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An important goal of population genetics is to determine the forces that have shaped the pattern of genetic variation in natural populations. We developed a maximum likelihood method that allows us to infer demographic changes and detect recent positive selection (selective sweeps) in populations of varying size from DNA polymorphism data. Applying this approach to single nucleotide polymorphism data at more than 250 noncoding loci on the X chromosome of Drosophila melanogaster from an (ancestral) African population and a (derived) European, we found that the African population expanded about 60,000 y ago and that the European population split off from the African lineage about 15,800 y ago, thereby suffering a severe population size bottleneck. We estimated that about 160 beneficial mutations (with selection coefficients s between 0.05% and 0.5%) were fixed in the euchromatic portion of the X in the African population since population size expansion, and about 60 mutations (with s around 0.5%) in the diverging European lineage.

The analysis of urban seismic signals offers valuable insights into urban environments and society. Yet, accurate detection and localization of seismic sources on a city-wide scale with conventional seismographic network is unavailable due to the prohibitive costs of ultra-dense seismic arrays required for imaging high-frequency anthropogenic sources. Here, we leverage existing fiber-optic networks as a distributed acoustic sensing system to accurately locate urban seismic sources and estimate how their intensity varies over time. By repurposing a 50-kilometer telecommunication fiber into an ultra-dense seismic array, we generate spatiotemporal maps of seismic source power (SSP) across San Jose, California. Our approach overcomes the proximity limitations of urban seismic sensing, enabling accurate localization of remote seismic sources generated by urban activities, such as traffic, construction, and school operations. We also show strong correlations between SSP values and environmental noise levels, as well as various persistent urban features, including land use patterns and demographics. This study leverages existing fiber-optic networks for urban sensing. By mapping Seismic Source Power, it reveals urban activities, land use patterns, and demographic trends, enabling scalable urban monitoring without additional sensor deployment.

Artemisinin and its derivatives are widely used in the world as the first-line antimalarial drug. Recently, growing evidences reveal that artemisinin and its derivatives also possess potent anti-inflammatory and immunoregulatory properties. Meanwhile, researchers around the world are still exploring the unknown bioactivities of artemisinin derivatives. In this review, we provide a comprehensive discussion on recent advances of artemisinin derivatives affecting inflammation and autoimmunity, the underlying molecular mechanisms, and also drug development of artemisinins beyond antimalarial functions.

To determine correlation between the Extended Disability Status Scale(EDSS) grade and the progression of neuromyelitis optica(NMO) patients’ levels of the chemokine CXC ligand 13 (CXCL13) in their serum and cerebrospinal fluid. This research included forty-one patients diagnosed with neuromyelitis optica(NMO) and forty-three patients diagnosed with multiple sclerosis(MS). The control group consisted of forty-three non-inflammatory neurological disease(NND) patients. The patients’ serum and cerebrospinal fluid CXCL13 levels were measured. Patients in NMO group and MS group had serum and cerebrospinal fluid with CXCL13 levels that were substantially greater than those in the NND group. When comparing the CXCL13 levels of blood and cerebrospinal fluid between patients in the EDSS ≥ 3.5 group and the EDSS<3.5 group, with the EDSS ≥ 3.5 group’s CXCL13 levels being greater( P <0.05). There was a positive correlation between the serum CXCL13 and the EDSS grades of both the NMO and MS groups( r  = 0.884, P  < 0.001); The cerebrospinal fluid CXCL13 of the NMO and MS groups showed a positive correlation with their EDSS grades( r  = 0.681, P  < 0.001). EDSS scores of NMO patients were positively correlated with their serum BLC-1 ( r  = 0.896, P  < 0.001); EDSS scores of NMO patients were positively correlated with their cerebrospinal fluid BLC-1 ( r  = 0.678, P  < 0.001).EDSS scores of MS patients were positively correlated with their serum BLC-1 ( r  = 0.852, P  < 0.001); EDSS scores of MS patients were positively correlated with their cerebrospinal fluid BLC-1 ( r  = 0.613, P  < 0.001). Serum and cerebrospinal fluid levels of CXCL13 may serve as an important biomarker for the presumptive assessment of the degree of disability in NMO and MS disease, providing a basis for the treatment and control of the disease.

Mirtazapine is a pharmacological agent commonly utilized as a first-line treatment for major depressive disorder, exhibiting both noradrenergic and selective serotonergic activity. Currently, there is an absence of a comprehensive and systematic review of the adverse events (AEs) associated with mirtazapine. Consequently, this study aims to assess the safety profile of mirtazapine in real-world clinical practice by performing an in-depth analysis of data from the Food and Drug Administration's Adverse Event Reporting System (FAERS). This study collected all real-world adverse event data related to mirtazapine from the FAERS database spanning from Q1 2004 to Q4 2024. Disproportionality analysis methods were employed, including Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-Item Gamma Poisson Shrinker (MGPS), to assess the associations between mirtazapine and major medical events. Our study identified a total of 14,237 adverse event reports related to mirtazapine from the FAERS database, of which 2,954 had available data. The results identified several previously unreported adverse events, including prolonged QT interval on electrocardiogram, insomnia, coma, falls, and drug toxicity. AEs related to mirtazapine occurred across 27 system organ classes (SOCs), primarily including Nervous System Disorders, Investigations, and Surgical and Medical Procedures. The onset of adverse events typically occurred within one month of mirtazapine administration, though it is important to note that the risk of AEs persists even in the following year. Our research findings are consistent with clinical observations. Additionally, we have identified new adverse reactions related to mirtazapine. These results provide valuable evidence for further guiding safety research on mirtazapine. They also help clinicians place greater emphasis on monitoring its AEs during use.

Radiotherapy plays an important role in the treatment of non-small cell lung cancer. Accurate segmentation of the gross target volume is very important for successful radiotherapy delivery. Deep learning techniques can obtain fast and accurate segmentation, which is independent of experts’ experience and saves time compared with manual delineation. In this paper, we introduce a modified version of ResNet and apply it to segment the gross target volume in computed tomography images of patients with non-small cell lung cancer. Normalization was applied to reduce the differences among images and data augmentation techniques were employed to further enrich the data of the training set. Two different residual convolutional blocks were used to efficiently extract the deep features of the computed tomography images, and the features from all levels of the ResNet were merged into a single output. This simple design achieved a fusion of deep semantic features and shallow appearance features to generate dense pixel outputs. The test loss tended to be stable after 50 training epochs, and the segmentation took 21 ms per computed tomography image. The average evaluation metrics were: Dice similarity coefficient, 0.73; Jaccard similarity coefficient, 0.68; true positive rate, 0.71; and false positive rate, 0.0012. Those results were better than those of U-Net, which was used as a benchmark. The modified ResNet directly extracted multi-scale context features from original input images. Thus, the proposed automatic segmentation method can quickly segment the gross target volume in non-small cell lung cancer cases and be applied to improve consistency in contouring.

Genetic recombination is a very important evolutionary mechanism that mixes parental haplotypes and produces new raw material for organismal evolution. As a result, information on recombination rates is critical for biological research. In this paper, we introduce a new extremely fast open-source software package (FastEPRR) that uses machine learning to estimate recombination rate ρ (=4Ner) from intraspecific DNA polymorphism data. When ρ>10 and the number of sampled diploid individuals is large enough (≥50), the variance of ρFastEPRR remains slightly smaller than that of ρLDhat. The new estimate ρcomb (calculated by averaging ρFastEPRR and ρLDhat) has the smallest variance of all cases. When estimating ρFastEPRR, the finite-site model was employed to analyze cases with a high rate of recurrent mutations, and an additional method is proposed to consider the effect of variable recombination rates within windows. Simulations encompassing a wide range of parameters demonstrate that different evolutionary factors, such as demography and selection, may not increase the false positive rate of recombination hotspots. Overall, accuracy of FastEPRR is similar to the well-known method, LDhat, but requires far less computation time. Genetic maps for each human population (YRI, CEU, and CHB) extracted from the 1000 Genomes OMNI data set were obtained in less than 3 d using just a single CPU core. The Pearson Pairwise correlation coefficient between the ρFastEPRR and ρLDhat maps is very high, ranging between 0.929 and 0.987 at a 5-Mb scale. Considering that sample sizes for these kinds of data are increasing dramatically with advances in next-generation sequencing technologies, FastEPRR (freely available at http://www.picb.ac.cn/evolgen/) is expected to become a widely used tool for establishing genetic maps and studying recombination hotspots in the population genomic era.

For the first time, we have interpreted and delineated the distribution of Jurassic strata in the Chaoshan Subbasin, Pearl River Basin, in unprecedented detail using the newly collected seismic data (>17,000 km 2D seismic data and >580 km2 3D seismic data). Four major reflection surfaces and three corresponding stratigraphic-structural layers were identified by analyzing seismic sections and borehole data from the LF35-1-1 well. Five distinct facies associations were identified within the stratigraphic-structural layers, including the shoreface, shallow marine, deep marine, deep-water fan, and mass-transport deposits. Based on outcrop observation, microscopic analysis, and geochemical evaluation, the Lower and Middle Jurassic deep marine mudstone has good source rock potential, and the Middle-Upper Jurassic deltaic sandstone and turbidite silty fine sandstone may be good reservoirs. Additional assessment of the study area’s hydrocarbon potential has been conducted using the aromatic hydrocarbon content of seafloor sediments, and favorable exploration areas have been identified using BTEP (benzene, toluene, ethylbenzene, and xylene) content anomalies. Further simulations indicate that the Middle Jurassic hydrocarbon migration is primarily controlled by sand body distribution and faults. In summary, we propose that the most promising exploration targets should be structural (fault blocks), stratigraphic (sandstone lenses), and a combination of both.

This systematic review and meta-analysis aim to comprehensively evaluate the efficacy of propranolol in the prevention and treatment of post-traumatic stress disorder (PTSD), with a focus on its improvement of core PTSD symptoms. A literature search was conducted across multiple databases (including PubMed, Cochrane, Web of Science, and Embase), with the search cutoff date in October 2024. The studies included randomized controlled trials (RCTs) investigating pharmacological treatments for PTSD. PTSD symptoms were assessed using standardized clinical scales, including the Clinician-Administered PTSD Scale (CAPS) and the PTSD Checklist (PCL). The primary outcome was the improvement in PTSD symptoms. Seven studies met the inclusion criteria for the meta-analysis. The studies showed low heterogeneity, with a chi-squared value of 2.56 (df = 6, p = 0.86) and I = 0%. The overall effect test indicated significant improvement in PTSD symptoms after propranolol intervention (Z = 2.32, p = 0.02). These findings suggest that propranolol has a statistically significant effect on reducing the severity of PTSD symptoms, with a moderate effect size according to Cohen's criteria. This systematic review and meta-analysis provide preliminary evidence supporting the possible role of propranolol in alleviating PTSD symptoms. Future researches are needed to further clarify the therapeutic potential, mechanisms of action, and long-term safety of propranolol in PTSD treatment.

Breast density has been recognised as an important biomarker that indicates the risk of developing breast cancer. Accurate classification of breast density plays a crucial role in developing a computer-aided detection (CADe) system for mammogram interpretation. This paper proposes a novel texture descriptor, namely, rotation invariant uniform local quinary patterns (RIU4-LQP), to describe texture patterns in mammograms and to improve the robustness of image features. In conventional processing schemes, image features are obtained by computing histograms from texture patterns. However, such processes ignore very important spatial information related to the texture features. This study designs a new feature vector, namely, K-spectrum, by using Baddeley’s K-inhom function to characterise the spatial distribution information of feature point sets. Texture features extracted by RIU4-LQP and K-spectrum are utilised to classify mammograms into BI-RADS density categories. Three feature selection methods are employed to optimise the feature set. In our experiment, two mammogram datasets, INbreast and MIAS, are used to test the proposed methods, and comparative analyses and statistical tests between different schemes are conducted. Experimental results show that our proposed method outperforms other approaches described in the literature, with the best classification accuracy of 92.76% (INbreast) and 86.96% (MIAS).