Sequential Search Research Articles

An Innovative Hybrid Approach Producing Trial Solutions for Global Optimization

Global optimization is critical in engineering, computer science, and various industrial applications as it aims to find optimal solutions for complex problems. The development of efficient algorithms has emerged from the need for optimization, with each algorithm offering specific advantages and disadvantages. An effective approach to solving complex problems is the hybrid method, which combines established global optimization algorithms. This paper presents a hybrid global optimization method, which produces trial solutions for an objective problem utilizing a genetic algorithm’s genetic operators and solutions obtained through a linear search process. Then, the generated solutions are used to form new test solutions, by applying differential evolution techniques. These operations are based on samples derived either from internal line searches or genetically modified samples in specific subsets of Euclidean space. Additionally, other relevant approaches are explored to enhance the method’s efficiency. The new method was applied on a wide series of benchmark problems from recent studies and comparison was made against other established methods of global optimization.

Protein Design by Integrating Machine Learning and Quantum-Encoded Optimization

The protein design problem involves finding polypeptide sequences folding into a given three-dimensional structure. Its rigorous algorithmic solution is computationally demanding, involving a nested search in sequence and structure spaces. Structure searches can now be bypassed thanks to recent machine-learning breakthroughs, which have enabled accurate and rapid structure predictions. Similarly, sequence searches might be entirely transformed by the advent of quantum annealing machines and by the required new encodings of the search problem, which could be performative even on classical machines. In this work, we introduce a general protein design scheme where algorithmic and technological advancements in machine learning and quantum-inspired algorithms can be integrated, and an optimal physics-based scoring function is iteratively learned. In this first proof-of-concept application, we apply the iterative method to a lattice protein model amenable to exhaustive benchmarks, finding that it can rapidly learn a physics-based scoring function and achieve promising design performances. Strikingly, our quantum-inspired reformulation outperforms conventional sequence optimization even when adopted on classical machines. The scheme is general and can be extended, e.g., to encompass off-lattice models, and it can integrate progress on various computational platforms, thus representing a new paradigm approach for protein design. Published by the American Physical Society 2024

Enhancing offshore parcel delivery efficiency through vessel-unmanned aerial vehicle collaborative routing

Offshore oil and gas production is vital for global energy supply, but it faces logistical challenges due to the high costs and inefficiencies of traditional supply methods. This paper introduces the vessel-unmanned aerial vehicle (UAV) routing problem with multiple visits in a single flight (VURP-M), a novel logistical model that addresses aimed at enhancing the replenishment of offshore platforms with small, essential items. The VURP-M allows the UAV to perform multiple visits during a single flight, optimising the delivery process. To tackle the VURP-M, we propose two mixed-integer second-order cone programs that capture the problem's complexities. Given its NP-hard nature, we employ an adaptive large neighbourhood search (ALNS) method, featuring a segmented initialisation process and problem-specific operators guided by a rule-based mechanism to improve solution efficiency. The ALNS formulates an initial solution by solving a travelling salesman problem to create a giant tour, which is then used to group sequential targets into multiple UAV flights. The subsequent optimal resolution of a SOCP determines the take-off and landing points for each flight. Subsequently, the ALNS refines the initial solution through destroy and repair operators, enhancing the search for superior sequences and allocation schemes. The effectiveness of our approach is demonstrated through a real-world case study and numerical experiments on random instances featuring up to 100 platforms. The results offer implications of the collaborative vessel-UAV model for the offshore logistics industry.

First Report of Leaf Blight of Syringa oblata Caused by Botryosphaeria dothidea in China.

As an important landscaping plant, Syringa oblata is widely planted in northern China with high ornamental, medicinal, and edible value (Men et al. 2023). In September 2023, a new leaf blight disease on S. oblata was observed in Tianjin (39.0916°N, 117.1019°E), China. The onset of foliar symptoms was marked by the appearance of yellow-brown spots that originated from the tip and margin, subsequently evolving into irregularly shaped brown lesions. Finally, the lesions are distributed throughout the leaf surface, causing the leaf to wilt and seriously affecting photosynthesis. To identify the pathogen responsible for leaf blight of S. oblata, symptomatic leaves were collected and cut into square leaf blocks with a size of 0.3 cm², which were sterilized by immersion in 75% ethanol for 60 s and 5% NaClO for 30 s, and rinsed three times with sterile distilled water. The sterilized leaf pieces were then placed on potato dextrose agar (PDA) and incubated at 25 °C for 3-5 days. The peripheral hyphae of the fungal colony which developed from the infected tissues were isolated onto PDA plates. The fungal cultures on PDA plates were used for morphological observation and identification of the fungus. Colonies of B. dothidea on PDA medium were initially off-white, batting-shaped and gradually grayish-black. Aerial hyphae were well developed and could reach the tip of the petri dish. To induce sporulation, the hyphae were picked into medium containing sterilized pine needles. Conidia were found on pine needles after 30 d of incubation at 25°C. Conidia were hyaline, unicellular, oblong to spindle-shaped, and 17.5-23.0 µm in size × 6.1-8.5 µm(n=50). Based on these characteristics, the isolates were preliminarily identified as B. dothidea (Phillips et al. 2005). To provide additional evidence for the classification of the isolate, genomic DNA was extracted from the isolates of B. dothidea and used for Polymerase Chain Reaction (PCR). The internal transcribed spacer (ITS), glyceraldehyde-3-phosphate dehydrogenase (GPDH), and actin (ACT) were amplified with the primer pairs ITS1/ITS4, GDP1/GDP2, and ACT-512F/ACT-783R, respectively (Jiang et al. 2022). A BLAST search of sequences showed the ITS, GPDH, and ACT sequences had >99% identity with homologous sequences from B. dothidea isolates Bb158-4(HQ392696.1), PPO-46523(MG761771.1), and CMW7779 (AY972117.1), respectively. Phylogenetic analysis determined that the isolate was in the same clade position as B. dothidea, which confirmed the above morphological identification. To assess pathogenicity, the fungal cakes (6 mm diameter) were obtained from the edge of a fresh colony (cultured on a PDA plate for 7 days) using a sterile perforator. Ten surface-sterilized leaves of healthy S. oblata with uniform growth condition were collected and inoculated with fungal cakes after wounding. Ten leaves were inoculated with sterile PDA medium blocks as control. The test was repeated three times. All leaves were kept at 25°C and sterile H2O was sprayed daily to keep leaves surface moist. After five days, all vaccination sites showed lesions similar to those of the S. oblata diseased leaves in the field, while the controls were asymptomatic. B. dothidea was reisolated from symptomatic tissues, thus fulfilling Koch's postulates. B. dothidea is a member of Botryosphaeriaceae. Currently, B. dothidea infection of Syringa spp. plants has not been reported. However, in China, it has been reported to cause stem rot on Forsythia suspense (He et al. 2022) and leaf dieback on sweet osmanthus (Ling et al. 2010), demonstrating that B. dothidea can infect Oleaceae species. This study found that S. oblata could be infected by B. dothidea. To the best of our knowledge, this is the first report of B. dothidea causing leaf blight on S. oblata in China. Identifying the pathogen of S. oblata leaf blight is essential for the prevention and management of disease associated with S. oblata.

Dynamic pricing and inventory model for perishable products with reference price and reference quality

Reference price (quality) is a benchmark point used by consumers to make price (quality) judgements. Combining reference price and reference quality with dynamic pricing and inventory management, this paper applies differential equations theory to construct a optimal control model for perishable products when the quality of products declines exponentially. The demand of products depends on price, quality, reference price and reference quality, among which reference price and reference quality are influenced by consumers’ past memory. The aim is to maximize the retailer’s profit during the period. The conclusions are as follows. Firstly, a optimal dynamic pricing and inventory model for perishable products with reference price and reference quality is constructed, and the model is extended to dual decision variables, stochastic demand, time-dependent effects, competitive and infinite planing horizon setups. Secondly, through the Pontryagin maximum principle, the analytical expression of the optimal dynamic price is derived. Thirdly, a linear search method to solve the optimal static price is proposed. Finally, the sensitivity of the main parameters is analyzed and the corresponding management enlightenments are given. By comparison of dynamic and static pricing, we find that dynamic pricing can achieve more profits and take a shorter selling period. In addition, for the sales problem of perishable products affected by both reference price and reference quality, retailers should adopt skimming pricing strategy (the optimal price decreases with time). Furthermore, to obtain more profit, retails should strive to increase the sensitivity coefficients of two types of reference, and the reference quality memory coefficient, while to decrease the reference price memory coefficient.

Vector space model, term frequency-inverse document frequency with linear search, and object-relational mapping Django on hadith data search

For Muslims, the Hadith ranks as the secondary legal authority following the Quran. This research leverages hadith data to streamline the search process within the nine imams’ compendium using the vector space model (VSM) approach. The primary objective of this research is to enhance the efficiency and effectiveness of the search process within Hadith collections by implementing pre-filtering techniques. This study aims to demonstrate the potential of linear search and Django object-relational mapping (ORM) filters in reducing search times and improving retrieval performance, thereby facilitating quicker and more accurate access to relevant Hadiths. Prior studies have indicated that VSM is efficient for large data sets because it assigns weights to every term across all documents, regardless of whether they include the search keywords. Consequently, the more documents there are, the more protracted the weighting phase becomes. To address this, the current research pre-filters documents prior to weighting, utilizing linear search and Django ORM as filters. Testing on 62,169 hadiths with 20 keywords revealed that the average VSM search duration was 51 seconds. However, with the implementation of linear and Django ORM filters, the times were reduced to 7.93 and 8.41 seconds, respectively. The recall@10 rates were 79% and 78.5%, with MAP scores of 0.819 and 0.814, accordingly.

ISOLATION AND MOLECULAR IDENTIFICATION OF INDIGENOUS BACTERIOCIN-PRODUCING WEISSELLA CIBARIA

Globally, over 6.22 million deaths are associated with antibiotic resistance. Bacteriocins, a set of antimicrobial peptides synthesized on the ribosomes, are widely viewed as a potential answer to this issue. This is due to their pore-forming ability and antimicrobial activity against antibiotic-resistant pathogens. The aim of this study is to isolate bacteriocin-producing Weissella cibaria and evaluate its antimicrobial activity against Pseudomonas aeruginosa, Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Salmonella typhi, Proteus mirabilis, Streptococcus sp., Candida sp. and Rhizopus stolonifer. Weissella cibaria man1 was isolated by inoculating deMan Rogosa Sharpe (MRS) broth with small pieces of ripe Mangifera indica (mango), 24-hour incubation at 370C, 10-fold serial dilution and plating on MRS agar. Molecular identification was achieved by DNA extraction, amplification of the 16S rRNA gene through polymerase chain reaction (PCR), agarose gel electrophoresis, gene sequencing, and BLASTN homology searches in the National Center for Biotechnology Information (NCBI). Antimicrobial activity of the bacteriocin was determined by agar well diffusion assay. Mangifera indica (mango) was found to harbor bacteriocin-producing Weissella cibaria man1. The bacteriocin (weissellicin man1) exhibited a broad spectrum of antimicrobial activity. Weissellicin man1 suppressed the growth of several target pathogens (Pseudomonas aeruginosa, Klebsiella pneumoniae, Salmonella typhi, Proteus mirabilis, Candida sp. and Rhizopus stolonifer) but had no inhibitory action against Escherichia coli, Streptococcus sp., Staphylococcus aureus. In conclusion, weissellicin man1 from Weissella cibaria man1 has a broad-spectrum of antimicrobial action. These findings will facilitate further evaluation of the antimicrobial potency of weissellicin man1.

A Task-driven Adversarial Channel Selection Method for Binary Classification Based on Magnetocardiography.

As the number of sensors in magnetocardiography (MCG) arrays increases to capture detailed cardiac activity, some channels contribute minimally to task performance, resulting in data redundancy and resource consumption. Although existing methods can reduce the number of channels required to meet task demands, they often struggle to balance computational time and the accuracy of the selected channels and overlook the scalability of the selected channels. This limitation means that when environmental conditions change, or when sensors malfunction, redesigning channel configurations becomes necessary, which increases experimental uncertainties. This study introduces a task-driven adversarial channel selection method tailored for binary classification of MCG signals. The optimal channel combination is determined through a group-wise search using a heuristic algorithm, whose objective function is designed to maximize the difference between the classification accuracy and cosine similarity of the selected channel. In evaluations using an MCG dataset from Qilu Hospital of Shandong University, the proposed method successfully reduced the number of channels from 36 to 5 without compromising classification performance. Furthermore, it outperforms existing hybrid sequential forward search method by achieving comparable accuracy with fewer channels, while also demonstrating superior scalability compared to both hybrid sequential forward search and pearson-rank methods. This approach strikes a balance between computational consumption and accuracy, while improving the scalability of the selected channel combinations, enhancing the efficiency and practicality of the MCG system.

Primary immunodeficiency in children: agammaglobulinemia (Bruton’s disease)

Primary Immunodeficiencies (PID) are a group of congenital immune system disorders caused by the loss, reduction, or impaired functioning of one or more components of the immune system. This article presents a detailed characterization of Bruton’s disease, the most common manifestation of inherited agammaglobulinemia, as well as modern approaches to its diagnosis and treatment. A clinical observation of the manifestation of primary immunodeficiency in a 3-year-old boy is described, while the diagnosis of “Primary Immunodeficiency: Bruton’s disease” was only established at the age of 8. The results of an 8-year follow-up of the patient are presented, and the difficulties of early diagnosis are analyzed. It is shown that a sequential diagnostic search allowed for the diagnosis of PID and the determination of the appropriate therapeutic strategy.

Origin, Evolution and Diversity of φ29-like Phages-Review and Bioinformatic Analysis.

Phage φ29 and related bacteriophages are currently the smallest known tailed viruses infecting various representatives of both Gram-positive and Gram-negative bacteria. They are characterised by genomic content features and distinctive properties that are unique among known tailed phages; their characteristics include protein primer-driven replication and a packaging process characteristic of this group. Searches conducted using public genomic databases revealed in excess of 2000 entries, including bacteriophages, phage plasmids and sequences identified as being archaeal that share the characteristic features of phage φ29. An analysis of predicted proteins, however, indicated that the metagenomic sequences attributed as archaeal appear to be misclassified and belong to bacteriophages. An analysis of the translated polypeptides of major capsid proteins (MCPs) of φ29-related phages indicated the dissimilarity of MCP sequences to those of almost all other known Caudoviricetes groups and a possible distant relationship to MCPs of T7-like (Autographiviridae) phages. Sequence searches conducted using HMM revealed the relatedness between the main structural proteins of φ29-like phages and an unusual lactococcal phage, KSY1 (Chopinvirus KSY1), whose genome contains two genes of RNA polymerase that are similar to the RNA polymerases of phages of the Autographiviridae and Schitoviridae (N4-like) families. An analysis of the tail tube proteins of φ29-like phages indicated their dissimilarity of the lower collar protein to tail proteins of all other viral groups, but revealed its possible distant relatedness with proteins of toxin translocation complexes. The combination of the unique features and distinctive origin of φ29-related phages suggests the categorisation of this vast group in a new order or as a new taxon of a higher rank.

Protocol for Designing De Novo Noncanonical Peptide Binders in OSPREY.

D-peptides, the mirror image of canonical L-peptides, offer numerous biological advantages that make them effective therapeutics. This article details how to use DexDesign, the newest OSPREY-based algorithm, for designing these D-peptides de novo. OSPREY physics-based models precisely mimic energy-equivariant reflection operations, enabling the generation of D-peptide scaffolds from L-peptide templates. Due to the scarcity of D-peptide:L-protein structural data, DexDesign calls a geometric hashing algorithm, Method of Accelerated Search for Tertiary Ensemble Representatives, as a subroutine to produce a synthetic structural dataset. DexDesign enables mixed-chirality designs with a new user interface and also reduces the conformation and sequence search space using three new design techniques: Minimum Flexible Set, Inverse Alanine Scanning, and K*-based Mutational Scanning.

Exploring “dark-matter” protein folds using deep learning

De novo protein design explores uncharted sequence and structure space to generate novel proteins not sampled by evolution. A main challenge in de novo design involves crafting "designable" structural templates to guide the sequence searches toward adopting target structures. We present a convolutional variational autoencoder that learns patterns of protein structure, dubbed Genesis. We coupled Genesis with trRosetta to design sequences for a set of protein folds and found that Genesis is capable of reconstructing native-like distance and angle distributions for five native folds and three novel, the so-called "dark-matter" folds as a demonstration of generalizability. We used a high-throughput assay to characterize the stability of the designs through protease resistance, obtaining encouraging success rates for folded proteins. Genesis enables exploration of the protein fold space within minutes, unrestricted by protein topologies. Our approach addresses the backbone designability problem, showing that small neural networks can efficiently learn structural patterns in proteins. A record of this paper's transparent peer review process is included in the supplemental information.

In Silico Evaluation of rbcL, matK, and psbA-trnH Regions on the Genus Spatholobus (Fabaceae)

Spatholobus is a genus that belongs to the Fabaceae that is known to contain various bioactive compounds and distributed across Asia, including Indonesia. However, exploration of Spatholobus in Indonesia is still rare. Therefore, DNA barcoding is used to support the exploration and conservation of Spatholobus in Indonesia. However, there is no universal marker that can be used across all plant species. In addition, there are still few studies related to DNA barcoding within the genus Spatholobus. The purpose of this study was to evaluate the rbcL, matK, and intergenic spacer psbA-trnH regions in silico that can be used as DNA barcodes for the genus Spatholobus. This study began with a sequence search on the NCBI database including the rbcL, matK, and intergenic spacer psbA-trnH genes in the genus Spatholobus and Phaseolus coccineus as the outgroup. Each sequence was then aligned with ClustalW. Then, a phylogenetic tree was constructed using the Maximum-Likelihood (ML) with 1000× bootstrap. As a result, the rbcL, matK, and psbA-trnH regions can be used as markers for DNA barcoding in the genus Spatholobus with different specifications. The rbcL and matK can be used to distinguish Spatholobus at the genus level, while the psbA-trnH can be used to distinguish Spatholobus at the species level.

Variability of a number of Pm gene sequences in representatives of Triticeae

Aim. Comparison of the sequences of selected resistance genes deposited in GenBank to find types of differences in their nucleotide composition and to determine the significance of these differences for primer design. Methods. Sequence searches were performed in the GenBank biological database. Comparison of different sequences of the same gene was carried out in DnaSP6 by calculating the Tajima test for Pm2, Pm3 and the Kolmogorov-Smirnov test in GeneDoc for Pm21. Multiple alignment was performed in CLUSTALW before using the specified programs. Primers were designed in Primer3. Results. Variability of the compared sequences is represented by nucleotide substitutions, insertions/deletions, the presence of a transposon in one of the Pm3 sequences. The regions coding for leucine-rich repeats are the most variable. The sequences isolated from Triticum dicoccoides have two such regions, within each of which some of the changes in the nucleotide sequence will be reflected in the amino acid composition of the proteins. Conclusions. The differences between the compared sequences of Pm2, Pm3, Pm21 (within each of the genes) are random (p < 0.05). According to the results of the sequences analysis, combinations of primers were developed for the identification of sequences characteristic to resistance genes on the available plant material.

PeptiHub: a curated repository of precisely annotated cancer-related peptides with advanced utilities for peptide exploration and discovery.

Peptihub (https://bioinformaticscollege.ir/peptihub/) is a meticulously curated repository of cancer-related peptides (CRPs) that have been documented in scientific literature. A diverse collection of CRPs is included in the PeptiHub, showcasing a spectrum of effects and activities. While some peptides demonstrated significant anticancer efficacy, others exhibited no discernible impact, and some even possessed alternative non-drug functionalities, including drug carrier or carcinogenic attributes. Presently, Peptihub houses 874 CRPs, subjected to evaluation across 10 distinct organism categories, 26 organs, and 438 cell lines. Each entry in the database is accompanied by easily accessible 3D conformations, obtained either experimentally or through predictive methodology. Users are provided with three search frameworks offering basic, advanced, and BLAST sequence search options. Furthermore, precise annotations of peptides enable users to explore CRPs based on their specific activities (anticancer, no effect, insignificant effect, carcinogen, and others) and their effectiveness (rate and IC50) under cancer conditions, specifically within individual organs. This unique property facilitates the construction of robust training and testing datasets. Additionally, PeptiHub offers 1141 features with the convenience of selecting the most pertinent features to address their specific research questions. Features include aaindex1 (in six main subcategories: alpha propensities, beta propensity, composition indices, hydrophobicity, physicochemical properties, and other properties), amino acid composition (Amino acid Composition and Dipeptide Composition), and Grouped Amino Acid Composition (Grouped amino acid composition, Grouped dipeptide composition, and Conjoint triad) categories. These utilities not only speed up machine learning-based peptide design but also facilitate peptide classification. Database URL: https://bioinformaticscollege.ir/peptihub/.

First report of basal rot of yellow dragon fruit (Selenicereus megalanthus) caused by Fusarium oxysporum in Peru.

Cultivation of yellow dragon fruit (Selenicereus megalanthus) in Peru has recently expanded (Verona-Ruiz et al. 2020). In August 2021, approximately 170 of 1,110 dragon fruit cuttings (15.3%) in the university's nursery (6°26'10'' S; 77°31'25'' W) showed basal rot symptoms. Initial symptoms included small brown spots on the base of stems, expanding towards the top that became soft and watery. All symptomatic plants eventually died, i.e., a severity of 100%. The disease was more prevalent on cuttings during the rooting phase than on well-established cuttings. We collected five symptomatic cuttings from throughout the nursery. Four sections of 1 × 1 cm2 of tissue adjacent to the diseased area were excised from each cutting, immersed for 1 min in 2% NaClO, rinsed twice with sterile distilled water, placed on potato dextrose agar (PDA) medium (four sections per Petri plate, five plates), and incubated at 25°C for 7 days. Morphologically similar mycelia grew from all sections, and five monosporic isolates were obtained, one per plate. Colonies grew fast, reaching 60 to 64 mm in 7 days, and produced violet-white cottony aerial mycelia with orange sporodochia on PDA, and abundant macro- and microconidia on synthetic nutrient-poor agar. Macroconidia were straight to slightly curved, typically with 2 to 3 septa, 16.6 to 23.3 × 1.7 to 3.7 µm (n = 30); microconidia were oval or kidney-shaped, and commonly hyaline, 6.7 to 16.4 × 2.5 to 4.7 µm (n = 40). Genomic DNA was extracted from isolate AFHP-100, then the ITS region and the TEF1 and RPB2 partial genes were amplified and sequenced (Accession numbers PP977433, OR437358, PP537149) following Gardes and Bruns (1993) and O'Donnell et al. (1998). We conducted a BLASTn search of ITS sequence against the NCBI "nr" database and local 'megablast' searches of TEF1 and RPB2 sequences against FUSARIUM-ID v.3.0 (Torres-Cruz et al. 2022). We found 100%, 98.19 to 99.84%, and 98.81 to 99.76% identities in ITS, TEF1, and RPB2 sequences, respectively, to the ex-epitype and other reference strains of Fusarium oxysporum (CBS 144134, NRRL26406, among others). A maximum likelihood phylogenetic analysis with a TEF1-RPB2 concatenated dataset with FUSARIUM-ID sequences also showed isolate AFHP-100 was F. oxysporum. A pathogenicity test was carried out by inoculating wounded healthy roots of three cuttings with submersion in a 5 × 106 conidia/ml suspension for 25 min. Then, the inoculated plants were planted in sterile soil. One cutting with wounded roots submerged in sterile water served as a control. In parallel, sterile soil was inoculated with 20 mL of the conidial suspension, and another three healthy cuttings were planted. A cutting planted in noninoculated soil also served as a control. Basal rot symptoms developed in all inoculated plants after 25 days. After re-isolation, the same fungus, corroborated based on micromorphology and TEF1 sequence (PP335689), was recovered, fulfilling Koch's postulates. The isolate was deposited in the KUELAP Herbarium (voucher KUELAP-3214), located and administered by the National University Toribio Rodriguez de Mendoza de Amazonas, in Chachapoyas, Peru. Fusarium oxysporum has been reported to cause basal stem rot in Bangladesh and Argentina (Mahmud et al. 2021; Wright et al. 2007), and stem blight in Malaysia (Mohd Hafifi et al. 2019) on dragon fruit. This is the first report of F. oxysporum causing basal rot in S. megalanthus in Peru. This fungus is among the most destructive plant pathogens, and the rapid expansion of the crop in Peru requires a comprehensive knowledge of the biotic factors influencing production. Therefore, this report is foundational to implementing proper control strategies.

Advantages and disadvantages of sequential vs. simultaneous search in simulated breast cancer screening

Advantages and disadvantages of sequential vs. simultaneous search in simulated breast cancer screening

Genome-wide identification of the E-class gene family in wheat: evolution, expression, and interaction.

Wheat (Triticum aestivum L.) is among themost important crop worldwide. Given a growing population and changing climate, enhancing wheat yield is of great importance. Yield is closely associated with flower and spike development, and E-class genes play important roles in the flower and kernel development of plants. Currently, the absence of systematic analysis on the E gene family hinders our comprehension of their roles in plant growth and development. Identify E-class genes based on homologous sequence searches. Analyze the identified E-class genes through a series of gene family analyses. Determine the expression levels of wheat E-class genes by searching public databases. Validate the functions of these genes by transforming them into Arabidopsis. Finally, determine the interactions between the genes through yeast two-hybrid experiments. Fifteen E-class genes (TaEs) were identified in common wheat. Nine E-class genes were detected in five ancestral/closely related species, including one in Aegilops tauschii (AtE), one in T. Urartu (TuEs), two in T. turgidum (TtEs), two in T. dicoccoides (TdEs), and three in T. spelta (TsEs). The 24 E-class genes were classified into three subgroups using a phylogenetic approach. All genes were highly expressed in spikes, and most were only highly expressed at the floret meristem stage. The effects of TaSEP5-A on flowering and growth cycles were confirmed in homologous mutants and transgenic Arabidopsis thaliana. The E-class genes were able to regulate the growth cycle of Arabidopsis. Finally, we confirmed the interactions between TaSEP5-A and other wheat E-class genes based on yeast two-hybrid assays. Our findings provide information regarding the E-class genes in wheat and will potentially promote the application of these genes in wheat improvement.

On the impact of co-optimizing station locations, trip assignment, and charging schedules for electric buses

As many public transportation systems around the world transition to electric buses, the planning and operation of fleets can be improved via tailored decision-support tools. In this work, we study the impact of jointly locating charging facilities, assigning electric buses to trips, and determining when and where to charge the buses. We propose a mixed integer linear program that co-optimizes planning and operational decisions jointly and an iterated local search heuristic to solve large-scale instances. Herein, we use a concurrent scheduler algorithm to generate an initial feasible solution, which serves as a starting point for our iterated local search algorithm. In the sequential case, we first optimize trip assignments and charging locations. Charging schedules are then determined after fixing the optimal decisions from the first level. The joint model, on the other hand, integrates charge scheduling within the local search procedure. The solution quality of the joint and sequential iterated local search models are compared for multiple real-world bus transit networks. Our results demonstrate that joint models can help further improve operating costs by 14.1% and lower total costs by about 4.1% on average compared with sequential models. In addition, energy consumption costs and contracted power capacity costs have been reduced significantly due to our integrated planning approach.

Approaching Optimal pH Enzyme Prediction with Large Language Models.

Enzymes are widely used in biotechnology due to their ability to catalyze chemical reactions: food making, laundry, pharmaceutics, textile, brewing─all these areas benefit from utilizing various enzymes. Proton concentration (pH) is one of the key factors that define the enzyme functioning and efficiency. Usually there is only a narrow range of pH values where the enzyme is active. This is a common problem in biotechnology to design an enzyme with optimal activity in a given pH range. A large part of this task can be completed in silico, by predicting the optimal pH of designed candidates. The success of such computational methods critically depends on the available data. In this study, we developed a language-model-based approach to predict the optimal pH range from the enzyme sequence. We used different splitting strategies based on sequence similarity, protein family annotation, and enzyme classification to validate the robustness of the proposed approach. The derived machine-learning models demonstrated high accuracy across proteins from different protein families and proteins with lower sequence similarities compared with the training set. The proposed method is fast enough for the high-throughput virtual exploration of protein space for the search for sequences with desired optimal pH levels.