Intensifying macrovertebrate reconnaissance together with refined age-dating of mid-Cretaceous assemblages in recent decades is producing a more nuanced understanding of the impact of the Cretaceous Thermal Maximum on terrestrial ecosystems. Here we report discovery of a new early-diverging ornithopod, Iani smithi gen. et sp. nov., from the Cenomanian-age lower Mussentuchit Member, Cedar Mountain Formation of Utah, USA. The single known specimen of this species (NCSM 29373) includes a well-preserved, disarticulated skull, partial axial column, and portions of the appendicular skeleton. Apomorphic traits are concentrated on the frontal, squamosal, braincase, and premaxilla, including the presence of three premaxillary teeth. Phylogenetic analyses using parsimony and Bayesian inference posit Iani as a North American rhabdodontomorph based on the presence of enlarged, spatulate teeth bearing up to 12 secondary ridges, maxillary teeth lacking a primary ridge, a laterally depressed maxillary process of the jugal, and a posttemporal foramen restricted to the squamosal, among other features. Prior to this discovery, neornithischian paleobiodiversity in the Mussentuchit Member was based primarily on isolated teeth, with only the hadrosauroid Eolambia caroljonesa named from macrovertebrate remains. Documentation of a possible rhabdodontomorph in this assemblage, along with published reports of an as-of-yet undescribed thescelosaurid, and fragmentary remains of ankylosaurians and ceratopsians confirms a minimum of five, cohabiting neornithischian clades in earliest Late Cretaceous terrestrial ecosystems of North America. Due to poor preservation and exploration of Turonian–Santonian assemblages, the timing of rhabdodontomorph extirpation in the Western Interior Basin is, as of yet, unclear. However, Iani documents survival of all three major clades of Early Cretaceous neornithischians (Thescelosauridae, Rhabdodontomorpha, and Ankylopollexia) into the dawn of the Late Cretaceous of North America.

Social networking is proliferating globally and in Vietnam, but this led to several negative aspects of adolescents’ health, including physical activity, sleep quality, and depressive and anxiety symptoms. This study aimed to identify the relationship between social media usage and examine risk factors (Fear of Missing out and Risk of Neglect) on social media usage, and the overall quality of life and mental health among individuals actively using social media networks. An online cross-sectional study was conducted in three cities in Vietnam (Hanoi, Tuyen Quang, and Can Tho) from September through to October 2021. A structured questionnaire assessed for characteristics of social media use and other associated factors. 1891 participants were recruited, with 98.4% having access to social media. s. Factors like “PHQ-9 score”, “Problematic Internet use”, and “Time average used social media per day”, were negatively associated with the EQ5D5L Index. By contrast, “Gender”, and “Using smartphone” were positive factors of the EQ5D5L Index. “FOMO score” and “self-harm and suicide” were positive factors of the PHQ-9 score while “Using smartphone” was negative. In terms of self-harm and suicide, “FOMO score” and “Problematic Internet use” were positive factors, by contrast, “Using smartphone” was a negative factor. This is the first study to examine social media addiction among Vietnamese adolescents, its relationship with FOMO score, stresses associated with rejection and neglect, and the overall quality of life. Our results highlighted there is a relationship between FOMO score and impaired overall quality of life, increased depressive symptoms, and an association between stresses relating to negative rejection and FOMO score.

Background: Miami, Florida is an epicenter of the HIV epidemic in the US, with 20% of new HIV infections occurring in women. Despite effectiveness of Pre-Exposure Prophylaxis (PrEP) in preventing HIV, only 10% of eligible women benefit from its use. Setting: This study evaluates PrEP awareness and use, and factors associated with PrEP awareness among sexually active women in Miami, Florida. Methods: Results reported in this study included cross-sectional data that were collected as part of a baseline visit from a parent study. Cis-gender, HIV-negative, 18-45-year-old, sexually active women were recruited as part of a study evaluating recurrent bacterial vaginosis and HIV risk. Participants completed questionnaires assessing socio-demographics, HIV risk factors, prior history of HIV testing and reproductive tract infections, PrEP awareness and use. Relationships between variables and PrEP awareness were analyzed and multivariable logistic regression identified variables strongly associated with PrEP awareness. Results: Among the 295 women enrolled, median age was 31 (24–38) years, 49% Black, 39% White, and 34% Hispanic. Of 63% who knew about PrEP, only 5% were on PrEP. Women with income below poverty line (OR = 2.00[1.04,3.87];p = 0.04), more male sexual partners in past month (OR = 1.30[1.01,1.68];p = 0.04), lifetime HIV testing (OR = 6.42[2.83,14.52];p<0.01), and current bacterial vaginosis (OR = 2.28[1.18,4.40];p = 0.01) were more likely to be aware of PrEP. Lower odds of PrEP awareness were associated with being Black (OR = 0.38[0.15,0.96];p = 0.04), Hispanic (OR = 0.18[0.08,0.39];p<0.01), heterosexual (OR = 0.29[0.11,0.77];p<0.01), and reporting inconsistent condom use during vaginal sex (OR = 0.21[0.08,0.56];p<0.01). Conclusion: PrEP awareness is low among reproductive age women in a high-risk setting. Culturally tailored interventions are needed to increase PrEP awareness and uptake, especially among Black and Hispanic women with inconsistent condom use during vaginal sex with male partners.

Background: There is increasing interest in collecting sociodemographic and social needs data in hospital settings to inform patient care and health equity. However, few studies have examined inpatients’ views on this data collection and what should be done to address social needs. This study describes internal medicine inpatients’ perspectives on the collection and use of sociodemographic and social needs information. Methods: A qualitative interpretive description methodology was used. Semi-structured interviews were conducted with 18 patients admitted to a large academic hospital in Toronto, Canada. Participants were recruited using maximum variation sampling for diverse genders, races, and those with and without social needs. Interviews were coded using a predominantly inductive approach and a thematic analysis was conducted. Results: Patients expressed that sociodemographic and social needs data collection is important to offer actionable solutions to address their needs. Patients described a gap between their ideal care which would attend to social needs, versus the reality that hospital-based teams are faced with competing priorities and pressures that make it unfeasible to provide such care. They also believed that this data collection could facilitate more holistic, integrated care. Patients conveyed a need to have a trusting and transparent relationship with their provider to alleviate concerns surrounding bias, discrimination, and confidentiality. Lastly, they indicated that sociodemographic and social needs data could be useful to inform care, support research to inspire social change, and assist them with navigating community resources or creating in-hospital programs to address unmet social needs. Conclusions: While the collection of sociodemographic and social needs information in hospital settings is generally acceptable, there were varied views on whether hospital staff should intervene, as their priority is medical care. The results can inform the implementation of social data collection and interventions in hospital settings.

Although over 180 freshwater fish species have been reported from Nepal, little is known of their ecology and distribution. This information is needed because their diversity may be threatened by developments like hydropower constructions. We conducted Nepal’s first environmental DNA (eDNA) based fish biodiversity assessment in two major river systems- Karnali River (KR), which is still pristine and Trishuli River (TR) with numerous hydropower plants. The eDNA was concentrated by filtering (0.45 μm pore size) two liters of water collected at different sampling points in each study site. A total of 224 eDNA samples (KR = 162 and TR = 62) were collected, from which fish species were identified by 12S rRNA metabarcording approach utilizing Illumina sequencing platform. Alpha and beta diversity of species between two river basins were compared. Also, in KR site, fish (N = 795) were caught, and identified using COI gene based DNA barcoding method- building Nepal’s first fish DNA reference database. Field sampling identified 21 species through morphology and DNA barcoding, where Barilius spp. and Schizothorax spp. were the most abundant. From 244 eDNA samples, 24 Operational Taxonomic Units (OTUs) were identified in TR and 46 in KR with 19 being common to both sites, 27 being unique in KR, and five in TR only. Most fishes were of Cypriniformes and Siluriformes orders, with Barilius spp. and Schizothorax spp. being the most abundant. Long distance migratory fish (Tor spp, Neolissochilus hexagonolepis) and non-native fishes were identified in eDNA samples as well. Alpha diversity in TR was significantly lower than in KR. High beta diversity between the two sites indicated low similarity in fish diversity between the TR and KR. This study demonstrated the utility of eDNA as a non-invasive technique for biodiversity assessment which is particularly useful in areas like Nepal with scarce data on fish species distribution.

Background: While evidence has emerged highlighting the potential benefits of the eye as a window to the central nervous system, research on severe mental illness (SMI) and eye health is rare. Aims: We examine the association of SMI with a range of ophthalmic health outcomes, and whether any relationship is modified by age. Methods: We used linked administrative data from general practitioner (GP), hospital and ophthalmic records to examine receipt of any Health and Social Care (HSC) eye-test; and (based on eligibility recorded for a sight test) any glaucoma, any diabetes, and any blindness among the Northern Ireland (NI) hospital population between January 2015 and November 2019 (N = 798,564). Results: When compared with non-SMI patients, those with SMI recorded a higher prevalence of having had a sight test, diabetes, and blindness. In fully adjusted logistic regression models, higher likelihood of an eye-test and diabetes (OR = 1.71: 95%CI = 1.63, 1.79 and OR = 1.29: 1.19, 1.40 respectively); and lower likelihood of glaucoma remained (OR = 0.69: 0.53, 0.90). Amongst persons with SMI there was evidence that the likelihood of having had an eye-test was lower in the older age-groups. Conclusion: Our study provides new evidence on ophthalmic health inequalities associated with SMI. While the study has immediate relevance to its NI context, we believe it is generalizable to wider UK health concerns. We emphasize the need for more research of this type, using large linkable electronic administrative databases to further our understanding of both health inequalities associated with SMI and poor eye health, and health outcomes in general.

With the advancement of ubiquitous computing, smartphone sensors are generating a vast amount of unlabeled data streams ubiquitously. This sensor data can potentially help to recognize various behavioral contexts in the natural environment. Accurate behavioral context recognition has a wide variety of applications in many domains like disease prevention and independent living. However, despite the availability of enormous amounts of sensor data, label acquisition, due to its dependence on users, is still a challenging task. In this work, we propose a novel context recognition approach i.e., Dissimilarity-Based Query Strategy (DBQS). Our approach DBQS leverages Active Learning based selective sampling to find the informative and diverse samples in the sensor data to train the model. Our approach overcomes the stagnation problem by considering only new and distinct samples from the pool that were not previously explored. Further, our model exploits temporal information in the data in order to further maintain diversity in the dataset. The key intuition behind the proposed approach is that the variations during the learning phase will train the model in diverse settings and it will outperform when assigned a context recognition task in the natural setting. Experimentation on a publicly available natural environment dataset demonstrates that our proposed approach improved overall average Balanced Accuracy(BA) by 6% with an overall 13% less training data requirement.

The burden of HIV and related diseases have been areas of great concern pre and post the emergence of COVID-19 in Zimbabwe. Machine learning models have been used to predict the risk of diseases, including HIV accurately. Therefore, this paper aimed to determine common risk factors of HIV positivity in Zimbabwe between the decade 2005 to 2015. The data were from three two staged population five-yearly surveys conducted between 2005 and 2015. The outcome variable was HIV status. The prediction model was fit by adopting 80% of the data for learning/training and 20% for testing/prediction. Resampling was done using the stratified 5-fold cross-validation procedure repeatedly. Feature selection was done using Lasso regression, and the best combination of selected features was determined using Sequential Forward Floating Selection. We compared six algorithms in both sexes based on the F1 score, which is the harmonic mean of precision and recall. The overall HIV prevalence for the combined dataset was 22.5% and 15.3% for females and males, respectively. The best-performing algorithm to identify individuals with a higher likelihood of HIV infection was XGBoost, with a high F1 score of 91.4% for males and 90.1% for females based on the combined surveys. The results from the prediction model identified six common features associated with HIV, with total number of lifetime sexual partners and cohabitation duration being the most influential variables for females and males, respectively. In addition to other risk reduction techniques, machine learning may aid in identifying those who might require Pre-exposure prophylaxis, particularly women who experience intimate partner violence. Furthermore, compared to traditional statistical approaches, machine learning uncovered patterns in predicting HIV infection with comparatively reduced uncertainty and, therefore, crucial for effective decision-making. Author summary: The Joint United Nations Programme (UNAIDS) set up fast-track targets to reach HIV epidemic control by 2030, where it is expected that 95% of people living with HIV know their status, and of those, 95% should be on treatment and of those on treatment 95% should have reached viral suppression. In Zimbabwe, by 2020, it was found that 86.8% of adults living with HIV were aware of their status and of those aware of their status, 97.0% were on antiretroviral treatment. Furthermore, among those on treatment, 90.3% achieved viral load suppression. In order to achieve these targets, modern predictive algorithms using machine learning have the power to enhance HIV prevention and prediction capability. Furthermore, studies have reported that machine learning could accurately predict future HIV infection. This paper, therefore, aims to use these machine learning tools to predict key HIV populations based on socio-behavioural characteristics obtained from survey data.

Eighty-two glass vessels, recovered from the excavations at the ancient Swahili settlement and port of Unguja Ukuu in Zanzibar, Eastern Africa, were analysed using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS). The results show that all of the glass samples are soda-lime-silica glass. Fifteen glass vessels belong to the natron glass type and are characterised by low MgO and K2O (<1.50%), suggesting they were made from natron, a mineral flux that was widely used during the Roman period and Late Antiquity. Sixty-seven glass vessels belong to the plant ash glass type, characterised by high magnesia and potash levels (>1.50%), suggesting plant ash was the main alkali flux. Based on the major, minor and trace elements, three different compositional groups were identified for the natron glass and three were identified for the plant ash glass: (1) UU Natron Type 1, (2) UU Natron Type 2, (3) UU Natron Type 3, (4) UU Plant ash Type 1, (5) UU Plant ash Type 2 and (6) UU Plan ash Type 3. Comparison with contemporary Middle Eastern glass groups shows that UU Natron Types 1, 2 and 3 correspond to Egypt II high Na2O, Levantine I and Levantine II respectively, while UU Plant ash Type 1 matches closely with Samarra Group 2. UU Plant ash Types 2 and 3 have unique chemical fingerprints that do not match any of the contemporary plant ash glass groups, but their chemical compositions show some affinity with the old Sassanian plant ash glass, suggesting a possible Mesopotamian provenance. Combined with existing research on early Islamic glass, the authors reveal a complex trading network in the globalisation of Islamic glass, particularly involving glass corresponding to modern Iraq and Syria, in the 7th– 9th centuries AD.

Meat processing plants have been at the center of the SARS-CoV-2 pandemic, with a recent report citing 90% of US facilities having multiple outbreaks during 2020 and 2021. We explored the potential for biofilms to act as a reservoir in protecting, harboring, and dispersing SARS-CoV-2 throughout the meat processing facility environment. To do this, we used Murine Hepatitis Virus (MHV), as a surrogate for SARS-CoV-2, and meat processing facility drain samples to develop mixed-species biofilms on materials found in meat processing facilities (stainless steel (SS), PVC, and ceramic tiles). After exposure to the biofilm organisms for five days post-inoculation at 7°C we conducted quantitative PCR (qPCR) and plaque assays to determine whether MHV could remain both detectable and viable. Our data provides evidence that coronaviruses can remain viable on all the surfaces tested and are also able to integrate within an environmental biofilm. Although a portion of MHV was able to remain infectious after incubation with the environmental biofilm, a large reduction in plaque numbers was identified when compared with the viral inoculum incubated without biofilm on all test surfaces, which ranged from 6.45–9.27-fold higher. Interestingly, we observed a 2-fold increase in the virus-environmental biofilm biovolume when compared to biofilm without virus, indicating that the biofilm bacteria both detected and reacted to the virus. These results indicate a complex virus-environmental biofilm interaction. Although we observed better survival of MHV on a variety of surfaces commonly found in meat processing plants alone than with the biofilm, there is the potential for biofilms to protect virions from disinfecting agents, which has implications for the potential of SARS-CoV-2 prevalence within the meat processing plant environment. Also given the highly infectious nature of SARS-CoV-2, particularly for some of the variant strains such as omicron, having even a residual level of virus present represents a serious health hazard. The increase in biofilm biovolume in response to virus is also a concern for food safety due to the potential of the same being seen with organisms associated with food poisoning and food spoilage.