Neural Regions Underlying Object and Action Naming: Complementary Evidence from Acute Stroke and Primary Progressive Aphasia

Background: Naming impairment is commonly noted in individuals with aphasia. However, object naming receives more attention than action naming. Furthermore, most studies include participants with aphasia due to only one aetiology, commonly stroke. We developed a new assessment, the Hopkins Action Naming Assessment (HANA), to evaluate action naming impairments. Methods \u3e& Procedures: Participants (N = 138 PPA, N = 37 acute stroke) completed the BNT and HANA. Behavioural performance was compared. A subset of participants (N = 31 PPA, N = 37 acute stroke) provided neuroimaging data. The whole brain was automatically segmented into regions of interest (ROIs). For participants with PPA, the image variables were the ROI volumes, normalised by brain volume. For participants with acute stroke, the image variables were the percentage of each ROI that was lesioned. The relationship between ROIs likely to be involved in naming performance was modelled with LASSO regression. Outcomes & Results: Behavioural results showed a double dissociation in performance: in each group, some participants displayed intact performance relative to healthy controls on actions but not objects and/or significantly better performance on actions than objects, while others showed the opposite pattern. These results support the need to assess both objects and actions when evaluating naming deficits. Neuroimaging results identified different regions associated with object vs. action naming, implicating overlapping but distinct networks of regions. Furthermore, results differed for participants with PPA vs. acute stroke, indicating that critical information may be missed when only one aetiology is considered. Conclusions: Overall, the study provides a more comprehensive picture of the neural bases of naming, underscoring the importance of assessing both objects and actions and considering different aetiologies of damage. It demonstrates the HANA\u27s utility

Environmental control of tropical cyclones in CMIP5: A ventilation perspective

The ventilation index serves as a theoretically based metric to assess possible changes in the statistics of tropical cyclones to combined changes in vertical wind shear, midlevel entropy deficit, and potential intensity in climate models. Model output from eight Coupled Model Intercomparison Project 5 models is used to calculate the ventilation index. The ventilation index and its relationship to tropical cyclone activity between two 20 year periods are compared: the historical experiment from 1981 to 2000 and the RCP8.5 experiment from 2081 to 2100. The general tendency is for an increase in the seasonal ventilation index in the majority of the tropical cyclone basins, with exception of the North Indian basin. All the models project an increase in the midlevel entropy deficit in the tropics, although the effects of this increase on the ventilation index itself are tempered by a compensating increase in the potential intensity and a decrease in the vertical wind shear in most tropical cyclone basins. The nonlinear combination of the terms in the ventilation index results in large regional and intermodel variability. Basin changes in the ventilation index are well correlated with changes in the frequency of tropical cyclone formation and rapid intensification in the climate models. However, there is large uncertainty in the projections of the ventilation index and the corresponding effects on changes in the statistics of tropical cyclone activity

Estimating the Population Size of Female Sex Workers in Zimbabwe: Comparison of Estimates Obtained Using Different Methods in Twenty Sites and Development of a National-Level Estimate.

BACKGROUND: National-level population size estimates (PSEs) for hidden populations are required for HIV programming and modelling. Various estimation methods are available at the site-level, but it remains unclear which are optimal and how best to obtain national-level estimates. SETTING: Zimbabwe. METHODS: Using 2015-2017 data from respondent-driven sampling (RDS) surveys among female sex workers (FSW) aged 18+ years, mappings, and program records, we calculated PSEs for each of the 20 sites across Zimbabwe, using up to 3 methods per site (service and unique object multipliers, census, and capture-recapture). We compared estimates from different methods, and calculated site medians. We estimated prevalence of sex work at each site using census data available on the number of 15-49-year-old women, generated a list of all "hotspot" sites for sex work nationally, and matched sites into strata in which the prevalence of sex work from sites with PSEs was applied to those without. Directly and indirectly estimated PSEs for all hotspot sites were summed to provide a national-level PSE, incorporating an adjustment accounting for sex work outside hotspots. RESULTS: Median site PSEs ranged from 12,863 in Harare to 247 in a rural growth-point. Multiplier methods produced the highest PSEs. We identified 55 hotspots estimated to include 95% of all FSW. FSW nationally were estimated to number 40,491, 1.23% of women aged 15-49 years, (plausibility bounds 28,177-58,797, 0.86-1.79%, those under 18 considered sexually exploited minors). CONCLUSION: There are large numbers of FSW estimated in Zimbabwe. Uncertainty in population size estimation should be reflected in policy-making

How Well Do Seasonal Climate Anomalies Match Expected El Niño-Southern Oscillation (ENSO) Impacts?

Abstract Did the strong 2023–24 El Niño live up to the hype? While climate prediction is inherently probabilistic, many users compare El Niño events against a deterministic map of expected impacts (e.g., wetter or drier regions). Here, using this event as a guide, we show that no El Niño perfectly matches the ideal image and that observed anomalies will only partially match what was anticipated. In fact, the degree to which the climate anomalies match the expected ENSO impacts tends to scale with the strength of the event. The 2023–24 event generally matched well with ENSO expectations around the United States. However, this will not always be the case, as the analysis shows larger deviations from the historical ENSO pattern of impacts are commonplace, with some climate variables more prone to inconsistencies (e.g., temperature) than others (e.g., precipitation). Users should incorporate this inherent uncertainty in their risk and decision-making analysis