Test Generation Algorithm Based on SVM with compressing Sample Space Methods

Test generation algorithm based on the SVM (support vector machine) generates test signals derived from the sample space of the output responses of the analog DUT. When the responses of the normal circuits are similar to those of the faulty circuits (i.e., the latter have only small parametric faults), the sample space is mixed and traditional algorithms have difficulty distinguishing the two groups. However, the SVM provides an effective result. The sample space contains redundant data, because successive impulse-response samples may get quite close. The redundancy will waste the needless computational load. So we propose three difference methods to compress the sample space. The compressing sample space methods are Equidistant compressional method, k-nearest neighbors method and maximal difference method. Numerical experiments prove that maximal difference method can ensure the precision of the test generation

Computational Approach to Investigating Key GO Terms and KEGG Pathways Associated with CNV

Choroidal neovascularization (CNV) is a severe eye disease that leads to blindness, especially in the elderly population. Various endogenous and exogenous regulatory factors promote its pathogenesis. However, the detailed molecular biological mechanisms of CNV have not been fully revealed. In this study, by using advanced computational tools, a number of key gene ontology (GO) terms and KEGG pathways were selected for CNV. A total of 29 validated genes associated with CNV and 17,639 nonvalidated genes were encoded based on the features derived from the GO terms and KEGG pathways by using the enrichment theory. The widely accepted feature selection method—maximum relevance and minimum redundancy (mRMR)—was applied to analyze and rank the features. An extensive literature review for the top 45 ranking features was conducted to confirm their close associations with CNV. Identifying the molecular biological mechanisms of CNV as described by the GO terms and KEGG pathways may contribute to improving the understanding of the pathogenesis of CNV

GADY: Unsupervised Anomaly Detection on Dynamic Graphs

Anomaly detection on dynamic graphs refers to detecting entities whose behaviors obviously deviate from the norms observed within graphs and their temporal information. This field has drawn increasing attention due to its application in finance, network security, social networks, and more. However, existing methods face two challenges: dynamic structure constructing challenge - difficulties in capturing graph structure with complex time information and negative sampling challenge - unable to construct excellent negative samples for unsupervised learning. To address these challenges, we propose Unsupervised Generative Anomaly Detection on Dynamic Graphs (GADY). To tackle the first challenge, we propose a continuous dynamic graph model to capture the fine-grained information, which breaks the limit of existing discrete methods. Specifically, we employ a message-passing framework combined with positional features to get edge embeddings, which are decoded to identify anomalies. For the second challenge, we pioneer the use of Generative Adversarial Networks to generate negative interactions. Moreover, we design a loss function to alter the training goal of the generator while ensuring the diversity and quality of generated samples. Extensive experiments demonstrate that our proposed GADY significantly outperforms the previous state-of-the-art method on three real-world datasets. Supplementary experiments further validate the effectiveness of our model design and the necessity of each module

A benchmark and an algorithm for detecting germline transposon insertions and measuring de novo transposon insertion frequencies

Transposons are genomic parasites, and their new insertions can cause instability and spur the evolution of their host genomes. Rapid accumulation of short-read whole-genome sequencing data provides a great opportunity for studying new transposon insertions and their impacts on the host genome. Although many algorithms are available for detecting transposon insertions, the task remains challenging and existing tools are not designed for identifying de novo insertions. Here, we present a new benchmark fly dataset based on PacBio long-read sequencing and a new method TEMP2 for detecting germline insertions and measuring de novo \u27singleton\u27 insertion frequencies in eukaryotic genomes. TEMP2 achieves high sensitivity and precision for detecting germline insertions when compared with existing tools using both simulated data in fly and experimental data in fly and human. Furthermore, TEMP2 can accurately assess the frequencies of de novo transposon insertions even with high levels of chimeric reads in simulated datasets; such chimeric reads often occur during the construction of short-read sequencing libraries. By applying TEMP2 to published data on hybrid dysgenic flies inflicted by de-repressed P-elements, we confirmed the continuous new insertions of P-elements in dysgenic offspring before they regain piRNAs for P-element repression. TEMP2 is freely available at Github: https://github.com/weng-lab/TEMP2

Comprehensive evaluation system for vegetation ecological quality: a case study of Sichuan ecological protection redline areas

Dynamic monitoring and evaluation of vegetation ecological quality (VEQ) is indispensable for ecological environment management and sustainable development. Single-indicator methods that have been widely used may cause biased results due to neglect of the variety of vegetation ecological elements. We developed the vegetation ecological quality index (VEQI) by coupling vegetation structure (vegetation cover) and function (carbon sequestration, water conservation, soil retention, and biodiversity maintenance) indicators. The changing characteristics of VEQ and the relative contribution of driving factors in the ecological protection redline areas in Sichuan Province (EPRA), China, from 2000 to 2021 were explored using VEQI, Sen’s slope, Mann-Kendall test, Hurst index, and residual analysis based on the XGBoost (Extreme gradient boosting regressor). The results showed that the VEQ in the EPRA has improved over the 22-year study period, but this trend may be unsustainable in the future. Temperature was the most influential climate factor. And human activities were the dominant factor with a relative contribution of 78.57% to VEQ changes. This study provides ideas for assessing ecological restoration in other regions, and can provide guidance for ecosystem management and conservation

Temperature vegetation dryness index (TVDI) for drought monitoring in the Guangdong Province from 2000 to 2019

Drought monitoring is crucial for assessing and mitigating the impacts of water scarcity on various sectors and ecosystems. Although traditional drought monitoring relies on soil moisture data, remote sensing technology has have significantly augmented the capabilities for drought monitoring. This study aims to evaluate the accuracy and applicability of two temperature vegetation drought indices (TVDI), TVDINDVI and TVDIEVI, constructed using the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) vegetation indices for drought monitoring. Using Guangdong Province as a case, enhanced versions of these indices, developed through Savitzky–Golay filtering and terrain correction were employed. Additionally, Pearson correlation analysis and F-tests were utilized to determine the suitability of the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) in correlation with TVDINDVI and TVDIEVI. The results show that TVDINDVI had more meteorological stations passing both significance test levels (P < 0.001 and P < 0.05) compared to TVDIEVI, and the average Pearson’R correlation coefficient was slightly higher than that of TVDIEVI, indicating that TVDINDVI responded better to drought in Guangdong Province. Our conclusion reveals that drought-prone regions in Guangdong Province are concentrated in the Leizhou Peninsula in southern Guangdong and the Pearl River Delta in central Guangdong. We also analyzed the phenomenon of winter-spring drought in Guangdong Province over the past 20 years. The area coverage of different drought levels was as follows: mild drought accounted for 42% to 64.6%, moderate drought accounted for 6.96% to 27.92%, and severe drought accounted for 0.002% to 1.84%. In 2003, the winter-spring drought in the entire province was the most severe, with a drought coverage rate of up to 84.2%, while in 2009, the drought area coverage was the lowest, at 49.02%. This study offers valuable insights the applicability of TVDI, and presents a viable methodology for drought monitoring in Guangdong Province, underlining its significance to agriculture, environmental conservation, and socio-economic facets in the region

Pre-treatment functional connectivity of the cingulate cortex predicts anti-suicidal effects of serial ketamine infusions

Abstract Background Although ketamine can rapidly decrease suicidal ideation (SI), its neurobiological mechanism of action remains unclear. Several areas of the cingulate cortex have been implicated in SI; therefore, we aimed to explore the neural correlates of the anti-suicidal effect of ketamine with cingulate cortex functional connectivity (FC) in depression. Methods Forty patients with unipolar or bipolar depression with SI underwent six infusions of ketamine over 2 weeks. Clinical symptoms and resting-state functional magnetic resonance imaging data were obtained at baseline and on day 13. Remitters were defined as those with complete remission of SI on day 13. Four pairs of cingulate cortex subregions were selected: the subgenual anterior cingulate cortex (sgACC), pregenual anterior cingulate cortex (pgACC), anterior mid-cingulate cortex (aMCC), and posterior mid-cingulate cortex (pMCC), and whole-brain FC for each seed region was calculated. Results Compared with non-remitters, remitters exhibited increased FC of the right pgACC–left middle occipital gyrus (MOG) and right aMCC–bilateral postcentral gyrus at baseline. A high area under the curve (0.91) indicated good accuracy of the combination of the above between-group differential FCs as a predictor of anti-suicidal effect. Moreover, the change of SI after ketamine infusion was positively correlated with altered right pgACC–left MOG FC in remitters (r = 0.66, p = 0.001). Conclusions Our findings suggest that the FC of some cingulate cortex subregions can predict the anti-suicidal effect of ketamine and that the anti-suicidal mechanism of action of ketamine may involve alteration of FC between the right pgACC and left MOG

Characterization of viral RNA splicing using whole-transcriptome datasets from host species

RNA alternative splicing (AS) is an important post-transcriptional mechanism enabling single genes to produce multiple proteins. It has been well demonstrated that viruses deploy host AS machinery for viral protein productions. However, knowledge on viral AS is limited to a few disease-causing viruses in model species. Here we report a novel approach to characterizing viral AS using whole transcriptome dataset from host species. Two insect transcriptomes (Acheta domesticus and Planococcus citri) generated in the 1,000 Insect Transcriptome Evolution (1KITE) project were used as a proof of concept using the new pipeline. Two closely related densoviruses (Acheta domesticus densovirus, AdDNV, and Planococcus citri densovirus, PcDNV, Ambidensovirus, Densovirinae, Parvoviridae) were detected and analyzed for AS patterns. The results suggested that although the two viruses shared major AS features, dramatic AS divergences were observed. Detailed analysis of the splicing junctions showed clusters of AS events occurred in two regions of the virus genome, demonstrating that transcriptome analysis could gain valuable insights into viral splicing. When applied to large-scale transcriptomics projects with diverse taxonomic sampling, our new method is expected to rapidly expand our knowledge on RNA splicing mechanisms for a wide range of viruses

Ferromagnetic-antiferromagnetic coexisting ground states and exchange bias effects in MnBi4Te7\bf{MnBi_4Te_7} and MnBi6Te10\bf{MnBi_6Te_{10}}

Natural superlattice structures $\rm{(MnBi_2Te_4)(Bi_2Te_3)}$$_n$ ($n$ = 1, 2,...), in which magnetic $\rm{MnBi_2Te_4}$ layers are separated by nonmagnetic $\rm{Bi_2Te_3}$ layers, hold band topology, magnetism and reduced interlayer coupling, providing a promising platform for the realization of exotic topological quantum states. However, their magnetism in the two-dimensional limit, which is crucial for further exploration of quantum phenomena, remains elusive. Here, complex ferromagnetic (FM)-antiferromagnetic (AFM) coexisting ground states that persist up to the 2-septuple layers (SLs) limit are observed and comprehensively investigated in $\rm{MnBi_4Te_7}$ ($n$ = 1) and $\rm{MnBi_6Te_{10}}$ ($n$ = 2). The ubiquitous Mn-Bi site mixing modifies or even changes the sign of the subtle inter-SL magnetic interactions, yielding a spatially inhomogeneous interlayer coupling. Further, a tunable exchange bias effect is observed in $\rm{(MnBi_2Te_4)(Bi_2Te_3)}$$_n$ ($n$ = 1, 2), arising from the coupling between the FM and AFM components in the ground state. Our work highlights a new approach toward the fine-tuning of magnetism and paves the way for further study of quantum phenomena in $\rm{(MnBi_2Te_4)(Bi_2Te_3)}$$_n$ ($n$ = 1, 2,...) as well as their magnetic applications.Comment: 9 pages, 4 figure

Multifunctional Biomimetic Nanovaccines Based on Photothermal and Weak-Immunostimulatory Nanoparticulate Cores for the Immunotherapy of Solid Tumors

An alternative strategy of choosing photothermal and weak-immunostimulatory porous silicon@Au nanocomposites as particulate cores to prepare a biomimetic nanovaccine is reported to improve its biosafety and immunotherapeutic efficacy for solid tumors. A quantitative analysis method is used to calculate the loading amount of cancer cell membranes onto porous silicon@Au nanocomposites. Assisted with foreign-body responses, these exogenous nanoparticulate cores with weak immunostimulatory effect can still efficiently deliver cancer cell membranes into dendritic cells to activate them and the downstream antitumor immunity, resulting in no occurrence of solid tumors and the survival of all immunized mice during 55 day observation. In addition, this nanovaccine, as a photothermal therapeutic agent, synergized with additional immunotherapies can significantly inhibit the growth and metastasis of established solid tumors, via the initiation of the antitumor immune responses in the body and the reversion of their immunosuppressive microenvironments. Considering the versatile surface engineering of porous silicon nanoparticles, the strategy developed here is beneficial to construct multifunctional nanovaccines with better biosafety and more diagnosis or therapeutic modalities against the occurrence, recurrence, or metastasis of solid tumors in future clinical practice.Peer reviewe