Equivalent Performance Research Articles

Efficacy of Digital and Non-Digital Compensatory Strategies in Supporting Prospective Memory Task Completion among Community-Dwelling Mid-Life and Older Adults.

Compensatory strategies can be used to help mid-life and older adults successfully manage instrumental activities of daily living that rely upon prospective memory. This study compared the quality of digital and non-digital compensatory strategies in supporting accurate completion of naturalistic, real-world prospective memory tasks. Participants included 70 community-dwelling mid-life and older adults. In this cross-sectional study, participants were tested remotely via Zoom in their homes. They were tasked with completing four real-world prospective memory tasks and encouraged to use their typical compensatory strategies. Utilized strategies were categorized, counted, and assigned quality scores (rating of thoroughness and utility), and prospective memory performance was coded. Participants who used any digital strategies utilized significantly more (ηp2 = .17) and better quality (ηp2 = .12) strategies than participants who did not use any digital strategies. However, the groups demonstrated equivalent prospective memory performance (ηp2 = .006). Within the digital group, participants' digital and non-digital strategies were of similar quality (d = .14) and resulted in similar prospective memory task accuracy (d = .01). Digital and non-digital strategies led to similar performance on naturalistic prospective memory tasks. Findings suggest that many different types of strategies can provide adequate prospective memory support to healthy mid-life and older adults. To inform development of compensatory strategy interventions, future studies should explore other factors that lead to successful prospective memory, such as the specific strategy type and task type match, across the continuum of cognitive impairment.

Constitutive knockout of interleukin-6 ameliorates memory deficits and entorhinal astrocytosis in the MRL/lpr mouse model of neuropsychiatric lupus.

Neuropsychiatric lupus (NPSLE) describes the cognitive, memory, and affective emotional burdens faced by many lupus patients. While NPSLE's pathogenesis has not been fully elucidated, clinical imaging studies and cerebrospinal fluid (CSF) findings, namely elevated interleukin-6 (IL-6) levels, point to ongoing neuroinflammation in affected patients. Not only linked to systemic autoimmunity, IL-6 can also activate neurotoxic glial cells the brain. A prior pre-clinical study demonstrated that IL-6 can acutely induce a loss of sucrose preference; the present study sought to assess the necessity of chronic IL-6 exposure in the NPSLE-like disease of MRL/lpr lupus mice. We quantified 1308 proteins in individual serum or pooled CSF samples from MRL/lpr and control MRL/mpj mice using protein microarrays. Serum IL-6 levels were plotted against characteristic NPSLE neurobehavioral deficits. Next, IL-6 knockout MRL/lpr (IL-6 KO; n = 15) and IL-6 wildtype MRL/lpr mice (IL-6 WT; n = 15) underwent behavioral testing, focusing on murine correlates of learning and memory deficits, depression, and anxiety. Using qPCR, we quantified the expression of inflammatory genes in the cortex and hippocampus of MRL/lpr IL-6 KO and WT mice. Immunofluorescent staining was performed to quantify numbers of microglia (Iba1 +) and astrocytes (GFAP +) in multiple cortical regions, the hippocampus, and the amygdala. MRL/lpr CSF analyses revealed increases in IL-17, MCP-1, TNF-α, and IL-6 (a priori p-value < 0.1). Serum levels of IL-6 correlated with learning and memory performance (R2 = 0.58; p = 0.03), but not motivated behavior, in MRL/lpr mice. Compared to MRL/lpr IL-6 WT, IL-6 KO mice exhibited improved novelty preference on object placement (45.4% vs 60.2%, p < 0.0001) and object recognition (48.9% vs 67.9%, p = 0.002) but equivalent performance in tests for anxiety-like disease and depression-like behavior. IL-6 KO mice displayed decreased cortical expression of aif1 (microglia; p = 0.049) and gfap (astrocytes; p = 0.044). Correspondingly, IL-6 KO mice exhibited decreased density of GFAP + cells compared to IL-6 WT in the entorhinal cortex (89 vs 148cells/mm2, p = 0.037), an area vital to memory. The inflammatory composition of MRL/lpr CSF resembles that of human NPSLE patients. Increased in the CNS, IL-6 is necessary to the development of learning and memory deficits in the MRL/lpr model of NPSLE. Furthermore, the stimulation of entorhinal astrocytosis appears to be a key mechanism by which IL-6 promotes these behavioral deficits.

Hardware in Loop Learning with Spin Stochastic Neurons

Despite the promise of superior efficiency and scalability, real‐world deployment of emerging nanoelectronic platforms for brain‐inspired computing have been limited thus far, primarily because of inter‐device variations and intrinsic non‐idealities. In this work, mitigation of these issues is demonstrated by performing learning directly on practical devices through a hardware‐in‐loop approach, utilizing stochastic neurons based on heavy metal/ferromagnetic spin–orbit torque heterostructures. The probabilistic switching and device‐to‐device variability of the fabricated devices of various sizes is characterized to showcase the effect of device dimension on the neuronal dynamics and its consequent impact on network‐level performance. The efficacy of the hardware‐in‐loop scheme is illustrated in a deep learning scenario achieving equivalent software performance. This work paves the way for future large‐scale implementations of neuromorphic hardware and realization of truly autonomous edge‐intelligent devices.

Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames.

To apply machine learning to extract radiomics features from thyroid two-dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) images to classify and predict benign and malignant thyroid nodules, classified according to the Chinese version of the thyroid imaging reporting and data system (C-TIRADS) as category 4. This retrospective study included 313 pathologically diagnosed thyroid nodules (203 malignant and 110 benign). Two 2D-US images and five CEUS key frames ("2nd second after the arrival time" frame, "time to peak" frame, "2nd second after peak" frame, "first-flash" frame, and "second-flash" frame) were selected to manually label the region of interest using the "Labelme" tool. A total of 7 images of each nodule and their annotates were imported into the Darwin Research Platform for radiomics analysis. The datasets were randomly split into training and test cohorts in a 9:1 ratio. Six classifiers, namely, support vector machine, logistic regression, decision tree, random forest (RF), gradient boosting decision tree and extreme gradient boosting, were used to construct and test the models. Performance was evaluated using a receiver operating characteristic curve analysis. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), and F1-score were calculated. One junior radiologist and one senior radiologist reviewed the 2D-US image and CEUS videos of each nodule and made a diagnosis. We then compared their AUC and ACC with those of our best model. The AUC of the diagnosis of US, CEUS and US combined CEUS by junior radiologist and senior radiologist were 0.755, 0.750, 0.784, 0.800, 0.873, 0.890, respectively. The RF classifier performed better than the other five, with an AUC of 1 for the training cohort and 0.94 (95% confidence interval 0.88-1) for the test cohort. The sensitivity, specificity, accuracy, PPV, NPV, and F1-score of the RF model in the test cohort were 0.82, 0.93, 0.90, 0.85, 0.92, and 0.84, respectively. The RF model with 2D-US combined with CEUS key frames achieved equivalent performance as the senior radiologist (AUC: 0.94 vs. 0.92, P = 0.798; ACC: 0.90 vs. 0.92) and outperformed the junior radiologist (AUC: 0.94 vs. 0.80, P = 0.039, ACC: 0.90 vs. 0.81) in the test cohort. Our model, based on 2D-US and CEUS key frames radiomics features, had good diagnostic efficacy for thyroid nodules, which are classified as C-TIRADS 4. It shows promising potential in assisting less experienced junior radiologists.

Herpesviridae and Atypical Bacteria Co-Detections in Lower Respiratory Tract Samples of SARS-CoV-2-Positive Patients Admitted to an Intensive Care Unit.

Shortly after the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), cases of viral, bacterial, and fungal coinfections in hospitalized patients became evident. This retrospective study investigates the prevalence of multiple pathogen co-detections in 1472 lower respiratory tract (LRT) samples from 229 SARS-CoV-2-positive patients treated in the largest intensive care unit (ICU) in Slovenia. In addition to SARS-CoV-2, (rt)RT-PCR tests were used to detect cytomegalovirus (CMV), Epstein-Barr virus (EBV), herpes simplex virus 1 (HSV-1), herpes simplex virus 2 (HSV-2), varicella zoster virus (VZV), and atypical bacteria: Chlamydia pneumoniae, Mycoplasma pneumoniae and Legionella pneumophila/spp. At least one co-detection was observed in 89.1% of patients. EBV, HSV-1, and CMV were the most common, with 74.7%, 58.1%, and 38.0% of positive patients, respectively. The median detection time of EBV, HSV-1, and CMV after initial SARS-CoV-2 confirmation was 11 to 20 days. Bronchoalveolar lavage (BAL) and tracheal aspirate (TA) samples showed equivalent performance for the detection of EBV, CMV, and HSV-1 in patients with both available samples. Our results indicate that SARS-CoV-2 infection could be a risk factor for latent herpesvirus reactivation, especially HSV-1, EBV, and CMV. However, additional studies are needed to elucidate the clinical importance of these findings.

Efficiency measurement of FL algorithms for image classification

Federated Learning (FL) has emerged as a promising approach to collaborative machine learning without the need to share raw data. It enables decentralized model updates while preserving the privacy of each device and reducing the communication overhead. This experiment evaluates the effectiveness of the personalized FL algorithms, namely FedAvg, APPLE, FedBABU and FedProto, in a decentralized setting, with a particular focus on the Fashion MNIST dataset, which is characterized by a non-ideal data distribution. The objective is to identify which algorithm performs optimally in image classification tasks. The experimental results show that both FedProto and APPLE have nearly equivalent and better performance compared to FedBABU and FedAvg. Interestingly, increasing the number of uploads in FedBABU leads to similar results to APPLE and FedProto. However, under limited upload conditions, FedBABU performs similarly to FedAvg. These results provide valuable insights into the differential performance of personalized FL algorithms in non-id data scenarios and provide guidance for their application in distributed environments, especially in sensitive domains such as medical, military and confidential image analysis tasks where privacy and communication efficiency are paramount concerns.

Comparative analysis of two evacuation simulation tools when applied to high-rise residential buildings

Currently there are numerous emergency evacuation simulation tools with varying levels of sophistication and differing capabilities. It is expected that investigating the same scenarios using different tools might not give the same outcome. This paper illustrates how a microscopic agent-based modelling tool (Pathfinder) and a macroscopic flow-based modelling tool (Evacuationz) can be setup to investigate high-rise residential building evacuations in comparable ways by varying configuration parameters. Both tools represent individual agents with associated characteristics (e.g., walking speed and pre-evacuation delay). These are varied in the scenarios examined to stress-test the designs or explore the impact of design/procedural changes.The two tools differ in how they deal with the building geometry and therefore have different algorithms to manage evacuee movement. In this work, assumed performance parameters (e.g., travel speeds, spacing, etc.) have been calibrated to better align the conditions represented in the tools and the outcomes produced across the scenarios examined. This then allows the tools to function at their respective levels of sophistication and granularity (providing different perspectives on performance) but adopt a more equivalent performance baseline given the calibration effort.The use of two tools increased confidence in the predictions, and also allowed for an examination of a wider range of scenario conditions such as number of stairs, stair width, and building size, given the different computational expense associated with the tools.

Feature-specific quantile normalization and feature-specific mean–variance normalization deliver robust bi-directional classification and feature selection performance between microarray and RNAseq data

BackgroundCross-platform normalization seeks to minimize technological bias between microarray and RNAseq whole-transcriptome data. Incorporating multiple gene expression platforms permits external validation of experimental findings, and augments training sets for machine learning models. Here, we compare the performance of Feature Specific Quantile Normalization (FSQN) to a previously used but unvalidated and uncharacterized method we label as Feature Specific Mean Variance Normalization (FSMVN). We evaluate the performance of these methods for bidirectional normalization in the context of nested feature selection.ResultsFSQN and FSMVN provided clinically equivalent bidirectional model performance with and without feature selection for colon CMS and breast PAM50 classification. Using principal component analysis, we determine that these methods eliminate batch effects related to technological platforms. Without feature selection, no statistical difference was identified between the performance of FSQN and FSMVN of cross-platform data compared to within-platform distributions. Under optimal feature selection conditions, balanced accuracy was FSQN and FSMVN were statistically equivalent to the within-platform distribution performance in multivariable linear regression analysis. FSQN and FSMVN also provided similar performance to within-platform distributions as the number of selected genes used to create models decreases.ConclusionsIn the context of generating supervised machine learning classifiers for molecular subtypes, FSQN and FSMVN are equally effective. Under optimal modeling conditions, FSQN and FSMVN provide equivalent model accuracy performance on cross-platform normalization data compared to within-platform data. Using cross-platform data should still be approached with caution as subtle performance differences may exist depending on the classification problem, training, and testing distributions.

Effects of intrinsically disordered regions in gp120 underlying HIV neutralization phenotypes

HIV envelope protein gp120 is considered a primary molecular determinant of viral neutralization phenotype due to its critical role in viral entry and immune evasion. The intrinsically disordered regions (IDRs) in gp120 are responsible for their extensive sequence variations and significant structural rearrangements. Despite HIV neutralization phenotype and sequence/structural information of gp120 have been experimentally characterized, there remains a gap in our understanding of the correlation between the viral phenotype and IDRs in gp120. Here, we combined machine learning (ML) techniques and molecular dynamics (MD) simulations to gain data-driven and molecule-mechanism insights into relationships between viral sequence, structure, and phenotypes from the perspective of IDRs in gp120. ML models, trained only on the length and disorder score of IDRs, achieved equivalent performance to the best baseline model using amino acid sequences to discriminate HIV neutralization phenotype, indicating that the lengths or disorder of specific IDRs are strongly related to HIV neutralization phenotypes. Comparative MD analysis reveals that gp120 with extreme neutralization phenotypes in multiple conformational states, especially some IDRs, exhibit significantly distinct structural dynamics, conformational flexibility, and thermodynamic distributions. Taken together, our study provided insights into the role of IDRs in gp120 responding to HIV neutralization phenotypes, which will advance the understanding of molecular mechanisms underlying viral function associated with HIV neutralization phenotype and help develop antiviral vaccines or drugs.

Durability of sodium carbonate alkali-activated slag concrete assessed by a performance-based approach

The study aims to understand the performance and durability of a sodium carbonate alkali-activated slag (AAS) based on a performance-approach. Equivalent performances were obtained between the AAS and CEM III/C-based concretes having a water/binder ratio of 0.4 regarding compressive strength, porosity, resistance to nitric acid, and sodium sulfate. AAS exhibited higher resistivity and better resistance to chloride, assessed by a chloride migration test. The AAS showed similar carbonation resistance to CEM III/C and CEM III/B concretes with w/b ratios of 0.47 and 0.53, respectively. A discussion of durability tests for their application to this type of binder is conducted.

Abstract 3675: Design of high-performance tumor-informed minimal residual disease (MRD) panels from low FFPE tumor input

Abstract Background: Minimal residual disease (MRD) testing can detect cancer recurrence months to years earlier than the current standard of care, enabling earlier treatment of recurrence and improved patient outcomes. Tumor-informed MRD assays typically utilize formalin-fixed paraffin-embedded (FFPE) tumor tissue, which is available in limited quantities for some patients, for example, following core needle biopsy (CNB), after neoadjuvant treatment or when patients need multiple tests from the same tumor sample. To determine the lower limit of tissue input, we evaluated our MRD assay performance across a range of extracted tumor volumes. Methods: Resected tumors and CNBs were sectioned, H&amp;E stained, and macro-dissected. Tumor gDNA was extracted, quantified, prepared into libraries and sequenced. Sequenced libraries were aligned and evaluated for depth of coverage, variation of coverage, and duplication rate. Somatic calling was performed on matched tumor and normal samples. Up to 1000 target sites were selected for high-depth targeted sequencing of the tumor and normal gDNA for confirmation of somatic variant calls. The positive predictive value (PPV) was computed as the percent of putative somatic variant sites that were present in the tumor capture library and absent in the normal capture library. Results: Extracted tumor volumes varied by almost two orders of magnitude, from 0.06mm3 (equivalent to needle core or fine needle aspirate biopsies) to 5mm3 (achievable with resected tumor). gDNA amount varied linearly (3.6ng to 1549ng) with tumor tissue input, indicating the low tissue to paraffin ratio did not have an adverse effect on yield. Tumor gDNA inputs into library prep ranged from 2.5ng to 100ng. DNA amounts above 100ng into library prep had no discernable benefit. Below 10 ng, depth of coverage and the coefficient of variation in coverage indicated poor-quality libraries. Samples with ~10 ng of gDNA input into library prep showed depth of coverage comparable to higher inputs and saturated the achievable library complexity. Additionally, PPV of somatic calling was consistent across the range of gDNA inputs from 10-100 ng, demonstrating equivalent assay performance. Conclusion: Tumor-informed MRD assays have immense potential for increasingly sensitive treatment response and recurrence monitoring that can inform better treatment decisions. FFPE tumor tissue is a critical input into MRD assays but is a limited resource. This study supports a minimum DNA input of 10 nanograms for a single attempt at extraction, corresponding to a tissue volume of 0.2mm3 or a single 10µm slide with a 20mm2 area, representing one of the lowest tissue input requirements for an MRD assay. Low FFPE tissue requirements expand the patient population that benefit from MRD testing by utilizing samples that have low tumor content, are post-neoadjuvant therapy or do not meet the tumor volume requirements of competing MRD offerings. Citation Format: Matt LaBella, Ashley Acevedo, Ravi Patel, Kiefer Haug, Sangita Ganesh, Elise Buser, Nafei Xu, Shalee Carlson, Kyle Trettin, Sarah Ratzel, Kieko Hara, Pavlos Msaouel, Kanishka Sircar, Chad Tang, Dale Muzzey, Genevieve Gould. Design of high-performance tumor-informed minimal residual disease (MRD) panels from low FFPE tumor input [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3675.

Abstract 2337: Accelerated optical genome mapping analysis with Stratys Compute and Guided Assembly

Abstract Background Optical genome maps (OGM) from Bionano enable the detection of genomic structural and copy number variants that cannot be detected by next-generation sequencing (NGS) technologies and are often missed by conventional cytogenetic techniques. Bionano has developed bioinformatics pipelines for calling structural and copy number variants including the Bionano Solve de novo assembly pipeline for constitutional analysis and the Rare Variant Analysis (RVA) pipeline for low-allele-fraction cancer applications. Both pipelines are computationally intensive and currently take 5-10 hours on the latest generation of the Bionano Saphyr® Compute which is deployed as a four-node compute cluster and requires significant IT resources. Methods To increase throughput and simplify the deployment of the compute resources needed to support the Stratys™ optical genome mapping instrument, Bionano has developed Stratys™ Compute. Stratys Compute improves both compute and analytical performance by adapting compute-intensive stages in the Solve pipeline to run on GPUs and by developing the Guided Assembly pipeline. Stratys Compute is powered by state-of-the-art NVIDIA RTX 6000 Ada generation cards and CUDA-optimized refinement, alignment, and structural variation detection kernels to accelerate OGM analysis. Stratys Compute is a standalone workstation placed adjacent to Stratys instrument. This will minimize the IT footprint and bypass the integration with the customer site’s data center. We have incorporated these optimizations into the new Guided Assembly pipeline, which aims to combine the low-allele fraction detection capability of the RVA pipeline with the whole genome coverage and ability to detect smaller structural variants enabled by the de novo assembly pipeline. The Guided Assembly pipeline uses the reference genome as an initial seed followed by extension, refinement, and structural variant calling. This new analysis method has been evaluated through comparison to previous results from both existing pipelines and standard benchmarking datasets used to estimate structural variant calling performance and is deployed on Stratys Compute for both constitutional and low allele-fraction applications. Results Guided Assembly has been adapted to run on GPU hardware in a simplified compute tower that can be deployed to a lab along with the Stratys instrument without the need for a dedicated server room. We found concordance between the guided assembly results and our previous de novo and RVA pipeline results. We also found increased sensitivity at low allele fractions for detecting insertion variants smaller than 5 kb and larger than 200 kb while finding equivalent performance for other variant types with the updated methods. The accelerated Guided Assembly for constitutional and low-allele-fraction applications will be available to early access customers in Q4 2023 with full commercial release in Q2 2024. Citation Format: Damla Senol Cali, Thomas Anantharaman, Martin Muggli, Samer Al-Saffar, Charles Schoonover, Neil Miller. Accelerated optical genome mapping analysis with Stratys Compute and Guided Assembly [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2337.

Contribution of irreversible non-180° domain to performance for multiphase coexisted potassium sodium niobate ceramics

Despite the dominance of lead-based piezoelectric materials with ultrahigh electric-field-induced strain in actuating applications, seeking eco-friendly substitutes with an equivalent performance remains an urgent demand. Here, a strategy of regulating the irreversible non-180° domain via phase engineering is introduced to optimize the available strain (the difference between the maximum strain and the remnant strain in a unipolar strain curve) in the lead-free potassium–sodium niobate-based piezoelectric ceramics. In situ synchrotron X-ray diffraction and Rayleigh analysis reveal the contribution of the non-180° domain to available strain in the tetragonal–orthorhombic–rhombohedral phase boundary. The reducing orthorhombic phase and increasing rhombohedral/tetragonal phase accompanied by the reduced irreversible non-180° domain are obtained with increasing doping of Sb5+, resulting in an enlarged available strain due to the significantly lowered remnant strain. This optimization is mainly attributed to the reduced irreversible non-180° domain wall motion and the increased lattice distortion, which are beneficial to decrease extrinsic contribution and enhance intrinsic contribution. The mesoscopic structure of miniaturized nanosized domain with facilitated domain switching also contributes to the enhancement of available strain due to the improved random field and decreased energy barrier. The study will shed light on the design of lead-free high-performance piezoelectric ceramics for actuator applications.

Ferbat's test: A Monte Carlo multiple comparison procedure with a control

The present work presented an alternative MCC to the Dunnett's test, called Ferbat's test. The test replaced the root of the mean square of the residue used within Dunnett's test with another non-biased $\sigma$ estimator. The distribution of the test statistics was determined by simulation using the Monte Carlo method. Comparing the performance evaluation of these two tests, the Ferbat's test performed better in some scenarios, such as control of the experimentwise error rate for all simulated situations and higher power when the number of treatments was small and when the number of replications increases. In the other evaluation situations, the tests presented equivalent performance.

DOSIMETRIC COMPARISON OF INTENSITY-MODULATED RADIATION THERAPY (IMRT) AND VOLUMETRIC-MODULATED ARC THERAPY (VMAT) IN RECTAL CANCER PATIENTS: A COMPREHENSIVE ANALYSIS

This study aimed to conduct a thorough dosimetric comparison between two advanced radiotherapy techniques, Intensity-Modulated Radiation Therapy (IMRT) and Volumetric-Modulated Arc Therapy (VMAT), to ascertain the superior modality in terms of dose distribution and organ-at-risk sparing for rectal cancer patients. Thirty-three patients with ECOG performance status 1, aged between 18 to 60 years, diagnosed with Stage II to III rectal cancer, were included in this prospective study. Each patient underwent treatment planning with IMRT and VMAT techniques using Varian system simulation computed tomography scans. The dosimetric analysis encompassed the evaluation of target volume dose homogeneity, conformity, and doses to at-risk organs. The dosimetric comparison was conducted utilizing seven-field IMRT and dual arc VMAT plans. Statistical analysis was performed using paired t-tests and SPSS 20 software. Treatment plan constraints adhered to the RTOG 9406 guidelines, ensuring adequate planning target volume (PTV) coverage, homogeneity index, conformity index, and dose limits for organs at risk, including small bowel V20 (&lt;200cc), bladder V45 (&lt;21Gy), and femoral head V50 (≤30 Gy). Volumetric arc therapy plans demonstrated superior dose homogeneity to fixed-field intensity-modulated plans (p-value = 0.04), particularly in femoral head dose-limiting toxicity (p-value = 0.00). However, intensity-modulated therapy exhibited equivalent or superior performance in other evaluated parameters. Our findings suggest that while VMAT offers advantages in PTV coverage homogeneity and femoral head dose-limiting toxicity, IMRT remains competitive and potentially preferable in certain dosimetric aspects. The choice between IMRT and VMAT should be carefully considered based on individual patient characteristics and treatment goals. This comprehensive dosimetric comparison contributes valuable insights into optimizing radiotherapy planning for rectal cancer patients, potentially guiding clinical decision-making and improving treatment outcomes.

Multicentric development and validation of a multi-scale and multi-task deep learning model for comprehensive lower extremity alignment analysis

Osteoarthritis of the knee, a widespread cause of knee disability, is commonly treated in orthopedics due to its rising prevalence. Lower extremity misalignment, pivotal in knee injury etiology and management, necessitates comprehensive mechanical alignment evaluation via frequently-requested weight-bearing long leg radiographs (LLR). Despite LLR’s routine use, current analysis techniques are error-prone and time-consuming. To address this, we conducted a multicentric study to develop and validate a deep learning (DL) model for fully automated leg alignment assessment on anterior–posterior LLR, targeting enhanced reliability and efficiency. The DL model, developed using 594 patients’ LLR and a 60%/10%/30% data split for training, validation, and testing, executed alignment analyses via a multi-step process, employing a detection network and nine specialized networks. It was designed to assess all vital anatomical and mechanical parameters for standard clinical leg deformity analysis and preoperative planning. Accuracy, reliability, and assessment duration were compared with three specialized orthopedic surgeons across two distinct institutional datasets (136 and 143 radiographs). The algorithm exhibited equivalent performance to the surgeons in terms of alignment accuracy (DL: 0.21 ± 0.18°to 1.06 ± 1.3°vs. OS: 0.21 ± 0.16°to 1.72 ± 1.96°), interrater reliability (ICC DL: 0.90 ± 0.05 to 1.0 ± 0.0 vs. ICC OS: 0.90 ± 0.03 to 1.0 ± 0.0), and clinically acceptable accuracy (DL: 53.9%–100% vs OS 30.8%–100%). Further, automated analysis significantly reduced analysis time compared to manual annotation (DL: 22 ± 0.6 s vs. OS; 101.7 ± 7 s, p ≤ 0.01). By demonstrating that our algorithm not only matches the precision of expert surgeons but also significantly outpaces them in both speed and consistency of measurements, our research underscores a pivotal advancement in harnessing AI to enhance clinical efficiency and decision-making in orthopaedics.

Six smart guidelines for high-tech manufacture on low-tech 3D printers: the case of the 3Flex

While articulated surgical instruments have enabled the proliferation of minimally invasive interventions, procedures such as laparo-endoscopic single-site surgery are waning in popularity. One potential reason for this decline is a lack of sufficiently dexterous instruments. Although multi-steerable instruments exist, these are often complex and therefore expensive assemblies. Even when 3D printing was used to simplify the design of these instruments, the requirement for high-performance 3D printers limited the reduction in manufacturing costs. To tackle this issue, we propose six guidelines for converting a 3D printed compliant medical instrument from printing on a Digital Light Processing (DLP) printer to a Fused Filament Fabrication (FFF) printer. These guidelines provide a framework to manage and compensate for differences in the two processes to achieve comparable results at a reduced cost. The proposed guidelines were evaluated by assembling a FFF 3D printed prototype that shows equivalent performance to its DLP 3D printed counterpart.

Acrylonitrile‐butadiene‐lignin thermoplastic rubber adhesive for enhanced metal‐to‐metal joining

AbstractWith the growing requirement for lightweight structural materials in automotive, aerospace, and infrastructure applications, multi‐material joints made with adhesive have attracted intense research interest. Commercial thermoset adhesives are one‐time cures, and difficult to disassemble the bonded components for repair and recycling. Our prior work with a thermoplastic acrylonitrile‐butadiene‐lignin rubber (ABL) addresses this sustainability/recycling challenge, but the adhesive exhibits deficient joining strength compared to standard thermosets. Here, we modify the ABL matrix by loading particulate fillers to enhance its modulus and toughness. The goal is to manufacture a cure‐free thermoplastic adhesive system with a simple dispensing protocol and characteristic ductility combined with a high yield stress for improved shear strength of a bonded joint. Fumed silica (FS) and epoxidized glass spheres (EGS) were used as fillers in the ABL to promote the dispersion of lignin particles that tailored the functionalities and free energy components of the adhesive surface. With optimal loading of FS (5 wt%) and EGS (30 wt%) in the ABL adhesive matrix, the lap‐shear strength of the bonded aluminum joint was elevated by 128%, compared to the neat ABL, reaching 21 MPa, which is 90% of the performance of a commercial epoxy‐based adhesive.Highlights A partly renewable filler‐toughened thermoplastic adhesive has been developed. This thermoplastic gives nearly equivalent performance of adhesively bonded aluminum joint compared to standard thermosets. Experimental and simulation data help understand the adhesive reinforcing mechanism by the fillers.

Comparison of the Simplexa GBS Direct and ARIES GBS assays for the detection of S. agalactiae in broth-enriched swab specimens.

We conducted a comparative evaluation of the FDA-cleared Simplexa GBS Direct and ARIES GBS molecular assays for the detection of Streptococcus agalactiae (Group B Streptococcus, GBS) in 386 prospectively collected, broth-enriched vaginal/rectal swab specimens. The sensitivity of each test was 96.2% and specificity was ≥98.7% when compared to a combined direct and enriched culture method using chromogenic culture medium. A total of four specimens were called positive by both molecular assays but negative by culture, likely representing specimens with a low burden of GBS in these specimens. Two specimens were reported positive by culture but negative by both molecular assays. One of these specimens demonstrated atypically colored colonies on chromogenic agar; the other yielded typically colored colonies only observed after broth enrichment. Our data demonstrate equivalent performance of Simplexa and ARIES molecular assays for the detection of GBS in clinical specimens.IMPORTANCEClinical laboratories often face decisions regarding which of the multiple available molecular platforms would best fit their needs based on cost, workflow, menu, and diagnostic performance. Therefore, objective clinical comparisons of similar molecular tests are valuable resources to aid these decisions. We provide a clinical comparison of two FDA-cleared tests to routine culture and to each other that can be used by clinical laboratories when determining which of the available molecular platforms would best fit their laboratory in terms of workflow, cost, and performance.

Incorporating long-range electrostatics in neural network potentials via variational charge equilibration from shortsighted ingredients

We present a new approach to construct machine-learned interatomic potentials including long-range electrostatic interactions based on a charge equilibration scheme. This new approach can accurately describe the potential energy surface of systems with ionic and covalent interactions as well as systems with multiple charge states. Moreover, it can either be regressed against known atomic charge decompositions or trained without charge targets, without compromising the accuracy of energy and forces. We benchmark our approach against other state-of-the-art models and prove it to have equivalent performances on a set of simple reference systems while being less computationally expensive. Finally, we demonstrate the accuracy of our approach on complex systems: solid and liquid state sodium chloride. We attain accuracy in energy and forces better than the model based on local descriptors and show that our electrostatic approach can capture the density functional theory tail of the potential energy surface of the isolated Na-Cl dimer, which the local descriptor-based model fails to describe.