Tool For Selection Research Articles

Use model averaging instead of model selection in pulsar timing

ABSTRACT Over the past decade and a half, adoption of Bayesian inference in pulsar timing analysis has led to increasingly sophisticated models. The recent announcement of evidence for a stochastic background of gravitational waves by various pulsar timing array (PTA) projects highlighted Bayesian inference as a central tool for parameter estimation and model selection. Despite its success, Bayesian inference is occasionally misused in the pulsar timing community. A common workflow is that the data is analysed in multiple steps: a first analysis of single pulsars individually, and a subsequent analysis of the whole array of pulsars. A mistake that is then sometimes introduced stems from using the posterior distribution to craft the prior for the analysis of the same data in a second step, a practice referred to in the statistics literature as ‘circular analysis’. This is done to prune the model for computational efficiency. Multiple recent high-profile searches for gravitational waves by PTA projects have this workflow. This letter highlights this error and suggests that Spike and Slab priors can be used to carry out model averaging instead of model selection in a single pass. Spike and Slab priors are proved to be equal to log-uniform priors.

Accounting for the impact of genotype and environment on variation in leaf respiration of wheat in Mexico and Australia.

An approach to improving radiation use efficiency (RUE) in wheat is to screen for variability in rates of leaf respiration in darkness (Rdark). We used a high-throughput system to quantify variation in Rdark among a diverse range of spring wheat genotypes (301 lines) grown in two countries (Mexico and Australia) and two seasons (2017 and 2018), and in doing so quantify the relative importance of genotype (G) and environment (E) in influencing variations in leaf Rdark. Through careful design, residual (unexplained) variation represented less than 10% of the total observed. Up to a third of the variation in Rdark (and related traits) was under genetic control. This suggests opportunities for breeders to use Rdark as a novel selection tool. In addition, E accounted for more than half of the total variation in area-based rates of Rdark. Here, the day of measurement was crucial, suggesting that day-to-day variations in the environment influence rates of Rdark measured at a common temperature. Overall, this study provides new insights into the role G and E play in determining variation in rates of leaf Rdark of one of the most important cereal crops, with implications for future improvements in carbon use efficiency and yield.

An efficient deep learning method for amino acid substitution model selection.

Amino acid substitution models play an important role in studying the evolutionary relationships among species from protein sequences. The amino acid substitution model consists of a large number of parameters; therefore, it is estimated from hundreds or thousands of alignments. Both general models and clade-specific models have been estimated and widely used in phylogenetic analyses. The maximum likelihood method is normally used to select the best fit model for a specific protein alignment under the study. A number of studies have discussed theoretical concerns as well as computational burden of the maximum likelihood methods in model selection. Recently, machine learning methods have been proposed for selecting nucleotide models. In this paper, we propose a method to measure substitution rates among amino acids (called summary statistics) from protein alignments to efficiently train a deep learning network of so-called ModelDetector for detecting amino acid substitution models. The ModelDetector network was trained from 2,246,400 alignments on a computer with 8 cores (without GPU) in about 3.3 hours. Experiments on simulation data showed that the accuracy of the ModelDetector was comparable with that of the maximum likelihood method ModelFinder. It was orders of magnitude faster than the maximum likelihood method in inferring amino acid substitution models and able to analyze genome alignments with millions of sites in minutes. The results indicate that the deep learning network can play as a promising tool for amino acid substitution model selection.

Eaglescope: an interactive visualization and cohort selection tool for biomedical data exploration.

Eaglescope: an interactive visualization and cohort selection tool for biomedical data exploration.

Comparison and validation of several scoring systems for non-variceal upper gastrointestinal bleeding: a retrospective study

Various scoring systems have been developed to predict outcomes in patients with non-variceal upper gastrointestinal bleeding (NVUGIB). However, their accuracy remains unclear. This study aimed to compare and validate the predictive performance of several established scoring systems in patients with NVUGIB: Glasgow–Blatchford score (GBS) and the age, blood tests, and comorbidities (ABC), mental status–anesthesiologist score–pulse–albumin–systolic blood pressure–hemoglobin (MAP(ASH)), Japanese, and Charlson comorbidity index-in-hospital onset–albumin-mental status–Eastern Cooperative Oncology Group performance status–steroids (CHAMPS) scores. We retrospectively reviewed the records of 1,241 patients who presented to the emergency department with NVUGIB and subsequently required hospitalization. Each scoring system was evaluated for its ability to predict in-hospital mortality, rebleeding, and the need for radiological or surgical intervention. The ABC score showed the highest accuracy in predicting in-hospital mortality. The MAP(ASH) score was the most effective predictor of rebleeding and the need for interventions. Different scoring systems have been optimized for various clinical outcomes. The ABC score was the best for predicting mortality, whereas the MAP(ASH) score excelled in identifying rebleeding risks and intervention needs. The selection of an appropriate scoring tool based on specific clinical scenarios can improve patient management and resource allocation in NVUGIB.

Drought-tolerant wheat for enhancing global food security.

Wheat is among the most produced grain crops of the world and alone provides a fifth of the world's calories and protein. Wheat has played a key role in food security since the crop served as a Neolithic founder crop for the establishment of world agriculture. Projections showing a decline in global wheat yields in changing climates imply that food security targets could be jeopardized. Increased frequency and intensity of drought occurrence is evident in major wheat-producing regions worldwide, and notably, the wheat-producing area under drought is projected to swell globally by 60% by the end of the 21st century. Wheat yields are significantly reduced due to changes in plant morphological, physiological, biochemical, and molecular activities in response to drought stress. Advances in wheat genetics, multi-omics technologies and plant phenotyping have enhanced the understanding of crop responses to drought conditions. Research has elucidated key genomic regions, candidate genes, signalling molecules and associated networks that orchestrate tolerance mechanisms under drought stress. Robust and low-cost selection tools are now available in wheat for screening genetic variations for drought tolerance traits. New breeding techniques and selection tools open a unique opportunity to tailor future wheat crop with optimal trait combinations that help withstand extreme drought. Adoption of the new wheat varieties will increase crop diversity in rain-fed agriculture and ensure sustainable improvements in crop yields to safeguard the world's food security in drier environments.

Early Diagnostic Prediction of Infective Endocarditis: Development and Validation of EndoPredict-Dx.

Infective endocarditis is a life-threatening disease with diverse clinical presentations, making diagnosis challenging and requiring a range of complementary tests. The level of suspicion, based on clinical judgment, guides decisions regarding the initiation of empirical treatment and the selection of appropriate diagnostic tools. This study aimed to develop and validate the EndoPredict-Dx score for early prediction of infective endocarditis diagnosis. Patients admitted to a specialized cardiovascular hospital emergency department with suspected infective endocarditis between January 2011 and January 2020 were included. The primary outcome was left-sided infective endocarditis according to the Duke criteria. Logistic regression was used to derive the scoring system, with internal validation performed through bootstrapping. Candidate variables were obtained from the admission medical history, physical examination, and laboratory parameters. Of the 805 individuals with suspected infective endocarditis (median age 56 years (40-73); 58.6% men), 530 confirmed the diagnosis based on the Duke criteria. The EndoPredict-Dx assigned points for male sex, previous endocarditis, petechiae, heart murmur, suspected embolism, symptoms lasting 14 or more days at the time of admission, hemoglobin level ≤ 12 g/dL, leukocyte level ≥ 10 × 109/L, C-reactive protein level ≥ 20 mg/L, and urine red blood cells ≥ 20,000 cells/mL. Patients were divided into three risk groups. The AUROC was 0.78 (95% CI 0.75-0.81) for the derivation cohort and 0.77 for the internal validation. The EndoPredict-Dx score accurately predicted the likelihood of infective endocarditis using clinical and laboratory data collected at admission.

What are microsatellites and how to choose the best tool: a user-friendly review of SSR and 74 SSR mining tools.

Microsatellites, also known as SSR or STR, are essential molecular markers in genomic research, playing crucial roles in genetic mapping, population genetics, and evolutionary studies. Their applications range from plant breeding to forensics, highlighting their diverse utility across disciplines. Despite their widespread use, traditional methods for SSR analysis are often laborious and time-consuming, requiring significant resources and expertise. To address these challenges, a variety of computational tools for SSR analysis have been developed, offering faster and more efficient alternatives to traditional methods. However, selecting the most appropriate tool can be daunting due to rapid technological advancements and the sheer number of options available. This study presents a comprehensive review and analysis of 74 SSR tools, aiming to provide researchers with a valuable resource for SSR analysis tool selection. The methodology employed includes thorough literature reviews, detailed tool comparisons, and in-depth analyses of tool functionality. By compiling and analyzing these tools, this study not only advances the field of genomic research but also contributes to the broader scientific community by facilitating informed decision-making in the selection of SSR analysis tools. Researchers seeking to understand SSRs and select the most appropriate tools for their projects will benefit from this comprehensive guide. Overall, this study enhances our understanding of SSR analysis tools, paving the way for more efficient and effective SSR research in various fields of study.

Toward Climate Resilient Asphalt Binder Selection in Canada

Climate change and extreme weather events present a fundamental challenge to pavement engineering and planning practice, given that transportation infrastructure has traditionally been planned and designed using historical climate data under the implicit assumption that climate is stationary and future conditions will resemble past ones. Transportation professionals should consider future risks scenarios in road design, material selection and system operations. This will be challenging given the inherent uncertainties in any climate projections. However, changes might be needed since transportation professionals are expected to deliver cost-effective and climate-resilient transportation infrastructure. The material selection plays an important role in the durability of flexible roads; therefore, it is important to consider the whole road design life cycle in terms of climate change and extreme weather events impact on asphalt selection as well as to consider different traffic speeds. This led to the development of the NRC Climate Adaptation and Asphalt Selection Tool (CAAST).

Embedded feature selection for robust probability learning machines

Methods:Feature selection is essential for building effective machine learning models in binary classification. Eliminating unnecessary features can reduce the risk of overfitting and improve classification performance. Moreover, the data we handle typically contains a stochastic component, making it important to develop robust models that are insensitive to data perturbations. Although there are numerous methods and tools for feature selection, relatively few studies address embedded feature selection within robust classification models using penalization techniques. Objective:In this work, we introduce robust classifiers with integrated feature selection capabilities, utilizing probability machines based on different penalization techniques, such as the ℓ1-norm or the elastic-net, combined with a novel Direct Feature Elimination process to improve model resilience and efficiency. Findings:Numerical experiments on standard datasets demonstrate the effectiveness and robustness of the proposed models in classification tasks even when using a reduced number of features. These experiments were evaluated using original performance indicators, highlighting the models’ ability to maintain high performance with fewer features. Novelty:The study discusses the trade-offs involved in combining different penalties to select the most relevant features while minimizing empirical risk. In particular, the integration of elastic-net and ℓ1-norm penalties within a unified framework, combined with the original Direct Feature Elimination approach, presents a novel method for improving both model accuracy and robustness.

BIOM-36. DETECTION OF CIRCULATING TUMOR DNA (CTDNA) IN CEREBROSPINAL FLUID (CSF) IN PATIENTS WITH GLIOBLASTOMA TREATED IN PHASE I CLINICAL TRIAL

Abstract BACKGROUND As circulating tumor DNA (ctDNA) has been shown to be a source of tumor-specific biomarkers, liquid biopsy has emerged as a novel noninvasive tool in diagnosis, disease monitoring and treatment selection in several solid tumors. However, its use in brain tumors remains challenging because ctDNA is commonly not detected in blood. Thus, CSF (cerebrospinal fluid) has emerged as a potential source of ctDNA in the context of brain tumors. PATIENTS AND METHODS We prospectively evaluated the detection of ctDNA in CSF (CSF-ctDNA) +/- blood of patients with pre-treated MTAP-deleted glioblastoma, currently treated in a phase I clinical trial evaluating PRMT5 inhibitors. We did CSF samples at C2D15 and at progression, after patient’s oral and written consent. CSF was collected by lumbar puncture and analyzed with a panel of 35 genes including IDH1-2, TERT promoter and EGFR. Blood was collected, synchronously with CSF, by peripheral veinous punction and analyzed with the Foundation One Liquid CDx panel. RESULTS Between January and April 2024, five patients were included. All of them undergone initial surgical resection and received at least one previous systemic treatment associated with radiotherapy. At least one somatic mutation was identified in each patient, including EGFR amplification in 2/5 patients (40%). CSF was not available at baseline. One patient provided CSF only at progression. Four patients provided CSF at C2D15 on treatment, and among them, one patient was also collected at progression. At data cut-off, one patient was still on treatment. We identified a detectable level of CSF-ctDNA in only one patient with occipital tumor localization, with detection of EGFR amplification known at diagnosis. Synchronous blood analysis showed only clonal hematopoiesis variants. CONCLUSION We showed in this small cohort of patients that detection of CSF-ctDNA is technically feasible. The type of panel used might be optimized for this population especially to monitor patients as a potential surrogate biomarker for treatment response.

Beyond conventional imaging: A systematic review and meta-analysis assessing the impact of computed tomography perfusion on ischemic stroke outcomes in the late window.

Non-contrast cranial computed tomography (NCCT) and CT angiogram (CTA) have become essential for endovascular treatment (EVT) in acute stroke. Patient selection may improve when CT perfusion (CTP) imaging is also added for patient selection. We aimed to analyze the effects of implementing CTP in acute ischemic stroke (AIS) patients' treatment to assess whether stroke outcomes differ in the late window. We searched the PubMed, Embase, and Web of Sciences databases to obtain articles related to CTA and CTP in EVT. Collected patient data were split into two groups: the CTP and control (NCCT + CTA) cohorts. Primary outcomes evaluated were modified Rankin Scale (mRS) scores, symptomatic intracranial hemorrhages (sICHs), mortality, and successful recanalization. There were 14 studies with 5809 total patients in the final analysis: 2602 received CTP and 3202 were in the control group. CTP/CTA patients showed significantly lower rates of 90-day stroke-related mortality (odds ratio (OR) = 0.72, 95% confidence interval (CI) = 0.60-0.87, p < 0.01) and significantly higher successful recanalization (OR = 1.42, 95% CI = 1.06-1.94, p < 0.01) compared with CTA-only patients. Analysis of other outcomes including functional independence (mRS = 0-2), critical times, and intracranial hemorrhages was non-significant (p > 0.05). The study highlights the usefulness of CTP-guided therapy as a supplementary tool in EVT selection in the late window. Although the addition of CTP resulted in lower mortality, the favorable outcomes did not improve. Further evidence is required to establish a clearer understanding of the potential advantages or limitations of incorporating CTP in stroke imaging.

Intra-regional classification and quality evaluation of honey from Mendoza (Argentina) based on multi-elemental analysis and chemometrics

Multi-elemental analysis of honey samples from Mendoza (Argentina) was performed with the aim of developing a reliable method for tracing honey provenance. The concentrations of twenty-six elements (Li, Na, Mg, Al, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Mo, Pd, Ag, Cd, Sn, Sb, Hg and Pb) were determined by inductively coupled plasma mass spectrometry (ICP-MS), considering the most abundant isotopes. Subsequently, a comparative machine learning approach for classification and for variable selection was applied to evaluate the possibility of using them as relevant markers to predict the region where honey was produced. Our results clearly demonstrate the potential of decision tree classifiers, such as Random Forest (RF), C5.0, recursive partitioning (rpart) and conditional inference tree (ctree), as simple and agile chemometric tools for honey origin identification. Moreover, the variable selection tools reduced the elemental data matrix to only six elements (Co, Sr, Zn, Na, Rb and Li) which were identified as the most important for predicting honey origin.

A mini-review of single-cell Hi-C embedding methods

Single-cell Hi-C (scHi-C) techniques have significantly advanced our understanding of the 3D genome organization, providing crucial insights into the spatial genome architecture within individual nuclei. Numerous computational and statistical methods have been developed to analyze scHi-C data, with embedding methods playing a key role. Embedding reduces the dimensionality of complex scHi-C contact maps, making it easier to extract biologically meaningful patterns. These methods not only enhance cell clustering based on chromatin structures but also facilitate visualization and other downstream analyses. Most scHi-C embedding methods incorporate strategies such as normalization and imputation to address the inherent sparsity of scHi-C data, thereby further improving data quality and interpretability. In this review, we systematically examine the existing methods designed for scHi-C embedding, outlining their methodologies and discussing their capabilities in handling normalization and imputation. Additionally, we present a comprehensive benchmarking analysis to compare both embedding techniques and their clustering performances. This review serves as a practical guide for researchers seeking to select suitable scHi-C embedding tools, ultimately contributing to the understanding of the 3D organization of the genome.

Overview on Current Selectable Marker Systems and Novel Marker Free Approaches in Fruit Tree Genetic Engineering.

Selectable marker genes are useful for recognizing which cells have integrated specific sequences in their genome after genetic transformation processes. They are especially important for fruit trees genetic transformation to individuate putatively genetically modified events, because most of the protocols used to genetic engineer these species are often unsuccessful or with low efficiency. Traditional selectable marker genes, mainly of bacterial origin, confer antibiotics/herbicides-resistance or metabolic advantages to transformed cells. Genes that allow the visual recognition of engineered tissues without using any selective agent, such as morphogenic regulators and reporter genes, are also used as selection tools to in vitro identify genetically modified regenerated lines. As final step, genetic engineered plants should be tested in field conditions, where selectable marker genes are no longer necessary, and strongly unpopular especially for the commercial development of the new products. Thus, different approaches, mainly based on the use of site-specific recombinases and/or editing nucleases, are being now used to recover marker-free fruit crops. This review describes and comments the most used and suitable selection tools of interest, particularly for fruit tree genetic engineering. Lastly, a spotlight highlights the biosafety aspects related to the use of selectable marker genes exploited for fruit species genetic engineering.

Optimization of Potent and Selective Cyclohexyl Acid ERAP1 Inhibitors Using Structure- and Property-Based Drug Design.

Endoplasmic reticulum aminopeptidase 1 (ERAP1) cleaves the N-terminal amino acids of peptides, which can then bind onto major histocompatibility class I (MHC-I) molecules for presentation onto the cell surface, driving the activation of adaptive immune responses. In cancer, overtrimming of mature antigenic peptides can reduce cytotoxic T-cell responses, and ERAP1 can generate self-antigenic peptides which contribute to autoimmune cellular responses. Therefore, modulation of ERAP1 activity has potential therapeutic indications for cancer immunotherapy and in autoimmune disease. Herein we describe the hit-to-lead optimization of a series of cyclohexyl acid ERAP1 inhibitors, found by X-ray crystallography to bind at an allosteric regulatory site. Structure-based drug design enabled a >1,000-fold increase in ERAP1 enzymatic and cellular activity, resulting in potent and selective tool molecules. For lead compound 7, rat pharmacokinetic properties showed moderate unbound clearance and oral bioavailability, thus highlighting the promise of the series for further optimization.

A supervised machine learning model to select a cost-effective directional drilling tool

With the increased directional drilling activities in the oil and gas industry, combined with the digital revolution amongst all industry aspects, the need became high to optimize all planning and operational drilling activities. One important step in planning a directional well is to select a directional tool that can deliver the well in a cost-effective manner. Rotary steerable systems (RSS) and positive displacement mud motors (PDM) are the two widely used tools, each with distinct advantages: RSS excels in hole cleaning, sticking avoidance and hole quality in general, while PDM offers versatility and lower operating costs. This paper presents a series of machine learning (ML) models to automate the selection of the optimal directional tool based on offset well data. By processing lithology, directional, drilling performance, tripping and casing running data, the model predicts section time and cost for upcoming wells. Historical data from offset wells were split into training and testing sets and different ML algorithms were tested to choose the most accurate one. The XGBoost algorithm provided the most accurate predictions during testing, outperforming other algorithms. The beauty of the model is that it successfully accounted for variations in formation thicknesses and drilling environment and adjusts tool recommendations accordingly. Results show that no universal rule favors either RSS or PDM; rather, tool selection is highly dependent on well-specific factors. This data-driven approach reduces human bias, enhances decision-making, and could significantly lower field development costs, particularly in aggressive drilling campaigns.

Bispecific killer engager for targeted depletion of PD-1 positive lymphocytes: A new avenue for autoimmune disease treatment

Bispecific killer cell engagers (BiKEs) are a powerful tool to incite the killing power of natural killer (NK) cells. Here, we posited that the BiKE technology could be utilized to deplete activated immune cells expressing programmed death-1 (PD-1+ cells), and hence treat autoimmune diseases since these cells drive the disorders. We designed and generated PD-1 BiKE that targets an activating NK cell receptor, CD16, and PD-1. PD-1 BiKE showed specific binding to PD-1+ cells and engaged CD16 simultaneously. PD-1 BiKE enhanced NK cell-mediated apoptosis and depletion of PD-1+ Raji cells, but not PD-1- Raji cells. Further, PD-1 BiKE induced apoptosis of primary PD-1+ T lymphocytes that are highly relevant to autoimmune disease progression. The BiKE depleted 42% of primary T cells that were stimulated in vitro. Importantly, those ablated primary T cells were activated cells. Meanwhile, naive T cells were spared by the BiKE treatment, supporting the crucial selectivity of PD-1 BiKE-directed cell depletion. Lastly, PD-1 BiKE is more effective than a conventional depleting antibody in the depletion of PD-1+ cells. The current work supports PD-1 BiKE is a selective, potent, and safe tool to deplete PD-1+ cells.

A preliminary study of developing an MRI-based model for postoperative recurrence prediction and treatment direction of intrahepatic cholangiocarcinoma.

To establish an MRI-based predictive model for postoperative recurrence in intrahepatic cholangiocarcinoma (iCCA) and further to evaluate the model utility in treatment direction for neoadjuvant and adjuvant therapies. Totally 114 iCCA patients with curative surgery were retrospectively included, including 38 patients in the neoadjuvant treatment, traditional surgery, and adjuvant treatment groups for each. Predictive variables associated with postoperative recurrence were identified using univariate and multivariate Cox regression analyses, and a prognostic model was formulated. Recurrence-free survival (RFS) curves were compared using log-rank test between MRI-predicted high-risk and low-risk iCCAs stratified by the optimal threshold. Tumor multiplicity (hazard ratio (HR) = 1.671 [95%CI 1.036, 2.695], P = 0.035), hemorrhage (HR = 2.391 [95%CI 1.189, 4.810], P = 0.015), peri-tumor diffusion-weighted hyperintensity (HR = 1.723 [95%CI 1.085, 2.734], P = 0.021), and positive regional lymph node (HR = 2.175 [95%CI 1.295, 3.653], P = 0.003) were independently associated with postoperative recurrence; treatment group was not significantly related to recurrence (P > 0.05). Independent variables above were incorporated for the recurrence prediction model, the 1-year and 2-year time-dependent area under the curve values were 0.723 (95%CI 0.631, 0.815) and 0.725 (95%CI 0.634, 0.816), respectively. After risk stratification, the MRI-predicted high-risk iCCA patients had higher cumulative incidences of recurrence and worse RFS than the low-risk patients (P < 0.001 for both). In the MRI-predicted high-risk patients, neoadjuvant therapy was associated with improved all-stage RFS (P = 0.034), and adjuvant therapy was associated with improved RFS after 4months (P = 0.014). The MRI-based iCCA recurrence predictive model may serve as a decision-making tool for both personalized prognostication and therapy selection.

Spatial Context of Immune Checkpoints as Predictors of Overall Survival in Patients with Resectable Colorectal Cancer Independent of Standard Tumor-Node-Metastasis Stages.

The identification of specific spatial patterns of immune checkpoint expression that correlate with overall survival in patients with colon cancer suggests a potential prognostic tool for risk stratification and treatment selection. These findings pave the way for the development of novel therapeutic strategies to enhance antitumor immune responses.