Diverse Protocols Research Articles

Mathematical modeling framework enhances clinical trial design for maintenance treatment in oncology

Clinical trials are costly and time-intensive endeavors, with a high rate of drug candidate failures. Moreover, the standard approaches often evaluate drugs under a limited number of protocols. In oncology, where multiple treatment protocols can yield divergent outcomes, addressing this issue is crucial. Here, we present a computational framework that simulates clinical trials through a combination of mathematical and statistical models. This approach offers a means to explore diverse treatment protocols efficiently and identify optimal strategies for oncological drug administration. We developed a computational framework with a stochastic mathematical model as its core, capable of simulating virtual clinical trials closely recapitulating the clinical scenarios. Testing our framework on the landmark SOLO-1 clinical trial investigating Poly-ADP-Ribose Polymerase maintenance treatment in high-grade serous ovarian cancer, we demonstrate that managing toxicity through treatment interruptions or dose reductions does not compromise treatment’s clinical benefits. Additionally, we provide evidence suggesting that further reduction of hematological toxicity could significantly improve the clinical outcomes. The value of this computational framework lies in its ability to expedite the exploration of new treatment protocols, delivering critical insights pivotal to shaping the landscape of upcoming clinical trials.

Abstract 4137465: Estimating Arterial Stiffness Using a Smart Ring and the Impact of HIIT Exercise: A Pilot Study

Introduction: Arterial stiffness is an important marker of cardiovascular health, but measuring it requires specialized equipment and trained personnel. This study aimed to evaluate a novel method for estimating arterial stiffness using photoplethysmography (PPG) signals recorded by a commercially available device (Oura Ring) and to study the impact of a high-intensity interval training (HIIT) intervention on arterial stiffness. Research Questions: 1. Can a smart ring provide accurate estimates of arterial stiffness comparable to the reference device (Sphygmocor)? 2. Does a three-month supervised HIIT cycling protocol reduce arterial stiffness? Aims: 1. Evaluate a PPG-based arterial stiffness estimation method using a smart ring. 2. Assess the efficacy of HIIT in reducing arterial stiffness and improving VO2max. Methods: The study comprised two parts: 1. A cross-sectional study (N=300, age 18-78) for collecting arterial stiffness data using the Sphygmocor and the Smart Ring to evaluate the PPG-based algorithm. 2. A randomized exercise intervention pilot study (N=80, a subset of the cross-sectional study), assigned to either a three-month HIIT protocol or a control group. Arterial stiffness was measured pre- and post-intervention using both devices. Pearson’s correlation coefficients assessed the agreement between the novel method and reference device, and effects of the intervention were analyzed using ANCOVA. Results: The novel PPG-based method demonstrated good correlation with the Sphygmocor device (R = 0.77 and 0.80 in pre- and post-timepoints, respectively). However, despite improving the participants’ aerobic fitness (average increase in VO2max of 1.41 ml/min/kg; 95% CI: [0.15, 2.67], P = 0.029) the HIIT intervention did not significantly improve arterial stiffness, as measured by both methods. Conclusions: The developed smart ring method provides a reliable, non-invasive, and continuous estimation of arterial stiffness. This innovative approach could significantly impact how arterial stiffness is monitored in an everyday setting. The null results of the HIIT intervention suggest that HIIT may not be effective in reducing arterial stiffness, highlighting the need for further research into different exercise modalities for cardiovascular health improvement. This study underscores the potential of wearable technology in cardiovascular health monitoring and the importance of evaluating diverse exercise protocols for arterial health.

AI-federated novel delay-aware link-scheduling for Industry 4.0 applications in IoT networks

PurposeWith the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy.Design/methodology/approachWith an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation.FindingsOur proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0.Originality/valueThe concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.

Riverbank plastic distributions and how to sample them

As plastic pollution exists in aquatic ecosystems globally, monitoring its abundance and distribution has become crucial for understanding transport pathways, sources, sinks, and impacts. Riverbanks are accumulation zones for plastic, but the selection of monitoring methods is constrained by research goals, available resources, and site-specific conditions. This diversity in approaches has led to disparate datasets, highlighting the need for standardized monitoring protocols. Here, we study the spatial distribution of plastic at the riverbank scale, quantify the uncertainty of existing riverbank methods, and provide recommendations for improved monitoring based on the balance between uncertainty loss and increase in effort. We measured riverbank plastic abundance at eight Dutch riverbanks, categorizing the items using 108 item categories (River-OSPAR). For every riverbank, an area of 100 by 25 meters was subdivided into five-by-five-meter squares, resulting in 100 individual monitored sub-areas. We found riverbank plastic exhibited high spatial variability, with deposition patterns ranging from parallel to the waterline to clustered, random, or uniform (Moran’s I between -0.050 and 0.301). Individual measurements from diverse sampling protocols are 5-49 times less accurate than estimates derived from extensive sampling, highlighting the diminishing impact of specific methods with increased data collection. Lastly, our findings suggest that increasing the sampling area quickly reaches diminishing returns in terms of accuracy. Reducing the sampled area by 80% only increases the uncertainty in estimating the true plastic density by 20%. While standardized protocols are essential for data comparability, a rigid, uniform sampling approach may be less efficient and resource-intensive than a flexible (step-wise) strategy that adapts to local conditions. By demonstrating that extensive sampling can mitigate the differences between unique sampling protocols, this study promotes a shift towards flexible and efficient riverbank plastic monitoring, ultimately accelerating global efforts to combat plastic pollution.

Evolution of Copolymers of Epoxides and CO2: Catalysts, Monomers, Architectures, and Applications.

The copolymerization of CO2 and epoxides presents a transformative approach to converting greenhouse gases into aliphatic polycarbonates (CO2-PCs), thereby reducing the polymer industry's dependence on fossil resources. Over the past 50 years, a wide array of metallic catalysts, both heterogeneous and homogeneous, have been developed to achieve precise control over polymer selectivity, sequence, regio-, and stereoselectivity. This review details the evolution of metal-based catalysts, with a particular focus on the emergence of organoborane catalysts, and explores how these catalysts effectively address kinetic and thermodynamic challenges in CO2/epoxides copoly2merization. Advances in the synthesis of CO2-PCs with varied sequence and chain architectures through diverse polymerization protocols are examined, alongside the applications of functional CO2-PCs produced by incorporating different epoxides. The review also underscores the contributions of computational techniques to our understanding of copolymerization mechanisms and highlights recent advances in the closed-loop chemical recycling of CO2-sourced polycarbonates. Finally, the industrialization efforts of CO2-PCs are discussed, offering readers a comprehensive understanding of the evolution and future potential of epoxide copolymerization with CO2.

Automated deep learning-based bone mineral density assessment for opportunistic osteoporosis screening using various CT protocols with multi-vendor scanners

This retrospective study examined the diagnostic efficacy of automated deep learning-based bone mineral density (DL-BMD) measurements for osteoporosis screening using 422 CT datasets from four vendors in two medical centers, encompassing 159 chest, 156 abdominal, and 107 lumbar spine datasets. DL-BMD values on L1 and L2 vertebral bodies were compared with manual BMD (m-BMD) measurements using Pearson’s correlation and intraclass correlation coefficients. Strong agreement was found between m-BMD and DL-BMD in total CT scans (r = 0.953, p < 0.001). The diagnostic performance of DL-BMD was assessed using receiver operating characteristic analysis for osteoporosis and low BMD by dual-energy x-ray absorptiometry (DXA) and m-BMD. Compared to DXA, DL-BMD demonstrated an AUC of 0.790 (95% CI 0.733–0.839) for low BMD and 0.769 (95% CI 0.710–0.820) for osteoporosis, with sensitivity, specificity, and accuracy of 80.8% (95% CI 74.2–86.3%), 56.3% (95% CI 43.4–68.6%), and 74.3% (95% CI 68.3–79.7%) for low BMD and 65.4% (95% CI 50.9–78.0%), 70.9% (95% CI 63.8–77.3%), and 69.7% (95% CI 63.5–75.4%) for osteoporosis, respectively. Compared to m-BMD, DL-BMD showed an AUC of 0.983 (95% CI 0.973–0.993) for low BMD and 0.972 (95% CI 0.958–0.987) for osteoporosis, with sensitivity, specificity, and accuracy of 97.3% (95% CI 94.5–98.9%), 85.2% (95% CI 78.8–90.3%), and 92.7% (95% CI 89.7–95.0%) for low BMD and 94.4% (95% CI 88.3–97.9%), 89.5% (95% CI 85.6–92.7%), and 90.8% (95% CI 87.6–93.4%) for osteoporosis, respectively. The DL-based method can provide accurate and reliable BMD assessments across diverse CT protocols and scanners.

HMLB: Holonic multi-agent approach for preventive controllers load-balancing in SDN-enabled smart grid

Smart grid networks present advantages like improving reliability, security, scalability, etc. However, designing an efficient communication infrastructure for smart grid networks is a great challenge. This is because of its dependency on proprietary protocols and specific vendors. Software-defined-enabled smart grid (SDN-SG) tackles this problem by incorporating diverse protocols and standards including open source platforms. One of the most important questions in Software-defined Networking (SDN) is the controller placement problem being NP-Hard in nature. Therefore, the predominant goal of this paper is to diminish the time complexity by modeling the controller placement problem based on the holonic multi-agent system. The hierarchical structure of a holonic organization improves the computational complexity through the divide and conquer mechanism. Such an idea also decreases the distributed controllers' synchronization overhead which is an issue in the realm of SDN. On the other hand, the proper functioning of the smart grid has a strict dependency on time-critical services. Accordingly, the controller placement is supposed to be a Quality of Service-aware (QoS-aware) one. Also, intermittent topology changes in the smart grid and the occasional joining and leaving of members result in an unsteady traffic pattern and dynamicity of controller load. This research is a pioneer in providing a QoS-aware and dynamic controller placement mechanism for SDN-SG. Experimental results certify the preponderance of the approach over similar ones concerning computational complexity, packet loss, controllers’ synchronization overhead, and also load-balancing overhead.

Characteristics of High-Intensity Interval Training Influence Anthropometrics, Glycemic Control, and Cardiorespiratory Fitness in Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Exercise is a non-pharmacological intervention for type 2 diabetes mellitus (T2DM), including moderate-intensity continuous training (MICT) and high-intensity interval training (HIIT). Despite diverse exercise protocol variations, the impact of these variations in HIIT on T2DM anthropometrics, glycemic control, and cardiorespiratory fitness (CRF) remains unclear. The aim was to examine the influence of HIIT protocol characteristics on anthropometrics, glycemic control, and CRF in T2DM patients and compare it to control (without exercise) and MICT. This review is registered in PROSPERO (CRD42021281398) and follows Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search, employing "high-intensity interval training" and "diabetes mellitus" in PubMed and Web of Science databases, with a "randomized controlled trial" filter, spanned articles up to January 2023. Of 190 records, 29 trials were included, categorized by HIIT interval duration, training volume, and intervention period. Long-duration, high-volume, and long-term HIIT yields superior outcomes compared to control conditions for body mass, waist circumference, fasting plasma glucose, Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), glycosylated hemoglobin (%HbA1c), and CRF. The findings favored HIIT over MICT for body mass in long-duration, high-volume, and short-term intervals (mean difference[MD] - 3.45, - 3.13, and - 5.42, respectively, all p < 0.05) and for CRF in long and medium work intervals and high volume (MD 1.91, 2.55, and 2.43, respectively, all p < 0.05), as well as in medium and long-term intervention (MD 2.66 and 2.21, respectively, all p < 0.05). Regardless of specific HIIT characteristics, no differences were found in the HIIT versus MICT comparison for glycemic control. Specific HIIT protocol characteristics influence changes in anthropometrics, glycemic control, and CRF compared to control groups. However, compared to MICT, only longer duration, higher volume, and short-term HIIT improved body mass, waist circumference, and CRF in individuals with T2DM.

Molecular screening of selected vector-borne pathogens circulating in owned dogs in the Caribbean archipelago of Guadeloupe (France)

Vector-borne diseases represent a major health challenge, both because of the complexity of their control, their common zoonotic nature, or the pathology they can cause in the individual. In tropical areas, surveillance of these diseases is even more important, since the activity of vectors is usually continuous throughout the year. To develop effective prophylaxis and surveillance programs, it is important to know the identity and prevalence of these pathogens as well as their distribution in a given territory. In Guadeloupe, a French archipelago located in the Lesser Antilles of the Caribbean, no information exists about vector-borne diseases in companion animals. With this aim, blood samples were obtained from 46 owned dogs with outdoor access from five different veterinary clinics located in the two mainland islands, and the presence of DNA of the main canine vector-borne pathogens (CVBP) was investigated through diverse PCR protocols. At least one pathogen was detected in 30.4 % of the dogs. The most frequently detected CVBP was Coxiella burnetii (17.4 %), followed by Dirofilaria immitis (8.7 %), and Candidatus Mycoplasma haematoparvum, Hepatozoon canis and Rickettsia spp. (2.2 % in all cases). One dog was coinfected with Candidatus M. haematoparvum and D. immitis. All samples were negative for Anaplasma spp., Ehrlichia spp., Bartonella spp., Borrelia burgdorferi sensu lato, piroplasmids, and Leishmania spp. No significant differences in pathogen occurrence were observed between the two main islands or according to the dog's sex and age groups. This study contributes to filling a relevant gap in the knowledge of vector-borne diseases in the Caribbean.

A fast and adaptive detection framework for genome-wide chromatin loop mapping from Hi-C data.

Chromatin loop identification plays an important role in molecular biology and 3D genomics research, as it constitutes a fundamental process in transcription and gene regulation. Such precise chromatin structures can be identified across genome-wide interaction matrices via Hi-C data analysis, which is essential for unraveling the intricacies of transcriptional regulation. Given the increasing number of genome-wide contact maps, derived from both in situ Hi-C and single-cell Hi-C experiments, there is a pressing need for efficient and resilient algorithms capable of processing data from diverse experiments rapidly and adaptively. Here, we propose YOLOOP, a novel detection-based framework that is different from the conventional paradigm. YOLOOP stands out for its speed, surpassing the performance of previous state-of-the-art (SOTA) chromatin loop detection methods. It achieves a 30-fold acceleration compared with classification-based methods, up to 20-fold acceleration compared with the SOTA kernel-based framework, and a fivefold acceleration compared with statistical algorithms. Furthermore, the proposed framework is capable of generalizing across various cell types, multiresolution Hi-C maps, and diverse experimental protocols. Compared with the existing paradigms, YOLOOP shows up to a 10% increase in recall and a 15% increase in F1-score, particularly noteworthy in the GM12878 cell line. YOLOOP also offers fast adaptability with straightforward fine-tuning, making it readily applicable to extremely sparse single-cell Hi-C contact maps. It maintains its exceptional speed, completing genome-wide detection at a 10 kb resolution for a single-cell contact map within 1 min and for a 900-cell-superimposed contact map within 3 min, enabling fast analysis of large-scale single-cell data.

Green Computing: Crafting A Low-Energy Amba APB Bridge Solution

Abstract—The Advanced High-performance Bus (AHB), a key component of the Advanced Microcontroller Bus Architecture (AMBA) family, stands as a high-performance, low-power, and high- bandwidth communication interface within system components. Bridging to low-bandwidth peripherals, the Advanced Peripheral Bus (APB) offers a simple, non-pipelined protocol for read- and-write communication, linking a bridge/master to numerous slaves via a shared bus. The AHB to APB bridge serves a critical role in integrating diverse bus protocols, facilitating efficient communication between high-performance processing units and slower peripherals, thereby enhancing the System-on-Chip (SoC)'s overall effectiveness and functionality. This approach addresses the functional verification of the AMBA AHB to APB Bridge protocol with a focus on completeness. We employ a layered testbench architecture in System Verilog to ensure thorough verification of functionality with maximal coverage. Our verification environment is constructed using System Verilog, and simulations are conducted on the EDA playground platform. Through this comprehensive approach, we aim to provide robust validation of the AHB to APB bridge protocol, ensuring its reliability and compatibility within complex SoC designs. Keywords— Advanced Peripheral Bus (APB), System-On-Chip (SOC), Advanced Microcontroller Bus Architecture.

Effect of Inspiratory Muscle Training on Outcomes After Cardiac Surgery: A Comprehensive Meta-Analysis of Randomized Controlled Trials.

Inspiratory muscle training (IMT) has emerged as a potential intervention to improve respiratory outcomes for patients undergoing cardiac surgery. However, the extent of the IMT effects on preoperative and postoperative respiratory metrics remains uncertain. Hence, we designed this study to determine the effects of IMT on various outcomes of patients undergoing cardiac surgery. We conducted a comprehensive meta-analysis of studies evaluating the impact of preoperative and postoperative IMT on various respiratory variables and postsurgical outcomes. We synthesized data from multiple studies, encompassing diverse patient populations and IMT protocols. The key outcomes included the maximal inspiratory pressure (MIP), forced expiratory volume in 1sec (FEV1), forced vital capacity (FVC), and others. Our meta-analysis results showed that preoperative IMT significantly improved the MIP values with a pooled standard mean difference (SMD) of 0.62. The hospital stay length was also reduced with a SMD of-0.4. Other variables such as FEV1 and FVC also improved significantly. Postoperative IMT improved the MIP and peak flow rate values, but the evidence was less robust than with preoperative interventions. We observed high heterogeneity across studies for several outcomes and found evidence of publication bias for some postoperative measures. Both preoperative and postoperative IMT offer benefits for patients undergoing operations, especially by enhancing respiratory muscle strength and potentially reducing hospital stays. However, the presence of heterogeneity and publication bias underscores the need for further standardized research to consolidate these findings and standardize IMT protocols for optimal patient outcomes.

A reference framework for standardization and harmonization of CT radiomics features on cadaveric sample

Radiomics features (RFs) serve as quantitative metrics to characterize shape, density/intensity, and texture patterns in radiological images. Despite their promise, RFs exhibit reproducibility challenges across acquisition settings, thus limiting implementation into clinical practice. In this investigation, we evaluate the effects of different CT scanners and CT acquisition protocols (KV, mA, field-of-view, and reconstruction kernel settings) on RFs extracted from lumbar vertebrae of a cadaveric trunk. Employing univariate and multivariate Generalized Linear Models (GLM), we evaluated the impact of each acquisition parameter on RFs. Our findings indicate that variations in mA had negligible effects on RFs, while alterations in kV resulted in exponential changes in several RFs, notably First Order (94.4%), GLCM (87.5%), and NGTDM (100%). Moreover, we demonstrated that a tailored GLM model was superior to the ComBat algorithm in harmonizing CT images. GLM achieved R2 > 0.90 in 21 RFs (19.6%), contrasting ComBat's mean R2 above 0.90 in only 1 RF (0.9%). This pioneering study unveils the effects of CT acquisition parameters on bone RFs in cadaveric specimens, highlighting significant variations across parameters and scanner datasets. The proposed GLM model presents a robust solution for mitigating these differences, potentially advancing harmonization efforts in Radiomics-based studies across diverse CT protocols and vendors.

The ‘ABC’ of split-nanoluciferase HIF heterodimerization bioassays: Applications, Benefits & Considerations

Hypoxia-inducible factors (HIF) are interesting targets for multiple therapeutic indications. While HIF activation is desired for the treatment of anemia-related and ischemic diseases, HIF inhibition is of tremendous interest to anti-cancer drug development. Different signaling events within the HIF pathway are being targeted by drug discovery programs, with a special interest in HIF-selective (possibly also HIF1/2 isoform-selective) compounds. In this study, we applied recently developed cell-based split-nanoluciferase HIF heterodimerization assays to study the effects of compounds, targeting HIF activity by various mechanisms of action. This study shows that the application of similar or diverse assay protocols allows to detect various influences on HIF heterodimerization as a key signaling event in the oxygen sensing pathway: increased HIF heterodimerization (roxadustat, MG-132), decreased HIF heterodimerization (PX-478, ibuprofen) and direct (HIF isoform-selective) heterodimerization inhibiting effects (PT-2385). Changes in treatment time and in the assay protocol allowed to assess direct and indirect effects on HIFα-HIFβ heterodimerization. In addition to the evaluation of applications of these new bioassays regarding pharmacological characterizations, benefits and considerations are discussed related to the use of cellular, luminescent-based bioassays. Briefly, benefits include the bidirectional nature of the biological readout, the upstream mechanism of detection, the differentiation between HIF1 and HIF2 effects and the simulation of various conditions. Specific and general considerations include cell-based, technical and disease/drug-related aspects (e.g., non-specific effects, color interference). In summary, the versatility of these bioassays offers benefits in widespread applications regarding drug discovery and pharmacological characterization of various therapeutics, applying either the same or optimized experimental protocols.

Multiple representation contrastive self-supervised learning for pulmonary nodule detection✰

Self-supervised learning aims to create semantic-enriched representation from unannotated data. A prevalent strategy in this field involves training a unified representation space that is invariant to various transformation combinations. However, creating a single invariant representation to multiple transformations poses several challenges. The efficacy of such a representation space depends on factors such as the intensity, sequence, and various combination scenarios of transformations. As a result, features generated in single representation space may exhibit limited adaptability for subsequent tasks. In contrast to the conventional SSL training approach, we introduce a novel method that involves constructing multiple atomic transformation-invariant representation subspaces. Each subspace in the proposed method is invariant to a specific atomic transformation from a predefined reference set. Our method offers increased flexibility by enabling the downstream task to weigh every atomic transformation-invariant subspace based on the desired feature space. A series of experiments were conducted to compare our approach to traditional self-supervised learning methods in order to assess its effectiveness. This evaluation encompassed diverse data regimes, datasets, evaluation protocols, and perspectives on source-destination data distribution. Our results highlight the superiority of our method compared to training strategies based on single transformation-invariant representation spaces. Additionally, our proposed method demonstrated superior performance in reducing false positives in the context of pulmonary nodule detection when compared to several recent supervised and self-supervised approaches.

The impact of multicentric datasets for the automated tumor delineation in primary prostate cancer using convolutional neural networks on 18F-PSMA-1007 PET

PurposeConvolutional Neural Networks (CNNs) have emerged as transformative tools in the field of radiation oncology, significantly advancing the precision of contouring practices. However, the adaptability of these algorithms across diverse scanners, institutions, and imaging protocols remains a considerable obstacle. This study aims to investigate the effects of incorporating institution-specific datasets into the training regimen of CNNs to assess their generalization ability in real-world clinical environments. Focusing on a data-centric analysis, the influence of varying multi- and single center training approaches on algorithm performance is conducted.MethodsnnU-Net is trained using a dataset comprising 161 18F-PSMA-1007 PET images collected from four distinct institutions (Freiburg: n = 96, Munich: n = 19, Cyprus: n = 32, Dresden: n = 14). The dataset is partitioned such that data from each center are systematically excluded from training and used solely for testing to assess the model's generalizability and adaptability to data from unfamiliar sources. Performance is compared through a 5-Fold Cross-Validation, providing a detailed comparison between models trained on datasets from single centers to those trained on aggregated multi-center datasets. Dice Similarity Score, Hausdorff distance and volumetric analysis are used as primary evaluation metrics.ResultsThe mixed training approach yielded a median DSC of 0.76 (IQR: 0.64–0.84) in a five-fold cross-validation, showing no significant differences (p = 0.18) compared to models trained with data exclusion from each center, which performed with a median DSC of 0.74 (IQR: 0.56–0.86). Significant performance improvements regarding multi-center training were observed for the Dresden cohort (multi-center median DSC 0.71, IQR: 0.58–0.80 vs. single-center 0.68, IQR: 0.50–0.80, p < 0.001) and Cyprus cohort (multi-center 0.74, IQR: 0.62–0.83 vs. single-center 0.72, IQR: 0.54–0.82, p < 0.01). While Munich and Freiburg also showed performance improvements with multi-center training, results showed no statistical significance (Munich: multi-center DSC 0.74, IQR: 0.60–0.80 vs. single-center 0.72, IQR: 0.59–0.82, p > 0.05; Freiburg: multi-center 0.78, IQR: 0.53–0.87 vs. single-center 0.71, IQR: 0.53–0.83, p = 0.23).ConclusionCNNs trained for auto contouring intraprostatic GTV in 18F-PSMA-1007 PET on a diverse dataset from multiple centers mostly generalize well to unseen data from other centers. Training on a multicentric dataset can improve performance compared to training exclusively with a single-center dataset regarding intraprostatic 18F-PSMA-1007 PET GTV segmentation. The segmentation performance of the same CNN can vary depending on the dataset employed for training and testing.

Quantification accuracy in photon-countingdetector CT for coronary artery calcium score: a pilot study.

To validate the accuracy of coronary artery calcium score (CACS) using photon-counting detector (PCD) CT under various scanning settings and explore the optimized scanning settings considering both the accuracy and the radiation dose. A CACS phantom containing six hollow cylindrical hydroxyapatite calcifications of two sizes with three densities and 12 patients underwent CACS scans. For PCD-CT, two scanning modes (sequence and flash [high-pitch spiral mode]) and five tube voltages (90kV, 120kV, 140kV, Sn100kV, and Sn140kV) at different image quality (IQ) levels were set for phantom, and patients were scanned with 120kV at IQ19 using flash mode. All acquisitions from PCD-CT were reconstructed at 70keV. Acquisitions in sequence mode at 120kV on an energy-integrating detector CT (EID-CT) was used as thereference. Agatston, mass, and volume scores were calculated. The CACS from PCD-CT exhibited excellent agreements with the reference (all intraclass correlation coefficient [ICC] > 0.99). The root mean square error (RMSE) between the Agatston score acquired from PCD-CT and the reference (5.4-11.5) was small. A radiation dose reduction (16-75%) from PCD-CT compared with the reference was obtained in all protocols using flash mode, albeit with IQ20 only at sequence mode (22-44%). For the patients, ICC ( all ICC > 0.98) and Bland-Altman analysis of CACS all showed high agreements between PCD-CT and the reference, without reclassifying CACS categories(P = 0.317). PCD-CT yields repeatable and accurate CACS across diverse scanning protocols according to our pilot study. Sn100kV, 90kV, and 120kV using flash mode at IQ20 are recommended for clinical applications considering both accuracy and radiation dose.

Can Complex Training Improve Acute and Long-Lasting Performance in Basketball Players? A Systematic Review

Basketball demands a sophisticated blend of tactical, technical, physical, and psychological skills, and various methods have been proposed to prepare players for these demands, including resistance training to enhance strength, power, speed, agility, and endurance. Complex training (CT) integrates diverse strength training methodologies by combining heavy-resistance exercises (e.g., squat at 90% of one repetition maximum) with high-velocity movements or plyometrics, both sharing the same biomechanical pattern. However, the optimal application of CT in basketball remains uncertain due to diverse protocols and a lack of consensus in the literature. The aim of this systematic review was to evaluate the acute and chronic effects of CT interventions on physical fitness performance in basketball players and identify the most effective characteristics of moderators. Methods: A bibliographic search was conducted using PubMed, SCOPUS, and Web of Science databases following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines using the PICOS strategy. Results: Fourteen studies met the inclusion criteria, three articles analyzed acute effects, and thirteen analyzed chronic effects. The total number of participants in the studies analyzing acute effects was 50, while for studies examining chronic effects, it was 362. Conclusions: Acutely, CT triggers post-activation potentiation and enhances sprint performance when coupled with brief rest intervals. Over time, these acute improvements contribute to more substantial, long-lasting benefits. Chronic effects of CT improve strength, as evidenced by enhanced 1 RM performance, jumps, sprints, and core muscle strength.

CleanUpRNAseq: An R/Bioconductor Package for Detecting and Correcting DNA Contamination in RNA-Seq Data.

RNA sequencing (RNA-seq) has become a standard method for profiling gene expression, yet genomic DNA (gDNA) contamination carried over to the sequencing library poses a significant challenge to data integrity. Detecting and correcting this contamination is vital for accurate downstream analyses. Particularly, when RNA samples are scarce and invaluable, it becomes essential not only to identify but also to correct gDNA contamination to maximize the data's utility. However, existing tools capable of correcting gDNA contamination are limited and lack thorough evaluation. To fill the gap, we developed CleanUpRNAseq, which offers a comprehensive set of functionalities for identifying and correcting gDNA-contaminated RNA-seq data. Our package offers three correction methods for unstranded RNA-seq data and a dedicated approach for stranded data. Through rigorous validation on published RNA-seq datasets with known levels of gDNA contamination and real-world RNA-seq data, we demonstrate CleanUpRNAseq's efficacy in detecting and correcting detrimental levels of gDNA contamination across diverse library protocols. CleanUpRNAseq thus serves as a valuable tool for post-alignment quality assessment of RNA-seq data and should be integrated into routine workflows for RNA-seq data analysis. Its incorporation into OneStopRNAseq should significantly bolster the accuracy of gene expression quantification and differential expression analysis of RNA-seq data.

Advancing IOT Interoperability: Dynamic Protocol Translation through Machine Learning For Enhanced Communication Efficiency

This study presents a pioneering methodology for Dynamic Protocol Translation in the Internet of Things (IoT), aiming to overcome challenges posed by diverse communication protocols among IoT devices. The primary objective is to develop a two-fold approach: first, acquiring data from IoT devices through their specific protocols, preprocessing it for consistency, and employing Natural Language Processing (NLP) techniques for semantic extraction and normalization; second, implementing a machine learning model, incorporating neural networks, to discern correlations between normalized representations and target protocol structures. The emphasis is on rigorous testing, validation, &amp; real-time translation capabilities. The main conclusions of the study demonstrate how well the suggested Logistic Regression model performed, with an accuracy of 96.76%, in contrast to an existing model (XML-JSON) that had an accuracy of 82.41%. The detailed evaluation metrics, which include F1 score, precision, and recall, demonstrate how well the suggested method works to solve protocol translation issues. The iterative feedback loop, real-time translation, and secure data transfer of the proposed system improve its adaptability and reliability. This research enhances the field of IoT communication by offering a comprehensive solution for smooth interoperability &amp; communication efficiency in a range of IoT applications.