INC Research (United States)

Fireball Extreme ChallengeTM is a coeducational high-intensity intermittent-explosive sport that lacks normative performance data; this study aimed to create multidimensional athlete profiles and establish reference benchmarks to support talent identification and training prescription. Twenty-one national-level athletes (13 males and 8 females; mean age 26.2±5.8 years) completed countermovement jump, one-repetition maximum strength assessment, bilateral handgrip testing, and the 30-15 intermittent fitness test under standardized indoor (21 °C, 50% relative humidity) and outdoor (37 °C, 68% relative humidity) field conditions representative of the athletes' typical training environment in coastal southern Mexico, recorded for ecological validity rather than thermal control. We calculated descriptive statistics and percentile distributions (25th, 50th, 75th), performed sex-stratified analyses adjusted for age, and conducted exploratory principal component and cluster analyses to examine interdomain associations and emergent performance groupings. Across all participants, the observed performance spectrum spanned a broad range of neuromuscular and metabolic capacities, with individual variability captured through percentile reference values (25th, 50th, 75th). The highest jump heights (up to 48 cm) and peak power outputs (≈2100 W) coexisted with moderate endurance and balanced heart rate responses, defining the multidimensional nature of Fireball Extreme Challenge™ performance. Sex-stratified distributions are presented descriptively but were not the primary analytical outcome. Principal component analysis identified two dominant performance domains - neuromuscular power and metabolic strain - explaining 59% of total variance. Hierarchical clustering revealed three mixed profiles that integrated both male and female athletes, illustrating overlapping phenotypes rather than categorical differences. This study provides the first multidimensional performance profile of Fireball Extreme Challenge™ athletes, establishing normative reference percentiles and identifying key physical domains - neuromuscular power and metabolic capacity - that characterize success in this coeducational, high-intensity team sport. The derived phenotypic clusters highlight overlapping attributes between male and female athletes, reflecting the integrated dynamics of mixed-team performance. These findings offer a foundational framework for evidence-based training design, athlete monitoring, and future validation in larger international cohorts.

Abstract We demonstrate the increased ability to forecast hurricane impacts with a coupled numerical modeling system by simulating ocean waves, water levels, currents, sediment transport, and structural damage to predict inundation, coastal morphological change, and residential building impacts. The Coupled‐Ocean‐Atmosphere‐Waves‐Sediment‐Transport (COAWST) modeling system is applied to simulate Hurricane Michael (category 5, 2018) that made landfall near Tyndall Air Force Base, FL, in the northern Gulf of America, causing severe devastation and flooding. Atmospheric forcings from the Coupled Ocean/Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS‐TC) are used to drive the ocean and wave models on a series of nested grids. Results identify that coastal inundation at Mexico Beach is due to surge from winds and waves, supplemented by pulses of infragravity wave motions that propagate landward into the inundation region. Seed lines observed on interior building walls also demonstrate variable changes in water level. In addition, a machine learning model was applied to hindcast structure damages, caused mostly by waves and winds, with a 72% accuracy estimate of substantial damage in proximity of landfall. The storm also created a breach across Cape San Blas, the adjacent barrier spit, due to large surge and low dune elevations. Dune locations with vegetated land cover are shown to reduce wave‐energy dissipation and reduce erosion, whereas locations without land cover had increased breaching potential. The breach occurred during the maximum ocean‐side water level, and the delayed high water on the bay side allowed a pressure gradient to drive flow seaward and promote breach development.

ABSTRACT How do people understand parties? Using data from two original surveys fielded nearly a decade apart, we shed light on people’s mental images of the parties. In the surveys, we asked which politicians immediately come to mind when you think about Republicans (Democrats). People’s mental images of parties are a narrow gallery of few prominent national politicians. Nearly 40% of the people struggle to list three politicians. Further, 44% of the mentions are of the last three presidents. People are also likelier to list more extreme politicians. To shed light on the source of these biases, we analyze a decade’s worth of national television news data. We find that the media coverage patterns of politicians are similar to the patterns in the survey data. A 10% increase in media mentions is associated with a 2% increase in the number of people recalling the politician.

The transformation of capitalism into a new socio-economic state formation modern discussion about the forms of alienation acquires a special character. The new social reality shows emergence of new forms of alienation not only in the economic sphere of society, but also forms of alienation that have arisen in other spheres of public life. The materials of modern scientific publications draw attention to the need for a careful study of human existence in the context of new forms of relationship between the individual and society.

Inpatient medical settings lack evidence-based spiritually integrated interventions to address patient care needs within a pluralistic religious landscape. To address this gap, CCSH™ (Compassion-Centered Spiritual Health) was developed to leverage the skillsets of healthcare chaplains to improve patient outcomes through spiritual consultation. Here, we report the results of a randomized, wait-list controlled, pre-registered (NCT03529812) study that evaluated the impact of CCSH on patient-reported depression and explored putative mediators of CCSH's effects. Chaplain residents were randomized to be trained in CCSH as part of their clinical pastoral education (CPE) residency in the fall (n = 8) or spring semester (n = 8). After fall training, all residents provided spiritual consultations with hospitalized patients (n = 119; n = 54 seen by CCSH-trained chaplains). Those not yet trained to deliver CCSH provided a traditional consult. Patients' pre-consult distress was measured using the National Comprehensive Cancer Network Distress Thermometer, and post-consult depression was measured using the Hospital Anxiety and Depression Scale (HADS). Consults were audio-recorded and transcribed verbatim, and we conducted linguistic analyses using LIWC 2015 software to quantify chaplain linguistic behavior. Patients seen by CCSH-trained chaplains had lower post-consult depression scores (M = 4.10, SD = 5.04) than patients who were seen by wait-listed chaplains (M = 6.12, SD = 5.08), after adjusting for pre-consult distress (p = .048). There was also a significant relationship between post-consult depression and chaplain LIWC clout scores (r = -0.24, p = .017), a linguistic measure thought to reflect the expressive confidence and other-oriented focus of the speaker. An exploratory mediation model revealed an indirect effect of CCSH on patient depression through chaplain clout language b = -0.11 (90% CI, -.257, -.003). These data suggest that CCSH decreases patient depression among inpatients, in part due to CCSH-trained chaplains' use of more inclusive, confident, and other-oriented language. We connect these findings with current understandings of effective clinical linguistic behavior and reflect on what this work may mean for integrated spiritual health care.

Monitoring the zoonotic potential of emerging SARS-CoV-2 variants in animals is a critical tool to protect public health. We conducted a longitudinal study in 47 households reporting people with COVID-19 in Texas in January-July 2022, during the first Omicron wave. We evaluated 105 people and 100 of their companion animals by RT-qPCR for SARS-CoV-2 at three sequential sampling events 1-2 weeks apart, starting 0-5 days after the first reported diagnosis of COVID-19 in the house. Of 47 households that reported people with COVID-19, SARS-CoV-2 was detected in 43, with 68% of people testing positive by RT-qPCR; 95.5% of people had antibodies to SARS-CoV-2. Dogs were the only animal species positive by RT-qPCR (5.4%; 3/55). Viral copies were consistently lower in dogs than household members, and no infectious virus was recovered in cell culture. Whole genome sequencing revealed household clusters of Omicron subvariants BA.1.1, BA.2.3.4 and BA.5.1.1 in people, dogs and a food bowl, confirming human-to-dog transmission within households, with no evidence of onward transmission from the infected dogs. Eleven dogs (n = 55) and two cats (n = 26) had neutralizing antibodies against SARS-CoV-2. Infection was not associated with clinical signs in pets; only two animals that tested negative for SARS-CoV-2 were reported to be sick. Nearly one-third (30.2%) of households with active COVID-19 had pets exposed to SARS-CoV-2, similar to our pre-Omicron studies, yet incidence of infection in cats was lower compared to pre-Omicron. These differences suggest that the zoonotic transmission dynamics in households may differ based on variant.

Cancer genetic data from Sub-Saharan African (SSA) are limited. Patients with female breast (fBC), male breast (mBC), and prostate cancer (PC) in Rwanda underwent germline genetic testing and counseling. Demographic and disease-specific information was collected. A multi-cancer gene panel was used to identify germline Pathogenic Variants (PV) and Variants of Uncertain Significance (VUS). 400 patients (201 with BC and 199 with PC) were consented and recruited to the study. Data was available for 342 patients: 180 with BC (175 women and 5 men) and 162 men with PC. PV were observed in 18.3% fBC, 4.3% PC, and 20% mBC. BRCA2 was the most common PV. Among non-PV carriers, 65% had ≥1 VUS: 31.8% in PC and 33.6% in BC (female and male). Our findings highlight the need for germline genetic testing and counseling in cancer management in SSA.

<strong class="journal-contentHeaderColor">Abstract.</strong> The uncertainty due to small number of ensemble members is a major source of uncertainty in future climate predictions. While the uncertainty due to the above in general circulation models (GCMs) projections can be mitigated by increasing the number of simulation ensembles, only a limited number of large-ensemble experiments are available in CMIP6 future scenario experiments. Here we propose a method that increases the sample ensemble size in evaluations of future hazard, by integrating multiple SSP-RCPs for a time period corresponding to a specific increase in temperature from the preindustrial level (i.e., X &deg;C warming). The success of the method was assessed by investigating whether the uncertainty due to small number of ensemble members could be reasonably reduced. First, the similarity in the spatial distributions of flood hazard projection at the same warming level was determined for different SSP-RCP scenarios. Under a 2 &deg;C warming, all SSP-RCPs had a similar distribution with respect to the change ratio of the flood magnitude. Additionally, we showed that the uncertainty due to the different SSP-RCPs (5 %&ndash;10 %) was smaller than the differences between different warming levels such as between 2 &deg;C and 3 &deg;C (around 20 %&ndash;30 %), which suggests that differences among SSP-RCPs as to future flood discharge change are relatively small. These results suggested that integrating SSP-RCPs to increase the ensemble size was a reasonable approach, reducing unbiased variance among GCMs in about 70 % of land grid points comparing to the result using SSP5-RCP8.5 alone.

International audience

BackgroundAccurate estimation of implant size before surgery is crucial in preparing for total knee arthroplasty. However, this task is time-consuming and labor-intensive. To alleviate this burden on surgeons, we developed a reliable artificial intelligence (AI) model to predict implant size.MethodsWe enrolled 714 patients with knee osteoarthritis who underwent total knee arthroplasty from March 2010 to February 2014. All surgeries were performed by the same surgeon using implants from the same manufacturer. We collected 1412 knee anteroposterior (AP) and lateral view x-ray images and retrospectively investigated the implant size. We trained the AI model using both AP and lateral images without any clinical or demographic information and performed data augmentation to resolve issues of uneven distribution and insufficient data. Using data augmentation techniques, we generated 500 images for each size of the femur and tibia, which were then used to train the model. Using data augmentation techniques, we generated 500 images for each size of the femur and tibia, which were then used to train the model. We used ResNet-101 and optimized the model with the aim of minimizing the cross-entropy loss function using both the Stochastic Gradient Descent (SGD) and Adam optimizer.ResultsThe SGD optimizer achieved the best performance in internal validation. The model showed micro F1-score 0.91 for femur and 0.87 for tibia. For predicting within ± one size, micro F1-score was 0.99 for femur and 0.98 for tibia.ConclusionWe developed a deep learning model with high predictive power for implant size using only simple x-ray images. This could help surgeons reduce the time and labor required for preoperative preparation in total knee arthroplasty. While similar studies have been conducted, our work is unique in its use of simple x-ray images without any other data, like demographic features, to achieve a model with strong predictive power.