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Abstract Hyperfocus (HF), or intense, deep concentration on a task, has gained significant research attention in recent years, particularly in regard to clinical populations such as Attention-Deficit/Hyperactivity Disorder (ADHD). The present work aims to provide validation of the 12-item dispositional adult hyperfocus questionnaire (AHQ-D) as a quantitative metric of HF in adults. We preregistered the study design and hypotheses. We administered the AHQ-D and several additional questionnaires to 347 adults (mean ± SD age: 33 ± 11 years; 47% female). Exploratory factor analysis revealed high factor loadings (0.57–0.81) on a single HF factor; item response theory analysis suggested that the questionnaire items had high discrimination and covered a wide range of responses; and we report strong internal consistency metrics (Cronbach’s alpha 0.93, mean split-half reliability 0.93). Replicating our previous work, HF was positively correlated with Conners’ Adult ADHD Rating Scale (CAARS) scores (r(345) = 0.53), suggesting that HF may be related to ADHD traits (though in this sample we did not specifically recruit individuals with ADHD). The AHQ-D demonstrated the hypothesized convergent validity; HF on the AHQ-D was positively correlated with HF measured using a different HF scale (r(344) = 0.69), as well as flow (r(345) = 0.12) and mind wandering (r(345) = 0.39) scores. AHQ-D HF scores showed a weak negative correlation with grit (r(345) = − 0.29). Though there was a weak negative correlation between HF and social desirability response tendency (r(345) = − 0.24), suggesting that those who care more about what others think may report less HF, there was no relationship between HF and extrasensory perception beliefs (r(345) = 0.01), suggesting that participants were not simply biased in their response tendencies. Taken together, we demonstrate strong scale metrics for the AHQ-D, the expected convergent validity, and a general lack of response bias, in addition to replicating our previous association of HF with ADHD traits. We suggest that the AHQ-D can be confidently used in future work as a valid way to measure HF in adults.

This paper describes a neural network model that can be used to detect at-risk students failing a particular course using only grade book data from a learning management system. By analyzing data extracted from the learning management system at the end of week 5, the model can predict with an accuracy of 88% whether the student will pass or fail a specific course. Data from the grade books from all course shells from the Spring 2022 semester (N = 22,041 rows) were analyzed, and four factors were found to be significant predictors of student success/failure: the current course grade after the fifth week of the semester and the presence of missing grades in weeks 3, 4, and 5. Several models were investigated before concluding that a neural network model had the best overall utility for the purpose of an early alert system. By categorizing students who are predicted to fail more than one course as being generally at risk, we provide a metric for those who use early warning systems to target resources to the most at-risk students and intervene before students drop out. Seventy-four percent of the students whom our model classified as being generally at risk ended up failing at least one course.

Pollinator communities could have nuanced effects on crop yield depending on their species and functional trait compositions. Here, the authors use a meta-analysis to show that, in addition to pollinator abundance, functional trait divergence also positively impacts yield of oilseed rape crops.

There is a growing desire for reliable 21st-century projections of water availability at the regional scale. Global climate models (GCMs) are typically used together with global hydrological models (GHMs) to generate such projections. GCMs alone are unsuitable, especially if they have biased representations of aridity. The Budyko framework represents how water availability varies as a non-linear function of aridity and is used here to constrain projections of runoff from GCMs, without the need for computationally expensive GHMs. Considering a Chinese case study, we first apply the framework to observations to show that the contribution of direct human impacts (water consumption) to the significant decline in Yellow River runoff was greater than the contribution of aridity change by a factor of approximately 2, although we are unable to rule out a significant contribution from the net effect of all other factors. We then show that the Budyko framework can be used to narrow the range of Yellow River runoff projections by 34 %, using a multi-model ensemble and the high-end Representative Concentration Pathway (RCP8.5) emissions scenario. This increases confidence that the Yellow River will see an increase in runoff due to aridity change by the end of the 21st century. Yangtze River runoff projections change little, since aridity biases in GCMs are less substantial. Our approach serves as a quick and inexpensive tool to rapidly update and correct projections from GCMs alone. This could serve as a valuable resource when determining the water management policies required to alleviate water stress for future generations.

Semen is a major vector for HIV transmission, but the semen HIV RNA viral load (VL) only correlates moderately with the blood VL. Viral shedding can be enhanced by genital infections and associated inflammation, but it can also occur in the absence of classical pathogens. Thus, we hypothesized that a dysregulated semen microbiome correlates with local HIV shedding. We analyzed semen samples from 49 men who have sex with men (MSM), including 22 HIV-uninfected and 27 HIV-infected men, at baseline and after starting antiretroviral therapy (ART) using 16S rRNA gene-based pyrosequencing and quantitative PCR. We studied the relationship of semen bacteria with HIV infection, semen cytokine levels, and semen VL by linear regression, non-metric multidimensional scaling, and goodness-of-fit test. Streptococcus, Corynebacterium, and Staphylococcus were common semen bacteria, irrespective of HIV status. While Ureaplasma was the more abundant Mollicutes in HIV-uninfected men, Mycoplasma dominated after HIV infection. HIV infection was associated with decreased semen microbiome diversity and richness, which were restored after six months of ART. In HIV-infected men, semen bacterial load correlated with seven pro-inflammatory semen cytokines, including IL-6 (p = 0.024), TNF-α (p = 0.009), and IL-1b (p = 0.002). IL-1b in particular was associated with semen VL (r(2) = 0.18, p = 0.02). Semen bacterial load was also directly linked to the semen HIV VL (r(2) = 0.15, p = 0.02). HIV infection reshapes the relationship between semen bacteria and pro-inflammatory cytokines, and both are linked to semen VL, which supports a role of the semen microbiome in HIV sexual transmission.

The blood HIV RNA viral load is the best-defined predictor of HIV transmission, in part due to ease of measurement and the correlation of blood and genital tract (semen or cervico-vaginal) viral load, although recent studies found semen HIV RNA concentration to be a stronger predictor of HIV transmission. There is currently no standardized method for semen collection when measuring HIV RNA concentration. Therefore, we compared two collection techniques in order to study of the impact of antiretroviral therapy on the semen viral load.Semen was collected by masturbation from HIV-infected, therapy-naïve men who have sex with men (MSM) either undiluted (Visit 1) or directly into transport medium (Visit 2). Seminal plasma was then isolated, and the HIV RNA concentration obtained with each collection technique was measured and corrected for dilution if necessary. Collection of semen directly into transport medium resulted in a median HIV RNA viral load that was 0.4 log10 higher than undiluted samples.The method of semen collection is an important consideration when quantifying the HIV RNA viral load in this compartment.

HS1136, a 7-page illustrated factsheet by Jonathan H. Crane, Jorge Peña, and J.L. Osborne, describes this insect-fungal pest attacking woody plants in the laurel family, including avocado. Includes descriptions, origin, detection and spread of the pest, plant hosts in the U.S., a map of counties with the beetle, plant host symptoms and damage, management strategies & restrictions, agencies working on the issue, research and extension efforts, and references. Published by the UF Department of Horticultural Sciences, February 2008. Revised December 2008.

HS1136, a 7-page illustrated factsheet by Jonathan H. Crane, Jorge Peña, and J.L. Osborne, describes this insect-fungal pest attacking woody plants in the laurel family, including avocado. Includes descriptions, origin, detection and spread of the pest, plant hosts in the U.S., a map of counties with the beetle, plant host symptoms and damage, management strategies & restrictions, agencies working on the issue, research and extension efforts, and references. Published by the UF Department of Horticultural Sciences, February 2008. Revised December 2008.

HS1136, a 7-page illustrated factsheet by Jonathan H. Crane, Jorge Peña, and J.L. Osborne, describes this insect-fungal pest attacking woody plants in the laurel family, including avocado. Includes descriptions, origin, detection and spread of the pest, plant hosts in the U.S., a map of counties with the beetle, plant host symptoms and damage, management strategies & restrictions, agencies working on the issue, research and extension efforts, and references. Published by the UF Department of Horticultural Sciences, February 2008. Revised December 2008.