In this work, two plasmonic structures with two different periodic nanostructures (nanodisk and nanostrip arrays) separated from the gold layer by an ultrathin dielectric Al2O3 layer are proposed, and their optical properties are simulated as 3-dimensional models using COMSOL Multiphysics software tool. The optical responses, such as the amplification of the electric field and the scattering, absorption and extinction cross sections of the two structures, are investigated and compared. The results show that both structures show strong field enhancement in their gap layer, and the structure with the periodic array of metal nanodisks shows stronger field enhancement and larger absorption, extinction, and scattering cross sections in its dipole resonance wavelength, which is defined as a bright plasmonic mode. In addition, some weaker resonant modes are observed for both structures and the reason for their generation is dis-cussed. The analysis of different possible modes in such structures is crucial for their application in various fields.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

Due to the long duration and high cost of traditional pumping tests, the response of well water level to seismic wave, earth tide, and barometric pressure provides a feasible method for determining continuous changes in aquifer hydraulic parameters. Aiming at the problem that the response phase shift of well BB water level to tidal tide M2 is greater than that of tide O1, this paper preferentially calculated the time series changes of S, Be, n and BKu based on the response mechanism of well water level to barometric pressure and earth tide and with the help of smooth moving method. Then by using the mixed flow model, the variation of transmissivity and leakage coefficient over time were simultaneously obtained, and the evolution process and characteristics of aquifer parameters near well BB caused by the Wenchuan earthquake were ultimately discussed. The calculation results are basically consistent with the previous pumping test, and have been verified and compared by using other scholars' methods. This solving process avoids the problems such as excessive dependence on initial values, multiple solutions, and unstable tide O1, which has a promoting effect on the study of the impact of seismic activity on aquifer systems.

GaN ultraviolet photodetector with metal-semiconductor-metal structure is achieved by growing on a periodic trapezoid column-shape patterned sapphire substrate using metalorganic chemical vapor deposition. Under 5-V reverse bias, the photodetector fabricated on such patterned sapphire substrate exhibits a lower dark current, a higher photocurrent, and a 476 % enhancement in the maximum responsivity as compare with those of the photodetector fabricated on conventional flat sapphire substrate. It is also found that the much larger UV-to-visible rejection ratio and the fact that responsivity drops in a smaller cut-off region are observed from photodetector fabricated by using a periodic trapezoid column-shape patterned sapphire substrate. These phenomena may all be attributed to the reduction of threading dislocation density and the improved quality of GaN film, as well as the internal reflection and/or scattering effect on the interface between GaN film and the periodic trapezoid column-shape pattern of the substrate.

Lipofuscin is indigestible garbage that accumulates in the autophagic vesicles and cytosol of post-mitotic cells with age. Drs. Brunk and Terman postulated that lipofuscin accumulation is the main or at least a major driving factor in aging. They even posited that the evolution of memory is the reason why we get lipofuscin at all, as stable synaptic connections must be maintained over time, meaning that the somas of neurons must also remain in the same locale. In other words, they cannot dilute out their garbage over time through cell division. Mechanistically, their position certainly makes sense given that rendering a large percentage of a post-mitotic cell’s lysosomes useless must almost certainly negatively affect that cell and the surrounding microenvironment. Here, I explore the possibility that the accumulation of lipofuscin may to some extent exacerbate every other kind of age-related damage. I do not think that lipofuscin removal will reverse/prevent all forms of aging, just the major component facing us currently. In this piece, I will review what is known about lipofuscin accumulation from evolutionary and mechanistic standpoints and discuss a method of removing it from single cells in vitro.

The search for periodic signals from blazars has become an actively pursued field of research in recent years. This is because periodic signals bring us information about the processes occurring near the innermost regions of blazars, which are mostly inaccessible to our direct view. Such signals provide insights into some of the extreme conditions that take place in the vicinity of supermassive black holes that lead to the launch of the relativistic jets. In addition, studies of characteristic timescales in blazar light curves shed light on some of the challenging issues in blazar physics that includes disk-jet connection, strong gravity near fast rotating supermassive black holes and release of gravitational waves from binary supermassive black hole systems. However, a number of issues associated with the search for quasi-periodic oscillations (QPOs) in blazars e.g., red-noise dominance, modest significance of the detection, periodic modulation lasting for only a couple of cycles and their transient nature, make it difficult to estimate the true significance of the detection. Consequently, it also becomes difficult to make meaningful inferences about the nature of the on-going processes. In this proceedings, results of study focused on searching for QPOs in a number of blazar multi-frequency light curves are summarized. The time series analyses of long term observations of the blazars revealed the presence of year-timescale QPOs in the sources including OJ 287 (optical), Mrk 501 (gamma-ray), J1043+2408 (radio) and PKS 0219 -164 (radio). As likely explanations, we discuss a number of scenarios including binary supermassive black hole systems, Lense–Thirring precession, and jet precession.

Open source software such as OpenDSS has given a lot of help to distribution network researchers and educators. With high penetration of distributed renewable energy resources into distribution network, tradition distribution steady state analysis software such as OpenDSS is faced with difficulty in handling distributed generators. Three-phase distributed generators are often modeled in sequence frame while unbalanced distribution network are usually modeled in phase frame. So a load flow in sequence-phase coupled frame is proposed to handle models described in both frames. Voltage controlled DGs which are difficult to cope with in OpenDSS are handled in proposed program. The steady state analysis platform is programmed with open source Modelica language and the main aim of this paper is to introduce an open source platform for active distribution network steady analysis include load flow and short circuit analysis which can be easily adopted and improved by other educators and researchers.

In this paper, we investigate the equilibrium points of following a system of difference equations x_n+1 = x_n 2y_n−1, y _n+1 = y_n 2x_n−1. We also study the asymptotic stability of related system of difference equations. Further we examine the periodic solutions of related system with period two. Additionally, we find out the invariant interval and periodic cycles of related system of difference equations.

We investigate the influence of the Abrikosov vortex lattice on the Casimir force in a setup constituted by high-temperature superconductors subject to an external magnetic field. The Abrikosov lattice is a property of type II superconductors in which normal and superconducting carriers coexist and these latter define a periodic pattern with squared symmetry. We find that the optical properties determined by spatial redistribution of the superconducting order parameter induce Casimir forces with a periodic structure whose minimal strengths coincide with the vortex cores.

Today massive amounts of sequenced metagenomic and -transcriptomic data from different ecological niches and environmental locations are available. Scientific progress depends critically on methods that allow extracting useful information from the various types of sequence data. Here, we will first discuss types of information contained in the various flavours of biological sequence data, and how this information can be interpreted to increase our scientific knowledge and understanding. We argue that a mechanistic understanding is required to consistently interpret experimental observations, and that this understanding is greatly facilitated by the generation and analysis of dynamic mathematical models. We conclude that, in order to construct mathematical models and to test mechanistic hypotheses, time-series data is of critical importance. We review diverse techniques to analyse time-series data and discuss various approaches by which time-series of biological sequence data was successfully used to derive and test mechanistic hypotheses. Analysing the bottlenecks of current strategies in the extraction of knowledge and understanding from data, we conclude that combined experimental and theoretical efforts should be implemented as early as possible during the planning phase of individual experiments and scientific research projects.

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. In contemporary times, the transition from conventional electricity grids to Smart Grids constitutes an area where extensive research is conducted on a global scale. Among the research challenges, the investigation of Deep Transfer Learning (DTL) in the field of electricity load forecasting represents a fundamental effort that imparts generality to Artificial Intelligence applications, due to new capabilities, such as knowledge transfer and computational power reduction. In this paper a comprehensive study is conducted for day-ahead electricity load forecasting. For this purpose, three Sequence-to-Sequence (Seq2seq) Deep Learning (DL) models are used, namely the Multilayer Perceptron (MLP), the Convolutional Neural Network (CNN) and the Ensemble Learning Model (ELM), which is consisted of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL and emphasizing the way the datasets are trained and fine-tuning for higher forecasting accuracy. In order to implement the forecasting strategies using Deep Learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1-24 hours) for each month of the year for Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. After several trials, four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) contains the training of the DL models using Lesvos dataset, and the testing period performed directly on the Chios dataset without fine-tuning. The fourth strategy is a Multi-task Deep Learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. In this paper, we explore the performance of DTL and compare the results with those produced with MTDL. The results demonstrated that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, the two cases with fine-tuning (DTL Case 1 and 2) outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it has been concluded that the MLP model presents best monthly forecasts with a MAPE of 6.24% and 6.01% for the first two cases, the CNN model presents best monthly forecasts with a MAPE of 5.57% and 5.60% respectively and the ELM model achieves best monthly forecasts with a MAPE of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

We report on a 2D-gap surface plasmon metasurface based composed of a bottom metal layer, middle insulator layer, and top metal nanostructure of gold nanoblocks (nanoantennas). The proposed structure enables us to generate simultaneous multi plasmonic resonances and offers the possibility to tune them, within the near-infrared domain. The simplicity of the metasurface makes it promising for compact optical platforms based on reflection mode operation with potential application in multi-channel biosensors, second-harmonic generation, and multi-wavelength surface-enhanced spectroscopy among other interesting fields

Periodic travelling waves are observed in various brain activities including visual, motor, language, sleep, and so on. There are several neural field models describing periodic waves assuming nonlocal interaction and, possibly, inhibition, time delay, or some other properties. In this work we study the influence of asymmetric connectivity functions and of time delay on the emergence of periodic waves and on their properties. Nonlinear wave dynamics is studied, including modulated and aperiodic waves. Multiplicity of waves for the same values of parameters is observed. External stimulation in order to restore wave propagation in a damaged tissue is discussed.

The aim of this paper is to use the elementary method and the properties of the second kind Stirling numbers to study the congruence properties of a new sequence, which is closely related to Fubini polynomials and Euler numbers, and give some interesting congruences for it. This solves a problem proposed by the first author at an unpublished paper.

Most non-infectious diseases are associated with dysfunction of proteins or protein complexes. Аssociation between sequence and structure is analyzed since a long time, and analysis of sequence organization in domains and motifs is actual research area. A mathematical method is proposed here to identify the hierarchical organization of protein sequences. The method is based on pentapeptide as a unit of protein sequences. This method was applied on a non-homologous dataset of protein sequences. The analysis revealed 11 hierarchical levels of protein sequence organization, showing the relationship of these multiple fragments of sequences. Using different examples, we illustrated how the fragments of the spatial structure of protein correspond to the elements of the hierarchical structure of the protein sequence. A hierarchical structure is observed in the protein sequence. This methodology is an interesting basis for mathematically based classification of elements of spatial organization of proteins. Elements of the hierarchical structure of different levels of the hierarchy can be used for biotechnological and medical problems.

Two-dimensional heat diffusion problem with heat source which is quasilinear parabolic problem is examined analytically and numerically. Periodic boundary conditions are used as a boundary conditions. Since the problem is not linear, Picard’s successive approximation theorem is used. Under certain conditions of natural regularity and consistency imposed on the input data, establish the existence, uniqueness and constant dependence of the solution on the data using the generalized Fourier method. As a numerical solution, implicit finite difference scheme is used. The results ob-tained from analytical and the numerical solutions are so close to each other.

A multisystem chronic autoimmune disorder, Sjogren syndrome, is characterized by lymphocytic infiltrates in the exocrine glands. Keratoconjunctivitis sicca and xerostomia are the two pathognomic findings in Sjogren syndrome. Here, we present a case of a 30-year-oldan old Indian woman presenting with primary Sjogren syndrome. The clinical features, diagnostic modalities, treatment options, and management of complications have been discussed.

Finding anomalies in the real-time system is recognized as one of most challenging study in information security. It has so many applications like IoT, and Stock-Market. In any IoT system the data generated are real-time, and temporal in nature. Since due to the extreme exposure to Internet and interconnectivity of devices, the IoT systems often face issues like fraud, anomalies, intrusions etc. Discovering anomaly in such domain can be interesting. Clustering and rough set theory have been tried in many cases. Considering the time-stamp associated with IoT data, time-dependent patterns like periodic clusters can be generated which could be helpful for the efficient detection of anomalies by providing more in-depth analysis of the system. In this paper, a mixed method comprising of nano topology, a modified k-means clustering and an interval superimposition technique is used for finding fuzzy periodic clusters in the subspace generated by the nano topology. For every clusters there will be an associated sequence of time-intervals where it exists. The sequence time-intervals accompanying with each clusters may exhibit some remarkable patterns. For example, there may exist different types of periodicity namely yearly, monthly, daily, and hourly etc. For finding such fuzzy periodicity, an operation called interval-superimposition has been used. The time-intervals associated with each cluster are superimposed if they have reasonable overlapping. Each superimposed time-interval generates a fuzzy time-interval. The data instances are thought to be anomalous if they either belong to sparse clusters or don't belong to any clusters. The efficacy of the method can be assessed by means of both time-complexity analysis and comparative studies with existing clustering-based anomaly detection algorithms with a real-life and a synthetic dataset. It can been found experimentally that our method can extract anomaly with 98% of accuracy and it runs cubic time approximately.

Classical numerical methods for solving ordinary differential equations often produce less accurate results when applied to problems with oscillatory or periodic behaviour. To adapt them for such problems, they are usually modified using the exponential fitting technique. This adaptation allows for the construction of new methods from their classical counterparts. The new methods are usually more accurate, efficient and suitable for handling the oscillatory or periodic behaviour of the problem. In this work, we construct a two-parameter exponentially-fitted Taylor method suitable for solving oscillatory or periodic problems that possess two frequencies. The construction algorithm is based on a proposed six-step flowchart discussed by authors in related literature. Two standard test problems were used to illustrate the accuracy and performance of the proposed method.

Search for periodic signals from blazars has become widely discussed topic in recent years. This is because periodic signals bear imprints of the processes occurring near the innermost regions of blazars which are mostly inaccessible to our direct view. Such signals provide insights into various aspect of blazar studies including disk-jet connection, magnetic field configuration and, more importantly, strong gravity near the supermassive black holes and release of gravitational waves from the binary supermassvie black hole systems. In this work, we report detection of a periodic signal in the radio light curves of the blazar J1043+2408 spanning ~10.5 years. We performed multiple methods of time series analysis, namely, epoch folding, Lomb-Scargle periodogram, and discrete auto-correlation function. All three methods consistently reveal a repeating signal with a periodicity of ~563 days. To robustly account for the red-noise processes usually dominant in the blazar variability and other possible artifacts, a large number of Monte Carlo simulations were performed. This allowed us to estimate a high significance (99.9% local and 99.4% global) against possible spurious detection. As possible explanations, we discuss a number of scenarios including binary supermassive black hole system, Lense-Thirring precession, and jet swing and precession.

Abstract: The study used whole-genome sequencing (WGS) and bioinformatics analysis for the genomic characterization of 60 isolates of Listeria monocytogenes isolated from cattle farms, cattle abattoirs, and retail outlets in Gauteng province, South Africa. The isolates' sequence types (STs), clonal complexes (CCs), and lineages were determined using in silico multilocus sequence typing (MLST). We used BLAST-based analyses to identify virulence and antimicrobial genes, plasmids, proviruses, and the CRISPR-Cas system. The study investigated any association of the detected genes to the origin in the beef production chain of the L. monocytogenes isolates. Overall, in 60 isolates of Listeria monocytogenes, there were 7 STs, 6 CCs, 44 putative virulence factors, 2 resistance genes, 1 plasmid with AMR genes and 3 with conjugative genes, 1 CRISPR gene, and all 60 isolates were positive for proviruses. Among the 7 STs detected, ST204 (46.7%) and ST2 (21.7%) were the most prominent, with ST frequency varying significantly (p<0.001). The predominant CC detected were CC2 (21.7%) and CC204 (46.7%) in lineages I and II, respectively. Of the 44 virulence factors detected, 26 (across Listeria Pathogenicity Islands, LIPIs) were present in all the isolates. The difference in the detection frequency varied significantly (P<0.001). The two AMR genes (fosX and vga(G)) detected were present in all 60 (100%) isolates of L. monocytogenes. The only plasmid, NF033156, was present in 3 (5%) isolates. A CRISPR-Cas system was detected in 6 (10%), and all the isolates carried proviruses. Significant differences were detected in the frequencies of STs and virulence factors regarding the source and sample type of the L. monocytogenes isolates. The presence of both fosX and vga(G) genes in all the isolates from the three industries (cattle farms, abattoirs, and retail outlets) can potentially cause therapeutic implications. Our study, which characterized L. monocytogenes recovered from the three levels in the beef production chain in the country, provides the first evidence of the distribution of the pathogen with potential food safety and therapeutic implications.

The nucleosome is a major modulator of DNA accessibility to other cellular factors. Nucleosome positioning has a critical importance in regulating cell processes such as transcription, replication, recombination or DNA repair. The DNA sequence is a major factor influencing the position of nucleosomes on genomes. Different sequence motifs can promote or inhibit the nucleosome formation, thus influencing the accessibility to the DNA. Sequence-encoded nucleosome positioning having functional consequences on cell processes can then be selected or counter-selected during evolution. We review the interplay between sequence evolution and nucleosome positioning evolution. We first focus on the different ways to encode nucleosome positions in the DNA sequence, and to which extent these mechanisms are responsible of genome-wide nucleosome positioning in vivo. Then, we discuss the findings about selection of sequences for their nucleosomal properties. Finally, we illustrate how the nucleosome can directly influence sequence evolution through its interactions with DNA damage and repair mechanisms. This review aims to provide an overview of the mutual influence of sequence evolution and nucleosome positioning evolution, possibly leading to complex evolutionary dynamics.

Personalized recommendation is an important part of ecommerce platform. In the recommendation system, the neural network is used to enhance collaborative filtering to accurately capture user preferences, so as to obtain better recommendation performance. Traditional recommendation methods focused on the results of a single user behavior, ignoring the modeling of multiple interactive behaviors of users, such as click, add to cart and purchase. Although many studies had also focused on Multi-Behavior modeling, two important challenges remained. 1)Since multiple behavioral context information was ignored, it was still a challenge to identify the multimodal relationships of behaviors. 2) Surveillance signals were still sparse. In order to solve this problem, this paper proposes Two-path Multi-behavior Sequence Modeling(TP_MB). First, a two-path learning strategy is introduced to maximize the multiple interaction information of user items learned by the two paths, which effectively enhances the robustness of the model. Second, a multi-behavior dependent encoder is designed. Contextual information is obtained through behavior dependencies in the interaction of different user. In addition, three contrastive learning methods are designed, which not only obtain additional auxiliary supervision signals, but also alleviate the problem of sparse supervision signals. Extensive experiments on two real datasets demonstrate that our method outperforms state-of-the-art multi-behavior recommendation methods.

We study various properties of the polygonal numbers; such as their recurrence relations; fundamental identities; weighted binomial and ordinary sums; partial sums and generating functions of their powers; and a continued fraction representation for them. A feature of our results is that they are presented naturally in terms of the polygonal numbers themselves and not in terms of arbitrary integers; unlike what obtains in most literature.

In this study, we performed periodicity analyzes of selected daily solar (FI, CI), geomagnetic (Ap, Dst, Scalar B) and cosmic (CRI) activity indices for the last solar Cycle 24 (December 2008 to December 2019). To investigate the periodic variations of above selected data sets the Multi Taper Method (MTM), Morlet wavelet, Cross Wavelet Transform (XWT), and Wavelet Coherence (WTC) analysis techniques were used. In results of our analysis we found that i) the 25-33 and 2048-day periodicities exist in all data sets without any exception, while all other detected periodicities have data preferences, ii) all detected periods located around the maximum phase of the cycle (Cycle 24), iii) the phase relations between compared data sets periodicities are gradually changing from small periods to large ones; there are no phase relations between small periodicities and they show mixed phases, while they are completely in phase/antiphase for large periodicities, iv) all detected FI periodicities, except 2048 days periodicity, are common periodicities with all other data sets used in this study.

The vibration feature of weak gear fault is often covered in strong background noise, which makes it necessary to establish weak feature enhancement methods. Among the enhancement methods, stochastic resonance (SR) has the unique advantage of transferring noise energy to weak signals and has a great application prospection in weak signal extraction. But the traditional SR potential model cannot form a richer potential structure and may lead to system instability when the noise is too great. To overcome these shortcomings, the article presents a periodic potential underdamping stochastic resonance (PPUSR) method after investigating the potential function and system signal-to-noise ratio (SNR). In addition, system parameters are further optimized by using ant colony algorithm. Through simulation and gear experiments, the effectiveness of the proposed method was verified. We concluded that compared with the traditional underdamped stochastic resonance (TUSR) method, the PPUSR method had a higher recognition degree and better frequency response capability.

The total enthalpies of the 16 different spin configurations that can be realized in the unit cell of the archetype spin crossover complex [Fe(phen)₂(NCS)₂] (phen=1,2-phenanthroline) were calculated applying periodic density functional theory combined with the Hubbard model and the Grimme-D2 dispersion correction (DFT+U/D2). The obtained enthalpy differences between the individual spin configurations were used to determine spin couplings of an Ising-like model and subsequent Monte Carlo simulations for this model allowed to estimate the phenomenological interaction parameter Г of the Slichter-Drickamer model, which is commonly used to describe the cooperativity of the spin transition. The calculational procedure described here, which led to an estimate of about 3 kJ/mol⁻¹ for Г, in good agreement with experiment, may be used to predict from first principles how modifications of spin crossover complexes can change the character of the spin transition from gradual to abrupt and vice versa.

The article is focused on detailed framework process for hierarchized construction of periodic freight train paths (PFTPs) – allocation of pre-arranged railway capacity to freight rail operators. The framework process considers fluctuations in demand for capacity from freight rail opera-tors, so the quality of a freight train path is related with its construction priority. Introduced framework process aims to offer freight rail operators attractive train paths, with low number of scheduled stops, and this way enhance competitiveness and decrease energy consumption of freight railway as a factor for sustainable development. The proposed generic process is in-tended for all time horizons of capacity allocation. Correctness of the framework process is tested on the example of mainline Prague – Dresden, in the context of prospective (denser) mod-el passenger timetable.

The purpose of this paper is to bring to light a method through which the global in time existence for arbitrary large in H¹ initial data of a strong solution to 3D periodic Navier-Stokes equations follows. The method consists of subdividing the time interval of existence into smaller sub-intervals carefully chosen. These sub-intervals are chosen based on the hypothesis that for any wavenumber m, one can find an interval of time on which the energy quantized in low-frequency components (up to m) of the solution u is lesser than the energy quantized in high-frequency components (down to m) or otherwise the opposite. We associate then a suitable number m to each one of the intervals and we prove that the norm ||u(t)||_H1 is bounded in both mentioned cases. The process can be continued until reaching the maximal time of existence T_max which yields the global in time existence of strong solution.

Reptilians or reptoids are staple characters of Hollywood cinema, science fiction literature, ufology and conspiracy theories. Despite its popularity, the reptoid has been dismissed by the scientific community as a figment of the imagination; born out of a speculation, paranoia and wanton anthropocentricism. In this paper, however, the Life Sequence Conjecture is advanced as a supportive framework for the notion of reptilian extraterrestrials. This paper is not an argument in support of the existence of extraterrestrials. Rather, it proposes that if extraterrestrials do exist, then a reptilian morphology is not untenable.

This article presents description of social structures such as, for example, states, in the context of their consideration as systems. Moreover, it seeks to develop K. Marx’s model in terms of systematic approach, as well as to justify the application of the Fibonacci sequence to systems in general and, in particular, to social systems. The description covers a wide range of issues, ranging from the assertion of the basic principle of living systems as patterns in resources flows to description of functions of various aspects of states and their economics in terms of objective needs of such structures. In addition, this article justifies synergetic effect based on proposed model, and the way this effect is applied to state systems. Moreover, it includes the description of main changes of socio-economic formations throughout the history as a natural development of social systems, including possible modern stages. The fundamental tool of this description is description of essence of the mechanism of property rights and its formation options. The differences of two extreme directions of such development are also described on the basis of the objective factor of resource provision. Special attention is given to the concept of surplus value as a feature of social production systems.

Mitochondrial carriers facilitate the transfer of small molecules across the inner mitochondrial membrane (IMM) to support mitochondrial function and core cellular processes. In addition to the classical mitochondrial carrier family SLC25, the past decade led to the discovery of additional protein families that exhibit IMM localization and transporter-like properties. These include mitochondrial pyruvate carriers, sideroflexins and mitochondrial cation/H+ exchangers that have been linked to vital physiological functions and disease. Their structures and transport mechanisms are still largely unknown and understudied. Protein sequence analysis per se can often pinpoint hotspots that are of functional or structural importance. In this review, we summarize current knowledge about sequence features of mitochondrial transporters with a special focus on the newly included SLC54, SLC55 and SLC56 families of the SLC solute carrier superfamily. Taking a step further, we combined sequence conservation analysis with transmembrane segment and secondary structure prediction methods to extract residue positions and sequence motifs that likely play a role in substrate binding, binding site gating or structural stability. We hope that our review will help guide future experimental efforts by the scientific community to unravel the transport mechanism and structure of these novel mitochondrial carriers.

In natural, laboratory and industrial conditions, the density of a fluid or gas, depending on temperature, pressure, concentration of dissolved substances or suspended particles, changes under the influence of a large number of physical factors. We assume that undisturbed liquid is stratified. The analysis is based on a system of fundamental equations for the transfer of energy, momentum and matter in periodic flows of a viscous compressible fluid. The propagation of periodic flows in viscous uniformly stratified fluids is considered. Taking into account the compatibility condition, dispersion relations are constructed for two-dimensional internal, acoustic and surface linear disturbances with a positive definite frequency and complex wave number in a compressible viscous fluid exponentially stratified by density. The temperature conductivity and diffusion effects are neglected. The obtained regularly perturbed solutions to the equations describe weakly damped waves. Singular solutions characterize the thin ligaments that accompany each type of wave. In limiting cases, the constructed solutions transform into known expressions for a viscous homogeneous and ideal fluid or degenerate.

The quest for accurate and more efficient methods for solving periodic/oscillatory problems is gaining more attention in recent time. This paper presents the construction and implementation of a family of exponentially–fitted Obrechkoff methods. A single–step Obrechkoff method involving terms up to the fourth derivatives was used as the base method. We also present the stability and convergence properties of the constructed family of methods. Two numerical examples were use to illustrate the performance of the constructed methods.

Histone deacetylases inhibitors (HDACi) have shown promising therapeutic outcomes in haematological malignancies such as leu-kaemia, multiple myeloma and lymphoma, with dis-appointing results in solid tumours when used as monotherapy. As a result, combination therapies either with radiation or other deoxyribonucleic acid (DNA) damaging agents have been suggested as ideal strategy to improve their efficacy in solid tumours. Numerous in vitro and in vivo studies have demonstrated that HDACi can sensitise malignant cells to both electromagnetic and particle types of radiation by inhibiting DNA damage repair. Although the radiosensitising ability of HDACi has been reported as early as 1990s, the mechanisms of radiosensitisation are yet to be fully understood. This review brings forth the various protocols used to sequence the administration of radiation and HDACi treatments in the different studies. The possible contribution of these various protocols on the ambiguity that surrounds the mechanisms of radiosensitisation is also highlighted.

In archaea, the interaction of HerA helicase and NurA nuclease is required for 3' end resection during DNA double-stranded break repair, and its disruption results in unfaithful DNA repair. Interactions transpire by the four conserved domains, including the N-terminal HAS domain (HerA-ATP synthase), which serves as a binding site for the exonuclease NurA. As a HerA hexamer between its two subunits, the conserved C-terminal brace extension functions as an adjacent brace. DBD serves as a location for DNA binding, which causes the DNA strands to be propelled into the HerA pocket to begin unwinding. The putative pore entrance is made up of four helix coils, which provide a suitable charged surface for guiding DNA into the pore. The crystal structure of the complex HerA-NurA allows for the identification of key residues for protein-protein interaction. This allows us to predict that most archaeal HerA orthologs will be able to interact with one another. Archaeal HerA is a homo-hexameric assembly featuring a DNA-binding domain that is conserved. It also features distinctive ATP and NurA binding domains. Following the structure and sequence conservation criteria, we find one HerA ortholog in Pyrobaculum calidifontis and multiple HerA paralogs, but the role of none is established yet.Interestingly, orthologous HerA is part of an Mre11-Rad50 gene neighborhood while paralogous HerA is not, which could be misleading. As a result, it sought to describe the criteria that may be used as a possible fingerprint for identifying orthologous genes, as well as the cloning procedures and tactics that would aid in the confirmation of the HerA helicase in P. calidifontis. Their confirmation is based on PCR cloning and characterization following HerA construct creation.

Children with history of prematurity are at higher risk of complications, comorbidities and iron deficiency. In this study we assess the prevalence of restless sleep disorder, as well as that of periodic leg movements during sleep (PLMS) in these children. Retrospective chart review of sleep studies in children aged 1-18 years, with history of prematurity. Only diagnostic studies in children without diagnosis of a genetic syndrome or airway surgery or tracheostomy were included. Three groups were compared, children with PLMS index>5, children with restless sleep disorder (RSD), and children with neither elevated PLMS index nor RSD. During the study, 2,577 sleep studies were conducted. Ninety-two studies fit our criteria and were included in analysis. Median birth age was 31 weeks, interquartile range (IQR) 27-34 weeks. Thirty-two (34.8%) children were referred for restless sleep and 55 (59.8%) for snoring; after polysomnography 18% were found to have PLMS index >5/hour, 14% fit the criteria for RSD. There were no statistically significant differences in polysomnographic parameters between the children with RSD, PLMS and the remaining group, except for lower obstructive apnea/hypopnea index (Kruskal-Wallis ANOVA 8.621, p=0.0135) in the RSD group (median 0.7, IQR 0.3-0.9) than in the PLMS (median 1.7, IQR 0.7-3.5), or than in the nonRSD/nonPLMS group (median 2.0, IQR 0.8-4.5). There was elevated frequency of RSD and elevated PLMS in children with history of prematurity that might be linked to the increased risk of iron deficiency in premature infants. These new results add new knowledge on the prevalence of RSD in these children.

In this paper, we investigate the recurrence properties of the generalized Tribonacci sequence and present how the generalized Tribonacci sequence at negative indices can be expressed by the sequence itself at positive indices.

Sequence alignment and comparison through pairwise, multiple, global and local techniques are the main principles that underpin comparative genomics. However, most of the algorithms used are alignment-based which imposed some limitations on their use and application. In an attempt to provide an alignment-free alternative approaches, a methodology of comparative optinalysis and statistical mirroring was used and adopted to provide a suitable alternative for multiple genomic sequence comparison. In this article, methods comparison with MUSCLE, MUFFT, Clustal Omega, and T-Coffee was designed to assess the suitability and statistical power of statistical mirroring as an alternative method for multiple genomic sequences comparison using different sets of logically generated biological sequence datasets with different problems and computational complications. The results of the comparisons validate that statistical mirroring is a suitable alignment-free alternative approach for multiple genomic sequence comparison. The applied method (statistical mirroring) distinguishes itself over MUSCLE, MUFFT, Clustal Omega, and T-Coffee in specificity to a position-specific changes, specificity to a base-specific changes, cladogram and phylogenetic linearity, alignment independency, computational simplicity, and limit of input capacity.

A critical discussion on the computation of the dispersion diagram in periodic one-dimensional guiding structures is carried out. In particular, an analysis is presented of the pros and cons of combined methods that make use of full-wave simulations done with commercial software packages with further analytical post-processing based on simplifications brought by an equivalent circuit model of the structure. Some of the most common methods reported in the literature are reviewed and their advantanges and limitations highlighted. Our discussion is complemented with several selected numerical examples in order to discuss the most relevant aspects that a potential user of these methods should be aware of. Special attention is paid to the relevant role played by the high-order coupling between the two halves of a symmetric unit cell of the periodic structure.

In this paper, a geometric body-centered model to simulate the periodic structure of unidirectional fibrous composites is presented. To this end, three prescribed configurations are introduced to predict in a deterministic manner the arrangement of internal and neighboring fibers inside the matrix. Thus, three different representative volume elements (RVEs) are established. Furthermore, the concept of the interphase has been taken into account, stating that each individual fiber is encircled by a thin layer of variable thermomechanical properties. Next, these three unit cells are transformed in a unified manner to a coaxial multilayer cylinder model. This advanced model includes the influence of fiber contiguity in parallel with the interphase concept on the thermomechanical properties of the overall material. Then, by the use of this model, the authors propose explicit expressions to evaluate the longitudinal and transverse thermal conductivity of this type of composite. The theoretical predictions were compared with experimental results, as well as with theoretical values yielded by some reliable formulae derived from other workers, and a reasonable agreement was found.

Taking a DNA-sequence, a word with letters/bases A, T, G and C, as the relation between the generators of an infinite group $\pi$, one can discriminate two important families: (i) the cardinality structure for conjugacy classes of subgroups of $\pi$ is that of a free group on $1$ to $4$ bases and the DNA word, viewed as a substitution sequence, is aperiodic. (ii) The cardinality structure for conjugacy classes of subgroups of $\pi$ is not that of a free group, the sequence is generally not aperiodic and topological properties of $\pi$ have to be determined differently. The two cases rely on DNA conformations such as A-DNA, B-DNA, Z-DNA, G-quadruplexes, etc. We found a few salient results: Z-DNA, when involved in transcription, replication and regulation in a healthy situation implies (i). The sequence of telomeric repeats comprising $3$ distinct bases, most of the time, satisfies (i). For $2$-base sequences in the free case (i) or non free case (ii), the topology of $\pi$ may be found in terms of the $SL(2,\mathbb{C})$ character variety of $\pi$ and the attached algebraic surfaces. The linking of two unknotted curves --the Hopf link-- may occur in the topology of $\pi$ in cases of biological importance, in telomeres, G-quadruplexes, hairpins and junctions, a feature we already found in the context of models of topological quantum computing. For $3$- and $4$ base sequences, other knotting configurations are noticed and a building block of the topology is the $4$-punctured sphere. Our methods have the potential to discriminate potential diseases associated to the sequences.

Assessment of the genetic distinctiveness of a cultivar through morphological descriptors is an important tool for both the registration and the protection. New mango genotypes have been improved using valuable diverse germplasm resources to ensure food security. DNA fingerprinting based simple sequence repeats (SSR)-markers have been the most broadly used, effective and accurate in evaluation of genetic characterization of a cultivar. Molecular breeding is an effective source of genetic gain after improvement of fruit trees using marker assisted genomic selection. Mango (Mangifera indica L.) is an allotetraploid (2n = 4X= 40) drupe fruit and has high nutritional value belongs to genus Mangifera and family Anacardiaceae. Mango cultivars are used with worldwide acceptance to pharmacological, ethnomedical, and phytochemical industries. This study investigated the molecular evaluation of a new mango cultivar ‘Azeem Chaunsa’ using a set of the most effective 50 hyper-variable polymorphic SSR markers. Highly specific DNA fingerprints were identified in the genome of this mango cultivar, ‘Azeem Chaunsa’ compared with three standard cultivars such as Sindhri, Samar Bahisht (S.B) Chaunsa and Sufaid Chaunsa. Our results showed that SSR markers could efficiently assess genetic diversity in mango. An agglomerative hierarchical clustering method was used to construct dendrogram based on the ‘Unweighted Pair- Group Method with Arithmetic Mean’ (UPGMA). The genetic similarity coefficients were recorded between the mangos cultivars ranged from 0.49 to 0.67. Cultivar identification (CID) evaluates association among standard cultivars and Azeem Chaunsa and further concludes significant variations. CID results concluded that cultivar ‘Azeem Chaunsa’ varied significantly from the check cultivar, Sindhri (46.2%), S.B Chaunsa (45%) and Sufaid Chaunsa (46.7%). The results obtained in this study will orient cultivar identification strategies for a successful future mango breeding programmes in the context of climate change.

The understanding of various health-oriented vital sign data generated from body sensor networks (BSN) and discovery of the association between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where the occupants’ health status is continuously monitored remotely, it is essential to provide required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach to mine the incomplete (partial) periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce the productive-associated partial periodic frequent patterns as the set of correlated partial periodical frequent items. The combination of these measures has the advantage of empowering healthcare providers and patients for quality of diagnosis, and also for better treatment and smart care, especially for the elderly people at smart home. We developed an efficient algorithm named PPFP-Growth (Productive Periodic Frequent Pattern growth) to discover all productive associated partial periodic patterns using these measures. PPFP-Growth is efficient, and the productiveness measure removes uncorrelated periodic items. An experimental evaluation on synthetic and real datasets shows the efficiency of the proposed PPFP-Growth algorithm, and can filter a huge number of partial periodic patterns to reveal only the correlated ones.

An addition sequence is an important operation in many applications of computer science, such as multi-modular exponentiation and outsourcing protocols. Finding an addition sequence for a set of positive integers with the shortest length is challenging due to the high computational time required to find the solution. In this paper, a new metaheuristic algorithm is designed based on the simulated annealing strategy to generate a short addition sequence. The efficiency of the proposed algorithm was proved experimentally by comparing it with the previous exact and heuristic algorithms in terms of running time and the length of the addition sequence.

While the language model using the stop sign as an independent token has been widely used to decide when the model should stop, it may lead to the growth of vocabulary dimensions and further problems. Similarly, present research on game algorithms usually estimate stopping point related problems based on the evaluation of the winning rate. However, information redundancy may also exist in such models, thus increasing the training difficulty. Above two types of tasks (and similar autoregressive tasks) show a common problem of stopping point prediction. In this paper, we describe a design of separated model, trying to separate the complexity of stopping point prediction from the main task model, so that the information used for estimating stopping point can be reduced. On this basis, in order to verify the rationality of using separated model, we propose a model-free test method. It judges the separability of transformed data based on point difference and sequence difference metrics. In this way, it can predict the credibility of the separated model inference.

Escherichia coli phage Eco_BIFF was isolated from several laboratory stocks of E. coli K-12 MG1655 derivatives. The source of the contamination is unknown. Eco_BIFF is a lytic phage that shows effective growth inhibition of E. coli K-12. Here, we announce the complete genome sequence of Eco_BIFF, and major findings from its genome annotation.

Background SARS-CoV-2 has generated a life-treating pandemic and is the main challenge of this century. Some untranslated regions (UTRs) in SARS-CoV-2 genome, specifically leader sequence and transcription regulatory sequence (TRS) in 5’UTR, can be considered as Achilles' heel of virus. Leader sequence are found at the 5' ends of all encoded transcripts that highlights its importance. TRS can explain the host range and pathogenicity of coronavirus. However, our knowledge on the evolution and the role of UTRs in SARS-CoV-2 pathogenicity is very limited. This study is a pioneering attempt to unravel the evolution of key regions in 5' UTR of SARS-CoV-2 and discover the inhibitory microRNAs against 5' UTR of virus. Methods Evolution of TRS and leader sequence was compared between human pathogenic (SARS-CoV-2, SARS, and MERS) and non-pathogenic (bovine) coronaviruses. Profiling of microRNAs that can inactive the key UTR regions of coronaviruses, UTR-inhibitory microRNAs, was carried out. Findings We found a distinguished pattern of evolution in leader sequence and TRS of SARS-CoV-2, compared to the other coronaviruses. Mining all available microRNA families against leader sequences of coronaviruses resulted in discovery of 39 microRNAs with an acceptable thermodynamic binding energy against SARS-COV-2, SARS, MERS, Bat Coronavirus, or Bovine Coronavirus. Multivariate analysis demonstrated a distinguished pattern of binding of leader sequence of SARS-CoV-2 against microRNAs, with a lower binding stability. hsa-MIR-5004-3p was the only human microRNA that can target leader sequence of SARS and SARS-CoV-2. However, its binding stability remarkably decreased in SARS-COV-2 (-19.4 kcal/mol), compared to SARS-COV-2 (-25.9 kcal/mol). We found an insertion-type mutation in leader sequence of SARS-COV-2 that results in lower binding stability and escaping of viral leader sequence from hsa-MIR-5004-3p. Altogether, we suggest lack of innate human inhibitory microRNAs to bind to leader sequence and TRS of SARS-CoV-2 contributes to its high replication in infected human cells. On the other hand, mining of two hundred million deposited human genomic variants led us to discovery of 49 missense and splice-disrupt mutations in genomic structure of hsa-MIR-5004-3p. These mutations can negatively affect hsa-MIR-5004-3p function in preventing SARS-CoV-2 replication. Interpretation This study unravels the evolution of key regions in 5’UTR of SARS-CoV-2. Inducing microRNAs to bind to the leader sequence and TRS regions by drugs or food supplements can reduce virus replication. Enhancing the microRNA defence machinery against TRS and leader of virus has a potential to prevent SARS-CoV-2 infection at the first place. The mentioned strategy is rapidly achievable against COVID-19. Missense variation in genomic sequence of 5’UTR inhibitory microRNAs, such as hsa-MIR-5004-3p, can be considered as risk factor of COVID-19.

Bioinformatics is a rapidly developing field enabling scientific experiments through computer models and simulations. Considering the vast databases of biological data available, it is ex-tremely important to develop efficient methods and algorithms for their processing. Sequence comparison is the best method for studying the evolutionary interaction between genes. It is based on alignment – the process of arranging two or more sequences to achieve the maximum level of identity and degree of similarity. The paper presents a new version of the algorithm for pairwise DNA sequences alignment, based on a new method called CAT, where a dependency with a previous match and the closes neighbor are taken in consideration to increase uniqueness of the CAT profile and to reduce possible collisions, i.e. two or more sequence having same CAT profiles. This makes proposed algorithm suitable for finding exact match of a concrete DNA se-quence in a big set of DNA data faster. The generation of CAT profiles is made once before data has been uploaded to the database, allowing the profiles to be used as metadata for the sequenc-es. It consists of an algorithm to calculate a CAT profile against the selected reference sequences and an algorithm to compare two sequences based on the calculated CAT profiles. Improve-ments in generation of the CAT profiles, are detailed described in the paper. Block scheme, pseudo code tables and figures were updated according to the proposed new version and ex-perimental results. Experiments have been carried out with the new version of the method and different datasets to align DNA sequences based on the CAT method. New experimental results in terms of collisions, speed, and efficiency of the proposed solutions are presented. Experiments related to the performance comparison with Needleman-Wunsch were re-executed with the new version of the algorithm to confirm that we have the same performance. An analysis of the per-formance of the proposed CAT based algorithm against Knuth–Morris–Pratt algorithm, which has a complexity of O(n) and is one of the most widely used for searching biological data, was performed. The impact of prior matching dependencies on uniqueness for generated CAT pro-files is investigated. The analysis of the experimental results obtained by sequence alignment shows a small deviation of the proposed algorithm based on the CAT method, which can be ig-nored if this deviation is acceptable at the expense of performance. The time efficiency of the CAT algorithm remains constant, regardless of the length of the sequences. Therefore, the ad-vantage of the proposed method is its fast processing in the alignment of large sequences, for which the execution of the exact algorithms takes a long time. The example code realization of the CAT Method, under the protection of the GNU General Public License v3.0, can be accessed on GitHub at: https://github.com/HristoS/CATSequenceAnalysis.

In response to a threefold increase in osteoporotic vertebral fractures (OVF) at our institution over the past decade and the subsequent development of new fractures within a year in a third of cases, we evaluated the effectiveness of STIR MRI scans in conjunction with CT scans for initial OVF diagnosis. This study, conducted over five months, involved full spine sagittal STIR MRI scans alongside CT scans for patients over 50 with minor injury-related vertebral fractures. The independent assessments by radiologists and neurosurgeons of 64 patients revealed that while CT scans identified 161 fresh fractures, of which only 68 were previously reported, MRI detected 266 fractures, 1.65 times more than CT. CT scans showed single fractures in 27% of patients and multiple fractures in 73%, whereas MRI identified single fractures in 9% and multiple fractures in 91% of patients. Additionally, MRI demonstrated a 1.5-fold increase in adjacent fracture detection. The use of MRI influenced treatment plans in 67% of cases. Given these findings, we advocate for including a STIR MRI of the entire spine alongside CT scans in OVF cases, as it significantly enhances fracture detection and can alter treatment strategies.

Many cryptographic systems require random numbers, and weak random numbers lead to insecure systems. In the modern world, there are several techniques for generating random numbers, of which the most fundamental and important methods are deterministic extractors proposed by von Neumann, Elias, and Peres. Elias’s extractor achieves the optimal rate (i.e., information theoretic upper bound) h(p) if the block size tends to infinity, where h(·) is the binary entropy function and p is probability that each bit of input sequences occurs. Peres’s extractor achieves the optimal rate h(p) if the length of input and the number of iterations tend to infinity. The previous researches related to both extractors did not mention practical aspects including running time and memory-size with finite input sequences. In this paper, based on some heuristics, we derive a lower bound on the maximum redundancy of Peres’s extractor, and we show that Elias’s extractor is better than Peres’s one in terms of the maximum redundancy (or the rates) if we do not pay attention to time complexity or space complexity. In addition, we perform numerical and non-asymptotic analysis of both extractors with a finite input sequence with any biased probability under the same environments. For doing it, we implemented both extractors on a general PC and simple environments. Our empirical results show that Peres’s extractor is much better than Elias’s one for given finite input sequences under the almost same running time. As a consequence, Peres’s extractor would be more suitable to generate uniformly random sequences in practice in applications such as cryptographic systems.

Three-dimensional flexible piezoresistive porous sensors have been used in health diagnosis and wearable devices. By utilizing 3D printed sacrificial mold casting with highly conductive polymer composites, porous sensors with high conductivity and complex structures were achieved. Traditional conductive polymer composites are usually heated using external heat sources, which are time and energy consuming. However, in this study, conductive porous sensors with triply periodic minimal surface (TPMS) structures were manufactured using self-resistance electric heating by utilizing the conductive nature of the polymers. The porous structures were designed based on Diamond (D), Gyroid (G), and I-WP (I) unit cells of the TPMS. The use of self-resistance electric heating has improved the compression performance and piezoresistive response of the structures. Among these structures, the D-based structure exhibited the maximum response strain (61 %), with a corresponding resistance response value of 0.97, which increased by 10.26 % compared to that of the structures heated by using the external heat sources. This study offers a new perspective on designing and optimizing porous materials with specific functionalities, particularly in the realm of flexible and portable piezoresistive sensors.

The calculation of moments of area is one of the most fundamental aspects of engineering mechanics for calculating the properties of beams or for the determination of invariants in different kind of geometries. While a variety of shapes such as circles, rectangles, ellipses or their combinations can be described symbolically, such symbolic expressions are missing for freeform cross-sections. In particular, periodic B-spline cross-sections are suitable for an alternative beam cross-section, e.g. for the representation of topology optimization results. In this work, therefore, a symbolic description of the moments of area of various parametric representations of such B-splines is computed. The expressions found are then compared with alternative computational methods and checked for validity. The resulting equations show a simple way for fast conceptual computation of such moments of area of periodic B-splines.

Objective: The main purpose of the study is to investigate the combined effects of polydeoxyribonucleotide (PDRN) and extracorporeal shock wave therapy (ESWT) sequences on the regenerative processes in atrophied animal muscles. Methods: Thirty male New Zealand rabbits, aged 12 weeks, were divided into five groups: normal saline, PDRN, ESWT, PDRN injection before ESWT, and PDRN injection after ESWT. Following 2 weeks of cast immobilization, the respective treatments were administered to the atrophied calf muscles. Radial ESWT was performed twice weekly. The evaluation included measurements of calf circumference, tibial nerve compound muscle action potential (CMAP), and gastrocnemius (GCM) muscle thickness after 2 weeks of treatment. Histological and immunohistochemical staining, as well as Western blot analysis, were conducted 2 weeks post-treatment. Staining intensity and extent were assessed using semi-quantitative scores. Results: Groups 4 and 5 demonstrated significantly greater calf muscle circumference, GCM muscle thickness, tibial nerve CMAP, and GCM muscle fiber cross-sectional area (types I, II, and total) than in the remaining three groups (p < 0.05), while they did not did not differ significantly in these parameters. Groups 2 and 3 showed the higher values for all the observations compared to group 1 (p < 0.05). Group 4 had the greatest ratio of vascular endothelial growth factor (VEGF) to platelet endothelial cell adhesion molecule-1 (PECAM-1) in the gastrocnemius muscle fibers than in the other four groups (p < 0.05). Western blot analysis revealed significantly higher expression of angiogenesis cytokines in groups 4 and 5 than in the remaining (p < 0.05). Conclusion: The combination of ESWT and PDRN injection demonstrated superior regenerative efficacy for atrophied calf muscle tissue in rabbit models than these techniques alone or saline. Particularly, administering ESWT after PDRN injection yielded the most favorable outcomes in specific parameters.

The Mobius function is commonly used to define Euler's totient function and the Mangoldt function. Similarly, the summatory Mobius function (the Mertens function) can be used to define the summatory totient function and the summatory Mangoldt function (the second Chebyshev function).

Sutherlandia frutescens (S. frutescens) has been traditionally used as an herbal medicine to ameliorate symptoms associated with cancer, infectious diseases, as well as inflammation. The objective of this investigation was to explore the impact of S. frutescens on the expression of genes in a murine macrophage cell line (i.e., RAW 264.7). We found that treatment with an ethanolic-extract of S. frutescens (SFE) 1 h prior to the stimulation with LPS and IFNγ for 24 h significantly affected the expression of 715 genes in RAW 264.7 cells. When the post-stimulation period was shortened to 8 h, the number of genes that were significantly impacted by SFE diminished to 50. Pathway analysis revealed that inflammatory signaling pathways, such as NF-κB, MAPK, and TNF, as well as signaling pathways associated with immune-related responses, were inhibited by SFE treatment. These findings are consistent with previously reported anti-inflammatory activity of SFE and enable better understanding of the immune-modulating properties of this botanical. To our knowledge, this represents the first report on the impact of S. frutescens on global gene expression in an immune cell population.

Thalassemia is a common monogenic autosomal disorder prevalent in India. HbE beta thalassemia is a compound heterozygous state of two different beta globin mutations (HBB), predominant in the Eastern India. In HbE-beta thalassemia (β⁺/β°) patients, one HBB mutation does not produce any functional protein (β°); another mutation produces a structural altered haemoglobin, variant E (β⁺). It has been observed that in HbE-beta thalassemia patients with same type of HBB mutations, there exist diverse phenotypic presentation ranging from very severe, with regular transfusion-dependence to clinically non severe requiring very less transfusion or no transfusion. To explore this phenotypic mystery, we performed trio based expanded whole exome sequencing (WES) of two extreme phenotypes of HbE beta thalassemia subjects with similar HBB genotype and their parents. For WES library preparation, Agilent Sure select V6+UTR kit was used and sequencing was done through the Illumina HiSeq 2500 platform. This study aimed to search for de novo or inherited variants associated with the clinical severity. Six significant inherited variants were found in the genes, ATOH8, TTLL4, P2RX7, PRSS54, SLC9C2 and VRK2 in severe subject, however only one significant inherited variant in MTRR gene was found in non-severe subject. Bioinformatic analysis also confirms that each variant significantly changes in respective protein structure. The identified genes were either associated with iron absorption, oxidative stress, hematopoietic stem cell differentiation, structural organization of RBC cytoskeleton or regulating anemia. No significant de novo variant were identified associated with clinical severity. The finding of this study indicates that the combined effect of mutation load in major pathways may responsible for severe phenotype.

The low-altitude quasi-periodic (LQP) E-region echoes observed with the upgraded HCOPAR located in Hainan Island of China (19.5°N, 109.1°E) are presented. With the interferometry technique, the interferometry equations can be developed to investigate the structures of the irregularities. The results show that the plasma structures of the LQP echoes with negative slope mainly drift southwestward almost horizontally, but they also drifted southeastward in some striations. According to the movement of the plasma structures, we find strong wind shear in the region where the LQP echoes are produced. Meanwhile, The periodicity of the LQP echoes may be related to the strength of the wind shear. It is proposed that the plasma structures are most likely produced by the Kelvin-Helmholtz instability.

We propose a novel electrochemical system to form precipitation patterns of Cu-Fe-based Prussian blue analogues (Cu-Fe PBA) in agarose gels, using an applied voltage to produce reactant ions. The spatiotemporal evolution, spatial distribution, and crystallite morphologies of the precipitates were investigated by visual inspection, Fe Kα intensity distribution measurements, and optical and scanning electron microscope observations. The precipitation patterns and their evolution depended on the applied voltage. Multicolored periodic precipitation bands were stochastically formed under cyclic alternating voltage (4 V for 1 h and then 1 V for 4 h per cycle). The distances between adjacent bands were randomly distributed (0.30 ± 0.25 mm). The sizes and shapes of the crystallites generated in the gel were position-dependent. Almost cubic but fairly irregular crystallites (0.1–0.8 μm) were formed in the periodic bands, whereas definitely cube-shaped crystallites (1–3 μm) appeared close to the anode. These cube-like reddish-brown crystallites were assigned to Cu-FeII PBA. In some periodic bands, plate-like blue crystallites (assigned to Cu(OH)2) were also present. Future issues for applications of the observed periodic banding were discussed.

Geometric Akaike Information Criteria (G-AICs) for generalized noise-level dependent crystallographic symmetry classifications of two-dimensional (2D) images that are more or less periodic in either two or one dimensions as well as Akaike weights for multi-model inferences and predictions are reviewed. Such novel classifications do not refer to a single crystallographic symmetry class exclusively in a qualitative and definitive way. Instead, they are quantitative, spread over a range of crystallographic symmetry classes, and provide opportunities for inferences from all classes (within the range) simultaneously. The novel classifications are based on information theory and depend only on information that has been extracted from the images themselves by means of maximal likelihood approaches so that these classifications are objective. This is in stark contrast to the common practice whereby arbitrarily set thresholds or null hypothesis tests are employed to force crystallographic symmetry classifications into apparently definitive/exclusive states, while the geometric feature extraction results on which they depend are never definitive in the presence of generalized noise, i.e. in all real world applications. Thus, there is unnecessary subjectivity in the currently practiced ways of making crystallographic symmetry classifications, which can be overcome by the approach outlined in this review.

Two strongly curved areas of the Carpathians in Romania are earthquake-prone: Vrancea - in the South-Eastern Carpathians, and Danubian – in the western edge of the Southern Carpathians. By far the highest seismicity episode in 2023 was recorded in the Southern Carpathians, near Targu Jiu city, of more than 4000 earthquakes. A first ML 5.2 mainshock was recorded on 13 February, followed by a second one, ML 5.7, on 14 February. Our purpose is to provide a first analysis of this sequence in order to explain its extent and unusual development, in time. The possible role of fluids in the middle crust, is discussed in connection with the rather unusual character of the space-time distribution of aftershocks. A large and high-quality dataset is provided by the dense array of seismic stations which has been gradually improved by adding new stations in the epicentral area. The thorough revision of the phase picking and location reveals a few interesting and intriguing features of seismicity that still remain worthy to be further investigated. The database built within this paper provides excellent information not only for Targu Jiu area, but for any other investigation in intra-continental regions characterized by diffuse and sporadic seismic activity.

The bacterial composition of oral samples has traditionally been determined by PCR amplicon sequencing of 16S rRNA genes. Recent amplicon sequence variant (ASV)-based analyses of 16S rRNA genes differ from that based on operational taxonomic unit (OTU) clustering in the way it deals with sequences having potential errors. However, little information is available on its application in oral microbiome studies. Here, we conducted ASV-based analysis of oral microbiome samples using QIIME 2. We investigated the optimal parameters for sequence denoising, using DADA2, and found the trimming of the first 20 nucleotides from 5′-end of both paired reads avoided excessive sequence loss during chimera removal. Truncating reads at positions 240–245 allowed the removal of low-quality sequences while maintaining sufficient length to merge matching paired ends. Taxonomic assignment, using the naïve Bayes classifier trained with the V3-V4 region of reference 16S rRNA sequences in the extended human oral microbiome database (eHOMD), resulted in bacterial compositions similar to those of OTU-based analyses. Contrary to OTU-based clustering, ASV-based analysis showed taxonomic abundance at the genus or species level to not differ significantly in tongue microbiomes, regardless of brushing. QIIME 2 can, therefore, be a standard pipeline for ASV-based analysis of oral microbiomes.

In this research, a design method for the graded triply periodic minimal surface (TPMS) structures with lightweight, high-strength, and high sound absorption properties is proposed. The graded TPMS structures are controlled by linear, quadratic, and sine functions. Uniform TPMS and graded TPMS were manufactured by laser powder bed fusion (LPBF) with AlSi7Mg powder, and an acoustic impedance tube test, compression test, and digital image correlation (DIC) test were ap-plied to obtain the sound absorption and compression properties. The sound absorption coefficient of a uniform Gyroid increases as the number of layers and surface thickness increase, and it in-creases as element size decreases. The sound absorption peak shifts to low frequencies as the number of layers increases. The sound absorption property of Type-Ⅰ graded TPMS is superior to the Type-Ⅱ, and the G-LinearⅠ has the highest sound absorption properties among the three functions. It reveals that the graded TPMS has superior sound absorption performance at low and medium frequencies. The compression and DIC results of graded TPMS also show excellent mechanical properties and energy absorption characteristics.

The intricate mechanisms of the immune response elicited by vaccines and the interplay between the immune system and the central nervous system (CNS) are poorly understood. This review explores these mechanisms, emphasizing the interplay between the immune system and the central nervous system (CNS). A critical aspect is the examination of how vaccine-induced immune responses can interact with the CNS, influencing neuroinflammation and neuroimmune interactions. Furthermore, the link between vaccination and persistent symptoms observed in long-COVID patients is discussed. Vaccination initiates a complex cascade of events, starting with the production of specific proteins, such as S1/S2 spike proteins, leading to robust immune activation in the axillary lymph nodes. The role of macrophages and antigen-presenting cells (APCs) in cytokine production, antigen presentation, and the stimulation of B and T cells highlights the sophistication of the adaptive immune response. The migration of immune cell-derived exosomes to the brain plays a dynamic role in CNS inflammation, neurodegenerative processes and long COVID. On the other hand, the prolonged presence of viral and vaccine-derived spike proteins may contribute to the complex pathophysiology of Long-COVID, shedding light on the delicate balance between beneficial immune responses and possible adverse neurological outcomes of vaccination. It is worth rigorous monitoring and further research to understand the mechanisms of neuroinflammation and the persistence of spike proteins and their long-term effects on the brain, emphasising the importance of a nuanced approach to vaccine safety and efficacy in the context of COVID-19 and beyond.Principio del formulario

In the coded pulse scanning light detection and ranging (LIDAR) system, the number of laser pulses used at a given measurement point changes depending on the modulation and the method of spreading used in optical code-division multiple access (OCDMA). The number of laser pulses determines the pulse width, output power, and duration of the pulse transmission of a measurement point. These parameters determine the maximum measurement distance of the laser and the number of measurement points that can be employed per second. In this paper, we evaluate the performance and characteristics of combinations of modulation and spreading technology that can be used for OCDMA, and study optimal combinations according to varying operating environments.

RaTG13 (a bat derived SARS-like CoV) is the closest relative sequence of SARS-CoV-2 reported till date. The sample from which RaTG13 was sequenced was a bat fecal swab collected in 2013 from Tongguan, Mojiang, Yunnan province, China. The Illumina based sequence of RaTG13, MN996532.1, was deposited on 27^th Jan 2020 and the raw data (Illumina), https://www.ncbi.nlm.nih.gov/sra/SRX7724752[accn]. There are discrepancies in dates about when the metagenome sequencing of RaTG13 sample was done (2018 or 2020), both stated by the same corresponding author. Comparison of the RNA Seq data of RaTG13 fecal swab to the corresponding data from the bat fecal swabs deposited by the same working group using the same methods indicated that the RaTG13 raw data seemed to be different in various aspects. The fecal swab sample showed abnormally less read of bacterial reads in the swab was exceptionally low, i.e. 0.7%, compared to the 20-90% abundance in other fecal swabs from bats processed by similar methods. Also, another raw data in the form of amplicon sequences was deposited in May 2020; however, the dates mentioned on the files of the sequenced amplicons were older (2017, 2018). The genome assembly of RaTG13 could not be done de-novo and the average coverage of the genome ~8%. Also, literature indicates that RaTG13 RBD cannot bind to Rhinolophus ACE-2 receptors. Collectively, the anomalies in the raw data of RaTG13 and other issues pose an important question about the overall authenticity of the RaTG13 genome sequence.

Unmanned aerial vehicles (UAVs, drones) are not just a technological achievement based on modern ideas of artificial intelligence; they also provide a sustainable solution for green technologies in logistics, transport and material handling. In particular, using battery-powered UAVs to transport products can significantly decrease energy and fuel expenses, reduce environmental pollution, and improve the efficiency of clean technologies through improved energy-saving efficiency. We consider the problem of maximizing the average environmental benefit of a fleet of drones given a periodic schedule of tasks performed by the fleet of vehicles. To solve the problem efficiently, we formulate it as an optimization problem on an infinite periodic graph and reduce it to a special type of parametric assignment problem. We exactly solve the problem under consideration in O(n³) time, where n is the number of tasks performed by vehicles.

With the continuous progress of remote sensing image object detection tasks in recent years, researchers in this field have gradually shifted the focus of their research from horizontal object detection to the study of object detection in arbitrary directions. It is worth noting that some properties are different from the horizontal object detection during oriented object detection that researchers have yet to notice much. This article presents the design of a straightforward and efficient arbitrary-oriented detection system, leveraging the inherent properties of the orientation task, including the rotation angle and box aspect ratio. In the detection of low aspect ratio objects, the angle is of little importance to the orientation bounding box, and it is even difficult to define the angle information in extreme categories. Conversely, in the detection of objects with high aspect ratios, the angle information plays a crucial role and can have a decisive impact on the quality of the detection results. By exploiting the aspect ratio of different targets, this letter proposes a ratio-balanced angle loss that allows the model to make a better trade-off between low-aspect ratio objects and high-aspect ratio objects. The rotation angle of each oriented object, which we naturally embed into a two-dimensional Euclidean space for regression, thus avoiding an overly redundant design and preserving the topological properties of the circular space. The performance of the UCAS-AOD, HRSC2016, and DLR-3K datasets show that the proposed model in this paper achieves a leading level in terms of both accuracy and speed. The code is released at https://github.com/minghuicode/Periodic-Pseudo-Domain.

Due to the numerous heat sources in electronic equipment, the periodic nature of stream-wise flow occurs in a cooling channel so often, necessitating the development of an effective technique to improve the heat-cooling convection in such a situation. This paper investigates thermal convection enhancement in a heated-block duct through the periodic boundary conditions using the element-by-element (EBE) treatment in a semi-implicit projection finite element method (FEM) through preconditioned conjugate gradient (PCG) solver. This paper used the time-mean Nusselt number ratio, friction factor, and thermal performance to study how installing rectangular cylinders in the channel works with periodic boundary conditions varying Reynolds numbers (100, 175, and 250). The outcomes show that the rectangular cylinders installed stream-wise above an upstream block promote thermal convection in the heated-block duct. However, increasing the number of rectangular cylinders can raise friction factor. As a result, the case for periodic boundary conditions with a rectangular cylinder above every two blocks has the best thermal performance.

With simple but rigorous arguments we prove that the ordinary second quantization of bosonic and fermionc fields is formally equivalent to constraining the elementary degrees of freedom of the classical fields to have intrinsically periodic dynamics in time. This result confirms and extends the formal equivalence obtained in previous papers for general Hamiltonian systems in terms of canonical quantization, for elementary relativistic particles also in terms of the Feynman Path Integrals, as well as other remarkable correspondences of both phenomenological and theoretical fundamental interest.

This paper proposes a radiation pattern synthesis of the almost periodic antenna arrays including mutual coupling effects (that extracted by the Floquet analysis according to our previous work), which principally has a high directivity and large bandwidth. For modeling the given structures, the moment method combined with the Generalized Equivalent Circuit (MoM-GEC) is proposed. The artificial neural network (ANN) as a powerful computational model has been successfully applied to the antenna array pattern synthesis. The results showed that the multilayer feedforward neural networks are rugged and can successfully and efficiently resolve various distinctive complex almost periodic antenna patterns (with different source amplitudes) (in particular, both periodic and randomly aperiodic structures are taken into account). However, the artificial neural network (ANN) is capable of quickly producing the synthesis results using generalization with the early stopping (ES) method. A significant time gain and memory consumption are achieved by using this given method to improve the generalization (called early stopping). To justify this work, several examples are developed and discussed.

In this paper, an icosahedral non – body centered model to simulate the periodic structure of homogeneous particulate composites, by predicting the particle arrangement, is presented. This model has yielded three different variations which correspond at three different deterministic particle configurations. In addition, the concept of boundary interphase between matrix and inclusions was taken into account. Thus, the influence of particle vicinity was examined in parallel with the interphase concept on the thermomechanical properties of the overall material. Next, by the use of this model the authors derived a closed – form expression to estimate the thermal conductivity of this type of composites. To test the validity of the model, the theoretical values arising from the proposed formula were compared with other theoretical predictions obtained from several accurate formulae found in the literature and an adequate accordance was observed.

Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence and structural knowledge of drugs, targets, and pockets using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model’s performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, the supremacy of S2DTA became strikingly apparent. It showcased a noteworthy 25.2% reduction in Mean Absolute Error (MAE) and an impressive 20.1% decrease in Root Mean Square Error (RMSE). Furthermore, S2DTA exhibited substantial advancements in other pivotal metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2. These metrics experienced remarkable increments of at least 19.6%, 17.5%, 8.1%, and a remarkable 49.4%, respectively. Finally, we conducted interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism, fully proving that S2DTA is a valuable and accurate tool for predicting DTA. For further exploration, the source data and code repository can be accessed at https://github.com/dldxzx/S2DTA.

Laser-induced periodic surface structures (LIPSS) have gained significant attention due to their ability to modify the surface morphology of materials at the micro-nanoscale and show great promise for surface functionalization application. In this study, we specifically investigate the formation of LIPSS in silicon substrates and explore their impact on Surface-Enhanced Raman Spectroscopy (SERS) applications. This study reveals a stepwise progression of LIPSS formation in silicon, involving three distinct stages of LIPSS: 1) integrated Low-Spatial-Frequency LIPSS (LSFL) and High-Spatial-Frequency LIPSS (HSFL), 2) principally LSFL and 3) LSFL at the edge of the irradiated spot, elucidating the complex interplay between laser fluence, pulse number, and resulting surface morphology. Furthermore, from an application standpoint, these high-quality multi-scale periodic patterns lead to the next step of texturing the entire silicon surface with homogeneous LIPSS for SERS application. The potential of LIPSS-fabricated silicon substrates for enhancing SERS performance is investigated using thiophenol as a test molecule. The results indicate that the Au-coated combination of LSFL and HSFL substrate showcased the highest enhancement factor (EF) of 1.38 × 10^6. This pronounced enhancement is attributed to the synergistic effects of Localized Surface Plasmon Resonance (LSPR) and Surface Plasmon Polaritons (SPPs), intricately linked to HSFL and LSFL characteristics. These findings contribute to understanding LIPSS formation in silicon and their applications in surface functionalization and SERS, paving the way for sensing platforms.

Attack investigation is an important research field in forensics analysis. Many existing supervised attack investigation methods rely on well-labeled data for effective training. While the unsupervised approach based on BERT can mitigate the issues, the high degree of similarity between certain real-world attack and normal behaviors makes it challenging to accurately identify disguised attacks. This paper proposes ConLBS, an attack investigation approach that combines the contrastive learning framework and multi-layer Transformer network to realize the classification of behavior sequences. Specifically, ConLBS constructs behavior sequences describing behavior patterns from audit logs, and a novel lemmatization strategy is proposed to map the semantics to the attack pattern layer. Four different augmentation strategies are explored to enhance the differentiation between attack and normal behavior sequences. Moreover, ConLBS can perform unsupervised representation learning on unlabeled sequences, and can be trained either supervised or unsupervised depending on the availability of labeled data. The performance of ConLBS is evaluated in two public datasets. The results show that ConLBS can effectively identify at-tack behavior sequences in the cases of unlabeled data or less labeled data to realize attack investigation, and achieve the superior effectiveness compared to existing methods and models.

Alopecurus myosuroides (blackgrass) is a problematic weed of Western European winter wheat and its success is largely due to widespread multiple-herbicide resistance. Previous analysis of F2 seed families derived from two distinct blackgrass populations exhibiting equivalent non-target site resistance (NTSR) phenotypes shows resistance is polygenic and evolves from standing genetic variation. Using a CIDER-seq pipeline we show that herbicide resistant (HR) and herbicide sensitive (HS) F3 plants from these F2 seed families as well as the parent populations they were derived from carry extra-chromosomal circular DNA (eccDNA). We identify the similarities and differences in the coding structures within and between resistant and sensitive populations. Although the numbers and size of detected eccDNAs varied between the populations, comparisons between HR and HS blackgrass populations identified shared and unique coding content, predicted genes, and functional protein domains. These include genes related to herbicide detoxification such as Cytochrome P450s, ATP-binding cassette transporters and glutathione transferases including AmGSTF1. eccDNA content was mapped to the Alopecurus myosuroides reference genome, revealing genomic regions at distal end of chromosome 5 and the near center of chromosome 1 and 7 as regions with high number of mapped eccDNA gene density. Mapping to 15 known herbicide-resistant QTL regions showed the eccDNA coding sequences match 12 with four QLT matching HS coding sequences one region only contained HR coding sequences. These findings establish that like other pernicious weeds, blackgrass has eccDNAs that contain homologs of chromosomal genes, and these may contribute genetic heterogeneity and evolutionary innovation to rapidly adapt to abiotic stresses including herbicide treatment.

Sequence analysis program is outlined that analyzes and investigates homology between various nucleic acid or protein sequence. The dot matrix technique compares the sequences and the consensus sequence is obtained by superimposing on each other all the dot matrices. Local Alignment and Global Alignment both sequence from start to end is the best possible alignment over the entire duration between the two sequences. This method is more important to align with two closely related sequences roughly the same length. This method may not able to generate optimal results for divergent sequences and variable length sequence because between the two sequences it does not recognize very similar local region.

The determination of crack propagation velocities can provide valuable information for a better understanding of damage processes of concrete. The spatio-temporal analysis of crack patterns developing at a speed of several hundred meters per second is a rather challenging task. In the paper, a photogrammetric procedure for the determination of crack propagation velocities in concrete specimens using high-speed camera image sequences is presented. A cascaded image sequence processing which starts with the computation of displacement vector fields for a dense pattern of points on the specimen’s surface between consecutive time steps of the image sequence chain has been developed. These surface points are triangulated into a mesh, and as representations of cracks, discontinuities in the displacement vector fields are found by a deformation analysis applied to all triangles of the mesh. Connected components of the deformed triangles are computed using region-growing techniques. Then, the crack tips are determined using principal component analysis. The tips are tracked in the image sequence and the velocities between the time stamps of the images are derived. A major advantage of this method as compared to established techniques is in the fact of its allowing for spatio-temporally resolved, full-field measurements rather than point-wise measurements and that information on crack width can be obtained simultaneously. To validate the experimentation, the authors processed image sequences of tests on four compact-tension specimens performed on a split-Hopkinson tension bar. The images were taken by a high-speed camera at a frame rate of 160,000 images per second. By applying to these datasets the image sequence processing procedure as developed, crack propagation velocities of about 800 m/s were determined with a precision in the order of 50 m/s.

Buildings have started to play a critical role in the stability and resilience of modern smart grids, leading to a refocusing of large scale energy management strategies from the supply side to the consumer side. When the buildings integrate local renewable energy generation in the form of renewable energy resources they become prosumers and this reflects into additional complexity into the operation of the interconnected complex energy systems. A class of methods of modelling the energy consumption patterns of the building have recently emerged as black-box input-output approaches with the ability to capture underlying consumption trends. These make use and require large quantities of quality data produces by non-deterministic processes underlying the energy consumption. We present an application of a class of neural networks, namely deep learning techniques for time series sequence modelling with the goal of accurate and reliable building energy load forecasting. The Recurrent Neural Network implementation uses Long Short-Term Memory layers in increasing density of nodes to quantify prediction accuracy. The case study is illustrated on four university buildings from temperate climates over one year of operation using a reference benchmarking dataset that allows replicable results. The obtained results are discussed in terms of accuracy metrics and computational and network architecture aspects and are considered suitable for further used in future in situ energy management at the building and neighbourhood levels.

In this paper, we introduce a new formulation based on Floquet (Fourier) spectral analysis combined with a spectral modulation technique (and its spatial form) to study strongly coupled sublattices predefined in the infinite and large finite extent of almost periodic antenna arrays (e.g metasurfaces). This analysis is very relevant for dense massive MIMO, intelligent surfaces, 5G, and 6G applications (used for very small areas with a large number of elements such as millimeter and terahertz waves applications). The numerical method that is adopted to model the structure is the method of moments simplified by equivalent circuits MoM GEC. Other numerical methods (as the ASM array scanning method and windowing Fourier method) used this analysis in their kernel that to treat periodic and pseudo-periodic (or quasi-periodic) arrays.

We present the case of a 35-year-old patient without pathological history who developed hemopneumothorax due to altitude barotrauma during a commercial airline flight. The computed tomography of the chest identified the presence of emphysema "blebs" and bubbles, and the histopathological examination of the lung fragment obtained during the diagnostic and therapeutic thoracoscopy identified elements suggestive for the diagnosis of respiratory bronchiolitis associated with desquamative interstitial pneumonia (DIP).

A simple mathematical interaction function, with the limit value w = e2π/e, derived as a basis for modelling emergent properties of nanoparticles, nanodrops and liquids, has been further developed. A correlation between physical systems of uniform particles and fundamental properties of natural numbers leads to a model, which acts as a bridge between properties of macroscopic systems and fundamental physical constants. The four fundamental coupling constants, and the masses of fundamental elementary particles, including the Higgs and Z bosons, are derived by using this interaction function. A proposal for the mass of the electron neutrino is given. Finally, the nuclear binding interaction is modeled with the interaction function. Only the experimental values of the Planck constant h and the speed of light c are needed for the model.

Studies have previously described the occurrence of multidrug-resistant (MDR) Escherichia coli in human and veterinary medical settings, livestock, and, to a lesser extent, in the environment and food. While they mostly analyzed foodborne E. coli regarding phenotypic and sometimes genotypic antibiotic resistance and basic phylogenetic classification, we have limited understanding of the in vitro and in vivo virulence characteristics and global phylogenetic contexts of these bacteria. Here, we investigated an E. coli strain (PBIO3502) isolated from a pork sausage in Germany in 2021 in-depth. Whole-genome sequence analysis revealed sequence type (ST)58, which is an emerging international high-risk clonal lineage. In addition to its MDR phenotype that mostly matched the genotype, PBIO3502 demonstrated pronounced virulence features, including in vitro biofilm formation, siderophore secretion, serum resilience, and in vivo mortality in Galleria mellonella larvae. Along with the genomic analysis indicating close phylogenetic relatedness of our strain with publicly available, clinically relevant representatives of the same ST, these results suggest the zoonotic and pathogenic character of PBIO3502 with the potential to cause infection in humans and animals. Also, our study highlights the necessity of the One Health approach while integrating human, animal, and environmental health, as well as the role of meat products and food chains in the putative transmission of MDR pathogens.

The chloroplast genome is conservative and stable, which can be employed to resolve genotypes. Currently, published nuclear sequences and molecular markers failed to differentiate the species from taxa robustly, including Machilus leptophylla, Hanceola exserta, Rubus bambusarum, and Rubus henryi. In this study, the four chloroplast genomes were characterized, and then their simple sequence repeats (SSRs) and phylogenetic positions were analyzed. The results demonstrated the four chloroplast genomes consisted of 152.624 kb, 153.296kb, 156.309 kb, and 158.953 kb in length, involving 124, 130, 129, and 131 genes, respectively. Moreover, the chloroplast genomes contained typical four regions. Six classes of SSR were identified from the four chloroplast genomes, in which mononucleotide was the class with the most members. The types of the repeats were various within individual classes of SSR. Phylogenetic trees indicated that M. leptophylla was clustered with M. yunnanensis, and H. exserta was confirmed under family Ocimeae. Additionally, R. bambusarum and R. henryi were clustered together, whereas they did not belong to the same species due to the differing SSR features. This research would provide evidence for resolving the species and contributed new genetic information for further study.

Deep neural networks (DNNs) have made a huge impact in the field of machine learning by providing unbeatable humanlike performance to solve real-world problems such as image processing and natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN) are two typical architectures that are widely used to solve such problems. Time sequence-dependent problems are generally very challenging, and RNN architectures have made an enormous improvement in a wide range of machine learning problems with sequential input involved. In this paper, different types of RNN architectures are compared. Special focus is put on two well-known gated-RNN’s Long Term Short Memory (LSTM) and Gated Recurrent Unit (GRU). We evaluated these models on the task of force estimation system in pouring. In this study, four different models including multi-layers LSTM, multi-layers GRU, single-layer LSTM and single-layer GRU) were created and trained. The result suggests that multi-layer GRU outperformed other three models.

Background: Most drug-resistant Escherichia coli isolates in dogs come from diseased dogs. Prior to this study, the prevalence and risk factors of fecal carriage drug-resistant E. coli and epidemic clone sequence type (ST) 131 (including subtypes) isolates in dogs were unknown. Methods: Rectal swabs were used for E. coli isolation from 299 dogs in a veterinary teaching hospital in Taiwan. Antibiotic resistance and multiplex PCR analyses of E. coli for major STs were performed. Result: There were 43.1% cefazolin-resistant, 22.1% fluoroquinolone-resistant, and 9.4% extended-spectrum beta-lactamase-producing E. coli in our cohort. In the phylogenetic study, B2 was the predominant group (30.1%). The cefazolin-resistant group and ciprofloxacin-resistant group had greater antibiotic exposure in the last 14 days (P &lt; 0.05). The age, sex, and dietary habits of the antibiotic-resistant and -susceptible groups were similar. In the seven isolates of ST131 in fecal colonization, the most predominant subtypes were FimH41 and FimH22. Conclusion: Recent antibiotic exposure was related to the fecal carriage of antibiotic-resistant E. coli isolates. Three major subtypes (FimH41, H22, and H30) of ST131 can thus be found in fecal carriage in dogs in Taiwan.

Legume plants enter symbiosis with soil nitrogen-fixing bacteria (rhizobia), thereby gaining access to the assimilable atmospheric nitrogen. This symbiosis is important for agriculture, thus, biofertilizers with effective strains of rhizobia are created for crop legumes to increase their yield and minimize the amounts of mineral fertilizers used. In this work, we sequenced and characterized the genome of Rhizobium leguminosarum bv. viciae strain RCAM1022, a component of the ‘Rhizotorfin’ biofertilizer produced in Russia and used for pea (Pisum sativum L.).

The market for microfluidic chips is experiencing significant growth; however, their development is hindered by a complex design process and low efficiency. Enhancing microfluidic chips’ design quality and efficiency has emerged as an integral approach to foster their advancement. Currently, the existing structural design schemes lack careful consideration regarding the impact of chip area, microchannel length, and the number of intersections on chip design. This inadequacy leads to redundant chip structures resulting from the separation of layout and wiring design. This study proposes a structural optimization method for microfluidic chips to address these issues utilizing a simulated annealing algorithm. The simulated annealing algorithm generates an initial solution in advance using the fast sequence pair algorithm. Subsequently, an improved simulated annealing algorithm is employed to obtain the optimal solution for the device layout. During the wiring stage, an advanced wiring method is used to designate the high wiring area, thereby increasing the success rate of microfluidic chip wiring. Furthermore, the connection between layout and routing is reinforced through an improved layout adjustment method, which reduces the length of microchannels and the number of intersections. Finally, the effectiveness of the structural optimization approach is validated through six sets of test cases, successfully achieving the objective of enhancing the design quality of microfluidic chips.

The world is facing a major health crisis, the global pandemic of COVID-19 caused by the SARS-CoV-2 coronavirus, for which no approved antiviral agents or vaccines are currently available. Here we describe a collection of codon-optimized coding sequences for SARS-CoV-2 cloned into Gateway-compatible entry vectors, which enable rapid transfer into a variety of expression and tagging vectors. The collection is freely available via Addgene. We hope that widespread availability of this SARS-CoV-2 resource will enable many subsequent molecular studies to better understand the viral life cycle and how to block it.

The purpose of this study was to identify candidate novel species performing beneficial ecological functions from soil. The hypothesis was that novel culturable species with antibiotic production potential could be readily found from soil in the Nature Canyon at Los Angeles Pierce College. 137 bacterial isolates from soil were grown on nutrient agar, ISP-6 medium, or LB agar and partial 16S was ascertained using the Sanger platform for regions V1-V4. One of the potentially novel isolates, Pseudomonas sp. 21-C3-ER, was isolated on nutrient agar from fallow soil in an agricultural nature preserve in Los Angeles. The soil conditions were 3.7% moisture, clay texture by touch, 13.37% organic matter, pH=6.63, TDS=638, 150 ppm nitrogen, 114 ppm phosphate, and 42 ppm potash. Whole genome sequencing was performed on the DNBseq platform and species identification was performed with TRUEBacID; the closest relative was Pseudomonas brassicacearum subsp. neoaurantiaca which was 92.05% similar by genomic evidence. 197 virulence factors were identified including flagellar biosynthesis, motor proteins, Beta-lactamase, Fosfomycin, copper, and sulfur drug resistance. Gram stain of strain 21-C3-ER revealed gram negative rods. Rods were visualized with scanning electron microscopy, revealing individuals approximately 1 µm in length. Secondary metabolite analysis using antiSMASH showed a variety of post-translationally modified proteins and a promysalin analog. The qualitative test for pyoverdine siderophore production using UV light was positive. Metabolic analysis using RAST indicated genes were present for pyoverdine production, antimicrobial resistance, and ammonification. The nitrate reduction test was positive. Denitrifying bacteria may prevent leaching of nitrates from soil, since ammonia has a higher affinity to soil and organic matter than nitrate. We propose Pseudomonas fernandeno as a novel species. The type stain is 21-C3-ER.

The precise molecular basis of anti-tumor immunity is not yet fully understood, although commensal Bifidobacterium (c-BIF) is expected to be one of the key players in cancer control via antigenic mimicry. We investigated the sequence similarity between c-BIF and cytotoxic T lymphocyte (CTL) epitope peptides against human tumor-associated antigens to better understand the molecular basis of antigenic mimicry. We used two different similarity analyses for this purpose, a linear sequence analysis and a similarity analysis of T cell receptor (TCR)-mediated recognition of CTL epitope peptides on antigen-presenting cells. The linear sequence analysis revealed 3,900 positive numbers of similarity sites between them with 126 mean per peptide and 107 median per peptide, while the TCR-mediated recognition analysis revealed 5,018 positive numbers with a mean of 162 and median of 132 per peptide. These results demonstrated the existence of durable and abundant sequence similarities between c-BIF and CTL epitope peptides, suggesting that the former play a pivotal in inducing cellular and humoral immunity against the latter in the absence of cancer cells via antigenic mimicry.

In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from e.g. diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19-54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyra^fit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps,obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.

Adeno-associated viruses (AAVs, genus dependoparvovirus) are promising gene therapy vectors. In strains AAV1-12, the capsid gene VP1 encodes a recently discovered protein, MAAP, in an overlapping frame. MAAP binds the cell membrane by an unknown mechanism. We discovered that MAAP is also encoded in bovine AAV and in porcine AAVs (which have shown promise for gene transfer into muscle tissues), in which it is probably translated from a non-canonical start codon. MAAP is predicted to be mostly disordered except for a predicted C-terminal, membrane-binding amphipathic α-helix. MAAP has a highly unusual composition. In particular, it lacks internal methionines, and is devoid of tyrosines in most strains. Unexpectedly, we discovered that the N-terminus of VP1 also lacks several amino acids. In all AAVs that encode MAAP, the first 200 aas of VP1 are devoid of internal methionines, probably owing to a selection against ATG codons that could prevent translation of MAAP and of capsid isoforms (VP2, VP3). The N-terminus of VP1 also lacks cysteines, likely to avoid the formation of disulfide bridges when it becomes exposed outside of the capsid during post-endocytic trafficking. Finally, the region common to VP1 and VP2 lacks tyrosine in the vast majority of AAVs that encode MAAP. Avoiding these "forbidden" aas in MAAP and VP1 when creating recombinant AAV capsids might increase the efficiency of capsid design. Conversely, the presence of "forbidden" aas in some rare strains probably indicates that they have unusual properties that could help us understand the viral cycle.

A novel effective vibrational mode was discovered in the conventional transducer with an array of orthogonal (square) regular piezoelectric rods in 1-3 piezocomposite, containing the damping backing and front matching layers. The operational resonance in the structure was determined as the Surface Acoustic Wave (SAW) on the backing boundary excited by the adjacent piezo-rods, with its frequency typically near 3 times lower the fundamental half-lambda conventional piezocomposite resonance. Pulse-echo sensitivity and transmitting sound-pressure-level (SPL) in air showed that the signal strength is roughly comparable to the industrial similar air transducers at the frequency range 100…700 kHz, where at these frequencies the lateral and longitudinal piezoelement dimensions in the conventional transducer design are typically close to each other causing interference with unwanted coupling modes. As was determined theoretically and proved in experiments, the backing SAW resonance effect in the transducer performance is inherent just to the regular periodic 1-3 piezocomposite structure, and does occur neither with randomly located/oriented piezo-rods, nor in the homogeneous piezo-plate at least with the same lateral cross-section as the connected to it backing. The purpose of the article is to investigate a newly discovered operational vibrational mode of a SAW type in 1-3 regular piezocomposite, other than piezoelectric resonance. The investigated phenomena can improve the transceiver sensitivity, bandwidth, providing lower drive voltage, and smaller and lighter weight ultrasonic transducers. Based on the piezocomposites with thickness’ 1…1.5 mm (rod resonance near 2…3 MHz), pillar width 0.2…0.8 mm, kerf width 0.1…0.4 mm, the transceivers with an operating frequency from 140 kHz to 650 kHz were designed and fabricated with a conventional backing of a mixture of high-density tungsten powder and epoxy, and a matching layer of a mixture of low-density glass bubbles and epoxy. Experimental evaluation of their acoustical performance showed expected characteristics suitable for practical applications.

Human cytomegalovirus (HCMV) tegument protein pp150 is essential for the completion of final steps in virion maturation. Earlier studies indicated that three pp150nt (N terminal one-third of pp150) conformers cluster on each triplex (Tri1, Tri2A and Tri2B) and extend towards small capsid proteins atop nearby major capsid proteins forming a net-like layer of tegument densities that enmesh and stabilize HCMV capsids. Based on this atomic detail, we designed several peptides targeting pp150nt. Our data show significant reduction in virus growth upon treatment with one of these peptides (pep-CR2) with an IC50 of 1.33 μM. Based on 3D modeling, pep-CR2 specifically interferes with the pp150-capsid binding interface. Cells pre-treated with pep-CR2 and infected with HCMV sequester pp150 in the nucleus indicating a mechanistic disruption of pp150 loading onto capsids and subsequent nuclear egress. To enhance the in-vivo inhibitory potential and bioavailability of pep-CR2, we conjugated it with a carrier molecule (elastin like polypeptide (ELP)). The ELP-pep-CR2 conjugate was expressed in E.coli and purified. Upon treatment with ELP-pep-CR2, HCMV showed significant titer reductions with no significant impact on cell viability. These results indicate that CR2 of pp150 is amenable to targeting by a peptide inhibitor and can be developed into an effective antiviral.

Carnivore protoparvovirus 1 (CPPV-1) is a DNA virus causing gastrointestinal disease and immunosuppression in various terrestrial carnivores. Domestic dogs and cats are considered the primary CPPV-1 reservoirs. The habitat overlaps of wild carnivores and free-roaming dogs increases the threat of CPPV-1 transmission between them. This study explored the CPPV-1 distribution among wild carnivores through PCR screening and compared the DNA sequences of the partial capsid protein (VP2) between wild and domestic carnivores. In total, 181 samples were screened for the CPPV-1 VP2 gene, including 32 masked palm civets (Paguma larvata), 63 Chinese ferret badgers (Melogale moschata), and 86 crab-eating mongooses (Herpestes urva), from 2015 to 2019 in Taiwan. The average prevalence of CPPV-1 was 17.7% (32/181), with the highest prevalence in masked palm civets (37.5%). In addition, a masked palm civet was coinfected with two CPPV-1 strains. Among the 33 partial VP2 gene sequences, 23 were identical to sequences amplified from domestic dogs and cats in Asia and the remaining 10 were identified for the first time. This study demonstrated that CPPV-1 has circulated between domestic and wild carnivores in rural Taiwan. Therefore, further population control and health management of free-roaming domestic carnivores are recommended.

The phylogenetic relationships among the Peninsular Malaysian orchid genus Coelogyne were studied by morphological characteristics and by sequencing the internal transcribed region (ITS) from the nuclear ribosomal DNA (nrDNA). Coelogyne is a large genus of about 200 species distributed in pantropical areas from the Himalayas, Sri Lanka, India, Southern China and throughout South East Asia to Papua New Guinea. The widely accepted previous classification system was exclusively based on floral morphology. There were very few molecular systematic studies of Coelogyne done in Peninsular Malaysia thus far. In this study, 59 Coelogyne taxa were collected from throughout Peninsular Malaysia and 57 of them were identified to the species level. To study the phylogeny of this genus, morphological characters were utilized together with molecular evidences to generate the systematic hypotheses. Cluster analysis was performed using both the vegetative and floral characters. The results showed that three sections of Peninsular Malaysian Coelogyne, namely Longifoliae, Speciosae and Fuliginosae were sister groups which were more closely related by forming one clade than they were with the other sections. Another clade consisted of four other sections, namely Flaccidae, Coelogynae, Tomentosae and Verrucosae. Molecular phylogenies obtained by using the Neighbour Joining method revealed the close relationship between the sections Tomentosae and Verrucosae, whereas usage of the Maximum Likelihood method demonstrated that three sections namely Longifoliae, Speciosae and Fuliginosae were sister groups since they formed a single clade.

Like other viruses, SARS-COV-2 too mutating and thus creating divergent variants across the world. Protein sequence variation occurs due to non-synonymous single-nucleotide polymorphism (SNP) that alter the amino acid. Amino acid substitutions on homooligomer interfaces may change the structure of the protein and hence alter the regular or known functional activities of a viral protein. Studies reveal that even a single point mutation in virus protein can significantly change their biology, leads to peculiar pathogenic properties. Therefore, an in-depth investigation of the amino acid substitution in the genomic signature of a protein is highly essential for the rapidly evolving virus-like SARS-COV-2. Investigation of world-wide and country-specific substitution features may be crucial and highly essential to decipher pathogenicity. These might be also helpful to precise structure prediction and identification of possible therapeutic targets for effective drug design. We perform extensive analysis towards highlighting and characterizing the amino acid substitution signature occurs in the four structural proteins (Spike-S, Nucleocapsid-N, Membrane-M, Envelope-E) of SARS-COV-2. We use a total of 9587 viral sequences reported from 49 different countries across the globe. In this study, we try to study the amino acid substitution patterns and its impact on change in biochemical properties, thereby possible changes in protein structures. We perform the following analysis: a) isolating and grouping variants we considered, for different protein sequences; b) identifying amino acid substitution type that are frequently and rarely occurring and reporting their location within the sequence; c) change in chemical properties due to amino acid substitution; and f) highlight country-specific divergent variation and substitution signature. In terms of mutational changes, E and M proteins are relatively stable than N and S proteins. A significant quantity of variations is observed in spike (S) proteins. Our study further reveals an interesting fact that the substitution location is random in N protein, whereas the substitution sites in M protein is less varying and almost stable. Substitutions specific to active sub-domains in S and N proteins reveals that sub-domains like Heptapeptide Repeat (HR2), Fusion peptides (FP), and Transmembrane (TM), which are involved in cellular membrane fusion and entry of the virus into the host cells, are significantly mutated. Majority of the substitutions leads to change in biochemical properties (side chain and hydropathy) of amino acid. A good number of exclusive variants are found specific to a particular country. We strongly believe that the current findings will be helpful for protein structure analysis of viral structural proteins and antiviral drug discovery.

In this paper, we generalize the concept of I-statistical convergence, which is a recently introduced summability method, with an infinite matrix of complex numbers and a modulus function. Lacunary sequences will also be included in our definitions. The name of our new method will be A^{I}-lacunary statistical convergence with respect to a sequence of modulus functions. We also define strongly A^{I}-lacunary convergence and we give some inclusion relations between these methods.

Laser powder bed fusion (LPBF) is an additive manufacturing technique that is gaining popularity for producing metallic parts in various industries. However, parts produced by LPBF are prone to residual stress, deformation, cracks and other quality defects due to uneven temperature distribution during the LPBF process. To address this issue, in prior work, the authors have proposed SmartScan, a method for determining laser scan sequence in LPBF using a model-based and optimization-driven approach, rather than using heuristics, and applied it to simple 2D geometries. This paper presents a generalized SmartScan methodology that is applicable to arbitrary 3D geometries. This is achieved by: (1) expanding the thermal model and optimization approach used in SmartScan to multiple layers; (2) enabling SmartScan to process shapes with arbitrary contours and infill patterns within each layer and (3) providing SmartScan with global foresight to make it less myopic in its optimization. Sample 3D parts are printed using the proposed generalized SmartScan and compared to those printed using standard heuristic scan sequences. Reductions of up to 93% in temperature inhomogeneity, 87% in residual stress, and 26% in maximum deformation were observed, without significantly sacrificing print speed. However, SmartScan was found to cause minor (<5%) to significant (up to 20%) increases in surface roughness compared to the heuristic approaches, depending on the scan pattern used.