Large Variations Research Articles

Comparative performance of YOLOv8, YOLOv9, YOLOv10, YOLOv11 and Faster R-CNN models for detection of multiple weed species

Weeds pose a serious production challenge in various agronomic crops by reducing their grain yields. Increasing cases of herbicide-resistant (HR) weed populations further exacerbate the problem. Future weed control tactics require the integration of non-chemical and reduced chemical-based strategies that can target site- and specie-specific weed management (SSSWM). Advanced machine learning technology has the potential to localize and detect weed seedlings to implement SSSWM. However, due to large biological variability among various weed species and environmental conditions where they grow, accurate and precise weed detection remains challenging. The main objectives of this research were to (1) develop an annotated image database of cocklebur (Xanthium strumarium L.), dandelion (Taraxacum officinale), common waterhemp (Amaranthus tuberculatus), Palmer amaranth (Amaranthus palmeri) and common lambsquarters (Chenopodium album L.), and (2) investigate the comparative performance (speed and accuracy) of YOLOv8, YOLOv9, YOLOv10, YOLOv11 and Faster R-CNN algorithms in detecting those weed species. A weed dataset with bounding box annotations for each weed species was created, consisting of images collected under variable field conditions which were preprocessed and augmented to create a dataset of 2348 color images. The YOLOv8, YOLOv9, YOLOv10, YOLOv11 and Faster R-CNN were trained using the annotated weed image database to detect each weed species. Results indicated that YOLOv11 was the fastest model with inference time of 13.5 milliseconds (ms) followed by YOLOv8 and YOLOv10 with inference time 23 and 19.3 milliseconds (ms), respectively. The YOLOv9 had the highest accuracy in detecting different weed species with an overall mean average precision (mAP@0.5) of 0.935. In contrast, the Detectron2 with Fast R-CNN configuration provided mAP@0.5 of 0.821 with an inference time of 63.8 ms. These results suggest that the YOLO series algorithms have the potential for real-time deployment for weed species detection more accurately and faster than Faster R-CNN in agricultural fields.

Defining the potential for sexual structures-sparing for prostate cancer external beam radiotherapy: A dosimetric study

PurposeThe purpose of the study was to evaluate the dosimetric impact of sexual-sparing radiotherapy for prostate cancer, with magnetic resonance-only treatment planning. Material and methodsFifteen consecutive patients receiving prostate cancer radiotherapy were selected. A synthetic CT was generated with a deep learning method from each T2-weighted MRI performed at the time of treatment planning. For each patient, two plans were performed: standard treatment planning and sexual-structures sparing treatment planning. The treatment plan was designed to deliver a dose of 78Gy to the prostate and 50Gy to the seminal vesicles in 2Gy daily fractions, using volumetric arc therapy. Dose–volume histograms were computed to compare treatment plans. ResultsAll plans fulfilled dosimetric objectives and were equivalent regarding planning target volume coverage. The doses delivered to both rectum, bladder, and femoral heads were similar between plans (P=0.20). Sexual-sparing plans enabled to decrease all dosimetric parameters on sexual organs-at-risk. The mean penile bulb dose in sexual-sparing plans was significantly reduced (21.1Gy±20.7 versus 13.4Gy±14.0, P<0.01), however with large variability observed between individuals. The mean dose delivered to the corpora cavernosa was also significantly reduced within sexual-sparing plans (13.1Gy±16.7 versus 8.6Gy±10.4, P<0.01). A significant reduction was also observed in the highest doses delivered to internal pudendal arteries (D10%: 48.4Gy±8.3 versus 33.1Gy±4.6, P<0.05; D5%: 52.0Gy±8.7 versus 36.8Gy±5.5, P<0.05). ConclusionSparing of sexual structures appears feasible, without compromising neither planning target volume coverage nor doses delivered to non-sexual organs at risk. The clinical significance of this dose-reduction requires prospective evaluation.

Characterization of soil organic carbon at profile scale in two forest soils under pine and holm oak

Context It is essential to promote soil carbon sequestration as a means to mitigate climate change. Thus, it is fundamental to know the distribution of C in the soil at profile scale, the characteristics of humic substances as indicators of soil organic matter (SOM) turnover and their relationship with other soil properties. Aims Two forest ecosystems under pine and holm oak developed under a Meso-Mediterranean climate in Spain were used to characterize SOM through the complete sequence of layers of the soil profile. Methods General soil analysis, infrared spectroscopic analysis and soil color measurements were conducted for the characterization. Key results Humus form under oak was found to be Mull mesotrophic–Mull acid while humus under pine Moder oligotrophic. The infrared spectrum determined that oxidation of the humic acids was more complete in the deeper horizons. Relationships between intensities of the main spectral bands in both soils followed similar maximum and minimum sequence values. The total humic extract (THE) color measured by reflection was found inversely related to the THE color measured by transmission. In the same way, the color spectrum between 350 and 800 nm in the THE showed an inverse relation between hue/brightness and absorbance values. Conclusions Infrared analysis and color measurements provided evidence of a different level of stabilization of humic substances from each soil, and between the different horizons. Large spatial variability in soil organic carbon quantity and quality was observed. Implications Better understanding of carbon sequestration behavior in different soil ecosystems in its crucial role within the global carbon cycle

The international multi-system OSEs/OSSEs by the UN Ocean Decade Project SynObs and its early results

“Synergistic Observing Network for Ocean Prediction (SynObs)” was launched in 2022 as a project of the United Nations Decade of Ocean Science for Sustainable Development to evaluate the importance of ocean observation systems and co-design the future evolution of the ocean observing network. SynObs is currently leading the flagship OSEs/OSSEs, an internationally coordinated activity in which observing system experiments (OSEs) and observing system simulation experiments (OSSEs) are conducted using a variety of ocean and coupled atmosphere–ocean prediction systems to evaluate ocean observation impacts consistent across most prediction systems. The flagship OSEs/OSSEs comprises the ocean prediction (OP) OSEs for high-resolution ocean predictions, the subseasonal-to-seasonal (S2S) OSEs for long-term lead-time coupled ocean–atmosphere predictions, and the OP OSSEs for evaluating new and future observing systems. SynObs plans to use the results of the flagship OSEs to contribute to the reports on the ocean observing network design made by international organizations and projects. Here, we introduce this initiative, and we report on some initial results. Some observation impacts consistent across four ocean prediction systems are found by a preliminary analysis of the analysis runs for the OP OSEs. For example, impacts of the altimetry data on the assimilated sea surface height (SSH) field are generally large in the westerly boundary current regions and around Antarctic Circumpolar Currents where SSH has large variability but are small in the tropical regions, despite the relatively large SSH variability there. The analysis also indicates the possibility that there are some characteristic differences in the observation impacts between low-resolution and eddy-resolving ocean prediction systems. Although OSE outputs of only four ocean prediction systems are available now, we will make further investigation, adding OSE outputs of other prediction systems that will be submitted in the near future.

Measuring the diagnostic management and follow-up imaging for glioma patients across Belgian hospitals between 2016 and 2019.

This study aimed to assess the diagnostic management and follow-up imaging for glioma patients across Belgian hospitals by calculating process indicators. Patients with newly diagnosed glioma in Belgium (2016-2019) were selected from the Belgian Cancer Registry. The National Social Security Number served as unique patient identifier, linking the Registry to vital status and reimbursement data. Nine measurable process related to diagnosis and follow-up imaging were identified, with reformulations for 7 due to data limitations. For each indicator, technical documentation sheets, containing all required details (rationale, numerator and denominator, target, limitations, benchmarking, subgroup analyses) were developed, reviewed by a multidisciplinary expert panel, and validated in six pilot hospitals. Per indicator, patients were assigned to the most relevant hospital per indicator using allocation algorithms. Results for process indicators assessing MRI use in glioma diagnosis and follow-up aligned with predefined targets (90%), except for early postoperative MRI (48.5% vs. target 90%). Mandatory reporting of the WHO performance status (89.3% vs. target 100%) and performance of full-spine (43.6% vs. target 90%) and follow-up MRI (73.5% vs. target 90%) in ependymoma were suboptimal. The largest variability across centers was noted for the indicator on early postoperative MRI. This calculation of process indicators identified opportunities for improvement in diagnosis and follow-up imaging for glioma patients in Belgium. Monitoring indicator results and providing individual feedback reports to the Belgian hospitals invites neuro-oncology care teams and hospital managements to reflect on their results and to take measures to continuously improve care for glioma.

Gas Transfer Across Air‐Water Interfaces in Inland Waters: From Micro‐Eddies to Super‐Statistics

AbstractIn inland water covering lakes, reservoirs, and ponds, the gas exchange of slightly soluble gases such as carbon dioxide, dimethyl sulfide, methane, or oxygen across a clean and nearly flat air‐water interface is routinely described using a water‐side mean gas transfer velocity , where overline indicates time or ensemble averaging. The micro‐eddy surface renewal model predicts , where is the molecular Schmidt number, is the water kinematic viscosity, and is the waterside mean turbulent kinetic energy dissipation rate at or near the interface. While has been reported across a number of data sets, others report large scatter or variability around this value range. It is shown here that this scatter can be partly explained by high temporal variability in instantaneous around , a mechanism that was not previously considered. As the coefficient of variation in increases, must be adjusted by a multiplier that was derived from a log‐normal model for the probability density function of . Reported variations in with a macro‐scale Reynolds number can also be partly attributed to intermittency effects in . Such intermittency is characterized by the long‐range (i.e., power‐law decay) spatial auto‐correlation function of . That varies with a macro‐scale Reynolds number does not necessarily violate the micro‐eddy model. Instead, it points to a coordination between the macro‐ and micro‐scales arising from the transfer of energy across scales in the energy cascade.

Influence of graphene nanoplatelets on the passivation and corrosion resistance of carbon steel bars in ordinary portland cement mortar

This study investigates the influence of adding graphene nanoplatelets (GNPs) to mortar mixtures on the corrosion behavior of embedded carbon steel rebars. Mortar mixtures were prepared with a water-to-cement ratio of 0.42 and GNP replacement levels of 0, 0.05, and 1 percent by weight of cement. After 28 days of curing in an environmental chamber, the passivity and kinetics of chloride induced corrosion initiation for the rebars were studied using a suite of electrochemical tests. The results suggest that incorporating GNPs at low concentrations may enhance the passivity and resistance to chloride-induced corrosion of carbon steel rebars; however, due to the large variability in the data, the findings are inconclusive. Potentiodynamic measurements revealed that the estimated average polarization resistance of steel rebars in 0 G (no GNP), 0.05 G, and 1 G specimens in the passive state was on average −182.3, −276.5, and −304.2 kΩ, respectively. Upon exposure to chloride solution, 0 G specimens, with an average corrosion potential of −600 mV vs. SCE, exhibited the lowest resistance, whereas 0.05 G specimens, with an average of −491 mV vs. SCE, showed the highest corrosion resistance. Results from electrical impedance spectroscopy were consistent with the potentiodynamic findings. Surface characterization also revealed that the inclusion of GNPs does not alter the surface roughness of the interface between steel rebar and paste. However, the interface between rebar ribs and mortar paste had higher surface roughness and porosity in all specimens regardless of GNPs replacement level. Visual inspections confirmed that the area around the rebar ribs at the interface is more conducive to the formation of crevice and pitting corrosion.

Scaling and Depth Variability of Source Parameters in Central and Southern Italy Using Regional Attenuation Models

ABSTRACT We study the scaling between seismic moment and corner frequency, and the spatial variability of the stress drop, in central and southern Italy. We analyze a data set generated by 28,943 earthquakes that occurred between 2005 and 2023 recorded by 1045 temporary or permanent stations. Considering the trade-off between source and propagation effects, we develop a spectral decomposition approach in which several attenuation models are derived for different subregions, and we perform an iterative decomposition to propagate the site amplification constraint to the whole data set. The use of multiple attenuation models has a significant impact on the source parameter estimation, resulting in stress-drop ratios on the order of a factor of 10 compared to values obtained without considering the attenuation differences. Once the attenuation variability is taken into account, the scaling between seismic moment and corner frequency shows different slopes for different subregions, with different degrees of self-similarity breakdown. Furthermore, we observe a clear trend in the slopes with depth for several subregions. Finally, the spatial variability of the stress-drop anomalies with respect to the average values expected for a given seismic moment shows a large lateral variability, which makes it difficult to detect increasing stress-drop trends with depth.

A newton interpolation network for smoke semantic segmentation

Smoke has large variances of visual appearances that are very adverse to visual segmentation. Furthermore, its semi-transparency often produces highly complicated mixtures of smoke and backgrounds. These factors lead to great difficulties in labelling and segmenting smoke regions. To improve accuracy of smoke segmentation, we propose a Newton Interpolation Network (NINet) for visual smoke semantic segmentation. Unlike simply concatenating or point-wisely adding multi-scale encoded feature maps for information fusion or re-usage, we design a Newton Interpolation Module (NIM) to extract structured information by analyzing the feature values in the same position but from encoded feature maps with different scales. Interpolated features by our NIM contain long-range dependency and semantic structures across different levels, but traditional fusion of multi-scale feature maps cannot model intrinsic structures embedded in these maps. To obtain multi-scale structured information, we repeatedly use the proposed NIM at different levels of the decoding stages. In addition, we use more encoded feature maps to construct a higher order Newton interpolation polynomial for extracting higher order information. Extensive experiments validate that our method significantly outperforms existing state-of-the-art algorithms on virtual and real smoke datasets, and ablation experiments also validate the effectiveness of our NIMs.

Out-of-pocket costs for patients diagnosed with high-grade glioma and their carers

Abstract Background This study aimed to describe the out-of-pocket costs incurred by patients diagnosed with high-grade glioma and their carers in the standard care arm of the Care-IS trial in the six to eight months following their diagnosis. Methods Carers completed monthly cost surveys detailing the out-of-pocket costs incurred by patients and carers over a six-month period. Seventy carers reported out-of-pocket costs at baseline (within two months following patient diagnosis), and a maximum of 50% of participants reported costs in any subsequent month. Costs were adjusted to 2023 AUD and reported as medians with interquartile range. Demographic factors were assessed to determine if any were significantly associated with being in the first or fourth quartile of total out-of-pocket costs at baseline. Results Median monthly costs for patient-carer dyads were highest at baseline ($535(IQR:$170-$930)), and two months post-recruitment ($314 (IQR:$150-$772)). The largest contributors to patient-carer costs were patient health service use and patient medications. Patient and carer health service use and medication costs varied over time. The median health service use and medication out-of-pocket costs for patients and carers were mostly below $100 per month, however, there was large variance in the upper 75th percentile for these cost categories. No factors were significantly associated with higher baseline out-of-pocket costs. Conclusion A HGG diagnosis has a significant and sustained financial impact on people who are diagnosed and their carers. Patients experience significant additional costs relating to their diagnosis and travel to receive care, and their carers also continue to experience sustained costs whilst managing the additional tasks associated with informal caregiving.

The Variability of the Salivary Antimicrobial Peptide Profile: Impact of Lifestyle.

Saliva is crucial in maintaining oral health; its composition reflects the body's physiological and diseased state. Among salivary components, antimicrobial peptides (AMPs) stand out for their broad antimicrobial activities and role in modulating the oral microbiota and innate immune response. Local and systemic diseases can affect the levels of AMPs in saliva, making them attractive biomarkers. However, the large variability in their concentrations hampers their use in diagnostics. Knowledge of the various factors influencing the profile of salivary AMPs is essential for their use as biomarkers. Here, we examine how lifestyle factors such as physical activity, dietary supplementation, tobacco smoking, and psychological stress impact salivary AMP levels. By understanding these sources of variability, we can take a step forward in using AMPs for diagnostics and prognostics and develop new tailored and preventative approaches.

Subject-aware PET Denoising with Contrastive Adversarial Domain Generalization.

Recent advances in deep learning (DL) have greatly improved the performance of positron emission tomography (PET) denoising performance. However, DL model performance can vary a lot across subjects, due to the large variability of the count levels and spatial distributions. A generalizable DL model that mitigates the subject-wise variations is highly expected toward a reliable and trustworthy system for clinical application. In this work, we propose a contrastive adversarial learning framework for subject-wise domain generalization (DG). Specifically, we configure a contrastive discriminator in addition to the UNet-based denoising module to check the subject-related information in the bottleneck feature, while the denoising module is adversarially trained to enforce the extraction of subject-invariant features. The sampled low-count realizations from the list-mode data are used as anchor-positive pairs to be close to each other, while the other subjects are used as negative samples to be distributed far away. We evaluated on 97 18F-MK6240 tau PET studies, each having 20 noise realizations with 25% fractions of events. Training, validation, and testing were implemented using 1400, 120, and 420 pairs of 3D image volumes in a subject-independent manner. The proposed contrastive adversarial DG demonstrated superior denoising performance than conventional UNet without subject-wise DG and cross-entropy-based adversarial DG.

Using Overlap Weights to Address Extreme Propensity Scores in Estimating Restricted Mean Counterfactual Survival Times.

While inverse probability of treatment weighting (IPTW) is a commonly used approach for treatment comparisons in observational data, the resulting estimates may be subject to bias and excessively large variance under lack of overlap. By smoothly down-weighting units with extreme propensity scores, i.e., those that are close (or equal) to zero or one, overlap weighting (OW) can help mitigate the bias and variance issues associated with IPTW. Although theoretical and simulation results have supported the use of OW with continuous and binary outcomes, its performance with survival outcomes remains to be further investigated, especially when the target estimand is defined based on the restricted mean survival time (RMST). We combine propensity score weighting and inverse probability of censoring weighting to estimate the restricted mean counterfactual survival times, and provide computationally-efficient variance estimators when the propensity scores are estimated by logistic regression and the censoring process is estimated by Cox regression. We conduct simulations to compare the performance of weighting methods in terms of bias, variance, and 95% interval coverage, under various degrees of overlap. Under moderate and weak overlap, we demonstrate the advantage of OW over IPTW, trimming and truncation, with respect to bias, variance, and coverage when estimating RMST.

Costs Analysis of Kidney Transplantation in Spain: Differences Between Regional Health Services

Background: For our society, chronic kidney disease is a major public health problem associated with high mortality, morbidity, reduced quality of life and a progressive increase in health costs. The aim of this study was to analyze and compare the current cost of kidney transplantation (KT) and kidney–pancreas transplantation (KPT) among the different Regional Health Services (RHS) in Spain. Methods: A descriptive comparative study analyzing the public prices of RHS in Spain. The Official Gazette of the different communities was consulted, where the latest available order on this type of cost was sought. A descriptive analysis was made of the stipulated cost of the KT and KPT, for each degree of severity, RHS, year of publication and cost calculation method. Mean cost and standard deviation were calculated. Results: KT prices were found for 15 of the 18 RHS (83.33%). The average cost of KT in Spain was €33,926.53 ± 6950.053 (range from €23,140.37 in the Canary Islands to €48,205.75 in Catalonia). For KPT, costs were found for 5 of the 18 RHS (27.8%). The mean cost of KPT was €65,792.38 ± 11,273.12 (ranging from €49,418.81 in Navarra to €78,363.20 in Andalusia). Conclusions: There is a large variability in KT and KPT costs in Spain between RHS. Our study underlines the importance of adopting standardized and updated costing methods for KT and KPT.

Strong Subseasonal Variability of Oxic Methane Production Challenges Methane Budgeting in Freshwater Lakes.

Methane (CH4) accumulation in the well-oxygenated lake epilimnion enhances the diffusive atmospheric CH4 emission. Both lateral transport and in situ oxic methane production (OMP) have been suggested as potential sources. While the latter has been recently supported by increasing evidence, quantifying the exact contribution of OMP to atmospheric emissions remains challenging. Based on a large high-resolution field data set collected during 2019-2020 in the deep stratified Lake Stechlin and on three-dimensional hydrodynamic modeling, we improved existing CH4 budgets by resolving each component of the mass balance model at a seasonal scale and therefore better constrained the residual OMP. All terms in our model showed a large temporal variability at scales from intraday to seasonal, and the modeled OMP was most sensitive to the surface CH4 flux estimates. Future efforts are needed to reduce the uncertainties in estimating OMP rates using the mass balance approach by increasing the frequency of atmospheric CH4 flux measurements.

Voltage-Gated Ion Channel Compensatory Effect in DEE: Implications for Future Therapies.

Developmental and Epileptic Encephalopathies (DEEs) represent a clinically and genetically heterogeneous group of rare and severe epilepsies. DEEs commonly begin early in infancy with frequent seizures of various types associated with intellectual disability and leading to a neurodevelopmental delay or regression. Disease-causing genomic variants have been identified in numerous genes and are implicated in over 100 types of DEEs. In this context, genes encoding voltage-gated ion channels (VGCs) play a significant role, and part of the large phenotypic variability observed in DEE patients carrying VGC mutations could be explained by the presence of genetic modifier alleles that can compensate for these mutations. This review will focus on the current knowledge of the compensatory effect of DEE-associated voltage-gated ion channels and their therapeutic implications in DEE. We will enter into detailed considerations regarding the sodium channels SCN1A, SCN2A, and SCN8A; the potassium channels KCNA1, KCNQ2, and KCNT1; and the calcium channels CACNA1A and CACNA1G.

Climate-adaptative management strategies for soybean production under ENSO scenarios in Southern Brazil: An in-silico analysis of crop failure risk

CONTEXTSoybeans (Glycine max L.) are a crucial crop for global food security, and the state of Rio Grande do Sul (RS), Brazil, plays a significant role. However, climate instability, particularly water stress (WS), is a major concern in this region, causing large interannual yield variability. OBJECTIVEThis study aims to address this issue using an in-silico approach to: i) characterize WS and spatial patterns and their frequency within ENSO events; and ii) explore climate-adaptative management strategies such as planting dates and maturity groups to mitigate the risk of crop failure and maximize seed yield and profits. METHODSCrop growth simulations were performed testing three soybean maturity groups (MG, 5.0, 5.8, and 6.4) and eight planting dates (from October 5th to January 20th) over 30 years at 187 locations in RS, Brazil, using APSIM Next Generation. Failure risk was calculated as the percentage of simulations that yielded less than the economic break-even soybean yield in a given scenario. RESULTS AND CONCLUSIONSThe simulated yields were clustered into four regions: Northeast, North, Central, and Southwest. Four WS seasonal patterns were then defined (no stress, early stress, late stress, and whole season stress). On average, WS reduced yields up to 2 Mg ha-1 (∼50 % relative to the maximum). WS varied among regions, with the SW experiencing more frequent and severe stress (up to 50 % of whole season stress during La Nina). ENSO events influenced WS frequency, with El Niño events associated with reduced stress and La Niña events to increased stress. The MG 5.0 resulted in a higher probability of failure risk in all regions. Early planting dates resulted in the highest yield variability (up to 5 Mg ha-1). Climate-adaptative management strategies, such as optimizing planting dates and maturity groups, resulted in a 15 % reduction in crop failure. SIGNIFICANCEOur findings provide valuable insights for developing targeted approaches to enhance soybean yield stability, thereby increasing the resilience of agriculture in the face of future climate uncertainties.

Asynchronous Functional Brain Network Construction with Spatiotemporal Transformer for MCI Classification.

Construction and analysis of functional brain networks (FBNs) with resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method to diagnose functional brain diseases. Nevertheless, the existing methods suffer from several limitations. First, the functional connectivities (FCs) of the FBN are usually measured by the temporal co-activation level between rs-fMRI time series from regions of interest (ROIs). While enjoying simplicity, the existing approach implicitly assumes simultaneous co-activation of all the ROIs, and models only their synchronous dependencies. However, the FCs are not necessarily always synchronous due to the time lag of information flow and cross-time interactions between ROIs. Therefore, it is desirable to model asynchronous FCs. Second, the traditional methods usually construct FBNs at individual level, leading to large variability and degraded diagnosis accuracy when modeling asynchronous FBN. Third, the FBN construction and analysis are conducted in two independent steps without joint alignment for the target diagnosis task. To address the first limitation, this paper proposes an effective sliding-window-based method to model spatiotemporal FCs in Transformer. Regarding the second limitation, we propose to learn common and individual FBNs adaptively with the common FBN as prior knowledge, thus alleviating the variability and enabling the network to focus on the individual disease-specific asynchronous FCs. To address the third limitation, the common and individual asynchronous FBNs are built and analyzed by an integrated network, enabling end-to-end training and improving the flexibility and discriminativity. The effectiveness of the proposed method is consistently demonstrated on three data sets for mild cognitive impairment (MCI) diagnosis.

Measurement reproducibility and storage impact on VOC/SVOC emission rate from decorative materials

Building materials are the major sources of Volatile and Semi-Volatile Organic Compounds (VOCs and SVOCs) in indoor air. Measurements of emission rates of these compounds are likely to be influenced by variation in certain environmental factors resulting in intra-specimen variability. This study aims to (i) evaluate the reproducibility of measurements between specimens and (ii) evaluate the impact of storage on VOC and SVOC emissions from antifungal acrylic paint (applied on polyester-cellulose). For this purpose, 15 discs of tested materials (1.63 ± 0.04 g) were prepared. From these, the emissions rates (ER) of 5 samples were analyzed simultaneously during three measurement campaigns (October 2021, January 2022 and March 2022). Between each campaign, specimens were stored in the dark at ambient temperature (25 ± 4 °C) and relative humidity (50 ± 20 %). Measurements were performed using the field and laboratory emission cell (FLEC) and characterized by gas chromatography (TD-GC-MS/FID) and liquid chromatography (HPLC). Intra-specimen reproducibility was assessed by comparing 5 ER of different specimens collected simultaneously. The impact of storage was evaluated by comparing the average VOC/SVOC ER between each campaign. The results show, concerning the reproducibility of the measurements, that the first measurement campaign provides ER with high variability (10–36 %) compared to the second and third measurement campaigns, which show lower intra-specimen variability (5–24 % and 8–20 % respectively). However, weakly emitted compounds (ER < 10 μg m−2 h−1) such as aromatics and aldehydes show large variabilities (6–100 % of variation) in all measurement campaigns. Regarding the effect of the 5-months storage a significant decrease in the ER of individual VOC/SVOCs (37–85 %) and of TVOCs (74 %) was noted, except for aldehydes, aromatic hydrocarbons, isopropylacetone and vinyl crotonate, which showed a stability or eventual increase (up to 100 %) in the ER over time, depending on the type of emitted compound.

Relativity, Rank, and the US News Health's Cardiology, Heart, and Vascular Surgery Best Hospitals.

Virtually anything can be ranked; the US News and World Report (USNWR or US News) ranks the top 50 hospitals specializing in cardiology, heart, and vascular surgery. Here the authors propose validating the effectiveness of rankings by comparing differences among the USNWR metrics across the top 50 hospitals. The ranking system for the top 50 hospitals specializing in cardiology, heart, and vascular surgery was derived from 16 variant scores. Each hospital's scores were collected from the USNWR. Hospitals were categorized into quintiles consisting of 10 institutions (1-10, 11-20, etc). An analysis of variance/χ2 comprehensive statistical analysis was run alongside a Wilcoxon/Kruskal-Wallis test to compare statistical outcomes. A significant threshold was deemed to be P < 0.05. Significant differences were noted between quintiles for advanced technologies (P = 0.05), US News specialty score (P < 0.001), number of patient referrals (P = 0.004), and expert opinion (P < 0.001). Non-statistically significant differences were found among patient experience, public transparency, Society of Thoracic Surgery transparency, American College of Cardiology transparency, recognition as a magnet hospital, and nursing staffing. Interestingly, a large variance was noted in the average number of referrals between the first quintile (13,371) and the last (6690). Expert opinion plays a critical role in the reputation of the USNWR's top 10 hospitals in cardiology, heart, and vascular surgery. Although many have argued about the merits of USNWR hospital rankings, taken together, rankings fill a strong customer demand and are sticky.