The generation of arithmetic sequences involves arithmetic operations. However, is it possible to generate such sequences without being subject to any arithmetic or related operations, all by just hand? The answer may be negative, as the very rule defining these sequences itself relies on arithmetic operations. Nonetheless, this paper presents a few results demonstrating that the exact sequences of $an$, where, $a \in \{ 2,3,\cdots,9,10\}$ and $n \in \mathbb N \cup \{0\}$, can indeed be generated up to any desired number of terms without relying on any arithmetic operations and others derived from such. This is done by the formation of a specialized table that leverages numerical patterns and the notion of concatenation.

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.

This study examines the contribution of early executive functions (EFs) in the association between fluency in reading and arithmetic. Kindergarten children (N = 1,185) were assessed on Executive Functions skills and on reading and arithmetic fluency in Grade 1 and Grade 3. The analysis revealed that beyond the connection within each domain there is a unidirectional effect between fluency measures, with Grade 1 reading fluency significantly influencing the development of arithmetic fluency in Grade 3. Furthermore, the findings indicate that kindergarten EFs significantly contribute to arithmetic fluency at both time points and to reading fluency in the first grade. Early EF skills also emerged as a mediator of associations between fluency performance in reading and arithmetic, suggesting that the influence of EFs extends beyond individual academic domains. These findings have implications for understanding the cognitive mechanisms that underlie the relations between these academic skills.

In a previous paper (https://dx.doi.org/10.2139/ssrn.3648127 ), an elementary and thoroughly arithmetical proof of Fermat’s last theorem by induction has been demonstrated if the case for “n = 3” is granted as proved only arithmetically (which is a fact a long time ago), furthermore in a way accessible to Fermat himself though without being absolutely and precisely correct. The present paper elucidates the contemporary mathematical background, from which an inductive proof of FLT can be inferred since its proof for the case for “n = 3” has been known for a long time. It needs “Hilbert mathematics”, which is inherently complete unlike the usual “Gödel mathematics”, and based on “Hilbert arithmetic” to generalize Peano arithmetic in a way to unify it with the qubit Hilbert space of quantum information. An “epoché to infinity” (similar to Husserl’s “epoché to reality”) is necessary to map Hilbert arithmetic into Peano arithmetic in order to be relevant to Fermat’s age. Furthermore, the two linked semigroups originating from addition and multiplication and from the Peano axioms in the final analysis can be postulated algebraically as independent of each other in a “Hamilton” modification of arithmetic supposedly equivalent to Peano arithmetic. The inductive proof of FLT can be deduced absolutely precisely in that Hamilton arithmetic and the pransfered as a corollary in the standard Peano arithmetic furthermore in a way accessible in Fermat’s epoch and thus, to himself in principle. A future, second part of the paper is outlined, getting directed to an eventual proof of the case “n=3” based on the qubit Hilbert space and the Kochen-Specker theorem inferable from it.

The theories of quantum mechanics and relativity dramatically altered our understanding of the universe ushering in the era of modern physics. Quantum theory deals with objects probabilistically at small scales, whereas relativity deals classically with motion in space and time. We show here that the mathematical structures of quantum theory and of relativity follow together from pure thought, defined and uniquely constrained by the same elementary ``combining and sequencing'' symmetries that underlie standard arithmetic and probability. The key is uncertainty, which inevitably accompanies observation of quantity and imposes the use of pairs of numbers. The symmetries then lead directly to the use of complex \sqrt{-1} arithmetic, the standard calculus of quantum mechanics, and the Lorentz transformations of relativistic spacetime. One dimension of time and three dimensions of space are thus derived as the profound and inevitable framework of physics.

The paper is a continuation of another paper (https://philpapers.org/rec/PENFLT-2) published as Part I. Now, the case of “n=3” is inferred as a corollary from the Kochen and Specker theorem (1967): the eventual solutions of Fermat’s equation for “n=3” would correspond to an admissible disjunctive division of qubit into two absolutely independent parts therefore versus the contextuality of any qubit, implied by the Kochen – Specker theorem. Incommensurability (implied by the absence of hidden variables) is considered as dual to quantum contextuality. The relevant mathematical structure is Hilbert arithmetic in a wide sense (https://dx.doi.org/10.2139/ssrn.3656179), in the framework of which Hilbert arithmetic in a narrow sense and the qubit Hilbert space are dual to each other. A few cases involving set theory are possible: (1) only within the case “n=3” and implicitly, within any next level of “n” in Fermat’s equation; (2) the identification of the case “n=3” and the general case utilizing the axiom of choice rather than the axiom of induction. If the former is the case, the application of set theory and arithmetic can remain disjunctively divided: set theory, “locally”, within any level; and arithmetic, “globally”, to all levels. If the latter is the case, the proof is thoroughly within set theory. Thus, the relevance of Yablo’s paradox to the statement of Fermat’s last theorem is avoided in both cases. The idea of “arithmetic mechanics” is sketched: it might deduce the basic physical dimensions of mechanics (mass, time, distance) from the axioms of arithmetic after a relevant generalization, Furthermore, a future Part III of the paper is suggested: FLT by mediation of Hilbert arithmetic in a wide sense can be considered as another expression of Gleason’s theorem in quantum mechanics: the exclusions about (n = 1, 2) in both theorems as well as the validity for all the rest values of “n” can be unified after the theory of quantum information. The availability (respectively, non-availability) of solutions of Fermat’s equation can be proved as equivalent to the non-availability (respectively, availability) of a single probabilistic measure as to Gleason’s theorem.

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.

In a safety-critical system typically not all provided services have the same criticality, which we call mixed-criticality systems. Criticality arithmetic, also called SIL arithmetic, is an approach to lower the development effort of a service by providing redundancy with tasks that are developed for a lower criticality level. In this paper we present ATMP-CA, which is a derivation of the multi-core scheduler ATMP. ATMP-CA is able to take into account the knowledge about the use of criticality arithmetic. ATMP-CA has a modified core allocation and procedure for utility optimisation, considering the context of the replicated tasks. We conducted experiments that show that ATMP-CA is able to provide the services using criticality arithmetic, while the reference schedulers were not.

This paper presents a unified reconfigurable coordinate rotation digital computer (CORDIC) processor for floating-point arithmetic. It can be configured to operate in multi-mode to achieve a variety of operations and replaces multiple single-mode CORDIC processors. A reconfigurable pipeline-parallel mixed architecture is proposed to adapt different operations, which maximizes the sharing of common hardware circuit and achieves the area-delay-efficiency. Compared with previous unified floating-point CORDIC processors, the consumption of hardware resources is greatly reduced. As a proof of concept, we apply it to 1638416384 points target Synthetic Aperture Radar (SAR) imaging system, which is implemented on Xilinx XC7VX690T FPGA platform. The maximum relative error of each phase function between hardware and software computation and the corresponding SAR imaging result can meet the accuracy index requirements.

In fuzzy group decision making problems and fuzzy shortest path problems, the addition and subtraction are the basic problems. For the mixed normal fuzzy numbers and trapezoidal fuzzy numbers, the addition and subtraction operations are approximated by the normal fuzzy numbers in this paper. The behaviors of approximated normal fuzzy numbers are the same as those of the normal distributions from the viewpoint of probability. An application of the addition and subtraction operations of mixed fuzzy numbers to the fuzzy sample mean is also proposed.

The paper proposes a new implementation of the PID algorithm in digital hardware. The proposed circuit implements an advanced PID formula, containing a non ideal derivative component, and weighting coefficients, which enable reducing influence of setpoint changes in the proportional and derivative components. The implementation operates on standard single precision (32 bit) floating-point numbers. The proposed circuit structure is optimized for cost. It uses just one arithmetic block, performing the multiply-and-add operation. The calculations are carried out in a sequential manner. The circuit was implemented in a Cyclone V FPGA device from Intel, using the Quartus Prime software. Proper operation of the circuit was verified by simulation. The proposed solution is comparable in terms of speed with other hardware implementations of the PID algorithm operating on standard single precision floating-point numbers, while being significantly cheaper. However, it outperforms by several orders of magnitude the speed of any software-based implementation, including solutions using PLCs, and CPU/MCUs. The proposed circuit structure, together with the overall regulator device concept, suit well the SoC (System on Chip), or SoPC (System on Programmable Chip) idea, i. e. a device, that contains a CPU core immersed in “FPGA fabric” - logic resources characteristic for FPGA devices.

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.

Background: Intrauterine transfusion (IUT) of the donor and partial exchange (pET) of the recipient is a temporizing treatment for pregnancies with Twin Anemia Polycythemia Sequence (TAPS). We sought to give a detailed description of the procedural approach and outcomes for sequential donor IUT and recipient pET in TAPS. Methods: Retrospective study of spontaneous TAPS referred to the Johns Hopkins Center for Fetal Therapy treated with donor IUT followed by recipient pET utilizing a double-syringe set up. Procedure characteristics and outcomes as well as accuracy of existing transfusion formulas were analyzed and compared with the literature. Results: 5 of 78 patients with spontaneous TAPS underwent a total of 19 combined IUT/pET procedures (median first procedure to delivery interval 5.6 weeks [interquartile range IQR 1.9-6.0]). One pET was stopped for fetal decelerations. Patients delivered at 33.0 weeks [IQR 31.9-33.3] with two survivors and no neonatal transfusion requirements. IUT volumes were 48 ml [IQR 39-63 ml] and pET volumes 32 ml [IQR 20-50] utilizing aliquots of 5 – 20 mls for the latter (p=0.021). For IUTs, assumption of a fetal blood volume below 150 ml/kg underestimated required transfusion volume. For pETs, all formulas required adjustment of dilution volume based on bedside testing (p<0.05 for all). Conclusions: Donor transfusion followed by partial exchange in the recipient can prolong pregnancy in spontaneous TAPS and obviate the need for neonatal transfusion. A double-syringe setup facilitates efficient saline exchange. Because the accuracy of volume formulas is limited, bedside testing is recommended to achieve the target hemoglobin.

Automatic and blind parameter estimation based on the inherent features of wireless signals is a major research area due to the fact that these techniques lead to the simplification of receivers, especially in terms of coarse synchronization, more importantly reduce down the signaling load at the control channels. Thus, in the literature many techniques are proposed to estimate a vast set of parameters including modulation types and orders, data and chip rates, phase and frequency offsets and so on. In this paper, a cyclostationary feature detection based method is proposed to estimate the carrier numbers and carrier spacing of carrier-aggregated direct sequence spread spectrum cellular signals blindly. The particular chip rate of the signal is also estimated through the process jointly. In the paper, after the proposed method is formulated, the measurement setup which is developed to collect the data for the validation of the proposed method is introduced. The measurement results are post-processed for performance analysis purposes. To that end, the method is investigated in terms of SNR values, different channel conditions, and measurement durations. Furthermore, the performance of the proposed method is compared with that of energy detection. Measurement results indicate superior performance of the proposed method under significant wireless channel impairments and in low-SNR regions.

Hilbert arithmetic in a wide sense, including Hilbert arithmetic in a narrow sense consisting by two dual and anti-isometric Peano arithmetics, on the one hand, and the qubit Hilbert space (originating for the standard separable complex Hilbert space of quantum mechanics), on the other hand, allows for an arithmetic version of Gentzen’s cut elimination and quantum measurement to be described uniformy as two processes occurring accordingly in those two branches. A philosophical reflection also justifying that unity by quantum neo-Pythagoreanism links it to the opposition of propositional logic, to which Gentzen’s cut rule refers immediately, on the one hand, and the linguistic and mathematical theory of metaphor therefore sharing the same structure borrowed from Hilbert arithmetic in a wide sense. An example by hermeneutical circle modeled as a dual pair of a syllogism (accomplishable also by a Turing machine) and a relevant metaphor (being a formal and logical mistake and thus fundamentally inaccessible to any Turing machine) visualizes human understanding corresponding also to Gentzen’s cut elimination and the Gödel dichotomy about the relation of arithmetic to set theory: either incompleteness or contradiction. The metaphor as the complementing “half” of any understanding of hermeneutical circle is what allows for that Gödel-like incompleteness to be overcome in human thought.

This work introduces an innovative approach that unites a PIDND2N2 controller and the balanced arithmetic optimization algorithm (b-AOA) to enhance the stability of an automatic voltage regulator (AVR) system. The PIDND2N2 controller, tailored for precision, stability, and responsiveness, mitigates the limitations of conventional methods. The b-AOA optimizer is obtained through integration of pattern search and elite opposition-based learning strategies into the arithmetic optimization algorithm. This integration optimizes the controller parameters and the AVR system's response, harmonizing exploration and exploitation. Extensive assessments, including evaluations on 23 classical benchmark functions, demonstrate the efficacy of the b-AOA. It consistently achieves accurate solutions, exhibits robustness in addressing a wide range of optimization problems, and stands out as a promising choice for various applications. In terms of AVR system, comparative analyses highlight the superiority of the proposed approach in transient response characteristics, with the shortest rise and settling times and zero overshoot. Additionally, the b-AOA approach excels in frequency response, ensuring robust stability and a broader bandwidth. Furthermore, the proposed approach is compared with various state-of-the-art control methods for the AVR system, showcasing impressive performance. These important results underscore the significance of this work, setting a new benchmark for AVR control by advancing stability, responsiveness, and reliability in power systems.

This paper presents a novel algorithm, the multi-objective nonlinear interval PageRank (MONIPR) Algorithm. This algorithm extends the conventional PageRank (PR) algorithm, integrating nonlinear interval computation and multi-objective considerations into the computation process. The MONIPR algorithm bridges a research gap in integrating nonlinear interval approaches and multi-objective problems into the PR algorithm, which were traditionally treated separately. In addition, this research adopts the constrained interval arithmetic proposed to address the limitations of the conventional PR algorithm, which tends to overlook uncertainties and results in deterministic outcomes. In addition, we use a numerical example to demonstrate the proposed algorithm, contrasting it with the conventional approaches. A numerical example demonstrates the algorithm's performance by comparing the rank intervals obtained with the traditional crisp Markov chain and PR algorithm. The results highlight the ability of the proposed algorithm to provide a range of possible rank intervals, considering both uncertainty and multi-objectives. The findings suggest potential applications in decision-making, uncertainty quantification, and systems analysis.

Based on an in-depth analysis of the factors influencing the compressive strength of ultra-high performance concrete (UHPC), this study examined the impact of both single factors and combined factors on UHPC performance using experimental data. The correlation analysis indicates that cement content, water content, steel fiber, and fly ash significantly affect the strength of UHPC, whereas silica fume, superplasticizers, and slag powder have a relatively smaller influence. This analysis provides a scientific basis for model development. Furthermore, the support vector regression (SVR) model was optimized using the arithmetic optimization algorithm (AOA). The superior performance and computational efficiency of the AOA-SVR model in predicting UHPC compressive strength were validated. Compared to SVR, support vector machine（SVM）, and other single models, the AOA-SVR model achieves the highest R2 value and the lowest error rates. The results demonstrate that the optimized AOA-SVR model possesses excellent generalization ability and can more accurately predict the compressive strength of UHPC.

In this paper, we derive a formula for sum of powers of integers from Abel’s Summation Formula. This formula enables us to generate the formula for the sum of k-th power of integers, denoted by Sk(n), given the formulas of S1(n), S2(n), ..., Sk−1(n). Furthermore, we shall extend this formula to compute the sum of powers of an arithmetic progression. Moreover, we can combine the formula with the result of Bernoulli to derive another result which enables us to find Bernoulli Numbers recursively.

Quantum-dot cellular automata (QCA) are a promising nanoscale computing technology that exploits the quantum mechanical tunneling of electrons between quantum dots in a cell and electrostatic interaction between dots, in neighboring cells. QCA can achieve higher speed, lower power, and smaller areas than conventional complementary metal–oxide–semiconductor (CMOS) technology. Developing QCA circuits in a logically and physically reversible manner can provide exceptional reductions in energy dissipation. The main challenge of reversible QCA design is to maintain reversibility down to the physical level. The arithmetic logic unit (ALU) is an essential component of the central processing unit (CPU) of a computer. It performs various arithmetic and logical operations on the data that the CPU processes. Current QCA ALU designs are either irreversible or logically reversible; however, they lack physical reversibility, a crucial requirement to increase energy efficiency. In this paper, we present a novel multilayer design for a logically and physically reversible QCA ALU based on majority gates as the key components, which can perform 16 different operations. We use QCADesigner-E software to simulate and evaluate energy dissipation. The proposed logically and physically reversible QCA ALU offers an improvement of 88.8% in energy efficiency. Compared to the next most efficient 16-operation QCA ALU, this ALU uses 51% fewer QCA cells and 47% less area.

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.

Butirosins are naturally occurring aminoglycoside (AG) antibiotics featuring a 4,5-disubstituted 2-deoxystreptamine (2-DOS) with a (2S)-4-amino-2-hydroxybutyrate (AHBA) side chain. This side chain has been shown to confer resistance against AG modifying enzymes, leading to ongoing studies on the butirosin biosynthetic pathway and the corresponding enzymes. Butirosin is produced by Niallia (formerly Bacillus) circulans and Bacillus vitellinus, with most research focused on the former strain. To date, no whole genome analysis has been performed on B. vitellinus. In this study, we sequenced the complete genome of B. vitellinus NBRC 13296 and performed a comparative analysis of different butirosin biosyntheric gene clusters (BGCs), including those from N. circulans. The complete genome of B. vitellinus NBRC 13296 comprises a 6,331,192 base circlular chromosome with a GC content of 52.68%. The annotation revealed the presence of 5,605 CDSs, 70 tRNA genes, 30 rRNA genes, and 3 ncRNA genes in NBRC 13296. The highest dDDH and ANI values between NBRC 13296 and the most closely related type strain, Paenibacillus chitinolyticus KCCM 41400, were 97.8% and 98.66%, respectively. Based on these genome-based comparative analyses, we propose reclassifying B. vitellinus NBRC 13296 as P. chitinolyticus. Genome mining revealed 18 gene clusters encoding the biosynthesis of diverse secondary metabolites in the genome of B. vitellinus NBRC 13296, indicating the enormous biosynthetic potential of this strain. The predicted structural diversity of secondary metabolites includes aminoglycoside, PKS, NRPS, PKS-NRPS hybrids, metallophores, phosphonates, terpenes, β-lactones, and RiPP peptides. We then comparatively characterized butorosin BGCs previously studied in several N. circulans strains. Additionally, comparative genome analysis revealed complete butirosin BGCs identified from P. chitinolyticus KCCM 41400, P. chitinolyticus NRRL B-23119, P. chitinolyticus NRRL B-23120, P. chitinolyticus B-14908, P. chitinolyticus YSY-3.1, P. chitinolyticus JMW06, Paenibacillus sp. GbtcB18, Paenibacillus sp. HGH0039, Paenibacillus sp. MZ04-78.2. Finally, we identified the core region consisting of BtrS, BtrN, BtrM, BtrL, BtrA, BtrB, BtrC, BtrD, BtrD, BtrE, BtrF, BtrG, BtrH, BtrI, BtrI, BtrJ, BtrK, BtrO, BtrP, and BtrV, followed by an upstream region organizing BtrQ, BtrW, BtrX, BtrY, and BtrZ in the same transcriptional direction and sequential genetic arrangement, and a downstream region organizing various proteins based on BtrT, BtrR2, BtrU, and BtrR1. Our study provided insights into the reclassification of B. vitellinus NBRC 13296 to P. chitinolyticus and suggested the need for continued studies on butirosin biosynthesis from an enzymatic perspective.

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.

Magnetic resonance (MR) image acquisitions are usually time-consuming, limiting utilization in neuroimaging. For multiple sclerosis (MS) patients, MR imaging plays a major role in drug therapy decision-making. The purpose of this study was to evaluate whether an ultrafast, T2-weighted (T2w), deep-learning-enhanced (DL), echo-planar-imaging-based (EPI) fluid-attenuated inversion recovery (FLAIR) sequence (FLAIRUF) that has been targeting neurological emergencies so far might even be an option to detect MS lesions of the brain compared to conventional FLAIR sequences. Therefore, 17 MS patients were enrolled prospectively in this exploratory study. Standard MR protocols and ultrafast acquisitions were conducted at 3 tesla (T), including three-dimensional (3D)-FLAIR, turbo/fast spin echo (TSE)-FLAIR, and FLAIRUF. Inflammatory lesions were grouped by size and location. Lesion conspicuity and image quality were rated on an ordinal five-point Likert scale, and lesion detection rates were calculated. Statistical analyses were performed to compare results. Altogether, 568 different lesions were found. Data indicated no significant differences in lesion detection (sensitivity and positive predictive value [PPV]) between FLAIRUF and axially reconstructed 3D-FLAIR (lesion size ≥ 3 mm × ≥ 2 mm) and no differences in sensitivity between FLAIRUF and TSE-FLAIR (lesion size ≥ 3 mm total). Lesion conspicuity in FLAIRUF was similar in all brain regions except for superior conspicuity in the occipital lobe and inferior conspicuity in central brain regions. Further findings include location-dependent limitations of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) as well as artifacts such as spatial distortions in FLAIRUF. In conclusion, FLAIRUF could potentially be an expedient alternative to conventional methods for brain imaging in MS patients since the acquisition can be performed in a fraction of time while maintaining good image quality.

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.

We make use of generalized iterations of the Sacks forcing to define cardinal-preserving generic extensions of the constructible universe L in which the axioms of ZF hold and in addition either 1) the parameter-free countable axiom of choice AC* fails, or 2) AC* holds but the full countable axiom of choice AC fails in the domain of reals. In another generic extension of L, we define a set X⊆P(ω), which is a model of the parameter-free part PA2* of the 2nd order Peano arithmetic PA2, in which CA(Σ21) (Comprehension for Σ21 formulas with parameters) holds, yet an instance of Comprehension CA for a more complex formula fails. Treating the iterated Sacks forcing as a class forcing over $L_{\omega_1}$, we infer the following consistency results as corollaries. If the 2nd order Peano arithmetic PA2 is formally consistent then so are the theories: 1) PA2 + negation of AC*, 2) PA2 + AC* + negation of AC, 3) PA2* + CA(Σ21) + negation of CA.

Deep Neural Network (DNN) inference demands substantial computing power, resulting in significant energy consumption. A large number of negative output activations in convolution layers are rendered zero due to the invocation of the ReLU activation function. This results in a substantial number of unnecessary computations that consume significant amounts of energy. This paper presents ECHO: Energy-efficient Computation Harnessing Onilne Arithmetic - A MSDF-based accelerator for DNN inference, designed for computation pruning, utilizing an unconventional arithmetic paradigm known as online/ most-significant digit first (MSDF) arithmetic which performs computations in a digit-serial manner. The MSDF digital serial computation of online arithmetic enables overlapped computation of successive operations leading to substantial performance improvements. The online arithmetic, coupled with a negative output detection scheme, facilitates early and precise recognition of negative outputs. This, in turn, allows for the timely termination of unnecessary computations, resulting in a reduction of energy consumption. The implemented design has been realized on the Xilinx Virtex-7 VU3P FPGA and subjected to a comprehensive evaluation through a rigorous comparative analysis involving widely used performance metrics. Experimental results demonstrate promising power and throughput improvements compared to contemporary methods. In particular, the proposed design achieved an average improvement in power consumption of up to 81%, 82.9%, and 40.6% for VGG-16, ResNet-18, and ResNet-50 workloads compared to the conventional bit-serial design, respectively. Furthermore, significant average speedups of 2.39×, 2.6×, and 2.42 were observed when comparing the proposed design to conventional bit-serial designs for VGG-16, ResNet-18, and ResNet-50 models respectively.

Nowadays, there are implemented devices whose purpose is to perform massive computations by saving resources at the time they reduce the latency of arithmetic operations. These devices are usually GPUs, FPGAs and other specialised devices such as "Coral". Neural networks, digital filters and numerical simulators take advantage of the massively parallel operations of such devices. One way to reduce the amount of resources used is to limit the size of the registers that store data. This has led to the proliferation of numeric formats with a length of less than 32 bits, known as short floating point or SFP. We have developed several SFP’s for use in our neural network accelerator design, allowing for different levels of accuracy. We use a 16-bit format for data transfer and different formats can be used simultaneously for internal operations. The internal operations can be performed in 16-bit, 20-bit and 24-bit. The use of registers larger than 16-bit allows the preservation of fractional information while increasing precision. By leveraging some of the FPGA’s arithmetic resources, our design outperforms designs implemented from scratch and is competitive with specialized arithmetic circuits already implemented in the FPGA.

Lewis Carroll, both logician and writer, suggested a logical paradox containing furthermore two connotations (connotations or metaphors are inherent in literature rather than in mathematics or logics). The paradox itself refers to implication demonstrating that an intermediate implication can be always inserted in an implication therefore postponing its ultimate conclusion for the next step and those insertions can be iteratively and indefinitely added ad lib, as if ad infinitum. Both connotations clear up links due to the shared formal structure with other well-known mathematical observations: (1) the paradox of Achilles and the Turtle; (2) the transitivity of the relation of equality. Analogically to (1), one can juxtapose the paradox of the Liar (for Lewis Carroll’s paradox) and that of the arrow (for “Achilles and the Turtle”), i.e. a logical paradox, on the one hand, and an aporia of motion, on the other hand, suggesting a shared formal structure of both, which can be called “ontological”, on which basis “motion” studied by physics and “conclusion” studied by logic can be unified being able to bridge logic and physics philosophically in a Hegelian manner: even more, the bridge can be continued to mathematics in virtue of (2), which forces the equality (for its property of transitivity) of any two quantities to be postponed analogically ad lib and ad infinitum. The paper shows that Hilbert arithmetic underlies naturally Lewis Carroll’s paradox admitting at least three interpretations linked to each other by it: mathematical, physical and logical. Thus, it can be considered as both generalization and solution of his paradox therefore naturally unifying the completeness of quantum mechanics (i.e. the absence of hidden variables) and eventual completeness of mathematics as the same and isomorphic to the completeness of propositional logic in relation to set theory as a first-order logic (in the sense of Gödel (1930)’s completeness theorems).

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.

Background: Fold change is widely used in biomedical research to quantify the magnitude of group differences in omics variables. However, the exact calculation method is often not reported, leading to inconsistent results. This study re-evaluates different fold-change calculation methods and provides a clear preference. Methods: Data scenarios with different distributions of treatment/test and reference data were created to challenge the assumption of interchangeable calculation methods. The main difference lies in the definition of the expected values of the groups used to calculate the log ratio, which is the basis of the fold change calculation. In addition, a multi-omics biomedical dataset was analyzed. Results: Using the arithmetic mean as the expected value for the treatment and reference groups resulted in incorrect fold change values more frequently than other methods, especially when the standard deviations between subgroups differed widely. Conclusions: The inferior arithmetic mean method is often perceived as the standard, although mathematically different equations are possible that differ mainly in the estimation of the expected value. Alternatives that define expectation by median, geometric mean, or paired fold change combinations are less susceptible to violations of equal variances or similar treatment/reference distributions.

In this paper defines the consecutive sum of the digits of a natural number, so far as it becomes less than ten, as an arithmetic function called and then introduces some important properties of this function by proving a few theorems in a way that they can be used as a powerful tool in many cases. As an instance, by introducing a test called test, it has been shown that we are able to examine many algebraic equalities in the form of in which and are arithmetic functions and to easily study many of the algebraic and diophantine equations in the domain of whole numbers. The importance of test for algebraic equalities can be considered equivalent to dimensional equation in physics relations and formulas. Additionally, this arithmetic function can also be useful in factorizing the composite odd numbers.

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.

The previous two parts of the paper (correspondingly, https://philpapers.org/rec/PENFLT-2 and https://philpapers.org/rec/PENFLT-3) demonstrate that the interpretation of Fermat’s last theorem (FLT) in Hilbert arithmetic meant both in a narrow sense and in a wide sense can suggest a proof by induction in Part I and by means of the Kochen - Specker theorem in Part II. The same interpretation can serve also for a proof FLT based on Gleason’s theorem and partly similar to that in Part II. The concept of (probabilistic) measure of a subspace of Hilbert space and especially its uniqueness can be unambiguously linked to that of partial algebra or incommensurability, or interpreted as a relation of the two dual branches of Hilbert arithmetic in a wide sense. The investigation of the last relation allows for FLT and Gleason’s theorem to be equated in a sense, as two dual counterparts, and the former to be inferred from the latter, as well as vice versa under an additional condition relevant to the Gödel incompleteness of arithmetic to set theory. The qubit Hilbert space itself in turn can be interpreted by the unity of FLT and Gleason’s theorem. The proof of such a fundamental result in number theory as FLT by means of Hilbert arithmetic in a wide sense can be generalized to an idea about “quantum number theory”. It is able to research mathematically the origin of Peano arithmetic from Hilbert arithmetic by mediation of the “nonstandard bijection” and its two dual branches inherently linking it to information theory. Then, infinitesimal analysis and its revolutionary application to physics can be also re-realized in that wider context, for example, as an exploration of the way for physical quantity of time (respectively, for time derivative in any temporal process considered in physics) to appear at all. Finally, the result admits a philosophical reflection of how any hierarchy arises or changes itself only thanks to its dual and idempotent counterpart.

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.

Let an odd integer be represented as $\sum \limits_{n > m} 2^n + 2^m - 1$ for $m \geq 1$. To transform $2^m - 1$ according to the Collatz rules, the $3x + 1$ step is applied, resulting in the even integer $2^{m + 1} + 2^m - 2$. This even integer, being exactly once divisible by 2, becomes the next odd integer $2^m + 2^{m - 1} - 1$. This represents the growth phase, as exactly one even step follows an odd step. However, while the overall value of the integer increases, terms with decreasing indices are generated after each odd-even cycle. After $m$ such cycles, a term $2^{m - m}$ (which equals 1) is generated, canceling the negative 1. Additional even steps are then performed until the integer becomes odd again, which occurs when the next smallest index becomes zero. The positive 1 thus obtained is written as $2^1 - 1$. If uninterrupted, the term $2^1 - 1$ has the trivial cycle $2^1 - 1 \rightarrow 2^2 \rightarrow 2^1 \rightarrow 2^1 - 1$. This represents the shrinkage phase, as two even steps follow an odd step. To interrupt the trivial cycle, a sequence such as $2^1 + 2^2 + \cdots$ must be available to combine with $2^1 - 1$ and produce $2^{\mathcal{M}} - 1$. This starts another growth phase that lasts for $\mathcal{M}$ odd-even cycles. Finally, there is an attempt to craft an integer that can escape the shrinkage phase in the Collatz dynamics.

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.

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.

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.

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.

Numerical simulations of the sub-critical flow past a circular cylinder at the Reynolds number, Re=130,000 are performed using two numerical platforms, commercial, Ansys Fluent and open source, OpenFoam (finite volume method and large eddy simulation based on a differential equation for the sub-grid kinetic energy). An overview of the available experimental data and similar large eddy simulation studies is presented. A detailed analysis of all accumulated data demonstrates satisfactory agreement between them with a dispersion of the order of 10% (for the main integral flow parameters). A detailed comparison of the results obtained by single- and double-precision numerical methods using Ansys Fluent did not reveal any noticeable discrepancies for the integral and local flow parameters, as well as spectral characteristics. It is shown that the behavior of the dynamic system of the fluid dynamic equations computed with single precision is stable by Lyapunov and does not lead to any loss of accuracy. The reconstructed attractors of the dynamic systems in the phase space are limited by an ellipsoid.

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.

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.

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.

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.

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.

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.

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.

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.

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 an intelligent (i.e., 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; (3) providing the optimization in SmartScan with a balance of exploration and exploitation to make it less myopic; and (4) improving SmartScan's computational efficiency via model order reduction using singular value decomposition. Sample 3D test artifacts are simulated and printed using SmartScan in comparison with common heuristic scan sequences. Reductions of up to 92% in temperature inhomogeneity, 86% in residual stress, 24% in maximum deformation and 50% in geometric inaccuracy were observed using SmartScan, without significantly sacrificing print speed. An approach for using SmartScan for printing complex 3D parts in practice, by integrating it as a plug-in to a commercial slicing software, was also demonstrated experimentally, along with its benefits in significantly improving printed part quality.

This study focuses on one of the most famous open problems in mathematics, namely the Collatz conjecture. The Collatz conjecture or 3x + 1 Problem is perhaps today's most enigmatic unsolved mathematical problem. It is named after Lothar Collatz, who rst proposed it in 1937. It may be stated as as follow: Take any positive integer n. If n is even then divide it by 2, else do \triple plus one" and get 3n + 1. The conjecture is that this process will eventually reach the number 1, regardless of which positive integer is chosen initially. In this paper, we present a simple proof for the Collatz conjecture.

We evaluate the nested sum $\sum_{a_{n - 1} = c}^{a_n } {\sum_{a_{n - 2} = c}^{a_{n - 1} } { \cdots \sum_{a_0 = c}^{a_1 } {x^{a_0 } } } }$ where $a_n$ and $c$ are any integers and $x$ is a real or complex variable. Consequently, we evaluate multiple sums involving the terms of a general second order sequence, the Horadam sequence (W_j (a, b; p, q)), defined for all non-negative integers j by the recurrence relation W₀ = a, W₁ = b; W_j = pW_j−1 − qW_j−2 (j≥2); where a, b, p and q are arbitrary complex numbers, with p ≠ 0, q ≠ 0.

Rigid-body visual tracking is an active research field with many practical applications including visual surveillance and intelligent transport system. In this paper, we define a new problem domain, called visual growth tracking, to track different parts of an object that grow non-uniformly over space and time for application in image-based plant phenotyping. The paper introduces a novel method to detect and track each leaf of a plant for automated leaf stage monitoring. The method has four phases: optimal view selection, plant architecture determination, leaf tracking and generation of a leaf status report. The proposed method uses a graph theoretic approach to reliably detect and track individual leaves by overcoming the challenge of leaf-losses based on temporal image sequence analysis for automatically generating the leaf status report containing the following phenotypes, i.e., the emergence timing of each leaf, total number of leaves present at any time, the day on which a particular leaf stopped growing, and the length and relative growth rate of individual leaves. The proposed method demonstrates high accuracy in detecting leaves and tracking them through the early vegetative stages of maize plants based on experimental evaluation on a publicly available benchmark dataset.

In the paper, using two inversion theorems for the Stirling numbers and binomial coecients, employing properties of the Bell polynomials of the second kind, and utilizing a higher order derivative formula for the ratio of two dierentiable functions, the authors present two explicit formulas, a determinantal expression, and a recursive relation for a sequence of unnamed polynomials, derive two identities connecting the sequence of unnamed polynomials with the Bell polynomials, and recover a known identity connecting the sequence of unnamed polynomials with the Bell polynomials.

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.

In this paper, we explore the surprising behavior of certain discrete maps, drawing inspiration from Collatz-like problems and advanced techniques in analytic number theory, dynamical systems, and differential geometry. Specifically, we investigate a driven cubic-quintic Duffing equation and predict the number of limit cycles around equilibrium points. Furthermore, we develop a novel theoretical framework for chaos suppression in damped-driven systems, leveraging sequences derived from Collatz-like problems. Our study also focuses on analyzing the density of these sequences in both Euclidean and Riemannian metric spaces, providing a comparative analysis of their distribution properties. We estimate the growth rate of sequence density analytically and numerically, highlighting the sensitivity of the density values to the choice of parameters in both metric spaces. Our results present new insights into the behavior of these sequences and their implications for number theory, chaotic dynamics, and geometric analysis.

Purpose: This study investigates the impact of different intensity normalization (IN) methods on the overall survival (OS) radiomics models’ performance of MR sequences in primary (pHGG) and recurrent high-grade glioma (rHGG). Methods: MR scans acquired before radiotherapy were retrieved from two independent cohorts (rHGG C1: 197, pHGG C2: 141) from multiple scanners (15, 14). The sequences are T1 weighted (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR. Sequence-specific significant features (SF) associated with OS, extracted from the tumour volume, were derived after applying 15 different IN methods. Survival analyses were conducted using Cox proportional hazard (CPH) and Poisson regression (POI) models. A ranking score was assigned based on the 10-fold cross-validated (CV) concordance index (C-I), mean square error (MSE), and the Akaike information criterion (AICs), to evaluate the methods’ performance. Results: Scatter plots of the 10-CV C-I and MSE against the AIC showed an impact on the survival predictions between the IN methods and MR sequences (C1/C2 C-I range: 0.62-0.71/0.61-0.72, MSE range: 0.20-0.42/0.13-0.22). White stripe showed stable results for T1wce (C1/C2 C-I: 0.71/0.65, MSE: 0.21/0.14). Combat (0.68/0.62, 0.22/0.15) and histogram matching (HM, 0.67/0.64, 0.22/0.15) showed consistent prediction results for T2w models. They were also the top-performing methods for T1w in C2 (Combat: 0.67, 0.13; HM: 0.67, 0.13), however, only HM achieved high predictions in C1 (0.66, 0.22). After eliminating IN impacted SF using Spearman’s rank-order correlation coefficient, a mean decrease in the C-I and MSE of 0.05 and 0.03 was observed in all four sequences. Conclusion: The IN method impacted the predictive power of survival models. Thus, performance is sequence-dependent.

Breadfruit [Artocarpus altilis (Parkinson) Fosberg] is recognized as a tropical fruit tree crop with great potential to contribute to food and nutrition security in the Caribbean and other tropical regions. However, the genetic diversity and identification of germplasm in the Caribbean and elsewhere is not well understood and documented. This hampers the effective conservation and use of the genetic resources of the tree crop for commercial activities. This study assessed the genetic identity, diversity, ancestry, and phylogeny of breadfruit germplasm existing in Caribbean and recently introduced accessions using 117 SNPs from 10 SSR amplicon sequences. The results showed that there was high genetic diversity in the breadfruit germplasm in the Caribbean, as well as the newly introduced breadfruit accessions based on nucleotide diversity (π_T) and nucleotide polymorphism (θ_W). Furthermore, based on these measures of genetic diversity, there were no significant differences between existing Caribbean breadfruit accessions and newly introduced breadfruit accessions which are known to be highly diverse. Ancestry and phylogeny analysis corroborated the genetic relatedness of these two groups with accessions of these groups being present in both main germplasm clusters. This suggests that the existing Caribbean breadfruit germplasm harbors a higher level of genetic diversity than expected.

MicroRNAs (miRNAs) are small non-coding RNAs that play a central role in the post-transcriptional regulation of biological processes. miRNAs regulate transcripts by direct binding involving the Argonaute protein family. The exact rules of binding are not known, and several in silico miRNA target prediction methods have been developed to date. Deep Learning has recently revolutionized miRNA target prediction. However, the higher predictive power comes with decreased ability to interpret increasingly complex models. Here, we present a novel interpretation technique, called attribution sequence alignment, for miRNA target site prediction models that can interpret such Deep Learning models on a two-dimensional representation of miRNA and putative target sequence. Our method produces a human readable visual representation of miRNA:target interactions and can be used as a proxy for further interpretation of biological concepts learned by the neural network. We demonstrate applications of this method in clustering of experimental data into binding classes, as well as using the method to narrow down predicted miRNA binding sites on long transcript sequences. Importantly, the presented method works with any neural network model trained on a two-dimensional representation of interactions and can be easily extended to further domains such as protein-protein interactions.

The Chinese honeybee (Apis cerana cerana) sacbrood virus (CSBV) causes death of larvae and colony collapse, and could damage the beekeeping industry in China. We sequenced complete genomes of CSBV strains derived from the Maerkang area, Wenjiang area, and Wanyuan area of Sichuan province of China. The genome length of CSBV strains from Sichuan was 8863bp, and it contained one complete Open Reading Frame of a gene with 8544 bp that encoded a protein with 2848 amino acids. The (G+C) % and (A+T) % composition ranged from 40.6 to 40.7 and 59.3 to 59.4, respectively. A phylogenetic tree was constructed using the three CSBV strains and previously reported SBV and CSBV sequences from other regions. We found that viral strains clustered based on their region of origin and host species. The genetic sequences of the CSBV strain from Maerkang were 98.7% and 99.6% similar to CSBV strains from Wanyuan and Wenjiang, respectively. In addition, CSBV from Maerkang had 88.4%-95.2% sequence similarity to previously published genomes of CSBV or SBV from other areas. The VP1 gene sequenced in our study had a 43 bp deletion compared to VP1 sequences of CSBV from other regions in Asia. We detected 10 antigenic determinants on the VP1 protein of CSBV form Aba. Our study provides new insight into the diversity of CSBV strains in China and may help with identifying methods to prevent infection of honeybee colonies.

The human androgen receptor (AR) is a ligand inducible transcription factor harboring an amino terminal domain (AR-NTD) hosting the ligand independent activation function. AR-NTD is intrinsically disordered and display aggregation properties conferred by the presence of a poly-glutamine (polyQ) sequence of 22 residues. The length of the polyQ sequence, as well as the presence of adjacent sequence motifs modulate this aggregation property. AR-NTD contains also a conserved sequence motif KELCKAVSVSM that displays an intrinsic property to form amyloid fibrils under mild oxidative conditions of its conserved cysteine residue. As peptide sequences with intrinsic ability to oligomerize are reported to have an impact on the aggregation of polyQ tract, we determined the effect of the KELCKAVSVSM on the polyQ stretch in the context of the AR NTD, using Atomic Force Microscopy (AFM). Here, we present evidence for a crosstalk between the amyloidogenic properties of the KELCKAVSVSM motif and the polyQ stretch at the AR NTD.

Pumpo is a Simmental breed and an essential livestock resource in the nucleus genetic cattle of Peru. This study provides a draft genome sequence of a top bull using a de novo assembly approach on the Illumina Novaseq X platform, yielding 208 GB of raw sequencing data with 150 bp paired-end reads. The final genome assembly resulted in a size of 2.06 Gb with an N50 contig length of 108 Mb and a completeness of 95.7% according to BUSCO analysis. A total of 973,925 simple sequence repeats (SSRs) were identified, with a predominance of mononucleotide repeats. The genome showed low heterozygosity (0.568%) and moderate repeatability (11.5%), aligning with other Bos taurus genomes. Reference-guided scaffolding improved the assembly quality significantly, producing an N50 scaffold value of 108 Mb. The SSR analysis of the Pumpo genome identified 973,925 SSRs with a frequency of 2,808 SSRs per kilobase, predominantly mononucleotide repeats, and 85,453 found in compound formations. Obtaining knowledge of the genome of a breeding Simmental bull is essential to optimize breeding programs and improve productivity.

Recent advancements in artificial intelligence (AI) and deep learning have revolutionized the field of protein engineering, particularly in the area of de novo protein design. This review article explores the impact of AI-driven approaches on protein design, with a specific focus on the role of structure prediction neural networks, such as AlphaFold and RoseTTAFold. The article discusses the paradigm shift brought about by these networks, which have enabled the design of proteins with unique structures and functions that are not found in nature. The review covers various aspects of AI-driven protein design, including the use of protein language models to harness evolutionary information, the development of de novo protein design workflows utilizing deep learning, and the application of generative models like RFdiffusion and RoseTTAFold All-Atom. The article also highlights the successes and applications of AI-driven protein design across diverse domains, such as enzyme engineering, antibody design, and vaccine development. Additionally, the review identifies current challenges and future directions in the field, emphasizing the need to address limitations in modeling conformational dynamics and designing proteins for in vivo functionality. The article concludes by underscoring the potential of AI-driven protein design to transform various aspects of science and technology, while also acknowledging the importance of interdisciplinary collaboration and the development of robust pipelines for the validation and optimization of designed proteins. Overall, this comprehensive review serves as a valuable resource for researchers and practitioners interested in understanding the current state and future prospects of AI-driven protein design.