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The interaction between protein and DNA plays an essential function in various critical natural processes, like DNA replication, transcription, splicing, and repair. Studying the binding affinity of proteins to DNA helps to understand the recognition mechanism of protein-DNA complexes. Since there are still many limitations on the protein-DNA binding affinity data measured by experiments, accurate and reliable calculation methods are necessarily required. So we put forward a computational approach in this paper, called PreDBA, that can forecast protein-DNA binding affinity effectively by using heterogeneous ensemble models. One hundred protein-DNA complexes are manually collected from the related literature as a data set for protein-DNA binding affinity. Then, 52 sequence and structural features are obtained. Based on this, the correlation between these 52 characteristics and protein-DNA binding affinity is calculated. Furthermore, we found that the protein-DNA binding affinity is affected by the DNA molecule structure of the compound. We classify all protein-DNA compounds into five classifications based on the DNA structure related to the proteins that make up the protein-DNA complexes. In each group, a stacked heterogeneous ensemble model is constructed based on the obtained features. In the end, based on the binding affinity data set, we used the leave-one-out cross-validation to evaluate the proposed method comprehensively. In the five categories, the Pearson correlation coefficient values of our recommended method range from 0.735 to 0.926. We have demonstrated the advantages of the proposed method compared to other machine learning methods and currently existing protein-DNA binding affinity prediction approach.

Control chart methods have been received much attentions in biosurvillance studies. The correlation between charting statistics or regions could be considerably important in monitoring the states of multiple outcomes or regions. In addition, the process variable distribution is unknown in most situations. In this paper, we propose a new nonparametric strategy for multivariate process monitoring when the distribution of a process variable is unknown. We discuss the EWMA control chart based on rank methods for a multivariate process, and the approach is completely nonparametric. A simulation study demonstrates that the proposed method is efficient in detecting shifts for multivariate processes. A real Japanese influenza data example is given to illustrate the performance of the proposed method.

The universal linguistic colour categorisation pattern as evident in the World Colour Survey (WCS) has been a principal focus of investigations on the relationship between language and cognition, yet most existing studies have failed to clarify whether this universality resulted primarily from individual perceptual constraints and/or socio-cultural transmissions. This paper designed an agent-based, unsupervised learning model to address the relative importance of these two aspects to linguistic colour categorisation. By directly comparing with the empirical data in the WCS, our study demonstrated that: the physical colour stimuli that reflect human perceptual constraints on colours trigger a categorisation pattern quantitatively resembling the WCS data, the randomised stimuli that distort such constraints lead to distinct categorisation patterns, and the processes of linguistic categorisation in both cases follow similar dynamics. These results reveal how perceptual and socio-cultural factors interact with each other to trigger linguistic universality, and serve as decisive evidence that human perceptual constraints induce the universality in linguistic categorisation, yet socio-cultural transmissions, though imperative, play an auxiliary role of transcribing perceptual constraints into common linguistic categories with slight variations.

Depressive disorder (DD) is a mental illness affecting more than 300 million people worldwide, whereas social stigma and subtle, variant symptoms impede diagnosis. Psychomotor retardation is a common component of DD with a negative impact on motor function, usually reflected on patients’ routine activities, including, nowadays, their interaction with mobile devices. Therefore, such interactions constitute an enticing source of information towards unsupervised screening for DD symptoms in daily life. In this vein, this paper proposes a machine learning-based method for discriminating between subjects with depressive tendency and healthy controls, as denoted by self-reported Patient Health Questionnaire-9 (PHQ-9) compound scores, based on typing patterns captured in-the-wild. The latter consisted of keystroke timing sequences and typing metadata, passively collected during natural typing on touchscreen smartphones by 11/14 subjects with/without depressive tendency. Statistical features were extracted and tested in univariate and multivariate classification pipelines to reach a decision on subjects’ status. The best-performing pipeline achieved an AUC = 0.89 (0.72–1.00; 95% Confidence Interval) and 0.82/0.86 sensitivity/specificity, with the outputted probabilities significantly correlating (>0.60) with the respective PHQ-9 scores. This work adds to the findings of previous research associating typing patterns with psycho-motor impairment and contributes to the development of an unobtrusive, high-frequency monitoring of depressive tendency in everyday living.

Extracellular vesicles (EV), including exosomes and microvesicles (MV), represent a rapidly expanding field of research with diagnostic and therapeutic applications. Although many aspects of EV function remain to be revealed and broad investigations are warranted, most published findings focus on only one vesicle category or a non-separated mix of EVs. In this paper, we investigated both MVs and exosomes from Ovalbumin (OVA)-pulsed dendritic cells for their immunostimulatory potential side-by-side in vivo. Only exosomes induced antigen-specific CD8+ T-cells, and were more efficient than MVs in eliciting antigen-specific IgG production. Further, mainly exosome-primed mouse splenocytes showed significant ex vivo interferon gamma production in response to antigen restimulation. Exosomes carried high levels of OVA, while OVA in MVs was barely detectable, which could explain the more potent antigen-specific response induced by exosomes. Moreover, exosomes induced increased germinal center B cell proportions, whereas MVs had no such effect. Immunisation with both vesicle types combined showed neither inhibitory nor synergistic effects. We conclude that DC-derived MVs and exosomes differ in their capacity to incorporate antigen and induce immune responses. The results are of importance for understanding the role of EVs in vivo, and for future design of vesicle-based immunotherapies and vaccines.

Liver X receptorβ (LXRβ) is a promising therapeutic target for lipid disorders, atherosclerosis, chronic inflammation, autoimmunity, cancer and neurodegenerative diseases. Druggable LXRβ agonists have been explored over the past decades. However, the pocket of LXRβ ligand-binding domain (LBD) is too large to predict LXRβ agonists with novel scaffolds based on either receptor or agonist structures. In this paper, we report a de novo algorithm which drives privileged LXRβ agonist fragments by starting with individual chemical bonds (de novo) from every molecule in a LXRβ agonist library, growing the bonds into substructures based on the agonist structures with isomorphic and homomorphic restrictions, and electing the privileged fragments from the substructures with a popularity threshold and background chemical and biological knowledge. Using these privileged fragments as queries, we were able to figure out the rules to reconstruct LXRβ agonist molecules from the fragments. The privileged fragments were validated by building regularized logistic regression (RLR) and supporting vector machine (SVM) models as descriptors to predict a LXRβ agonist activities.

A selective colon cancer cell therapy was effectively achieved with catalase-mediated intra-cellular heterogeneous Fenton reactions triggered by cellular uptake of SnFe2O4 nanocrystals. The treatment was proven effective for eradicating colon cancer cells, whereas was benign to normal colon cells, thus effectively realizing the selective colon cancer cell therapeutics. Cancer cells possess much higher innate hydrogen peroxide (H2O2) but much lower catalase levels than normal cells. Catalase, an effective H2O2 scavenger, prevented attacks on cells by reactive oxygen species induced from H2O2. The above intrinsic difference between cancer and normal cells was utilized to achieve selective colon cancer cell eradication through endocytosing efficient heterogeneous Fenton catalysts to trigger the formation of highly reactive oxygen species from H2O2. In this paper, SnFe2O4 nanocrystals, a newly noted outstanding paramagnetic heterogeneous Fenton catalyst, have been verified an effective selective colon cancerous cell treatment reagent of satisfactory blood compatibility.

Chitin is a major component of insect exoskeleton, tracheal system and gut where it is synthesized by chitin synthase (CHS) enzymes. In this paper, we report the isolation and RNAi of chitin synthase A (PhoCHSA) from the potato tuber moth Phthorimaea operculella. The full-length cDNA of PhoCHSA is 5,627 bp with 4,689 bp open reading frame coding for 1,563 amino acids. Structural analysis of conceptual amino acid translation showed three distinct regions found in all known insect CHS proteins; N-terminus region having 9 transmembrane helices, middle catalytic region containing several conserved domains identified in insect CHS enzymes, and C-terminus region containing seven transmembrane spans. Phylogenetic analysis showed that PhoCHSA protein clustered with CHSA enzymes identified from insects from different insect orders. RNAi targeting three different regions of the gene showed different efficacy against potato tuber moth larvae and dsRNA targeting the 5′ region has the highest efficacy. Results were verified by qRT-PCR which showed that dsRNA targeting the 5′ region caused the highest reduction in PhoCHSA mRNA level. Our results show the importance of selecting the RNAi target region and that chitin synthase A can be a suitable RNAi target for the potato tuber moth control.

Imaging flow cytometry (IFC) is an emerging technology that acquires single-cell images at high-throughput for analysis of a cell population. Rich information that comes from high sensitivity and spatial resolution of a single-cell microscopic image is beneficial for single-cell analysis in various biological applications. In this paper, we present a fast image-processing pipeline (R-MOD: Real-time Moving Object Detector) based on deep learning for high-throughput microscopy-based label-free IFC in a microfluidic chip. The R-MOD pipeline acquires all single-cell images of cells in flow, and identifies the acquired images as a real-time process with minimum hardware that consists of a microscope and a high-speed camera. Experiments show that R-MOD has the fast and reliable accuracy (500 fps and 93.3% mAP), and is expected to be used as a powerful tool for biomedical and clinical applications.

Hepatocellular carcinoma with bile duct tumor thrombus (BDTT) is a malignant disease. The most commonly used diagnosis methods for BDTT are MRCP/ERCP, ultrasonic diagnosis or CT scan. However, BDTT is often misdiagnosed as other bile duct diseases, such as extrahepatic cholangiocarcinoma (EHCC), choledochal cyst (Cyst) and common bile duct stone (Stone). Diagnostic methods, which are more accurate and less destructive, are urgently needed. In this paper, we analyzed the small molecule metabolites in the serum of BDTT, Stone, Cyst and EHCC patients and normal people using untargeted GC-MS, and identified 21 metabolites that show different levels among different samples. Using targeted UHPLC-QQQ-MS analysis, we found that several metabolites are significantly changed. ROC curve analysis revealed two metabolites, L-citrulline and D-aspartic acid, as potential biomarkers that can distinguish BDTT from other bile duct diseases.

Brain imaging genetics intends to uncover associations between genetic markers and neuroimaging quantitative traits. Sparse canonical correlation analysis (SCCA) can discover bi-multivariate associations and select relevant features, and is becoming popular in imaging genetic studies. The L1-norm function is not only convex, but also singular at the origin, which is a necessary condition for sparsity. Thus most SCCA methods impose $${\ell }_{{\bf{1}}}$$ ℓ 1 -norm onto the individual feature or the structure level of features to pursuit corresponding sparsity. However, the $${\ell }_{{\bf{1}}}$$ ℓ 1 -norm penalty over-penalizes large coefficients and may incurs estimation bias. A number of non-convex penalties are proposed to reduce the estimation bias in regression tasks. But using them in SCCA remains largely unexplored. In this paper, we design a unified non-convex SCCA model, based on seven non-convex functions, for unbiased estimation and stable feature selection simultaneously. We also propose an efficient optimization algorithm. The proposed method obtains both higher correlation coefficients and better canonical loading patterns. Specifically, these SCCA methods with non-convex penalties discover a strong association between the APOE e4 rs429358 SNP and the hippocampus region of the brain. They both are Alzheimer’s disease related biomarkers, indicating the potential and power of the non-convex methods in brain imaging genetics.

Liquid marble is a liquid droplet coated with hydrophobic powder that can be used as a bioreactor. This paper reports the three-dimensional self-assembly and culture of a cell toroid in a slow-releasing, non-adhesive and evaporation-reducing bioreactor platform based on a liquid marble. The bioreactor is constructed by embedding a hydrogel sphere containing growth factor into a liquid marble filled with a suspension of dissociated cells. The hydrogel maintains the water content and concurrently acts as a slow-release carrier. The concentration gradient of growth factor induces cell migration and assembly into toroidal aggregates. An optimum cell concentration resulted in the toroidal (doughnut-like) tissue after 12 hours. The harvested cell toroids showed rapid closure of the inner opening when treated with the growth factor. We also present a geometric growth model to describe the shape of the toroidal tissue over time. In analogy to the classical two-dimensional scratch assay, we propose that the cell toroids reported here open up new possibilities to screen drugs affecting cell migration in three dimensions.

For the first time, a paper-based fluorescence resonance energy transfer (FRET) determination with cyclic AMP (cAMP)-specific phosphodiesterase 4B (PDE4B) inhibitory assay using an inkjet-printing technique is proposed. Non-fabricated parchment paper is found to constitute a unique substrate to measure fluorescent energy transfer, due to its insignificant self-absorption, and enables efficient sample interaction. Here, we report the responsive FRET signals generated on paper, upon sequentially printing reaction components on parchment paper using a conventional inkjet printer equipped with four cartridges. After printing, the energy emitted by Eu chelate was transferred by FRET to ULight molecule on paper, detected at 665 nm. In the absence of free cAMP, a maximum FRET signal was achieved on paper, while a decrease in FRET signals was recorded when free cAMP produced by PDE4B inhibitors compete with Eu-cAMP, binding with ULight-mAb. The IM50 value was determined as 2.46 × 10−13 mole for roliparm and 1.86 × 10−13 mole for roflumilast, to effectively inhibit PDE4B activity. Inkjet printing-based FRET signal determination utilizes components that are less than the femtomole range, which was four-orders less than the standard assay method. The methodology reported here constitutes an innovative approach towards the determination of FRET signals generated on paper.

Subunit viral vaccines are typically not as efficient as live attenuated or inactivated vaccines at inducing protective immune responses. This paper describes an alternative ‘biomimetic’ technology; whereby viral antigens were formulated around a polymeric shell in a rationally arranged fashion with a surface glycoprotein coated on to the surface and non-structural antigen and adjuvant encapsulated. We evaluated this model using BVDV E2 and NS3 proteins formulated in poly-(D, L-lactic-co-glycolic acid) (PLGA) nanoparticles adjuvanted with polyinosinic:polycytidylic acid (poly(I:C) as an adjuvant (Vaccine-NP). This Vaccine-NP was compared to ovalbumin and poly(I:C) formulated in a similar manner (Control-NP) and a commercial adjuvanted inactivated BVDV vaccine (IAV), all inoculated subcutaneously and boosted prior to BVDV-1 challenge. Significant virus-neutralizing activity, and E2 and NS3 specific antibodies were observed in both Vaccine-NP and IAV groups following the booster immunisation. IFN-γ responses were observed in ex vivo PBMC stimulated with E2 and NS3 proteins in both vaccinated groups. We observed that the protection afforded by the particulate vaccine was comparable to the licenced IAV formulation. In conclusion, the biomimetic particulates showed a promising immunogenicity and efficacy profile that may be improved by virtue of being a customisable mode of delivery.

Tunable interparticle interactions in colloidal suspensions are of great interest because of their fundamental and practical significance. In this paper we present a new experimental setup for self-assembly of colloidal particles in two-dimensional systems, where the interactions are controlled by external rotating electric fields. The maximal magnitude of the field in a suspension is 25 V/mm, the field homogeneity is better than 1% over the horizontal distance of 250 μm, and the rotation frequency is in the range of 40 Hz to 30 kHz. Based on numerical electrostatic calculations for the developed setup with eight planar electrodes, we found optimal experimental conditions and performed demonstration experiments with a suspension of 2.12 μm silica particles in water. Thanks to its technological flexibility, the setup is well suited for particle-resolved studies of fundamental generic phenomena occurring in classical liquids and solids, and therefore it should be of interest for a broad community of soft matter, photonics, and material science.

Traumatic brain injury (TBI) is one of the most important death and disability cause, involving substantial costs, also in economic terms, when considering the young age of the involved subject. Aim of this paper is to report a series of patients treated at our institutions, to verify neurological results at six months or survival; in fatal cases we searched for βAPP, GFAP, IL-1β, NFL, Spectrin II, TUNEL and miR-21, miR-16, and miR-92 expressions in brain samples, to verify DAI diagnosis and grade as strong predictor of survival and inflammatory response. Concentrations of 8OHdG as measurement of oxidative stress was performed. Immunoreaction of β-APP, IL-1β, GFAP, NFL, Spectrin II and 8OHdG were significantly increased in the TBI group with respect to control group subjects. Cell apoptosis, measured by TUNEL assay, were significantly higher in the study group than control cases. Results indicated that miR-21, miR-92 and miR-16 have a high predictive power in discriminating trauma brain cases from controls and could represent promising biomarkers as strong predictor of survival, and for the diagnosis of postmortem traumatic brain injury.

In this paper, the memristive switching behavior of Cu/ HfO2/p++ Si devices fabricated by an organic-polymer-assisted sol-gel spin-coating method, coupled with post-annealing and shadow-mask metal sputtering steps, is examined. HfO2 layers of about 190 nm and 80 nm, are established using cost-effective spin-coating method, at deposition speeds of 2000 and 4000 rotations per minute (RPM), respectively. For two types of devices, the memristive characteristics (Von, Ion, and Vreset) and device-to-device electrical repeatability are primarily discussed in correlation with the oxide layer uniformity and thickness. The devices presented in this work exhibit an electroforming free and bipolar memory-resistive switching behavior that is typical of an Electrochemical Metallization (ECM) I-V fingerprint. The sample devices deposited at 4000 RPM generally show less variation in electrical performance parameters compared to those prepared at halved spin-coating speed. Typically, the samples prepared at 4000 RPM (n = 8) display a mean switching voltage Von of 3.0 V (±0.3) and mean reset voltage Vreset of −1.1 V (±0.5) over 50 consecutive sweep cycles. These devices exhibit a large Roff/Ron window (up to 104), and sufficient electrical endurance and retention properties to be further examined for radiation sensing. As they exhibit less statistical uncertainty compared to the samples fabricated at 2000 RPM, the devices prepared at 4000 RPM are tested for the detection of soft gamma rays (emitted from low-activity Cs-137 and Am-241 radioactive sources), by assessing the variation in the on-state resistance value upon exposure. The analysis of the probability distributions of the logarithmic Ron values measured over repeated ON-OFF cycles, before, during and after exposing the devices to radiation, demonstrate a statistical difference. These results pave the way for the fabrication and development of cost-effective soft-gamma ray detectors.

In this paper, we use a model modified from classic corticothalamic network(CT) to explore the mechanism of absence seizures appearing on specific relay nuclei (SRN) of the thalamus. It is found that typical seizure states appear on SRN through tuning several critical connection strengths in the model. In view of previous experimental and theoretical works which were mainly on epilepsy seizure phenomena appearing on excitatory pyramidal neurons (EPN) of the cortex, this is a novel model to consider the seizure observed on thalamus. In particular, the onset mechanism is different from previous theoretical studies. Inspired by some previous clinical and experimental studies, we employ the external stimuli voltage on EPN and SRN in the network, and observe that the seizure can be well inhibited by tuning the stimulus intensity appropriately. We further explore the effect of the signal transmission delays on seizures, and found that the polyspike phenomenon appears only when the delay is sufficiently large. The experimental data also confirmed our model. Since there is a complex network in the brain and all organizations are interacting closely with each other, the results obtained in this paper provide not only biological insights into the regulatory mechanisms but also a reference for the prevention and treatment of epilepsy in future.

The monospecific fern genus Cystodium (Cystodiaceae; Polypodiales) occurs exclusively in the tropical forests of the Malay Archipelago, the Admiralty Islands, the Louisiade Archipelago, and the Solomon Islands. Divergence time estimates suggest that the genus originated in the Mesozoic; however, fossil evidence to validate this suggestion has been lacking. Amber from Myanmar (Burmese amber) is an important source of new information on the diversity of vascular cryptogams in the Cretaceous. This paper describes the fossil taxon Cystodium sorbifolioides nov. sp. based on a fragment of a fertile leaf preserved in Burmese amber that represents the first fossil evidence of the family Cystodiaceae. Cystodium sorbifolioides is used to obtain a minimum age estimate for the Cystodiaceae and the closely related, monogeneric Lonchitidaceae and Lindsaeaceae. The fossil strengthens the hypothesis that the forest ecosystems of Malesia and Melanesia represent refugia for many tropical plant lineages that originated in the Cretaceous.

Tumor metastasis is mainly caused by somatic genomic alterations (SGAs) that perturb pathways regulating metastasis-relevant activities and thus help the primary tumor to adapt to the new microenvironment. Identifying drivers of metastasis, i.e. SGAs, sheds light on the metastasis mechanism and provides guidance for targeted therapy. In this paper, we introduce a novel method to search for SGAs driving breast cancer metastasis to the lung. First, we search for transcriptomic modules with genes that are differentially expressed in breast cell lines with strong metastatic activities to the lung and co-expressed in a large number of breast tumors. Then, for each transcriptomic module, we search for a set of SGA genes (driver modules) such that genes in each driver module carry a common signal regulating the transcriptomic module. Evaluations indicate that many genes in driver modules are indeed related to metastasis, and our methods have identified many new driver candidates. We further choose two novel metastatic driver genes, BCL2L11 and CDH9, for in vitro verification. The wound healing assay reveals that inhibiting either BCL2L11 or CDH9 will enhance the migration of cell lines, which provides evidence that these two genes are suppressors of tumor metastasis.

Variations in DNA copy number carry important information on genome evolution and regulation of DNA replication in cancer cells. The rapid development of single-cell sequencing technology enables exploration of gene-expression heterogeneity among single cells, providing important information on cell evolution. Evolutionary relationships in accumulated sequence data can be visualized by adjacent positioning of similar cells so that similar copy-number profiles are shown by block patterns. However, single-cell DNA sequencing data usually have low amount of starting genome, which requires an extra step of amplification to accumulate sufficient samples, introducing noise and making regular pattern-finding challenging. In this paper, we will propose to tackle this issue of recovering the hidden blocks within single-cell DNA-sequencing data through continuous sample permutations such that similar samples are positioned adjacently. The permutation is guided by the total variational norm of the recovered copy number profiles, and is continued until the total variational norm is minimized when similar samples are stacked together to reveal block patterns. An efficient numerical scheme for finding this permutation is designed, tailored from the alternating direction method of multipliers. Application of this method to both simulated and real data demonstrates its ability to recover the hidden structures of single-cell DNA sequences.

Inappropriate use of ethanol (EtOH) had led to noticeable health problems, but a beneficial phenomenon was found that EtOH displayed unique influences for toxic trans-crotonaldehyde (TCA) derived from mitochondrial lipid peroxidation. The influences of EtOH on the structure of TCA were systematically probed by UV-vis & Raman spectroscopy in the absence and presence of mitochondria, respectively. The maximum UV-vis peak at 301 nm of TCA was red shifted by hydroxyl (-OH) and methyl (-CH3) of EtOH, respectively. Raman stretching band of aldehyde (-CH=O) of TCA (TCA-CH=O) was split by the -CH3 of EtOH. The -CH3 increased TCA-CH=O stretching frequency while the -OH induced it. The more exposed -OH, the less stretching frequency. The ectopic -CH3 red shifted the UV-vis peak at 301 nm and Raman band of TCA-CH=O. In mitochondria, EtOH red shifted Raman stretching band of TCA-CH=O. Raman stretching bands of C-H, C-O and C-C of EtOH were red shifted, while Raman stretching bands of -CH2 and C-C-O of EtOH disappeared. The paper unearths the influences of EtOH to trap and transform the structure of TCA-CH=O. This discovery has an important contribution to eliminate TCA in order to protect and repair mtDNA by means of the decrease of 8-oxoG.

Genome-wide association study is especially challenging in detecting high-order disease-causing models due to model diversity, possible low or even no marginal effect of the model, and extraordinary search and computations. In this paper, we propose a niche harmony search algorithm where joint entropy is utilized as a heuristic factor to guide the search for low or no marginal effect model, and two computationally lightweight scores are selected to evaluate and adapt to diverse of disease models. In order to obtain all possible suspected pathogenic models, niche technique merges with HS, which serves as a taboo region to avoid HS trapping into local search. From the resultant set of candidate SNP-combinations, we use G-test statistic for testing true positives. Experiments were performed on twenty typical simulation datasets in which 12 models are with marginal effect and eight ones are with no marginal effect. Our results indicate that the proposed algorithm has very high detection power for searching suspected disease models in the first stage and it is superior to some typical existing approaches in both detection power and CPU runtime for all these datasets. Application to age-related macular degeneration (AMD) demonstrates our method is promising in detecting high-order disease-causing models.

Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated by these platforms, and rely on experts to hand-select a small number of features for training prediction models. In this paper, we demonstrate how deep learning and Bayesian optimization methods that have been remarkably successful in general high-dimensional prediction tasks can be adapted to the problem of predicting cancer outcomes. We perform an extensive comparison of Bayesian optimized deep survival models and other state of the art machine learning methods for survival analysis, and describe a framework for interpreting deep survival models using a risk backpropagation technique. Finally, we illustrate that deep survival models can successfully transfer information across diseases to improve prognostic accuracy. We provide an open-source software implementation of this framework called SurvivalNet that enables automatic training, evaluation and interpretation of deep survival models.

Recent studies have demonstrated the involvement of colorectal cancer (CRC) stem cells (CSC) in transformation, cancer progression and metastasis. The main goal of this paper was to examine the molecular mechanisms by which SATB2 induced malignant transformation of colorectal epithelial cells. SATB2 induced malignant transformation and these transformed cells gained the characteristics of CSCs by expressing stem cell markers (CD44, CD133, LGR5 and DCLK1) and transcription factors (c-Myc, Nanog and Sox2). Overexpression of SATB2 in normal colorectal epithelial cells increased cell motility, migration and invasion, which were associated with an increase in N-cadherin and Zeb1, and decrease in E-cadherin expression. SATB2 overexpression also upregulated XIAP and cyclin D1, suggesting its role in cell survival and cell cycle. Furthermore, the expression of SATB2 was positively correlated with β-catenin expression in CRC. In contrary, depletion of SATB2 inhibited cell proliferation, colony formation, cell motility and expression of β-catenin, Snail, Slug, Zeb1 and N-cadherin, and upregulated E-cadherin. Furthermore, SATB2 silencing inhibited the expression of stem cell markers, pluripotency maintaining transcription factors, cell cycle and cell proliferation/survival genes and TCF/LEF targets. Finally, β-catenin/TCF-LEF pathway mediated the biological effects of SATB2 in CSCs. These studies support the role of SATB2/β-catenin/TCF-LEF pathway in transformation and carcinogenesis.

Regulatory T cells (Tregs) play crucial role in maintenance of peripheral tolerance. Recent clinical trials confirmed safety and efficacy of Treg treatment of deleterious immune responses. However, Tregs lose their characteristic phenotype and suppressive potential during expansion ex vivo. Therefore, multiple research teams have been studding Treg biology in aim to improve their stability in vitro. In the current paper, we demonstrate that mild hypothermia of 33 °C induces robust proliferation of Tregs, preserves expression of FoxP3, CD25 and Helios, and prevents TSDR methylation during culture in vitro. Tregs expanded at 33 °C have stronger immunosuppressive potential and remarkably anti-inflammatory phenotype demonstrated by the whole transcriptome sequencing. These observations shed new light on impact of temperature on regulation of immune response. We show that just a simple change in temperature can preserve Treg stability, function and accelerate their proliferation, responding to unanswered question- how to preserve Treg stability in vitro.

2-Hydroxyglutaric acid disodium salt (2HG) is a unique biomarker existing in glioma, which can be used for recognizing cancer development stage and identifying the boundary between the ordinary tissue and cancer tissue. However, the most efficient detection method for 2HG now is Magnetic Resonance Spectroscopy (MRS), whose testing time is at least twenty minutes and the variability of 2HG (continuous synthesis and decomposition) determines it cannot be used as the real-time image in medical surgery. In this paper, by using the Terahertz Time-domain Spectroscopy (THz-TDS) System, we investigate the vibration spectra of 2HG isomers and further distinguish their physical properties by using Density Functional Theory. The differences between isomers are mainly attributed to the proton transfer inside the carbon chain. These results indicate that terahertz technology can identify the isomers of 2HG accurate and fast, which has important significance for the further investigation of glioma and clinical surgery.

We investigated cytosolic calcium (Ca2+) and sarcoplasmic reticulum Ca2+ regulation in skeletal muscle fibers of hibernating Daurian ground squirrels (Spermophilus dauricus), non-hibernating hindlimb-unloaded (HLU) squirrels, and HLU rats to clarify the molecular mechanisms involved in preventing muscle atrophy in hibernators. The Na+, K+-ATPase and Ca2+-ATPase activities in the soleus muscle (SOL) of squirrels were maintained in hibernation, decreased during interbout arousal (IB-A), and increased to autumn/pre-hibernation (AUT/Pre-H) levels in torpor after interbout arousal (Post-IBA), whereas activities in the extensor digitorum longus muscle (EDL) were stable during hibernation, but increased during post-hibernation (Post-H). Activities increased in the SOL of HLU rats, but were stable in HLU squirrels. Sarco/endoplasmic reticulum Ca2+-ATPase (SERCA) activity in the SOL decreased in IB-A squirrels, but returned to AUT/Pre-H levels in the Post-IBA group; no significant changes were found in the EDL. SERCA activity increased in the EDL of HLU squirrels and SOL of HLU rats. Compared with AUT/Pre-H, SERCA type 2 protein expression increased in the SOL and EDL of IB-A and Post-IBA squirrels, but increased in the SOL only in HLU animals. We also describe the protein kinase A changes in this paper. Thus, hibernating ground squirrels displayed remarkable Na+, K+-ATPase, Ca2+-ATPase, and SERCA plasticity.

As an ancient protein family, the WD40 repeat proteins often play essential roles in fundamental cellular processes in eukaryotes. Although investigations of eukaryotic WD40 proteins have been frequently reported, prokaryotic ones remain largely uncharacterized. In this paper, we report a systematic analysis of prokaryotic WD40 proteins and detailed comparisons with eukaryotic ones. About 4,000 prokaryotic WD40 proteins have been identified, accounting for 6.5% of all WD40s. While their abundances are less than 0.1% in most prokaryotes, they are enriched in certain species from Cyanobacteria and Planctomycetes, and participate in various functions such as prokaryotic signal transduction and nutrient synthesis. Comparisons show that a higher proportion of prokaryotic WD40s tend to contain multiple WD40 domains and a large number of hydrogen bond networks. The observation that prokaryotic WD40 proteins tend to show high internal sequence identity suggests that a substantial proportion of them (~20%) should be formed by recent or young repeat duplication events. Further studies demonstrate that the very young WD40 proteins, i.e., Highly-Repetitive WD40s, should be of higher stability. Our results have presented a catalogue of prokaryotic WD40 proteins, and have shed light on their evolutionary origins.

The current economics of the fish protein industry demand rapid, accurate and expressive prediction algorithms at every step of protein production especially with the challenge of global climate change. This help to predict and analyze functional and nutritional quality then consequently control food allergies in hyper allergic patients. As, it is quite expensive and time-consuming to know these concentrations by the lab experimental tests, especially to conduct large-scale projects. Therefore, this paper introduced a new intelligent algorithm using adaptive neuro-fuzzy inference system based on whale optimization algorithm. This algorithm is used to predict the concentration levels of bioactive amino acids in fish protein hydrolysates at different times during the year. The whale optimization algorithm is used to determine the optimal parameters in adaptive neuro-fuzzy inference system. The results of proposed algorithm are compared with others and it is indicated the higher performance of the proposed algorithm.

Revealing the underlying similarity of various complex networks has become both a popular and interdisciplinary topic, with a plethora of relevant application domains. The essence of the similarity here is that network features of the same network type are highly similar, while the features of different kinds of networks present low similarity. In this paper, we introduce and explore a new method for comparing various complex networks based on the cut distance. We show correspondence between the cut distance and the similarity of two networks. This correspondence allows us to consider a broad range of complex networks and explicitly compare various networks with high accuracy. Various machine learning technologies such as genetic algorithms, nearest neighbor classification, and model selection are employed during the comparison process. Our cut method is shown to be suited for comparisons of undirected networks and directed networks, as well as weighted networks. In the model selection process, the results demonstrate that our approach outperforms other state-of-the-art methods with respect to accuracy.

Johne’s disease (JD) is a chronic enteric disease in cattle caused by Mycobacterium avian subsp. paratuberculosis (MAP). Eradicating JD is a difficult task due to the long incubation period of MAP, inefficient diagnostic tests, and delayed clinical signs. Effective control strategies can help farmers to reduce prevalence, but those most acceptable to farmers combine specific information about lactation performance and testing results, which existing models do not provide. This paper presents an individual-based model of MAP infection dynamics and assesses the relative performance of the applied alternative control strategies. The base dairy herd model included the daily life events of a dairy cow and reflects several current dairy management processes. We then integrated MAP infection dynamics into the model. The model adopted four different test-based control strategies based on risk-based culling decisions and three hygiene scenarios. The model tracked the source of each infection and quantified the efficacy of each control strategy in reducing the risks of different transmission routes. The results suggest that risk-based culling can reduce prevalence compared with no control, but cannot eliminate the infection. Overall, this work provides not only a valuable tool to investigate MAP transmission dynamics but also offers adaptability to model similar infectious diseases.

The quantitative monitoring of airborne urediniospores of Puccinia striiformis f. sp. tritici (Pst) using spore trap devices in wheat fields is an important process for devising strategies early and effectively controlling wheat stripe rust. The traditional microscopic spore counting method mainly relies on naked-eye observation. Because of the great number of trapped spores, this method is labour intensive and time-consuming and has low counting efficiency, sometimes leading to huge errors; thus, an alternative method is required. In this paper, a new algorithm was proposed for the automatic detection and counting of urediniospores of Pst, based on digital image processing. First, images of urediniospores were collected using portable volumetric spore traps in an indoor simulation. Then, the urediniospores were automatically detected and counted using a series of image processing approaches, including image segmentation using the K-means clustering algorithm, image pre-processing, the identification of touching urediniospores based on their shape factor and area, and touching urediniospore contour segmentation based on concavity and contour segment merging. This automatic counting algorithm was compared with the watershed transformation algorithm. The results show that the proposed algorithm is efficient and accurate for the automatic detection and counting of trapped urediniospores. It can provide technical support for the development of online airborne urediniospore monitoring equipment.

Increasing evidence suggests that the brain dynamics can be interpreted from the viewpoint of nonlinear dynamical systems. The aim of this paper is to investigate the behavior of a thalamocortical model from this perspective. The model includes both cortical and sensory inputs that can affect the dynamic nature of the model. Driving response of the model subjected to various harmonic stimulations is considered to identify the effects of stimulus parameters on the cortical output. Detailed numerical studies including phase portraits, Poincare maps and bifurcation diagrams reveal a wide range of complex dynamics including period doubling and chaos in the output. Transition between different states can occur as the stimulation parameters are changed. In addition, the amplitude jump phenomena and hysteresis are shown to be possible as a result of the bending in the frequency response curve. These results suggest that the jump phenomenon due to the brain nonlinear resonance can be responsible for the transitions between ictal and interictal states.

AbstarctExisting isothermal nucleic acid amplification (INAA) relying on the strand displacement activity of DNA polymerase usually requires at least two primers. However, in this paper, we report an unusual isothermal multimerization and amplification (UIMA) which only needs one primer and is efficiently initiated by the strand-displacing DNA polymerases with reverse transcription activities. On electrophoresis, the products of UIMA present a cascade-shape band and they are confirmed to be multimeric DNAs with repeated target sequences. In contrast to current methods, UIMA is simple to product multimeric DNA, due to the independent of multiple primers and rolling circle structures. Through assaying the synthesized single-stranded DNA targets, UIMA performs high sensitivity and specificity, as well as the universality. In addition, a plausible mechanism of UIMA is proposed, involving short DNA bending, mismatch extension, and template slippage. UIMA is a good explanation for why nonspecific amplification easily happens in existing INAAs. As the simplest INAA till now, UIMA provides a new insight for deeply understanding INAA and opens a new avenue for thoroughly addressing nonspecific amplification.

Cytosolic phospholipase A2 (cPLA2) is an enzyme that releases arachidonic acid (AA) for the synthesis of eicosanoids and lysophospholipids which play critical roles in the initiation and modulation of oxidative stress and neuroinflammation. In the central nervous system, cPLA2 activation is implicated in the pathogenesis of various neurodegenerative diseases that involves neuroinflammation, thus making it an important pharmacological target. In this paper, a new class of arachidonic acid (AA) analogues was synthesized and evaluated for their ability to inhibit cPLA2. Several compounds were found to inhibit cPLA2 more strongly than arachidonyl trifluoromethyl ketone (AACOCF3), an inhibitor that is commonly used in the study of cPLA2-related neurodegenerative diseases. Subsequent experiments concluded that one of the inhibitors was found to be cPLA2-selective, non-cytotoxic, cell and brain penetrant and capable of reducing reactive oxygen species (ROS) and nitric oxide (NO) production in stimulated microglial cells. Computational studies were employed to understand how the compound interacts with cPLA2.

Gene selection is an attractive and important task in cancer survival analysis. Most existing supervised learning methods can only use the labeled biological data, while the censored data (weakly labeled data) far more than the labeled data are ignored in model building. Trying to utilize such information in the censored data, a semi-supervised learning framework (Cox-AFT model) combined with Cox proportional hazard (Cox) and accelerated failure time (AFT) model was used in cancer research, which has better performance than the single Cox or AFT model. This method, however, is easily affected by noise. To alleviate this problem, in this paper we combine the Cox-AFT model with self-paced learning (SPL) method to more effectively employ the information in the censored data in a self-learning way. SPL is a kind of reliable and stable learning mechanism, which is recently proposed for simulating the human learning process to help the AFT model automatically identify and include samples of high confidence into training, minimizing interference from high noise. Utilizing the SPL method produces two direct advantages: (1) The utilization of censored data is further promoted; (2) the noise delivered to the model is greatly decreased. The experimental results demonstrate the effectiveness of the proposed model compared to the traditional Cox-AFT model.

Addition of glucose to starved Saccharomyces cerevisiae initiates collective NADH dynamics termed glycolytic oscillations. Numerous questions remain about the extent to which single cells can oscillate, if oscillations occur in natural conditions, and potential physiological consequences of oscillations. In this paper, we report sustained glycolytic oscillations in single cells without the need for cyanide. Glucose addition to immobilized cells induced pH oscillations that could be imaged with fluorescent sensors. A population of cells had oscillations that were heterogeneous in frequency, start time, stop time, duration and amplitude. These changes in cytoplasmic pH were necessary and sufficient to drive changes in NADH. Oscillators had lower mitochondrial membrane potentials and budded more slowly than non-oscillators. We also uncovered a new type of oscillation during recovery from H2O2 challenge. Our data show that pH in S. cerevisiae changes over several time scales, and that imaging pH offers a new way to measure glycolytic oscillations on individual cells.

Porous silica is an attractive biomaterial in many applications, including drug-delivery systems, bone-graft fillers and medical devices. The issue with porous silica biomaterials is the rate at which they resorb and the significant role played by interfacial chemistry on the host response in vivo. This paper explores the potential of diatom-biosilica as a model tool to assist in the task of mapping and quantifying the role of surface topography and chemical cues on cell fate. Diatoms are unicellular microalgae whose cell walls are composed of, amorphous nanopatterned biosilica that cannot be replicated synthetically. Their unique nanotopography has the potential to improve understanding of interface reactions between materials and cells. This study used Cyclotella meneghiniana as a test subject to assess cytotoxicity and pro-inflammatory reactions to diatom-biosilica. The results suggest that diatom-biosilica is non-cytotoxic to J774.2 macrophage cells, and supports cell proliferation and growth. The addition of amine and thiol linkers have shown a significant effect on cytotoxicity, growth and cytokine response, thus warranting further investigation into the interfacial effects of small chemical modifications to substrate surfaces. The overall findings suggest diatom-biosilica offers a unique platform for in-depth investigation of the role played by nanotopography and chemistry in biomedical applications.

DJ-1 (also known as PARK7) has been identified as a causal gene for hereditary recessive Parkinson’s disease (PD). Consequently, the full elucidation of DJ-1 function will help decipher the molecular mechanisms underlying PD pathogenesis. However, because various, and sometimes inconsistent, roles for DJ-1 have been reported, the molecular function of DJ-1 remains controversial. Recently, a number of papers have suggested that DJ-1 family proteins are involved in aldehyde detoxification. We found that DJ-1 indeed converts methylglyoxal (pyruvaldehyde)-adducted glutathione (GSH) to intact GSH and lactate. Based on evidence that DJ-1 functions in mitochondrial homeostasis, we focused on the possibility that DJ-1 protects co-enzyme A (CoA) and its precursor in the CoA synthetic pathway from aldehyde attack. Here, we show that intact CoA and β-alanine, an intermediate in CoA synthesis, are recovered from methylglyoxal-adducts by recombinant DJ-1 purified from E. coli. In this process, methylglyoxal is converted to L-lactate rather than the D-lactate produced by a conventional glyoxalase. PD-related pathogenic mutations of DJ-1 (L10P, M26I, A104T, D149A, and L166P) impair or abolish detoxification activity, suggesting a pathological significance. We infer that a key to understanding the biological function of DJ-1 resides in its methylglyoxal-adduct hydrolase activity, which protects low-molecular thiols, including CoA, from aldehydes.

Single molecule microarrays have been used in quantitative proteomics, in particular, single cell analysis requiring high sensitivity and ultra-low limits of detection. In this paper, several image analysis methods are evaluated for their ability to accurately enumerate single molecules bound to a microarray spot. Crucially, protein abundance in single cells can vary significantly and may span several orders of magnitude. This poses a challenge to single molecule image analysis. In order to quantitatively assess the performance of each method, synthetic image datasets are generated with known ground truth whereby the number of single molecules varies over 5 orders of magnitude with a range of signal to noise ratios. Experiments were performed on synthetic datasets whereby the number of single molecules per spot corresponds to realistic single cell distributions whose ground truth summary statistics are known. The methods of image analysis are assessed in their ability to accurately estimate the distribution parameters. It is shown that super-resolution image analysis methods can significantly improve counting accuracy and better cope with single molecule congestion. The results highlight the challenge posed by quantitative single cell analysis and the implications to performing such analyses using microarray based approaches are discussed.

Identifying drug target genes in gene expression profiles is not straightforward. Because a drug targets proteins and not mRNAs, the mRNA expression of drug target genes is not always altered. In addition, the interaction between a drug and protein can be context dependent; this means that simple drug incubation experiments on cell lines do not always reflect the real situation during active disease. In this paper, I applied tensor-decomposition-based unsupervised feature extraction to the integrated analysis using a mathematical product of gene expression in various diseases and gene expression in the DrugMatrix dataset, where comprehensive data on gene expression during various drug treatments of rats are reported. I found that this strategy, in a fully unsupervised manner, enables researchers to identify a combined set of genes and compounds that significantly overlap with gene and drug interactions identified in the past. As an example illustrating the usefulness of this strategy in drug discovery experiments, I considered cirrhosis, for which no effective drugs have ever been proposed. The present strategy identified two promising therapeutic-target genes, CYPOR and HNFA4; for their protein products, bezafibrate was identified as a promising candidate drug, supported by in silico docking analysis.

Investigating how genes jointly affect complex human diseases is important, yet challenging. The network approach (e.g., weighted gene co-expression network analysis (WGCNA)) is a powerful tool. However, genomic data usually contain substantial batch effects, which could mask true genomic signals. Paired design is a powerful tool that can reduce batch effects. However, it is currently unclear how to appropriately apply WGCNA to genomic data from paired design. In this paper, we modified the current WGCNA pipeline to analyse high-throughput genomic data from paired design. We illustrated the modified WGCNA pipeline by analysing the miRNA dataset provided by Shiah et al. (2014), which contains forty oral squamous cell carcinoma (OSCC) specimens and their matched non-tumourous epithelial counterparts. OSCC is the sixth most common cancer worldwide. The modified WGCNA pipeline identified two sets of novel miRNAs associated with OSCC, in addition to the existing miRNAs reported by Shiah et al. (2014). Thus, this work will be of great interest to readers of various scientific disciplines, in particular, genetic and genomic scientists as well as medical scientists working on cancer.

The validity of the Lophophorata as a monophyletic group remains controversial. New data on the innervation of the lophophore, which is a unique feature of the lophophorates, may help clarify the status of the Lophophorata and provide new information on the early evolution of the group. In this paper, the organization of the nervous system of the lophophore is described in adults of the minute phoronid Phoronis ovalis. The lophophore nervous system includes a dorsal ganglion, a tentacular nerve ring, an inner ganglion, an inner nerve ring, and six nerves in each tentacle. The inner ganglion and inner nerve ring, which is associated with sensory cells, are described for the first time in adult phoronids. The general plan of the nervous system of the lophophore and tentacles is similar in P. ovalis and bryozoans. These new results suggest the presence of two nerve centers and two nerve rings in the last common ancestor of phoronids and bryozoans. During evolution, bryozoans may have lost the outer nerve center and outer nerve ring, whereas phoronids may have lost the inner nerve center and inner nerve ring. These morphological results evidence the lophophorates are monophyletic.

Irrigation with eutrophic water containing cyanobacteria toxins poses a potential risk to soil animals. To evaluate ecotoxicological effect of microcystins (MCs) on earthworms, filter paper acute toxicity test, avoidance test and a 14-d artificial soil test were carried out. No acute toxicity was found in the filter paper test, and earthworms showed no avoidance response to MCs exposure. In the artificial soil test, Eisenia fetida were allowed to grow in presence or absence of MCs (0, 1, 10, 100, 1000 μg kg−1 of soil) for 1, 7, and 14 d. Results showed that MCs could bioaccumulated in earthworm. A stimulatory effect on catalase and glutathione oxidase activities induced by MCs was found on day 1, and both of them were significantly inhibited at 100 and 1000 μg kg−1 on days 14. The superoxide dismutase activity was relatively insensitive. Significant increase of malondialdehyde content and decrease of neutral red retention time were observed at 100 and 1000 μg kg−1 on days 7 and 14. Our results suggest that MCs induces oxidative stress on earthworms, which leads to disruption of the antioxidant system and lipid peroxidation, as well as alterations in lysosomal membrane stability.

Text mining has become an important tool in bioinformatics research with the massive growth in the biomedical literature over the past decade. Mining the biomedical literature has resulted in an incredible number of computational algorithms that assist many bioinformatics researchers. In this paper, we present a text mining system called Gene Interaction Rare Event Miner (GIREM) that constructs gene-gene-interaction networks for human genome using information extracted from biomedical literature. GIREM identifies functionally related genes based on their co-occurrences in the abstracts of biomedical literature. For a given gene g, GIREM first extracts the set of genes found within the abstracts of biomedical literature associated with g. GIREM aims at enhancing biological text mining approaches by identifying the semantic relationship between each co-occurrence of a pair of genes in abstracts using the syntactic structures of sentences and linguistics theories. It uses a supervised learning algorithm, weighted logistic regression to label pairs of genes to related or un-related classes, and to reflect the population proportion using smaller samples. We evaluated GIREM by comparing it experimentally with other well-known approaches and a protein-protein interactions database. Results showed marked improvement.

This paper addresses a fundamental question, are eyes closed and eyes open resting states equivalent baseline conditions, or do they have consistently different electrophysiological signatures? We compare the functional connectivity patterns in an eyes closed resting state with an eyes open resting state to investigate the alpha desynchronization hypothesis. The change in functional connectivity from eyes closed to eyes open, is here, for the first time, studied with intracranial recordings. We perform network connectivity analysis in iEEG and we find that phase-based connectivity is sensitive to the transition from eyes closed to eyes open only in interhemispheral and frontal electrodes. Power based connectivity, on the other hand, consistently discriminates between the two conditions in temporal and interhemispheral electrodes. Additionally, we provide a calculation for the wiring cost, defined in terms of the connectivity between electrodes weighted by distance. We find that the wiring cost variation from eyes closed to eyes open is sensitive to the eyes closed and eyes open conditions. We extend the standard network-based approach using the filtration method from algebraic topology which does not rely on the threshold selection problem. Both the wiring cost measure defined here and this novel methodology provide a new avenue for understanding the electrophysiology of resting state.

In this paper we use Bayesian networks to determine and visualise the interactions among various Class III malocclusion maxillofacial features during growth and treatment. We start from a sample of 143 patients characterised through a series of a maximum of 21 different craniofacial features. We estimate a network model from these data and we test its consistency by verifying some commonly accepted hypotheses on the evolution of these disharmonies by means of Bayesian statistics. We show that untreated subjects develop different Class III craniofacial growth patterns as compared to patients submitted to orthodontic treatment with rapid maxillary expantion and facemask therapy. Among treated patients the CoA segment (the maxillary length) and the ANB angle (the antero-posterior relation of the maxilla to the mandible) seem to be the skeletal subspaces that receive the main effect of the treatment., 15 pages, 10 figures

DNA-binding proteins play a very important role in the structural composition of the DNA. In addition, they regulate and effect various cellular processes like transcription, DNA replication, DNA recombination, repair and modification. The experimental methods used to identify DNA-binding proteins are expensive and time consuming and thus attracted researchers from computational field to address the problem. In this paper, we present iDNAProt-ES, a DNA-binding protein prediction method that utilizes both sequence based evolutionary and structure based features of proteins to identify their DNA-binding functionality. We used recursive feature elimination to extract an optimal set of features and train them using Support Vector Machine (SVM) with linear kernel to select the final model. Our proposed method significantly outperforms the existing state-of-the-art predictors on standard benchmark dataset. The accuracy of the predictor is 90.18% using jack knife test and 88.87% using 10-fold cross validation on the benchmark dataset. The accuracy of the predictor on the independent dataset is 80.64% which is also significantly better than the state-of-the-art methods. iDNAProt-ES is a novel prediction method that uses evolutionary and structural based features. We believe the superior performance of iDNAProt-ES will motivate the researchers to use this method to identify DNA-binding proteins. iDNAProt-ES is publicly available as a web server at: http://brl.uiu.ac.bd/iDNAProt-ES/.

A conventional anaerobic baffled reactors (ABRs) treating high strength sweet potato starch wastewater at ambient temperatures resulted in acidification and bad performances. After modification, the acidification was remitted and COD removal efficiencies reached 92.73% at high temperatures and were maintained at 71.19% at low temperatures. Moreover, as much as 1.014 ± 0.056 L CH4/L/d were collected at Stage III. The q-PCR results revealed that the largest methanogen populations emerged at Stage III as well, which was 5.29 × 108 mcrA copies per milliliter sludge. A comparable shift in the archaeal community structure at different stages and acetoclastic methanogens Methanosaeta predominated the archaeal community in every compartment in Stages I (63.73%) and II (48.63%). Finally, the net energy gains analysis at mesophilic, thermophilic, and ambient temperature revealed that modified ABR at ambient temperature was not only economical but also profitable and could generated 3.68 KJ energy per gram COD removed.