ort for smoke and tobacco-free campus policymaking efforts at community colleges, as internal and external support is demonstrated for more comprehensive policies. This study investigates whether recreational marijuana legislation and perceived social norms (descriptive and injunctive) affect college students' propensity to share pro-marijuana messages. We examine which referent group (close friends, typical student, parents) most influence those norms. https://www.selleckchem.com/products/PHA-793887.html A sample of 343 college students participated in the study. Of these students, 214 were from Washington State, where recreational marijuana is legal, and 129 were from Wyoming, where recreational marijuana is illegal. Data, from an online survey, were analyzed through PROCESS analyses. College students in Washington State who believed a typical peer would want them to share pro-marijuana messaging were marginally more likely to share pro-marijuana messages than their counterparts in Wyoming. However, among students who thought a typical peer would not approve of them sharing pro-marijuana messaging, the opposite pattern emerged. Restrictive recreational marijuana legislation does not uniformly abate relatedonal marijuana legislation does not uniformly abate related message sharing on social media. Musculoskeletal injuries from patient handling are significant problems among health care workers. In California, legislation requiring hospitals to implement safe patient handling (SPH) programs was enacted in 2011. This qualitative study explored workers' experiences and perceptions about the law, their hospital's SPH policies and programs, patient handling practices, and work environment. Three focus groups were conducted with 21 participants (19 nurses and 2 patient handling specialists) recruited from 12 hospitals located in the San Francisco Bay Area and San Joaquin Valley. Qualitative content analysis was used for data analysis. Multiple themes emerged from diverse experiences and perceptions. Positive perceptions included empowerment to advocate for safety, increased awareness of SPH policies and programs, increased provision of patient handling equipment and training, increased lift use, and improvement in safety culture. Perceived concerns included continuing barriers to safe practices and life positive impacts of the SPH law but also notes the potential limitations of this legislation in the view of health care workers.Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19), which is an ongoing global health concern. The exact source of the virus has not been identified, but it is believed that this novel coronavirus originated in animals; bats in particular have been implicated as the primary reservoir of the virus. SARS-CoV-2 can also be transmitted from humans to other animals, including tigers, cats, and mink. Consequently, infected people who work directly with bats could transfer the virus to a wild North American bat, resulting in a new natural reservoir for the virus, and lead to new outbreaks of human disease. We evaluated a reverse-transcription real-time PCR panel for detection of SARS-CoV-2 in bat guano. We found the panel to be highly specific for SARS-CoV-2, and able to detect the virus in bat guano samples spiked with SARS-CoV-2 viral RNA. Our panel could be utilized by wildlife agencies to test bats in rehabilitation facilities prior to their release to the wild, minimizing the risk of spreading this virus to wild bat populations.EEG is the gold standard for seizure detection in the newborn infant, but EEG interpretation in the preterm group is particularly challenging; trained experts are scarce and the task of interpreting EEG in real-time is arduous. Preterm infants are reported to have a higher incidence of seizures compared to term infants. Preterm EEG morphology differs from that of term infants, which implies that seizure detection algorithms trained on term EEG may not be appropriate. The task of developing preterm specific algorithms becomes extra-challenging given the limited amount of annotated preterm EEG data available. This paper explores novel deep learning (DL) architectures for the task of neonatal seizure detection in preterm infants. The study tests and compares several approaches to address the problem training on data from full-term infants; training on data from preterm infants; training on age-specific preterm data and transfer learning. The system performance is assessed on a large database of continuous EEG recordings of 575[Formula see text]h in duration. It is shown that the accuracy of a validated term-trained EEG seizure detection algorithm, based on a support vector machine classifier, when tested on preterm infants falls well short of the performance achieved for full-term infants. An AUC of 88.3% was obtained when tested on preterm EEG as compared to 96.6% obtained when tested on term EEG. When re-trained on preterm EEG, the performance marginally increases to 89.7%. An alternative DL approach shows a more stable trend when tested on the preterm cohort, starting with an AUC of 93.3% for the term-trained algorithm and reaching 95.0% by transfer learning from the term model using available preterm data. The proposed DL approach avoids time-consuming explicit feature engineering and leverages the existence of the term seizure detection model, resulting in accurate predictions with a minimum amount of annotated preterm data.Electroencephalogram (EEG) plays an important role in recording brain activity to diagnose epilepsy. However, it is not only laborious, but also not very cost effective for medical experts to manually identify the features on EEG. Therefore, automatic seizure detection in accordance with the EEG recordings is significant for the diagnosis and treatment of epilepsy. Here, a new method for detecting seizures using tensor distance (TD) is proposed. First, the time-frequency characteristics of EEG signals are obtained by wavelet transformation, and the tensor representation of EEG signals is then obtained. Tucker decomposition is used to obtain the principal components of the EEG tensor. After, the distances between different categories of EEG tensors are calculated as the EEG features. Finally, the TD features are classified through the Bayesian Linear Discriminant Analysis (Bayesian LDA) classifier. The performance of this method is measured by the sensitivity, specificity, and recognition accuracy. Results indicate 95.