-mediated transgenic method. We also demonstrated that MsSKIP23_1 and MsFBL23_1 (two XinJiangDaYe-specific PAV genes) regulated leaf size by transient overexpression and virus-induced gene silencing analysis. Our study provides a high-quality reference genome of an important diploid alfalfa germplasm and a valuable resource of variation landscape between diploid and autotetraploid, which will facilitate the functional gene discovery and molecular-based breeding for the cultivars in the future.

Click-through Rate Prediction and Uncertainty Quantification Based on Bayesian Deep Learning.

Click-through rate (CTR) prediction is a research point for measuring recommendation systems and calculating AD traffic. Existing studies have proved that deep learning performs very well in prediction tasks, but most of the existing studies are based on deterministic models, and there is a big gap in capturing uncertainty. Modeling uncertainty is a major challenge when using machine learning solutions to solve real-world problems in various domains. In order to quantify the uncertainty of the model and achieve accurate and reliable prediction results. This paper designs a CTR prediction framework combining feature selection and feature interaction. In this framework, a CTR prediction model based on Bayesian deep learning is proposed to quantify the uncertainty in the prediction model. On the squeeze network and DNN parallel prediction model framework, the approximate posterior parameter distribution of the model is obtained using the Monte Carlo dropout, and obtains the integrated prediction results. Epistemic and aleatoric uncertainty are defined and adopt information entropy to calculate the sum of the two kinds of uncertainties. Epistemic uncertainty could be measured by mutual information. Experimental results show that the model proposed is superior to other models in terms of prediction performance and has the ability to quantify uncertainty.

Spiking Neural Network Based on Multi-Scale Saliency Fusion for Breast Cancer Detection.

Deep neural networks have been successfully applied in the field of image recognition and object detection, and the recognition results are close to or even superior to those from human beings. A deep neural network takes the activation function as the basic unit. It is inferior to the spiking neural network, which takes the spiking neuron model as the basic unit in the aspect of biological interpretability. The spiking neural network is considered as the third-generation artificial neural network, which is event-driven and has low power consumption. It modulates the process of nerve cells from receiving a stimulus to firing spikes. However, it is difficult to train spiking neural network directly due to the non-differentiable spiking neurons. In particular, it is impossible to train a spiking neural network using the back-propagation algorithm directly. Therefore, the application scenarios of spiking neural network are not as extensive as deep neural network, and a spiking neural network is mostly used in simple image classification tasks. This paper proposed a spiking neural network method for the field of object detection based on medical images using the method of converting a deep neural network to spiking neural network. The detection framework relies on the YOLO structure and uses the feature pyramid structure to obtain the multi-scale features of the image. By fusing the high resolution of low-level features and the strong semantic information of high-level features, the detection precision of the network is improved. The proposed method is applied to detect the location and classification of breast lesions with ultrasound and X-ray datasets, and the results are 90.67% and 92.81%, respectively.

A Parallel Spiking Neural Network Based on Adaptive Lateral Inhibition Mechanism for Objective Recognition.

Spiking neural network (SNN) has attracted extensive attention in the field of machine learning because of its biological interpretability and low power consumption. However, the accuracy of pattern recognition cannot completely surpass deep neural networks (DNNs). The main reason is that the inherent nondifferentiability of spiking neurons makes SNN unable to be trained directly by the gradient descent algorithm, and there is also no unified training algorithm for SNN. Inspired by the biological vision system, this paper proposes a parallel convolution SNN structure combined with an adaptive lateral inhibition mechanism. And, a way of dynamically evolving the time constant with the training of SNN is proposed to ensure the diversity of neurons. This paper verifies the effectiveness of the proposed methods on static datasets and neuromorphic datasets and extends it to the recognition of breast tumors. Experimental results show that the SNN has obvious advantages in dynamical datasets. For breast tumors, it is also an edge-based task, because the edge of a medical image contains the most important information in the image. This kind of information can provide great help for the noninvasive and accurate diagnosis of diseases. The Experimental results show that the proposed method is very close to the recognition results of DNNs on static datasets, and its performance on neuromorphic datasets exceeds that of DNNs.

Innate immune memory and its application to artificial immune systems.

The study of innate immune-based algorithms is an important research domain in Artificial Immune System (AIS), such as Dendritic Cell Algorithm (DCA), Toll-Like Receptor algorithm (TLRA). The parameters in these algorithms usually require either manually pre-defined usually provided by the immunologists, or empirically derived from the training dataset, and result in poor self-adaptation and self-learning. The fundamental reason is that the original innate immune mechanisms lack adaptive biological theory. To solve this problem, a theory called â€ËœTrained Immunity™ or Innate Immune Memory (IIM)™ that thinks innate immunity can also build immunological memory to enhance the immune system™s learning and adaptive reactions to the second stimulus is introduced into AIS to improve the innate immune algorithms™ adaptability. In this study, we present an overview of IIM with particular emphasis on analogies in the AIS world, and a modified DCA with an effective automated tuning mechanism based on IIM (IIM-DCA) to optimize migration threshold of DCA. The migration threshold of Dendritic Cells (DCs) determines the lifespan of the antigen collected by DCs, and directly affect the detection speed and accuracy of DCA. Experiments on real datasets show that our proposed IIM-DCA which integrates Innate Immune Memory mechanism delivers more accurate results.

A joint network of non-linear graph attention and temporal attraction force for geo-sensory time series prediction.

Geo-sensory time series, such as the air quality and water distribution, are collected from numerous sensors at different geospatial locations in the same time interval. Each sensor monitors multiple parameters and generates multivariate time series. These time series change over time and vary geographically; hence, geo-sensory time series contain multi-scale spatial-temporal correlations, namely inter-sensor spatial-temporal correlations and intra-sensor spatial-temporal correlations. To capture spatial-temporal correlations, although various deep learning models have been developed, few of the models focus on capturing both correlations. To solve this problem, we propose simultaneously capture the inter- and intra-sensor spatial-temporal correlations by designing a joint network of non-linear graph attention and temporal attraction force(J-NGT) consisting two graph attention mechanisms. The non-linear graph attention mechanism can characterize node affinities for adaptively selecting the relevant exogenous series and relevant sensor series. The temporal attraction force mechanism can weigh the effect of past values on current values to represent the temporal correlation. To prove the superiority and effectiveness of our model, we evaluate our model in three real-world datasets from different fields. Experimental results show that our model can achieve better prediction performance than eight state-of-the-art models, including statistical models, machine learning models, and deep learning models. Furthermore, we conducted experiments to capture inter- and intra-sensor spatial-temporal correlations. Experimental results indicate that our model significantly improves performance by capturing both inter- and intra-sensor spatial-temporal correlations. This fully shows that our model has a greater advantage in geo-sensory time series prediction.

A Temporal Window Attention-Based Window-Dependent Long Short-Term Memory Network for Multivariate Time Series Prediction.

Multivariate time series prediction models perform the required operation on a specific window length of a given input. However, capturing complex and nonlinear interdependencies in each temporal window remains challenging. The typical attention mechanisms assign a weight for a variable at the same time or the features of each previous time step to capture spatio-temporal correlations. However, it fails to directly extract each time step's relevant features that affect future values to learn the spatio-temporal pattern from a global perspective. To this end, a temporal window attention-based window-dependent long short-term memory network (TWA-WDLSTM) is proposed to enhance the temporal dependencies, which exploits the encoder-decoder framework. In the encoder, we design a temporal window attention mechanism to select relevant exogenous series in a temporal window. Furthermore, we introduce a window-dependent long short-term memory network (WDLSTM) to encode the input sequences in a temporal window into a feature representation and capture very long term dependencies. In the decoder, we use WDLSTM to generate the prediction values. We applied our model to four real-world datasets in comparison to a variety of state-of-the-art models. The experimental results suggest that TWA-WDLSTM can outperform comparison models. In addition, the temporal window attention mechanism has good interpretability. We can observe which variable contributes to the future value.

A Multi-Objective Multi-Label Feature Selection Algorithm Based on Shapley Value.

Multi-label learning is dedicated to learning functions so that each sample is labeled with a true label set. With the increase of data knowledge, the feature dimensionality is increasing. However, high-dimensional information may contain noisy data, making the process of multi-label learning difficult. Feature selection is a technical approach that can effectively reduce the data dimension. In the study of feature selection, the multi-objective optimization algorithm has shown an excellent global optimization performance. The Pareto relationship can handle contradictory objectives in the multi-objective problem well. Therefore, a Shapley value-fused feature selection algorithm for multi-label learning (SHAPFS-ML) is proposed. The method takes multi-label criteria as the optimization objectives and the proposed crossover and mutation operators based on Shapley value are conducive to identifying relevant, redundant and irrelevant features. The comparison of experimental results on real-world datasets reveals that SHAPFS-ML is an effective feature selection method for multi-label classification, which can reduce the classification algorithm's computational complexity and improve the classification accuracy.

Bacteriostatic effects of nanometer silver disinfectant on the biofilms in dental unit water lines.

BACKGROUND/PURPOSE: Dental unit water lines (DUWLs) may be contaminated by aerobic bacteria in clinical settings and comprehensive disinfecting methods should be considered without delay. Herein, this study aims to investigate the timeliness and dynamic bacteriostatic effects of different forms of nanometer silver (NMS) disinfectant on bio-film in DUWLs. MATERIALS AND METHODS: Bacterial DUWLs samples were respectively treated with different NMS forms, including liquid phase and solid phase at the concentrations of 0.25%, 0.5%, 1% and 2% and their bacteriostatic effects were observed at the 1st, 4th, 7th, 14th, 28th day. RESULTS: The bacteriostatic effects of liquid phase NMS at all concentrations were unsatisfactory and the bacteriostatic rate was only 20% at the 1st day. However, there appeared massive bacteria growth at the 4th, 7th, 14th, 28th day. Comparatively, no bacteria growth was found at the 1st, 4th, 7th, 14th, 28th day after sterilizing with different concentrations of solid phase NMS and the bacteriostatic rate was 100%. CONCLUSION: Microbial contamination in DUWLs can be disinfected by different NMS forms, among which solid phase NMS is more bactericidal against bacteria bio-films, demonstrating significant roles of solid phase NMS in preventing DUWL contamination.

Adaptive feature selection using v-shaped binary particle swarm optimization.

Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers.

Chronic Glutathione Depletion Confers Protection against Alcohol-induced Steatosis: Implication for Redox Activation of AMP-activated Protein Kinase Pathway.

The pathogenesis of alcoholic liver disease (ALD) is not well established. However, oxidative stress and associated decreases in levels of glutathione (GSH) are known to play a central role in ALD. The present study examines the effect of GSH deficiency on alcohol-induced liver steatosis in Gclm knockout (KO) mice that constitutively have ≈15% normal hepatic levels of GSH. Following chronic (6 week) feeding with an ethanol-containing liquid diet, the Gclm KO mice were unexpectedly found to be protected against steatosis despite showing increased oxidative stress (as reflected in elevated levels of CYP2E1 and protein carbonyls). Gclm KO mice also exhibit constitutive activation of liver AMP-activated protein kinase (AMPK) pathway and nuclear factor-erythroid 2-related factor 2 target genes, and show enhanced ethanol clearance, altered hepatic lipid profiles in favor of increased levels of polyunsaturated fatty acids and concordant changes in expression of genes associated with lipogenesis and fatty acid oxidation. In summary, our data implicate a novel mechanism protecting against liver steatosis via an oxidative stress adaptive response that activates the AMPK pathway. We propose redox activation of the AMPK may represent a new therapeutic strategy for preventing ALD.

Collaborative Filtering Recommendation on Users' Interest Sequences.

As an important factor for improving recommendations, time information has been introduced to model users' dynamic preferences in many papers. However, the sequence of users' behaviour is rarely studied in recommender systems. Due to the users' unique behavior evolution patterns and personalized interest transitions among items, users' similarity in sequential dimension should be introduced to further distinguish users' preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users' interest sequences (IS) that rank users' ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users' longest common sub-IS (LCSIS) and the count of users' total common sub-IS (ACSIS). Then, these semantics are utilized to obtain users' IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users' preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction.

Update of the human and mouse Fanconi anemia genes.

Fanconi anemia (FA) is a recessively inherited disease manifesting developmental abnormalities, bone marrow failure, and increased risk of malignancies. Whereas FA has been studied for nearly 90 years, only in the last 20 years have increasing numbers of genes been implicated in the pathogenesis associated with this genetic disease. To date, 19 genes have been identified that encode Fanconi anemia complementation group proteins, all of which are named or aliased, using the root symbol "FANC." Fanconi anemia subtype (FANC) proteins function in a common DNA repair pathway called "the FA pathway," which is essential for maintaining genomic integrity. The various FANC mutant proteins contribute to distinct steps associated with FA pathogenesis. Herein, we provide a review update of the 19 human FANC and their mouse orthologs, an evolutionary perspective on the FANC genes, and the functional significance of the FA DNA repair pathway in association with clinical disorders. This is an example of a set of genes--known to exist in vertebrates, invertebrates, plants, and yeast--that are grouped together on the basis of shared biochemical and physiological functions, rather than evolutionary phylogeny, and have been named on this basis by the HUGO Gene Nomenclature Committee (HGNC).

ALDH1B1 links alcohol consumption and diabetes.

Aldehyde dehydrogenase 1B1 (ALDH1B1) is a mitochondrial enzyme sharing 65% and 72% sequence identity with ALDH1A1 and ALDH2 proteins, respectively. Compared to the latter two ALDH isozymes, little is known about the physiological functions of ALDH1B1. Studies in humans indicate that ALDH1B1 may be associated with alcohol sensitivity and stem cells. Our recent in vitro studies using human ALDH1B1 showed that it metabolizes acetaldehyde and retinaldehyde. To investigate the in vivo role of ALDH1B1, we generated and characterized a global Aldh1b1 knockout mouse line. These knockout (KO) mice are fertile and show overtly good health. However, ethanol pharmacokinetic analysis revealed ∼40% increase in blood acetaldehyde levels in KO mice. Interestingly, the KO mice exhibited higher fasting blood glucose levels. Collectively, we show for the first time the functional in vivo role of ALDH1B1 in acetaldehyde metabolism and in maintaining glucose homeostasis. This mouse model is a valuable tool to investigate the mechanism by which alcohol may promote the development of diabetes.

Transgenic mouse models for alcohol metabolism, toxicity, and cancer.

Alcohol abuse leads to tissue damage including a variety of cancers; however, the molecular mechanisms by which this damage occurs remain to be fully understood. The primary enzymes involved in ethanol metabolism include alcohol dehydrogenase (ADH), cytochrome P450 isoform 2E1, (CYP2E1), catalase (CAT), and aldehyde dehydrogenases (ALDH). Genetic polymorphisms in human genes encoding these enzymes are associated with increased risks of alcohol-related tissue damage, as well as differences in alcohol consumption and dependence. Oxidative stress resulting from ethanol oxidation is one established pathogenic event in alcohol-induced toxicity. Ethanol metabolism generates free radicals, such as reactive oxygen species (ROS) and reactive nitrogen species (RNS), and has been associated with diminished glutathione (GSH) levels as well as changes in other antioxidant mechanisms. In addition, the formation of protein and DNA adducts associated with the accumulation of ethanol-derived aldehydes can adversely affect critical biological functions and thereby promote cellular and tissue pathology. Animal models have proven to be valuable tools for investigating mechanisms underlying pathogenesis caused by alcohol. In this review, we provide a brief discussion on several animal models with genetic defects in alcohol-metabolizing enzymes and GSH-synthesizing enzymes and their relevance to alcohol research.

Mitochondrial targeting of mouse NQO1 and CYP1B1 proteins.

Four dioxin-inducible enzymes--NAD(P)H: quinone oxidoreductase-1 (NQO1) and three cytochromes P450 (CYP1A1, CYP1A2 & CYP1B1)--are implicated in both detoxication and metabolic activation of various endobiotics and xenobiotics. NQO1 is generally regarded as a cytosolic enzyme; whereas CYP1 proteins are located primarily in endoplasmic reticulum (ER), CYP1A1 and CYP1A2 proteins are also targeted to mitochondria. This lab has generated Cyp1a1(mc/mc) and Cyp1a1(mtt/mtt) knock-in mouse lines in which CYP1A1 protein is targeted exclusively to ER (microsomes) and mitochondria, respectively. Comparing dioxin-treated Cyp1(+/+) wild-type, Cyp1a1(mc/mc), Cyp1a1(mtt/mtt), and Cyp1a1(-/-), Cyp1b1(-/-) and Nqo1(-/-) knockout mice, in the present study we show that [a] NQO1 protein locates to cytosol, ER and mitochondria, [b] CYP1B1 protein (similar to CYP1A1 and CYP1A2 proteins) traffics to mitochondria as well as ER, and [c] NQO1 and CYP1B1 targeting to mitochondrial or ER membranes is independent of CYP1A1 presence in that membrane.