Accurate Prediction Of Response Research Articles

A semi-analytical framework for predicting far-field responses of complex elastic waves emitters

Applications of guided waves in various fields of engineering and science rely on elastic wave emitters for wave generation. Accurate prediction and understanding of the far-field responses of these wave emitters are crucial for the reliable and efficient application of guided waves-based technologies. In this paper, we propose a novel semi-analytical framework capable of predicting the far-field response of complex wave emitters of arbitrary shape and internal structure in any type of substrate. This framework is general, and is not confined to specific methods, enhancing its versatility. We applied the proposed semi-analytical framework to predict the directivity patterns of two different macro-fiber composite transducers, accurately modeled using their exact topologies. The framework’s validity was experimentally confirmed by comparing the predicted directivity patterns with the results obtained from experimental measurements.

Evaluating the predictive efficacy of real-time 3D echocardiography in cardiac resynchronization therapy

BackgroundThe aim of this study is to assess the predictive efficacy of real-time three-dimensional echocardiography (RT-3DE) and QRS wave duration in determining the response to cardiac resynchronization therapy (CRT) and assessing left ventricular systolic function pre- and post-CRT device implantation.MethodA total of 51 patients with heart failure undergoing CRT at the Second Affiliated Hospital of Nantong University between January 1, 2013, and October 31, 2020, were enrolled in this study. Traditional two-dimensional echocardiography and RT-3DE were performed pre and post-CRT, with QRS wave width data from electrocardiograms and additional clinical information collected. Patients were categorized into CRT responder (n = 36) and CRT non-responder (n = 15) groups based on their response to CRT device implantation. Comparative analyses were conducted on the general characteristics of both groups, as well as the predictive efficacy of RT-3DE and QRS wave width for CRT responsiveness and left ventricular systolic function. Data on the standard deviation (Tmsv16-SD, Tmsv12-SD, Tmsv6-SD) and maximum difference (Tmsv16-Dif, Tmsv12-Dif, Tmsv6-Dif) of left ventricular end-systolic volume (LVESV) at segments 16, 12, and 6, as well as QRS wave width, were collected and analyzed.ResultsThe indicators Tmsv6-Dif, Tmsv12-Dif, Tmsv16-Dif, Tmsv6-SD, Tmsv12-SD, Tmsv16-SD, and QRS wave width exhibited significantly higher values in the CRT responder group when compared to the CRT non-responder group (P < 0.05). Among these, Tmsv16-SD demonstrated superior predictive performance for post-CRT response, with a sensitivity of 88.9%, specificity of 80.0%, and a diagnostic cut-off value of 6.19%. This predictive capability exceeded that of the conventional indicator, QRS wave width.ConclusionRT-3DE enables accurate prediction of post-CRT patient response and significantly facilitates quantitative assessment of CRT therapy efficacy.

Hybrid Somatostatin Receptor PET/MRI of the Head and Neck.

Hybrid PET/MRI has the potential to transform neuro-oncologic imaging, particularly in diagnosis and treatment planning of somatostatin receptor-expressing tumors of the head and neck. Hybrid PET/MRI combines high-resolution MRI with functional information from PET, providing precise anatomic information and overcoming difficulties in localization inherent to PET alone. There is a range of tumors in the head and neck that overexpress somatostatin receptors and are therefore amenable to evaluation with somatostatin receptor PET/MRI. These include meningiomas, paragangliomas, olfactory neuroblastomas, pituitary neuroendocrine tumors, middle ear neuroendocrine tumors, and medullary thyroid carcinomas. The combination of PET and MRI is superior to either modality alone and can address several unique diagnostic challenges associated with these lesions. The authors discuss the superior capabilities of somatostatin receptor PET/MRI, including improved lesion localization, more sensitive demonstration of disease extent, enhanced surveillance, optimized radiation therapy planning, and accurate prediction of response to somatostatin analog therapy. Although there are only a few dedicated PET/MRI units available in clinical practice, commercial software is now available that can automatically fuse PET/CT data with recently acquired MRI data, increasing the availability of this approach. Radiologists should be aware of the advantages of somatostatin receptor PET/MRI in evaluation of head and neck tumors as well as the potential pitfalls of this approach so that they can accurately advise clinicians and better interpret these studies. ©RSNA, 2024 See the invited commentary by Shatzkes and Strauss in this issue.

Influence of reinforcement reductions on the response of reinforced concrete half-joints

Reinforced concrete half-joints are susceptible to deterioration mechanisms primarily due to the leakage of surface water, often contaminated with chlorides from de-icing salts, through the expansion joint. Due to the joint geometric form, favourable conditions can be created inside the joint for the corrosion of steel reinforcement. To this end, seven reinforced concrete half-joints were tested to failure under three-point bending to investigate the effect of reinforcement reductions that simulate common deterioration scenarios arising from the corrosion of reinforcement. The results were then compared to the predictions of nonlinear finite element analyses and the strut-and-tie method. The results demonstrated that a reduction in the amount of reinforcement close to the re-entrant corner has major influence on the load-deflection response of reinforced concrete half-joints. The largest reductions in load-carrying capacity were obtained from the half-joints with a full removal of diagonal bars and a combined removal of diagonal and U-bars, both showing reductions in load capacity of more than 50 %. The failure mode in each case of simulated deterioration scenarios involved a shear failure at the nib, except one case which displayed a shear failure in the full depth section due to the reduced anchorage of the bottom reinforcement. Furthermore, it was shown that nonlinear finite element analyses provide accurate predictions of the load-deflection response and failure crack patterns. The strut-and-tie method was found to result in overly conservative predictions, with an average observed-to-predicted load ratio of 1.48 and a coefficient of variation (COV) of 10.2 %. This is considerably higher than an average load ratio of 1.05 and a COV of only 3.1 % obtained from the nonlinear finite element analyses.

A nonlinear structural pulse-like seismic response prediction method based on pulse-like identification and decomposition learning

Abstract Accurate prediction of structural responses under seismic action is important for the performance evaluation of structures. However, depending on the differences in the characteristics of ground motions, particularly whether they contain impulses, the seismic-response characteristics of structures may differ significantly. Hence, a new method, i.e., WD–AttDL, is proposed in this study to predict structural response under pulse-like ground motions. This method innovatively combines a wavelet decomposition-based velocity pulse-identification method with decomposition learning, where decomposed pulses and high-frequency features are used as inputs to the neural-network model, thus simplifying the identification of pulse features for the model. The decomposition learning module integrates different types of submodules, such as CNN feature-extraction, LSTM temporal-learning, and self-attention mechanism submodules. To reduce the nonlinear time-range analysis calculations required to generate training data, a screening method for impulsive ground motions based on time-series feature clustering is proposed. The accuracy and applicability of the proposed method are verified by numerically simulating three different cycles of reinforced-concrete frame structures and a three-dimensional masonry structure. Compared with existing structural seismic-response models, WD–AttDL synergistically integrates different modules and thus offers a higher prediction accuracy. In particular, it reduces the peak error of the predicted response, which is important for the evaluation of structural performance. Additionally, based on the response predicted by WD–AttDL, a vulnerability analysis of the structure under pulse-like seismic effects can be performed rapidly and accurately.&amp;#xD;

Circulating miRNA panels as a novel non-invasive diagnostic, prognostic, and potential predictive biomarkers in non-small cell lung cancer (NSCLC).

Non-small cell lung cancer (NSCLC) is characterised by its aggressiveness and poor prognosis. Early detection and accurate prediction of therapeutic responses remain critical for improving patient outcomes. In the present study, we investigated the potential of circulating microRNA (miRNA) as non-invasive biomarkers in patients with NSCLC. We quantified miRNA expression in plasma from 122 participants (78 NSCLC; 44 healthy controls). Bioinformatic tools were employed to identify miRNA panels for accurate NSCLC diagnosis. Validation was performed using an independent publicly available dataset of more than 4000 NSCLC patients. Next, we correlated miRNA expression with clinicopathological information to identify independent prognostic miRNAs and those predictive of anti-PD-1 treatment response. We identified miRNA panels for lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) diagnosis. The LUAD panel consists of seven circulating miRNAs (miR-9-3p, miR-96-5p, miR-147b-3p, miR-196a-5p, miR-708-3p, miR-708-5p, miR-4652-5p), while the LUSC panel comprises nine miRNAs (miR-130b-3p, miR-269-3p, miR-301a-5p, miR-301b-5p, miR-744-3p, miR-760, miR-767-5p, miR-4652-5p, miR-6499-3p). Additionally, miR-135b-5p, miR-196a-5p, miR-31-5p (LUAD), and miR-205 (LUSC) serve as independent prognostic markers for survival. Furthermore, two miRNA clusters, namely miR-183/96/182 and miR-767/105, exhibit predictive potential in anti-PD-1-treated LUAD patients. Circulating miRNA signatures demonstrate diagnostic and prognostic value for NSCLC and may guide treatment decisions in clinical practice.

DrugFormer: Graph-Enhanced Language Model to Predict Drug Sensitivity.

Drug resistance poses a crucial challenge in healthcare, with response rates to chemotherapy and targeted therapy remaining low. Individual patient's resistance is exacerbated by the intricate heterogeneity of tumor cells, presenting significant obstacles to effective treatment. To address this challenge, DrugFormer, a novel graph-augmented large language model designed to predict drug resistance at single-cell level is proposed. DrugFormer integrates both serialized gene tokens and gene-based knowledge graphs for the accurate predictions of drug response. After training on comprehensive single-cell data with drug response information, DrugFormer model presents outperformance, with higher F1, precision, and recall in predicting drug response. Based on the scRNA-seq data from refractory multiple myeloma (MM) and acute myeloid leukemia (AML) patients, DrugFormer demonstrates high efficacy in identifying resistant cells and uncovering underlying molecular mechanisms. Through pseudotime trajectory analysisunique drug-resistant cellular states associated with poor patient outcomes are revealed. Furthermore, DrugFormer identifies potential therapeutic targets, such as COX8A, for overcoming drug resistance across different cancer types. In conclusion, DrugFormer represents a significant advancement in the field of drug resistance prediction, offering a powerful tool for unraveling the heterogeneity of cellular response to drugs and guiding personalized treatment strategies.

Analytical solutions of noncircular tunnels in transversely isotropic rheological rock masses

In the field of tunnelling applications, it is often found that the rock masses exhibit anisotropy and rheological properties. To optimize the utilization of underground space, the use of noncircular tunnels is often preferred. However, it is important to note that these noncircular tunnels can lead to high-stress concentrations and significant displacements.This article presents a thorough analytical study on the time-dependent ground responses induced by the excavation of noncircular tunnels in transversely isotropic viscoelastic rock masses. The study considers a comprehensive set of engineering factors, including the viscoelastic characteristics of the surrounding rock, any anisotropic angle, and arbitrary tunnel shapes.Using the generalized corresponding principle of anisotropic elasticity and anisotropic viscoelasticity, an analytical model is introduced. This model can accurately and swiftly address the problem of deformation and stresses around noncircular tunnels in anisotropic rheological rock masses. The analytical solutions are verified by their good agreement with the Finite Element Method (FEM) results under identical assumptions. Moreover, the qualitative agreement between the analytical solutions and field data further validates the practical application of the analytical solution.A parametric analysis is then performed to investigate the effects of anisotropy ratio, anisotropy angle, and coefficient of lateral pressure on stresses and displacements.The proposed analytical solutions can help reveal the particular mechanical mechanism of the time-dependent ground responses due to the combination of rock anisotropy and rheology. Furthermore, they can provide a more accurate prediction of the ground response, which may be useful to optimize the design of tunnel excavation in anisotropic rheological rock masses.

Trajectory modeling and response prediction in transcranial magnetic stimulation for depression

BackgroundRepetitive transcranial magnetic stimulation (rTMS) therapy could be improved by more accurate and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modeling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models are useful in predicting clinically meaningful change in symptom severity, i.e. categorical (non)response as opposed to continuous scores. MethodsWe compared LCMM and NLME approaches to model the antidepressant response to TMS in a naturalistic sample of 238 patients receiving rTMS for treatment resistant depression, across multiple coils and protocols. We then compared the predictive power of those models. ResultsLCMM trajectories were influenced largely by baseline symptom severity, but baseline symptoms provided little predictive power for later antidepressant response. Rather, the optimal LCMM model was a nonlinear two-class model that accounted for baseline symptoms. This model accurately predicted patient response at 4 weeks of treatment (AUC=0.70, 95 % CI=[0.52–0.87]), but not before. NLME offered slightly improved predictive performance at 4 weeks of treatment (AUC=0.76, 95 % CI=[0.58–0.94], but likewise, not before. ConclusionsIn showing the predictive validity of these approaches to model response trajectories to rTMS, we provided preliminary evidence that trajectory modeling could be used to guide future treatment decisions.

Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review

BackgroundNavigating the complexity of chronic myeloid leukemia (CML) diagnosis and management poses significant challenges, including the need for accurate prediction of disease progression and response to treatment. Artificial intelligence (AI) presents a transformative approach that enables the development of sophisticated predictive models and personalized treatment strategies that enhance early detection and improve therapeutic interventions for better patient outcomes.MethodsAn extensive search was conducted to retrieve relevant articles from PubMed, Scopus, and Web of Science databases up to April 24, 2023. Data were collected using a standardized extraction form, and the results are presented in tables and graphs, showing frequencies and percentages. The authors adhered to the PRISMA-ScR checklist to ensure transparent reporting of the study.ResultsOf the 176 articles initially identified, 12 were selected for our study after removing duplicates and applying the inclusion and exclusion criteria. AI’s primary applications of AI in managing CML included tumor diagnosis/classification (n = 9, 75%), prediction/prognosis (n = 2, 17%), and treatment (n = 1, 8%). For tumor diagnosis, AI is categorized into blood smear image-based (n = 5), clinical parameter-based (n = 2), and gene profiling-based (n = 2) approaches. The most commonly employed AI models include Support Vector Machine (SVM) (n = 5), eXtreme Gradient Boosting (XGBoost) (n = 4), and various neural network methods, such as Artificial Neural Network (ANN) (n = 3). Furthermore, Hybrid Convolutional Neural Network with Interactive Autodidactic School (HCNN-IAS) achieved 100% accuracy and sensitivity in organizing leukemia data types, whereas MayGAN attained 99.8% accuracy and high performance in diagnosing CML from blood smear images.ConclusionsAI offers groundbreaking insights and tools for enhancing prediction, prognosis, and personalized treatment in chronic myeloid leukemia. Integrated AI systems empower healthcare practitioners with advanced analytics, optimizing patient care and improving clinical outcomes in CML management.

A new seismic wave input method for canyon sites based on the finite element method-indirect boundary integral equation method

The seismic input is the basis for the seismic safety analysis of dams, but current seismic input methods have not reasonably considered the influence of canyon scattering effects on the dynamic response of dams. In this paper, the total motion field of the canyon is deconstructed into the free field of a uniform half space and the scattering field of the canyon and they are obtained separately. The indirect boundary integral equation method (IBIEM) is used to determine the scattering field of the canyon, which is superimposed with the free field to obtain the seismic ground motion field (total motion field) of the canyon. The accuracy of the total motion field of the canyon is verified through reference solutions. In the finite element analysis of seismic ground motion field in the canyon, the total motion field at the truncation boundary of the canyon foundation is transformed into the equivalent seismic input loads and the interior of the canyon are simulated using the finite element method (FEM). Then, based on the wave input for the free field combined with the viscoelastic artificial boundary, a new wave input method for the total motion field combined with the viscoelastic artificial boundary is established. The seismic wave input method based on the finite element-indirect boundary integral equation method is applied to numerically determine the seismic response of a trapezoidal canyon. The differences in the seismic response of the canyon under free-field input and total motion field input are discussed. The results show that there is a certain deviation between the displacement and waveform obtained from the free-field input and the solutions obtained from the indirect boundary integral equation method. There is a large error for a large angle of incidence, and the errors increase with increasing seismic wave frequency. When the incident angle is 60°, the maximum error in the frequency domain reaches 92.3 %, and the maximum error in the time domain reaches 250 %. The displacement amplitude and waveform of the canyon obtained from the total motion field input showed agreement with the results of the indirect boundary integral equation method. The wave input method established in this paper has high calculation accuracy and reasonably considers the scattering effect of canyons, which provides a basis for more accurate predictions of the seismic response of dams on canyon foundations.

External validation of the natriuretic response prediction equation to discriminate diuretic response in heart failure.

Incomplete decongestion due to lack of titration of diuretics to effective doses is a common reason for readmission in patients with acute decompensated heart failure (ADHF). The natriuretic response prediction equation (NRPE) is a novel tool that proved to be rapid and accurate to predict natriuretic response and does not need urine collection. However, the NRPE has not been externally validated. The goal of this study was to externally validate the discrimination capacity of the NRPE in patients with ADHF and fluid overload. Patients admitted with ADHF who required intravenous loop diuretics were included. A spot urine sample was obtained ~2h following diuretic administration, and a timed 6-h urine collection by study staff was carried out. Urine sodium and urine creatinine from the spot urine sample were used to predict the 6-h natriuretic response using the NRPE. The primary goal was to validate the NRPE to discriminate poor loop diuretic natriuretic response (sodium output <50 mmol in the 6h following diuretic administration). The NRPE was compared with urine sodium and measured urine output which are the methods currently recommended by international guidelines to assess diuretic response. Eighty-seven diuretic administrations from 49 patients were analysed. Mean age of patients was 57±17 years and 67% were male. Mean estimated glomerular filtration rate was 65±28mL/min/1.73m2, and ejection fraction was 35±15%. Median dose of intravenous furosemide equivalents administered the day of the study was 80 mg (IQR 40 - 160). Poor natriuretic response occurred in 39% of the visits. The AUC of the NRPE to predict poor natriuretic response during the 6-h urine collection was 0.91 (95% CI 0.85-0.98). Compared with the NRPE, spot urine sodium concentration (AUC 0.75) and urine output during the corresponding nursing shift (AUC 0.74) showed lower discrimination capacity. In this cohort of patients with ADHF, the NRPE outperformed spot urine sodium concentration and all other metrics related to diuretic response to predict poor natriuretic response. Our findings support the use of this equation at other settings to allow rapid and accurate prediction of natriuretic response.

Accuracy of MRI Versus PET/CT in the Prediction of Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer.

Background Breast-conserving surgeries have significantly advanced breast cancer treatment, offering favorable oncological outcomes, enhanced cosmetic results, reduced postoperative morbidity, and better psychological acceptance compared to mastectomy. The introduction of neoadjuvant therapy has expanded the applicability of breast conservation surgery to include locally advanced tumors. Tumor response to neoadjuvant chemotherapy is evaluated using imaging modalities such as breast ultrasound, breast magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT). Accurate prediction of therapeutic response facilitates the planning of surgical and adjuvant treatments. This study aims to compare the diagnostic accuracy of MRI and PET/CT in predicting treatment response to neoadjuvant chemotherapy in breast cancer patients. Methods This retrospective study was conducted at a tertiary care center in Bahrain. A total of 138 patients with locally advanced breast cancer or human epidermal growth factor receptor-2 (HER2) positive, hormone receptor-negative cancers who underwent breast-conserving surgeries between June 2018 and December 2022 were included. The inclusion criteria focused on patients achieving a complete pathological responsefollowing neoadjuvant systemic therapy, ensuring a homogenous study population. Patients with hormone receptor-positive early breast cancers or metastatic tumors, ineligible for neoadjuvant chemotherapy, were excluded. Non-responders and partial responders were also excluded from the study. Statistical analysis was performed using IBM SPSS v26.0 (IBM Corp., Armonk, US). Response rates for the imaging modalities and histopathology results were assessed. Agreement between histology and imaging modalities was computed using kappa statistics. Diagnostic performance for predicting "no residual" disease was evaluated using the McNemar Test. All tests were two-tailed, with a p-value <0.05 considered statistically significant. Results The study included 138 patients, of whom 73 (52.9%) had an incomplete response or residual disease, while 65 (47.1%) had a complete response or no residual disease according to histology reports. There was slight agreement between post-neoadjuvant MRI and histology results (Cohen's kappa 0.172, p=0.010), while substantial agreement was observed between post-neoadjuvant PET/CT and histology results (Cohen's kappa 0.614, p=0.000). PET/CT demonstrated a higher sensitivity of 93.8% (p<0.001) and a specificity of 68.5%. Although MRI was more specific, the positive predictive value was comparable for both PET/CT and MRI. Conclusion PET/CT shows higher sensitivity and can serve as an early marker for predicting complete pathological response in post-neoadjuvant breast cancer patients. However, the prediction of residual disease is optimized by combining both MRI and PET/CT as diagnostic modalities.

Research on vibration response distorted similitude of cylindrical shells under modal distribution disorder

Similitude theory enables local-scale models to reflect the dynamic characteristics of full-scale models and is widely employed in engineering experimentation. However, shell structures, constrained by wall thickness, cannot undergo experiments through proportional downsizing. Existing similitude studies with retained wall thickness exhibit only minimal distortion, and accurate prediction of vibration responses is hindered by modal distribution disorder. To address these challenges, a method based on modified frequency response functions of natural frequencies and mode shapes is proposed. Scaling laws for natural frequencies and mode shapes of cylindrical shells with retained wall thickness are established. This method yields a maximum prediction error of 4.29 % for prototype natural frequencies, compared to 50.57 % for existing methods. Predicted prototype vibration acceleration responses correspond well with theoretical calculations in both frequency and amplitude, while existing methods fail entirely. The phenomenon of modal distribution disorder and the effectiveness of the proposed method in mitigating it are verified by modal experiments and response tests. Research reveals that the sensitivity of different modal frequencies to the wall thickness scaling factor varies, serving as the cause of modal distribution disorder. The modal distribution with the same circumferential wave number is unaffected by wall thickness. Modes above the bending frequency, with axial half-wavelength consistent with bending modes, are prone to disorder due to wall thickness distortion. During wall thickness distortion, a mode originally with the lowest frequency is surpassed towards lower frequency by the mode with larger circumferential wave number and the same axial half-wave number, leading to disorder. The proposed method addresses the challenge of minimal distortion in structural vibration response and poor performance, advancing the development of distorted similitude.

Information transfer between tip leakage vortex and blade aerodynamic force of a compressor cascade

The compressor blade exhibits intricate flow patterns near stall conditions, with the tip leakage vortex and the main flow shedding vortex from the blade's trailing edge identified as factors influencing the blade's aerodynamic force oscillation. The tip leakage vortex is recognized as the primary contributor to flow structures during non-synchronous vibration (NSV), although existing compressor NSV reduced order models often attribute the fluid oscillation to the passage flow shedding vortex. A tool capable of distinguishing between flow structures and blade aerodynamic force is crucial for resolving this discrepancy. Detached eddy simulations (DES) in a compressor cascade form the basis for gathering data on vortex structures and blade aerodynamic force. This study employs an information thesis method to elucidate the cause-and-effect relationship between the tip leakage vortex and blade aerodynamic force. Through a series of unsteady single passage simulations and experimental validation, the mechanisms leading to variations in blade aerodynamic force are demonstrated. Notably, this research utilizes Proper Orthogonal Decomposition (POD) mode time coefficients in span and axial directions to represent dynamic information on vortex structures. Additionally, transfer entropy is introduced as a metric to evaluate the causal interaction between turbulent flow of the tip leakage and the blade's aerodynamic force, marking a significant advancement. The analysis of information transfer entropy aids in identifying the vortex structure with a stronger relationship to aerodynamic force, a critical finding. By applying this approach, the study examines tip leakage vortex structures in various tip clearance sizes concerning aerodynamic force. The Tip3C and Tip5C tip geometries exhibit tip leakage vortex features with radial vortex characteristics and backflow, akin to structures associated with non-synchronous vibration. These tip vortex structures in the Tip3C and Tip5C tip geometry cascades demonstrate substantial correlations with aerodynamic force oscillation on the blade surface. Conversely, the Tip1C tip geometry cascade displays distinct tip leakage vortex features compared to Tip3C and Tip5C, with weaker ties to blade aerodynamic force oscillation. As simulation models progress, consideration of vortex structures in the passage and leakage vortex at the leading edge is imperative for accurate predictions of aerodynamic force oscillation response.

Taller trees exhibit greater hydraulic vulnerability in southern Amazonian forests

Potential increases in drought frequency and vapour pressure deficit pose a risk to the future function of tropical trees. Previous studies have found that taller tropical trees show a stronger increase in mortality than shorter trees in response to dry anomalies, but the mechanisms behind this are unclear. Here we investigate whether canopy branches of taller tropical trees have different hydraulic traits compared to shorter conspecifics. We determined xylem resistance to embolism (P50), hydraulic safety margin (HSM), xylem functional traits and xylem theoretical hydraulic conductivity for canopy branches of four tree species across a range of tree heights (sapling to maximum tree height) in an ecotonal forest near the Amazonia-Cerrado transition. We found that canopy branches of taller trees within each species have lower HSM, suggesting that they are more susceptible to hydraulic failure under drought than smaller conspecifics. Height-related trends in HSM were driven by variation in P50 with height and not by variation in leaf water potential which did not vary with height. We find that canopy branches with greater xylem vessel diameters are generally more vulnerable to embolism, suggesting a potential role for a diameter-safety linkage in explaining observed patterns of decreasing HSM with height. However, we find no evidence of a branch-level trade-off between theoretical hydraulic conductivity and hydraulic vulnerability. The greater hydraulic vulnerability of larger trees provides a potential explanation for the higher drought-induced mortality observed in taller tropical trees. The consistency of the height-P50 relationship across species opens the door to a more accurate prediction of southern Amazon forest responses to future droughts. Whether the findings for forests in southern Amazonia can be generalized to other Amazonian regions remains an open question.

Eosinophils, Eosinophilic Gastrointestinal Diseases, and Inflammatory Bowel Disease: A Critical Review.

Inflammatory bowel disease (IBD) and eosinophilic gastrointestinal diseases (EGIDs) are complex, multifactorial chronic inflammatory disorders affecting the gastrointestinal tract. Their epidemiology, particularly for eosinophilic esophagitis (EoE), is increasing worldwide, with a rise in the co-diagnosis of IBD and EGIDs. Both disorders share common risk factors, such as early exposure to antibiotics or specific dietary habits. Moreover, from a molecular perspective, eosinophilic infiltration is crucial in the diagnosis of eosinophilic disorders, and it also plays a pivotal role in IBD histological diagnosis. Indeed, recent evidence highlights the significant role of eosinophils in the health of the intestinal mucosal barrier and as mediators between innate and acquired immunity, even indicating a potential role in IBD pathogenesis. This narrative review aims to summarize the current evidence regarding the common clinical and molecular aspects of EGIDs and IBD and the current state of knowledge regarding overlap conditions and their pathogenesis. Pubmed was searched until May 2023 to assess relevant studies describing the epidemiology, pathophysiology, and therapy of EGIDs in IBD. The immune pathways and mechanisms underlying both EGIDs and IBD remain partially known. An improved understanding of the role of eosinophils in overlapping conditions could lead to enhanced diagnostic precision, the development of more effective future therapeutic strategies, and a more accurate prediction of patient response. Consequently, the identification of red flags indicative of an eosinophilic disorder in IBD patients is of paramount importance and must be evaluated on a case-by-case basis.

Integrating Tumor and Nodal Radiomics to Predict the Response to Neoadjuvant Chemotherapy and Recurrence Risk for Locally Advanced Gastric Cancer.

To develop and evaluate machine learning models using tumor and nodal radiomics features for predicting the response to neoadjuvant chemotherapy (NAC) and recurrence risk in locally advanced gastric cancer (LAGC). Early and accurate response prediction is vital to stratify LAGC patients and select proper candidates for NAC. A total of 218 patients with LAGC undergoing NAC followed by gastrectomy were enrolled in our study and were randomly divided into a training cohort (n = 153) and a validation cohort (n = 65). We extracted 1316 radiomics features from the volume of interest of the primary lesion and maximal lymph node on venous phase CT images. We built 3 radiomics signatures for distinguishing good responders and poor responders based on tumor radiomics (TR), nodal radiomics (NR), and a combination of the two (TNR), respectively. A nomogram was then developed by integrating the radiomics signature and clinical factors. Kaplan- Meier survival curves were used to evaluate the prognostic value of the nomogram. The TNR signature achieved improved predictive value, with AUCs of 0.755 and 0.744 in the training and validation cohorts. Our proposed nomogram model (TNRN) showed a good performance for GR prediction in the prediction efficacy, calibration ability, and clinical benefit, with AUCs of 0.779 and 0.732 in the training and validation cohorts, superior to the clinical model. Moreover, the TNRN could accurately classify the patients into high-risk and low-risk groups in both training and validation cohorts with regard to postoperative recurrence and metastasis. The TNRN performed well in identifying good responders and provided valuable information for predicting progression-free survival time (PFS) in patients with LAGC who underwent NAC.

Seismic response prediction and fragility assessment of high-speed railway bridges using machine learning technology

Nowadays, reliable and accurate prediction of the seismic response of high-speed railway bridges and then rapid assessment of their seismic fragility is crucial for the risk assessment of regional high-speed railway bridges. However, the traditional seismic fragility assessment based on nonlinear time history analysis (NTHA) has a characteristic of high computational cost. Therefore, this study explores different machine learning (ML) methods to develop a reliable rapid seismic fragility assessment framework for high-speed railway bridges. Taking a typical high-speed railway continuous (HRC) bridge as a case, a large number of numerical model and ground motion pairs considering various structural and ground motion features are established. Then, the two types of models including six popular ML methods are investigated, in which Lasso regression, artificial neural network (ANN) and support vector machine (SVM) are single models, and random forest (RF), extreme gradient boosting (XGBoost) and light gradient boosting machine (Light GBM) are ensemble models. A database containing a large number of seismic responses of different components is established through the NTHA to develop prediction models, and the optimal hyperparameters of different ML models are determined by ten-fold cross-validation and grid search. The study results show that the ensemble model can reliably predict the seismic response of the HRC bridge compared with the single model, and the XGBoost model is recommended for seismic fragility assessment. On this basis, the optimal ML model obtained by the performance evaluation is applied to develop seismic fragility curves of the HRC bridge, which are compared with the fragility curves based on NTHA. The results indicate that the XGBoost model can effectively replace the traditional NTHA for rapid seismic fragility assessment of the HRC bridge, which can achieve a sufficient assessment level while saving calculation costs.

Predicting emergent animal biodiversity patterns across multiple scales.

Restoring biodiversity-based resilience and ecosystem multi-functionality needs to be informed by more accurate predictions of animal biodiversity responses to environmental change. Ecological models make a substantial contribution to this understanding, especially when they encode the biological mechanisms and processes that give rise to emergent patterns (population, community, ecosystem properties and dynamics). Here, a distinction between 'mechanistic' and 'process-based' ecological models is established to review existing approaches. Mechanistic and process-based ecological models have made key advances to understanding the structure, function and dynamics of animal biodiversity, but are typically designed to account for specific levels of biological organisation and spatiotemporal scales. Cross-scale ecological models, which predict emergent co-occurring biodiversity patterns at interacting scales of space, time and biological organisation, is a critical next step in predictive ecology. A way forward is to first capitalise on existing models to systematically evaluate the ability of scale-explicit mechanisms and processes to predict emergent patterns at alternative scales. Such model intercomparisons will reveal mechanism to process transitions across fine to broad scales, overcome approach-specific barriers to model realism or tractability and identify gaps which necessitate the development of new fundamental principles. Key challenges surrounding model complexity and uncertainty would need to be addressed, and while opportunities from big data can streamline the integration of multiple scale-explicit biodiversity patterns, ambitious cross-scale field studies are also needed. Crucially, overcoming cross-scale ecological modelling challenges would unite disparate fields of ecology with the common goal of improving the evidence-base to safeguard biodiversity and ecosystems under novel environmental change.