An application of neighbourhoods in digraphs to the classification of binary dynamics

A binary state on a graph means an assignment of binary values to its vertices. A time dependent sequence of binary states is referred to as binary dynamics. We describe a method for the classification of binary dynamics of digraphs, using particular choices of closed neighbourhoods. Our motivation and application comes from neuroscience, where a directed graph is an abstraction of neurons and their connections, and where the simplification of large amounts of data is key to any computation. We present a topological/graph theoretic method for extracting information out of binary dynamics on a graph, based on a selection of a relatively small number of vertices and their neighbourhoods. We consider existing and introduce new real-valued functions on closed neighbourhoods, comparing them by their ability to accurately classify different binary dynamics. We describe a classification algorithm that uses two parameters and sets up a machine learning pipeline. We demonstrate the effectiveness of the method on simulated activity on a digital reconstruction of cortical tissue of a rat, and on a non-biological random graph with similar density.

Moderate deviation principle for $ m $-dependent random variables under the sub-linear expectation

<abstract><p>Let $ \{X_n, n\geq1\} $ be a sequence of $ m $-dependent strictly stationary random variables in a sub-linear expectation $ (\Omega, \mathcal{H}, \mathbb{E}) $. In this article, we give the definition of $ m $-dependent sequence of random variables under sub-linear expectation spaces taking values in $ \mathbb{R} $. Then we establish moderate deviation principle for this kind of sequence which is strictly stationary. The results in this paper generalize the result that in the case of independent identically distributed samples. It provides a basis to discuss the moderate deviation principle for other types of dependent sequences.</p></abstract>

NGLY1: Insights from C. elegans

Summary Peptide:N-glycanase is an evolutionarily conserved deglycosylating enzyme that catalyzes the removal of N-linked glycans from cytosolic glycoproteins. Recessive mutations that inactivate this enzyme cause NGLY1 deficiency, a multisystemic disorder with symptoms including developmental delay and defects in cognition and motor control. Developing treatments for NGLY1 deficiency will require an understanding of how failure to deglycosylate NGLY1 substrates perturbs cellular and organismal function. In this review, I highlight insights into peptide:N-glycanase biology gained by studies in the highly tractable genetic model animal C. elegans. I focus on the recent discovery of SKN-1A/Nrf1, an N-glycosylated transcription factor, as a peptide:N-glycanase substrate critical for regulation of the proteasome. I describe the elaborate post-translational mechanism that culminates in activation of SKN-1A/Nrf1 via NGLY1-dependent ‘sequence editing’ and discuss the implications of these findings for our understanding of NGLY1 deficiency.

A Molecular Survey of Bacterial Species in the Guts of Black Soldier Fly Larvae (Hermetia illucens) Reared on Two Urban Organic Waste Streams in Kenya

Globally, the expansion of livestock and fisheries production is severely constrained due to the increasing costs and ecological footprint of feed constituents. The utilization of black soldier fly (BSF) as an alternative protein ingredient to fishmeal and soybean in animal feed has been widely documented. The black soldier fly larvae (BSFL) used are known to voraciously feed and grow in contaminated organic wastes. Thus, several concerns about their safety for inclusion into animal feed remain largely unaddressed. This study evaluated both culture-dependent sequence-based and 16S rDNA amplification analysis to isolate and identify bacterial species associated with BSFL fed on chicken manure (CM) and kitchen waste (KW). The bacteria species from the CM and KW were also isolated and investigated. Results from the culture-dependent isolation strategies revealed that Providencia sp. was the most dominant bacterial species detected from the guts of BSFL reared on CM and KW. Morganella sp. and Brevibacterium sp. were detected in CM, while Staphylococcus sp. and Bordetella sp. were specific to KW. However, metagenomic studies showed that Providencia and Bordetella were the dominant genera observed in BSFL gut and processed waste substrates. Pseudomonas and Comamonas were recorded in the raw waste substrates. The diversity of bacterial genera recorded from the fresh rearing substrates was significantly higher compared to the diversity observed in the gut of the BSFL and BSF frass (leftovers of the rearing substrates). These findings demonstrate that the presence and abundance of microbiota in BSFL and their associated waste vary considerably. However, the presence of clinically pathogenic strains of bacteria in the gut of BSFL fed both substrates highlight the biosafety risk of potential vertical transmission that might occur, if appropriate pre-and-postharvest measures are not enforced.

Rose Exctracts and In-Vitro Inhibition of SARS-CoV-2 Spike: The Ace-2 Interaction

Objective The study was performed to evaluate the novel potential of red rose extract to inhibit SARS-CoV-2 spike protein-Ace2 receptor interaction in vitro. Methods ACE2 receptors were His-labelled, and the interaction was studied by chemiluminescence after the addition of anti-His HRP and HRP substrate. The inhibition of SARS-CoV-2 and ACE2 was assessed in a dose-dependent sequence. Results The 50% inhibitory concentration was observed at 0.75 percent v/v of the rose extract, and the 90% inhibition was seen at about 1.8 percent v/v. Steam inhalation or nebulization could be simple methods of delivering rose extracts to the lower respiratory tract and pulmonary tissues. Conclusion Rose extracts have a potential for inhibition of SARS-CoV-2 and ACE2 receptor in vitro, which could add beneficial effects in Covid-19 treatment. Further tests need to be performed to study their therapeutic benefits in vivo.

Utility Mining Across Multi-Dimensional Sequences

Knowledge extraction from database is the fundamental task in database and data mining community, which has been applied to a wide range of real-world applications and situations. Different from the support-based mining models, the utility-oriented mining framework integrates the utility theory to provide more informative and useful patterns. Time-dependent sequence data are commonly seen in real life. Sequence data have been widely utilized in many applications, such as analyzing sequential user behavior on the Web, influence maximization, route planning, and targeted marketing. Unfortunately, all the existing algorithms lose sight of the fact that the processed data not only contain rich features (e.g., occur quantity, risk, and profit), but also may be associated with multi-dimensional auxiliary information, e.g., transaction sequence can be associated with purchaser profile information. In this article, we first formulate the problem of utility mining across multi-dimensional sequences, and propose a novel framework named MDUS to extract <underline>M</underline>ulti-<underline>D</underline>imensional <underline>U</underline>tility-oriented <underline>S</underline>equential useful patterns. To the best of our knowledge, this is the first study that incorporates the time-dependent sequence-order, quantitative information, utility factor, and auxiliary dimension. Two algorithms respectively named MDUS EM and MDUS SD are presented to address the formulated problem. The former algorithm is based on database transformation, and the later one performs pattern joins and a searching method to identify desired patterns across multi-dimensional sequences. Extensive experiments are carried on six real-life datasets and one synthetic dataset to show that the proposed algorithms can effectively and efficiently discover the useful knowledge from multi-dimensional sequential databases. Moreover, the MDUS framework can provide better insight, and it is more adaptable to real-life situations than the current existing models.

The Convergence Rate of High-Dimensional Sample Quantiles for φ-Mixing Observation Sequences

In this paper, we obtain the convergence rate for the high-dimensional sample quantiles with the φ-mixing dependent sequence. The resulting convergence rate is shown to be faster than that obtained by the Hoeffding-type inequalities. Moreover, the convergence rate of the high-dimensional sample quantiles for the observation sequence taking discrete values is also provided.

Bladder Semantic Segmentation

Obtaining information about the shape and volume of the bladder plays a significant role in determining the pathologies of this organ. To collect the relevant data, the first thing to do is to separate the bladder from the background on the ultrasound image. The article is devoted to automation this process using an algorithm based on the Unet architecture with a pretrained imagenet encoder (encoder – ResNet50). The article gives a comparative analysis of some well-known methods in the literature that improve the accuracy of the proposed algorithm. The quality of the basic architecture has been improved by more than 4 percent on the PR AUC metric (from 84.49% to 89.62%) in the series of experiments with the help of automatic annotation of previously unmarked data. In addition, there are two important results showing practical effectiveness of using the data from another medical task (which raised the accuracy to 88.50%) and using time-dependent sequence of frames inside the video (raised the quality to 88.19%).

Ligation of random oligomers leads to emergence of autocatalytic sequence network

ABSTRACTThe emergence of longer information-carrying and functional nucleotide polymers from random short strands was a major stepping stone at the dawn of life. But the formation of those polymers under temperature oscillation required some form of selection. A plausible mechanism is template-based ligation where theoretical work already suggested a reduction in information entropy.Here, we show how nontrivial sequence patterns emerge in a system of random 12mer DNA sequences subject to enzyme-based templated ligation reaction and temperature cycling. The strands acted both as a template and substrates of the reaction and thereby formed longer oligomers. The selection for templating sequences leads to the development of a multiscale ligation landscape. A position-dependent sequence pattern emerged with a segregation into mutually complementary pools of A-rich and T-rich sequences. Even without selection for function, the base pairing of DNA with ligation showed a dynamics resembling Darwinian evolution.