Variation in Sex Allocation and Floral Morphology in an Expanding Distylous Plant Hybrid Complex

Premise of research. Sex allocation, the relative energy devoted to producing pollen, ovules, and floral displays, can significantly affect reproductive output and population dynamics. In this study, we investigated floral morphology and gamete production in bisexual, distylous plants from a self-incompatible hybrid complex (Piriqueta cistoides ssp. caroliniana Walter [Arbo]; Turneraceae). Sampling focused on two parent types (C, V) and their stable hybrid derivative (H). Since H morphotypes are heterotic for growth and fruit production, we hypothesized that they would produce larger flowers with more gametes. We also anticipated that plants with long styles (long morphs) would produce less pollen than short morphs, since long-morph pollen is larger. Methodology. Over two consecutive summers, flowers were collected from 1465 individual plants in 28 field populations. Floral parameters were measured digitally, and each flower’s pollen number, ovule number, and stigma-anther separation was quantified under a dissecting microscope. Gamete production (n = 332) and stigma-anther separation (n = 119) were also quantified for plants from a greenhouse accession. Pivotal results. Floral display differed among morphotypes, with H plants producing the largest flowers and C plants displaying the least petal separation. Hybrid morphotypes produced significantly more pollen than parental morphotypes, and pollen quantity was significantly greater for long morphs. Ovule production, however, was greatest for V flowers. Stigma-anther separation differed between years and style morphs (greater for short morphs) but not among morphotypes or within a single season. Conclusions. Differences in pollen production between morphs were not consistent with trade-offs in pollen size and number or selection for increased male function in short morphs. Greater stigma-anther separation in short morphs supported the hypothesis of selection to reduce pollen interference. Enhanced floral display and pollen production followed other heterotic traits observed in H morphotypes. The superior ability of H morphotypes to attract pollinators and sire seeds might partially explain this hybrid zone’s continuing expansion

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Risk factor optimization and guideline-directed medical therapy in us veterans with peripheral arterial and ischemic cerebrovascular disease compared to veterans with coronary heart disease

Cardiovascular disease (CVD) is a systemic process involving multiple vascular beds and includes coronary heart disease (CHD), ischemic cerebrovascular disease (ICVD), and peripheral arterial disease (PAD). All these manifestations are associated with an increased risk of subsequent myocardial infarction, stroke, and death. Guideline-directed medical therapy is recommended for all patients with CVD. In a cohort of US veterans, we identified 1,242,015 patients with CVD receiving care in 130 Veterans Affairs facilities from October 1, 2013 to September 30, 2014. CVD included diagnoses of CHD, PAD, or ICVD. We assessed the frequency of risk factor optimization and the use of guideline-directed medical therapy in patients with CHD, PAD alone, ICVD alone, and PAD + ICVD groups. A composite of 4 measures (blood pressure \u3c140/90 mm Hg, A1c \u3c7% in diabetics, statin use, and antiplatelet use in eligible patients), termed optimal medical therapy (OMT) was compared among groups. Multivariate logistic regression was performed with CHD as the referent category. CHD comprised 989,380 (79.7%), PAD alone 70,404 (5.7%), ICVD alone 163,730 (13.2%), and PAD + ICVD 18,501 (1.5%) of the cohort. Overall, only 36% received OMT with adjusted odds ratios of 0.54 (95% CI 0.53 to 0.55), 0.77 (0.76 to 0.78), and 0.97 (0.94 to 1.00) for patients with PAD alone, ICVD alone, and PAD + ICVD, respectively, compared with patients with CHD. In conclusion, OMT was low in all groups. Patients with PAD alone and ICVD alone were less likely to receive OMT than those with CHD and PAD + ICVD

Artificial Intelligence Support for Informal Patient Caregivers: A Systematic Review

This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI’s role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI’s role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers’ effectiveness and well-being

Association of Neutralizing Antispike Monoclonal Antibody Treatment With Coronavirus Disease 2019 Hospitalization and Assessment of the Monoclonal Antibody Screening Score

Objective: To test the hypothesis that the Monoclonal Antibody Screening Score performs consistently better in identifying the need for monoclonal antibody infusion throughout each “wave” of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant predominance during the coronavirus disease 2019 (COVID-19) pandemic and that the infusion of contemporary monoclonal antibody treatments is associated with a lower risk of hospitalization. Patients and Methods: In this retrospective cohort study, we evaluated the efficacy of monoclonal antibody treatment compared with that of no monoclonal antibody treatment in symptomatic adults who tested positive for SARS-CoV-2 regardless of their risk factors for disease progression or vaccination status during different periods of SARS-CoV-2 variant predominance. The primary outcome was hospitalization within 28 days after COVID-19 diagnosis. The study was conducted on patients with a diagnosis of COVID-19 from November 19, 2020, through May 12, 2022. Results: Of the included 118,936 eligible patients, hospitalization within 28 days of COVID-19 diagnosis occurred in 2.52% (456/18,090) of patients who received monoclonal antibody treatment and 6.98% (7,037/100,846) of patients who did not. Treatment with monoclonal antibody therapies was associated with a lower risk of hospitalization when using stratified data analytics, propensity scoring, and regression and machine learning models with and without adjustments for putative confounding variables, such as advanced age and coexisting medical conditions (eg, relative risk, 0.15; 95% CI, 0.14-0.17). Conclusion: Among patients with mild to moderate COVID-19, including those who have been vaccinated, monoclonal antibody treatment was associated with a lower risk of hospital admission during each wave of the COVID-19 pandemic