Miracle-dz/UniFAR-FTUs huggingface dataset:
Summer shifts of bacterial communities associated with the invasive brown seaweed Sargassum muticum are location and tissue dependent

Seaweed-associated microbiota experience spatial and temporal shifts in response to changing environmental conditions and seaweed physiology. These shifts may result in structural, functional and behavioral changes in the host with potential consequences for its fitness. They, thus, may help the host to adapt to changing environmental conditions. The current knowledge of seasonal variation of seaweed-associated microbiota is however still limited. In this study, we explored temporal and spatial variation of microbial communities associated with the invasive brown seaweed S. muticum. We sampled in northern and southern Portugal, in September, March and July-August (summer). In addition, as (pseudo-)perennial seaweeds display seasonal reproductive phenology, we sampled various parts of the individuals to disentangle the effect of temporal changes from those due to structural development variations. The diversity and structure of associated microbial communities were determined using next generation sequencing of the variable regions V5-7 of the 16S rDNA. We expected to find differentiation in associated microbial communities between regions and sampling months, but with differences depending on the seaweed structure examined. As expected, the study revealed substantial temporal shifts in S. muticum microbiome, for instance with large abundance of Rhodobacteraceae and Loktanella in September-March but prevalence of Pirellulales during the summer months. Variations between regions and tissues were also observed: in northern Portugal and on basal structures, bacterial diversity was higher as compared to the South and apical parts. All examined seaweed structures showed temporal differences in associated microbial community structure over time, except for holdfasts between September and March. Bacteria contributing to these changes varied spatially. Conversely to all other structures, the holdfast also did not show differences in associated community structure between southern and northern regions. Our study highlights the importance of structural microscale differentiations within seaweeds hosts with regard to their associated microbial communities and their importance across temporal and spatial dimensions.
How do reference montage and electrodes setup affect the measured scalp EEG potentials?

Objective. Human scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time resolution. However, the representativeness of the measured EEG potentials for the underneath neural activities is still a problem under debate. This study aims to investigate systematically how both reference montage and electrodes setup affect the accuracy of EEG potentials. Approach. First, the standard EEG potentials are generated by the forward calculation with a single dipole in the neural source space, for eleven channel numbers (10, 16, 21, 32, 64, 85, 96, 128, 129, 257, 335). Here, the reference is the ideal infinity implicitly determined by forward theory. Then, the standard EEG potentials are transformed to recordings with different references including five mono-polar references (Left earlobe, Fz, Pz, Oz, Cz), and three re-references (linked mastoids (LM), average reference (AR) and reference electrode standardization technique (REST)). Finally, the relative errors between the standard EEG potentials and the transformed ones are evaluated in terms of channel number, scalp regions, electrodes layout, dipole source position and orientation, as well as sensor noise and head model. Main results. Mono-polar reference recordings are usually of large distortions; thus, a re-reference after online mono-polar recording should be adopted in general to mitigate this effect. Among the three re-references, REST is generally superior to AR for all factors compared, and LM performs worst. REST is insensitive to head model perturbation. AR is subject to electrodes coverage and dipole orientation but no close relation with channel number. Significance. These results indicate that REST would be the first choice of re-reference and AR may be an alternative option for high level sensor noise case. Our findings may provide the helpful suggestions on how to obtain the EEG potentials as accurately as possible for cognitive neuroscientists and clinicians.
Monoclonal antibody against human Tim‐3 enhances antiviral immune response

T cell immunoglobulin and mucin domain protein 3 (Tim‐3) is an immune checkpoint inhibitor in T cells and innate immune cells. The deregulated upregulation of Tim‐3 is related to immune exhaustion in tumour and viral infection. To overcome Tim‐3‐mediated immune tolerance, we developed a novel monoclonal antibody against human Tim‐3 (L3G) and investigated its roles in inhibiting Tim‐3 signalling and overcoming immune tolerance in T cells and monocytes/macrophages. The administration of L3G to cultured peripheral blood mononuclear cells (PBMCs) significantly increased the production of IFN‐γ and IL‐2 and the expression of type I interferon. The administration of L3G also increased the production of IFN‐γ, IL‐8 and type I interferon in U937 cells and primary monocytes. We investigated the mechanisms by which L3G enhances pro‐inflammatory cytokine expression, and our data show that L3G enhances STAT1 phosphorylation in both monocytes/macrophages and T cells. Finally, in an H1N1 infection model of PBMCs and U937 cells, L3G decreased the viral load and enhanced the expression of interferon. Thus, we developed a functional antibody with therapeutic potential against Tim‐3‐mediated infection tolerance.
Enzymatic lipid oxidation by eosinophils propagates coagulation, hemostasis, and thrombotic disease

Blood coagulation is essential for physiological hemostasis but simultaneously contributes to thrombotic disease. However, molecular and cellular events controlling initiation and propagation of coagulation are still incompletely understood. In this study, we demonstrate an unexpected role of eosinophils during plasmatic coagulation, hemostasis, and thrombosis. Using a large-scale epidemiological approach, we identified eosinophil cationic protein as an independent and predictive risk factor for thrombotic events in humans. Concurrent experiments showed that eosinophils contributed to intravascular thrombosis by exhibiting a strong endogenous thrombin-generation capacity that relied on the enzymatic generation and active provision of a procoagulant phospholipid surface enriched in 12/15-lipoxygenase–derived hydroxyeicosatetraenoic acid–phosphatidylethanolamines. Our findings reveal a previously unrecognized role of eosinophils and enzymatic lipid oxidation as regulatory elements that facilitate both hemostasis and thrombosis in response to vascular injury, thus identifying promising new targets for the treatment of thrombotic disease.
Secretomes of apoptotic mononuclear cells ameliorate neurological damage in rats with focal ischemia

The pursuit of targeting multiple pathways in the ischemic cascade of cerebral stroke is a promising treatment option. We examined the regenerative potential of conditioned medium derived from rat and human apoptotic mononuclear cells (MNC), rMNC apo sec and hMNC apo sec, in experimental stroke. We performed middle cerebral artery occlusion on Wistar rats and administered apoptotic MNC-secretomes intraperitoneally in two experimental settings. Ischemic lesion volumes were determined 48 hours after cerebral ischemia. Neurological evaluations were performed after 6, 24 and 48 hours. Immunoblots were conducted to analyze neuroprotective signal-transduction in human primary glia cells and neurons. Neuronal sprouting assays were performed and neurotrophic factors in both hMNC apo sec and rat plasma were quantified using ELISA. Administration of rat as well as human apoptotic MNC-secretomes significantly reduced ischemic lesion volumes by 36% and 37%, respectively. Neurological examinations revealed improvement after stroke in both treatment groups. Co-incubation of human astrocytes, Schwann cells and neurons with hMNC apo sec resulted in activation of several signaling cascades associated with the regulation of cytoprotective gene products and enhanced neuronal sprouting in vitro. Analysis of neurotrophic factors in hMNC apo sec and rat plasma revealed high levels of brain derived neurotrophic factor (BDNF). Our data indicate that apoptotic MNC-secretomes elicit neuroprotective effects on rats that have undergone ischemic stroke.
Both Intrinsically Disordered Regions and Structural Domains Evolve Rapidly in Immune-Related Mammalian Proteins

Eukaryotic proteins consist of structural domains (SDs) and intrinsically disordered regions (IDRs), i.e., regions that by themselves do not assume unique three-dimensional structures. IDRs are generally subject to less constraint and evolve more rapidly than SDs. Proteins with a lower number of protein-to-protein interactions (PPIs) are also less constrained and tend to evolve fast. Extracellular proteins of mammals, especially immune-related extracellular proteins, on average have relatively high evolution rates. This article aims to examine if a high evolution rate in IDRs or that in SDs accounts for the rapid evolution of extracellular proteins. To this end, we classified eukaryotic proteins based on their cellular localizations and analyzed them. Moreover, we divided proteins into SDs and IDRs and calculated the respective evolution rate. Fractional IDR content is positively correlated with evolution rate. For their fractional IDR content, immune-related extracellular proteins show an aberrantly high evolution rate. IDRs evolve more rapidly than SDs in most subcellular localizations. In extracellular proteins, however, the difference is diminished. For immune-related proteins in mammals in particular, the evolution rates in SDs come close to those in IDRs. Thus high evolution rates in both IDRs and SDs account for the rapid evolution of immune-related proteins.
The effectiveness of topical therapy combined with 308‐nm excimer laser on vitiligo compared to excimer laser monotherapy in pediatric patients

We evaluated the efficiency of using a 308‐nm excimer laser with either tacrolimus, pimecrolimus, or halometasone for the treatment of childhood vitiligo. Patients who received combined treatments had significantly higher rates of repigmentation in comparison with individuals who underwent excimer laser alone therapy. Moreover, patients who received treatment with excimer laser and halometasone had significantly higher rates of repigmentation, even higher than individuals who were treated with tacrolimus combined or pimecrolimus combined. This regimen was more effective on the face, neck, and limbs. We conclude that the use of a combined excimer laser and halometasone cream has a greater therapeutic effect than laser alone.
Feature Tracking by Two-Step Optimization

Tracking the temporal evolution of features in time-varying data is a key method in visualization. For typical feature definitions, such as vortices, objects are sparsely distributed over the data domain. In this paper, we present a novel approach for tracking both sparse and space-filling features. While the former comprise only a small fraction of the domain, the latter form a set of objects whose union covers the domain entirely while the individual objects are mutually disjunct. Our approach determines the assignment of features between two successive time-steps by solving two graph optimization problems. It first resolves one-to-one assignments of features by computing a maximum-weight, maximum-cardinality matching on a weighted bi-partite graph. Second, our algorithm detects events by creating a graph of potentially conflicting event explanations and finding a weighted, independent set in it. We demonstrate our method's effectiveness on synthetic and simulation data sets, the former of which enables quantitative evaluation because of the availability of ground-truth information. Here, our method performs on par or better than a well-established reference algorithm. In addition, manual visual inspection by our collaborators confirm the results’ plausibility for simulation data.
The Development and Application of Bayesian Networks Used in Data Mining Under Big Data

Big data era has arrived. With the rapid development of big data, the machine learning based on probability statistics has drawn great attention in recent years from both industry and academia. It also has great applications in the fields of the Internet, finance, natural language, biology, among which the Bayesian network has experienced significant development last year and has become a kind of important machine learning method. This paper summarizes the data mining concept principle and basic method of data mining under the background of big data. Since data mining has strong statistic characteristic, it can be integrated with the Bayesian networks naturally. Therefore, this paper starts with the Bayesian networks, stretching out the Bayesian formula, Bayesian network technology, as well as the advantages of the combination of data mining and the Bayesian networks. Finally, with the biomedicine as an example, it summarizes its development and applications.
Integrated Analysis of Mouse and Human Gastric Neoplasms Identifies Conserved microRNA Networks in Gastric Carcinogenesis.

BACKGROUND & AIMS microRNAs (miRNAs) are small noncoding RNAs that bind to the 3' untranslated regions of mRNAs to promote their degradation or block their translation. Mice with disruption of the trefoil factor 1 gene (Tff1) develop gastric neoplasms. We studied these mice to identify conserved miRNA networks involved in gastric carcinogenesis. METHODS We performed next-generation miRNA sequencing analysis of normal gastric tissues (based on histology) from patients without evidence of gastric neoplasm (n = 64) and from TFF1-knockout mice (n = 22). We validated our findings using 270 normal gastric tissues (including 61 samples from patients without evidence of neoplastic lesions) and 234 gastric tumor tissues from 3 separate cohorts of patients and from mice. We performed molecular and functional assays using cell lines (MKN28, MKN45, STKM2, and AGS cells), gastric organoids, and mice with xenograft tumors. RESULTS We identified 117 miRNAs that were significantly deregulated in mouse and human gastric tumor tissues compared with nontumor tissues. We validated changes in levels of 6 miRNAs by quantitative real-time polymerase chain reaction analyses of neoplastic gastric tissues from mice (n = 39) and 3 independent patient cohorts (n = 332 patients total). We found levels of MIR135B-5p, MIR196B-5p, and MIR92A-5p to be increased in tumor tissues, whereas levels of MIR143-3p, MIR204-5p, and MIR133-3p were decreased in tumor tissues. Levels of MIR143-3p were reduced not only in gastric cancer tissues but also in normal tissues adjacent to tumors in humans and low-grade dysplasia in mice. Transgenic expression of MIR143-3p in gastric cancer cell lines reduced their proliferation and restored their sensitivity to cisplatin. AGS cells with stable transgenic expression of MIR143-3p grew more slowly as xenograft tumors in mice than control AGS cells; tumor growth from AGS cells that expressed MIR143-3p, but not control cells, was sensitive to cisplatin. We identified and validated bromodomain containing 2 (BRD2) as a direct target of MIR143-3p; increased levels of BRD2 in gastric tumors was associated with shorter survival times for patients. CONCLUSIONS In an analysis of miRNA profiles of gastric tumors from mice and human patients, we identified a conserved signature associated with the early stages of gastric tumorigenesis. Strategies to restore MIR143-3p or inhibit BRD2 might be developed for treatment of gastric cancer.
Chromatin remodeling modulates radiosensitivity of the daughter cells derived from cell population exposed to low- and high-LET irradiation

Radiation effects are dependent of linear energy transfer (LET), but it is still obscure whether the daughter cells (DCs) derived from irradiated population are radioresistance and much less the underlying mechanism. With the measurements of survival, proliferation and γH2AX foci, this study shows that the DCs from γ-ray irradiated cells (DCs-γ) became more radioresistant than its parent control without irradiation, but the radiosensitivity of DCs from α-particle irradiated cells (DCs-α) was not altered. After irradiation with equivalent doses of γ-rays and α-particles, the foci number of histone H3 lysine 9 dimethylation (H3K9me3) and the activity of histone deacetylase (HDAC) in DCs-γ was extensively higher than these in DCs-α and its parent control, indicating that a higher level of heterochromatin was formed in DCs-γ but not in DCs-α. Treatment of cells with SAHA (an inhibitor of HDAC) decreased the level of heterochromatin domains by inhibiting the expressions of H3K9m3 and HP-1a proteins and triggering the expression of acetylated core histone H3 (Ac-H3). When cells were treated with SAHA, the radioresistance phenotype of DCs-γ was eliminated so that the radiosensitivities of DCs-γ, DCs-α and their parent cells approached to same levels. Our current results reveal that γ-rays but not α-particles could induce chromatin remodeling and heterochromatinization which results in the occurrence of radioresistance of DCs, indicating that the combination treatment of irradiation and HDAC inhibitor could serve as a potential cancer therapy strategy, especially for the fraction radiotherapy of low-LET irradiation.
seq2graph: Discovering Dynamic Dependencies from Multivariate Time Series with Multi-level Attention

Discovering temporal lagged and inter-dependencies in multivariate time series data is an important task. However, in many real-world applications, such as commercial cloud management, manufacturing predictive maintenance, and portfolios performance analysis, such dependencies can be non-linear and time-variant, which makes it more challenging to extract such dependencies through traditional methods such as Granger causality or clustering. In this work, we present a novel deep learning model that uses multiple layers of customized gated recurrent units (GRUs) for discovering both time lagged behaviors as well as inter-timeseries dependencies in the form of directed weighted graphs. We introduce a key component of Dual-purpose recurrent neural network that decodes information in the temporal domain to discover lagged dependencies within each time series, and encodes them into a set of vectors which, collected from all component time series, form the informative inputs to discover inter-dependencies. Though the discovery of two types of dependencies are separated at different hierarchical levels, they are tightly connected and jointly trained in an end-to-end manner. With this joint training, learning of one type of dependency immediately impacts the learning of the other one, leading to overall accurate dependencies discovery. We empirically test our model on synthetic time series data in which the exact form of (non-linear) dependencies is known. We also evaluate its performance on two real-world applications, (i) performance monitoring data from a commercial cloud provider, which exhibit highly dynamic, non-linear, and volatile behavior and, (ii) sensor data from a manufacturing plant. We further show how our approach is able to capture these dependency behaviors via intuitive and interpretable dependency graphs and use them to generate highly accurate forecasts.
A Regularized Weighted Smoothed L0 Norm Minimization Method for Underdetermined Blind Source Separation

Compressed sensing (CS) theory has attracted widespread attention in recent years and has been widely used in signal and image processing, such as underdetermined blind source separation (UBSS), magnetic resonance imaging (MRI), etc. As the main link of CS, the goal of sparse signal reconstruction is how to recover accurately and effectively the original signal from an underdetermined linear system of equations (ULSE). For this problem, we propose a new algorithm called the weighted regularized smoothed L0-norm minimization algorithm (WReSL0). Under the framework of this algorithm, we have done three things: (1) proposed a new smoothed function called the compound inverse proportional function (CIPF); (2) proposed a new weighted function; and (3) a new regularization form is derived and constructed. In this algorithm, the weighted function and the new smoothed function are combined as the sparsity-promoting object, and a new regularization form is derived and constructed to enhance de-noising performance. Performance simulation experiments on both the real signal and real images show that the proposed WReSL0 algorithm outperforms other popular approaches, such as SL0, BPDN, NSL0, and Lp-RLSand achieves better performances when it is used for UBSS.
Valuation of EQ-5D-5L health states: a comparison of seven Asian populations

ABSTRACT Objectives: To compare the time trade-off (TTO) utility values of EQ-5D-5L health states elicited from different general populations in Asia. Methods: We analyzed the TTO data from seven Asian EQ-5D-5L valuation studies in which utility values of 86 EQ-5D-5L health states were elicited from general population samples. An eight-parameter multiplicative regression model including five dimension parameters (mobility [MO], self-care, usual activities [UA], pain/discomfort, anxiety/depression) and three level parameters (level 2 [L2], level 3 [L3], and level 4 [L4]) was used to model the data from each of the populations. The model coefficients were compared to understand how the valuations of EQ-5D-5L health states differ. Results: For dimension parameters, Korea and Indonesia generally had the highest and lowest values among the populations, respectively; UA and MO commonly had the highest and lowest values among the parameters, respectively. For level parameters, Singapore and Korea generally had the highest and lowest values, respectively; L2 showed less variance compared to L3 and L4. Koreans, Indonesians, and Singaporeans appeared to have different health preferences compared with other populations. Conclusion: Utility values of EQ-5D-5L health states differ among Asian populations, suggesting that each health system should establish and use its own value set.
An Efficient Classifier for Alzheimer’s Disease Genes Identification

Alzheimer’s disease (AD) is considered to one of 10 key diseases leading to death in humans. AD is considered the main cause of brain degeneration, and will lead to dementia. It is beneficial for affected patients to be diagnosed with the disease at an early stage so that efforts to manage the patient can begin as soon as possible. Most existing protocols diagnose AD by way of magnetic resonance imaging (MRI). However, because the size of the images produced is large, existing techniques that employ MRI technology are expensive and time-consuming to perform. With this in mind, in the current study, AD is predicted instead by the use of a support vector machine (SVM) method based on gene-coding protein sequence information. In our proposed method, the frequency of two consecutive amino acids is used to describe the sequence information. The accuracy of the proposed method for identifying AD is 85.7%, which is demonstrated by the obtained experimental results. The experimental results also show that the sequence information of gene-coding proteins can be used to predict AD.