Diversity of Epiphytic Bacterial Communities on Male and Female Porphyra Haitanensis

PurposeEpiphytic bacteria play an important role in macroalgae growth, development, and morphogenesis. However, epiphytic bacterial communities on male and female macroalgae have not been reported. Porphyra haitanensis is one of the main economic macroalgae.In order to explore the similarities and differences of epiphytic bacterial community structure between male and female macroalgae of Porphyra haitanensis.MethodsWe investigated the composition, diversity of epiphytic bacterial communities between male and female Porphyra haitanensis by 16S rDNA high-throughput sequencing.ResultsThe divergences of bacterial community compositions occurred between males and females. Both males and females had their unique bacterial microbiota, such as, Armatimonadetes and Rokubacteria are the unique phyla of male Porphyra haitanensis, Chlamydiae is a unique phylum of female Porphyra haitanensis. The epiphytic bacteria on both male and female Porphyra haitanensis have the similar predictive functions, but they also have their own specific functions, respectively.The specific functions of epiphytic bacteria on female Porphyra haitanensis were sulfite_respiration, nitrogen_fixation, nitrate_ammonification, chlorate_reducers and anoxygenic_photoautotrophy_S_oxidizing. ConclusionsThis study provides a basis for exploring the mechanism of epiphytic bacterial communities on dioecious algae and are of great significance for further understanding the relationships between epiphytic microbial communities and the sex of algae.

The Human Cerebellum as a Hub of the Predictive Brain

Although the cerebellum has long been believed to be involved uniquely in sensorimotor processes, recent research works pointed to its participation in a wide range of cognitive predictive functions. Here, we review the available evidence supporting a generalized role of the cerebellum in predictive computation. We then discuss the anatomo-physiological properties that make the cerebellum the ideal hub of the predictive brain. We further argue that cerebellar involvement in cognition may follow a continuous gradient, with higher cerebellar activity occurring for tasks relying more on predictive processes, and outline the empirical scenarios to probe this hypothesis.

Experimental confirmation that an uncommon, yet clinically relevant mutation (G878A) in the rrs gene of Mycobacterium tuberculosis confers resistance to streptomycin.

The effective treatment of patients diagnosed with drug resistant tuberculosis (TB) is highly dependent upon the ability to rapidly and accurately determine the antibiotic resistance/ susceptibility profile of the Mycobacterium tuberculosis isolate(s) involved. Thus, as more and more clinical microbiology laboratories advance towards the routine use of DNA sequence-based diagnostics, it is imperative that their predictive functions extend beyond the well-known resistance-conferring mutations, in order to also encompass as many of the lower-frequency mutations as possible. However, in most cases, the fundamental experimental proof that links these uncommon mutations with phenotypic resistance is still lacking. One such example is the G878A polymorphism within the rrs gene encoding the 16s rRNA. We, and others, have identified this mutation within a small number of drug-resistant M. tuberculosis isolates, although prior to this study a consensus regarding exactly which aminoglycoside antibiotic(s) it conferred resistance toward seems not to have been reached. Here we have employed oligo-mediated recombineering to specifically introduce the G878A polymorphism into the rrs gene of M. bovis BCG - a species very closely related to M. tuberculosis - and demonstrate that it confers low-level resistance to streptomycin alone. In our hands, it does not confer cross-resistance towards amikacin, capreomycin, nor kanamycin. We also demonstrate that the rrsG878A mutation exerts a substantial fitness defect in vitro, that may at least in part explain why clinical M. tuberculosis isolates bearing this mutation appear to be quite rare. Overall, this study provides clarity to the resistance phenotype attributable to the rrsG878A mutation and is relevant to the future implementation of genomics-based diagnostics, as well as the clinical management of patients in situations where this particular polymorphism is encountered.

Predictive Functions of H3K27me3 and H4K20me3 in Primate Hippocampal Stem and Progenitor Cells

Epigenetic regulations play important roles in cell fate determination during neurogenesis, a process by which different types of neurons are generated from neural stem and progenitor cells (NSPCs). Although some epigenetic changes are part of developmental and aging processes, the role of tri-methylation on histone 3 lysine 27 (H3K27me3) and histone 4 lysine 20 (H4K20me3) in primate hippocampal NSPCs remains elusive. This task is best assessed within a context resembling the human brain. As more studies emerge, the baboon represents an excellent model of human central nervous system in addition to their genomic similarity. With a focus on H3K27me3 and H4K20me3, the overarching goal of this work is to reveal their respective epigenetic landscapes in NSPCs of non-human primate baboon hippocampus. We identified putative targets of H3K27me3 and H4K20me3 that suggests a protective mechanism by dual H3K27me3/H4K20me3-mediated repression of specific-lineage gene activation important for differentiation processes while controlling the progression of the cell cycle.

Entamoeba histolytica: Proteomics Bioinformatics Reveal Predictive Functions and Protein–Protein Interactions of Differentially Abundant Membrane and Cytosolic Proteins

Amoebiasis is caused by Entamoeba histolytica and ranked second for parasitic diseases causing death after malaria. E. histolytica membrane and cytosolic proteins play important roles in the pathogenesis. Our previous study had shown several cytosolic proteins were found in the membrane fraction. Therefore, this study aimed to quantify the differential abundance of membrane and cytosolic proteins in membrane versus cytosolic fractions and analyze their predicted functions and interaction. Previous LC-ESI-MS/MS data were analyzed by PERSEUS software for the differentially abundant proteins, then they were classified into their functional annotations and the protein networks were summarized using PantherDB and STRiNG, respectively. The results showed 24 (44.4%) out of the 54 proteins that increased in abundance were membrane proteins and 30 were cytosolic proteins. Meanwhile, 45 cytosolic proteins were found to decrease in abundance. Functional analysis showed differential abundance proteins involved in the molecular function, biological process, and cellular component with 18.88%, 33.04% and, 48.07%, respectively. The STRiNG server predicted that the decreased abundance proteins had more protein–protein network interactions compared to increased abundance proteins. Overall, this study has confirmed the presence of the differentially abundant membrane and cytosolic proteins and provided the predictive functions and interactions between them.

Using data science for medical decision making case: role of gut microbiome in multiple sclerosis

Background A decade ago, the advancements in the microbiome data sequencing techniques initiated the development of research of the microbiome and its relationship with the host organism. The development of sophisticated bioinformatics and data science tools for the analysis of large amounts of data followed. Since then, the analyzed gut microbiome data, where microbiome is defined as a network of microorganisms inhabiting the human intestinal system, has been associated with several conditions such as irritable bowel syndrome - IBS, colorectal cancer, diabetes, obesity, and metabolic syndrome, and lately in the study of Parkinson’s and Alzheimer’s diseases as well. This paper aims to provide an understanding of differences between microbial data of individuals who have been diagnosed with multiple sclerosis and those who were not by exploiting data science techniques on publicly available data. Methods This study examines the relationship between multiple sclerosis (MS), an autoimmune central nervous system disease, and gut microbial community composition, using the samples acquired by 16s rRNA sequencing technique. We have used three different sets of MS samples sequenced during three independent studies (Jangi et al, Nat Commun 7:1–11, 2016), (Miyake et al, PLoS ONE 10:0137429, 2015), (McDonald et al, Msystems 3:00031–18, 2018) and this approach strengthens our results. Analyzed sequences were from healthy control and MS groups of sequences. The extracted set of statistically significant bacteria from the (Jangi et al, Nat Commun 7:1–11, 2016) dataset samples and their statistically significant predictive functions were used to develop a Random Forest classifier. In total, 8 models based on two criteria: bacteria abundance (at six taxonomic levels) and predictive functions (at two levels), were constructed and evaluated. These include using taxa abundances at different taxonomy levels as well as predictive function analysis at different hierarchical levels of KEGG pathways. Results The highest accuracy of the classification model was obtained at the genus level of taxonomy (76.82%) and the third hierarchical level of KEGG pathways (70.95%). The second dataset’s 18 MS samples (Miyake et al, PLoS ONE 10:0137429, 2015) and 18 self-reported healthy samples from the (McDonald et al, Msystems 3:00031–18, 2018) dataset were used to validate the developed classification model. The significance of this step is to show that the model is not overtrained for a specific dataset but can also be used on other independent datasets. Again, the highest classification model accuracy for both validating datasets combined was obtained at the genus level of taxonomy (70.98%) and third hierarchical level of KEGG pathways (67.24%). The accuracy of the independent set remained very relevant. Conclusions Our results demonstrate that the developed classification model provides a good tool that can be used to suggest the presence or absence of MS condition by collecting and analyzing gut microbiome samples. The accuracy of the model can be further increased by using sequencing methods that allow higher taxa resolution (i.e. shotgun metagenomic sequencing).

Combining Interior Orientation Variables to Predict the Accuracy of Rpas–Sfm 3D Models

Remotely piloted aerial systems (RPAS) have been recognized as an effective low-cost tool to acquire photogrammetric data of low accessible areas reducing collection and processing time. Data processing techniques like structure from motion (SfM) and multiview stereo (MVS) techniques, can nowadays provide detailed 3D models with an accuracy comparable to the one generated by other conventional approaches. Accuracy of RPAS-based measures is strongly dependent on the type of adopted sensors. Nevertheless, up to now, no investigation was done about relationships between camera calibration parameters and final accuracy of measures. In this work, authors tried to fill this gap by exploring those dependencies with the aim of proposing a prediction function able to quantify the potential final error in respect of camera parameters. Predictive functions were estimated by combining multivariate and linear statistical techniques. Four photogrammetric RPAS acquisitions were considered, supported by ground surveys, to calibrate the predictive model while a further acquisition was used to test and validate it. Results are preliminary, but promising. The calibrated predictive functions relating camera internal orientation (I.O.) parameters with final accuracy of measures (root mean squared error) showed high reliability and accuracy.

E-Bayesian Prediction for the Burr XII Model Based on Type-II Censored Data with Two Samples

Type-II censored data is an important scheme of data in lifetime studies. The purpose of this paper is to obtain E-Bayesian predictive functions which are based on observed order statistics with two samples from two parameter Burr XII model. Predictive functions are developed to derive both point prediction and interval prediction based on type-II censored data, where the median Bayesian estimation is a novel formulation to get Bayesian sample prediction, as the integral for calculating the Bayesian prediction directly does not exist. All kinds of predictions are obtained with symmetric and asymmetric loss functions. Two sample techniques are considered, and gamma conjugate prior density is assumed. Illustrative examples are provided for all the scenarios considered in this article. Both illustrative examples with real data and the Monte Carlo simulation are carried out to show the new method is acceptable. The results show that Bayesian and E-Bayesian predictions with the two kinds of loss functions have little difference for the point prediction, and E-Bayesian confidence interval (CI) with the two kinds of loss functions are almost similar and they are more accurate for the interval prediction.

Predictive Analysis of Robotic Manipulators Through Inertial Sensors and Pattern Recognition

Industrial robotics is constantly evolving, with installation forecast of about 2 million new robots in 2020. The predictive maintenance focused on industrial robots is beginning to be applied more, but its possibilities have not yet been fully exploited. The present study focuses on the applications offered by inertial sensors in the field of industrial robotics, specifically the possibility of measuring the “real” rotation angle of a robotic arm and comparing it with its own system of measure. The study will focus on the measurement of the backlash existing in the gearbox of the axis of a robot. Data received from the sensor will be analysed using the wavelet transform, and the mechanical state of the system could be determined. The introduction of this sensing system is safe, dynamic, and non-destructive, and it allows one to perform the measurement remotely, in the own installation of the robot and in working conditions. These features allow one to use the device in different predictive functions.