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Quite a few methods have been proposed to infer sibship and parentage among individuals from their multilocus marker genotypes. They are all based on Mendelian laws either qualitatively (exclusion methods) or quantitatively (likelihood methods), have different optimization criteria, and use different algorithms in searching for the optimal solution. The full-likelihood method assigns sibship and parentage relationships among all sampled individuals jointly. It is by far the most accurate method, but is computationally prohibitive for large data sets with many individuals and many loci. In this article I propose a new likelihood-based method that is computationally efficient enough to handle large data sets. The method uses the sum of the log likelihoods of pairwise relationships in a configuration as the score to measure its plausibility, where log likelihoods of pairwise relationships are calculated only once and stored for repeated use. By analyzing several empirical and many simulated data sets, I show that the new method is more accurate than pairwise likelihood and exclusion-based methods, but is slightly less accurate than the full-likelihood method. However, the new method is computationally much more efficient than the full-likelihood method, and for the cases of both sexes polygamous and markers with genotyping errors, it can be several orders faster. The new method can handle a large sample with thousands of individuals and the number of markers limited only by the computer memory.

Cardiovascular disease is a major global public health problem, and intelligent diagnostic approaches play an increasingly important role in the analysis of electrocardiograms (ECGs). Convenient wearable ECG devices enable the detection of transient arrhythmias and improve patient health by making it possible to seek intervention during continuous monitoring. We collected 658,486 wearable 12-lead ECGs, among which 164,538 were annotated, and the remaining 493,948 were without diagnostic. We present four data augmentation operations and a self-supervised learning classification framework that can recognize 60 ECG diagnostic terms. Our model achieves an average area under the receiver-operating characteristic curve (AUROC) and average F1 score on the offline test of 0.975 and 0.575. The average sensitivity, specificity and F1-score during the 2-month online test are 0.736, 0.954 and 0.468, respectively. This approach offers real-time intelligent diagnosis, and detects abnormal segments in long-term ECG monitoring in the clinical setting for further diagnosis by cardiologists. Intelligent diagnostic algorithms for ECG are becoming increasingly important to reduce the workload of cardiologists, enable telemedicine and real-time monitoring. Here, the authors show a model based on self-supervised learning that can classify 60 diagnostic terms for ECG.

Severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) causes the infectious disease COVID-19 (coronavirus disease 2019), which was first reported in Wuhan, China, in December 2019. Despite extensive efforts to control the disease, COVID-19 has now spread to more than 100 countries and caused a global pandemic. SARS-CoV-2 is thought to have originated in bats; however, the intermediate animal sources of the virus are unknown. In this study, we investigated the susceptibility of ferrets and animals in close contact with humans to SARS-CoV-2. We found that SARS-CoV-2 replicates poorly in dogs, pigs, chickens, and ducks, but ferrets and cats are permissive to infection. Additionally, cats are susceptible to airborne transmission. Our study provides insights into the animal models for SARS-CoV-2 and animal management for COVID-19 control.

Inferring changes in effective population size (Ne) in the recent past is of special interest for conservation of endangered species and for human history research. Current methods for estimating the very recent historical Ne are unable to detect complex demographic trajectories involving multiple episodes of bottlenecks, drops, and expansions. We develop a theoretical and computational framework to infer the demographic history of a population within the past 100 generations from the observed spectrum of linkage disequilibrium (LD) of pairs of loci over a wide range of recombination rates in a sample of contemporary individuals. The cumulative contributions of all of the previous generations to the observed LD are included in our model, and a genetic algorithm is used to search for the sequence of historical Ne values that best explains the observed LD spectrum. The method can be applied from large samples to samples of fewer than ten individuals using a variety of genotyping and DNA sequencing data: haploid, diploid with phased or unphased genotypes and pseudohaploid data from low-coverage sequencing. The method was tested by computer simulation for sensitivity to genotyping errors, temporal heterogeneity of samples, population admixture, and structural division into subpopulations, showing high tolerance to deviations from the assumptions of the model. Computer simulations also show that the proposed method outperforms other leading approaches when the inference concerns recent timeframes. Analysis of data from a variety of human and animal populations gave results in agreement with previous estimations by other methods or with records of historical events.

The performance of solar cells based on molecular electronic materials is limited by relatively high nonradiative voltage losses. The primary pathway for nonradiative recombination in organic donor-acceptor heterojunction devices is believed to be the decay of a charge-transfer (CT) excited state to the ground state via energy transfer to vibrational modes. Recently, nonradiative voltage losses have been related to properties of the charge-transfer state such as the Franck-Condon factor describing the overlap of the CT and ground-state vibrational states and, therefore, to the energy of the CT state. However, experimental data do not always follow the trends suggested by the simple model. Here, we extend this recombination model to include other factors that influence the nonradiative decay-rate constant, and therefore the open-circuit voltage, but have not yet been explored in detail. We use the extended model to understand the observed behavior of series of small molecules:fullerene blend devices, where open-circuit voltage appears insensitive to nonradiative loss. The trend could be explained only in terms of a microstructure-dependent CT-state oscillator strength, showing that parameters other than CT-state energy can control nonradiative recombination. We present design rules for improving open-circuit voltage via the control of material parameters and propose a realistic limit to the power-conversion efficiency of organic solar cells.

The discovery of ferromagnetic two-dimensional van der Waals materials has opened up opportunities to explore intriguing physics and to develop innovative spintronic devices. However, controllable synthesis of these 2D ferromagnets and enhancing their stability under ambient conditions remain challenging. Here, we report chemical vapor deposition growth of air-stable 2D metallic 1T-CrTe 2 ultrathin crystals with controlled thickness. Their long-range ferromagnetic ordering is confirmed by a robust anomalous Hall effect, which has seldom been observed in other layered 2D materials grown by chemical vapor deposition. With reducing the thickness of 1T-CrTe 2 from tens of nanometers to several nanometers, the easy axis changes from in-plane to out-of-plane. Monotonic increase of Curie temperature with the thickness decreasing from ~130.0 to ~7.6 nm is observed. Theoretical calculations indicate that the weakening of the Coulomb screening in the two-dimensional limit plays a crucial role in the change of magnetic properties. Here, the authors report chemical vapor deposition growth of metallic 1T-CrTe 2 ultrathin crystals with controlled thickness and long-range ferromagnetic ordering, and observe a monotonic increase of the Curie temperature with decreasing thickness.

Wildland fires dramatically affect forest ecosystems, altering the loss of their biodiversity and their sustainability. In addition, they have a strong impact on the global carbon balance and, ultimately, on climate change. This review attempts to provide a comprehensive meta-analysis of studies on remotely sensed methods and data used for estimation of forest burnt area, burn severity, post-fire effects, and forest recovery patterns at the global level by using the PRISMA framework. In the study, we discuss the results of the analysis based on 329 selected papers on the main aspects of the study area published in 48 journals within the past two decades (2000–2020). In the first part of this review, we analyse characteristics of the papers, including journals, spatial extent, geographic distribution, types of remote sensing sensors, ecological zoning, tree species, spectral indices, and accuracy metrics used in the studies. The second part of this review discusses the main tendencies, challenges, and increasing added value of different remote sensing techniques in forest burnt area, burn severity, and post-fire recovery assessments. Finally, it identifies potential opportunities for future research with the use of the new generation of remote sensing systems, classification and cloud performing techniques, and emerging processes platforms for regional and large-scale applications in the field of study.

Ivonescimab is a bispecific antibody against programmed cell death protein 1 and vascular endothelial growth factor, yielding promising clinical outcomes for patients with advanced non-small cell lung cancer in early-phase studies. We compared the efficacy and safety of ivonescimab with pembrolizumab in patients with programmed cell death ligand-1 (PD-L1)-positive advanced non-small cell lung cancer. HARMONi-2 is a randomised, double-blind, phase 3 trial across 55 hospitals in China. Eligible patients were aged 18 years or older and had locally advanced or metastatic PD-L1-positive non-small cell lung cancer without sensitising epidermal growth factor receptor mutations or anaplastic lymphoma kinase translocations and an Eastern Cooperative Oncology Group performance-status of 0 or 1. Patients were randomly assigned (1:1) to receive 20 mg/kg ivonescimab or 200 mg pembrolizumab intravenously every 3 weeks. Randomisation was stratified by histology, clinical stage, and PD-L1 expression. The primary endpoint was progression-free survival (PFS) assessed by a masked independent radiographic review committee per RECIST v1.1 in the intention-to-treat population. This study is registered with ClinicalTrials.gov, NCT05499390; recruitment is complete, with the trial ongoing and final analysis to be reported later. Between Nov 9, 2022, and Aug 26, 2023, 398 (45%) of 879 screened patients were randomly assigned to receive ivonescimab (n=198) or pembrolizumab (n=200). At the preplanned interim analysis, median PFS was significantly longer with ivonescimab than with pembrolizumab (11·1 vs 5·8 months; stratified hazard ratio [HR] 0·51 [95% CI 0·38–0·69]; one-sided p<0·0001). The PFS benefit of ivonescimab over pembrolizumab was broadly consistent within prespecified subgroups, including patients with PD-L1 tumour proportion score (TPS) 1–49% (HR 0·54 [95% CI 0·37–0·78]) and PD-L1 TPS of 50% of higher (HR 0·48 [0·29–0·79]). Grade 3 or higher treatment-related adverse events occurred in 58 (29%) patients with ivonescimab and 31 (16%) patients with pembrolizumab. Immune-related adverse events of grade 3 or higher were observed in 14 (7%) of 197 patients on ivonescimab and 16 (8%) of 199 patients on pembrolizumab. Ivonescimab demonstrated a manageable safety profile in patients with both squamous and non-squamous non-small cell lung cancer. In patients with squamous cell carcinoma, grade 3 or higher treatment-related adverse events were comparable between the two groups. Ivonescimab significantly improved PFS compared with pembrolizumab in previously untreated patients with advanced PD-L1 positive non-small cell lung cancer. Therefore, ivonescimab might represent another treatment option in the first-line setting for PD-L1-positive advanced non-small cell lung cancer. Akeso Biopharma.

The unprecedented coronavirus disease 2019 (COVID-19) epidemic has created a worldwide public health emergency, and there is an urgent need to develop an effective vaccine to control this severe infectious disease. Here, we find that a single vaccination with a replication-defective human type 5 adenovirus encoding the SARS-CoV-2 spike protein (Ad5-nCoV) protect mice completely against mouse-adapted SARS-CoV-2 infection in the upper and lower respiratory tracts. Additionally, a single vaccination with Ad5-nCoV protects ferrets from wild-type SARS-CoV-2 infection in the upper respiratory tract. This study suggests that the mucosal vaccination may provide a desirable protective efficacy and this delivery mode is worth further investigation in human clinical trials. A vaccine preventing infection and transmission of SARS-CoV-2 is needed. Here, Wu et al. generate an adenovirus-vector vaccine expressing SARS-CoV-2 spike protein and show that a single dose of mucosal vaccination protects mice and ferrets from infection and inhibits virus replication in the upper respiratory tract.

Tea is the most popular drink worldwide, and China is the largest producer of tea. Therefore, tea is an important commercial crop in China, playing a significant role in domestic and foreign markets. It is necessary to make accurate and timely maps of the distribution of tea plantation areas for plantation management and decision making. In the present study, we propose a novel mapping method to map tea plantation. The town of Menghai in the Xishuangbanna Dai Autonomous Prefecture, Yunnan Province, China, was chosen as the study area, andgg GF-1 remotely sensed data from 2014–2017 were chosen as the data source. Image texture, spectral and geometrical features were integrated, while feature space was built by SEparability and THresholds algorithms (SEaTH) with decorrelation. Object-Oriented Image Analysis (OOIA) with a Support Vector Machine (SVM) algorithm was utilized to map tea plantation areas. The overall accuracy and Kappa coefficient ofh the proposed method were 93.14% and 0.81, respectively, 3.61% and 0.05, 6.99% and 0.14, 6.44% and 0.16 better than the results of CART method, Maximum likelihood method and CNN based method. The tea plantation area increased by 4,095.36 acre from 2014 to 2017, while the fastest-growing period is 2015 to 2016.