Search

Abstract Neuroimaging studies have revealed that the mechanisms of auditory hallucinations are related to morphological changes in multiple cortical regions, but studies on brain network properties are lacking. This study aims to construct intra-individual structural covariance networks and reveal network changes related to auditory hallucinations. T1-weighted MRI images were acquired from 90 schizophrenia patients with persistent auditory hallucinations (pAH group), 55 schizophrenia patients without auditory hallucinations (non-pAH group), and 83 healthy controls (HC group). Networks were constructed using the voxel-based gray matter volume and the intra-individual structural covariance was based on the similarity between the morphological variations of any two regions. One-way ANCOVA was employed to compare global and local network metrics among the three groups, and edge analysis was conducted via network-based statistics. In the pAH group, Pearson correlation analysis between network metrics and clinical symptoms was conducted. Compared with the HC group, both the pAH group (p = 0.01) and the non-pAH group (p = 3.56 × 10−4) had lower nodal efficiency of the left medial superior frontal gyrus. Compared to the non-pAH group and HC group, the pAH group presented lower nodal efficiency of the temporal pole of the left superior temporal gyrus (p = 1.09 × 10−3; p = 7.67 × 10−4) and right insula (p = 0.02; p = 8.99 × 10−6), and lower degree centrality of the right insula (p = 0.04; p = 1.65 × 10−5). The pAH group had a subnetwork with reduced structural covariance centered by the left temporal pole of the superior temporal gyrus. In the pAH group, the normalized clustering coefficient (r = −0.36, p = 8.45 × 10−3) and small-worldness (r = −0.35, p = 9.89 × 10−3) were negatively correlated with the PANSS positive scale score. This study revealed network changes in schizophrenia patients with persistent auditory hallucinations, and provided new insights into the structural architecture related to auditory hallucinations at the network level.

Foam concrete is a prospective material in defense engineering to protect structures due to its high energy absorption capability resulted from the long plateau stage. However, stress enhancement rather than stress mitigation may happen when foam concrete is used as sacrificial claddings placed in the path of an incoming blast load. To investigate this interesting phenomenon, a one-dimensional difference model for blast wave propagation in foam concrete is firstly proposed and numerically solved by improving the second-order Godunov method. The difference model and numerical algorithm are validated against experimental results including both the stress mitigation and the stress enhancement. The difference model is then used to numerically analyze the blast wave propagation and deformation of material in which the effects of blast loads, stress–strain relation and length of foam concrete are considered. In particular, the concept of minimum thickness of foam concrete to avoid stress enhancement is proposed. Finally, non-dimensional analysis on the minimum thickness is conducted and an empirical formula is proposed by curve-fitting the numerical data, which can provide a reference for the application of foam concrete in defense engineering.

Objective To identify the effective components and key molecules of Yiqi Huoxu compound(YQHX) in the treatment of pulmonary inflammation and fibrosis in scleroderma, providing basis for the development of drug targets for scleroderma. Methods A mouse model of pulmonary fibrosis was established by a single tracheal perfusion of bleomycin. Twenty-four mice were randomly divided into four groups, i.e. intragastric PBS, bleomycin alone, intragastric Yiqi Huoxue compound, and control. HE staining and Masson staining were used to verify the therapeutic effect of Yiqi Huoxue compound on lung inflammation and fibrosis. The active components and target genes of anti-fiber salvia miltiorrhiza were obtained from TCMSP database. PPI network, GO/KEGG enrichment analysis, molecular docking and other bioinformatics analyses were integrated to identify the main active ingredients and key targets of salvia miltiorrhiza in the treatment of scleroderma, and to analyze the relevant signaling pathways. The expression levels of CD3 antibody, p-PI3K antibody and p-AKT antibody were analyzed by immunohistochemistry to verify the effects of Yiqi Huoxu compound on inflammation and key pathways. Results The results of HE staining and Masson staining showed that the severity of fibrosis and inflammatory infiltration were decreased, suggesting that Yiqi Huoxue compound could significantly improve the pulmonary fibrosis, inflammation and tissue damages. The key component of salvia miltiorrhiza was mainly luteolin. PPI network analysis showed that AKT1, EGFR, ESR1, TNF and IL6 were the core targets of salvia miltiorrhiza in the treatment of scleroderma. KEGG enrichment analysis showed that the genes were mainly enriched in the PI3K-AKT pathway. Molecular docking analysis showed the potential binding sites between luteolin and four key targets. The immunohistochemical results showed that YQHX significantly decreased bleomycin-induced CD3+T cell infiltration, p-PI3K and p-AKT levels in mouse lung tissue. Conclusions YQHX has significant therapeutic effects on lung inflammation and fibrosis. Luteolin may be a key effective molecule in YQHX, and treat scleroderma lung inflammation and fibrosis by targeting the PI3K-AKT pathway.

This study analyzed the fate and transfer of PAHs through multiple media in Dianchi Lake and the sediment records of polycyclic aromatic hydrocarbons (PAHs) in sediments. The model simulation results showed the simulated concentrations to be in good agreement with the measured values. The concentration and total amount of PAHs were the highest in the sediment, which is an important sink for PAHs in lake systems. The main transport direction of the PAHs in the three phases was from the atmosphere to the water to the sediment. Advection and emission inputs were the primary input pathways for PAHs in the atmosphere, and advection output was the main removal path. The main input sources pathways for PAHs in sediments was the sedimentation of particulate matter in water, with diffusion and degradation being the primary methods of removal. Advection and atmospheric dry and wet deposition were the main sources of PAHs in water, with particulate deposition being the primary removal process. The range of PAHs was 336–3520 ng/g in sediments in Dianchi Lake, with an average of 1569 ng/g, peaking in 2012. Among the predicted future PAH concentrations in the sediments under the five shared socioeconomic pathways (SSPs), the lowest PAH concentration was found under SSP3 from 2025 to 2035, whereas the lowest PAH concentration was found under SSP4 from 2035 to 2050. In the sustainable path of SSP1, although the concentration of PAH pollutants showed an increasing trend in the short term, the increase in PAHs concentration in sediments slowed in the long run.

Abstract Background Constructing molecular interaction networks from microarray data and then identifying disease module biomarkers can provide insight into the underlying pathogenic mechanisms of non-small cell lung cancer. A promising approach for identifying disease modules in the network is community detection. Results In order to identify disease modules from gene co-expression networks, a community detection method is proposed based on multi-objective optimization genetic algorithm with decomposition. The method is named DM-MOGA and possesses two highlights. First, the boundary correction strategy is designed for the modules obtained in the process of local module detection and pre-simplification. Second, during the evolution, we introduce Davies–Bouldin index and clustering coefficient as fitness functions which are improved and migrated to weighted networks. In order to identify modules that are more relevant to diseases, the above strategies are designed to consider the network topology of genes and the strength of connections with other genes at the same time. Experimental results of different gene expression datasets of non-small cell lung cancer demonstrate that the core modules obtained by DM-MOGA are more effective than those obtained by several other advanced module identification methods. Conclusions The proposed method identifies disease-relevant modules by optimizing two novel fitness functions to simultaneously consider the local topology of each gene and its connection strength with other genes. The association of the identified core modules with lung cancer has been confirmed by pathway and gene ontology enrichment analysis.

Cancer is the world’s leading cause of human death today, and the treatment process of cancer is highly complex. Chemotherapy and targeted therapy are commonly used in cancer treatment, and the emergence of drug resistance is a significant problem in cancer treatment. Therefore, the mechanism of drug resistance during cancer treatment has become a hot issue in current research. A series of studies have found that lipid metabolism is closely related to cancer drug resistance. This paper details the changes of lipid metabolism in drug resistance and how lipid metabolism affects drug resistance. More importantly, most studies have reported that combination therapy may lead to changes in lipid-related metabolic pathways, which may reverse the development of cancer drug resistance and enhance or rescue the sensitivity to therapeutic drugs. This paper summarizes the progress of drug design targeting lipid metabolism in improving drug resistance, and providing new ideas and strategies for future tumor treatment. Therefore, this paper reviews the issues of combining medications with lipid metabolism and drug resistance.

Background Early diagnosis and treatment can improve the survival rates of patients with laryngeal squamous cell carcinoma (LSCC). Therefore, it is necessary to discover new biomarkers for laryngeal cancer screening and early diagnosis. Methods We collected fasting plasma from LSCC patients and healthy volunteers, as well as cancer and para-carcinoma tissues from LSCC patients, and performed quantitative detection of amino acid levels using liquid chromatography-mass spectrometry. We used overall analysis and multivariate statistical analysis to screen out the statistically significant differential amino acids in the plasma and tissue samples, conducted receiver operating characteristic (ROC) analysis of the differential amino acids to evaluate the sensitivity and specificity of the differential amino acids, and finally determined the diagnostic value of amino acids for laryngeal cancer. Additionally, we identified amino acids in the plasma and tissue samples that are valuable for the early diagnosis of laryngeal cancer classified according to the tumor-node-metastasis (TNM) classification. Results Asparagine (Asp) and homocysteine (Hcy) were two amino acids of common significance in plasma and tissue samples, and their specificity and sensitivity analysis showed that they may be new biomarkers for the diagnosis and treatment of LSCC. According to the TNM staging system, phenylalanine (Phe) and isoleucine (Ile) were screened out in the plasma of LSCC patients at early (I and II) and advanced (III and IV) stages; ornithine hydrochloride (Orn), glutamic acid (Glu), and Glycine (Gly) were selected in the tissue. These dysregulated amino acids found in LSCC patients may be useful as clinical biomarkers for the early diagnosis and screening of LSCC.

Alpine lakes on the Tibetan Plateau are highly sensitive to global change and have been recognized as the sentinel of climate warming. However, anthropogenic impacts in populated area are migrating to these remote areas via transporting particulate nutrients by atmospheric deposition. Whether warming and nutrient deposition would impose additive or synergistic effects on the lake ecosystem remains largely unknown. Here, we present multi-proxy (sediment pigment and geochemistry) records during the past two centuries at the Cuoqia Lake in the southeast Tibetan Plateau. We found that the lake exhibited rapid ecological changes since 1980 AD characterized by an increase in primary productivity due to algal proliferation, with more rapid growth of green algae and diatoms. These findings are in concert with many other lakes (e.g., Moon Lake and Shade Co) in the same area, suggesting a consistent pattern of ecosystem evolution at the region scale. Statistical analyses suggested that nutrient deposition and climate warming were strongly associated with the variations in primary productivity and algae composition, exerting both individual and interactive effects. In addition, scenario analyses with a well-established process-based ecosystem model further revealed that the two factors not only individually, but also synergistically promoted the algal proliferation and community succession. Such synergy is evident in that the effect of lake warming would be more pronounced under higher nutrient deposition scenario, which is potentially due to higher temperature-driven mineralization in warmer conditions, and higher efficiency of nutrient utilization under enhanced light availability attributing to declining ice thickness and duration in cold seasons. Overall, our study proposes the existence and quantifies the synergistic impacts of climate warming and anthropogenic activities in driving the ecological changes in remote alpine lakes on the Tibetan Plateau. The lake ecological consequences driven by individual factor would be worsen by such synergy, so that we cannot predict the lake ecosystem trajectory in the future based on each factor separately, and more efforts than previously expected would be needed for the lake restoration and management.

Humans can flexibly represent both categorical and coordinate spatial relations. Previous research has mainly focused on hemisphere lateralization in representing these two types of spatial relations, but little is known about how distinct network organization states support representations of the two. Here we used dynamic resting-state functional connectivity (FC) to explore this question. To do this, we separated a meta-identified navigation network into a ventral and two other subnetworks. We revealed a Weak State and a Strong State within the ventral subnetwork and a Negative State and a Positive State between the ventral and other subnetworks. Further, we found the Weak State (i.e., weak but positive FC) within the ventral subnetwork was related to the ability of categorical relation recognition, suggesting that the representation of categorical spatial relations was related to weak integration among focal regions in the navigation network. In contrast, the Negative State (i.e., negative FC) between the ventral and other subnetworks was associated with the ability of coordinate relation processing, suggesting that the representation of coordinate spatial relations may require competitive interactions among widely distributed regions. In sum, our study provides the first empirical evidence revealing different focal and distributed organizations of the navigation network in representing different types of spatial information.

The emulsion gels that can be used as solid fat replacers were produced with different polysaccharides (κ-carrageenan, κC; high-acyl gellan, HA; konjac glucomannanon, and KGM), pea protein isolate (PPI) and sunflower seed oil. The effect of polysaccharide concentration on the texture, rheological property, microstructure, and water holding capacity of the mixed emulsion gels were investigated. Rheological results showed that the presence of polysaccharides enhanced the hardness, storage modulus and resistance against deformation of emulsion gel, where PPI/κC system exhibited superior hardness with a similar level of pig back fat, due to the self-gelation behavior of κC. CLSM and SEM results showed that the presence of κC, HA, and KGM broke the uniform structure of gel network and formed irregular, threadlike, and oval shaped inclusions respectively, resulting in the broken and coalescence of oil droplets. The α-helix content of emulsion gels decreased, while β-sheet, β-turn and random coils slightly increased due to the unfolding of protein during gel formation. This study may offer a valuable strategy for the development of solid fat mimetic with the characteristics closing to the pig back fat.

Dispositional optimism (hereinafter, optimism), as a vital character strength, reflects the tendency to hold generalized positive expectancies for future outcomes. A great number of studies have consistently shown the importance of optimism to a spectrum of physical and mental health outcomes. However, less attention has been given to the intrinsic neurodevelopmental patterns associated with interindividual differences in optimism. Here, we investigated this important question in a large sample comprising 231 healthy adolescents (16–20 years old) via structural magnetic resonance imaging and behavioral tests. We constructed individual structural covariance networks based on cortical gyrification using a recent novel approach combining probability density estimation and Kullback-Leibler divergence and estimated global (global efficiency, local efficiency and small-worldness) and regional (betweenness centrality) properties of these constructed networks using graph theoretical analysis. Partial correlations adjusted for age, sex and estimated total intracranial volume showed that optimism was positively related to global and local efficiency but not small-worldness. Partial least squares correlations indicated that optimism was positively linked to a pronounced betweenness centrality pattern, in which twelve cognition-, emotion-, and motivation-related regions made robust and reliable contributions. These findings remained basically consistent after additionally controlling for family socioeconomic status and showed significant correlations with optimism scores from 2.5 years before, which replicated the main findings. The current work, for the first time, delineated characteristics of the cortical gyrification covariance network associated with optimism, extending previous neurobiological understandings of optimism, which may navigate the development of interventions on a neural network level aimed at raising optimism.

Hemispheric lateralization constitutes a core architectural principle of human brain organization underlying cognition, often argued to represent a stable, trait-like feature. However, emerging evidence underlines the inherently dynamic nature of brain networks, in which time-resolved alterations in functional lateralization remain uncharted. Integrating dynamic network approaches with the concept of hemispheric laterality, we map the spatiotemporal architecture of whole-brain lateralization in a large sample of high-quality resting-state fMRI data (N = 991, Human Connectome Project). We reveal distinct laterality dynamics across lower-order sensorimotor systems and higher-order associative networks. Specifically, we expose 2 aspects of the laterality dynamics: laterality fluctuations (LF), defined as the standard deviation of laterality time series, and laterality reversal (LR), referring to the number of zero crossings in laterality time series. These 2 measures are associated with moderate and extreme changes in laterality over time, respectively. While LF depict positive association with language function and cognitive flexibility, LR shows a negative association with the same cognitive abilities. These opposing interactions indicate a dynamic balance between intra and interhemispheric communication, i.e., segregation and integration of information across hemispheres. Furthermore, in their time-resolved laterality index, the default mode and language networks correlate negatively with visual/sensorimotor and attention networks, which are linked to better cognitive abilities. Finally, the laterality dynamics are associated with functional connectivity changes of higher-order brain networks and correlate with regional metabolism and structural connectivity. Our results provide insights into the adaptive nature of the lateralized brain and new perspectives for future studies of human cognition, genetics, and brain disorders. Hemispheric lateralization constitutes a core architectural principle of human brain organization, often argued to represent a stable, trait-like feature, but how does this fit with our increasing appreciation of the inherently dynamic nature of brain networks? This neuroimaging study reveals the dynamic nature of functional brain lateralization at resting-state and its relationship with language function and cognitive flexibility.

BackgroundAtrial fibrillation (AF) is the most common cardiac arrhythmia and precursor to other cardiac diseases. Catheter ablation is associated with limited success rates in patients with persistent AF. Currently, existing mapping systems fail to identify critical target sites for ablation. Recently, we proposed and validated several individual techniques, such as dominant frequency (DF), multiscale frequency (MSF), kurtosis (Kt), and multiscale entropy (MSE), to identify active sites of arrhythmias using simulated intracardiac electrograms (iEGMs). However, the individual performances of these techniques to identify arrhythmogenic substrates are not reliable.ObjectiveThis study aimed to develop a similarity score using various iEGM analysis techniques to more accurately identify the spatial location of active sites of arrhythmia in patients with AF.MethodsClinical bipolar iEGMs were obtained from patients with AF who underwent either successful (m = 4) or unsuccessful (m = 4) catheter ablation. A similarity score (0–3) was developed via the earth mover’s distance (EMD) approach based on a combination of DF, MSF, MSE, and Kt techniques.ResultsIndividual techniques successfully discriminated between successful and unsuccessful AF ablation patients but were not reliable in identifying active spatial sites of AF. However, the proposed similarity score was able to pinpoint the spatial sites with high values (active AF sites) that were observed only in patients with unsuccessful AF termination, suggesting that these active sites were missed during the ablation procedure.ConclusionArrhythmogenic substrates with abnormal electrical activity are identified in patients with unsuccessful AF termination after catheter ablation, suggesting clinical efficacy of similarity score.

Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Signal-processing approaches are widely used for the analysis of intracardiac electrograms (iEGMs), which are collected during catheter ablation from patients with AF. In order to identify possible targets for ablation therapy, dominant frequency (DF) is widely used and incorporated in electroanatomical mapping systems. Recently, a more robust measure, multiscale frequency (MSF), for iEGM data analysis was adopted and validated. However, before completing any iEGM analysis, a suitable bandpass (BP) filter must be applied to remove noise. Currently, no clear guidelines for BP filter characteristics exist. The lower bound of the BP filter is usually set to 3–5 Hz, while the upper bound (BP¯th) of the BP filter varies from 15 Hz to 50 Hz according to many researchers. This large range of BP¯th subsequently affects the efficiency of further analysis. In this paper, we aimed to develop a data-driven preprocessing framework for iEGM analysis, and validate it based on DF and MSF techniques. To achieve this goal, we optimized the BP¯th using a data-driven approach (DBSCAN clustering) and demonstrated the effects of different BP¯th on subsequent DF and MSF analysis of clinically recorded iEGMs from patients with AF. Our results demonstrated that our preprocessing framework with BP¯th = 15 Hz has the best performance in terms of the highest Dunn index. We further demonstrated that the removal of noisy and contact-loss leads is necessary for performing correct data iEGMs data analysis.

Humans possess the essential capacity to navigate in environment, supported by multiple brain regions constituting the navigation network. Recent studies on development of the navigation network mainly examined activation changes in the medial temporal regions. It is unclear how the large-scale organization of the whole navigation network develops and whether the network organizations under resting-state and task-state develop differently. We addressed these questions by examining functional connectivity (FC) of the navigation network in 122 children (10-13 years) and 260 adults. First, we identified a modular structure in the navigation network during resting-state that included a ventral and a dorsal module. Then, we found that the intrinsic modular structure was strengthened from children to adults, that is, adults showed stronger FC within the ventral module and weaker FC between ventral and dorsal modules than children. Further, the intrinsic modular structure was loosened when performing scene-viewing task, that is, both adults and children showed decreased within-ventral FC and increased between-module FC during task- than resting-state. Finally, the task-modulated FC changes were greater in adults than in children. In sum, our study reveals age-related changes in the navigation network organization as increasing modularity under resting-state and increasing flexibility under task-state.

As an important approach to cancer classification, cancer sample clustering is of particular importance for cancer research. For high dimensional gene expression data, examining approaches to selecting characteristic genes with high identification for cancer sample clustering is an important research area in the bioinformatics field. In this paper, we propose a novel integrated framework for cancer clustering known as the non-negative symmetric low-rank representation with graph regularization based on score function (NSLRG-S). First, a lowest rank matrix is obtained after NSLRG decomposition. The lowest rank matrix preserves the local data manifold information and the global data structure information of the gene expression data. Second, we construct the Score function based on the lowest rank matrix to weight all of the features of the gene expression data and calculate the score of each feature. Third, we rank the features according to their scores and select the feature genes for cancer sample clustering. Finally, based on selected feature genes, we use the K-means method to cluster the cancer samples. The experiments are conducted on The Cancer Genome Atlas (TCGA) data. Comparative experiments demonstrate that the NSLRG-S framework can significantly improve the clustering performance.

Background/Aims: Drug resistance remains a main obstacle to the treatment of non- small cell lung cancer (NSCLC). The aim of this study was to identify the expression profiles of microRNAs (miRNAs) in drug-resistant NSCLC cell lines. Methods: The expression profiles of miRNAs in drug-resistant NSCLC cell lines were examined using miRNA sequencing, and the common dysregulated miRNAs in these cell lines were identified and analyzed by bioinformatics methods. Results: A total of 29 upregulated miRNAs and 36 downregulated miRNAs were found in the drug-resistant NSCLC cell lines, of which 26 upregulated and 36 downregulated miRNAs were found to be involved in the Ras signaling pathway. The expression levels, survival analysis, and receiver operating characteristic curve of the dysregulated miRNAs based on The Cancer Genome Atlas database for lung adenocarcinoma showed that hsa-mir-192, hsa-mir-1293, hsa-mir-194, hsa-mir-561, hsa-mir-205, hsa-mir-30a, and hsa-mir-30c were related to lung cancer, whereas only hsa-mir-1293 and hsa-mir-561 were not involved in drug resistance. Conclusion: The results of this study may provide novel biomarkers for drug resistance in NSCLC and potential therapies for overcoming drug resistance, and may also reveal the potential mechanisms underlying drug resistance in this disease.

The residual levels of 16 priority polycyclic aromatic hydrocarbons (PAHs) in environment media and freshwater fish were collected and measured from Lake Chaohu by using Gas chromatography-mass spectrometry. Potential atmospheric sources were identified by molecular diagnostic ratios and the positive matrix factorization (PMF) method. PAH exposure doses through inhalation, intake of water and freshwater fish ingestion were estimated by the assessment model recommended by US EPA. The carcinogenic risks of PAH exposure were evaluated by probabilistic risk assessment and Monte Carlo simulation. The following results were obtained: (1) The PAH16 levels in gaseous, particulate phase, water and fish muscles were 59.4 ng·m−3, 14.2 ng·m−3, 170 ng·L−1 and 114 ng·g−1, respectively. No significant urban-rural difference was found between two sampling sites except gaseous BaPeq. The relationship between gaseous PAHs and PAH in water was detected by the application of Spearman correlation analysis. (2) Three potential sources were identified by the PMF model. The sources from biomass combustions, coal combustion and vehicle emission accounted for 43.6%, 30.6% and 25.8% of the total PAHs, respectively. (3) Fish intake has the highest lifetime average daily dose (LADD) of 3.01 × 10−6 mg·kg−1·d−1, followed by the particle inhalation with LADD of 2.94 × 10−6 mg·kg−1·d−1. (4) As a result of probabilistic cancer risk assessment, the median ILCRs were 3.1 × 10−5 to 3.3 × 10−5 in urban and rural residents, which were lower than the suggested serious level but higher than the acceptable level. In summary, the result suggests that potential carcinogenic risk exists among residents around Lake Chaohu. Fish ingestion and inhalation are two major PAH exposure pathways.

After reviewing the species- and community-level ecological risk assessments (ERAs) of chemicals in the aquatic environment, the present study attempted to propose a third stage of ERA, i.e., the ecosystem-level ERA. Based on the species sensitivity distribution model (SSD) and thermodynamic theory, the exergy and biomass indicators of communities from various trophic levels (TLs) were introduced to improve ecological connotation of SSDs. The species were classified into three TLs based on algae (TL1), invertebrates (TL2), and vertebrates (TL3), and the weight of each TL was determined based on relative biomass and β value, which indicated a holistic contribution of each species or community to the ecosystem. Then, a system-level ERA protocol was successfully established, and the community- and system-level ecological risks of 10 typical toxic micro-organic pollutants in the western area of Lake Chaohu and its inflowing rivers were evaluated. System-level ERA curves (ExSSD) were mainly affected by the community-level SSD at TL2 for most chemicals in the present study. The uncertain boundary of ExSSD was mostly related to TLs with a wider uncertain boundary, but had little relation to the weight of each TL. The results of system-level ERAs revealed that dibutyl phthalate had the highest eco-risk, whereas γ-hexachlorocyclohexane presented the lowest eco-risk. Results of the system-level ERA were not fully consistent with the those of community-level ERA owing to the lack of a sufficient dataset, SSD model type, and ecosystem structure, as indicated by the weight of each TL. The successful application of ExSSD in Lake Chaohu signifies the start of the third stage of ERA at the system-level, and it also provides a scientific basis for ecosystem-level ERA, aquatic ecosystem protection, and future water safety management. However, there were some limitations, including sufficient data dependence, neglect of ecological interactions, and neglect of environmental parameters such as natural organic matter. We propose to employ toxicogenomics to enrich the toxicity database, to simulate the interaction using the ecological dynamic model, and to introduce the chemical fate model into the system-level ERA. Keywords: System-level ecological risk assessment, Exergy, Species sensitivity distribution model, Micro-organic pollutant, Lake Chaohu

Streptococcus sanguinis is an early colonizer of tooth surfaces and a key player in plaque biofilm development. However, the mechanism of biofilm formation of S. sanguinis is still unclear. Here, we showed that deletion of a transcription factor, brpL, promotes cell aggregation and biofilm formation in S. sanguinis SK36. Glucan, a polysaccharide synthesized from sucrose, was over-produced and aggregated in the biofilm of ΔbrpL, which was necessary for better biofilm formation ability of ΔbrpL. Quantitative RT-PCR demonstrated that gtfP was significantly up-regulated in ΔbrpL, which increased the productions of water-insoluble and water-soluble glucans. The ΔbrpLΔgtfP double mutant decreased biofilm formation ability of ΔbrpL to a level similar like that of ΔgtfP. Interestingly, the biofilm of ΔbrpL had an increased tolerance to ampicillin treatment, which might be due to better biofilm formation ability through the mechanisms of cellular and glucan aggregation. RNA sequencing and quantitative RT-PCR revealed the modulation of a group of genes in ΔbrpL was mediated by activating the expression of ciaR, another gtfP-related biofilm formation regulator. Double deletion of brpL and ciaR decreased biofilm formation ability to the phenotype of a ΔciaR mutant. Additionally, RNA sequencing elucidated a broad range of genes, related to carbohydrate metabolism and uptake, were activated in ΔbrpL. SSA_0222, a gene involved in the phosphotransferase system, was dramatically up-regulated in ΔbrpL and essential for S. sanguinis survival under our experimental conditions. In summary, brpL modulates glucan production, cell aggregation and biofilm formation by regulating the expression of ciaR in S. sanguinis SK36.

The coordinate system has been proposed as a fundamental and cross-culturally used spatial representation, through which people code location and direction information in the environment. Here we provided direct evidence demonstrating that daily experience with the cardinal coordinate system (i.e., east, west, north, and south) contributed to the representation of cognitive maps. Behaviorally, we found that individuals who relied more on the cardinal coordinate system for daily navigation made smaller errors in an indoor pointing task, suggesting that the cardinal coordinate system is an important element of cognitive maps. Neurally, the extent to which individuals relied on the cardinal coordinate system was positively correlated with the gray matter volume of the entorhinal cortex, suggesting that the entorhinal cortex may serve as the neuroanatomical basis of coordinate-based navigation (the entorhinal coordinate area, ECA). Further analyses on the resting-state functional connectivity revealed that the intrinsic interaction between the ECA and two hippocampal sub-regions, the subiculum and cornu ammonis, might be linked with the representation precision of cognitive maps. In sum, our study reveals an association between daily experience with the cardinal coordinate system and cognitive maps, and suggests that the ECA works in collaboration with hippocampal sub-regions to represent cognitive maps.

The organic contaminants, including polycyclic aromatic hydrocarbons (PAHs), organochlorine pesticides (OCPs), polybrominated diphenyl ethers (PBDEs), and polychlorinated biphenyls (PCBs), are of priority concern because of their persistence, toxicity, and long-distance transportation in global environment. Their residues in a daily consumed fish (grass carp) pose potential threat to human health and aquatic ecosystems. The present study optimized an analytical protocol of microwave-assisted extraction (MAE), lip-removal by gel permeation chromatography (GPC), cleanup by solid phase cartridge (SC) or adsorption chromatography column (CC), and gas chromatography-mass spectrometry (GC/MS). Besides traditional statistical parameters, some indicators were calculated to judge the performances of extraction by various methods. The optimization experiment showed that n-hexane/acetone was the best MEA extraction solvent; an optimal fraction time of 10–39 min could simultaneously elute all the target chemicals in a single GPC run. Both CC and SC showed good recoveries. However, CC performed better than SC (p

A highly selective procedure to extract thiol-containing peptides (TCPs) from complicated soy glycinin hydrolysates (SGHs) was described. This procedure included the reduction of disulfide bonds by 1,4-dithiothreitol (DTT) and enrichment of TCPs through Thiopropyl-Sephrose 6B covalent chromatography. TCPs were confirmed using a strategy based on mass shift after differential alkylation of sulfhydryl groups with iodoacetamide and N-ethylmaleimide by matrix-assisted laser desorption ionization mass spectrometry (MALDI-TOF-MS). The antioxidant activities of TCPs were evaluated using chemical assays. DTT reduction increased the concentration of sulfhydryl groups from 1.8 μmol/g to 113.8 μmol/g. The efficiency of the extraction was improved by optimizing the loading of sample, extraction and desorption time and the content of desorption reagent. Both of the adsorption and desorption process were found to fit a pseudo-second order model. MALDI-TOF-MS showed that 36 of the 45 extracted peptides were TCPs. The EC50 of TCPs for DPPH, hydroxyl radical, and superoxide anion radical was 0.1, 1.49 and 0.084 mg/mL, respectively. The reducing power of TCPs (0.2 mg/mL) was of 0.375. These results suggest that the combination of DTT reduction and Thiopropyl-Sepharose 6B covalent chromatograph was a successful pathway to extract TCPs from SGHs and the TCPs could be used as potential antioxidants.

Video target tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in target tracking for nonlinear and non-Gaussian estimation problems. Although most existing algorithms are able to track targets well in controlled environments, it is often difficult to achieve automated and robust tracking of pedestrians in video sequences if there are various changes in target appearance or surrounding illumination. To surmount these difficulties, this paper presents multitarget tracking of pedestrians in video sequences based on particle filters. In order to improve the efficiency and accuracy of the detection, the algorithm firstly obtains target regions in training frames by combining the methods of background subtraction and Histogram of Oriented Gradient (HOG) and then establishes discriminative appearance model by generating patches and constructing codebooks using superpixel and Local Binary Pattern (LBP) features in those target regions. During the process of tracking, the algorithm uses the similarity between candidates and codebooks as observation likelihood function and processes severe occlusion condition to prevent drift and loss phenomenon caused by target occlusion. Experimental results demonstrate that our algorithm improves the tracking performance in complicated real scenarios.