Combined Dataset Research Articles

Determining the Interstellar Wind Longitudinal Inflow Evolution Using Pickup Ions in the Helium Focusing Cone

The size, shape, and composition of the heliosphere are affected by its interaction with the local interstellar medium. A means of quantifying this interaction is by measuring the relative motion of the two media. We determine the flow direction, λISN , of the interstellar wind through the heliosphere and its trend over an 11 yr solar cycle using Advanced Composition Explorer/Solar Wind Ion Composition Spectrometer He+ double-coincidence pickup ion (PUI) measurements from a data set spanning 13 full orbits, with focusing cone crossings occurring between the years 1998 and 2010. We measure the flow direction by fitting a kappa function to the focusing cone signature in the count rates of He+ at the PUI cutoff measured as a function of ecliptic longitude. We determine λISN for each three-orbit boxcar centered on the years 1999 through 2009 and find the trend of the resulting linear fit. We account for solar transients by removing measurements associated with CMEs. However, there still may be effects from compression regions, such as stream–stream and corotating interaction regions. We additionally make considerations to ensure the PUI torus is in view, to account for varying interplanetary magnetic field angles, to ensure that we analyze newly injected interstellar PUIs largely unaffected by transport effects, and to account for general solar activity. We measure a slope in the flow direction to be 0.00° ± 0.51°​​​​ yr−1, indicating that we do not measure a varying trend over this period. We repeat the focusing cone analysis for the combined data set and determine an overall flow direction of 75.37° ± 0.43°.

Novel phylogenetic analysis of the Mesozoic common gymnosperm Xenoxylon Gothan reveals close affinity with extant Podocarpaceae (Coniferales)

AbstractFossil wood is one of the crucial proxies for understanding terrestrial vegetation composition and development in the Earth's history. The gymnosperm wood taxon Xenoxylon Gothan is a significant member of the Mesozoic flora. To date, more than 20 species of Xenoxylon have been described. However, its botanical affinities have remained enigmatic ever since it was described, over a century ago. Here we perform a phylogenetic analysis of Xenoxylon to understand the systematic relationship with extant conifers. Data come from four nucleotide regions (trnL‐F, trnK‐matK, rbcL, psbA‐trnH), xylological characters, and biomolecular composition of five extant conifer families; for Xenoxylon, no nucleotide data are available. Using maximum parsimony in Tree Analysis using New Technology, Xenoxylon appeared basal to Araucariaceae in the data set combining genes and xylological characters, whereas Xenoxylon is placed next to Podocarpaceae in the data set combining genes and biomolecular characters. To find a reliable systematic placement of Xenoxylon, a combined data set of genes, xylological and biomolecular characters is analyzed. Our results and interpretations indicate that Xenoxylon is closely related to Podocarpaceae. This first phylogenetic analysis of Xenoxylon fills the knowledge gap of the systematic relationship of this taxon and contributes to a better understanding of the evolution of extant Podocarpaceae.

Variation in spatial population structure in the Anopheles gambiae species complex.

Anopheles gambiae, Anopheles coluzzii, and Anopheles arabiensis are three of the most widespread vectors of malaria parasites, with geographical ranges stretching across wide swaths of Africa. Understanding the population structure of these closely related species, including the extent to which populations are connected by gene flow, is essential for understanding how vector control implemented in one location might indirectly affect vector populations in other locations. Here, we assessed the population structure of each species based on a combined data set of publicly available and newly processed whole-genome sequences. The data set included single nucleotide polymorphisms from whole genomes of 2,410 individual mosquitoes sampled from 128 locations across 19 African countries. We found that A. gambiae sampled from several countries in West and Central Africa showed low genetic differentiation from each other according to principal components analysis (PCA) and ADMIXTURE modeling. Using Estimated Effective Migration Surfaces (EEMS), we showed that this low genetic differentiation indicates high effective migration rates for A. gambiae across this region. Outside of this region, we found eight groups of sampling locations from Central, East, and Southern Africa for which A. gambiae showed higher genetic differentiation, and lower effective migration rates, between each other and the West/Central Africa group. These results indicate that the barriers to and corridors for migration between populations of A. gambiae differ across the geographical range of this malaria vector species. Using the same methods, we found higher genetic differentiation and lower migration rates between populations of A. coluzzii in West and Central Africa than for A. gambiae in the same region. In contrast, we found lower genetic differentiation and higher migration rates between populations of A. arabiensis in Tanzania, compared to A. gambiae in the same region. These differences between A. gambiae, A. coluzzii, and A. arabiensis indicate that migration barriers and corridors may vary, even between very closely related species. Overall, our results demonstrate that migration rates vary both within and between species of Anopheles mosquitoes, presumably based on species-specific responses to the ecological or environmental conditions that may impede or facilitate migration, and the geographical patterns of these conditions across the landscape. Together with previous findings, this study provides robust evidence that migration rates between populations of malaria vectors depend on the ecological context, which should be considered when planning surveillance of vector populations, monitoring for insecticide resistance, and evaluating interventions.

Permafrost Hydrogeology of Taylor Valley, Antarctica: Insights From Deep Electrical Resistivity Tomography

AbstractGlobal warming has prompted globally widespread permafrost thawing, resulting in enhanced greenhouse gas release into the atmosphere. Studies conducted in the Northern Hemisphere reveal an alarming increase in permafrost thawing. However, similar data from Antarctica are scarce. We conducted a 2‐D Deep Electrical Resistivity Tomography (DERT) survey in Taylor Valley, Antarctica, to image the distribution of permafrost, its thicknesses, lower boundaries, and hydrogeology. Results show resistive, discontinuous domains that we suggest represent permafrost units. We also find highly conductive layers (5–10 Ω·m), between 300–350 m and 600–650 m below ground level and a shallower (∼50–100 m depth) conductive layer. The combined data set reveals a broad brine system in Taylor Valley, implying multi‐tiered groundwater circulation: a shallow, localized system linked with surface water bodies and a separate deeper, regional circulation system. The arrangement of these brines across different levels, coupled with the uneven permafrost distribution, underscores potential interplay between the two systems.

Ultraslow cooling of an ultrahot orogen

To constrain the rate of cooling of lower-crustal rocks from an ultrahot orogen, we determined both the age and equilibration temperature of metamorphic zircon from six widely spaced samples of metasedimentary garnet−sillimanite gneiss from the Eastern Ghats Province in eastern India. For the combined data set of metamorphic zircon, concordant dates decrease continuously within 2σ uncertainty from around 950 Ma to 800 Ma, consistent with ∼150 m.y. of zircon crystallization. Ti-in-zircon temperatures for each dated spot during this period decrease with age, corresponding to linear cooling rates ranging from 0.26 to 0.90 °C/m.y. We propose that retention of heat-producing elements in the lower crust of the Eastern Ghats Province and a low net erosion rate were responsible for ∼150 m.y. of ultraslow cooling.

Plasma Proteomics to Identify Drug Targets and Potential Drugs for Retinal Artery Occlusion: An Integrated Analysis in the UK Biobank.

Retinal artery occlusion (RAO), which is positively correlated with acute ischemic stroke (IS) and results in severe visual impairment, lacks effective intervention drugs. This study aims to perform integrated analysis using UK Biobank plasma proteome data of RAO and IS to identify potential targets and preventive drugs. A total of 7191 participants (22 RAO patients, 1457 IS patients, 8 individuals with both RAO and IS, and 5704 healthy age-gender-matched controls) were included in this study. Unique 1461 protein expression profiles of RAO, IS, and the combined data set, extracted from UK Biobank Plasma proteomics projects, were analyzed using both differential expression analysis and elastic network regression (Enet) methods to identify shared key proteins. Subsequent analyses, including single cell type expression assessment, pathway enrichment, and druggability analysis, were conducted for verifying shared key proteins and discovery of new drugs. Five proteins were found to be shared among the samples, with all of them showing upregulation. Notably, adhesion G-protein coupled receptor G1 (ADGRG1) exhibited high expression in glial cells of the brain and eye tissues. Gene set enrichment analysis revealed pathways associated with lipid metabolism and vascular regulation and inflammation. Druggability analysis unveiled 15 drug candidates targeting ADGRG1, which demonstrated protective effects against RAO, especially troglitazone (-8.5 kcal/mol). Our study identified novel risk proteins and therapeutic drugs associated with the rare disease RAO, providing valuable insights into potential intervention strategies.

Crustal and upper mantle 3-D Vs structure of the Pannonian region from joint earthquake and ambient noise Rayleigh wave tomography

SUMMARY The Pannonian Basin, situated in Central Europe, is surrounded by the Alpine, Carpathian and Dinaric orogens. To understand its tectonic characteristics and evolution, we determine a shear wave velocity model of its crust, mantle lithosphere and asthenosphere consistently by jointly inverting Rayleigh wave phase velocities measured consistently from earthquake (EQ) and ambient noise (AN) data. For the AN data, continuous waveform data were collected from 1254 stations, covering an area within 9° from the centre of the Pannonian Basin during the time period from 2006 to 2018. This data set enabled the extraction of over 164 464 interstation Rayleigh phase-velocity curves, after applying a strict quality control workflow. For the EQ data set more than 2000 seismic events and about 1350 seismic stations were used in the broader Central and Eastern European region between the time-span of 1990 to 2015, allowing us to extract 139 987 quality controlled Rayleigh wave phase-velocity curve. Using the combined data set, a small period- and distance-dependent bias between ambient noise and earthquake measurements, mostly below 1 per cent but becoming larger towards longer periods has been found. After applying a period and distance dependent correction, we generated phase-velocity maps, spanning periods from 5 to 250 s. 33 981 local dispersion curves were extracted and a new approach is introduced to link their period-dependent roughness to the standard deviation. Using a non-linear stochastic particle swarm optimization, a consistent 3-D shear wave velocity model (PanREA2023) encompassing the crust and upper mantle down to 300 km depth was obtained with a lateral resolution reaching about 50 km at the centre of the study area for shorter periods. The crust beneath the Carpathian orogen exhibits a distinct low-velocity anomaly extending down to the Moho. It is referred to as Peri-Carpathian anomaly. Similar anomalies were observed in the Northern Apennines, while the Eastern Alps and Dinarides, as collisional orogens, generally demonstrate higher velocities in the upper crust. High crustal shear wave velocities are also evident in the Bohemian Massif and the East European Craton. The brittle upper crust of the Pannonian Basin is characterized by alternating NE–SW trending high- and low-velocity anomalies: the western and central Pannonian low-velocity anomalies and the Transdanubian and Apuseni high-velocity anomalies related to Miocene sedimentary basins and intervening intervening interbasinal highs exposing Pre-Cenozoic rocks including crystalline basement rocks. Beneath the Southeastern Carpathians, a NE-dipping slab was identified, extending to depths of at least 200 km, while a slab gap is evident beneath the Western Carpathians. A short south-dipping Eurasian slab was imaged beneath the Eastern Alps down to only 150–200 km depth. The Adriatic lithosphere is subducting near-vertically dipping beneath the Northern Apennines, and a slab gap was observed beneath the Central Apennines. In the Northern Dinarides, a short slab was evident, reaching depths of around 150 km. The Southern Dinarides featured a thinned but possibly incompletely detached slab.

The progenitor star of SN 2023ixf: a massive red supergiant with enhanced, episodic pre-supernova mass loss

ABSTRACT We identify the progenitor star of SN 2023ixf in Messier 101 using Keck/NIRC2 adaptive optics imaging and pre-explosion Hubble Space Telescope (HST)/Advanced Camera for Surveys (ACS) images. The supernova, localized with diffraction spikes and high-precision astrometry, unambiguously coincides with a progenitor candidate of $m_\text{F814W}=24.87\pm 0.05$ (AB). Given its reported infrared excess and semiregular variability, we fit a time-dependent spectral energy distribution (SED) model of a dusty red supergiant (RSG) to a combined data set of HST optical, ground-based near-infrared, and Spitzer Infrared Array Camera (IRAC) [3.6], [4.5] photometry. The progenitor resembles an RSG of $T_\text{eff}=3488\pm 39$ K and $\log (L/\mathrm{L}_\odot)=5.15\pm 0.02$, with a $0.13\pm 0.01$ dex ($31.1\pm 1.7$ per cent) luminosity variation at a period of $P=1144.7\pm 4.8$ d, obscured by a dusty envelope of $\tau =2.92\pm 0.02$ at $1\, \mu \text{m}$ in optical depth (or $A_\text{V}=8.43\pm 0.11$ mag). The signatures match a post-main-sequence star of $18.2_{-0.6}^{+1.3}\, \mathrm{M}_\odot$ in zero-age main-sequence mass, among the most massive SN II progenitor, with a pulsation-enhanced mass-loss rate of $\dot{M}=(4.32\pm 0.26)\times 10^{-4} \, \mathrm{M}_\odot \, \text{yr}^{-1}$. The dense and confined circumstellar material is ejected during the last episode of radial pulsation before the explosion. Notably, we find strong evidence for variations of $\tau$ or $T_\text{eff}$ along with luminosity, a necessary assumption to reproduce the wavelength-dependent variability, which implies periodic dust sublimation and condensation. Given the observed SED, partial dust obscuration remains possible, but any unobstructed binary companion over $5.6\, \mathrm{ M}_\odot$ can be ruled out.

Inference of host-pathogen interaction matrices from genome-wide polymorphism data.

Host-pathogen coevolution is defined as the reciprocal evolutionary changes in both species due to genotype x genotype (GxG) interactions at the genetic level determining the outcome and severity of infection. While co-analyses of host and pathogen genomes (co-GWAs) allow us to pinpoint the interacting genes, these do not reveal which host genotype(s) is/are resistant to which pathogen genotype(s). The knowledge of this so-called infection matrix is important for agriculture and medicine. Building on established theories of host-pathogen interactions, we here derive four novel indices capturing the characteristics of the infection matrix. These indices can be computed from full genome polymorphism data of randomly sampled uninfected hosts, as well as infected hosts and their pathogen strains. We use these indices in an Approximate Bayesian Computation method to pinpoint loci with relevant GxG interactions and to infer their underlying interaction matrix. In a combined SNP data set of 451 European humans and their infecting Hepatitis C Virus (HCV) strains and 503 uninfected individuals, we reveal a new human candidate gene for resistance to HCV and new virus mutations matching human genes. For two groups of significant human-HCV (GxG) associations, we infer a gene-for-gene infection matrix, which is commonly assumed to be typical of plant-pathogen interactions. Our model-based inference framework bridges theoretical models of GxG interactions with host and pathogen genomic data. It, therefore, paves the way for understanding the evolution of key GxG interactions underpinning HCV adaptation to the European human population after a recent expansion.

Common sleep data pipeline for combined data sets.

Over the past few years, sleep research has shown impressive performance of deep neural networks in the area of automatic sleep-staging. Recent studies have demonstrated the necessity of combining multiple data sets to obtain sufficiently generalizing results. However, working with large amounts of sleep data can be challenging, both from a hardware perspective and because of the different preprocessing steps necessary for distinct data sources. Here we review the possible obstacles and present an open-source pipeline for automatic data loading. Our solution includes both a standardized data store as well as a 'data serving' portion which can be used to train neural networks on the standardized data, allowing for different configuration options for different studies and machine learning designs. The pipeline, including implementation, is made public to ensure better and more reproducible sleep research.

Variational Supertrees for Bayesian Phylogenetics

Bayesian phylogenetic inference is powerful but computationally intensive. Researchers may find themselves with two phylogenetic posteriors on overlapping data sets and may wish to approximate a combined result without having to re-run potentially expensive Markov chains on the combined data set. This raises the question: given overlapping subsets of a set of taxa (e.g. species or virus samples), and given posterior distributions on phylogenetic tree topologies for each of these taxon sets, how can we optimize a probability distribution on phylogenetic tree topologies for the entire taxon set? In this paper we develop a variational approach to this problem and demonstrate its effectiveness. Specifically, we develop an algorithm to find a suitable support of the variational tree topology distribution on the entire taxon set, as well as a gradient-descent algorithm to minimize the divergence from the restrictions of the variational distribution to each of the given per-subset probability distributions, in an effort to approximate the posterior distribution on the entire taxon set.

Speak Up: How Hearing Loss and the Lack of Hearing Aids Affect Conversations in Quiet.

The study examines the effect of hearing loss and hearing aid (HA) amplification on the conversational dynamics between hearing-impaired (HI) and normal-hearing (NH) interlocutors. Combining data from the current and a prior study, we explore how the speech levels of both interlocutors correlate and relate to HI interlocutors' degree of hearing loss. Sixteen pairs of younger NH and elderly HI interlocutors conversed in quiet, with the HI interlocutor either unaided or wearing HAs. We analyzed the effect of hearing status and HA amplification on the conversational dynamics, including turn-taking times (floor-transfer offsets), utterance lengths, and speech levels. Furthermore, we conducted an in-depth analysis of the speech levels using combined data sets from the current and previously published data by Petersen, MacDonald, and Sørensen (2022). Unaided HI interlocutors were slower and more variable at timing their turns, but wearing HAs reduced the differences between the HI and NH interlocutors. Conversations were less interactive, and pairs were slower at solving the conversational tasks when the HI interlocutor was unaided. Both interlocutors spoke louder when the HI interlocutor was unaided. The speech level of the NH interlocutors was related to that of the HI interlocutors, with the HI speech levels also correlating with their own degree of hearing loss. Despite typically being unchallenging for HI individuals, one-on-one conversations in quiet were impacted by the HI interlocutor not wearing HAs. Additionally, combining data sets revealed that NH interlocutors adjusted their speech level to match that of HI interlocutors.

TOI-1685 b Is a Hot Rocky Super-Earth: Updates to the Stellar and Planet Parameters of a Popular JWST Cycle 2 Target

We present an updated characterization of the TOI-1685 planetary system, which consists of a P b = 0.69 day ultra-short-period super-Earth planet orbiting a nearby (d = 37.6 pc) M2.5V star (TIC 28900646, 2MASS J04342248+4302148). This planet was previously featured in two contemporaneous discovery papers, but the best-fit planet mass, radius, and bulk density values were discrepant, allowing it to be interpreted either as a hot, bare rock or a 50% H2O/50% MgSiO3 water world. TOI-1685 b will be observed in three independent JWST Cycle 2 programs, two of which assume the planet is a water world, while the third assumes that it is a hot rocky planet. Here we include a refined stellar classification with a focus on addressing the host star’s metallicity, an updated planet radius measurement that includes two sectors of TESS data and multicolor photometry from a variety of ground-based facilities, and a more accurate dynamical mass measurement from a combined CARMENES, InfraRed Doppler, and MAROON-X radial velocity data set. We find that the star is very metal-rich ([Fe/H] ≃ +0.3) and that the planet is systematically smaller, lower mass, and higher density than initially reported, with new best-fit parameters of R pl = 1.468 −0.051+0.050 R ⊕ and M pl = 3.03−0.32+0.33 M ⊕. These results fall in between the previously derived values and suggest that TOI-1685 b is a hot rocky planet with an Earth-like density (ρ pl = 5.3 ± 0.8 g cm−3, or 0.96 ρ ⊕), high equilibrium temperature (T eq = 1062 ± 27 K), and negligible volatiles, rather than a water world.

Adjusting Central and Eastern United States ground-motion models for use in the Coastal Plain considering the sediment thickness

This study aims to develop adjustment factors for ground-motion models such as Pezeshk et al., which were developed for regions outside the Coastal Plain to be used for sites within the Coastal Plain region. The adjustment factors developed are a function of sediment thickness and rupture distance [Formula: see text] in the Coastal Plain region. We use the newly developed sediment thickness contour maps by Boyd et al. Also, the adjustment factors are developed using a combined data set, which consists of the Next Generation Attenuation (NGA)-East original data set, the data set from Chapman and Guo, and the newly compiled and verified data set by Thompson et al. We calculate residuals by taking the difference between the logarithms of the observed data and those predicted by the Pezeshk et al. ground-motion model (GMM), considering the site amplification model of Stewart et al. and utilizing the combined data set. This model is applicable for [Formula: see text] as far as 1000 km. We perform residual analyses using a mixed-effects regression to partition the total residuals into between-event and within-event components. To develop the correction factors for stations within the Coastal Plain region, we fit the within-event residuals to an equation that is a function of sediment thickness and [Formula: see text]. The results indicate that for stations within the Coastal Plain region, for most periods, the residual trend has been eliminated (with respect to sediment thickness, [Formula: see text], and time-averaged shear wave velocity in the first 30 m under the surface, V S30) using the proposed adjustment factors. The results of this study can be utilized in seismic hazard and risk analyses for sites within the Coastal Plain.

A confirmation of the predictive utility of the Antibiotic Use Questionnaire

BackgroundThe change in the efficacy of antimicrobial agents due to their misuse is implicated in extensive health and mortality related concerns. The Antibiotics Use Questionnaire (AUQ) is a theory driven measure based on the Theory of Planned Behaviour (TpB) factors that is designed to investigate drivers of antibiotic use behaviour. The objective of this study is to replicate the factor structure from the pilot study within a similar Australian confirmation cohort, and to extend this through investigating if the factor structure holds in a Chinese-identifying cohort.MethodsThe AUQ was disseminated to two cohorts: a confirmation cohort similar to the original study, and a Chinese identifying cohort. Data analysis was completed on the two data sets independently, and on a combined data set. An orthogonal principal components analysis with varimax rotation was used to assess the factor structure, followed by general linear models to determine the influence of the TpB factors on reported antibiotic use.Results370 participant responses from the confirmation cohort, and 384 responses from the Chinese-identifying cohort were retained for analysis following review of the data. Results showed modest but acceptable levels of internal reliability across both cohorts. Social norms, and the interaction between attitudes and beliefs and knowledge were significant predictors of self-reported antibiotic use in both cohorts. In the confirmation cohort healthcare training was a significant predictor, and in the Chinese-identifying cohort education was a significant predictor. All other predictors tested produced a nonsignificant relationship with the outcome variable of self-reported antibiotic use.ConclusionsThis study successfully replicated the factor structure of the AUQ in a confirmation cohort, as well as a cohort that identified as culturally or legally Chinese, determining that the factor structure is retained when investigated across cultures. The research additionally highlights the need for a measure such as the AUQ, which can identify how differing social, cultural, and community factors can influence what predicts indiscriminate antibiotic use. Future research will be required to determine the full extent to which this tool can be used to guide bespoke community level interventions to assist in the management of antimicrobial resistance.

First report of Alternaria alternata causing leaf spot on Polygonatum kingianum in China.

Polygonatum kingianum is a Chinese herbal medicine that belongs to the genus Polygonatum of the family Liliaceae. In June 2023, Polygonatum kingianum Coll. et Hemsl. in nurseries in Qujing, Yunnan Province, China, showed irregular brown spots on the leaves, whole leaf necrosis, and plant death in serious cases, with an incidence of 10-20% (Fig. S1). To identify the pathogens of P. kingianum, six diseased samples were collected from nurseries with 0.6 acre. These diseased sample leaves were soaked in 0.1% HgCl2 for 1 min and 75% ethanol for 2 min and then rinsed thrice with sterile water. Treated leaves were cut into small pieces (5×5 mm) and cultured on potato dextrose agar (PDA) for five days at 28°C. Total thirteen fungal strains were isolated from PDA medium. The nuclear ribosomal internal transcribed spacer of ribosomal DNA (ITS rDNA) region of these 13 strains was amplified by polymerase chain reaction (PCR) using universal primers ITSI/ITS4 (White et al. 1990). Sequencing and BLAST of the ITS region on NCBI showed that 11 out of 13 fungal strains belonged to the genus Alternaria, with an identity ≥99%. We selected one of the Alternaria strains, HJ-A1, for further study. The HJ-A1 colony appeared grayish brown white-to-gray with a flocculent texture on the front side and a dark gray underside on the PDA medium (Fig. S1). The conidiophores appeared brown, either single or branched, and produced numerous short conidial chains. The conidia were obclavate to obpyriform or ellipsoid in shape and contained 1-4 transverse septa and 0-2 oblique septa. The conidial diameter was 27.30µm in length and 12.27µm in width. (Fig. S1). To further determine the species of HJA1, the genomic DNA of HJ-A1 was extracted using the Lysis Buffer for PCR (AG, Hunan, China). Four Alternaria genomic DNA regions including the ITS, translation elongation factor 1-α gene (TEF1-α), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and Alternaria major allergen gene (Alt a1) were amplified by PCR using the primers as previously reported (Woudenberg et al. 2013, Hong et al. 2005). Sequence analysis revealed that the ITS (484bp) of HJ-A1 (NCBI No. PP082633), TEF1-α (267bp) of HJ-A1 (NCBI No. PP419893), GAPDH (582bp) of HJ-A1 (NCBI No. PP419892), and Alt a1 (522bp) of HJ-A1 (NCBI No. PP228046) shared the highest identity with A. alternata respectively (99≥%). A maximum likelihood phylogenetic tree was constructed with the combined sequence data sets of ITS, GAPDH, TEF, and Alt a1 using MEGA 7. The results showed that HJ-A1 strain clustered with A. alternate (Fig. S2). The pathogenicity of HJ-A1 was tested according to Koch's postulates by inoculating HJ-A1 conidia suspension (2×105 conidia/mL) into leaves of 1-year-old P. kingianum, with sterile water as a control. Each treatment group included 3 plants with 3 replicates. The tested plants were planted in a phytotron at 28℃ and 90% humidity. Three days after inoculation, symptoms similar to those under natural conditions were observed in the HJ-A1-inoculated plants, whereas no symptoms were observed in the control plants (Fig. S1). The same fungal strains were re-isolated from inoculated leaves and identified by morphologically and sequence of ITS. Previous studies showed that Alternaria alternata funji cause many plant diseases, such as fig fruit rot (Latinović N et al. 2014)，daylily leaf spot (Huang D et al. 2022), fruit blight on sesame (Cheng H et al. 2021)，leaf spot of Cynanchum atratum Bunge (Sun H et al. 2021) and so on. To our knowledge, this is the first report of A. alternata causing P. kingianum leaf spot in China. The discovery of this pathogen will help to guide the protection and control of P. kingianum disease.

Alpha and beta‐diversity and community structure of dung beetle assemblages in an East African protected savannah

AbstractFew studies have examined how dung beetle species composition changes across several mammalian dung types. Species composition and abundance of dung beetles were studied in dung samples of seven African mammals along animal trails and using 14 baited‐pitfall traps in both the park and the buffer zone resulting in a combined data set of 113 samples. Both the number of species (18) and the number of individual dung beetles (600) encountered were low for an area close to the Equator. Few species (2) and herbivores (13) tunnellers (9) dominated the dung beetles community, feeding and guild structure respectively. Alpha diversity was moderate 2.06 (1 < H′ ≤ 3). Beta diversity ranged from low 0.8 (high similarity) to high (zero, no similarity). Dung beetle species composition varied with dung type with the degree of pairwise similarity between two dung types in 19 of the total (21) dung beetle communities comparisons showing low similarity (0.4) to no similarity (zero). The observed discrepancies may be attributed to dung size, composition and consistency as well as to the low number of dung of the different animal species sampled. Furthermore, further research is needed as rarefaction curves suggest that sampling was incomplete.

Scalable Ab Initio Electronic Structure Methods with Near Chemical Accuracy for Main Group Chemistry.

This study evaluates the precision of widely recognized quantum chemical methodologies, CCSD(T), DLPNO-CCSD(T), and localized ph-AFQMC, for determining the thermochemistry of main group elements. DLPNO-CCSD(T) and localized ph-AFQMC, which offer greater scalability compared to canonical CCSD(T), have emerged over the past decade as pivotal in producing precise benchmark chemical data. Our investigation includes closed-shell, neutral molecules, focusing on their heat of formation and atomization energy sourced from four specific small molecule data sets. First, we selected molecules from the G2 and G3 data sets, noted for their reliable experimental heat of formation data. Additionally, we incorporate molecules from the W4-11 and W4-17 sets, which provide high-level theoretical reference values for atomization energy at 0 K. Our findings reveal that both DLPNO-CCSD(T) and ph-AFQMC methods are capable of achieving a root-mean-square deviation of less than 1 kcal/mol across the combined data set, aligning with the threshold for chemical accuracy. Moreover, we make efforts to confine the maximum deviations within 2 kcal/mol, a degree of precision that significantly broadens the applicability of these methods in fields such as biology and materials science.

The β Pictoris b Hill sphere transit campaign

The β Pictoris system is the closest known stellar system with directly detected gas giant planets, an edge-on circumstellar disc, and evidence of falling sublimating bodies and transiting exocomets. The inner planet, β Pictoris c, has also been indirectly detected with radial velocity (RV) measurements. The star is a known δ Scuti pulsator, and the long-term stability of these pulsations opens up the possibility of indirectly detecting the gas giant planets through time delays of the pulsations due to a varying light travel time. We search for phase shifts in the δ Scuti pulsations consistent with the known planets β Pictoris b and c and carry out an analysis of the stellar pulsations of β Pictoris over a multi-year timescale. We used photometric data collected by the BRITE-Constellation, bRing, ASTEP, and TESS to derive a list of the strongest and most significant δ Scuti pulsations. We carried out an analysis with the open-source python package maelstrom to study the stability of the pulsation modes of β Pictoris in order to determine the long-term trends in the observed pulsations. We did not detect the expected signal for β Pictoris b or β Pictoris c. The expected time delay is 6 s for β Pictoris c and 24 s for β Pictoris b. With simulations, we determined that the photometric noise in all the combined data sets cannot reach the sensitivity needed to detect the expected timing drifts. An analysis of the pulsational modes of β Pictoris using maelstrom showed that the modes themselves drift on the timescale of a year, fundamentally limiting our ability to detect exoplanets around β Pictoris via pulsation timing.

Explainable coronary artery disease prediction model based on AutoGluon from AutoML framework.

This study focuses on the innovative application of Automated Machine Learning (AutoML) technology in cardiovascular medicine to construct an explainable Coronary Artery Disease (CAD) prediction model to support the clinical diagnosis of CAD. This study utilizes a combined data set of five public data sets related to CAD. An ensemble model is constructed using the AutoML open-source framework AutoGluon to evaluate the feasibility of AutoML in constructing a disease prediction model in cardiovascular medicine. The performance of the ensemble model is compared against individual baseline models. Finally, the disease prediction ensemble model is explained using SHapley Additive exPlanations (SHAP). The experimental results show that the AutoGluon-based ensemble model performs better than the individual baseline models in predicting CAD. It achieved an accuracy of 0.9167 and an AUC of 0.9562 in 4-fold cross-bagging. SHAP measures the importance of each feature to the prediction of the model and explains the prediction results of the model. This study demonstrates the feasibility and efficacy of AutoML technology in cardiovascular medicine and highlights its potential in disease prediction. AutoML reduces the barriers to model building and significantly improves prediction accuracy. Additionally, the integration of SHAP enhances model transparency and explainability, which is critical to ensuring model credibility and widespread adoption in cardiovascular medicine.