Self-Discriminative Modeling for Anomalous Graph Detection

This paper studies the problem of detecting anomalous graphs using a machine learning model trained on only normal graphs, which has many applications in molecule, biology, and social network data analysis. We present a self-discriminative modeling framework for anomalous graph detection. The key idea, mathematically and numerically illustrated, is to learn a discriminator (classifier) from the given normal graphs together with pseudo-anomalous graphs generated by a model jointly trained, where we never use any true anomalous graphs and we hope that the generated pseudo-anomalous graphs interpolate between normal ones and (real) anomalous ones. Under the framework, we provide three algorithms with different computational efficiencies and stabilities for anomalous graph detection. The three algorithms are compared with several state-of-the-art graph-level anomaly detection baselines on nine popular graph datasets (four with small size and five with moderate size) and show significant improvement in terms of AUC. The success of our algorithms stems from the integration of the discriminative classifier and the well-posed pseudo-anomalous graphs, which provide new insights for anomaly detection. Moreover, we investigate our algorithms for large-scale imbalanced graph datasets. Surprisingly, our algorithms, though fully unsupervised, are able to significantly outperform supervised learning algorithms of anomalous graph detection. The corresponding reason is also analyzed.Comment: This work was submitted to NeurIPS 2023 but was unfortunately rejecte

Optimizing Methanol Blending Performance of Electronically Controlled Diesel Engines through Fuzzy Analysis

This paper presents a comprehensive optimization approach for enhancing the performance of a methanol/diesel Exhaust Gas Recirculation (EGR) engine. Initially, a hybrid fuel engine combustion chamber model was developed using AVL-FIRE software, and the simulated results were compared with the values obtained from bench tests. An orthogonal experimental design was employed to optimize five key factors, namely methanol blending ratio, EGR rate, injection advance angle, intake pressure, and intake temperature. Evaluation indexes were established, with indicated power and NO emissions assigned weights of 0.35 and 0.65, respectively. The optimal parameter combinations were determined as follows: methanol blending ratio (a1=20%), EGR rate (a2=12.5%), injection advance angle (a3=16.6°CA), intake temperature (a4 = 315.15 K), and intake pressure (a5=0.173 MPa). The indicated power of the optimized configuration reached 47.8 kW, slightly lower than the original 55 kW, while the NO emission mass fraction decreased to 1.9×10-4%, representing a significant reduction of 77.6% compared to the original value of 8.5×10-4%. This optimization methodology demonstrates the effective reduction of NO emissions without compromising power performance in methanol/diesel EGR engines

Effect of Injection Advance Angle on the Performance of Butanol-Diesel Dual-fuel Engines

In order to further investigate the performance of the butanol-diesel dual-fuel engine, this paper uses the 4190ZLC-2 marine medium-speed diesel engine as a prototype and establishes a dual-fuel engine high-pressure cycle model using AVL-FIRE simulation software. The injection advance angle was set to 16.6°, 18.6°, 20.6° and 22.6° respectively, and its effect on the performance of the dual-fuel engine was investigated by varying the injection advance angle. The results show that as the injection advance angle increases, the incylinder pressure and temperature also increase. When the injection advance angle is 22.6°CA, compared with the original engine, CO emission is reduced by 16.8%, NO emission is increased by 7.4%, carbon smoke emission is reduced by 16.9%, and the indicated power is 52.6kW, which is increased by 1.8%

RNA sequencing reveals CircRNA expression profiles in chicken embryo fibroblasts infected with velogenic Newcastle disease virus

IntroductionNewcastle disease virus (NDV) is an important avian pathogen prevalent worldwide; it has an extensive host range and seriously harms the poultry industry. Velogenic NDV strains exhibit high pathogenicity and mortality in chickens. Circular RNAs (circRNAs) are among the most abundant and conserved eukaryotic transcripts. They are part of the innate immunity and antiviral response. However, the relationship between circRNAs and NDV infection is unclear.MethodsIn this study, we used circRNA transcriptome sequencing to analyze the differences in circRNA expression profiles post velogenic NDV infection in chicken embryo fibroblasts (CEFs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to reveal significant enrichment of differentially expressed (DE) circRNAs. The circRNA- miRNA-mRNA interaction networks were further predicted. Moreover, circ-EZH2 was selected to determine its effect on NDV infection in CEFs.ResultsNDV infection altered circRNA expression profiles in CEFs, and 86 significantly DE circRNAs were identified. GO and KEGG enrichment analyses revealed significant enrichment of DE circRNAs for metabolism-related pathways, such as lysine degradation, glutaminergic synapse, and alanine, aspartic-acid, and glutamic-acid metabolism. The circRNA- miRNA-mRNA interaction networks further demonstrated that CEFs might combat NDV infection by regulating metabolism through circRNA-targeted mRNAs and miRNAs. Furthermore, we verified that circ-EZH2 overexpression and knockdown inhibited and promoted NDV replication, respectively, indicating that circRNAs are involved in NDV replication.ConclusionsThese results demonstrate that CEFs exert antiviral responses by forming circRNAs, offering new insights into the mechanisms underlying NDV-host interactions

Electrocatalytic reduction of CO2 to ethylene and ethanol through hydrogen-assisted C-C coupling over fluorine-modified copper

精准控制C1分子C-C偶联合成特定C2+化合物是C1化学中极具挑战性的难题。由于C2+化合物(如乙烯和乙醇)在化工和能源领域具有重要用途，将CO2直接转化为C2+产物极具吸引力。发展高效催化剂，实现高电流密度、高C2+选择性、高稳定性的“三高”性能，是推进电催化还原CO2走向实际应用的关键。研究团队针对电催化还原CO2中高CO2还原法拉第效率的催化剂常常活性低的问题，提出了适当提高催化剂活化水的能力对增加CO2还原活性的重要性，发展出氢助碳碳偶联(hydrogen-assisted C-C coupling)的新策略，在氟修饰的铜(F-Cu)催化剂上实现了CO2电催化还原制乙烯和乙醇的新突破。该研究工作实验部分主要由王野、张庆红教授指导，能源材料协同创新中心iChEM2016级博士生马文超、固体表面物理化学国家重点实验室高级工程师谢顺吉(共同第一作者)完成；理论计算部分由程俊教授指导，2017级硕士生刘彤彤(共同第一作者)、2016级博士生樊祺源完成。叶进裕博士为原位红外测试提供了支持。上海光源姜政研究员、孙凡飞博士、杨若欧为同步辐射表征提供了支持。 这是投稿的最终版本，正式出版的论文版本请访问官方链接（https://doi.org/10.1038/s41929-020-0450-0）。Electrocatalytic reduction of CO2 into multi-carbon (C2+) products is a highly attractive route for CO2 utilization. However, the yield of C2+ products remains low because of the limited C2+ selectivity at high CO2 conversion rate. Here, we report a fluorine-modified copper catalyst that exhibits an ultrahigh current density of 1.6 A cm−2 at C2+ (mainly ethylene and ethanol) Faradaic efficiency of 80% for electrocatalytic CO2 reduction in a flow cell. The C2-4 selectivity reaches 85.8% at a single-pass yield of 16.5%. We show a hydrogen-assisted C−C coupling mechanism between adsorbed formyl (CHO) intermediates for C2+ formation. Fluorine enhances water activation, CO adsorption and hydrogenation of adsorbed CO to CHO intermediate that can readily undergo coupling. Our findings offer an opportunity to design highly active and selective CO2 electroreduction catalysts with potential for practical applicationThis work was supported by the National Key Research and Development Program of the Ministry of Science and Technology of China (No. 2017YFB0602201), the National Natural Science Foundation of China (Nos. 21690082, 91545203, 21503176 and 21802110), We thank staffs at the BL14W1 beamline of the Shanghai Synchrotron Radiation Facilities (SSRF) for assistance with the EXAFS measurements.研究工作得到科技部重点研发计划（批准号：2017YFB0602201）和国家自然科学基金（批准号：21690082、91545203、21503176、21802110）项目的资助

Functionally Orthologous Viral and Cellular MicroRNAs Studied by a Novel Dual-Fluorescent Reporter System

Recent research raised the possibility that some viral microRNAs (miRNAs) may function as orthologs of cellular miRNAs. In the present work, to study the functional orthologous relationships of viral and cellular miRNAs, we first constructed a dual-fluorescent protein reporter vector system for the easy determination of miRNA function. By expressing the miRNAs and the indicator and internal control fluorescent proteins individually from a single vector, this simple reporter system can be used for miRNA functional assays that include visualizing miRNA activity in live cells. Sequence alignments indicated that the simian virus 40 (SV40) encoded miRNA sv40-mir-S1-5p contains a seed region identical to that of the human miRNA hsa-miR423-5p. Using the new reporter system, it was found that sv40-mir-S1-5p and hsa-miR423-5p downregulate the expression of common artificial target mRNAs and some predicted biological targets of hsa-miR423-5p, demonstrating that they are functional orthologs. The human immunodeficiency virus 1 (HIV-1) encoded hiv1-miR-N367 also contains a seed sequence identical to that of the human miRNA hsa-miR192. Functional assays showed that hiv1-miR-N367 and hsa-miR192 could downregulate common artificial and predicted biological targets, suggesting that these miRNAs may also act as functional orthologs. Thus, this study presents a simple and universal system for testing miRNA function and identifies two new pairs of functional orthologs, sv40-mir-S1-5p and hsa-miR423-5p as well as hiv-1-miR-N367 and hsa-miR192. These findings also expand upon our current knowledge of functional homology and imply that a more general phenomenon of orthologous relationships exists between viral and cellular miRNAs

A survey of green plant tRNA 3'-end processing enzyme tRNase Zs, homologs of the candidate prostate cancer susceptibility protein ELAC2

<p>Abstract</p> <p>Background</p> <p>tRNase Z removes the 3'-trailer sequences from precursor tRNAs, which is an essential step preceding the addition of the CCA sequence. tRNase Z exists in the short (tRNase Z^S) and long (tRNase Z^L) forms. Based on the sequence characteristics, they can be divided into two major types: bacterial-type tRNase Z^Sand eukaryotic-type tRNase Z^L, and one minor type, <it>Thermotoga maritima </it>(TM)-type tRNase Z^S. The number of tRNase Zs is highly variable, with the largest number being identified experimentally in the flowering plant <it>Arabidopsis thaliana</it>. It is unknown whether multiple tRNase Zs found in <it>A. thaliana </it>is common to the plant kingdom. Also unknown is the extent of sequence and structural conservation among tRNase Zs from the plant kingdom.</p> <p>Results</p> <p>We report the identification and analysis of candidate tRNase Zs in 27 fully sequenced genomes of green plants, the great majority of which are flowering plants. It appears that green plants contain multiple distinct tRNase Zs predicted to reside in different subcellular compartments. Furthermore, while the bacterial-type tRNase Z^Ss are present only in basal land plants and green algae, the TM-type tRNase Z^Ss are widespread in green plants. The protein sequences of the TM-type tRNase Z^Ss identified in green plants are similar to those of the bacterial-type tRNase Z^Ss but have distinct features, including the TM-type flexible arm, the variant catalytic HEAT and HST motifs, and a lack of the PxKxRN motif involved in CCA anti-determination (inhibition of tRNase Z activity by CCA), which prevents tRNase Z cleavage of mature tRNAs. Examination of flowering plant chloroplast tRNA genes reveals that many of these genes encode partial CCA sequences. Based on our results and previous studies, we predict that the plant TM-type tRNase Z^Ss may not recognize the CCA sequence as an anti-determinant.</p> <p>Conclusions</p> <p>Our findings substantially expand the current repertoire of the TM-type tRNase Z^Ss and hint at the possibility that these proteins may have been selected for their ability to process chloroplast pre-tRNAs with whole or partial CCA sequences. Our results also support the coevolution of tRNase Zs and tRNA 3'-trailer sequences in plants.</p

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Unsupervised Deep Discriminant Analysis Based Clustering

This work presents an unsupervised deep discriminant analysis for clustering. The method is based on deep neural networks and aims to minimize the intra-cluster discrepancy and maximize the inter-cluster discrepancy in an unsupervised manner. The method is able to project the data into a nonlinear low-dimensional latent space with compact and distinct distribution patterns such that the data clusters can be effectively identified. We further provide an extension of the method such that available graph information can be effectively exploited to improve the clustering performance. Extensive numerical results on image and non-image data with or without graph information demonstrate the effectiveness of the proposed methods

Some results on the value distribution of differential polynomials

In this article, we study some results on the value distribution of differential polynomials and mainly prove the following theorem: suppose that PP is a polynomial with degP≥3{\rm{\deg }}\hspace{0.33em}P\ge 3 and ff is a transcendental meromorphic function. Let α\alpha be a small function of ff. If α\alpha is a constant, we also require that there exists a constant A≠αA\ne \alpha such that P(z)−AP\left(z)-A has a zero of multiplicity at least 3. Then, for any 0<ε<10\lt \varepsilon \lt 1, we have T(r,f)≤kN¯r,1P(f)−α+S(r,f),T\left(r,f)\le k\overline{N}\left(r,\frac{1}{P(f)-\alpha }\right)+S\left(r,f), where if P′(z)P^{\prime} \left(z) has only one zero, then k=1degP−2k=\frac{1}{{\rm{\deg }}\hspace{0.33em}P-2}; if P′(z)P^{\prime} \left(z) has two distinct zeros aa and bb with P(a)≠P(b)P\left(a)\ne P\left(b) and α\alpha is nonconstant, then k=11−εk=\frac{1}{1-\varepsilon }; otherwise k=1k=1