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Abstract Background White-rot fungi are known to naturally produce high quantities of laccase, which exhibit commendable stability and catalytic efficiency. However, their laccase production does not meet the demands for industrial-scale applications. To address this limitation, it is crucial to optimize the conditions for laccase production. However, the regulatory mechanisms underlying different conditions remain unclear. This knowledge gap hinders the cost-effective application of laccases. Results In this study, we utilized transcriptomic and metabolomic data to investigate a promising laccase producer, Cerrena unicolor 87613, cultivated with fructose as the carbon source. Our comprehensive analysis of differentially expressed genes (DEGs) and differentially abundant metabolites (DAMs) aimed to identify changes in cellular processes that could affect laccase production. As a result, we discovered a complex metabolic network primarily involving carbon metabolism and amino acid metabolism, which exhibited contrasting changes between transcription and metabolic patterns. Within this network, we identified five biomarkers, including succinate, serine, methionine, glutamate and reduced glutathione, that played crucial roles in co-determining laccase production levels. Conclusions Our study proposed a complex metabolic network and identified key biomarkers that determine the production level of laccase in the commercially promising Cerrena unicolor 87613. These findings not only shed light on the regulatory mechanisms of carbon sources in laccase production, but also provide a theoretical foundation for enhancing laccase production through strategic reprogramming of metabolic pathways, especially related to the citrate cycle and specific amino acid metabolism.

Wuyi rock tea residues, generated after brewing or aroma extraction, are rich in nutrients. This study aimed to prepare ACE inhibitory peptides from the protein hydrolysate of Wuyi rock tea residues and investigate their stability. The optimal proteolysis conditions for neutral protease were optimized as: a substrate concentration of 2% (w/v), pH 7.0, hydrolysis temperature of 45°C, enzymatic digestion time of 3 h, and enzyme dosage of 6800 U/g. Under these conditions, the ACE inhibition rate of protein hydrolysate of Wuyi rock tea residues reached 70.47 ± 0.29% at a concentration of 0.5 mg/mL. Additionally, the prepared ACE inhibitory peptides demonstrated excellent stability against acid, base, heat, and certain metal ions. These findings suggest that the proteins extracted from Wuyi rock tea residues can serve as a promising source to produce highly active ACE inhibitory peptides. These peptides could be utilized in the development of nutraceuticals, functional foods, and pharmaceuticals.

ABSTRACTThe white rot fungus Cerrena unicolor 87613 has been previously shown to be a promising resource in laccase production, an enzyme with significant biotechnological applications. Conventional methods face technical challenges in improving laccase activity. Attempts are still being made to develop novel approaches for further enhancing laccase activity. This study aimed to understand the regulation of laccase activity in C. unicolor 87613 for a better exploration of the novel approach. Transcriptomic and metabolomic analyses were performed to identify key genes and metabolites involved in extracellular laccase activity. The findings indicated a strong correlation between the glutathione metabolism pathway and laccase activity. Subsequently, experimental verifications were conducted by manipulating the pathway using chemical approaches. The additive reduced glutathione (GSH) dose-dependently repressed laccase activity, while the GSH inhibitors (APR-246) and reactive oxygen species (ROS) inducer (H2O2) enhanced laccase activity. Changes in GSH levels could determine the intracellular redox homeostasis in interaction with ROS and partially affect the expression level of laccase genes in C. unicolor 87613 in turn. In addition, GSH synthetase was found to mediate GSH abundance in a feedback loop. This study suggests that laccase activity is negatively influenced by GSH metabolism and provides a theoretical basis for a novel strategy to enhance laccase activity by reprogramming glutathione metabolism at a specific cultivation stage.IMPORTANCEThe production of laccase activity is limited by various conventional approaches, such as heterologous expression, strain screening, and optimization of incubation conditions. There is an urgent need for a new strategy to meet industrial requirements more effectively. In this study, we conducted a comprehensive analysis of the transcriptome and metabolome of Cerrena unicolor 87613. For the first time, we discovered a negative role played by reduced glutathione (GSH) and its metabolic pathway in influencing extracellular laccase activity. Furthermore, we identified a feedback loop involving GSH, GSH synthetase gene, and GSH synthetase within this metabolic pathway. These deductions were confirmed through experimental investigations. These findings not only advanced our understanding of laccase activity regulation in its natural producer but also provide a theoretical foundation for a strategy to enhance laccase activity by reprogramming glutathione metabolism at a specific cultivation stage.

Herein, a doxorubicin-loaded carbon-based drug delivery system, denoted as PC-DOX, composed of pH-responsive imine bond was developed for the tumor-targeted treatment. PC-DOX with a uniform particle size around 180 nm was synthesized by coating of as-synthesized hollow carbon-based nanoparticles (NPs) with dialdehyde PEG, which was used as carrier to attach DOX covalently through dynamic covalent bond. The unique structure endowed the advantages of specific tumor targeting and tumor microenvironment (TME) specific drug delivery capacity with PC-DOX. For the one hand, the tumor targeting caused by the enhanced permeability and retention (EPR) effect could significantly improve the tumor cellular uptake. For the other hand, the pH-responsiveness could realize the effective DOX accumulation in tumor tissues, avoiding the unwanted side effect to the normal tissues. As a result, PC-DOX with high DOX loading capacity (70.12%) and excellent biocompatibility, concurrently, presented a significant anti-tumor effect at a low mass concentration (DOX equivalent dose: 20 μg/mL). Another attractive characteristic of PC-DOX was the remarkable protective effect towards DOX-induced cardiotoxicity, which could be clearly observed from in vitro cellular, and animal assays. Compared with free DOX, the cardiomyocyte viability increased by average 30.58%, and the heart function was also significantly improved. This novel drug delivery nanoplatform provides a new method for the future clinical application of DOX in the cancer’s therapeutics.

AIM: To observe the effect of soluble glycoprotein 130(sgp130)on expression of p-STAT3 and vascular endothelial growth factor(VEGF)-A in retina of mice with diabetes mellitus(DM), and explore the possibility of sgp130 in interfering with inflammatory damage of diabetic retinopathy(DR).METHODS: A total of 45 mice were randomly divided into normal group, DM group and sgp130 group. DM models were made in DM group and sgp130 group with streptozotocin. No special intervention was given to normal group and DM group, but sgp130 group was given intravitreal injection of 1.5mg/mL sgp130 2μL at the 1 and 5wk. After 10wk, all the mice were sacrificed to assess the protein expression of interleukin 6(IL-6), p-STAT3 and VEGF-A in the retina.RESULTS: The expressions of IL-6, p-STAT3 and VEGF-A in retina of DM group were higher than those of normal group at 10wk(all P

Because of the health benefits and economic opportunities, extracting bioactive peptides from plant proteins, often food processing by-products, garners significant interest. However, the high enzyme costs and the emergence of bitter peptides have posed significant challenges in production. This study achieved the immobilization of Alcalase and Flavorzyme using cost-effective SiO2 microparticles. Mussel-inspired chemistry and biocompatible polymers were employed, with genipin replacing glutaraldehyde for safer crosslinking. This approach yielded an enzyme loading capacity of approximately 25 mg/g support, with specific activity levels reaching around 180 U/mg for immobilized Alcalase (IA) and 35 U/mg for immobilized Flavorzyme (IF). These immobilized proteases exhibited improved activity and stability across a broader pH and temperature range. During the hydrolysis of soy proteins, the use of immobilized proteases avoided the thermal inactivation step, resulting in fewer peptide aggregates. Moreover, this study applied peptidomics and bioinformatics to profile peptides in each hydrolysate and identify bioactive ones. Cascade hydrolysis with IA and IF reduced the presence of bitter peptides by approximately 20%. Additionally, 50% of the identified peptides were predicted to have bioactive properties after in silico digestion simulation. This work offers a cost-effective way of generating bioactive peptides from soy proteins with reducing potential bitterness.

The complete mitochondrial genome of Phyllonorycter ringoniella (Matsumura, 1931) is characterized by a circular with 15,729 bp in size, containing 37 encoded genes and a control region. The gene order and nucleotide composition are similar to the known gracillariid mitogenomes. All protein-coding genes (PCGs) initiate with ATN and terminate with TAN, while cox1 starts with CGA, cox1, cox2, nad3, and nad5 terminate with an incomplete codon TA– or T–. All transfer RNA genes (tRNAs) can fold into typical clover-leaf structure, except for trnS1 (AGN), in which dihydrouridine stem is simplified to form a loop structure. The control region is located between 12S rRNA and trnM with relatively strong AT bias. The phylogenetic trees reveal that two subfamilies Oecophyllembiinae and Acrocercopinae were clustered together, and that clade was sister to the subfamily Lithocolletinae. All species of the genus Phyllonorycter grouped into a monophyletic clade and the P. ringoniella was closely related to P. platani.

AIM: To establish three-dimensional(3D)model of rat retinal angiogenesis in vitro based on retinal microvascular endothelial cells(ECs)and retinal microvascular pericytes(RMPs). METHODS: The identified ECs and RMPs of third generation to seventh generation were used for research after isolated, purified and cultured. The cells were stained with cell tracer. Then, it were mixed and inoculated on Matrigel by the surface culture method for dynamic observation. The expression of VEGF-A was assessed during angiogenesis. RESULTS: At 12h of co-culture, RMPs were recruited by ECs and gathered into cell masses with different sizes. At 24h, ECs/RMPs formed a complex 3D vascular spline network. At 48h, the reticular structure disintegrated obviously, and only a small amount of incomplete and simple reticular structure remained. At 72h, the vascular spline cable network disintegrated completely. In the development of 3D model, the expression of VEGF-A increased, but decreased when it degenerated. CONCLUSION: This study successfully established a 3D model of rat retinal angiogenesis in vitro based on ECs and RMPs.

In this work, four novel angiotensin I-converting enzyme inhibitory (ACEI) peptides were isolated and identified from peony (Paeonia suffruticosa Andr.) seed hydrolysate (PSH) by consecutive chromatography and UPLC-ESI-QTOF-MS/MS. The IC50 values of HWS (0.640 ±0.016 mg/mL) and VLSGF (0.328 ± 0.040 mg/mL) were significantly (p < 0.05) lower than those of the other two peptides. The Lineweaver-Burk plots showed that VLSGF is competitive inhibition against ACE and HWS acted as a mixed-type inhibitor. Meanwhile, the results of molecular docking showed hydrogen interactions between VLSGF/HWS and ACE. In human endothelial cells, the pretreatments of VLSGF and HWS could increase the production of nitric oxide (NO) and the expression of endothelial nitric oxide synthase (eNOS). In addition, they also decreased the reactive oxygen species (ROS) production. These results indicated that it may be possible to use peony seed protein hydrolysate to create ingredients for functional foods.

Abstract Background Osteoporosis is a gradually recognized health problem with risks related to disease history and living habits. This study aims to establish the optimal prediction model by comparing the performance of four prediction models that incorporated disease history and living habits in predicting the risk of Osteoporosis in Chongqing adults. Methods We conduct a cross-sectional survey with convenience sampling in this study. We use a questionnaire From January 2019 to December 2019 to collect data on disease history and adults’ living habits who got dual-energy X-ray absorptiometry. We established the prediction models of osteoporosis in three steps. Firstly, we performed feature selection to identify risk factors related to osteoporosis. Secondly, the qualified participants were randomly divided into a training set and a test set in the ratio of 7:3. Then the prediction models of osteoporosis were established based on Artificial Neural Network (ANN), Deep Belief Network (DBN), Support Vector Machine (SVM) and combinatorial heuristic method (Genetic Algorithm - Decision Tree (GA-DT)). Finally, we compared the prediction models’ performance through accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) to select the optimal prediction model. Results The univariate logistic model found that taking calcium tablet (odds ratio [OR] = 0.431), SBP (OR = 1.010), fracture (OR = 1.796), coronary heart disease (OR = 4.299), drinking alcohol (OR = 1.835), physical exercise (OR = 0.747) and other factors were related to the risk of osteoporosis. The AUCs of the training set and test set of the prediction models based on ANN, DBN, SVM and GA-DT were 0.901, 0.762; 0.622, 0.618; 0.698, 0.627; 0.744, 0.724, respectively. After evaluating four prediction models’ performance, we selected a three-layer back propagation neural network (BPNN) with 18, 4, and 1 neuron in the input layer, hidden and output layers respectively, as the optimal prediction model. When the probability was greater than 0.330, osteoporosis would occur. Conclusions Compared with DBN, SVM and GA-DT, the established ANN model had the best prediction ability and can be used to predict the risk of osteoporosis in physical examination of the Chongqing population. The model needs to be further improved through large sample research.

It is well documented that massive dynamic information is contained in the resting-state fMRI. Recent studies have identified recurring states dominated by similar coactivation patterns (CAPs) and revealed their temporal dynamics. However, the reproducibility and generalizability of the CAP analysis are unclear. To address this question, the effects of methodological pipelines on CAP are comprehensively evaluated in this study, including the preprocessing, network construction, cluster number and three independent cohorts. The CAP state dynamics are characterized by the fraction of time, persistence, counts, and transition probability. Results demonstrate six reliable CAP states and their dynamic characteristics are also reproducible. The state transition probability is found to be positively associated with the spatial similarity. Furthermore, the aberrant CAP states in schizophrenia have been investigated by using the reproducible method on three cohorts. Schizophrenia patients spend less time in CAP states that involve the fronto-parietal network, but more time in CAP states that involve the default mode and salience network. The aberrant dynamic characteristics of CAP states are correlated with the symptom severity. These results reveal the reproducibility and generalizability of the CAP analysis, which can provide novel insights into the neuropathological mechanism associated with aberrant brain network dynamics of schizophrenia.

The corpus callosum serves as a crucial organization for understanding the information integration between the two hemispheres. Our previous study explored the functional connectivity between the corpus callosum and white-matter functional networks (WM-FNs), but the corresponding physical connectivity remains unknown. The current study uses the resting-state fMRI of Human Connectome Project data to identify ten WM-FNs in 108 healthy subjects, and then independently maps the structural and functional connectivity between the corpus callosum and above WM-FNs using the diffusion tensor images (DTI) tractography and resting-state functional connectivity (RSFC). Our results demonstrated that the structural and functional connectivity between the human corpus callosum and WM-FNs have the following high overall correspondence: orbitofrontal WM-FN, DTI map = 89% and RSFC map = 92%; sensorimotor middle WM-FN, DTI map = 47% and RSFC map = 77%; deep WM-FN, DTI map = 50% and RSFC map = 79%; posterior corona radiata WM-FN, DTI map = 82% and RSFC map = 73%. These findings reinforce the notion that the corpus callosum has unique spatial distribution patterns connecting to distinct WM-FNs. However, important differences between the structural and functional connectivity mapping results were also observed, which demonstrated a synergy between DTI tractography and RSFC toward better understanding the information integration of primary and higher-order functional systems in the human brain.

Consumers play an important role as one of the main actors in food safety social co-governance. To create a pattern of food safety social co-governance, the active and effective participation of consumers is critical. To encourage consumers to participate in food safety social co-governance voluntarily and positively, we attempted to develop and preliminarily validate a multidimensional questionnaire on consumer psychological capital that could be used to measure the degree of consumer participation in food safety social co-governance. The aim of the initial sample (N = 170) and test sample 2 (N = 204) was to investigate the factor structure of a preliminary measure of consumer psychological capital. A 4-factor model with 23 items explained 61.05% of the total variance in item scores. The aim of test sample 3 (N = 30) was to measure the retest reliability. Test sample 4 (N = 1,076) was randomly allocated to the modeling sample (N = 538) and validation sample (N = 538) to verify questionnaire reliability and validity. Convergent validity, discriminant validity, and the internal inconsistency coefficients of the questionnaire were assessed in the modeling sample. While processing CFA, we deleted 9 items with small standardized factor loadings. The remaining 14 items in the final revised 4-factor model included self-efficacy, resilience, hope, and optimism. The fit indices of the revised four-factor model and second-order factor model in the modeling sample revealed an acceptable model fit. The convergent validity and discriminant validity of the revised model were good and acceptable, respectively. A cross-validation procedure confirmed the appropriateness of the revised four-factor model and second-order factor model in the validation sample. The cross-validation results confirmed that the fit indices of the revised four-factor model fitted the data well and the second-order factor model in the validation sample reached acceptable values. We concluded that the questionnaire developed in this study had good reliability and stable and acceptable construct validity. It could provide a theoretical basis for measuring psychological capital in food safety co-governance.

BackgroundCoronary artery disease (CAD) is a type of cardiovascular disease that greatly hurts the health of human beings. Diabetic status is one of the largest clinical factors affecting CAD-associated gene expression changes. Most of the studies focus on diabetic patients, whereas few have been done for non-diabetic patients. Since the pathophysiological processes may vary among these patients, we cannot simply follow the standard based on the data from diabetic patients. Therefore, the prognostic and predictive diagnostic biomarkers for CAD in non-diabetic patient need to be fully recognized.Materials and MethodsTo screen out candidate genes associated with CAD in non-diabetic patients, weighted gene co-expression network analysis (WGCNA) was constructed to conduct an analysis of microarray expression profiling in patients with CAD. First, the microarray data GSE20680 and GSE20681 were downloaded from NCBI. We constructed co-expression modules via WGCNA after excluding the diabetic patients. As a result, 18 co-expression modules were screened out, including 1,225 differentially expressed genes (DEGs) that were obtained from 152 patients (luminal stenosis ≥50% in at least one major vessel) and 170 patients (stenosis of

The linearity and stationarity of fMRI time series need to be understood due to their important roles in the choice of approach for brain network analysis. In this paper, we investigated the stationarity and linearity of resting-state fMRI (rs-fMRI) time-series data from the Midnight Scan Club datasets. The degree of stationarity (DS) and the degree of non-linearity (DN) were, respectively, estimated for the time series of all gray matter voxels. The similarity and difference between the DS and DN were assessed in terms of voxels and intrinsic brain networks, including the visual network, somatomotor network, dorsal attention network, ventral attention network, limbic network, frontoparietal network, and default-mode network. The test-retest scans were utilized to quantify the reliability of DS and DN. We found that DS and DN maps had overlapping spatial distribution. Meanwhile, the probability density estimate function of DS had a long tail, and that of DN had a more normal distribution. Specifically, stronger DS was present in the somatomotor, limbic, and ventral attention networks compared to other networks, and stronger DN was found in the somatomotor, visual, limbic, ventral attention, and default-mode networks. The percentage of overlapping voxels between DS and DN in different networks demonstrated a decreasing trend in the order default mode, ventral attention, somatomotor, frontoparietal, dorsal attention, visual, and limbic. Furthermore, the ICC values of DS were higher than those of DN. Our results suggest that different functional networks have distinct properties of non-stationarity and non-linearity owing to the complexity of rs-fMRI time series. Thus, caution should be taken when analyzing fMRI data (both resting-state and task-activation) using simplified models.

Alterations in large-scale brain intrinsic functional connectivity (FC), i.e., coherence between fluctuations of ongoing activity, have been implicated in major depressive disorder (MDD). Yet, little is known about the frequency-dependent alterations of FC in MDD. We calculated frequency specific degree centrality (DC) – a measure of overall FC of a brain region – within 10 distinct frequency sub-bands accessible from the full range of resting-state fMRI BOLD fluctuations (i.e., 0.01–0.25 Hz) in 24 healthy controls and 24 MDD patients. In healthy controls, results reveal a frequency-specific spatial distribution of highly connected brain regions – i.e., hubs – which play a fundamental role in information integration in the brain. MDD patients exhibited significant deviations from the healthy DC patterns, with decreased overall connectedness of widespread regions, in a frequency-specific manner. Decreased DC in MDD patients was observed predominantly in the occipital cortex at low frequencies (0.01–0.1 Hz), in the middle cingulate cortex, sensorimotor cortex, lateral parietal cortex, and the precuneus at middle frequencies (0.1–0.175 Hz), and in the anterior cingulate cortex at high frequencies (0.175–0.25 Hz). Additionally, decreased DC of distinct parts of the insula was observed across low, middle, and high frequency bands. Frequency-specific alterations in the DC of the temporal, insular, and lateral parietal cortices correlated with symptom severity. Importantly, our results indicate that frequency-resolved analysis within the full range of frequencies accessible from the BOLD signal – also including higher frequencies (>0.1 Hz) – reveals unique information about brain organization and its changes, which can otherwise be overlooked.

In addition to a face-to-face classroom learning environment, today's learners in higher education are likely to have also experienced a blended learning or an online learning environment. These learning environments not only differ in their delivery modes, but also learning activities, class interactions, assessment approaches, etc. Learners tend to have differing perceptions about the effectiveness of different learning environments. This study therefore investigates whether the reasons learners like or dislike a learning environment reveal learner characteristics that may explain why some learners are more inclined towards a particular learning environment. This study also examines whether learner demographics influence learner characteristics and their preference for a particular learning environment. Using an exploratory sequential mixed methods research design, this study first conducted several focus group discussions and then administered an online questionnaire survey to collect input from students at a local university. Analyses derived four learner characteristics (i.e. desire for direct support, digital readiness, learning independence, and online hesitancy) based on the reasons why the students liked or disliked face-to-face classroom learning, blended learning, or online learning environments. A cluster analysis further distinguished the students into three groups (i.e. classroom learners, insecure learners, and online learners) based on the four learner characteristics. Analyses also found that learners' demographics largely had no effect on learners' characteristics and their preference for a particular learning environment. The findings suggest that learner characteristics may provide a clue to why certain learners have a preference for a face-to-face classroom learning, a blended learning, or an online learning environment. A better understanding of the relationship between learner characteristics and learners' inclination towards a particular learning environment can be helpful to educational institutions and academics to design a range of engaging learning activities for learners with different characteristics.

The increased use of digital technology in higher education requires university students to be digitally proficient. The main objective of this study is to examine the extent to which university students' proficiency in various digital competences could be helpful towards their learning performance. This study adopted DigComp 2.2 to design a data collection instrument for an online questionnaire survey and conducted data analysis on the valid 314 responses using partial least squares structural equation modelling (PLS-SEM). The findings indicate that, with the exception of safety, the digital competence areas of information and data literacy, communication and collaboration, digital content creation, and problem solving were significant to student learning performance. The findings also revealed that students' current semester had no significant moderating effect on the relationships between their proficiency in digital competence areas and learning performance, with the exception of a relationship between information and data literacy and learning performance, as well as a relationship between problem solving and learning performance, between respondents in semesters 3 and 4, and between respondents in semesters 5 and 6, respectively. The findings contribute to a better understanding of DigComp 2.2's applicability in higher education and can be used by educational institutions and educators to design learning activities and formative assessments that can help students improve their digital competence proficiency.

A real-time electric nose (E-nose) with a metal oxide sensor (MOS) array was developed to monitor 5 highly flammable liquids (ethanol, tetrahydrofuran, turpentine, lacquer thinner, and gasoline) in this work. We found that temperature had a significant impact on the test results and temperature control could efficiently improve the performance of our E-nose. The results of our qualitative analysis showed that principal component analysis (PCA) could not efficiently distinguish these samples compared to a back-propagation artificial neural network (BP-ANN) which had a 100% accuracy rate on the test samples. Quantitative analysis was performed by regression analysis and the average errors were 9.1%−18.4%. In addition, through anti-interference training, the E-nose could filter out the potential false alarm caused by mosquito repellent, perfume and hair jelly.

Ongoing, slowly fluctuating brain activity is organized in resting-state networks (RSNs) of spatially coherent fluctuations. Beyond spatial coherence, RSN activity is governed in a frequency-specific manner. The more detailed architecture of frequency spectra across RSNs is, however, poorly understood. Here we propose a novel measure–the Spectral Centroid (SC)–which represents the center of gravity of the full power spectrum of RSN signal fluctuations. We examine whether spectral underpinnings of network fluctuations are distinct across RSNs. We hypothesize that spectral content differs across networks in a consistent way, thus, the aggregate representation–SC–systematically differs across RSNs. We therefore test for a significant grading (i.e., ordering) of SC across RSNs in healthy subjects. Moreover, we hypothesize that such grading is biologically significant by demonstrating its RSN-specific change through brain disease, namely major depressive disorder. Our results yield a highly organized grading of SC across RSNs in 820 healthy subjects. This ordering was largely replicated in an independent dataset of 25 healthy subjects, pointing toward the validity and consistency of found SC grading across RSNs. Furthermore, we demonstrated the biological relevance of SC grading, as the SC of the salience network–a RSN well known to be implicated in depression–was specifically increased in patients compared to healthy controls. In summary, results provide evidence for a distinct grading of spectra across RSNs, which is sensitive to major depression.

Proteins were extracted from perilla (Perilla frutescens L. Britton) seed by-products and hydrolyzed with an alkaline protease. Antioxidant peptides were purified from the hydrolysate by size-exclusion chromatography and RP-HPLC. Two peptides with strong antioxidant activity were identified as Tyr-Leu (YL) and Phe-Tyr (FY) with the molecular mass of 294.33 Da and 328.33 Da, respectively. Synthesized YL and FY efficiently quenched free radicals (DPPH, ABTS and hydroxyl radicals) and showed high oxygen radical absorbance capacity. The two peptides also inhibited lipid peroxidation in the rat liver. Furthermore, YL and FY could protect HepG-2 cells against hydrogen peroxide-induced oxidative damage without cytotoxicity. Based on the structure-activity analysis, the Tyr residue was crucial for the antioxidant activity of YL and FY. The results indicate that the protein hydrolysate from perilla seed by-products possessed potent biological activity and can be utilized to develop health-related nutraceutical ingredients.

Very preterm birth is associated with an increased prevalence of attention problems and may especially impair executive attention, i.e., top-down control of attentional selection in situations where distracting information interferes with the processing of task-relevant stimuli. While there are initial findings linking structural brain alterations in preterm-born individuals with attention problems, the functional basis of these problems are not well understood. The present study used an fMRI adaptation of the Attentional Network Test to examine the neural correlates of executive attention in a large sample of N = 86 adults born very preterm and/or with very low birth weight (VP/VLBW), and N = 100 term-born controls. Executive attention was measured by comparing task behavior and brain activations associated with the processing of incongruent vs. congruent arrow flanker stimuli. Consistent with subtle impairments of executive attention, the VP/VLBW group showed lower accuracy and a tendency for increased response times during the processing of incongruent stimuli. Both groups showed similar activation patters, especially within expected fronto-cingulo-parietal areas, but no significant between-group differences. Our results argue for a maintained attention-relevant network organization in high-functioning preterm born adults in spite of subtle deficits in executive attention. Gestational age and neonatal treatment variables showed associations with task behavior, and brain activation in the dorsal ACC and lateral occipital areas, suggesting that the degree of prematurity (and related neonatal complications) has subtle modulatory influences on executive attention processing.

The volatile anesthetic sevoflurane is capable of inducing preconditioning and postconditioning effects in the brain. In this study, we investigated the effects of sevoflurane postconditioning on antioxidant and immunity indexes in cerebral ischemia reperfusion (CIR) rats. Rats were randomly assigned to five separate experimental groups I–V. In the sham group (I), rats were subjected to the same surgery procedures except for occlusion of the middle cerebral artery and exposed to 1.0 MAC sevoflurane 90 min after surgery for 30 min. IR control rats (group II) were subjected to middle cerebral artery occlusion (MCAO) for 90 min and exposed to O2 for 30 min at the beginning of reperfusion. Sevoflurane 0.5, 1.0 and 1.5 groups (III, IV, V) were all subjected to MCAO for 90 min, but at the beginning of reperfusion exposed to 0.5 MAC, 1.0 MAC or 1.5 MAC sevoflurane for 30 min, respectively. Results showed that sevoflurane postconditioning can decrease serum tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), nitric oxide (NO), nitric oxide synthase (NOS) and increase serum interleukin-10 (IL-10) levels in cerebral ischemia reperfusion rats. In addition, sevoflurane postconditioning can still decrease blood lipid, malondialdehyde (MDA) levels, infarct volume and increase antioxidant enzymes activities, normal pyramidal neurons density in cerebral ischemia reperfusion rats. It can be concluded that sevoflurane postconditioning may decrease blood and brain oxidative injury and enhance immunity indexes in cerebral ischemia reperfusion rats.

An improved aerobiological virus sampling method was developed based on adding adsorptive nanoparticles in samplers for concentrating viruses in sampling liquid buffers. The objectives of this research were to select effective adsorptive materials and optimize sampling parameters for increasing recovery of airborne viruses, such as influenza A virus or respiratory syndrome virus (RSV). Three kinds of polycation nanoparticles were evaluated for direct effects on absorption and desorption of influenza virus hemagglutinin and DNA. Chitosan particles showed good performance in absorption and desorption for both influenza virus hemagglutinin and DNA. A subsequent study evaluated the effects of collection buffer, pH and sampling time on the recovery of aerosolized viruses using a method for making direct comparisons of three treatments. The results demonstrated that various components in air-sampling collection buffer, impinger model, and sampling time, independently influenced the recovery of viruses. It was shown that adsorptive samplers with air disperser had the highest levels of sensitivity and repeatability in virus sampling. Both unspecifically adsorptive chitosan particles and specifically adsorptive particles labeled specific antibody to virus significantly enhanced recovery rate of aerosolized viruses. We succeeded to sample low level different pathogen viruses in outdoor environments with the optimized sampling system.

The Gastric Cancer (Biomarkers) Knowledgebase (GCBKB) (http://biomarkers.bii.a-star.edu.sg/background/gastricCancerBiomarkersKb.php) is a curated and fully integrated knowledgebase that provides data relating to putative biomarkers that may be used in the diagnosis and prognosis of gastric cancer. It is freely available to all users. The data contained in the knowledgebase was derived from a large literature source and the putative biomarkers therein have been annotated with data from the public domain. The knowledgebase is maintained by a curation team who update the data from a defined source. As well as mining data from the literature, the knowledgebase will also be populated with unpublished experimental data from investigators working in the gastric cancer biomarker discovery field. Users can perform searches to identify potential markers defined by experiment type, tissue type and disease state. Search results may be saved, manipulated and retrieved at a later date. As far as the authors are aware this is the first open access database dedicated to the discovery and investigation of gastric cancer biomarkers.

Although developmental stuttering has been extensively studied with structural and task-based functional magnetic resonance imaging (fMRI), few studies have focused on resting-state brain activity in this disorder. We investigated resting-state brain activity of stuttering subjects by analyzing the amplitude of low-frequency fluctuation (ALFF), region of interest (ROI)-based functional connectivity (FC) and independent component analysis (ICA)-based FC. Forty-four adult males with developmental stuttering and 46 age-matched fluent male controls were scanned using resting-state fMRI. ALFF, ROI-based FCs and ICA-based FCs were compared between male stuttering subjects and fluent controls in a voxel-wise manner. Compared with fluent controls, stuttering subjects showed increased ALFF in left brain areas related to speech motor and auditory functions and bilateral prefrontal cortices related to cognitive control. However, stuttering subjects showed decreased ALFF in the left posterior language reception area and bilateral non-speech motor areas. ROI-based FC analysis revealed decreased FC between the posterior language area involved in the perception and decoding of sensory information and anterior brain area involved in the initiation of speech motor function, as well as increased FC within anterior or posterior speech- and language-associated areas and between the prefrontal areas and default-mode network (DMN) in stuttering subjects. ICA showed that stuttering subjects had decreased FC in the DMN and increased FC in the sensorimotor network. Our findings support the concept that stuttering subjects have deficits in multiple functional systems (motor, language, auditory and DMN) and in the connections between them.

Purpose: This study intends to examine whether the reasons learners like or dislike a learning environment can help explain the differences in the characteristics of the learner and whether learner characteristics can influence a learner's preference for a learning environment. Design/methodology/approach: This study adopted an exploratory sequential mixed methods research design. The study first conducted focus groups with university students to uncover their learner characteristics by examining the reasons they liked or disliked a learning environment. This was followed by a questionnaire survey to explore how these learner characteristics influenced learner inclination for a learning environment. The survey data were analysed using exploratory and confirmatory factor analysis (partial least squares structural equation modelling). Findings: The findings showed that two types of learner characteristics, i.e. online learner characteristics and classroom learner characteristics, significantly affected learner inclination for a learning environment. Analyses also indicated that learner demographics had no major moderating effect between learner characteristics and learner inclination for a learning environment. Practical implications: The findings can be useful for education institutions, learning designers and academics to design engaging learning activities to better support different learning needs. Originality/value: This study makes a novel attempt to distinguish learner characteristics based on the reasons learners like or dislike a learning environment and establishes that individual learners' characteristics play a role in influencing their preference for a specific learning environment.

BACKGROUND: Shared neuropathological features between schizophrenic patients and their first-degree relatives have potential as indicators of genetic vulnerability to schizophrenia. We sought to explore genetic influences on brain morphology and function in schizophrenic patients and their relatives. METHODS: Using a multimodal imaging strategy, we studied 33 schizophrenic patients, 55 of their unaffected parents, 30 healthy controls for patients, and 29 healthy controls for parents with voxel-based morphometry of structural MRI scans and functional connectivity analysis of resting-state functional MRI data. RESULTS: Schizophrenic patients showed widespread gray matter reductions in the bilateral frontal cortices, bilateral insulae, bilateral occipital cortices, left amygdala and right thalamus, whereas their parents showed more localized reductions in the left amygdala, left thalamus and right orbitofrontal cortex. Patients and their parents shared gray matter loss in the left amygdala. Further investigation of the resting-state functional connectivity of the amygdala in the patients showed abnormal functional connectivity with the bilateral orbitofrontal cortices, bilateral precunei, bilateral dorsolateral frontal cortices and right insula. Their parents showed slightly less, but similar changes in the pattern in the amygdala connectivity. Co-occurrences of abnormal connectivity of the left amygdala with the left orbitofrontal cortex, right dorsolateral frontal cortex and right precuneus were observed in schizophrenic patients and their parents. CONCLUSIONS: Our findings suggest a potential genetic influence on structural and functional abnormalities of the amygdala in schizophrenia. Such information could help future efforts to identify the endophenotypes that characterize the complex disorder of schizophrenia.

Today, online learning is prospering from the widely available and easily accessible connection to the Web. Massive open online course (MOOC) platforms such as Coursera, edX, and Udemy have made available several thousands of short courses at several difficulty levels in a wide variety of disciplines, ranging from business, computer science to literature, for learners to select from. Learners who are looking to earn credentials for career advancement or personal interest would find MOOCs attractive not only because of the time and place flexibility these courses offer, but also because of the free enrollment or the very small certificate fee upon completion, as well as the emergent recognition these courses are receiving for their high quality learning delivery from leading educational institutions. Learners who enroll in a MOOC would typically need to participate in various learning activities and complete a few assessment tasks to complete the course. However, it has been commonly reported that the completion rates of MOOCs are low. Based on the common notion that when learners are more motivated to learn, they are likely to better engage in learning and have a higher likelihood to complete a MOOC, this study adopted the Motivation and Engagement Scale (MES) by Martin (2007, 2009) to collect responses from university students to examine whether positive motivation resulted in positive engagement; whether negative motivation resulted in negative engagement; and how positive or negative engagement swayed learners' inclination to complete a MOOC if they were to enroll in one. Findings show that there was a statistically significant positive relationship between positive motivation and positive engagement, between negative motivation and negative engagement, and between positive engagement and inclination to complete a MOOC. However, the relationship between negative engagement and inclination to complete a MOOC was statistically not significant. Findings of this study can be useful to MOOC providers and learners in their effort to develop strategies to increase completion rates of MOOCs.