Phylogenetic Evolution and Phylogeography of Tibetan Sheep Based on mtDNA D-Loop Sequences

The molecular and population genetic evidence of the phylogenetic status of the Tibetan sheep (Ovis aries) is not well understood, and little is known about this species’ genetic diversity. Phylogenetic relationship and phylogeography of 636 individual Tibetan sheep which were collected from the Qinghai-Tibetan Plateau area in China and were assessed using 642 complete sequences of the mitochondrial DNA D-loop. Reference data were obtained from the six reference breed sequences available in GenBank. Phylogeography analysis showed that all four previously defined haplogroups were found in the 15 Tibetan sheep populations but that only one haplogroup was found in Linzhou sheep. Furthermore, clustering analysis divided the 636 individual Tibetan sheep into at least two clusters. The estimated genetic distance and genetic differentiation associate with altitude, suggesting geographic and adaptive effects in Tibetan sheep. These results contribute to the knowledge of Tibetan sheep populations and will help inform future conservation programs about the Tibetan sheep native to the Qinghai-Tibetan Plateau in China

Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection

Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority. It has gained substantial attention in various domains of information security, including network intrusion, financial fraud, and malicious comments, et al. Existing methods are primarily developed in an unsupervised manner due to the challenge in obtaining labeled data. For lack of guidance from prior knowledge in unsupervised manner, the identified anomalies may prove to be data noise or individual data instances. In real-world scenarios, a limited batch of labeled anomalies can be captured, making it crucial to investigate the few-shot problem in graph anomaly detection. Taking advantage of this potential, we propose a novel few-shot Graph Anomaly Detection model called FMGAD (Few-shot Message-Enhanced Contrastive-based Graph Anomaly Detector). FMGAD leverages a self-supervised contrastive learning strategy within and across views to capture intrinsic and transferable structural representations. Furthermore, we propose the Deep-GNN message-enhanced reconstruction module, which extensively exploits the few-shot label information and enables long-range propagation to disseminate supervision signals to deeper unlabeled nodes. This module in turn assists in the training of self-supervised contrastive learning. Comprehensive experimental results on six real-world datasets demonstrate that FMGAD can achieve better performance than other state-of-the-art methods, regardless of artificially injected anomalies or domain-organic anomalies

An Enhanced Differential Evolution with Elite Chaotic Local Search

Differential evolution (DE) is a simple yet efficient evolutionary algorithm for real-world engineering problems. However, its search ability should be further enhanced to obtain better solutions when DE is applied to solve complex optimization problems. This paper presents an enhanced differential evolution with elite chaotic local search (DEECL). In DEECL, it utilizes a chaotic search strategy based on the heuristic information from the elite individuals to promote the exploitation power. Moreover, DEECL employs a simple and effective parameter adaptation mechanism to enhance the robustness. Experiments are conducted on a set of classical test functions. The experimental results show that DEECL is very competitive on the majority of the test functions

Operational Calibration: Debugging Confidence Errors for DNNs in the Field

Trained DNN models are increasingly adopted as integral parts of software systems, but they often perform deficiently in the field. A particularly damaging problem is that DNN models often give false predictions with high confidence, due to the unavoidable slight divergences between operation data and training data. To minimize the loss caused by inaccurate confidence, operational calibration, i.e., calibrating the confidence function of a DNN classifier against its operation domain, becomes a necessary debugging step in the engineering of the whole system. Operational calibration is difficult considering the limited budget of labeling operation data and the weak interpretability of DNN models. We propose a Bayesian approach to operational calibration that gradually corrects the confidence given by the model under calibration with a small number of labeled operation data deliberately selected from a larger set of unlabeled operation data. The approach is made effective and efficient by leveraging the locality of the learned representation of the DNN model and modeling the calibration as Gaussian Process Regression. Comprehensive experiments with various practical datasets and DNN models show that it significantly outperformed alternative methods, and in some difficult tasks it eliminated about 71% to 97% high-confidence (>0.9) errors with only about 10\% of the minimal amount of labeled operation data needed for practical learning techniques to barely work.Comment: Published in the Proceedings of the 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2020

Effectiveness of neoadjuvant immunochemotherapy compared to neoadjuvant chemotherapy in non-small cell lung cancer patients: Real-world data of a retrospective, dual-center study

BackgroundStudying the application of neoadjuvant immunochemotherapy (NICT) in the real world and evaluating its effectiveness and safety in comparison with neoadjuvant chemotherapy (NCT) are critically important.MethodsThis study included the II-IIIB stage non-small cell lung cancer (NSCLC) patients receiving NCT with or without PD-1 inhibitors and undergoing surgery after neoadjuvant treatments between January 2019 to August 2022. The clinical characteristics and treatment outcomes were retrospectively reviewed and analyzed.ResultsA total of 66 patients receiving NICT and 101 patients receiving NCT were included in this study. As compared to NCT, NICT showed similar safety while not increasing the surgical difficulty. The ORR in the NICT and NCT groups was 74.2% and 53.5%, respectively, P = 0.009. A total of 44 patients (66.7%) in the NICT group and 21 patients (20.8%) in the NCT group showed major pathology response (MPR) (P <0.001). The pathology complete response (pCR) rate was also significantly higher in NICT group than that in NCT group (45.5% vs. 10.9%, P <0.001). After Propensity Score Matching (PSM), 42 pairs of patients were included in the analysis. The results showed no significant difference in the ORR between the two groups (52.3% vs. 43.2%, P = 0.118), and the proportions of MPR (76.2%) and pCR (50.0%) in NICT group were significantly higher than those of MPR (11.9%) and pCR (4.7%) in the NCT group (P <0.001). The patients with driver mutations might also benefit from NICT.ConclusionsAs compared to NCT, the NICT could significantly increase the proportions of patients with pCR and MPR without increasing the operation-related bleeding and operation time

Evaluation of 17 microsatellite markers for parentage testing and individual identification of domestic yak (Bos grunniens)

Background Yak (Bos grunniens) is the most important domestic animal for people living at high altitudes. Yak ordinarily feed by grazing, and this behavior impacts the accuracy of the pedigree record because it is difficult to control mating in grazing yak. This study aimed to evaluate the pedigree system and individual identification in polled yak. Methods A total of 71 microsatellite loci were selected from the literature, mostly from the studies on cattle. A total of 35 microsatellite loci generated excellent PCR results and were evaluated for the parentage testing and individual identification of 236 unrelated polled yaks. A total of 17 of these 35 microsatellite loci had polymorphic information content (PIC) values greater than 0.5, and these loci were in Hardy–Weinberg equilibrium without linkage disequilibrium. Results Using multiplex PCR, capillary electrophoresis, and genotyping, very high exclusion probabilities were obtained for the combined core set of 17 loci. The exclusion probability (PE) for one candidate parent when the genotype of the other parent is not known was 0.99718116. PE for one candidate parent when the genotype of the other parent is known was 0.99997381. PE for a known candidate parent pair was 0.99999998. The combined PEI (PE for identity of two unrelated individuals) and PESI (PE for identity of two siblings) were >0.99999999 and 0.99999899, respectively. These findings indicated that the combination of 17 microsatellite markers could be useful for efficient and reliable parentage testing and individual identification in polled yak. Discussion Many microsatellite loci have been investigated for cattle paternity testing. Nevertheless, these loci cannot be directly applied to yak identification because the two bovid species have different genomic sequences and organization. A total of 17 loci were selected from 71 microsatellite loci based on efficient amplification, unambiguous genotyping, and high PIC values for polled yaks, and were suitable for parentage analysis in polled yak populations

Effects of nanobubble water on the growth of Lactobacillus acidophilus 1028 and its lactic acid production

Nanobubble water (NBW) has been applied in various fields due to the unique properties of nanobubbles (NBs) including long-term stability, negative zeta potential and generation of free radicals. In this study, the performance of four kinds of NBW from different gases (air, N2, H2, and CO2) in addition to deionized water (DW) were investigated and compared in terms of the growth of the probiotic Lactobacillus acidophilus 1028. The NB density, size distribution, zeta potential, pH and dissolved oxygen (DO) of the NBW were firstly investigated. Results indicate that N2-NBW had the highest absolute value of zeta potential and NB density (−25.3 ± 5.43 mV and 5.73 ± 1.0 × 107 particles per mL, respectively), while the lowest was detected in CO2-NBW (−6.96 ± 2.36 mV and 3.39 ± 1.73 × 107 particles per mL, respectively). With the exception of CO2-NBW, all the other types of NBW showed promotion effects on the growth of the strain at the lag and logarithmic phases. Among them, N2-NBW demonstrated the best performance, achieving the highest increase ratio of 51.1% after 6 h cultivation. The kinetic models (Logistic and Gompertz) indicate that the culture with N2-NBW had the shortest lag phase and the maximum specific growth rate when compared to the H2-NBW and DW groups under the same cultivation conditions. Preliminary analysis on the mechanisms suggested that these effects were related to the properties (zeta potential and density) of the NBs, which might affect the transport of substances. This study suggests that NBW has the potential for promoting the production efficiency of probiotics via fermentation

Ultra-short lifetime isomer studies from photonuclear reactions using laser-driven ultra-intense {\gamma}-ray

Isomers, ubiquitous populations of relatively long-lived nuclear excited states, play a crucial role in nuclear physics. However, isomers with half-life times of several seconds or less barely had experimental cross section data due to the lack of a suitable measuring method. We report a method of online {\gamma} spectroscopy for ultra-short-lived isomers from photonuclear reactions using laser-driven ultra-intense {\gamma}-rays. The fastest time resolution can reach sub-ps level with {\gamma}-ray intensities >10^{19}/s ({\geqslant} 8 MeV). The ^{115}In({\gamma}, n)^{114m2}In reaction (T_{1/2} = 43.1 ms) was first measured in the high-energy region which shed light on the nuclear structure studies of In element. Simulations showed it would be an efficient way to study ^{229m}Th (T_{1/2} = 7 {\mu}s), which is believed to be the next generation of nuclear clock. This work offered a unique way of gaining insight into ultra-short lifetimes and promised an effective way to fill the gap in relevant experimental data

Frictional Wear Analysis of Armature Rails by Temperature Field Effect

Frictional wear between the armature and the rails directly affects the armature-rails contact condition, thereby affecting the service life and launch efficiency of the electromagnetic rail launcher. In order to explore the influence of temperature on the frictional wear of electromagnetic rail launcher, the source of heat load on the launcher is analyzed, a frictional wear model under the effect of temperature is established and the effect of temperature on the wear between armature and rail is analyzed. Using the finite element method, the pulse forming network is used to power the launcher, the contact resistance variation curve with time is found out, the 3D finite element calculation model is established considering the electromagnetic field-temperature field-stress field, etc., and the data related to the armature wear amount in the two states considering the temperature field and not considering the temperature field are compared and analyzed. The results show that with the armature movement, the temperature of the contact surface of the armature-rails gradually increases, the elastic modulus and hardness of the material in the contact area decrease, and the change trends of the wear volume and wear rate of the armature in the two states are the same. The maximum wear rate of the armature when considering the temperature field is 1.15 mm3/ms, which is 1.2 times of the maximum wear rate of the armature without considering the temperature field