Copy number variants in locally raised Chinese chicken genomes determined using array comparative genomic hybridization

BACKGROUND: Copy number variants contribute to genetic variation in birds. Analyses of copy number variants in chicken breeds had focused primarily on those from commercial varieties with nothing known about the occurrence and diversity of copy number variants in locally raised Chinese chicken breeds. To address this deficiency, we characterized copy number variants in 11 chicken breeds and compared the variation among these breeds. RESULTS: We presented a detailed analysis of the copy number variants in locally raised Chinese chicken breeds identified using a customized comparative genomic hybridization array. We identified 833 copy number variants contained within 308 copy number variant regions. The median and mean sizes of the copy number variant regions were 14.6 kb and 35.1 kb, respectively. Of the copy number variant regions, 138 (45%) involved gain of DNA, 159 (52%) involved loss of DNA, and 11 (3%) involved both gain and loss of DNA. Principal component analysis and agglomerative hierarchical clustering revealed the close relatedness of the four locally raised chicken breeds, Shek-Ki, Langshan, Qingyuan partridge, and Wenchang. Biological process enrichment analysis of the copy number variant regions confirmed the greater variation among the four aforementioned varieties than among the seven other breeds studied. CONCLUSION: Our description of the distribution of the copy number variants and comparison of the differences among the copy number variant regions of the 11 chicken breeds supplemented the information available concerning the copy number variants of other Chinese chicken breeds. In addition to its relevance for functional analysis, our results provided the first insight into how chicken breeds can be clustered on the basis of their genomic copy number variation

Analysis of Immunoglobulin Transcripts in the Ostrich Struthio camelus, a Primitive Avian Species

Previous studies on the immunoglobulin (Ig) genes in avian species are limited (mainly to galliformes and anseriformes) but have revealed several interesting features, including the absence of the IgD and Igκ encoding genes, inversion of the IgA encoding gene and the use of gene conversion as the primary mechanism to generate an antibody repertoire. To better understand the Ig genes and their evolutionary development in birds, we analyzed the Ig genes in the ostrich (Struthio camelus), which is one of the most primitive birds. Similar to the chicken and duck, the ostrich expressed only three IgH chain isotypes (IgM, IgA and IgY) and λ light chains. The IgM and IgY constant domains are similar to their counterparts described in other vertebrates. Although conventional IgM, IgA and IgY cDNAs were identified in the ostrich, we also detected a transcript encoding a short membrane-bound form of IgA (lacking the last two CH exons) that was undetectable at the protein level. No IgD or κ encoding genes were identified. The presence of a single leader peptide in the expressed heavy chain and light chain V regions indicates that gene conversion also plays a major role in the generation of antibody diversity in the ostrich. Because the ostrich is one of the most primitive living aves, this study suggests that the distinct features of the bird Ig genes appeared very early during the divergence of the avian species and are thus shared by most, if not all, avian species

Utility of S100A12 as an Early Biomarker in Patients With ST-Segment Elevation Myocardial Infarction

Importance: S100A12 is a calcium binding protein which is involved in inflammation and progression of atherosclerosis. Objective: We sought to investigate the utility of S100A12 as a biomarker for the early diagnosis and prognostication of patients presenting with ST-segment elevation myocardial infarction (STEMI). Design, Setting, and Participants: S100A12 was measured in 1023 patients presenting to the emergency department with acute chest pain between June 2012 and November 2015. An independent cohort of 398 patients enrolled at 3 different hospitals served as a validation cohort. Main Outcomes and Measures: The primary clinical endpoint of interest was major adverse cardiac and cerebral events (MACCE) defined as a composite of all-cause death, MI, stroke, or hospitalization for heart failure. Results: A total of 438/1023 patients (42.8%) in the diagnosis cohort were adjudicated as STEMI, among whom plasma S100A12 levels increased within 30 min and peaked 1–2 h after symptom onset. Compared with high-sensitivity cardiac troponin T and creatine kinase-MB isoenzyme, S100A12 more accurately identified STEMI, especially within the first 2 h after symptom onset (area under the curve 0.963 compared with 0.860 for hscTnT and 0.711 for CK-MB, both P \u3c 0.05). These results were consistent in the 243-patient validation cohort. The 1-year rate of MACCE was greatest in patients in the highest peak S100A12 tertile, intermediate in the middle tertile and least in the lowest tertile (9.3 vs. 5.7 vs. 3.0% respectively, Ptrend = 0.0006). By multivariable analysis the peak plasma concentration of S100A12 was an independent predictor of MACCE within 1 year after STEMI (HR, 1.001, 95%CI, 1.000–1.002; P = 0.0104). Zhang et al. S100A12 as a STEMI Biomarker Conclusions and Relevance: S100A12 rapidly identified patients with STEMI, more accurately than other cardiac biomarkers, especially within the first 2 h after symptom onset. The peak plasma S100A12 level was a strong predictor of 1-year prognosis after STEMI

Milestones in autonomous driving and intelligent vehicles: survey of surveys

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks, lack of systematic summary and research directions in the future. Here we propose a Survey of Surveys (SoS) for total technologies of AD and IVs that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. To our knowledge, this article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical surveys. We anticipate that this article will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future

Mineralogical Characteristics and Arsenic Release of High Arsenic Coals from Southwestern Guizhou, China during Pyrolysis Process

Coal is the primary energy source in China, and coal pyrolysis is considered an essential and efficient method for clean coal utilization. Three high arsenic coals collected from the southwestern Guizhou province of China were chosen in this study. Low-temperature ashing plus X-ray diffraction analysis (XRD) was used to identify the minerals in coals. The three coals were pyrolyzed in a tube furnace in an N2 atmosphere at 950 °C, 1200 °C, and 1400 °C, respectively. Environment scanning electron microscope (ESEM), XRD, X-ray fluorescence analysis (XRF), and inductively coupled plasma-mass spectrometry (ICP-MS) were adopted to determine the morphology, mineral compositions, and element compositions and arsenic contents of the coal pyrolysis ashes, respectively. It can be found that minerals in coal are mainly composed of quartz, pyrite, muscovite, and rutile. The minerals in the ashes generated from coal pyrolysis mainly contain quartz, dehydroxylated muscovite, iron oxide minerals, mullite, and silicon nitride. Oldhamite and gupeite exist at 950 °C and 1400 °C, respectively. The morphologies of oldhamite and gupeite at these temperatures are irregular block-shaped particles and irregular spherical particles, respectively. The mineralogical transformations in the process of coal pyrolysis affect coal utilization. The arsenic release rate is higher than 87% during pyrolysis at 1400 °C. The arsenic in organic matter is more able to be volatilized than mineral components. The retention time can slightly influence the arsenic release rate, and the influence of temperature is much more significant than the influence of retention time. The understanding of mineral evolution and arsenic environmental emission is helpful for the safety of high-arsenic coal pyrolysis

Serum metabolic profile and metabolome genome-wide association study in chicken

Abstract Background Chickens provide globally important livestock products. Understanding the genetic and molecular mechanisms underpinning chicken economic traits is crucial for improving their selective breeding. Influenced by a combination of genetic and environmental factors, metabolites are the ultimate expression of physiological processes and can provide key insights into livestock economic traits. However, the serum metabolite profile and genetic architecture of the metabolome in chickens have not been well studied. Results Here, comprehensive metabolome detection was performed using non-targeted LC–MS/MS on serum from a chicken advanced intercross line (AIL). In total, 7,191 metabolites were used to construct a chicken serum metabolomics dataset and to comprehensively characterize the serum metabolism of the chicken AIL population. Regulatory loci affecting metabolites were identified in a metabolome genome-wide association study (mGWAS). There were 10,061 significant SNPs associated with 253 metabolites that were widely distributed across the entire chicken genome. Many functional genes affect metabolite synthesis, metabolism, and regulation. We highlight the key roles of TDH and AASS in amino acids, and ABCB1 and CD36 in lipids. Conclusions We constructed a chicken serum metabolite dataset containing 7,191 metabolites to provide a reference for future chicken metabolome characterization work. Meanwhile, we used mGWAS to analyze the genetic basis of chicken metabolic traits and metabolites and to improve chicken breeding

Weighted Pseudo-Labels and Bounding Boxes for Semisupervised SAR Target Detection

Synthetic aperture radar (SAR) image target detection methods based on semisupervised learning, such as the mean teacher framework, have shown promise in diminishing the issue of limited labeled data. However, several challenges exist in current methods. First, data augmentation techniques designed for optical images may not be suitable for SAR images due to differences in imaging methods. In addition, the contribution of pseudo labels remains constant during the initial retraining stage can lead to degradation in prediction results. Moreover, the low quality of predicted bounding boxes poses a challenge for effective retraining. To address these challenges, we propose an end-to-end semisupervised detection method based on the mean teacher framework. To enhance the robustness of training, we first introduce SAR-specific data augmentation techniques, including multiplicative noise, which effectively increase the diversity of training samples. Second, we propose a method that weights the losses of pseudo-labeled data using a hard-sigmoid function, gradually improving the importance of pseudo-labeled data during retraining, thereby alleviating their potential negative impact on the training process. Finally, we propose an IoU-aware subnetwork to incorporate high-quality pseudo-labeled bounding boxes into retraining, allowing them to contribute to network adjustments while mitigating the impact of low-quality samples. Experimental evaluations on publicly available SAR image datasets demonstrate the effectiveness of our proposed method in improving the target detection capability of semisupervised SAR target detection

In Situ Chemical Modification with Zwitterionic Copolymers of Nanofiltration Membranes:Cure for the Trade-Off between Filtration and Antifouling Performance

Breaking the trade-off between filtration performance and antifouling property is critical to enabling a thin-film nanocomposite (TFC) nanofiltration (NF) membrane for a wide range of feed streams. We proposed a novel design route for TFC NF membranes by grafting well-defined zwitterionic copolymers of [2-(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl)ammonium hydroxide (SBMA) and 2-aminoethyl methacrylate hydrochloride (AEMA) on the polyamide surfaces via an in situ surface chemical modification process. The successful grafting of a zwitterionic copolymer imparted the modified NF membranes with better surface hydrophilicity, a larger actual surface area (i.e., nodular structures), and a thinner polyamide layer. As a result, the water permeability of the modified membrane (i.e., TFC-10) was triple that of the pristine TFC membrane while maintaining high Na2SO4 rejection. We further demonstrated that the TFC-10 membrane possessed exceptional antifouling properties in both static adsorption tests and three cycles of dynamic protein and humic acid fouling tests. To recap, this work provides valuable insights and strategies for the fabrication of TFC NF membranes with simultaneously enhanced filtration performance and antifouling property.</p

A High-dynamic Wavelength Tracking and Millimeter-level Ranging Inter-satellite Laser Communication Link with Feedback-Homodyne Detection

The Integrated Laser Communication/Ranging System, which uses coded signal as the ranging information carrier, is of great importance to the next large-capacity inter-satellite information network. In this paper, a system design with high-sensitivity feedback-homodyne detection scheme and asynchronous ranging algorithm is demonstrated with real-time FPGA implementation. The parallel FFT estimation is applied to improve the speed and range of wavelength drift tracking, which can handle dynamic wavelength drift up to 2.4 pm/s (300 MHz/s). Meanwhile, for clock sources with subtle dynamic frequency offset and sufficient stability, the proposed fractional symbol ranging method is proven to achieve millimeter-level measurement accuracy. The designed system is shown to perform well in terms of both laser linewidth tolerance and noise resistance