Search

RNA binding protein (RBP) is extensively involved in various cellular regulatory processes through the interaction with RNAs. Capturing the RBP binding preferences is fundamental for revealing the pathogenesis of complex diseases. Many experimental detection techniques are still time-consuming and labor-intensive, therefore, it is indispensable to develop a computational method with convincing accuracy. In this study, we proposed a CNN-BLSTM hybrid deep learning framework, named DeepDW, for predicting the RBP binding sites on RNAs with high-order encoding features of RNA sequence and secondary structure. The high-order encoding strategy was used to characterize the dependencies among adjacency nucleotides. For CNN-BLSTM hybrid model, DeepDW firstly employed two 1-D convolutional neural networks (CNNs) for learning the local features from high-order encoded matrices of RNA sequence and structure separately, and then applied two bidirectional long short-term memory networks (BLSTMs) to capture the global information in a higher level. Moreover, a series of experiments were carried out on 31 public datasets to evaluate our proposed framework, and DeepDW achieved superior performance than the state-of-the-art methods. The results indicated that the combination of high-order encoding method and CNN-BLSTM hybrid model had advantages in identifying RBP-RNA binding sites.

Incomplete knowledge of metabolic processes impairs the accuracy of GEnome-scale Metabolic models (GEMs), hindering advancements in systems biology and metabolic engineering. To close this critical gap, we present CLOSEgaps, a machine learning-based algorithm that considers the hypergraph topology of metabolic networks and hypothetical reactions to predict missing reactions and identify gaps in GEMs. Extensive results show that CLOSEgaps accurately gap-filled metabolic networks, filling over 96% of artificially introduced gaps, and enhances the predictability of fermentation products in 24 wild-type GEMs. Furthermore, we integrate CLOSEgaps into a generalized workflow for automated metabolic network reconstruction, hereby named NICEgame, and found a notable improvement in producing four crucial metabolites (Lactate, Ethanol, Propionate, and Succinate) in two organisms. As a broadly applicable solution for any GEM or reaction, CLOSEgaps promises to enhance biotechnological and biomedical applications by improving GEM-based predictions and automating the NICEgame workflow.

A Gram-stain-negative, aerobic and rod-shaped bacterium, strain Y2R2T, was isolated from a saline–alkali soil sample collected from Binhai New Area, Tianjin, PR China. Growth of strain Y2R2T was observed at 10–45 °C (optimum, 30 °C), at pH 6.0–11.0 (optimum, pH 9.0) and in the presence of 0–15 % (w/v) NaCl (optimum, 9.0 %). Phylogenetic analysis based on 16S rRNA gene sequences showed that strain Y2R2T was affiliated with the genus Halomonas and showed the highest similarity to Halomonas huangheensis BJGMM-B45T (99.0%) and Halomonas cupida DSM 4740T (98.4%). The digital DNA–DNA hybridization and average nucleotide identity values of 21.0–22.8 % and 73.3–75.7 % with the closely related species H. huangheensis BJGMM-B45T, H. cupida DSM 4740T, H. ventosae AL12T, H. stenophila N12T and H. litopenaei SYSU ZJ2214T were lower than the threshold recommended for species discrimination. The major respiratory quinone of strain Y2R2T was Q-9 and the major cellular fatty acids consisted of C16 : 0, C19 : 0 cyclo ω8c and summed feature 8 (C18 : 1 ω7c and/or C18 : 1 ω6c). The DNA G+C content of strain Y2R2T was 57.0 mol%. On the basis of this polyphasic taxonomic study, strain Y2R2T is considered to represent a novel species of the genus Halomonas , for which the name Halomonas binhaiensis sp. nov. is proposed. The type strain is Y2R2T (=CGMCC 1.16974T=KCTC 72578T).

Introduction Consumer wearables use accelerometry and heart rate to predict sleep, the same data signals used by research-grade devices to classify sleep/wake patterns. However, metrics predicted by consumer wearables are based on black box algorithms limiting their use in research. The objective of this study was to develop algorithms for predicting sleep in children based on the raw accelerometry and heart rate data from a popular consumer wearable device, therefore bypassing the onboard black box algorithms. Methods 38 children (M=8.5 years, SD=2.4, 42% black, 61% male) underwent overnight laboratory-based polysomnography (PSG) while wearing an Apple Watch Series 7. Heart rate and accelerometry data were collected via the Apple Watch application program interface. Features extracted included (1) age, (2) heart rate, (3) y-axis offset angle, (4) y-axis angle relative to x-axis (y-angle), (5) x-axis fast Fourier transformation (FFT) 4Hz, (6) x-axis FFT 9Hz, (7) vector magnitude (VM) FFT 9Hz, (8) VM FFT 14Hz, (9) mean power dispersion (MPD), (10) bandpass filter followed by Euclidean norm/vector magnitude (BFEN), (11) dominant signal power at 0.6–2.5Hz (PMAXBAND), and (12) activity counts. Four machine learning models: logistic regression (LR), K-nearest neighbor (KNN), random forest (RF), and neural network (NN) predicted sleep or wake. Model performance was evaluated by F1 score, precision, sensitivity, and specificity. Feature importance was evaluated using the RF model. Results LR achieved the best performance according to F1 score (RF: 85.04, LR: 86.50, KNN: 82.52, NN: 85.13). LR also had the highest precision (RF:88.33, LR:88.94, KNN: 86.77, NN:87.95), and nearly the highest sensitivity (RF:84.52, LR:86.11, KNN:84.73, NN:86.55) and specificity (RF:75.72, LR:75.00, KNN:54.01, NN:60.57). BFEN was the most important feature, followed by MPD, age, PMAXBAND, VM FFT 14Hz, x-axis FFT 9Hz, heart rate, y-axis offset angle, y-angle, activity count, VM FFT 9Hz, x-axis FFT 4Hz. Conclusion Raw accelerometry data combined with heart rate data from the Apple Watch Series 7 was able to detect sleep and wake in children. Leveraging the raw accelerometry data is a viable alternative to relying on black-box algorithms for sleep classification in consumer wearable devices. Support (if any)

Pathogens producing β-lactamase pose a great challenge to antibiotic-resistant infection treatment; thus, it is urgent to discover novel β-lactamase inhibitors for drug development. Conventional high-throughput screening is very costly, and structure-based virtual screening is limited with mechanisms. In this study, we construct a novel multichannel deep neural network (DeepBLI) for β-lactamase inhibitor screening, pretrained with a label reversal KIBA data set and fine-tuned on β-lactamase-inhibitor pairs from BindingDB. First, the pairs of encoders (Conv and Att) fuse the information spatially and sequentially for both enzymes and inhibitors. Then, a co-attention module creates the connection between the inhibitor and enzyme embeddings. Finally, multichannel outputs fuse with an element-wise product and then are fed into 3-layer fully connected networks to predict interactions. Comparing the state-of-the-art methods, DeepBLI yields an AUROC of 0.9240 and an AUPRC of 0.9715, which indicates that it can identify new β-lactamase-inhibitor interactions. To demonstrate its prediction ability, an application of DeepBLI is described to screen potential inhibitor compounds for metallo-β-lactamase AIM-1 and repurpose rottlerin for four classes of β-lactamase targets, showing the possibility of being a broad-spectrum inhibitor. DeepBLI provides an effective way for antibacterial drug development, contributing to antibiotic-resistant therapeutics.

Motivation Metabolomics has developed rapidly in recent years, and metabolism-related databases are also gradually constructed. Nowadays, more and more studies are being carried out on diverse microbes, metabolites and diseases. However, the logics of various associations among microbes, metabolites and diseases are limited understanding in the biomedicine of gut microbial system. The collection and analysis of relevant microbial bioinformation play an important role in the revelation of microbe–metabolite–disease associations. Therefore, the dataset that integrates multiple relationships and the method based on complex heterogeneous graphs need to be developed. Results In this study, we integrated some databases and extracted a variety of associations data among microbes, metabolites and diseases. After obtaining the three interconnected bilateral association data (microbe–metabolite, metabolite–disease and disease–microbe), we considered building a heterogeneous graph to describe the association data. In our model, microbes were used as a bridge between diseases and metabolites. In order to fuse the information of disease–microbe–metabolite graph, we used the bipartite graph attention network on the disease–microbe and metabolite–microbe bipartite graph. The experimental results show that our model has good performance in the prediction of various disease–metabolite associations. Through the case study of type 2 diabetes mellitus, Parkinson’s disease, inflammatory bowel disease and liver cirrhosis, it is noted that our proposed methodology are valuable for the mining of other associations and the prediction of biomarkers for different human diseases. Availability and implementation: https://github.com/Selenefreeze/DiMiMe.git

A new kind of photolabile protecting group (PLPG) for carboxyl moieties was designed and synthesized as the linker between resin and peptide. This group can be used for the protection of amino acid carboxyl groups. The peptide was synthesized on Nph (2‐hydroxy‐3‐(2‐nitrophenyl)‐heptanoic acid)‐derivatized resins and could be cleaved under UV exposure, thus avoiding the necessity for harsh acid‐mediated resin cleavage. The PLPG has been successfully used for solid‐phase synthesis of peptides., A photolabile protecting group (PLPG) for carboxyl derivatives was designed and synthesized. The newly derived molecule can be used for the protection of amino acid carboxyl groups and can be removed rapidly under the condition of 365 nm.

In this work, a series of novel benzyl naphthyl sulfoxides/sulfones derived from Rigosertib were designed and synthesized as potential antitumor agents. The in vitro cytotoxicity against four human cancer cell lines (HeLa, MCF-7, HepG2 and SCC-15) and two normal human cell lines (HUVEC and 293T) indicated that some of the sulfones and sulfoxides possessed potent antineoplastic activity that reached nanomolar levels and relatively low toxicity to normal cells. Among them, (2-methoxy-5-((naphthalen-2-ylsulfonyl)methyl)phenyl)glycine (15b) was found to be a promising antitumor drug candidate that could significantly inhibit tumor cell migration and induce tumor cell apoptosis via the p53-Bcl-2-Bax signaling pathway at nanomolar concentrations.

openaire: EC/H2020/101016775/EU//INTERVENE Identifying DNA N4-methylcytosine (4mC) sites is of great significance in biological research, such as chromatin structure, DNA stability, DNA-protein interaction and controlling gene expression. However, the traditional sequencing technology to identify 4mC sites is very time-consuming. In order to detect 4mC sites, we develop a multi-view learning method for achieving more effectively via merging multiple feature spaces. Furthermore, we think about whether the multi-view learning method can improve the across species classification ability by fusing data of multiple species. In our study, we propose a multi-view Laplacian kernel sparse representation-based classifier, called MvLapKSRC-HSIC. First, we make use of three feature extraction methods (PSTNP, NCP, DPP) to extract the DNA sequence features. MvLapKSRC-HSIC uses a kernel sparse representation-based classifier with graph regularization. In order to maintain the independence between various views, we add a multi-view regularization term constructed by Hilbert-Schmidt independence criterion (HSIC). In the experiments, MvLapKSRC-HSIC is applied on six datasets, so as to compare with other popular methods in single species and cross-species experiments. All experimental results show that MvLapKSRC-HSIC is superior to other outstanding methods on both single species and cross-species. Importantly, MvLapKSRC-HSIC can identify a series of potential DNA 4mC sites, which have not yet been experimentally evaluate on multiple species and merit further research.

Nowadays, the success of drilling operations has become more sensitive to cost control. As one of the most effective cost reductions is the elimination of extra trips, real-time bit wear prediction has been a challenge for appropriate decision-making in drilling operations to reach the highest drilling performance and avoid serious bit issues. This present study shows the combination of mechanical specific energy (MSE), principal component analysis (PCA) and wavelet analysis in real-time bit wear prediction with mud logger data for the Kymera bit. The aforementioned mathematical approaches were combined with the traditional MSE method to monitor the drilling efficiency, especially to eliminate the loss of cone. Novel analytical approaches like PCA and wavelet analysis were introduced to predict the real-time bit wear grade for PDC part of the Kymera bit. Two field cases with several applications were selected for this study to illustrate the advantages of the aforementioned methods and demonstrate their efficiency in bit wear prediction. The first case study shows the bit wear prediction at pulling depth as Grade 5, while the actual dull grade of PDC part is Grade 5-5 without losing the cone. The second case study shows the bit wear prediction at the pulling depth is Grade 1, while the actual dull grade of PDC part is Grade 1-1 without losing the cone. The novelty of this bit wear prediction model is the ability to predict the Kymera bit wear in real time using mathematical approaches with practical applications and valid results in the mentioned study area.

Background Identifying potential associations between genes and diseases via biomedical experiments must be the time-consuming and expensive research works. The computational technologies based on machine learning models have been widely utilized to explore genetic information related to complex diseases. Importantly, the gene-disease association detection can be defined as the link prediction problem in bipartite network. However, many existing methods do not utilize multiple sources of biological information; Additionally, they do not extract higher-order relationships among genes and diseases. Results In this study, we propose a novel method called Dual Hypergraph Regularized Least Squares (DHRLS) with Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL), in order to detect all potential gene-disease associations. First, we construct multiple kernels based on various biological data sources in gene and disease spaces respectively. After that, we use CAK-MKL to obtain the optimal kernels in the two spaces respectively. To specific, hypergraph can be employed to establish higher-order relationships. Finally, our DHRLS model is solved by the Alternating Least squares algorithm (ALSA), for predicting gene-disease associations. Conclusion Comparing with many outstanding prediction tools, DHRLS achieves best performance on gene-disease associations network under two types of cross validation. To verify robustness, our proposed approach has excellent prediction performance on six real-world networks. Our research work can effectively discover potential disease-associated genes and provide guidance for the follow-up verification methods of complex diseases.

Predicting associations in drug–disease network provides effective information for the drug repositioning. Therefore, it is an important task to develop an effective drug–disease association prediction method. In this paper, we propose a model, called the Multiple Kernel-based Dual Graph Regularized Least Squares Model (MKDGRLS), to predict potential drug–disease associations. First, we calculate the multiple kernels of drug and disease spaces respectively. Moreover, we utilize multiple kernels and related Laplacian regularization terms to construct MKDGRLS. Finally, we solve the object function of MKDGRLS by the Alternating Least squares algorithm (ALSA), for predicting drug–disease associations. Simultaneously, we design a variant MKDGRLS-A, which is added one dimension as the bias for compensate the inexact solution of linear systems. Our proposed approach has better prediction performance than existing prediction tools under two types of cross validation on the three real drug–disease association network datasets. The results of the case studies of the two diseases prove that our model can effectively predict new associations. We also test MKDGRLS on six real-world networks and it achieves better performance than other methods. In conclusion, our research work can effectively discover potential drug–disease associations, and can provide effective help for related drug repositioning work.

It has been reported that vitexin has anti-inflammatory effects in osteoarthritis (OA) rats. However, the effects of vitexin on interleukins-1β (IL-1β)-stimulated OA patient-derived chondrocytes have not been reported. The purpose of this study was to investigate the anti-inflammatory effects of vitexin on IL-1β-stimulated human osteoarthritis chondrocytes and to reveal the involvement of hypoxia-inducible factor 1α (HIF-1α) pathway. Enzyme-linked immunosorbent assay, quantitative real-time PCR and Western blotting assays were employed. ELISA results demonstrated that the proinflammatory cytokine levels of interleukins-6 (IL-6) and tumour necrosis factor α (TNF-α) in the serum and synovial fluid and HIF-1α level in the synovial fluid were significantly elevated in OA patients compared to normal healthy subjects. Moreover, the Western blotting results indicated that the protein expression of HIF-1α was significantly higher in the cartilage tissues of OA patients. OA patient-derived chondrocytes were stimulated by IL-1β and treated with different concentration of vitexin for 24 hours. Vitexin showed no cytotoxicity and increased the survival of chondrocytes under IL-1β stimulation. Vitexin suppressed IL-1β-induced production of NO and prostaglandin E2 (PGE2 ) in chondrocytes culture. The treatment of vitexin significantly inhibited IL-1β-induced expressions of proinflammatory cytokine levels of IL-6, TNF-α, matrix metalloproteinase (MMP)-1, MMP-3 and MMP-13. Furthermore, Western blotting results demonstrated that HIF-1α is involved in vitexin's protective effects on IL-1β-stimulated injuries in OA patient-derived chondrocytes. Our study demonstrates that vitexin alleviates IL-1β-induced inflammatory responses in chondrocytes from osteoarthritis patients, which may be attributed partly to the inhibition of HIF-1α pathway.

Sphingomonas sanxanigenens, a new species of the genus Sphingomonas, synthesizes extracellular biopolymer termed sanxan. Sanxan polysaccharide was purified from the fermentation broth by Sephacryl S-400 column chromatography. The molecular weight of sanxan polysaccharide was 408kDa by the method of size-exclusion chromatography combined with laser light scattering. Based on FT-IR, periodate oxidation, Smith degradation, composition analysis and nuclear magnetic resonance experiments, the structure of sanxan polysaccharide was elucidated as follows: The solution of sanxan polysaccharide showed properties of high viscosity and shear-thinning. By cooling hot solutions, sanxan polysaccharide could form elastic thermoreversible gel.

Objective: To examine protein changes in the hippocampus of APP/PS1 transgenic mice after blueberry extracts (BB) intervention.Methods: Eight APP/PS1 transgenic mice were randomly assigned to Alzheimer's disease (AD)+BB group (n=4) and AD+control group (n=4). After a 16-week treatment, 2-DE and MALDI-TOF-MS were used to compare the proteomic profiles of the hippocampus in the two groups and Western blot was used to confirm the important differentially expressed proteins.Results: Twelve proteins were differentially expressed between the two groups. Nine of them were identified. Cytochrome b-c1 complex subunit 6, beta-actin, dynamin 1, and heat shock cognate 71 were up-regulated in AD+BB group, while a-enolase, stress-induced-phosphoprotein 1, malate dehydrogenase (MDH), MDH 1, and T-complex protein 1 subunit beta were down-regulated, respectively. Importantly, some of the identified proteins (e.g. dynamin 1) are known to be involved in cognitive impairment. Western blot analysis of hippocampus dynamin 1 expression confirmed the proteomic findings.Conclusions: The consumption of BB modulates the expression of proteins that are linked to the improvements of cognitive dysfunction in hippocampus of APP/PS1 transgenic mice.

Mulberry, which contained high amounts of anthocyanins, has been used in traditional Chinese medicine. Mulberry fruit extracts (ME) have demonstrated the antioxidant activity and neuroprotection. The study was to investigate the neuroprotective efficacy of ME againstβ-amyloid 25–35- (Aβ25–35-) induced PC12 cells injury. Cells preincubated with or without ME (200 μg/mL) for 24 h were treated with Aβ25–35(20 μmol/L) for another 24 h. Cell viability was assessed by MTT, gene expression profiles were examined by cDNA microarrays, and RT-PCR were used to confirm the results of microarray assays. ME pretreatment was found to neutralize the cytotoxicity and prevent Aβ25–35-induced cells injury. Analyses of gene expression profile revealed that genes involving cell adhesion, peptidase activity, cytokine activity, ion binding activity, and angiogenesis regulation were significantly modulated by ME pretreatment. Among those genes, Apaf1, Bace2, and Plcb4 were enriched in the “Alzheimer’s disease-reference pathway” and downregulated after ME intervention. RT-PCR results showed that ME preincubation could significantly inhibit Aβ25–35increased mRNA levels of these three genes. Overall, ME pretreatment could substantially alleviate PC12 cells injury and downregulate expression of AD-related genes, such as Apaf1, Bace2, and Plcb4. This study has a great nutrigenomics interest and brings new and important light in the field of AD intervention.

BACKGROUND Blueberry (BB) can provide a wide range of antioxidant benefits for AD. There is evidence that BB extracts could improve brain functions. However, the details are still unknown. OBJECTIVE In the present study, we aimed to investigate the possible mechanism involved in the improvement of learning and memory capacity from BB extracts in AD. METHODS APP/PS1 transgenic mice were fed BB extracts for 16 weeks. The capacity of learning and memory was assessed by Morris water maze (MWM) test, and long-term potentiation (LTP) was determined to evaluate hippocampal neuronal plasticity at the end of administration. Pathological changes in the brain were observed, and the expressions of brain-derived neurotrophic factor (BDNF) and extracellular signal-related kinase (ERK1/2) were determined to explore the mechanism of BB extract-induced benefits. RESULTS AD mice exhibited more difficulties to learn and remember the exact position of the platform in the MWM test. The data showed that AD mice lacked effective learning in the platform search. In contrast, AD mice exhibited better performance both in the training phase and probe test of MWM after the BB treatment. Moreover, LTP was enhanced and the neuron loss was alleviated with BB treatment, while we did not find any obvious effect on the elimination of amyloid-β. In the AD mice, the expression of ERK1/2 was significantly increased (p

Although Zinc depletion induces apoptosis in different cells and tissues, exact mechanism of this action of zinc depletion is not completely understood. In our previous study, the results suggested that the significant down-regulation of MEK/ERK signaling pathway was observed in zinc deficiency neurons. Here, we investigate whether, in hippocampal neurons, this increased rate of apoptosis induced by zinc depletion is the result of hypophosphorylation of ERK pathway. In this study, we found that NGF, ERK agonist, prevented neurons against TPEN-induced apoptosis, whereas TPEN-induced apoptosis was potentiated by U0126, inhibitors of ERK. Moreover, TPEN-induced caspase-3 activity was further increased by the pretreatment with U0126, but it was further decreased by the pretreatment with NGF. However, pretreatment of the cells with U0126 or NGF had no effect on the changes of Bcl-2 and Bax protein expression induced by zinc depletion. Thus, the results indicate that TPEN induces apoptosis of hippocampal neurons through inhibition of ERK and, in turn, activation of caspase-3

Microbe Abstract. In this paper, different proportion of activated carbon was added to the Rehmannia glutinosa continuous cropping soils including first and second stubble to adjust the phenolic acids content and microbial biomass and improve the soil environment after continuous cropping. Results showed that vanillic acid, vanillin and ferulic acid in the continuous cropping soils were all somewhat decreased after a growth cycle, half a year. The ratio of fungi to bacteria was increased in the soil cropped Rehmannia glutinosa for one year, while decreased in the second stubble soil after addition of activated carbon. Taken together, the effect, 0.8 percent addition of activated carbon to the soil cropped Rehmannia glutinosa for two years, was the best.

An experiment was performed to observe the changes in Raf-1 kinase/mitogen-activated protein kinase ERK (MEK)/extracellular signal-regulated kinase (ERK) signaling pathways in cultured hippocampal neurons and its correlation with neurons apoptosis induced by intracellular zinc depletion. Cultured hippocampal neurons were exposed to a cell membrane-permeant zinc chelator TPEN (2 µM), and to TPEN plus zinc sulfate (5 µM) for 24 h. Cultures were then processed to detect neuronal viability by the methyl thiazolyl tetrazolium assay, while apoptosis rate was simultaneously observed by the flow cytometric analysis. Caspase-3, Raf-1, pMEK, pERK1/2, and pCREB protein levels were examined by Western blot assays. The viability in TPEN-incubated neurons was notably decreased, apoptosis rate and expression of caspase-3 significantly increased compared to untreated controls. The significant down-regulation of Raf/MEK/ERK signaling pathway and expression of pCREB were decreased in TPEN-treated neurons. Co-addition of zinc almost completely reversed TPEN-induced alterations described. The results demonstrated zinc-modulated apoptosis and the expression of Raf/MEK/ERK at the protein level in hippocampal neurons. It is possible that zinc depletion-induced apoptosis in cultured hippocampal neurons may be relevant to the changes of Raf/MEK/ERK signaling pathway.

Relationships between hyperhomocysteinemia (HHE) and neurodegenerative diseases have been widely studied. However, the impact of serum total homocysteine (tHcy) levels on cognitive function has not been confirmed. C677T polymorphisms in the 5,10-methylenetetrahydrofolate reductase (MTHFR) gene have impacts on tHcy level; it is suspected to influence cognitive function, but only few investigations have assessed its effects on non-dementia adults and the results have been controversial. Moreover, there is no report about Chinese subjects. In the present study, we determined C677T/MTHFR genotype, serum tHcy concentration and cognition in 182 nondemented subjects aged 55-88 years to probe the associations between MTHFRC677T mutation, increased tHcy levels and decreased cognitive function in a northern city in China. A serum tHcy levelor = 16 micromol/l was deemed HHE. Cognitive function was assessed by the Mini Mental State Examination (MMSE) and Basic Cognitive Aptitude Tests (BCAT). Results showed that: (i) subjects with the T allele had higher serum tHcy levels than those without, especially in lower folate status; (ii) T allele and CT/TT genotype frequencies in subjects with HHE were higher than in non-HHE subjects (P0.05); and (iii) serum tHcy level was inversely related to total BCAT score (P0.05) but MTHFR677 C to T polymorphism had no association with it. Our results confirmed that the MTHFR 677 C to T mutation, especially in lower serum folate concentration status, results in the increase of serum tHcy levels which is bad for cognitive function and indicates that higher serum folate level is of benefit in keeping lower serum tHcy level and better cognitive function. The results provide some valuable clues for individualized nutrition intervention of HHE and cognition decline in the middle-aged and the elderly.

Security is an important issue in the field of wireless channel. In this paper, the security problem of Gaussian MIMO wireless hybrid channel is considered where a transmitter with multiple antennas sends information to an intended receiver with one antenna in the presence of an eavesdropper with multiple antennas. Through utilizing some of the power to produce ‘artificial noise’, the transmitter can only degrade the eavesdropper's channel to ensure the security of the communication. But there is an inherent weakness in this scheme. Then a Hybrid Blind Space Elimination (HBSE) scheme is proposed and proved to fix the design flaw in order to strengthen the original scheme.

Objective: An intervention study was performed to determine if supplement containing folic acid, vitamin B6, and vitamin B12 could improve cognitive function and lower homocysteine in middle-aged and elderly patients with hyperhomocysteinemia.Methods: One hundred and four participants with hyperhomocysteinemia were recruited in Tianjin, China, aged 55–94 years old. Fifty-seven individuals with hyperhomocysteinemia were included in the intervention group (vitamin B group, which received 800 µg/day of folate, with 10 mg of vitamin B6 and 25 µg of vitamin B12) and 47 patients in the placebo group. The endpoint was the improvement in cognitive function as evaluated by Basic Cognitive Aptitude Tests (BCATs). All parameters were measured before and after the treatment period of 14 weeks.Results: The BCAT total score and four sub-tests scores (digit copy, Chinese character rotation, digital working memory, and recognition of meaningless figure) of BCAT at 14 weeks significantly increased only for the vitamin B g...

To investigate the neuroprotective effect of mulberry extracts (ME) against Abeta25-35-induced injury in cultured PC12 cells and explore the possible mechanisms involved.PC12 cells at logarithmic growth phase were divided into the normal control group, Abeta25-35 group (20 micromol/L for 24h) and ME pretreating groups at the concentrations of 25, 50, 100, 200, 400, 800 microg/ml. Cell viability was determined by MTT assay. On that basis, PC12 cells were divided into the normal control group, ME group (200 microg/ml ME incubation alone for 24h), Abeta25-35 group (20 micromol/L for 24h) and ME plus Abeta25-35 group (after pretreatment with 200 microg/ml ME for 24h, cells were exposed to 201 micromol/L Abeta25-35 for another 24h). Intracellular reactive oxidative species (ROS) was measured by using the fluorescent DCFH-DA and the apoptotic cells were observed by Hoechst33342 staining method.(1) The results showed that exposure of PC12 cells to Abeta22-35 (20 micromol/L) for 24h induced notably neuronal injury (P0.05) as compared with the control group, while pretreatment with 100 or 200 microg/ml ME almost completely reversed Abeta25-35 induced neuronal injury (P0.05). (2) There were no remarkable changes in ROS levels and apoptosis rate of ME group as compared with the control group (P0.05). Compared with the control group, exposure to 20 micromol/L Abeta25-35 for 24h resulted in a significant decrease in cell viability (P0.05) while increase in ROS levels and cell apoptosis rate (P0.05). But pretreatment of PC12 cells with 200 microg/ml ME could counteract ROS formation and inhibit apoptosis (P0.05).These results suggest that mulberry extracts rich in phenolics and anthocyanins could alleviate Abeta25-35-induced injury in PC12 cells, which might be associated with the antioxidative and antiapoptosis effects.

Zinc supplementation can help maintain learning and memory function in rodents. In this study, we hypothesized that zinc supplementation could antagonize the neurotoxicity induced by aluminum in rats. Animals were fed a diet containing different doses of zinc (50, 100, 200 mg/kg) for 9 weeks, and orally administered aluminum chloride (300 mg/kg daily) from the third week for 7 consecutive weeks. Open-field behavioral test results showed that the number of rearings in the group given the 100 mg/kg zinc supplement was significantly increased compared with the group given the 50 mg/kg zinc supplement. Malondialdehyde content in the cerebrum was significantly decreased, while dopamine and 5-hydroxytryptamine levels were increased in the groups given the diet supplemented with 100 and 200 mg/kg zinc, compared with the group given the diet supplemented with 50 mg/kg zinc. The acetylcholinesterase activity in the cerebrum was significantly decreased in the group given the 100 mg/kg zinc supplement. Hematoxylin-eosin staining revealed evident pathological damage in the hippocampus of rats in the group given the diet supplemented with 50 mg/kg zinc, but the damage was attenuated in the groups given the diet supplemented with 100 and 200 mg/kg zinc. Our findings suggest that zinc is a potential neuroprotective agent against aluminum-induced neurotoxicity in rats, and the optimal dosages are 100 and 200 mg/kg.