Estimating the population size of shy and elusive species is challenging but necessary to inform appropriate conservation actions for threatened or declining species. Using camera-trap surveys conducted during 2017–2021, we estimated and compared African clawless otter Aonyx capensis population densities and activity times in six conserved areas in southern Africa. We used two different models to estimate densities: random encounter models and camera-trap distance sampling. Our results highlight a general pattern of higher estimated densities and narrower confidence intervals using random encounter models compared to camera-trap distance sampling. We found substantial variation in densities between study areas, with random encounter model estimates ranging between 0.9 and 4.2 otters/km2. Our camera-trap distance sampling estimates supported the relative density estimates obtained from random encounter models but were generally lower and more variable, ranging from 0.8 to 4.0 otters/km2. We found significant differences in otter activity patterns, with populations either being nocturnal, mostly nocturnal or cathemeral. As all study areas experience little human disturbance, our results suggest that there are large natural variations in otter densities and activity patterns between regions. When densities are converted to metrics that are comparable to previous studies, our estimates suggest that African clawless otter population numbers are generally lower than previously reported. This highlights a need for broader spatial coverage of otter population assessments and future studies to assess potential environmental drivers of spatial, and potentially temporal, variation in population numbers and activity patterns.

What determines why some protest events last only a single day while others can stretch over multiple days? This study presents the first cross-national quantitative analysis of the factors that shape protest event duration. This study argues that protest event duration is the function of factors that increase momentum (e.g. protest size, location and participants) while also examining whether repression attenuates such momentum. Using the Armed Conflict Location Event Data, this study employs two multilevel statistical methods to examine the factors that matter. First, the study examines the day-by-day factors that shape whether a protest will continue the next day. Second, the study examines the overall duration of events. The analyses find strong support that protests in capitals and urban areas, as well as protests featuring students, labour unions and professional organizations, last longer, while repression does truncate events.

Several neural networks have been proposed in the general literature for pattern recognition and clustering, but little empirical comparison with traditional methods has been done. The results reported here compare neural networks using Kohonen learning with a traditional clustering method (K-means) in an experimental design using simulated data with known cluster solutions. Two types of neural networks were examined, both of which used unsupervised learning to perform the clustering. One used Kohonen learning with a conscience and the other used Kohonen learning without a conscience mechanism. The performance of these nets was examined with respect to changes in the number of attributes, the number of clusters, and the amount of error in the data. Generally, the K-means procedure had fewer points misclassified while the classification accuracy of neural networks worsened as the number of clusters in the data increased from two to five.

Statesman and scholar Alexis de Tocqueville (1876) once noted, “History is a gallery of pictures in which there are few originals and many copies.” In other words, history has a habit of repeating itself, and we can deduce cycles and patterns that likely will recur. Such stability and inertia should bode well for prediction. Nevertheless, when it comes to election forecasting, especially in the United States, most prognostications rely on short-term political fundamentals measuring macroeconomic performance or government or leader popularity. In this contribution, we adopt a structural approach but depart from existing literature by focusing on historical party and governance dynamics in the vein of de Tocqueville to establish whether they offer solid guidance regarding the performance of Democrats in US congressional elections. Our ex-post political history models provide solid predictions of which party will control Congress and the Democrats’ seat tally in each chamber between 1946 and 2022. This creates conditions to assume that political history may help us forecast Campaign 2024. Our study applied this political history model to predict the 2024 congressional elections. It forecast Democrats to lose control of the Senate with a net loss of three seats and estimated an exceptionally close race for House control, with the point estimate for the House suggesting that the Democrats would fall short of winning control.

Our political economy model, as it has come to be called, has offered up forecasts of the American presidential election outcome since the early 1980s. The model, based on referendum theory, as measured by the job performance of the president and the economy (1948 to the present), yields a forecast from data available in the summer of the election year. We consider alternative specifications of this parsimonious model, examining the possible effects of other economic measures, Covid-19, and incumbency advantage on forecasting. The current point estimate of the core political economy model predicts the Democratic candidate will receive 48 percent of the two-party popular vote, which translates into a narrow Electoral College loss for the incumbent party. This point forecast, however, comes with a considerable amount of uncertainty. There is an 11-point spread around our point estimate, which effectively means we have a horserace on our hands, with both horses close to the finish line.

In the initial wave of the opioid crisis, uninformed prescribing practices and lax oversight were the drivers of opioid addiction and death. Although opioid prescriptions have decreased by 44.4 percent between 2011-2020,1 the number of deaths linked to prescription opioids has decreased only marginally.2 The marked fall in opioid prescribing without a concomitant reduction in opioid-related deaths suggests that an at-risk population continued to receive prescription opioids, whether directly or indirectly, from a medical professional. Currently, illicitly manufactured fentanyl (IMF) is the culprit for the majority of the approximately 81,000 annual opioid-related deaths.3 This finding has been misleadingly used to suggest that prescription opioids for chronic pain are no longer (and never were) a relevant concern,4 while the reality is that their lethal consequences are simply dwarfed by the marked rise in IMF deaths.5

Background: Efgartigimod, a human immunoglobulin G (IgG)1 antibody Fc fragment, blocks the neonatal Fc receptor, decreasing IgG recycling and reducing pathogenic IgG autoantibody levels. ADHERE assessed the efficacy and safety of efgartigimod PH20 subcutaneous (SC; co-formulated with recombinant human hyaluronidase PH20) in chronic inflammatory demyelinating polyneuropathy (CIDP). Methods: ADHERE enrolled participants with CIDP (treatment naive or on standard treatments withdrawn during run-in period) and consisted of open-label Stage A (efgartigimod PH20 SC once weekly [QW]), and randomized (1:1) Stage B (efgartigimod or placebo QW). Primary outcomes were clinical improvement (assessed with aINCAT, I-RODS, or mean grip strength; Stage A) and time to first aINCAT score deterioration (relapse; Stage B). Secondary outcomes included treatment-emergent adverse events (TEAEs) incidence. Results: 322 participants entered Stage A. 214 (66.5%) were considered responders, randomized, and treated in Stage B. Efgartigimod significantly reduced the risk of relapse (HR: 0.394; 95% CI: 0.25–0.61) versus placebo (p=0.000039). Reduced risk of relapse occurred in participants receiving corticosteroids, intravenous or SC immunoglobulin, or no treatment before study entry. Most TEAEs were mild to moderate; 3 deaths occurred, none related to efgartigimod. Conclusions: Participants treated with efgartigimod PH20 SC maintained a clinical response and remained relapse-free longer than those treated with placebo.

Helminth parasites were studied in the wood mouse, Apodemus sylvaticus, in southern England in September of each of four successive years (1994–1997). Nine species of helminths were recorded: five nematodes (Heligmosomoides polygyrus, Syphacia stroma, Pelodera strongyloides, Trichuris muris, Capillaria murissylvatici), two cestodes (Microsomacanthus crenata, Taenia taeniaeformis) and two trematodes (Corrigia vitta, Brachylaemus recurvum). In total, 134 mice were examined and 91.8% carried at least one species of helminth. The majority of mice carried two to three species (60.5%) and the highest combination was six of the nine species recorded in the study. The patterns of between-year variations in the prevalence and abundance of infection were different for each of the six species for which sufficient quantitative data were available to enable statistical analysis. For H. polygyrus, the most important source of variation arose from between-year differences, host age and the interaction of these factors: abundance increased with host age but in 1995 the age pattern was markedly different from that in the remaining years. The abundance of C. vitta also varied significantly between years but additionally there was a strong independent age effect. For M. crenata, the year × age interaction was significant, indicating that abundance among different age cohorts varied from year to year but there was also a weak significant main effect of age arising from the youngest age cohort carrying no parasites and the oldest age cohort the heaviest infections. For P. strongyloides the only significant factor was between-year variation with 1995 being a year of exceptionally low prevalence and abundance of infection. No significant between-year variation was detected for S. stroma but there was a strong sex effect (males carrying heavier infections) and an age effect (older mice of both sexes carrying heavier infections). The abundance of Trichuris muris varied only in relation to host age, worm burdens growing in intensity with increasing age, but there was also a significant interaction between year and host sex with respect to prevalence. For the remaining three species, the prevalence of infections was too low (< 8.2%) to enable any meaningful interpretation. This analysis emphasizes the need for carefully controlled statistical procedures in aiding the interpretation and the prioritization of the factors affecting worm burdens in wild rodents.

OBJECTIVES/GOALS: We designed the Biocascade Exhaled Breath Sampler (BEBS) to characterize viral aerosol shedding among individuals with influenza and other respiratory virus infections. We first aimed to test the BEBS on volunteer COVID-19 cases and report the aerodynamic size distribution of exhaled breath aerosol particles carrying SARS-CoV-2 RNA. METHODS/STUDY POPULATION: From June 15 through December 15, 2022, we recruited 27 PCR-confirmed COVID-19 cases from a college campus and the surrounding community to provide 30-minute breath samples into a well-validated Gesundheit-II (G-II) exhaled breath aerosol sampler. Among these individuals, 17 provided an additional exhaled breath sample into the newly designed BEBS. We quantified samples for viral RNA using reverse transcription digital polymerase chain reaction (RT-dPCR) and determined the viral RNA copies collected within two aerosol size fractions (≤5 µm and >5 µm in diameter) from the G-II, and four aerosol size fractions (<1.15 µm, 1.15–3.2 µm, 3.3–8.2 µm, and >8.2 µm) from the BEBS. RESULTS/ANTICIPATED RESULTS: Individuals with a SARS-CoV-2 Omicron BA.4 or BA.5 infection shed virus in aerosols at an average rate of 7.5x103 RNA copies per 30-minute G-II sample, with 78% of the total RNA in aerosols ≤5 µm in diameter. Among the BEBS samples, 10% of the total viral RNA was detected in aerosols <1.15 µm, 43% in 1.15–3.2 µm, 37% in 3.3–8.2 µm, and 10% in the >8.2 µm size fraction. Based on viral RNA loads, our results indicate that exhaled aerosols ≤3.2 µm contribute the majority of SARS-CoV-2 inhalation exposure. DISCUSSION/SIGNIFICANCE: Our data provide additional evidence that respirable aerosols contribute to the spread of SARS-CoV-2. Thus, our data suggest that mitigation measures designed to reduce infectious aerosol inhalation, such as ventilation and the use of air cleaners and respirators, are needed to control the spread.

The California Department of Public Health (CDPH) reviewed 109 cases of healthcare personnel (HCP) with laboratory-confirmed mpox to understand transmission risk in healthcare settings. Overall, 90% of HCP with mpox had nonoccupational exposure risk factors. One occupationally acquired case was associated with sharps injury while unroofing a patient’s lesion for diagnostic testing.