Real-time Methods Research Articles

Parental Experiences of Administering Pediatric Tuina for Sleep and Appetite in Early School-Aged Children With Attention-Deficit/Hyperactivity Disorder: Qualitative Study in Hong Kong.

Previous research suggested that parent-administered pediatric tuina could improve symptoms of attention-deficit/hyperactivity disorder (ADHD), such as sleep quality and appetite. This study aimed to explore the experiences and perceptions of parents administering pediatric tuina to school-aged children with ADHD in Hong Kong. This qualitative study was embedded in a pilot randomized controlled trial on parent-administered pediatric tuina for improving sleep and appetite in school-aged children diagnosed with ADHD. Purposive sampling was used to invite 12 parents who attended a pediatric tuina training program and delivered the intervention to their children at home for at least 8 weeks. Data were collected through semistructured focus group interviews and individual interviews, which were audio-recorded, transcribed verbatim, and analyzed using thematic analysis. Two main themes emerged: (1) effects of parent-administered pediatric tuina and (2) parents' experience of administering pediatric tuina. Parents reported significant improvements in children's sleep quality, appetite, behavior, mental state, and academic performance. Facilitators provided professional guidance and applied a user-friendly course design. Challenges included difficulties in mastering techniques, locating acupuncture points, and time management. Participants suggested the need for more traditional Chinese medicine pattern diagnostic sessions, real-time supervision methods, and extended follow-up to better observe long-term effects. Parent-administered pediatric tuina was perceived to improve children's sleep quality and appetite significantly, along with other aspects of well-being. Professional guidance and a structured training program facilitated implementation, and challenges highlighted the need for more frequent diagnostic sessions, real-time supervision, and extended follow-up.

Online Bayesian State Estimation for Real-Time Monitoring of Growth Kinetics in Thin Film Synthesis.

Rapid validation of newly predicted materials through autonomous synthesis requires real-time adaptive control methods that exploit physics knowledge, a capability that is lacking in most systems. Here, we demonstrate an approach to enable real-time control of thin film synthesis by combining in situ optical diagnostics with a Bayesian state estimation method. We developed a physical model for film growth and applied the direct filter (DF) method for real-time estimation of nucleation and growth rates during pulsed laser deposition (PLD). We validated the approach using simulated and experimental reflectivity data for WSe2 growth and ultimately deployed the algorithm on an autonomous PLD system during the growth of 1T'-MoTe2. The DF robustly estimates growth parameters in real time at early stages of growth, down to 15% monolayer area coverage. This fusion of in situ diagnostics, data assimilation, and physical modeling opens new opportunities in adaptive control of synthesis trajectories toward desired material states.

Identifying Dietary Triggers Among Individuals with Overweight and Obesity: An Ecological Momentary Assessment Study

Background/Objectives: Excess adiposity, affecting 43% of the global adult population, is a major contributor to cardiometabolic diseases. Lifestyle behaviours, specifically dietary habits, play a key role in weight management. Real-time assessment methods such as Ecological Momentary Assessment (EMA) provide context-rich data that reduce recall bias and offer insights into dietary triggers and lapses. This study examines dietary triggers among adults with excess adiposity in Singapore using EMA, focusing on factors influencing dietary adherence and lapses. Methods: A total of 250 participants with a BMI ≥ 23 kg/m2 were recruited to track dietary habits for one week, at least three times a day, using the Eating Behaviour Lapse Inventory Survey Singapore (eBLISS) embedded within the Eating Trigger Response Inhibition Program (eTRIP© V.1) smartphone app. Logistic regression analysis was used to identify predictors of dietary adherence. Results: Of the 4708 responses, 76.4% of the responses were indicative of adherence to dietary plans. Non-adherence was primarily associated with food accessibility and negative emotions (stress, nervousness, and sadness). Factors such as meals prepared by domestic helpers and self-preparation were significantly associated with adherence. Negative emotions and premenstrual syndrome were identified as significant predictors of dietary lapses. Conclusions: EMA offers valuable insights into dietary behaviours by identifying real-time triggers for dietary lapses. Future interventions can utilise technology-driven approaches to predict and prevent lapses, potentially improving adherence and weight management outcomes.

295. Methods for Cost-efficient Real-time Whole Genome Sequencing Surveillance for Enhanced Detection of Outbreaks in a Hospital Setting

Abstract Background Healthcare associated infections (HAI) are common hospital complications associated with increased length of stay, economic burden, morbidity, and mortality. Traditionally, HAI outbreaks were detected using reactive whole genome sequencing (WGS); however, with costs decreasing over time, WGS has become a practical tool for the real time detection of nosocomial outbreaks. This study describes such methods, provides a cost analysis, and reviews notable outbreaks observed while performing real time WGS surveillance of HAIs. EDS-HAT real-time genomic surveillance methodsFigure 1:EDS-HAT real-time genomic surveillance methods A) collect list generation, B) sample collection C) sample processing, D) DNA extraction, E) Library preparation, F) WGS, and G-I) Data analysis, including determining SNPs between samples and infection clusters Methods The Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT) was devised to identify potential transmitted HAIs in real time among patients admitted to the hospital for ≥ 3 days or with a previous exposure in the prior 30-days (Fig 1). We performed a cost analysis for real time genomic HAI surveillance, based on an average cutoff of 35× coverage per genome, considering personnel, reagents, supplies, and WGS platform. We also considered maximum sample counts that could be sequenced on two platforms (MiSeq or NextSeq), based on genome size (Fig 2). Bioinformatic analyses were completed following WGS, and epidemiological links were investigated for isolates that clustered with ≤ 15 pair-wise SNP differences (C. diff ≤ 2 SNPs). Maximum Sample CountsFigure 2:Maximum number of genomes that can be run on a MiSeq v3 600 cycle flow cell and NextSeq500 v2.5 300 cycle flow cell based on size (Mb). Sample counts were calculated using Illumina coverage calculator based on 80× coverage criteria. Genome size of an average run by the MiGEL lab is shown in comparison Results From November 1, 2021 to October 31, 2023, we sequenced 4,723 isolates (weekly mean N=49). The per sample cost depended on platform used and sample count, totaling $49 or $19 for MiSeq (N=32) and NextSeq (N=96), respectively (Table 1). With the inclusion of personnel costs for 1 lab technician and 1 biostatistician (salary only, based on percent effort) the average weekly cost to run EDS-HAT was $4,293 (range $3,626-$4,758). We highlight multiple outbreaks involving a common hospital unit, equipment, or location (Table 2). Following Infection Prevention (IP) intervention, no additional cases were detected for these clusters on the associated routes. Cost per sample BreakdownTable 1.Cost per sample across MiSeq, NextSeq, and commercial WGS Conclusion We describe time- and cost-efficient methods for real time outbreak surveillance and detection using WGS. These genomic methods, as described in EDS-HAT, can be utilized with traditional epidemiological methods to guide hospital IP teams to mitigate and stop the spread of deadly transmitted HAIs. Notable OutbreaksTable 2.Description of notable outbreaks detected by real-time EDS-HAT HAI surveillance Disclosures Alexander Sundermann, DrPH, CIC, FAPIC, OpGen: Honoraria Lee Harrison, MD, GSK: Advisor/Consultant|Merck: Advisor/Consultant|Pfizer: Advisor/Consultant|Sanofi: Advisor/Consultant

Point Cloud-Based Deep Learning in Industrial Production: A Survey

With the rapid development of 3D acquisition technology, point clouds have received increasing attention. In recent years, point cloud-based deep learning has been applied to various industrial scenarios, promoting industrial intelligence. However, there is still a lack of review on the application of point cloud-based deep learning in industrial production. To bridge this gap and inspire future research, this paper provides a review of current point cloud-based deep learning methods applied to industrial production from the perspective of different application scenarios, including pose estimation, defect inspection, measurement and estimation, etc. Considering the real-time requirement of industrial production, this paper also summarizes real-time point cloud-based deep learning methods in each application scenario. Then, this paper introduces commonly used evaluation metrics and public industrial point cloud datasets. Finally, from the aspects of the dataset, speed and industrial product specificity, the challenges faced by current point cloud-based deep learning methods in industrial production are discussed, and future research directions are prospected.

Real-time CBCT imaging and motion tracking via a single arbitrarily-angled x-ray projection by a joint dynamic reconstruction and motion estimation (DREME) framework

Objective.Real-time cone-beam computed tomography (CBCT) provides instantaneous visualization of patient anatomy for image guidance, motion tracking, and online treatment adaptation in radiotherapy. While many real-time imaging and motion tracking methods leveraged patient-specific prior information to alleviate under-sampling challenges and meet the temporal constraint (<500 ms), the prior information can be outdated and introduce biases, thus compromising the imaging and motion tracking accuracy. To address this challenge, we developed a frameworkdynamicreconstruction andmotionestimation (DREME) for real-time CBCT imaging and motion estimation, without relying on patient-specific prior knowledge.Approach.DREME incorporates a deep learning-based real-time CBCT imaging and motion estimation method into a dynamic CBCT reconstruction framework. The reconstruction framework reconstructs a dynamic sequence of CBCTs in a data-driven manner from a standard pre-treatment scan, without requiring patient-specific prior knowledge. Meanwhile, a convolutional neural network-based motion encoder is jointly trained during the reconstruction to learn motion-related features relevant for real-time motion estimation, based on a single arbitrarily-angled x-ray projection. DREME was tested on digital phantom simulations and real patient studies.Main Results.DREME accurately solved 3D respiration-induced anatomical motion in real time (∼1.5 ms inference time for each x-ray projection). For the digital phantom studies, it achieved an average lung tumor center-of-mass localization error of 1.2 ± 0.9 mm (Mean ± SD). For the patient studies, it achieved a real-time tumor localization accuracy of 1.6 ± 1.6 mm in the projection domain.Significance.DREME achieves CBCT and volumetric motion estimation in real time from a single x-ray projection at arbitrary angles, paving the way for future clinical applications in intra-fractional motion management. In addition, it can be used for dose tracking and treatment assessment, when combined with real-time dose calculation.

Comparison of Real-Time Methods Demonstrating the Effects of Reduced Glutathione on Olfactory Neuroblasts

Comparison of Real-Time Methods Demonstrating the Effects of Reduced Glutathione on Olfactory Neuroblasts

A large geno-spatial cluster of multi-drug resistant tuberculosis outbreak in a western district of Thailand.

The growing issue of drug resistance, particularly multidrug-resistant TB (MDR-TB), has exacerbated this problem. The rise of drug resistance TB is a severe global health concern. In Thailand, a persistent community outbreak of primary MDR-TB has been confirmed in the Tha Maka district of Kanchanaburi province, with an increasing prevalence of MDR-TB among newly diagnosed pulmonary tuberculosis cases. It was the first site in Thailand where a cluster of MDR-TB, caused by the Asian African 3 Modern Beijing strain, and XDR-TB, caused by L2.1, outbreaks were reported. This study aims to assess the MDR-TB outbreak in detail by characterizing the genomic profiles of the prevalent MDR-TB strains and examining their geographical distribution within the affected district. Through whole-genome sequencing (WGS) and bioinformatic analysis of 188 MTB isolates, the study identified three major phylogenetic lineages: the East Asian lineage (L2, 92 %), the Indo-Oceanic lineage (L1, 5.9 %), and the Euro-American lineage (L4, 2.1 %). The detailed sub-lineage distribution offers valuable insights into the predominant genetic clusters of M. tuberculosis within the sampled population. Notably, Lineage 2, specifically the L2.2.M3 sub-lineage, stood out as the dominant strain of MDR-TB, accounting for 77.7 % of the isolates. This finding underscores the significant prevalence of the L2.2.M3 sub-lineage and its potential role in the local transmission dynamics of tuberculosis. The high proportion and genetic homogeneity of the L2.2.M3 cluster among MDR-TB patients may indicate the strain's adaptation for more effective transmission within the Thai population. The increasing prevalence of this pathogenic strain could significantly impact tuberculosis control programs. Early diagnosis and contact tracing with chemotherapeutic preventive therapy for MDR-TB will be essential in inhibiting the spread and reactivation of these strains. Additionally, further studies are needed to prospectively identify transmission routes through contact tracing and real-time genotypic methods. It will also be crucial to ensure that future vaccines and/or recommended chemoprophylaxis therapy for MDR-TB will provide protection against these emerging strains.

Real-Time Pharmacovigilance: Transforming Population-Based Monitoring of Post-Approval Vaccine Safety Through Rapid Cycle Analysis (RCA)-A Review of the Published Literature.

Background/Objectives: Rapid cycle analysis (RCA) is an established and efficient methodology that has been traditionally utilized by United States health authorities to monitor post-approval vaccine safety. Initially developed in the Vaccine Safety Datalink (VSD) in early 2000s, RCA has evolved into a valuable approach for timely post-approval signal detection. Due to the availability of additional near real-time data sources and enhanced analytic approaches, the use of RCA has expanded. This narrative review provides an in-depth assessment of studies that utilized RCA for safety surveillance to detect and evaluate safety signals in post-approval vaccine monitoring. Methods: Embase and Medline were searched on 8 August 2024 to identify post-approval non-interventional vaccine safety studies using RCA or other near real-time surveillance methods published from 1 January 2018 to 31 July 2024. Data on study characteristics (e.g., study population, data source, outcomes) and RCA methodological characteristics (e.g., type of comparator, sequential testing method, confounding control method) were extracted from the eligible RCA studies. Results: Of 1128 articles screened, 18 RCA vaccine safety studies were included, of which 17 (94.4%) were conducted in the United States (US). Twelve (67%) aimed at signal detection and six (33%) conducted further signal evaluation. Over 60% examined COVID-19 vaccine safety, with half using VSD. Over 80% conducted the RCA weekly or monthly and about 78% of the studies used a database-specific historical comparator group. Conclusions: This review indicates that most of the published articles on the application of the RCA methodology in vaccine safety studies are based on research conducted in the US. With increasing availability of near real-time data sources and advanced analytic methods capabilities, RCA is expected to be more widely deployed as an active surveillance tool to complement traditional pharmacovigilance. Future studies should explore the extension of vaccine RCA methodology for non-vaccine medicinal products.

Investigating the Effect of Syringe Infiltration on Nicotiana tabacum (Tobacco).

Plant infiltration techniques, particularly agroinfiltration, have transformed plant science and biotechnology by enabling transient gene expression for genetic engineering of plants or genomic studies. Recently, the use of infiltration has expanded to introduce nanomaterials and polymers in plants to enable nonnative functionalities. Despite its wide use, the impact of the infiltration process per se on plant physiology needs to be better understood. This study investigates the effect of syringe infiltration, a commonly employed technique in plants, using a typical infiltration buffer solution. Noninvasive and real-time monitoring methods, including high-resolution thermal imaging and a porometer/fluorometer, were used to study the physiological responses and stress levels of the infiltrated plants. Our results revealed localized cell damage at the infiltration site due to syringe compression, but the overall cell viability and tissue integrity were largely unaffected. Thermography showed a temporary temperature increase of the leaves and stomatal conductance alterations postinfiltration, with leaf recovery in 3-6 days. Additionally, fluorescence measurements indicated a 6% decrease in maximum quantum efficiency (F v/F m) and a 34% decrease in photosystem II (ΦPSII) quantum yield, persisting for 5 days after infiltration, suggesting sustained photosystem efficiency changes. Our work highlights the need to consider the effect of infiltration when performing biological studies and aims to facilitate the optimization of protocols commonly used in plant science and biotechnology.

Real-Time Monitoring of Metal Ions during the Molten Salt Electrolysis Process by Optical Emission Spectrometry Based on Microplasma.

Molten salt electrolysis has been widely used in the production and separation of metals, but it still lacks in situ real-time analysis methods to monitor the electrolysis process. In this work, a microplasma spectroscopic real-time analysis (MIPECA) system is developed based on noncontact direct current (DC) glow discharge. With the MIPECA system, the atomic emission spectroscopy of Li and K could be obtained in situ in LiCl-KCl molten salt, and the impact of different operating conditions on spectral signals was investigated. Then, the characteristic spectra of ten representative elements, including alkali metals (Na, Cs), alkaline earth metals (Mg, Ca, Sr, Ba), transition metals (Ag, Cu, Ni), and lanthanide metals (Ce), were all successfully excited for qualitative analysis. Under the optimal operating conditions, quantitative analysis of Ag, Cs, and Sr was achieved with high sensitivity and low limits of detection (LOD) of about 0.063, 0.021, and 0.073 wt %, respectively. Finally, the electrolysis process of Ag+ in LiCl-KCl molten salt was real-time monitored by using this MIPECA system, showing its application potential in molten salt electrolysis.

Impact of Nuclear Peripheral Chromatin Lamin LMNB1 Gene in the Proliferation and Migration of Glioma Cells.

The goal of this study is to explore the role of the LMNB1 gene in glioma. A cohort of 160 patients who underwent glioma surgery were randomly selected of this study. The LMNB1 expression was assessed employing immunohistochemical and real-time quantitative polymerase chain reaction methods. Initially, RNA interference technology was applied to suppress gene expression, followed by the evaluation of tumor cell proliferation, apoptosis, cell cycle dynamics, and migration. The underlying molecular mechanisms of LMNB1 function were examined by a human phospho-kinase array and immunoblotting. And we established the xenograft models to determine the effect of tumor growth as well as the degree of invasion in shLMNB1 mice. Elevated LMNB1 expression correlated with unfavorable overall survival and disease-free survival. A substantial inhibition in cell growth was observed subsequent to LMNB1 knockdown in SHG-44 and U251 glioma cells. SHG-44-shLMNB1 cells exhibited a reduction in the S phase population, along with an increase in cells in G1 and G2 phases. Similarly, shLMNB1 U251 cells showed fewer cells in the S phase and an elevation in cells in G1 phase. Notably, increased apoptosis was observed in U251-shLMNB1 cells and SHG-44-shLMNB1 cells. Wound healing and Transwell migration assays demonstrated a significant decrease in the migration rate of both SHG-44-shLMNB1 and U251-shLMNB1 cells. The phosphorylation levels of Akt1/2/3, as well as the expressions of PI3K, AKT, and p-AKT proteins, were reduced in the shLMNB1 group. Downregulation of LMNB1 repressed tumor progress in vivo. The silencing of LMNB1 was found to significantly reduce the proliferation of human glioma cells, induce apoptosis in tumor cells, impede the progression of the cell cycle, and inhibit the migration of tumor cells. Consequently, we hypothesize that LMNB1 promotes glioma cell proliferation through mechanisms involving the inhibition of tumor cell apoptosis, acceleration of the cell cycle, and enhancement of tumor cell migration. We found that LMNB1 exert critical roles in glioma progression may via regulation of PI3K/Akt signaling pathway. These observations suggest that LMNB1 holds clinical potential for diagnostic and prognostic applications in glioma, presenting novel targets for drug development.

Inter-rater agreement of trained and untrained observers in estimating plate waste using digital method

Most hospitals in Indonesia use real-time visual methods to measure the amount of inpatient plate waste. However, research showed the reliability of the digital method in estimating plate waste in hospital and school food service settings abroad. This study aimed to investigate the inter-rater agreement of two trained observers and mix-trained and untrained observers in measuring inpatient plate waste using the digital method. A total of 104 trays of inpatient lunch were collected. Each food item (rice, animal protein side dishes, plant-based protein side dishes and vegetables) was photographed before and after patient consumption. A trained observer weighed each item of food as gold. Then, two trained observers estimated the amount of plate waste from each food item image. In addition, an untrained observer also has the same procedures as the trained observer did by the digital method. This study showed the kappa of the trained observers was 0.84 for rice, 0.85 for animal protein side dishes, 0.77 for plant-based protein side dishes, and 0.65 for vegetables. On the other hand, the kappa of the trained and untrained observer was 0.62 for rice, 0.53 for animal protein side dishes, 0.67 for plant-based protein side dishes, and 0.49 for vegetables. In conclusion, the agreement between the two trained observers was higher. Meanwhile, the agreement between a trained observer and an untrained observer tends to be lower.

Radar and communication integration under FPGA platform: real-time waveform generation with time division multiplexing and waveform sharing strategy

Abstract The integration of radar and communication technologies is gaining prominence in electronic information systems. This paper explores advanced waveform strategies—LFM (Linear Frequency Modulation) + MSK (Minimum Shift Keying) based on time-division multiplexing and LFM-MSK utilizing waveform sharing. These methods enhance spectrum efficiency and system capabilities in detecting targets and transmitting data reliably amidst complex environments. Based on the FPGA (Field Programmable Gate Array) platform, this study proposes real-time waveform generation methods for integrated radar communication waveforms, specifically LFM+MSK and LFM-MSK. It focuses on discussing FPGA implementation solutions for these methods and validates their effectiveness. This research addresses a critical gap in the field and provides practical insights for radar communication integration systems.

Vulnerability and Malware Detection

The increasingly linked digital world oftoday has made cybersecurity a top priority. The rise in cyber dangers, notably vulnerabilities and malware, poses major risks to individuals, organizations, and governments. This review article offers a thorough analysis of the approachesand instruments currently in use for malware and vulnerability detection. It examines and contrasts several detection methods, such as machine learning techniques, static and dynamic analysis, and signature-based detection. The study also examines new developments in the sector, such as the application of big data analytics and artificial intelligence (AI) to the detection of complex threats. It also covers the need for real-time detection methods, changing threat environments, and the difficulties posed by false positives.

An Adaptive Multimodal Fusion 3D Object Detection Algorithm for Unmanned Systems in Adverse Weather

Unmanned systems encounter challenging weather conditions during obstacle removal tasks. Researching stable, real-time, and accurate environmental perception methods under such conditions is crucial. Cameras and LiDAR sensors provide different and complementary data. However, the integration of disparate data presents challenges such as feature mismatches and the fusion of sparse and dense information, which can degrade algorithmic performance. Adverse weather conditions, like rain and snow, introduce noise that further reduces perception accuracy. To address these issues, we propose a novel weather-adaptive bird’s-eye view multi-level co-attention fusion 3D object detection algorithm (BEV-MCAF). This algorithm employs an improved feature extraction network to obtain more effective features. A multimodal feature fusion module has been constructed with BEV image feature generation and a co-attention mechanism for better fusion effects. A multi-scale multimodal joint domain adversarial network (M2-DANet) is proposed to enhance adaptability to adverse weather conditions. The efficacy of BEV-MCAF has been validated on both the nuScenes and Ithaca365 datasets, confirming its robustness and good generalization capability in a variety of bad weather conditions. The findings indicate that our proposed algorithm performs better than the benchmark, showing improved adaptability to harsh weather conditions and enhancing the robustness of UVs, ensuring reliable perception under challenging conditions.

A Novel Technique for Monitoring Carbonate and Scale Precipitation Using a Batch-Process-Based Hetero-Core Fiber Optic Sensor.

Techniques for monitoring calcium carbonate and silica deposits (scale) in geothermal power plants and hot spring facilities using fiber optic sensors have already been reported. These sensors continuously measure changes in light transmittance with a detector and, when applied to field tests, require the installation of a power supply and sensor monitoring equipment. However, on some sites, a power supply may not be available, or a specialist skilled in handling scale sensors is required. To overcome this problem, we have developed a method for evaluating scale formation that is based on a batch process that can be used by anyone. In brief, this method involves depositing scale on a section of the optical fiber sensor and then fusing this section to the optical fiber and measuring it. Using this sensor, a technician in the field can simply place the sensor in the desired location, collect the samples at any given time, and send them to the laboratory to measure their transmittance. This simple and easy method was achieved by using a hetero-core type of fiber optic. This evaluation method can measure with the same sensitivity as conventional real-time methods, while its transmittance response for the sensor corresponds to the saturation index (SI) changes in the scale components in the solution due to increases in temperature and concentration. In the field of carbon dioxide capture and storage (CCS), this evaluation method can be used to quantitatively measure the formation of carbonate minerals, and it can also be used as an indicator for determining the conditions for CO2 mineral fixation, as well as in experiments using batch-type autoclaves in laboratory testing. It is also expected to be used in geothermal power plants as a method for evaluating scale formation, such as that of amorphous silica, and to protect against agents that hinder stable operation.

A Real-Time Accounting Method for Carbon Dioxide Emissions in High-Energy-Consuming Industrial Parks

Industrial parks play a crucial role in carbon control and reduction. With energy-intensive enterprises at their core, such parks feature highly concentrated carbon emission sources and a significant demand for carbon trading. Nonetheless, the absence of precise and real-time carbon accounting methods makes it difficult for them to effectively manage and regulate carbon emissions. The real-time accounting methods for carbon dioxide emissions in high-energy-consuming industrial parks urgently need further study. Therefore, this paper initially examines three areas—fuel combustion, industrial engineering, and electricity usage—and proposes a real-time framework to account for carbon dioxide emissions in high-energy industrial parks. Secondly, it extracts real-time elements from each part and proposes a real-time carbon dioxide emission accounting method tailored to the actual needs of high-energy-consuming industrial parks. Finally, an empirical analysis is carried out on an aluminum production park as an example to verify the feasibility and effectiveness of the real-time accounting method for carbon dioxide emissions in high-energy-consuming industrial parks.

The TROG 15.01 stereotactic prostate adaptive radiotherapy utilizing kilovoltage intrafraction monitoring (SPARK) clinical trial database.

The US National Institutes of Health state that Sharing of clinical trial data has great potential to accelerate scientific progress and ultimately improve public health by generating better evidence on the safety and effectiveness of therapies for patients (https://www.ncbi.nlm.nih.gov/books/NBK285999/ accessed 2024-01-24.). Aligned with this initiative, the Trial Management Committee of the Trans-Tasman Radiation Oncology Group (TROG) 15.01 Stereotactic Prostate Adaptive Radiotherapy utilizing Kilovoltage intrafraction monitoring (KIM) (SPARK) clinical trial supported the public sharing of the clinical trial data. The data originate from the TROG 15.01 SPARK clinical trial. The SPARK trial was a phase II prospective multi-institutional clinical trial (NCT02397317). The aim of the SPARK clinical trial was to measure the geometric and dosimetric cancer targeting accuracy achieved with a real-time image-guided radiotherapy technology named KIM for 48 prostate cancer patients treated in 5 treatment sessions. During treatment, real-time tumor translational and rotational motion were determined from x-ray images using the KIM technology. A dose reconstruction method was used to evaluate the dose delivered to the target and organs-at-risk. Patient-reported outcomes and toxicity data were monitored up to 2 years after the completion of the treatment. The dataset contains planning CT images, treatment plans, structure sets, planned and motion-included dose-volume histograms, intrafraction kilovoltage, and megavoltage projection images, tumor translational and rotational motion determined by KIM, tumor motion ground truth data, the linear accelerator trajectory traces, and patient treatment outcomes. The dataset is publicly hosted by the University of Sydney eScholarship Repository at https://doi.org/10.25910/qg5d-6058. The 3.6Tb dataset, with approximately 1 million patient images, could be used for a variety of applications, including the development of real-time image-guided methods, adaptation strategies, tumor, and normal tissue control modeling, and prostate-specific antigen kinetics.

Multisite community-scale monitoring of respiratory and enteric viruses in the effluent of a nursing home and in the inlet of the local wastewater treatment plant.

The aim of this study was to evaluate whether community-level monitoring of respiratory and enteric viruses in wastewater can provide a comprehensive picture of local virus circulation. Wastewater samples were collected weekly at the wastewater treatment plant (WWTP) inlet and at the outlet of a nearby nursing home (NH) in Burgundy, France, during the winter period of 2022/2023. We searched for the pepper mild mottle virus as an indicator of fecal content as well as for the main respiratory viruses [severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza, and respiratory syncytial virus] and enteric viruses (rotavirus, sapovirus, norovirus, astrovirus, and adenovirus). Samples were analyzed using real-time reverse transcription PCR-based methods. SARS-CoV-2 was the most frequently detected respiratory virus, with 66.7% of positive samples from the WWTP and 28.6% from the NH. Peaks of SARS-CoV-2 were consistent with the chronological incidence of infections recorded in the sentinel surveillance and the nearby hospital databases. The number of positive samples was lower in the NH than in WWTP for the three respiratory viruses. Enteric viruses were frequently detected, most often sapovirus and norovirus genogroup II, accounting both for 77.8% of positive samples in the WWTP and 57.1% and 37%, respectively, in the NH. The large circulation of sapovirus was unexpected in particular in the NH. Combined wastewater surveillance using simple optimized methods can be a valuable tool for monitoring viral circulation and may serve as a suitable early warning system for identifying both local outbreaks and the onset of epidemics. These results encourage the application of wastewater-based surveillance (WBS) to SARS-CoV2, norovirus, and sapovirus.IMPORTANCEWBS provides valuable information on the spread of epidemic viruses in the environment using appropriate and sensitive detection methods. By monitoring the circulation of viruses using reverse transcription PCR methods in wastewater from the inlet of a wastewater treatment plant and the outlet of a nearby retirement home (connected to the same collective sewer network), we aimed to demonstrate that implementing combined WBS at key community sites allows effective detection of the occurrence of respiratory (influenza, respiratory syncytial virus, and SARS-CoV-2) and enteric (norovirus, rotavirus, and sapovirus) virus infections within a given population. This analysis on a localized scale provided new information on the viral circulation in the two different sites. Implementing WBS to monitor the circulation or the emergence of infectious diseases is an important means of alerting the authorities and improving public health management. WBS could participate actively to the health of humans, animals, and the environment.