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Changes in the character of rainfall are assessed using a holistic set of statistics based on rainfall frequency and amount distributions in climate change experiments with three conventional and superparameterized versions of the Community Atmosphere Model (CAM and SPCAM). Previous work has shown that high-order statistics of present-day rainfall intensity are significantly improved with superparameterization, especially in regions of tropical convection. Globally, the two modeling approaches project a similar future increase in mean rainfall, especially across the Inter-Tropical Convergence Zone (ITCZ) and at high latitudes, but over land, SPCAM predicts a smaller mean change than CAM. Changes in high-order statistics are similar at high latitudes in the two models but diverge at lower latitudes. In the tropics, SPCAM projects a large intensification of moderate and extreme rain rates in regions of organized convection associated with the Madden Julian Oscillation, ITCZ, monsoons, and tropical waves. In contrast, this signal is missing in all versions of CAM, which are found to be prone to predicting increases in the amount but not intensity of moderate rates. Predictions from SPCAM exhibit a scale-insensitive behavior with little dependence on horizontal resolution for extreme rates, while lower resolution (∼2°) versions of CAM are not able to capture the response simulated with higher resolution (∼1°). Moderate rain rates analyzed by the “amount mode” and “amount median” are found to be especially telling as a diagnostic for evaluating climate model performance and tracing future changes in rainfall statistics to tropical wave modes in SPCAM.

This study evaluates several important statistics of daily rainfall based on frequency and amount distributions as simulated by a global climate model whose precipitation does not depend on convective parameterization - Super-Parameterized Community Atmosphere Model (SPCAM). Three superparameterized and conventional versions of CAM, coupled within the Community Earth System Model (CESM1 and CCSM4), are compared against two modern rainfall products (GPCP 1DD and TRMM 3B42) to discriminate robust effects of superparameterization that emerge across multiple versions. The geographic pattern of annual-mean rainfall is mostly insensitive to superparameterization, with only slight improvements in the double-ITCZ bias. However, unfolding intensity distributions reveal several improvements in the character of rainfall simulated by SPCAM. The rainfall rate that delivers the most accumulated rain (i.e., amount mode) is systematically too weak in all versions of CAM relative to TRMM 3B42 and does not improve with horizontal resolution. It is improved by superparameterization though, with higher modes in regions of tropical wave, Madden-Julian Oscillation, and monsoon activity. Superparameterization produces better representations of extreme rates compared to TRMM 3B42, without sensitivity to horizontal resolution seen in CAM. SPCAM produces more dry days over land and fewer over the ocean. Updates to CAM's low cloud parameterizations have narrowed the frequency peak of light rain, converging toward SPCAM. Poleward of 50°, where more rainfall is produced by resolved-scale processes in CAM, few differences discriminate the rainfall properties of the two models. These results are discussed in light of their implication for future rainfall changes in response to climate forcing.

Summary: Background: In 2022 and 2023, novel reassortant H3N8 influenza viruses infected three people, marking the first human infections with viruses of this subtype. Methods: Here, we generated one of these viruses (A/Henan/4-10CNIC/2022; hereafter called A/Henan/2022 virus) by using reverse genetics and characterized it. Findings: In intranasally infected mice, reverse genetics-generated A/Henan/2022 virus caused weight loss in all five animals (one of which had to be euthanized) and replicated efficiently in the respiratory tract. Intranasal infection of ferrets resulted in minor weight loss and moderate fever but no mortality. Reverse genetics-generated A/Henan/2022 virus replicated efficiently in the upper respiratory tract of ferrets but was not detected in the lungs. Virus transmission via respiratory droplets occurred in one of four pairs of ferrets. Deep-sequencing of nasal swab samples from inoculated and exposed ferrets revealed sequence polymorphisms in the haemagglutinin protein that may affect receptor-binding specificity. We also tested 90 human sera for neutralizing antibodies against reverse genetics-generated A/Henan/2022 virus and found that some of them possessed neutralizing antibody titres, especially sera from older donors with likely exposure to earlier human H3N2 viruses. Interpretation: Our data demonstrate that reverse genetics-generated A/Henan/2022 virus is a low pathogenic influenza virus (of avian influenza virus descent) with some antigenic resemblance to older human H3N2 viruses and limited respiratory droplet transmissibility in ferrets. Funding: This work was supported by the Japan Program for Infectious Diseases Research and Infrastructure (JP23wm0125002), and the Japan Initiative for World-leading Vaccine Research and Development Centers (JP233fa627001) from the Japan Agency for Medical Research and Development (AMED).

Observations of ionized outflows from active galactic nuclei (AGNs) provide evidence of energy and momentum transfer from the AGN radiation to the plasma. The AGN radiation is very energetic. Therefore, at distances of parsec scale, where gravity is relatively weak, energy transfer alone can lead to outflow. Much closer to the black hole, gravity dominates thermal energy and the gas is in the so-called “cold” regime. Only magnetic or radiation forces can lead to outflow. However, it is unclear when the radiation force is efficient in overcoming gravity because of its dependence on the spectral energy distribution (SED) of the radiation and opacity. In this work, we survey the parameter space of radiation forces due to spectral lines resulting from blackbody SEDs with temperatures ranging from ∼10 ^4 to 10 ^6 K. The objective was to identify the radiation temperature above which line driving becomes inefficient. We find that the temperature ≲4 × 10 ^5 K marks such a transition. We also self-consistently calculate heating and cooling balance to estimate gas temperature and identify the transition where thermal driving becomes comparable to line driving. We summarize hydrodynamical simulations of radial outflows to illustrate how wind properties change during the transition from line to thermal driving and their dependence on outflow parameters and SED.

In disk-wind models for active galactic nuclei outflows, high-energy radiation poses a significant problem wherein the gas can become overionized, effectively disabling what is often inferred to be the largest force acting on the gas: the radiation force due to spectral line opacity. Calculations of this radiation force depend on the magnitude of ionizing radiation, which can strongly depend on the position above a disk where the radiation is anisotropic. As our first step to quantify the position and direction dependence of the radiation field, we assumed free streaming of photons and computed energy distributions of the mean intensity and components of flux as well as energy-integrated quantities such as mean photon energy. We find a significant dependence of radiation-field properties on position, but this dependence is not necessarily the same for different field quantities. A key example is that the mean intensity is much softer than the radial flux at many points near the disk. Because the mean intensity largely controls ionization, this softening decreases the severity of the overionization problem. The position dependence of mean intensity implies the position dependence of gas opacity, which we illustrate by computing the radiation force a fluid element feels in an accelerating wind. We find that in a vertical accelerating flow, the force due to radiation is not parallel to the radiation flux. This misalignment is due to the force’s geometric weighting by both the velocity field’s directionality and the position dependence of the mean intensity.

Techniques of 3D modeling have earned increasing popularity in scientific studies as they offered unprecedented traits in representing objects. As with all mathematical models, the 3D model will be useful once its accuracy has been validated with direct measurements and the robustness of its predictive capability tested. Although measures of body mass and volume are essential to comprehend the life history of animals, such morphometrics, especially total volume, are challenging to obtain from marine mammals due to their elusive nature, aquatic lifestyle, and large size. In this study, accurate measurements of body volume were directly detected from fresh carcasses of eleven finless porpoises (Neophocaena asiaeorientalis sunameri) and used to validate 3D models recreated from the same animals using Blender 3D graphics. Published models using truncated cones or elliptical shapes, based on 3, 5, 8, or 19 measurements of girths or heights & widths along the body, were also applied to porpoises to verify their accuracy. The Blender-generated 3D model produced the most accurate estimates of body volume compared to conventional truncated models, with a mean error of only 2.5% to the direct volume measurements. When photogrammetric images are available, the model can predict the body volumes based on total length alone. Similar accuracy was possible with the elliptical model using 19 height & width measurements (5% increments in total length). However, significant (p < 0.001) inaccuracy resulted from truncated models with 3, 5, or 8 girth measurements and elliptical models with 3 or 5 height & width measurements, and the accuracy of these models also decreased significantly as the number of measurement sites was reduced. Moreover, Blender 3D models can be extrapolated to animals with images absent, and such predicted volumes were tested to be trustworthy (overall skill = 0.998, r = 0.998, p < 0.01). Therefore, we recommend that researchers use either digital 3D models or elliptical models with 5% increments sectioning the torso to obtain accurate estimates of the body volumes of free-ranging marine mammals.

The recent increase in online instruction caused by the 2020 pandemic has reignited the debate over the efficacy of online instruction compared to in-person instruction. This study explored the impact various modes of instruction had on student achievement and compared students’ stated modality preference with their actual attendance behavior. Results show that while most students preferred in-person instruction (74%), only 47% of those who indicated they preferred in-person instruction attended class only in-person. Many attended classes utilizing a modality other than in-person, and several students switched their preferred modality away from in-person instruction. In general, students who attended class using a mixed modality or in-person achieved slightly better results than those attending class live online. However, the only statistically significant difference was between those participating predominantly in person and those watching class recordings (88% compared to 84% on average). Overall, many students achieved high grades regardless of their chosen instructional modality. In addition, students reported appreciating the option of multiple modalities when they could not attend class in their preferred modality. These results suggest offering a choice of modalities benefited many students.

Aims: Reduced pre-operative cognitive functioning in older adults is a risk factor for postoperative complications, but it is unknown if preoperative digitally-acquired clock drawing test (CDT) cognitive screening variables, which allow for more nuanced examination of patient performance, may predict lengthier hospital stay and greater cost of hospital care. This issue is particularly relevant for older adults undergoing transcatheter aortic valve replacement (TAVR), as this surgical procedure is chosen for intermediate-risk older adults needing aortic replacement. This proof of concept research explored if specific latency and graphomotor variables indicative of planning from digitally-acquired command and copy clock drawing would predict post-TAVR duration and cost of hospitalization, over and above age, education, American Society of Anesthesiologists (ASA) physical status classification score, and frailty. Methods: Form January 2018 to December 2019, 162 out of 190 individuals electing TAVR completed digital clock drawing as part of a hospital wide cognitive screening program. Separate hierarchical regressions were computed for the command and copy conditions of the CDT and assessed how a-priori selected clock drawing metrics (total time to completion, ideal digit placement difference, and hour hand distance from center; included within the same block) incrementally predicted outcome, as measured by R2 change significance values. Results: Above and beyond age, education, ASA physical status classification score, and frailty, only digitally-acquired CDT copy performance explained significant variance for length of hospital stay (9.5%) and cost of care (8.9%). Conclusions: Digital variables from clock copy condition provided predictive value over common demographic and comorbidity variables. We hypothesize this is due to the sensitivity of the copy condition to executive dysfunction, as has been shown in previous studies for subtypes of cognitive impairment. Individuals undergoing TAVR procedures are often frail and executively compromised due to their cerebrovascular disease. We encourage additional research on the value of digitally-acquired clock drawing within different surgery types. Type of cognitive impairment and the value of digitally-acquired CDT command and copy parameters in other surgeries remain unknown.

We examined the construct of mental planning by quantifying digital clock drawing digit placement accuracy in command and copy conditions, and by investigating its underlying neuropsychological correlates and functional connectivity. We hypothesized greater digit misplacement would associate with attention, abstract reasoning, and visuospatial function, as well as functional connectivity from a major source of acetylcholine throughout the brain: the basal nucleus of Meynert (BNM). Participants (n = 201) included non-demented older adults who completed all metrics within 24 h of one another. A participant subset met research criteria for mild cognitive impairment (MCI; n = 28) and was compared to non-MCI participants on digit misplacement accuracy and expected functional connectivity differences. Digit misplacement and a comparison dissociate variable of total completion time were acquired for command and copy conditions. a priori fMRI seeds were the bilateral BNM. Command digit misplacement is negatively associated with semantics, visuospatial, visuoconstructional, and reasoning (p’s < 0.01) and negatively associated with connectivity from the BNM to the anterior cingulate cortex (ACC; p = 0.001). Individuals with MCI had more misplacement and less BNM-ACC connectivity (p = 0.007). Total completion time involved posterior and cerebellar associations only. Findings suggest clock drawing digit placement accuracy may be a unique metric of mental planning and provide insight into neurodegenerative disease.

Developing tools for efficiently measuring cognitive change specifically and brain health generally—whether for clinical use or as endpoints in clinical trials—is a major challenge, particularly for conditions such as Alzheimer's disease. Technology such as connected devices and advances in artificial intelligence offer the possibility of creating and deploying clinical-grade tools with high sensitivity, rapidly, cheaply, and non-intrusively. Starting from a widely-used paper and pencil cognitive status test—The Clock Drawing Test—we combined a digital input device to capture time-stamped drawing coordinates with a machine learning analysis of drawing behavior to create DCTclock™, an automated analysis of nuances in cognitive performance beyond successful task completion. Development and validation was conducted on a dataset of 1,833 presumed cognitively unimpaired and clinically diagnosed cognitively impaired individuals with varied neurological conditions. We benchmarked DCTclock against existing clock scoring systems and the Mini-Mental Status Examination, a widely-used but lengthier cognitive test, and showed that DCTclock offered a significant improvement in the detection of early cognitive impairment and the ability to characterize individuals along the Alzheimer's disease trajectory. This offers an example of a robust framework for creating digital biomarkers that can be used clinically and in research for assessing neurological function.

While access to higher education has grown over the past few years, significant barriers exist for nontraditional students attempting to prepare for and complete postsecondary education. For these students, the traditional methods for acknowledging student achievement do not always work. This research explored the impact of earning professional certificates on nontraditional students’ confidence, motivation, and persistence. Specifically, this study evaluated the matriculation rates between two cohorts of students who participated in the online PathwayConnect program. The mixed-method study found that matriculation rates for students who were encouraged to earn a certificate increased compared to those who followed a traditional path. Analysis of these results suggests that earning a certificate increased students’ confidence and motivation to persist in school. Students reported that (a) earning certificates represented a significant achievement and encouraged them to earn more, (b) the certificate allowed participants to obtain better employment, and (c) the satisfaction of this accomplishment improved their desire to acquire new skills. This approach seemed to positively impact on the development of a strong support network that helped diminish some of the traditional barriers to matriculation. Institutions seeking to improve matriculation rates and student persistence might consider providing professional certificates programs in addition to current academic offerings.

Analyzing the learning analytics from a course provides insights that can impact instructional design decisions. This study used educational data mining techniques, specifically a longitudinal k-means cluster analysis, to identify the strategies students used when completing the online portion of an online flipped spreadsheet course. An analysis of these results showed that students did tend to follow a specific learning strategy as they completed this course. However, students also self-regulated to some degree, based on the topic and context of specific lessons. These insights not only improve our understanding about the students taking the course, but they also provide guidance for how the instructional design of the course might be improved. Of note is the practical value of this proof-of-concept study in using educational data mining to improve the instructional design of a course.

Broad assumptions are commonly made on how technology, including anything from computers, whiteboards, and iPads, can transform and revolutionize teaching, independent learning, assessment, and other educational initiatives. Furthermore, changes in school programs are at times instituted at break-neck speeds with little understanding of the effects that technology can have on the actual learning process. This "perspectives" piece calls for a need to maintain realistic expectations and to invest in training, not devices