Sequence Selectivity Research Articles

ESR Essentials: Image evaluation of patients with seizures and epilepsy-practice recommendations by the European Society of Neuroradiology.

Epilepsy, a neurological disorder characterised by recurrent seizures, poses significant challenges in diagnosis, treatment, and management. Understanding the underlying causes and identifying precise anatomical locations of epileptogenic foci are critical for effective management strategies, particularly in drug-resistant patients. Neuroimaging techniques, particularly magnetic resonance (MR), play a pivotal role in the evaluation of epilepsy patients, offering insights into structural abnormalities, epileptogenic lesions, and functional alterations within the brain. Diverse clinical scenarios that warrant neuroimaging in epilepsy patients, ranging from first-onset seizures to drug-resistant epilepsy, will be presented, elucidating the considerations and recommendations for imaging modalities. The dedicated MR protocol for epilepsy patients will be discussed, justifying the rationale behind sequence selection and optimisation strategies and providing clues about how to read these magnetic resonance imaging (MRI) exams. Finally, MR findings associated with common epileptogenic lesions, such as hippocampal sclerosis, focal cortical dysplasia, and long-term epilepsy-associated tumours, will be described. This article reviews essential concepts, including definitions, classification, imaging indications, protocols, and neuroradiological findings, aiming to understand how neuroimaging contributes to diagnosing and managing epilepsy comprehensively. KEY POINTS: MR should be performed in adults and children with a recent diagnosis of epilepsy of unknown aetiology, a first seizure, and a negative CT. Performing a dedicated MR protocol in focal epilepsy is essential for increasing the detection of potentially epileptogenic lesions. For presurgical evaluations, MR abnormalities should correlate with the electric pattern, semiology data, or other neuroimaging examination to be considered the epileptogenic lesion.

Awake rodent fMRI: gradient-echo echo planar imaging versus compressed-sensing fast low angle shot

Abstract Awake rodent fMRI is increasingly becoming a reliable neuroimaging technique to study neuronal activity at both the whole brain and high-resolution laminar scales. Prior studies have focused on developing acclimation protocols, experimental paradigms, and hardware to optimize outcomes. However, little effort has been made to address the impact of pulse sequence selection on detecting brain activation in awake fMRI experiments. In the current study, we compare gradient-echo echo planar imaging (GE-EPI) and compressed-sensing fast low angle shot (CS-FLASH) sequences with cerebral blood volume-weighted (CBVw) contrast enhancement to investigate their sensitivity to hemodynamic activity in the olfactory bulb of awake rodents. Compared to GE-EPI, CS-FLASH had comparable motion parameters but was more sensitive to large motions, often resulting in corruption of the image quality. The use of framewise displacement as a motion censoring technique may over censor the data, requiring alternative approaches, such as spatial correlation censoring. CS-FLASH images were qualitatively sharper than GE-EPI; however, the contrast-to-noise ratio for odor activation was consistently greater for GE-EPI compared to CS-FLASH that cannot be explained by olfactory adaptation alone. The activation maps of CS-FLASH to four different odors showed spatially unique patterns consistent with GE-EPI, but with lower z-scores or detection sensitivity. Activation maps were consistent with previously established histological findings. Additionally, odor-evoked laminar activation was greatest in the superficial layers that decreased with laminar depth, consistent with prior findings. We conclude that CS-FLASH produces sharper images with equivalent spatial activation maps to GE-EPI, albeit with lower statistical strength and CNR; and without being prohibited by motion-related image distortion.

Localized In Vivo Electro Gene Therapy (LiveGT)-Mediated Skeletal Muscle Protein Factory Reprogramming

Gene electrotransfer (GET) has gained significant momentum as a non-viral gene delivery method for various clinical applications, primarily in the cancer immunotherapy and vaccine development space. Preclinical studies have demonstrated exogenous gene delivery and expression in various tissues, including the liver, skin, cardiac muscle, and skeletal muscle. However, protein replacement applications of this technology have yet to be fully actuated. Plasmid DNA skeletal muscle delivery has been shown to maintain expression for up to 18 months. In the current study, we evaluated localized skeletal muscle delivery for protein replacement applications. We developed localized in vivo electro gene therapy (liveGT) protocols utilizing mono- and biphasic pulse sequences for localized pulse delivery directly to skeletal muscle with a custom monopolar platinum electrode. Plasmid DNA encoding human insulin and human glucokinase were chosen for this study to evaluate the liveGT platform for protein replacement potential. Initial in vitro GET was performed in mouse myoblasts to evaluate human insulin and glucokinase co-delivery. This was followed by liveGT-mediated reporter gene delivery in the skeletal muscle of Sprague–Dawley rats for pulse sequence selection. Protein replacement potential was evaluated in healthy (non-diabetic) rats with liveGT-mediated human insulin and glucokinase co-delivery to skeletal muscle. Human and rat insulin levels were measured via ELISA over the course of 3 months. Fed-state blood glucose measurements were monitored in correlation with serum human insulin levels. LiveGT-mediated skeletal muscle reprogramming successfully produced physiological levels of human insulin in serum over the course of 3 months. Hypo- and hyperglycemic events were not observed. Therefore, liveGT is a safe and viable platform for potential protein replacement therapies.

Shapelet selection for time series classification

In recent times, increasing attention has been given to shapelet-based methods for time series classification. However, in the majority of current methods, similar subsequences were often selected as shapelets, thereby reducing the final interpretability of these methods. Aiming to circumvent the selection of similar subsequences as the final shapelets, a novel shapelet selection method (SSM) was proposed in this paper. Firstly, shapelet candidates were generated by SSM through time series segmentation to avoid excessive generation of similar candidates from a single time series. Secondly, all shapelet candidates were evaluated simultaneously to improve evaluation efficiency. Finally, SSM introduced a position-based filter to prevent the selection of similar sequences repeatedly. The results obtained on the UCR TSC archive demonstrated the effectiveness of the proposed method.

“A show of confidence and authority”: Philopseudes and More’s Lucian

Of the three Lucianic dialogues translated into Latin by Thomas More and published in 1506, Philopseudes is the last, longest, and most narratively complex. The majority of it is taken up by a recital of incredible lies, yet it presents a nuanced view of lying that accounts for its appeal to More, who calls it both “amusing” and “instructive” in his dedicatory letter to Thomas Ruthall. Lucian’s Philopseudes instructs by demonstrating various strategies of plausible liars and suggesting that “truth and sound reason” are an antidote ( alexipharmakon) to the drug ( pharmakon) or infectious disease of lying. But this solution is undercut, with typical Lucianic irony, by the failings of the characters who suggest it. More’s translation of Philopseudes is alert to Lucian’s ironies, but more confidently suggests a union of divinely revealed truth and human reason as a cure to the plague of lies. After analyzing both versions of Philopseudes, I defend three claims about the selection and arrangement of the whole sequence of dialogues in More’s Lucian— Cynicus, Menippus, and Philopseudes. 1) They form a unified progression of explicit and implicit critiques from which none of their characters escape, and which threaten to involve the author and his readers. 2) Later dialogues in More’s selection encourage ironic readings of earlier ones, guiding readers from a simple understanding of explicit critiques to a deeper understanding of implicit critiques, and finally to self-examination. 3) Beyond selecting and arranging the dialogues in this enlightening way, More scatters humorous interpretive mistakes and dubious allusions to authority throughout his own prefatory letter to Ruthall, even as he warns his readers not to fall for “a show of confidence and authority.” More’s ironic preface to Lucian thus requires of us, and trains us in, the prudent reading that Lucian himself demands.

The dynamics of sea otter prey selection under population growth and expansion

AbstractSea otters (Enhydra lutris) were extirpated from much of their range in the North Pacific by the early 1900s but have made a remarkable recovery in Southeast Alaska. Sea otter populations have been particularly successful in Glacier Bay, Alaska, a protected tidewater glacier fjord with a diverse and productive nearshore habitat. Collection of sea otter foraging observations in Glacier Bay began in 1993, along with high‐resolution aerial surveys that provide estimates of sea otter abundance and distribution. We integrated these two data sources to investigate how sea otter diet changed in space and time as sea otters established and spread across Glacier Bay. Specifically, we developed a multilevel Bayesian model to capture how sea otter diet at a location (the number, type, and size of prey collected) changed as a function of local cumulative otter abundance and the year in which the location was first occupied. This framework enabled us to estimate the sequence of sea otter prey selection and switching as prey populations responded to sea otter foraging pressure. We found that local sea otter diet changed substantially as the population established, shifting away from large urchins, crabs, and clams to Modiolus mussels and small urchins, and lastly to small clams and Mytilus mussels. We also found that sea otter diet at newly occupied sites changed as otters spread over the main channel and into the arms of Glacier Bay. Further, by 2019, sea otters across the bay were primarily foraging on small prey, regardless of the local occupancy history. The absence of a spatial gradient in the size of prey captured late in the study suggests that feedbacks between the top‐down effects of sea otter foraging, sea otter dispersal processes, and local variation in habitat productivity may have homogenized the size structure of available prey across Glacier Bay.

Igome Graphs Suggest the Changes in the IgM and IgG Repertoires in Antiphospholipid Syndrome Are in Part Idiotypically Defined

Antiphospholipid Syndrome (APS) is characterized by the persistence of high-affinity antiphospholipid antibodies, manifesting clinically as pregnancy complications, thrombosis, or neurological symptoms. This study explores the changes in the IgM and IgG antibody repertoires in APS patients using igome graphs, which represent antibody repertoires through peptide libraries selected from phage display on the complete immunoglobulin fraction of the serum. Serum samples from healthy women and APS patients were processed to isolate IgM and IgG fractions, which were then used for phage selection and next-generation sequencing. Peptide sequences were analyzed using graph representation, clustering, and spectral embedding to identify significant differences between the repertoires. The study revealed that while IgM repertoires in APS patients exhibit a loss of public reactivities, the IgG repertoires do not follow this trend. Three orthogonal sequence motifs were identified in the IgM and IgG repertoires, suggesting potential idiotypic interactions. These findings highlight the intricate mechanisms of antibody selection and cross-reactivity in APS, offering insights into potential therapeutic approaches. The study underscores the importance of system-level analysis in understanding immune mechanisms and suggests that idiotypic interactions, though complex, could play a crucial role in APS pathogenesis.

Detection of drug resistance in Escherichia coli from calves with diarrhea in the Tongliao region: an analysis of multidrug-resistant strains.

Escherichia coli is a major pathogen responsible for calf diarrhea, which has been exacerbated by the irrational and unscientific use of antimicrobial drugs, leading to significant drug resistance. This study focused on the isolation and identification of E. coli from calf diarrhea samples in the Tongliao area of China. Isolation was conducted using selective media, Gram staining, and 16S rRNA sequencing. The minimum inhibitory concentration (MIC) of E. coli was determined through the microbroth dilution method. Additionally, the presence of antibiotic-resistant genes was detected, and multidrug-resistant strains were selected for whole-genome sequencing (WGS). The results revealed that all 40 isolated strains of E. coli exhibited resistance to sulfadiazine sodium, enrofloxacin, and ciprofloxacin, with 90% of the strains being susceptible to polymyxin B. Notably, strains 11, 23, and 24 demonstrated severe resistance. The detection rates of the antibiotic resistance genes TEM-1, TEM-206, strA, strB, qacH, and blaCTX were 100%, indicating a high prevalence of these genes. Moreover, the majority of strains carried antibiotic resistance genes consistent with their resistance phenotypes. WGS of strains 11, 23, and 24 revealed genome sizes of 4,897,185 bp, 4,920,234 bp, and 4,912,320 bp, respectively. These strains carried two, one, and two plasmids, respectively. The prediction of antibiotic resistance genes showed a substantial number of these genes within the genomes, with strain 24 harboring the highest number, totaling 77 subspecies containing 88 antibiotic resistance genes. In conclusion, all 40 isolated strains of E. coli from calf diarrhea in this study were multidrug-resistant, exhibiting a broad distribution of antibiotic resistance genes and mobile components. This poses a significant risk of horizontal gene transfer, highlighting the critical situation of antibiotic resistance in this region.

NIMG-41. MULTI-SITE, PROSPECTIVE TRIAL EVALUATING FET-PET IN GLIOBLASTOMA (FIG) STUDY (TROG 18.06): PRELIMINARY RESULTS OF CENTRAL NUCLEAR MEDICINE REVIEW OF FET-PET BIOLOGIC TARGET VOLUME DELINEATION FOR RADIATION PLANNING

Abstract BACKGROUND To provide an update from the multi-site trial evaluating O-(2-[18F]-fluoroethyl)-L-tyrosine Positron Emission Tomography (FET-PET) in Glioblastoma (FIG) study and 2) assess the impact of central Nuclear Medicine physician (NMP) review of prospective FET-PET1 delineation of the biological target volume (BTV) for radiotherapy (RT) planning. Material and methods Up to 210 adult GBM participants across 11 Australian sites will undergo FET-PET post-surgery/pre-chemo-RT [CRT] (FET-PET1), one month post CRT (FET-PET2) and at suspected progression (FET-PET3). Group 1 participants enter at timepoint 1 (FET-PET1 with MRI1), with Group 2 entering at timepoint 2. Adjuvant RT target volumes are derived per standard contrast MRI with hybrid post-hoc RT volumes then incorporating the FET-PET1 NM-derived BTV utilising MiM version 7.0. All trial sites and NMP have passed credentialling which included three benchmarking cases involving BTV delineation. RESULTS Trial recruitment commenced in January 2021, with 189 (n=126 Group 1 and n=63 Group 2) participants enrolled to date, with a target of 140 Group 1 participants. During trial credentialling, results demonstrated variations in FET-PET1-derived BTV in 25/72 (34.7%) - 13 minor and 12 major.Currently, 42 of 112 participant FET-PET1 with BTV delineation cases across 10 sites have undergone central review, with 7/42 (16.7%) requiring resubmission. Reasons for resubmission/protocol deviation included incorrect imaging sequence selection within the MiM workflow (n=3), Static GTV overcontouring (n=3) and dynamic volume of interest change in size/position during MiM workflow use (n=1). CONCLUSION The FIG trial will complete recruitment in 2024 with analyses planned at one year post CRT completion. Despite improvements in resubmission rates compared to the credentialling phase, central review of prospective FET-PET1-derived BTV delineation remains important in ensuring protocol adherence. The FIG study is the largest prospective multi-site study of its kind addressing FET-PET’s impact on adjuvant radiation planning and its role in management of pseudoprogression and prognostication.

Challenges and Prospects of DNA-Encoded Library Data Interpretation.

DNA-encoded library (DEL) technology is a powerful platform for the efficient identification of novel chemical matter in the early drug discovery process enabled by parallel screening of vast libraries of encoded small molecules through affinity selection and deep sequencing. While DEL selections provide rich data sets for computational drug discovery, the underlying technical factors influencing DEL data remain incompletely understood. This review systematically examines the key parameters affecting the chemical information in DEL data and their impact on hit triaging and machine learning integration. The need for rigorous data handling and interpretation is emphasized, with standardized methods being critical for the success of DEL-based approaches. Major challenges include the relationship between sequence counts and binding affinities, frequent hitters, and the influence of factors such as inhomogeneous library composition, DNA damage, and linkers on binding modes. Experimental artifacts, such as those caused by protein immobilization and screening matrix effects, further complicate data interpretation. Recent advancements in using machine learning to denoise DEL data and predict drug candidates are highlighted. This review offers practical guidance on adopting best practices for integrating robust methodologies, comprehensive data analysis, and computational tools to improve the accuracy and efficacy of DEL-driven hit discovery.

TCellR2Vec: efficient feature selection for TCR sequences for cancer classification.

Cancer remains one of the leading causes of death globally. New immunotherapies that harness the patient's immune system to fight cancer show promise, but their development requires analyzing the diversity of immune cells called T-cells. T-cells have receptors that recognize and bind to cancer cells. Sequencing these T-cell receptors allows to provide insights into their immune response, but extracting useful information is challenging. In this study, we propose a new computational method, TCellR2Vec, to select key features from T-cell receptor sequences for classifying different cancer types. We extracted features like amino acid composition, charge, and diversity measures and combined them with other sequence embedding techniques. For our experiments, we used a dataset of over 50,000 T-cell receptor sequences from five cancer types, which showed that TCellR2Vec improved classification accuracy and efficiency over baseline methods. These results demonstrate TCellR2Vec's ability to capture informative aspects of complex T-cell receptor sequences. By improving computational analysis of the immune response, TCellR2Vec could aid the development of personalized immunotherapies tailored to each patient's T-cells. This has important implications for creating more effective cancer treatments based on the individual's immune system.

STALL-seq: mRNA-display selection of bacterial and eukaryotic translational arrest sequences from large random-sequence libraries

Translational arrest is a phenomenon wherein a temporary pause or slowing of the translation elongation reaction occurs due to the interaction between ribosome and nascent peptide. Recent studies have revealed that translational arrest peptides are involved in intracellular protein homeostasis regulatory functions, such as gene expression regulation at the translational level and regulation of cotranslational protein folding. Herein, we established a method for the large-scale in vitro selection of translational arrest peptides from DNA libraries by combining a modified mRNA display method and deep sequencing. We performed in vitro selection of translational arrest sequences from random-sequence libraries via mRNA display based on the E. coli PURE system or wheat germ extract. Following several rounds of affinity selection, we obtained various candidate sequences that were not similar to known arrest peptides and subsequently confirmed their ribosome stalling activity by peptidyl-tRNA detection and toeprinting assay. Following the site-directed mutagenesis of the selected sequences, these clones were found to contain novel arrest peptide motifs. This method, termed as STALL-seq (Selection of Translational Arrest sequences from Large Library sequencing), could be useful for the large-scale investigation of translational arrest sequences acting on both bacterial and eukaryotic ribosomes and could help discover novel intracellular regulatory mechanisms.

Synergistic solvent extraction system composed of bis(pyridin-2-ylmethyl) dodecan-1-amine and dinonylnaphthalene for enhanced selective extraction of nickel and cobalt

Simultaneous recovery of Ni and Co from Fe(III) and Al is a critical challenge in hydrometallurgical processes. Recognized solvent extraction systems often struggle with selectivity and effective performance in mixed metal ion environments. Herein, a new synergistic solvent extraction (SSX) system comprised of a novel pyridine analog, N,N-bis(pyridin-2-ylmethyl) dodecan-1-amine (BPMDA), and dinonylnaphthalene sulfonic acid (DNNSA) with tributyl phosphate as phase modifier is introduced. The SSX system demonstrates high extraction performance achieving >90% for Ni and >97% for Co in a single-stage extraction process, with high selectivity. Under optimal conditions, the selectivity sequence is observed as Co2+ (>97%) > Ni2+ (>90%) > Mn2+ (<20%) > Fe3+ (<10%) > Mg2+ (<5%) > Al3+ (<2%) > Ca2+ (<1%). Spectroscopic analysis evidences the preferential binding of BPMDA with Ni and Co in the presence of DNNSA, concurrently achieving a significant reduction in the co-extraction of Fe(III) and Al. The selective complexation of Ni and Co using the SSX system offers a highly efficient and selective approach for their extraction, with promising potential for applications in recovery-based processes.

HTD-TS3: Weakly Supervised Hyperspectral Target Detection Based on Transformer via Spectral-Spatial Similarity.

As an advanced technique in remote sensing, hyperspectral target detection (HTD) is widely concerned in civilian and military applications. However, the limitation of prior and heterogeneous backgrounds makes HTD models sensitive to data corruption under various interference from the environment. In this article, a novel united HTD framework based on the concept of transformer is proposed to extract [HTD based on transformer via spectral-spatial similarity (HTD-TS3)] under weak supervision, which opens up more flexible ways to study HTD. For the first time, the transformer mechanism is introduced into the HTD task to extract spectral and spatial features in a unified optimization procedure. By modeling long-range dependence among spectra, it realizes spectral-spatial joint inference based on long-range context, which addresses the issues of insufficient utilization of spatial information. To provide samples for weakly supervised learning (WSL), the coarse sample selection and spectral sequence construction in an efficient way are proposed, which makes full use of limited prior information. Finally, an exponential constrained nonlinear function is adopted to acquire pixel-level prediction via combining discriminative spectral-spatial features and coarse spatial information. Experiments on real hyperspectral images (HSIs) captured by different sensors at various scenes verify the effectiveness and efficiency of HTD-TS3.

A Test Report Optimization Method Fusing Reinforcement Learning and Genetic Algorithms

Filtering high-variability and high-severity defect reports from large test report databases is a challenging task in crowdtesting. Traditional optimization algorithms based on clustering and distance techniques have made progress but are limited by initial parameter settings and significantly decrease in efficiency with an increasing number of reports. To address this issue, this paper proposes a method that integrates reinforcement learning with genetic algorithms for crowdsourced testing report optimization, called Reinforcement Learning-based Genetic Algorithm for Crowdsourced Testing Report Optimization (RLGA). Its core goal is to identify distinct, high-severity defect reports from a large set. The method uses genetic algorithms to generate the optimal report selection sequence and adjusts the crossover probability (Pc) and mutation probability (Pm) dynamically with reinforcement learning based on the population’s average fitness, best fitness, and diversity. The reinforcement learning component uses a hybrid SARSA and Q-Learning strategy to update the Q-value table, allowing the algorithm to learn quickly in early iterations and expand the search space later to avoid local optima, thereby improving efficiency. To validate the RLGA method, this paper uses four public datasets and compares RLGA with six classic methods. The results indicate that RLGA outperforms BDDIV in terms of execution time and is less sensitive to the total number of test reports. In terms of optimization objectives, the test reports selected by RLGA have higher levels of defect severity and diversity than those selected by the random choice, BDDIV, and TSE methods. Regarding population diversity, RLGA effectively enhances the uniformity and diversity of individuals compared to random initialization. In terms of convergence speed, RLGA is superior to the GA, GA-SARSA, and GA-Q methods.

Crystal structure of the HMGA AT-hook 1 domain bound to the minor groove of AT-rich DNA and inhibition by antikinetoplastid drugs

High mobility group (HMG) proteins are intrinsically disordered nuclear non-histone chromosomal proteins that play an essential role in many biological processes by regulating the expression of numerous genes in eukaryote cells. HMGA proteins contain three DNA binding motifs, the “AT-hooks”, that bind preferentially to AT-rich sequences in the minor groove of B-form DNA. Understanding the interactions of AT-hook domains with DNA is very relevant from a medical point of view because HMGA proteins are involved in different conditions including cancer and parasitic diseases. We present here the first crystal structure (1.40 Å resolution) of the HMGA AT-hook 1 domain, bound to the minor groove of AT-rich DNA. In contrast to AT-hook 3 which bends DNA and shows a larger minor groove widening, AT-hook 1 binds neighbouring DNA molecules and displays moderate widening of DNA upon binding. The binding affinity and thermodynamics of binding were studied in solution with surface plasmon resonance (SPR)-biosensor and isothermal titration calorimetry (ITC) experiments. AT-hook 1 forms an entropy-driven 2:1 complex with (TTAA)2-containing DNA with relatively slow kinetics of association/dissociation. We show that N-phenylbenzamide-derived antikinetoplastid compounds (1–3) bind strongly and specifically to the minor groove of AT-DNA and compete with AT-hook 1 for binding. The central core of the molecule is the basis for the observed sequence selectivity of these compounds. These findings provide clues regarding a possible mode of action of DNA minor groove binding compounds that are relevant to major neglected tropical diseases such as leishmaniasis and trypanosomiasis.

CMMamba: channel mixing Mamba for time series forecasting

Transformer-based methods have achieved excellent results in the field of time series forecasting due to their powerful ability to model sequences and capture their long-term dependencies. However, Transformer-based methods are still computationally inefficient and have high costs when modeling long sequences. In addition, previous work ignored the destruction of the original in-channel features by too many mixed features when performing channel mixing. To address the above issues, we introduce Mamba into the field of time series forecasting. The selective state-space model Mamba models long sequences more efficiently by expanding linearly according to the sequence length. Based on this, we propose Channel Mixing Mamba (CMMamba), which uses bidirectional Mamba to model sequences and uses a channel mixing mechanism to select appropriate mixed features to enhance the feature representation within the original channel. By introducing Mamba, we achieve efficient modeling of sequences. We also propose Deep Convolutional Structure (DCS) to learn cross-channel dependencies and temporal order information. Extensive experimental results on six real-world public datasets demonstrate the effectiveness of CMMamba.

Two remarks on properties of functions of bounded variation

In terms of variations, a sufficient condition for the uniform convergence of sequences of continuous functions is proved. Using this result, we obtain an addition to the classical Helly theorem on the selection of convergent sequences of functions with uniformly bounded variations in the case when the limit function is continuous. Also, by using an example we show that the condition of continuous differentiability of a function, ensuring the differentiability of its variation with the variable upper limit, is in a certain sense sharp.

An Expectation-Maximization framework for Personalized Itinerary Recommendation with POI Categories and Must-see POIs

In this article, we introduce a novel deterministic method based on Expectation Maximization (EM) to solve the rather complex problem of designing a tourist trip or Personalized Itinerary Recommendation (PIR). PIR objective is to recommend a personalized tour consisting of successive Points of Interest (POIs), which maximizes user satisfaction and respects user time-frame constraints. On top of that, the POIs are divided into categories, in order for travelers to be able to set limits on the maximum (and minimum) number of POIs that belong to one category and are included in the itinerary. In the proposed framework, emphasis is given on the POIs sequence selection, which exploits the customized POI recommendations offered by a recommender system. Additionally, the proposed methodology with POIs categories is able to solve the TourMustSee problem, so that the tour includes a set of POIs that must be visited. The proposed system has been successfully incorporated into a mobile app, offering a complete tourist trip design. The high performance, resilience, and computational efficiency of the proposed framework are demonstrated by experimental findings and comparisons to existing approaches on numerous synthetic and real datasets.

A Novel and Rapid Selective Viral Genome Amplification and Sequencing Method for African Swine Fever Virus.

African swine fever virus (ASFV) is the etiological agent of African swine fever, a highly contagious hemorrhagic disease affecting both wild boars and domestic pigs with lethality rates up to 100%. Until now, the most effective measure to prevent an outbreak of ASFV was early detection. In this situation, whole genome sequencing (WGS) allows the gathering of detailed information about the identity and epidemiology of the virus. However, due to the large genome size and complex genome ends, WGS is challenging. Current WGS workflows require either elaborate enrichment methods or are based on tiled PCR approaches, which are susceptible to genetic differences between ASFV strains. To overcome this, we developed a novel approach for WGS of ASFV, using the Phi29 DNA polymerase-based multiple displacement amplification in combination with only seven primers. Furthermore, we applied an alkaline-based DNA denaturation step to significantly increase the number of viral reads, which resolves the near-full genome of ASFV. This novel isothermal WGS approach can be used in authorized laboratories for the genomic epidemiological analysis of ASFV outbreaks caused by different genotypes.