A Study of NCAA Gambling Prevention Videos on Gambling Perceptions Within a NCAA Division II Baseball Team

The purpose of this study was to investigate gambling attitudes among collegiate baseball players. We wanted to know if online educational videos developed by the NCAA would be successful in altering gambling attitudes and behaviors. A Randomized Controlled Trial (RCT) with a pretest-posttest design was conducted for this study. Subjects included a convenience sample of 33 baseball studentathletes from a NCAA Division II University in the mid-Atlantic region. Gambling attitudes and behaviors of collegiate baseball student-athletes were determined through survey questions and the administration of the South Oaks Gambling Screen. Findings indicated that baseball student-athletes participated in a variety of gambling activities, and that NCAA educational videos are moderately successful in altering student-athletes’ attitudes toward gambling. This study is important for college and university athletics departments as they work to counter the progambling messages that student-athletes receive

Carbon monoxide poisoning: Novel magnetic resonance imaging pattern in the acute setting

The presentation of carbon monoxide (CO) poisoning is non-specific and highly variable. The diagnosis is made when a compatible history and examination occur in a patient with elevated carboxyhaemoglobin levels. The severity of intoxication is difficult to assess accurately based on laboratory markers alone. Magnetic resonance imaging (MRI) has been shown to have superior sensitivity to computed tomography for the detection of abnormalities post CO poisoning. We report a novel imaging pattern on MRI undertaken in the acute setting in a patient with CO intoxication. We also discuss the management and follow up of patients with CO poisoning

MALT1 substrate cleavage: what is it good for?

CARD-BCL10-MALT1 (CBM) signalosomes connect distal signaling of innate and adaptive immune receptors to proximal signaling pathways and immune activation. Four CARD scaffold proteins (CARD9, 10, 11, 14) can form seeds that nucleate the assembly of BCL10-MALT1 filaments in a cell- and stimulus-specific manner. MALT1 (also known as PCASP1) serves a dual function within the assembled CBM complexes. By recruiting TRAF6, MALT1 acts as a molecular scaffold that initiates IκB kinase (IKK)/NF-κB and c-Jun N-terminal kinase (JNK)/AP-1 signaling. In parallel, proximity-induced dimerization of the paracaspase domain activates the MALT1 protease which exerts its function by cleaving a set of specific substrates. While complete MALT1 ablation leads to immune deficiency, selective destruction of either scaffolding or protease function provokes autoimmune inflammation. Thus, balanced MALT1-TRAF6 recruitment and MALT1 substrate cleavage are critical to maintain immune homeostasis and to promote optimal immune activation. Further, MALT1 protease activity drives the survival of aggressive lymphomas and other non-hematologic solid cancers. However, little is known about the relevance of the cleavage of individual substrates for the pathophysiological functions of MALT1. Unbiased serendipity, screening and computational predictions have identified and validated ~20 substrates, indicating that MALT1 targets a quite distinct set of proteins. Known substrates are involved in CBM auto-regulation (MALT1, BCL10 and CARD10), regulation of signaling and adhesion (A20, CYLD, HOIL-1 and Tensin-3), or transcription (RelB) and mRNA stability/translation (Regnase-1, Roquin-1/2 and N4BP1), indicating that MALT1 often targets multiple proteins involved in similar cellular processes. Here, we will summarize what is known about the fate and functions of individual MALT1 substrates and how their cleavage contributes to the biological functions of the MALT1 protease. We will outline what is needed to better connect critical pathophysiological roles of the MALT1 protease with the cleavage of distinct substrates

Killing fields generated by multiple solutions to the Fischer–Marsden equation II

In the process of finding Einstein metrics in dimension [Formula: see text], we can search metrics critical for the scalar curvature among fixed-volume metrics of constant scalar curvature on a closed oriented manifold. This leads to a system of PDEs (which we call the Fischer–Marsden Equation, after a conjecture concerning this system) for scalar functions, involving the linearization of the scalar curvature. The Fischer–Marsden conjecture said that, if the equation admits a solution, the underlying Riemannian manifold is Einstein. Counter-examples are known by Kobayashi and Lafontaine, and by our first paper. Multiple solutions to this system yield Killing vector fields. We showed in our first paper that the dimension of the solution space [Formula: see text] can be at most [Formula: see text], with equality implying that [Formula: see text] is a sphere with constant sectional curvatures. Moreover, we also showed there that the identity component of the isometry group has a factor [Formula: see text]. In this second paper, we apply our results in the first paper to show that either [Formula: see text] is a standard sphere or the dimension of the space of Fischer–Marsden solutions can be at most [Formula: see text]

Extreme Fermi surface smearing in a maximally disordered concentrated solid solution

We show that the Fermi surface can survive the presence of extreme compositional disorder in the equiatomic alloy Ni0.25Fe0.25Co0.25Cr0.25. Our high-resolution Compton scattering experiments reveal a Fermi surface which is smeared across a significant fraction of the Brillouin zone (up to 40% of 2π/a). The extent of this smearing and its variation on and between different sheets of the Fermi surface have been determined, and estimates of the electron mean free path and residual resistivity have been made by connecting this smearing with the coherence length of the quasiparticle states

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8–20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke. Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health

Evolutionary development of tensegrity structures

Contributions from the emerging fields of molecular genetics and evo-devo (evolutionary developmental biology) are greatly benefiting the field of evolutionary computation, initiating a promise of renewal in the traditional methodology. While direct encoding has constituted a dominant paradigm, indirect ways to encode the solutions have been reported, yet little attention has been paid to the benefits of the proposed methods to real problems. In this work, we study the biological properties that emerge by means of using indirect encodings in the context of form-finding problems. A novel indirect encoding model for artificial development has been defined and applied to an engineering structural-design problem, specifically to the discovery of tensegrity structures. This model has been compared with a direct encoding scheme. While the direct encoding performs similarly well to the proposed method, indirect-based results typically outperform the direct-based results in aspects not directly linked to the nature of the problem itself, but to the emergence of properties found in biological organisms, like organicity, generalization capacity, or modularity aspects which are highly valuable in engineering

Assessing the ecological impacts of invasive species based on their functional responses and abundances

Invasive species management requires allocation of limited resources towards the proactive mitigation of those species that could elicit the highest ecological impacts. However, we lack predictive capacity with respect to the identities and degree of ecological impacts of invasive species. Here, we combine the relative per capita effects and relative field abundances of invader as compared to native species into a new metric, “Relative Impact Potential” (RIP), and test whether this metric can reliably predict high impact invaders. This metric tests the impact of invaders relative to the baseline impacts of natives on the broader ecological community. We first derived the functional responses (i.e. per capita effects) of two ecologically damaging invasive fish species in Europe, the Ponto-Caspian round goby (Neogobius melanostomus) and Asian topmouth gudgeon (Pseudorasbora parva), and their native trophic analogues, the bullhead (Cottus gobio; also C. bairdi) and bitterling (Rhodeus amarus), towards several prey species. This establishes the existence and relative strengths of the predator-prey relationships. Then, we derived ecologically comparable field abundance estimates of the invader and native fish from surveys and literature. This establishes the multipliers for the above per capita effects. Despite both predators having known severe detrimental field impacts, their functional responses alone were of modest predictive power in this regard; however, incorporation of their abundances relative to natives into the RIP metric gave high predictive power. We present invader/native RIP biplots that provide an intuitive visualisation of comparisons among the invasive and native species, reflecting the known broad ecological impacts of the invaders. Thus, we provide a mechanistic understanding of invasive species impacts and a predictive tool for use by practitioners, for example, in risk assessments

Impact of cognitive stimulation on ripples within human epileptic and non-epileptic hippocampus

Background: Until now there has been no way of distinguishing between physiological and epileptic hippocampal ripples in intracranial recordings. In the present study we addressed this by investigating the effect of cognitive stimulation on interictal high frequency oscillations in the ripple range (80-250 Hz) within epileptic (EH) and non-epileptic hippocampus (NH). Methods: We analyzed depth EEG recordings in 10 patients with intractable epilepsy, in whom hippocampal activity was recorded initially during quiet wakefulness and subsequently during a simple cognitive task. Using automated detection of ripples based on amplitude of the power envelope, we analyzed ripple rate (RR) in the cognitive and resting period, within EH and NH. Results: Compared to quiet wakefulness we observed a significant reduction of RR during cognitive stimulation in EH, while it remained statistically marginal in NH. Further, we investigated the direct impact of cognitive stimuli on ripples (i.e. immediately post-stimulus), which showed a transient statistically significant suppression of ripples in the first second after stimuli onset in NH only. Conclusion: Our results point to a differential reactivity of ripples within EH and NH to cognitive stimulation