Optimal Target Research Articles

When Invariant Representation Learning Meets Label Shift: Insufficiency and Theoretical Insights.

As a crucial step toward real-world learning scenarios with changing environments, dataset shift theory and invariant representation learning algorithm have been extensively studied to relax the identical distribution assumption in classical learning setting. Among the different assumptions on the essential of shifting distributions, generalized label shift (GLS) is the latest developed one which shows great potential to deal with the complex factors within the shift. In this paper, we aim to explore the limitations of current dataset shift theory and algorithm, and further provide new insights by presenting a comprehensive understanding of GLS. From theoretical aspect, two informative generalization bounds are derived, and the GLS learner are proved to be sufficiently close to optimal target model from the Bayesian perspective. The main results show the insufficiency of invariant representation learning, and prove the sufficiency and necessity of GLS correction for generalization, which provide theoretical supports and innovations for exploring generalizable model under dataset shift. From methodological aspect, we provide a unified view of existing shift correction frameworks, and propose a kernel embedding-based correction algorithm (KECA) to minimize the generalization error and achieve successful knowledge transfer. Both theoretical results and extensive experiment evaluations demonstrate the sufficiency and necessity of GLS correction for addressing dataset shift and the superiority of proposed algorithm.

Impact of Therapeutic Inertia on Patient-Reported Outcomes in Moderate-to-Severe Atopic Dermatitis: A 12-Month Longitudinal Study from the TARGET-DERM AD Registry

Background: Therapeutic inertia, the delay or reluctance to modify treatment when goals are unmet, is a significant challenge in managing chronic diseases, including atopic dermatitis (AD). This inertia can lead to suboptimal disease control and affect patient outcomes. Objectives: This study evaluates the effect of therapeutic inertia on patient-reported outcomes (PROs) in moderate-to-severe AD patients undergoing systemic treatment over 3 to 12 months. Methods: We analyzed longitudinal data from the TARGET-DERM AD registry, which includes 3,457 patients with moderate-to-severe AD from 39 centers across the U.S. and Canada. Eligible patients had documented patient-reported outcomes (PROs) at the initiation of systemic therapy and at subsequent 3-month intervals up to 12 months of follow-up. We assessed the proportion of patients not meeting treatment targets based on expert consensus. Patients had a validated Investigator Global Assessment (vIGA-AD) score of 3 or more at initiation of either an advanced systemic therapy (AST) such as biologics or JAK inhibitors or a conventional systemic therapy (CST) such as cyclosporine, methotrexate, or prednisone. PROs were evaluated at 3-month intervals up to 12 months, assessing achievement against the predefined treatment targets. PRO measures included Worst-Itch (PROMIS Itch-Severity), POEM, PO-SCORAD, NRS-sleep, and NRS-pain with specific moderate and optimal target levels established for each. Itch-Severity (range: 0–10; moderate target: ≥4-point reduction, optimal target: score ≤1), POEM (range: 0-28; moderate target: ≥4-point reduction, optimal target: score ≤2), PO-SCORAD (range: 0-103; moderate target: score ≤24; optimal target: score ≤10), NRS-sleep and NRS-pain (range: 0-10; moderate target: reduction ≥3-points; optimal target: score ≤1). Results: Out of 2107 patients with moderate-to-severe AD, 445 qualifying participants were included (63.8% adult, 62.0% female, 45.4% Non-Hispanic White, mean age of 31 years). Most patients (88.8%) initiated AST, with dupilumab being the most common (86.5%). At 6 months, significant proportions of AST-treated patients failed to reach moderate and optimal targets for itch (67% and 79%, respectively), POEM (46% and 69%), and NRS-sleep (59% and 47%). By 12 months, these figures were similar, with 66% and 88% failing to meet itch targets, 53% and 73% failing to meet POEM targets, and 62% and 45% failing to meet NRS-sleep targets, respectively. A similar pattern was observed for other PROs. CST-treated patients exhibited similar trends. Conclusions: The study highlights the profound impact of therapeutic inertia on the quality of life of patients with moderate-to-severe AD. Despite systemic therapy, a considerable proportion failed to meet treatment targets over a 12-month period, underscoring the need for more proactive and responsive treatment strategies in AD management.

Why Do Optimal Targets for Itch and Skin Clearance Matter in Atopic Dermatitis Treatment? Insights from TARGET-DERM AD Registry

Introduction/Background: Atopic dermatitis (AD) patients undergoing treatment may only experience partial improvement in itch and skin lesions, often leading to suboptimal outcomes. The Aiming High in Eczema/Atopic Dermatitis (AHEAD) treat-to-target recommendations emphasize the importance of achieving optimal treatment targets, such as complete or near-complete itch relief and skin clearance. However, there is limited evidence on the impact of achieving these higher efficacy targets on patient-reported outcomes and quality of life in AD. Objectives: To evaluate the independent and combined effects of achieving optimal treatment targets for itch and skin clearance on patient-reported outcomes (PROs) in AD, based on the AHEAD treat-to-target recommendations. Method: A cross-sectional analysis was conducted on adult participants in TARGET-DERM AD, a longitudinal study with over 4,000 participants across 52 U.S. and Canadian clinical-practice sites (2019-2024). Itch severity was measured using the PROMIS Itch-Severity question (NRS-Itch, 0–10 scale), with scores of 0/1 indicating no or minimal itch. Skin severity was assessed using the validated Investigator Global Assessment (vIGA-AD), where 0/1 represents clear or almost clear skin. Associations between itch and skin severity with optimal patient outcomes including POEM 0–2 (clear/almost-clear disease), DLQI 0/1 (minimal/no impact on quality of life), NRS-Sleep 0/1, and NRS-Pain 0/1 were evaluated. Logistic regression models examined the main and interaction effects of itch and skin severity. Results: Among 1,920 patients (58.6% female; 54.5% Non-Hispanic White; 93.8% US; mean age 45 years), optimal DLQI, POEM, NRS-Sleep, and NRS-Pain occurred most frequently among those achieving the optimal treatment targets for itch (WI-NRS 0/1; 52.1%, 53.7%, 57.3%, and 83.1%, respectively) and skin clearance (vIGA-AD 0/1; 44.7%, 44.3%, 44.7%, and 74.3%, respectively). Compared to partial improvement, the adjusted odds ratios (aOR) of optimal PROs were greatest for participants with complete or near-complete resolution of both itch and skin lesions (DLQI 0/1: 20.0; POEM 0-2: 41.7; Sleep-NRS: 16.1; Pain-NRS: 6.0). Conclusions: Achieving optimal treatment targets for both itch and skin lesions markedly enhances patient-reported outcomes in AD. The results of this real-world study support treat-to-optimal targets to assess therapeutic effectiveness and optimize patient outcomes.

Long-Term Maintenance of Optimal Treatment Targets for Skin and Itch Outcomes With Upadacitinib in Moderate-to-Severe Atopic Dermatitis: 140-Week Results From the Phase 3 Measure Up 1 and 2 Studies

Introduction: Despite undergoing prolonged systemic therapy, many patients with moderate-to-severe atopic dermatitis (AD) do not achieve optimal targets for skin and itch outcomes as defined by Aiming High in Eczema/AD (AHEAD) recommendations (eg, ≥90% improvement from baseline in Eczema Area and Severity Index [EASI 90] and Worst Pruritus Numerical Rating Scale [WP-NRS] score of 0/1 [no/minimal itch]). We evaluated long-term maintenance of optimal treatment targets with upadacitinib, an oral selective JAK inhibitor approved for moderate-to-severe AD in adolescents and adults. Methods: This 140-week interim analysis included adolescents and adults with moderate-to-severe AD who were randomized at baseline to upadacitinib 15 mg (UPA15) or 30 mg (UPA30) in the ongoing, phase 3, double-blind Measure Up 1 and 2 studies. Assessments included the proportion of patients (at the population level) who maintained EASI 90, WP-NRS 0/1, or simultaneous achievement of EASI 90 + WP-NRS 0/1 responses from week 16 (end of the placebo-controlled period) onwards. Data are reported as observed cases with no imputation for missing data. Results: Among patients who achieved EASI 90 at week 16 (UPA15, n=298; UPA30, n=384), EASI 90 was maintained by 79.8% (UPA15) and 83.7% (UPA30) at week 52, 76.7% (UPA15) and 83.0% (UPA30) at week 100, and 74.0% (UPA15) and 84.2% (UPA30) at week 140. Of patients achieving WP-NRS 0/1 at week 16 (UPA15, n=193; UPA30, n=281), WP-NRS 0/1 was maintained by 69.4% (UPA15) and 72.0% (UPA30) at week 52, 65.6% (UPA15) and 68.4% (UPA30) at week 100, and 62.3% (UPA15) and 66.0% (UPA30) at week 140. Of patients simultaneously achieving EASI 90 + WP-NRS 0/1 at week 16 (UPA15, n=156; UPA30, n=243), maintenance was achieved by 68.1% (UPA15) and 70.8% (UPA30) at week 52, 62.7% (UPA15) and 66.5% (UPA30) at week 100, and 58.7% (UPA15) and 64.4% (UPA30) at week 140. Among patients who did not maintain optimal outcomes, most still achieved clinically meaningful responses. Conclusion: Most patients maintained optimal skin and itch outcomes through 140 weeks of upadacitinib treatment, demonstrating its potential to provide sustained long-term disease control in atopic dermatitis.

Switching from Dupilumab to Upadacitinib in Adults and Adolescents with Moderate-to-Severe Atopic Dermatitis and Inadequate Response to Dupilumab: Efficacy and Safety Results from the Phase 3b/4 LEVEL UP Study

Atopic dermatitis (AD) is a chronic skin disease characterized by intense itch and eczematous skin lesions. Upadacitinib (UPA), a selective oral Janus kinase (JAK) inhibitor, and dupilumab (DUPI), a monoclonal antibody targeting interleukin-4 and interleukin-13 signaling, are both approved treatments for moderate-to-severe AD. LEVEL UP is a phase 3b/4 efficacy assessor blinded monotherapy study comparing UPA to DUPI for treatment of moderate-to-severe AD in adults and adolescents over a 16-week period (Period 1). Patients not achieving ≥75% improvement in the Eczema Area and Severity Index (EASI 75) from baseline at Week 16 entered an additional 16-week extension phase (Period 2). In Period 2, patients either continued/escalated to UPA 30 mg (UPA/UPA 30) or switched from DUPI to UPA 15 mg (DUPI/UPA), with the potential to escalate to 30 mg based on clinical response. Efficacy for skin and itch outcomes in Period 2 were assessed using observed case analysis while on treatment, without statistical comparisons. Here we report efficacy and safety results for the DUPI/UPA switch group. A total of 355 patients who did not achieve EASI 75 at Week 16 entered Period 2 of the study (DUPI/UPA, N=208). At Week 32, response rates in the DUPI/UPA group were: 79.6%, 58.7%, and 19.9% achieving EASI 75, EASI 90, and EASI 100, respectively; 60.2% achieving WP-NRS improvement ≥4 among those with baseline WP-NRS ≥4; 37.0% achieving WP-NRS 0/1 among those with baseline WP-NRS >1; and 26.8% simultaneously achieving EASI 90 and WP-NRS 0/1 by Week 32. Clinically meaningful outcomes were also observed at an earlier visit (Week 20). No new safety signals through Week 32 were identified compared to the established safety profile of UPA. Most patients with an inadequate response to DUPI at Week 16 experienced clinically meaningful improvements in skin clearance and itch at 4 weeks post-switch to UPA, with additional patients achieving these outcomes by 16 weeks post-switch. These findings suggest that switching from DUPI to UPA is an effective treatment strategy for patients who do not meet moderate or optimal treatment targets with DUPI.

J-shaped association of serum uric acid with all-cause and cardiovascular mortality in patients with diabetic kidney disease.

The association of serum uric acid (SUA) with mortality remains unclear in patients with diabetic kidney disease (DKD). Thus, this prospective cohort study aimed to explore the association of SUA with all-cause and cardiovascular disease (CVD) mortality among patients with DKD in a large, nationally representative sample. This cohort study included data from the National Health and Nutrition Examination Survey (NHANES) 1999-2018 and the National Death Index mortality data until 31 December 2019. The restricted cubic spline and the Cox proportional hazards regression were conducted to describe the association of SUA with all-cause and CVD mortality and evaluate potential nonlinear associations. The analysis included 3470 patients with DKD from NHANES 1999-2018. During the follow-up time of 24,633 person-years, we recorded 1489 all-cause deaths, including 542 CVD deaths. We identified a J-shaped association of SUA with all-cause and CVD mortality. The corresponding inflection points were observed at 5.1 and 5.7mg/dL. When SUA were higher than inflection points, each 1mg/dL increase in SUA was linked to a 13% and 22% higher risk of all-cause (HR: 1.13; 95% CI: 1.07-1.20;P < 0.001) and CVD (HR: 1.22; 95% CI: 1.06-1.41;P = 0.006) mortality, respectively. This study indicated the J-shaped association of SUA with all-cause and CVD mortality in patients with DKD. The corresponding inflection points were 5.1 mg/dL for all causes and 5.7mg/dL for CVD, respectively. More clinical randomized trials are needed to confirm the optimal uric acid-lowering target.

Early postnatal corticosteroids in preventing mortality and bronchopulmonary dysplasia: the pivotal importance of selecting an appropriate treated population.

These findings underscore that the baseline event rates in the control group are potentially correlated with the efficacy of PCS in preventing BPD or mortality, offering more precise guidance beyond the general "high-risk" category and supporting baseline risk stratification for more targeted PCS therapy in clinical practice. • Early postnatal corticosteroids (PCS) have been widely studied for their anti-inflammatory effects in preventing bronchopulmonary dysplasia (BPD) in preterm infants. • Determining the optimal target population for PCS remains challenging due to varying baseline risks and associated neurodevelopmental concerns. • Stratifying infants by baseline BPD or mortality rates reveals that PCS shows greater efficacy in high-risk groups, particularly those with baseline event rates above 50%. • Considering baseline risk stratification represents a key direction for future clinical decision-making.

SPEED OF ADJUSTMENT OF CAPITAL STRUCTURE IN LISTED COMPANIES ON THE INDONESIA STOCK EXCHANGE

This study aims to analyze the speed of adjustment of capital structure in companies listed on the Indonesia Stock Exchange (BEI) by considering key variables such as SoA, Distance, Profitability (RoA), Growth, and Tangibility. SoA refers to the speed at which companies adjust to their target capital structure, and the results indicate that companies in Indonesia have not yet reached an optimal capital structure target. Distance reflects the difference between the actual and target capital structure, while profitability reflects the financial performance of the company. Growth and Tangibility refer to company growth and the level of tangible assets that can be pledged as collateral. This study involved 717 companies listed on the BEI from 2020 to 2022, with 2020 as the estimation period and 2021-2022 as the research period. The One Sample t-test with a test value of 1 and multiple linear regression analysis with classical assumption testing were employed to examine the relationship between these variables and SoA. Each variable that has been used in this study shows that the speed of adjustment of capital structure in companies listed on the Indonesia Stock Exchange is not at the optimal target. Of the five hypotheses made, it turns out that only three hypotheses were accepted while the other two hypotheses were rejected.

Assessing the Impact and Cost-Effectiveness of Exposome Interventions on Alzheimer’s Disease: A Review of Agent-Based Modeling and Other Data Science Methods for Causal Inference

Background: The exposome (e.g., totality of environmental exposures) and its role in Alzheimer’s Disease and Alzheimer’s Disease and Related Dementias (AD/ADRD) are increasingly critical areas of study. However, little is known about how interventions on the exposome, including personal behavioral modification or policy-level interventions, may impact AD/ADRD disease burden at the population level in real-world settings and the cost-effectiveness of interventions. Methods: We performed a critical review to discuss the challenges in modeling exposome interventions on population-level AD/ADRD burden and the potential of using agent-based modeling (ABM) and other advanced data science methods for causal inference to achieve this. Results: We describe how ABM can be used for empirical causal inference modeling and provide a virtual laboratory for simulating the impacts of personal and policy-level interventions. These hypothetical experiments can provide insight into the optimal timing, targeting, and duration of interventions, identifying optimal combinations of interventions, and can be augmented with economic analyses to evaluate the cost-effectiveness of interventions. We also discuss other data science methods, including structural equation modeling and Mendelian randomization. Lastly, we discuss challenges in modeling the complex exposome, including high dimensional and sparse data, the need to account for dynamic changes over time and over the life course, and the role of exposome burden scores developed using item response theory models and artificial intelligence to address these challenges. Conclusions: This critical review highlights opportunities and challenges in modeling exposome interventions on population-level AD/ADRD disease burden while considering the cost-effectiveness of different interventions, which can be used to aid data-driven policy decisions.

Structure-guided design and evaluation of CRM197-scaffolded vaccine targeting GnRH for animal immunocastration

Immunocastration is a humane alternative to surgical castration for controlling population and estrous behaviors in animals. Gonadotropin-releasing hormone (GnRH), the pivotal initiating hormone of the hormonal cascade in mammals, is the optimal target for immunocastration vaccine development. Cognate antigen-mediated cross-linking of B cell receptors (BCRs) is a strong activation signal for B cells and is facilitated by repetitive surface organizations of antigens. In this study, we describe the structure-guided design of highly immunogenic chimeric proteins with variant numbers of GnRH peptide insertion by epitope grafting. Linear B-cell epitopes of cross-reacting material 197 (CRM197) were replaced with multiple copies of GnRH peptide, and the inserts were displayed on the surface of the designs while maintaining the overall folding of CRM197. Among the seven designs, TCG13, which carries 13 copies of GnRH peptide, was the most immunogenic, and its immunocastration efficacy was evaluated in male mice. Vaccination with the BFA03-adjuvanted TCG13 induced potent humoral immunity and reduced the serum testosterone concentration in mice. It could significantly decrease sperm quality and severely impair gonadal function and fertility. These results demonstrate that CRM197 holds great value as a scaffold for epitope presentation in peptide-based vaccine development and supports TCG13 as a promising vaccine candidate for animal immunocastration.Key points• Provide a feasible way to design chimeric immunogen targeting GnRH by epitope grafting.• CRM197 can accommodate the insertion of multiple copies of heterologous epitope peptides.• Administration with the most immunogenic design led to effective immunocastration in male mice.

Determining optimal clinical target volume margins based on microscopic extracapsular extension of metastatic nodes in patients with non-small-cell lung cancer after chemotherapy or chemotherapy combined with immunotherapy

BackgroundNo standard has been established for the clinical target volume (CTV) margins of lymph nodes (LNs) in patients with non-small-cell lung cancer (NSCLC) receiving chemotherapy or chemotherapy combined with immunotherapy followed by radiotherapy. This study aimed to discuss the CTV range of NSCLC after chemotherapy or chemotherapy combined with immunotherapy by observing the microscopic extent of tumor spread beyond the LN capsule.MethodsWe retrospectively analyzed the data of 240 patients with stage II and III NSCLC who underwent surgery without neoadjuvant therapy, with neoadjuvant chemotherapy (NAC), or with NAC combined with immunotherapy (NACI). We measured the maximal distance of extracapsular extension (ECE) using a digital microscope, analyzed the correlation between clinicopathological features and ECE distance, and determined the CTV margins of metastatic LN under different treatment methods.ResultsThe ECE distance differed significantly among the three groups (p < 0.001). We determined appropriate margin widths based on a 5% error risk as 3.00, 2.30, and 1.40 mm for direct surgery, NAC, and NACI, respectively. Multivariate analysis revealed that the ECE of metastatic LN correlated with the treatment methods and LN size.ConclusionThe existing CTV delineation standards may increase the radiation toxicity of patients. We believe that different CTV margins should be used for LN in patients with NSCLC receiving different treatments. To ensure 95% coverage of ECE, the gross tumor volume of untreated, chemotherapy-treated, and chemotherapy combined with immunotherapy-treated patients should be expanded by 3.00, 2.30, and 1.40 mm, respectively, to obtain the CTV.

INTEGRAL search for magnetar giant flares from the Virgo Cluster and in nearby galaxies with high star formation rate

Abstract Giant flares from magnetars can reach, for a fraction of a second, luminosities greater than 1047 erg s−1 in the hard X-ray/soft γ-ray range. This makes them visible at distances of several megaparsecs. However, at extragalactic distances (farther than the Magellanic Clouds) they are difficult to distinguish from the short γ-ray bursts, which occur much more frequently. Since magnetars are young neutron stars, nearby galaxies with a high rate of star formation are optimal targets to search for magnetar giant flares (MGFs). Here we report the results of a search for MGFs in observations of the Virgo cluster and in a small sample of nearby galaxies obtained with the IBIS instrument on the INTEGRAL satellite. From the currently known MGF sample we find that their energy distribution is well described by a power law with slope γ=2 (with 90 per cent c.l. interval [1.7-2.2]). From the lack of detections in this extensive data set (besides 231115A in M82) we derive a 90 per cent c.l. upper limit on the rate of MGF with E &amp;gt; 3 × 1045 erg of ∼2 × 10−3 yr−1 per magnetar and a lower limit of R(E) &amp;gt; ∼4 × 10−4 yr−1 magnetar−1 for E &amp;lt; 1045 erg.

Toward Robust Car-Following Dynamics Modeling via Blackbox Models: Methodology, Analysis, and Recommendations

The selection of the target variable is critical for the performance of classical car-following (CF) models. There is a vast body of literature on which target variable is optimal for classical CF models, but there is no study that evaluates the selection of optimal target variables for black-box models prevalent in machine learning, which are increasingly being used to model CF behavior. This paper evaluates different target variables, for example acceleration, velocity, and headway, for three models: Gaussian processes (GP), kernel ridge regression, and long short-term memory (LSTM). These models have different objective functions and work in different vector spaces, for example, GP work in function space, and LSTM in parameter space. Furthermore, this paper evaluates the efficacy of black box and classical CF models across datasets, including diverse traffic scenarios and human versus automated control. Finally, this paper evaluates the coupling of design choices across selection of model type, target variable during model calibration, and dataset characteristics. The experiments show that the optimal target variable recommendations for black-box models differ from classical CF models, depending on the objective function and the vector space. Statistical analysis indicates that selection of models couples greatly with target variables, while recommended target variables do not depend on the datasets used during model calibration. These results provide a path toward more robust CF models, potentially resulting in more accurate and generalizable capacity and/or traffic flow estimates for roadways, even as driving behaviors change with the introduction of new types of automation.

Caloric requirement targets for nutritional support in adult autoimmune encephalitis: a retrospective cohort study.

Autoimmune encephalitis (AE) is a severe neurological disease often accompanied by consciousness disturbances, severe swallowing difficulties, and gastrointestinal dysfunction, increasing the risk of malnutrition. However, the optimal caloric intake target during the acute phase of AE remains unclear. This study aims to evaluate the impact of caloric intake on short-term clinical outcomes in AE patients, specifically focusing on the improvement in Clinical Assessment Scale for Autoimmune Encephalitis (CASE) scores, to provide recommendations for nutritional support during the acute phase. A retrospective study analyzed clinical data from 128 adult AE patients requiring nutritional support, admitted to West China Hospital, Sichuan University, from January 2020 to January 2024. Patients were categorized into low-calorie intake (below 70% of requirements), standard-calorie intake (70-100% of requirements), and high-calorie intake (above 100% of requirements) groups. Multivariate linear and logistic regression models were used to quantify the associations. Higher caloric intake was significantly associated with improved CASE scores (β = 8.58, SE = 3.75, 95% CI = 1.14 to 16.03, p = 0.02). Low caloric intake negatively impacted the improvement of CASE scores (p = 0.049), particularly in seizures and speech problems. The low-calorie intake group had significantly longer hospital stays and nutrition therapy durations (45.79 ± 30.98 days, p < 0.01; 40.39 ± 31.92 days, p = 0.02). Adequate caloric intake has a significant positive impact on the short-term clinical outcomes of AE patients, suggesting that meeting or exceeding caloric requirements may promote neurological recovery in AE patients. Future prospective studies are needed to validate these findings and further optimize nutritional support strategies.

Water quality estimates using machine learning techniques in an experimental watershed

ABSTRACT This study aims to identify the best machine learning (ML) approach to predict concentrations of biochemical oxygen demand (BOD), nitrate, and phosphate. Four ML techniques including Decision tree, Random Forest, Gradient Boosting and XGBoost were compared to estimate the water quality parameters based on biophysical (i.e., population, basin area, river slope, water level, and stream flow), and physicochemical properties (i.e., conductivity, turbidity, pH, temperature, and dissolved oxygen) input parameters. The innovation lies in the combination of on-the-spot variables with additional characteristics of the watershed. The model performances were evaluated using coefficient of determination (R2), Nash-Sutcliffe efficiency coefficient (NSE), Root Mean Squared Error (RMSE) and Kling-Gupta Efficiency (KGE) coefficient. The robust five-fold cross-validation, along with hyperparameter tuning, achieved R2 values of 0.71, 0.66, and 0.69 for phosphate, nitrate, and BOD; NSE values of 0.67, 0.65, and 0.62, and KGE values of 0.64, 0.75, and 0.60, respectively. XGBoost yielded good results, showcasing superior performance when considering all analysis performed, but his performance was closely match by other algorithms. The overall modeling design and approach, which includes careful consideration of data preprocessing, dataset splitting, statistical evaluation metrics, feature analysis, and learning curve analysis, are just as important as algorithm selection.

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization

Abstract Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. The associated notebooks allow to reproduce the reported analyses and apply them to other systems.2

Individualized rTMS Intervention Targeting Sleep Deprivation-Induced Vigilance Decline: Task fMRI-Guided Approach.

Sleep deprivation (SD) is prevalent in our increasingly round-the-clock society. Optimal countermeasures such as ample recovery sleep are often unfeasible, and brief naps, while helpful, do not fully restore cognitive performance following SD. Thus, we propose that targeted interventions, such as repetitive transcranial magnetic stimulation (rTMS), may enhance cognitive performance recovery post-SD. We recruited 50 participants for two SD experiments. In the first experiment, participants performed a psychomotor vigilance task (PVT) under three conditions: normal sleep (resting wakefulness), after 24 h of SD, and following a subsequent 30-min nap. We analyzed dynamic changes in PVT outcomes and cerebral responses across conditions to identify the optimal stimulation target. Experiment 2 adopted the same protocol except that, after the nap, 10-Hz, sham-controlled, individualized rTMS was administrated. Then, an analysis of variance was conducted to investigate the ability of stimulation to improve the PVT reaction times. Through task-related functional magnetic resonance imaging, we identified cerebral responses within the right middle frontal gyrus (MFG) as the optimal stimulation target. Subsequent application of individualized 10-Hz rTMS over the right MFG attenuated SD-induced deterioration of vigilance. Our findings suggest that combining a brief nap with individualized rTMS can significantly aid the recovery of vigilance following SD. This approach, through modulating neural activity within functional brain networks, is a promising strategy to counteract the cognitive effects of SD.

A larval expressed chemosensory protein involved in recognition of anthocyanins in Spodoptera litura (Lepidoptera: Noctuidae).

Anthocyanins are secondary metabolites which act as diverse functions during plant growth. Insects can discriminate host plants by their sensitive gustatory systems. It is hypothetical that chemosensory proteins (CSPs) play a crucial role in regulating this behavioral process. However, the underlying molecular mechanisms remain obscure. In the present study, we characterized a CSP SlitCSP8 from the Spodoptera litura (Fabricius) (Lepidoptera: Noctuidae). Quantitative real-time-polymerase chain reaction analysis demonstrated that SlitCSP8 was mainly expressed in the head of the 7th S. litura larvae, especially labrum. Further, recombinant SlitCSP8 was obtained using bacterial expression system. Fluorescence competitive binding assays demonstrated that the purified SlitCSP8 exhibited a strong binding affinity to anthocyanins, a natural compound derived from the host plant. Silencing SlitCSP8 through RNAi significantly reduced the sensitivity of S. litura larvae to anthocyanins-treated leaf disks, the development from larva to pupae was not affected. These data provide insight into the molecular basis that CSP8 can detect anthocyanins in host plants by chemosensory system of insects. It can be further used in designing novel optimal food attractant targeting to the CSPs for pest control.

Efficient CRISPR/Cas9 Knock-in Approaches for Manipulation of Endogenous Genes in Human B Lymphoma Cells.

Precise understanding of temporally controlled protein-protein interactions, localization, and expression is often difficult to achieve using traditional overexpression techniques. Recent advances have made CRISPR-based knock-in approaches efficient, which enables rapid derivation of cells with tagged endogenous proteins. However, the high degree of variability in knock-in efficiency across cell types and gene loci poses challenges, in particular with B lymphocytes, which are refractory to lipid transfection. Here, we present detailed protocols for efficient B lymphoma cell CRISPR/Cas9-mediated knock-in. We address knock-in efficiency in two ways. First, we provide a detailed approach for assessing cutting efficiency to select the most efficient single guide RNA for the gene region of interest. Second, we provide detailed approaches for tagging endogenous proteins with a fluorescent marker or instead for co-expressing them with an unlinked fluorescent marker. Either approach facilitates downstream selection of single-cell or bulk populations with the desired knock-in, particularly when knock-in efficiency is low. The utility of this approach is demonstrated via examples of engineering tags onto endogenous protein N- or C-termini, together with downstream analyses. We anticipate that this workflow can be applied more broadly to other cell types for efficient knock-in into endogenous loci. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Choosing an optimal knock-in target site and single guide RNA (sgRNA) design Basic Protocol 2: Assessment of Cas9 editing efficiency at the desired B cell genomic knock-in site Basic Protocol 3: Cloning the sgRNA dual guide construct Basic Protocol 4: Repair template design and cloning Basic Protocol 5: Electroporation and selection of engineered B cells Basic Protocol 6: Single-cell cloning of engineered B cells.

Luminosity Evolution of the Hot Gas in Normal Galaxies from the Near Universe to z = 0.5

We explore the evolution of the ∼107 K hot gas in normal galaxies out to redshift = 0.5 (lookback time = 5 Gyr), using X-ray luminosity functions (XLFs) built from a sample of 575 normal galaxies with z < 0.6 detected in five high-galactic-latitude Chandra wide-field surveys. After estimating the emission due to the hot gas component (reducing the sample to ∼400 galaxies), we compared the XLF in three redshift bins (z = 0.1, 0.3, and 0.5), finding increases in the number of galaxies per unit comoving volume from z = 0.1 to 0.3 and then from z = 0.3 to 0.5. These XLF changes suggest a significant (∼5σ) X-ray luminosity evolution of the hot gas, with L X,GAS decreasing by a factor of 6–10 in the last 5 Gyr (from z = 0.5 to 0.1). The relative abundance of L X,GAS ∼ 1041 erg s−1 galaxies at higher z suggests that high-z, moderate-L X,GAS galaxies may be the optimal target to solve the missing baryon problem. In early-type galaxies, this observational trend is qualitatively consistent with (but larger than) the expected time-dependent mass-loss rate in cooling flow models without active galactic nucleus feedback. In late-type galaxies, the observational trend is also qualitatively consistent with (but larger than) the effect of the z-dependent star formation rate.