Alpha-1 antitrypsin is a potential biomarker for hepatitis B

<p>Abstract</p> <p>Background</p> <p>Function exertion of specific proteins are key factors in disease progression, thus the systematical identification of those specific proteins is a prerequisite to understand various diseases. Though many proteins have been verified to impact on hepatitis, no systematical protein screening has been documented to hepatitis B virus (HBV) induced hepatitis, hindering the comprehensive understanding to this severe disease.</p> <p>Aim</p> <p>To identify the major proteins in the progression of HBV infection from mild stage to severe stage.</p> <p>Methods</p> <p>We performed an integrated strategy by combining two-dimensional electrophoresis (2-DE), peptide mass fingerprinting (PMF) analysis, and tissue microarray techniques to screen the functional proteins and detect the localization of those proteins.</p> <p>Results</p> <p>Interestingly, MS/MS identification revealed the expression level of alpha-1 antitrypsin (AAT) was significantly elevated in serum samples from patients with severe chronic hepatitis. Immunoblotting with a specific AAT antibody confirmed that AAT is highly expressed in serum samples from patients with hepatic carcinoma and severe chronic hepatitis. Furthermore, we observed that AAT is with highest expression in normal tissue and cells, but lowest in hepatic carcinoma and severe chronic hepatitis tissues and cells, suggesting the specific secretion of AAT from tissues and cells to serum.</p> <p>Conclusion</p> <p>These results suggest the possibility of AAT as a potential biomarker for hepatitis B in diagnosis.</p

Effects of waterlogging and elevated salinity on the allocation of photosynthetic carbon in estuarine tidal marsh: a mesocosm experiment

Embargo until September 10, 2023Background and aim Coastal marshes and wetlands hosting blue carbon ecosystems have shown vulnerability to sea-level rise (SLR) and its consequent effects. In this study, we explored the effects of waterlogging and elevated salinity on the accumulation and allocation of photosynthetic carbon (C) in a widely distributed species in marsh lands. Methods The plant–soil mesocosms of Phragmites australis were grown under waterlogging and elevated salinity conditions to investigate the responses of photosynthetic C allocation in different C pools (plant organs and soils) based on 13CO2 pulse-labeling technology. Results Both waterlogging and elevated salinity treatments decreased photosynthetic C fixation. The hydrological treatments also reduced 13C transport to the plant organs of P. australis while significantly increased 13C allocation percentage in roots. Waterlogging and low salinity had no significant effects on 13C allocation to rhizosphere soils, while high salinity (15 and 30 ppt) significantly reduced 13C allocation to soils, indicating a decreased root C export in saline environments. Waterlogging enhanced the effects of salinity on the 13C allocation pattern, particularly during the late growing season. The responses of flooding and elevated salinity on C allocation in plant organs and rhizosphere soils can be related to changes in nutrient, ionic concentrations and microbial biomass. Conclusion The adaptation strategy of P. australis led to increased C allocation in belowground organs under changed hydrology. Expected global SLR projection might decrease total C stocks in P. australis and alter the C allocation pattern in marsh plant-soil systems, due to amplified effects of flooding and elevated salinities.acceptedVersio

InterFace:Adjustable Angular Margin Inter-class Loss for Deep Face Recognition

In the field of face recognition, it is always a hot research topic to improve the loss solution to make the face features extracted by the network have greater discriminative power. Research works in recent years has improved the discriminative power of the face model by normalizing softmax to the cosine space step by step and then adding a fixed penalty margin to reduce the intra-class distance to increase the inter-class distance. Although a great deal of previous work has been done to optimize the boundary penalty to improve the discriminative power of the model, adding a fixed margin penalty to the depth feature and the corresponding weight is not consistent with the pattern of data in the real scenario. To address this issue, in this paper, we propose a novel loss function, InterFace, releasing the constraint of adding a margin penalty only between the depth feature and the corresponding weight to push the separability of classes by adding corresponding margin penalties between the depth features and all weights. To illustrate the advantages of InterFace over a fixed penalty margin, we explained geometrically and comparisons on a set of mainstream benchmarks. From a wider perspective, our InterFace has advanced the state-of-the-art face recognition performance on five out of thirteen mainstream benchmarks. All training codes, pre-trained models, and training logs, are publicly released \footnote{$https://github.com/iamsangmeng/InterFace$}.Comment: arXiv admin note: text overlap with arXiv:2109.09416 by other author

Toll-like receptor 2 -196 to -174 del polymorphism influences the susceptibility of Han Chinese people to Alzheimer's disease

<p>Abstract</p> <p>Background</p> <p>Toll-like receptor 2 (<it>TLR2</it>) represents a reasonable functional and positional candidate gene for Alzheimer's disease (AD) as it is located under the linkage region of AD on chromosome 4q, and functionally is involved in the microglia-mediated inflammatory response and amyloid-β clearance. The -196 to -174 del polymorphism affects the <it>TLR2 </it>gene and alters its promoter activity.</p> <p>Methods</p> <p>We recruited 800 unrelated Northern Han Chinese individuals comprising 400 late-onset AD (LOAD) patients and 400 healthy controls matched for gender and age. The -196 to -174 del polymorphism in the <it>TLR2 </it>gene was genotyped using the polymerase chain reaction (PCR) method.</p> <p>Results</p> <p>There were significant differences in genotype (P = 0.026) and allele (P = 0.009) frequencies of the -196 to -174 del polymorphism between LOAD patients and controls. The del allele was associated with an increased risk of LOAD (OR = 1.31, 95% CI = 1.07-1.60, Power = 84.9%). When these data were stratified by apolipoprotein E (<it>ApoE</it>) ε4 status, the observed association was confined to <it>ApoE </it>ε4 non-carriers. Logistic regression analysis suggested an association of LOAD with the polymorphism in a recessive model (OR = 1.64, 95% CI = 1.13-2.39, Bonferroni corrected P = 0.03).</p> <p>Conclusions</p> <p>Our data suggest that the -196 to -174 del/del genotype of <it>TLR2 </it>may increase risk of LOAD in a Northern Han Chinese population.</p

Accelerating dynamic graph analytics on GPUs

As graph analytics often involves compute-intensive operations, GPUs have been extensively used to accelerate the processing. However, in many applications such as social networks, cyber security, and fraud detection, their representative graphs evolve frequently and one has to perform a rebuild of the graph structure on GPUs to incorporate the updates. Hence, rebuilding the graphs becomes the bottleneck of processing high-speed graph streams. In this paper, we propose a GPU-based dynamic graph storage scheme to support existing graph algorithms easily. Furthermore, we propose parallel update algorithms to support efficient stream updates so that the maintained graph is immediately available for high-speed analytic processing on GPUs. Our extensive experiments with three streaming applications on large-scale real and synthetic datasets demonstrate the superior performance of our proposed approach.</jats:p

SaPt-CNN-LSTM-AR-EA: a hybrid ensemble learning framework for time series-based multivariate DNA sequence prediction

Biological sequence data mining is hot spot in bioinformatics. A biological sequence can be regarded as a set of characters. Time series is similar to biological sequences in terms of both representation and mechanism. Therefore, in the article, biological sequences are represented with time series to obtain biological time sequence (BTS). Hybrid ensemble learning framework (SaPt-CNN-LSTM-AR-EA) for BTS is proposed. Single-sequence and multi-sequence models are respectively constructed with self-adaption pre-training one-dimensional convolutional recurrent neural network and autoregressive fractional integrated moving average fused evolutionary algorithm. In DNA sequence experiments with six viruses, SaPt-CNN-LSTM-AR-EA realized the good overall prediction performance and the prediction accuracy and correlation respectively reached 1.7073 and 0.9186. SaPt-CNN-LSTM-AR-EA was compared with other five benchmark models so as to verify its effectiveness and stability. SaPt-CNN-LSTM-AR-EA increased the average accuracy by about 30%. The framework proposed in this article is significant in biology, biomedicine, and computer science, and can be widely applied in sequence splicing, computational biology, bioinformation, and other fields

2-Amino­pyridinium 1-phenyl­cyclo­propane-1-carboxyl­ate

In the title salt, C5H7N2 +·C10H9O2 −, 2-amino­pyridine and 1-phenyl­cyclo­propane-1-carb­oxy­lic acid crystallize together, forming a 2-amino­pyridinium–carboxyl­ate supra­molecular heterosynthon involving two N—H⋯O hydrogen bonds, which in turn dimerizes to form a four-component supra­molecular unit also sustained by N—H⋯O hydrogen bonding. A C—H⋯π inter­action between a pyridine C—H group and the centroid of the phenyl ring of the anion further stabilizes the four-component supra­molecular unit. The overall crystal packing also features C—H⋯O inter­actions

Context-dependent pro- and anti-resection roles of ZKSCAN3 in the regulation of fork processing during replication stress

Uncontrolled resection of replication forks under stress can cause genomic instability and influence cancer formation. Extensive fork resection has also been implicated in the chemosensitivity of BReast CAncer gene BRCA-deficient cancers. However, how fork resection is controlled in different genetic contexts and how it affects chromosomal stability and cell survival remains incompletely understood. Here, we report a novel function of the transcription repressor ZKSCAN3 in fork protection and chromosomal stability maintenance under replication stress. We show disruption of ZKSCAN3 function causes excessive resection of replication forks by the exonuclease Exo1 and homologous DNA recombination/repair protein Mre11 following fork reversal. Interestingly, in BRCA1-deficient cells, we found ZKSCAN3 actually promotes fork resection upon replication stress. We demonstrate these anti- and pro-resection roles of ZKSCAN3, consisting of a SCAN box, Kruppel-associated box, and zinc finger domain, are mediated by its SCAN box domain and do not require the Kruppel-associated box or zinc finger domains, suggesting that the transcriptional function of ZKSCAN3 is not involved. Furthermore, despite the severe impact on fork structure and chromosomal stability, depletion of ZKSCAN3 did not affect the short-term survival of BRCA1-proficient or BRCA1-deficient cells after treatment with cancer drugs hydroxyurea, PARPi, or cisplatin. Our findings reveal a unique relationship between ZKSCAN3 and BRCA1 in fork protection and add to our understanding of the relationships between replication fork protection, chromosomal instability, and chemosensitivity

Frequency and longitudinal clinical outcomes of Alzheimer's AT(N) biomarker profiles: A longitudinal study.

INTRODUCTION: We aimed to estimate the frequency of each AT(N) (β-amyloid deposition [A], pathologic tau [T], and neurodegeneration [N]) profile in different clinical diagnosis groups and to describe the longitudinal change in clinical outcomes of individuals in each group. METHODS: Longitudinal change in clinical outcomes and conversion risk of AT(N) profiles are assessed using linear mixed-effects models and multivariate Cox proportional-hazard models, respectively. RESULTS: Participants with A+T+N+ showed faster clinical progression than those with A-T-N- and A+T±N-. Compared with A-T-N-, participants with A+T+N± had an increased risk of conversion from cognitively normal (CN) to incident prodromal stage of Alzheimer's disease (AD), and from MCI to AD dementia. A+T+N+ showed an increased conversion risk when compared with A+T±N-. DISCUSSION: The 2018 research framework may provide prognostic information of clinical change and progression. It may also be useful for targeted recruitment of participants with AD into clinical trials