Cryptanalysis of Homophonic Substitution Cipher Using Hidden Markov Models

We investigate the effectiveness of a Hidden Markov Model (HMM) with random restarts as a mean of breaking a homophonic substitution cipher. Based on extensive experiments, we find that such an HMM-based attack outperforms a previously de- veloped nested hill climb approach, particularly when the ciphertext message is short. We then consider a combination cipher, consisting of a homophonic substitution and a column transposition. We develop and analyze an attack on such a cipher. This attack employs an HMM (with random restarts), together with a hill climb to recover the column permutation. We show that this attack can succeed on relatively short ci- phertext messages. Finally, we test this combined attack on the unsolved Zodiac 340 cipher

PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation

To help merchants/customers to provide/access a variety of services through miniapps, online service platforms have occupied a critical position in the effective content delivery, in which how to recommend items in the new domain launched by the service provider for customers has become more urgent. However, the non-negligible gap between the source and diversified target domains poses a considerable challenge to cross-domain recommendation systems, which often leads to performance bottlenecks in industrial settings. While entity graphs have the potential to serve as a bridge between domains, rudimentary utilization still fail to distill useful knowledge and even induce the negative transfer issue. To this end, we propose PEACE, a Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation. For domain gap bridging, PEACE is built upon a multi-interest and entity-oriented pre-training architecture which could not only benefit the learning of generalized knowledge in a multi-granularity manner, but also help leverage more structural information in the entity graph. Then, we bring the prototype learning into the pre-training over source domains, so that representations of users and items are greatly improved by the contrastive prototype learning module and the prototype enhanced attention mechanism for adaptive knowledge utilization. To ease the pressure of online serving, PEACE is carefully deployed in a lightweight manner, and significant performance improvements are observed in both online and offline environments.Comment: Accepted by WSDM 202

Leader peptide removal in lasso peptide biosynthesis based on penultimate isoleucine residue

Lasso peptides are ribosomally synthesized peptides that undergo post-translational modifications including leader peptide removal by B (or the segregated B1 and B2) proteins and core peptide macrolactamization by C proteins to form a unique lariat topology. A conserved threonine residue at the penultimate position of leader peptide is hitherto found in lasso peptide precursors and shown to be a critical recognition element for effective enzymatic processing. We identified a lasso peptide biosynthetic gene cluster (bsf) from Bradymonas sediminis FA350, a Gram-negative and facultatively prey-dependent bacterium that belongs to a novel bacterial order Bradymonadales in the class Deltaproteobacteria. The kinase BsfK specifically catalyzes the phosphorylation of the precursor peptide BsfA on the Ser3 residue. BsfB1 performs dual functions to accelerate the post-translational phosphorylation and assist BsfB2 in leader peptide removal. Most importantly, the penultimate residue of leader peptide is an isoleucine rather than the conserved threonine and this isoleucine has a marked impact on the phosphorylation of Ser3 as well as leader peptide removal, implying that BsfB1 and BsfB2 exhibit a new substrate selectivity for leader peptide binding and excision. This is the first experimentally validated penultimate isoleucine residue in a lasso peptide precursor to our knowledge. In silico analysis reveals that the leader peptide Ile/Val(-2) residue is rare but not uncommon in phosphorylated lasso peptides, as this residue is also discovered in Acidobacteriaceae and Sphingomonadales in addition to Bradymonadales

Photocatalytic Activity Enhancement of Anatase TiO 2

We employed high-energy ball-milling technique to fabricate TiO/TiO2 heterogeneous nanostructures. XRD proved the existence of TiO/TiO2 heterogeneous structures. SEM and HRTEM investigation evidenced that the mean particle size and mean grain size of the as-prepared samples are 23 nm and 13 nm, respectively. UV-Vis spectra exhibited that TiO has enhanced the visible light absorption of TiO2 and has changed the Eg of TiO2. UPS examination indicated that the electron work function (EWF) of TiO is higher than that of TiO2. Photocatalytic degradation experiments revealed that an appropriate TiO content can enhance the photocatalytic activity of pure anatase TiO2. The best photocatalytic activity of TiO/TiO2 heterogeneous nanostructures is even better than that of Au-deposited TiO2 by keeping high degradation efficiency of 93%. The internal electrical field producing in TiO/TiO2 heterogeneous nanostructures was considered to be dominantly responsible for the enhanced photocatalytic activity. Therefore, the substitution of TiO with noble metal in TiO2 will be widely used in the future due to its low cost. This study also provides a clear direction of enhancing photocatalytic activity of TiO2: incorporating a guest compound into TiO2 with an appropriate content if the compound has much higher electron work function than that of TiO2

Pan-Genomic Study of Mycobacterium tuberculosis Reflecting the Primary/Secondary Genes, Generality/Individuality, and the Interconversion Through Copy Number Variations

Tuberculosis (TB) has surpassed HIV as the leading infectious disease killer worldwide since 2014. The main pathogen, Mycobacterium tuberculosis (Mtb), contains ~4,000 genes that account for ~90% of the genome. However, it is still unclear which of these genes are primary/secondary, which are responsible for generality/individuality, and which interconvert during evolution. Here we utilized a pan-genomic analysis of 36 Mtb genomes to address these questions. We identified 3,679 Mtb core (i.e., primary) genes, determining their phenotypic generality (e.g., virulence, slow growth, dormancy). We also observed 1,122 dispensable and 964 strain-specific secondary genes, reflecting partially shared and lineage-/strain-specific individualities. Among which, five L2 lineage-specific genes might be related to the increased virulence of the L2 lineage. Notably, we discovered 28 Mtb “Super Core Genes” (SCGs: more than a copy in at least 90% strains), which might be of increased importance, and reflected the “super phenotype generality.” Most SCGs encode PE/PPE, virulence factors, antigens, and transposases, and have been verified as playing crucial roles in Mtb pathogenicity. Further investigation of the 28 SCGs demonstrated the interconversion among SCGs, single-copy core, dispensable, and strain-specific genes through copy number variations (CNVs) during evolution; different mutations on different copies highlight the delicate adaptive-evolution regulation amongst Mtb lineages. This reflects that the importance of genes varied through CNVs, which might be driven by selective pressure from environment/host-adaptation. In addition, compared with Mycobacterium bovis (Mbo), Mtb possesses 48 specific single core genes that partially reflect the differences between Mtb and Mbo individuality

Fault Diagnosis Method for UHVDC Transmission Based on Deep Learning under Cloud-Edge Architecture

Aiming at the problem of fault diagnosis after the UHVDC system fails, a deep learning-based UHVDC fault diagnosis method under the cloud-edge architecture is proposed. First, based on the edge computing framework of the “cloud” + “edge terminal,” a four-layer fault diagnosis structure including the data integration layer, edge prediction layer, cloud diagnosis layer, and human-computer interaction layer is constructed. Then, a fault data set is constructed by finding effective information that can fully reflect the DC fault in the huge power grid environmental information, and the data set is screened, processed by classification feature fields, and linearly normalized. Finally, a deep convolutional generative adversarial network (DCGAN) is constructed by introducing a deep convolutional neural network (DCNN) into the traditional generative adversarial network (GAN) for data training and DC fault diagnosis. In addition, the corresponding process is given. The proposed method and the other three methods are compared and analyzed by simulation experiments. The results show that the method proposed has the highest accuracy and smallest error loss value of 95.6% and 0.18, respectively. It has the highest diagnosis accuracy under different fault types, and its performance is better than the other three comparison methods

Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China

With the continuous progress of human production and life, air quality has become the focus of attention. In this paper, Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces were taken as the study area, where there are 58 air quality monitoring stations from which daily and monthly data are obtained. Firstly, the temporal characteristics of the air quality index (AQI) are explored. Then, the spatial distribution of the AQI is mapped by the inverse distance weighted (IDW) method, the ordinary kriging (OK) method and the Bayesian maximum entropy (BME) method. Additionally, cross-validation is utilized to evaluate the mapping results of these methods with two indexes: mean absolute error and root mean square interpolation error. Furthermore, the correlation analysis of meteorological factors, including precipitation anomaly percentage, precipitation, mean wind speed, average temperature, average water vapor pressure and average relative humidity, potentially affecting the AQI was carried out on both daily and monthly scales. In the study area and period, AQI shows a clear periodicity, although overall, it has a downward trend. The peak of AQI appeared in November, December and January. BME interpolation has a higher accuracy than OK. IDW has the maximum error. Overall, the AQI of winter (November), spring (February) is much worse than summer (May) and autumn (August). Additionally, the air quality has improved during the study period. The most polluted areas of air quality are concentrated in Beijing, the southern part of Tianjin, the central-southern part of Hebei, the central-northern part of Henan and the western part of Shandong. The average wind speed and average relative humidity have real correlation with AQI. The effect of meteorological factors such as wind, precipitation and humidity on AQI is putative to have temporal lag to different extents. AQI of cities with poor air quality will fluctuate greater than that of others when weather changes and has higher correlation with meteorological factors

Unusual Post-Translational Modifications in the Biosynthesis of Lasso Peptides

Lasso peptides are a subclass of ribosomally synthesized and post-translationally modified peptides (RiPPs) and feature the threaded, lariat knot-like topology. The basic post-translational modifications (PTMs) of lasso peptide contain two steps, including the leader peptide removal of the ribosome-derived linear precursor peptide by an ATP-dependent cysteine protease, and the macrolactam cyclization by an ATP-dependent macrolactam synthetase. Recently, advanced bioinformatic tools combined with genome mining have paved the way to uncover a rapidly growing number of lasso peptides as well as a series of PTMs other than the general class-defining processes. Despite abundant reviews focusing on lasso peptide discoveries, structures, properties, and physiological functionalities, few summaries concerned their unique PTMs. In this review, we summarized all the unique PTMs of lasso peptides uncovered to date, shedding light on the related investigations in the future

Efficacy and safety of glucocorticoids use in patients with COVID-19: a systematic review and network meta‑analysis

Abstract Background Currently, some meta-analyses on COVID-19 have suggested that glucocorticoids use can reduce the mortality rate of COVID-19 patients, utilization rate of invasive ventilation, and improve the prognosis of patients. However, optimal regimen and dosages of glucocorticoid remain unclear. Therefore, the purpose of this network meta-analysis is to analyze the efficacy and safety of glucocorticoids in treating COVID-19 at regimens. Methods This meta-analysis retrieved randomized controlled trials from the earliest records to December 30, 2022, published in PubMed, Embase, Cochrane Library, CNKI Database and Wanfang Database, which compared glucocorticoids with placebos for their efficacy and safety in the treatment of COVID-19, Effects of different treatment regimens, types and dosages (high-dose methylprednisolone, very high-dose methylprednisolone, Pulse therapy methylprednisolone, medium-dose hydrocortisone, high-dose hydrocortisone, high-dose dexamethasone, very high-dose dexamethasone and placebo) on 28-day all-caused hospitalization mortality, hospitalization duration, mechanical ventilation requirement, ICU admission and safety outcome were compared. Results In this network meta-analysis, a total of 10,544 patients from 19 randomized controlled trials were finally included, involving a total of 9 glucocorticoid treatment regimens of different types and dosages. According to the analysis results, the 28-day all-cause mortality rate was the lowest in the treatment with pulse therapy methylprednisolone (OR 0.08, 95% CI 0.02, 0.42), but the use of high-dose methylprednisolone (OR 0.85, 95% CI 0.59, 1.22), very high-dose dexamethasone (OR 0.95, 95% CI 0.67, 1.35), high-dose hydrocortisone (OR 0.64, 95% CI 0.34, 1.22), medium-dose hydrocortisone (OR 0.80, 95% CI 0.49, 1.31) showed no benefit in prolonging the 28-day survival of patient. Compared with placebo, the treatment with very high-dose methylprednisolone (MD = -3.09;95%CI: -4.10, -2.08) had the shortest length of hospital stay, while high-dose dexamethasone (MD = -1.55;95%CI: -3.13,0.03) and very high-dose dexamethasone (MD = -1.06;95%CI: -2.78,0.67) did not benefit patients in terms of length of stay. Conclusions Considering the available evidence, this network meta‑analysis suggests that the prognostic impact of glucocorticoids in patients with COVID-19 may depend on the regimens of glucocorticoids. It is suggested that pulse therapy methylprednisolone is associated with lower 28-day all-cause mortality, very high-dose methylprednisolone had the shortest length of hospital stay in patients with COVID-19. Trial registration PROSPERO CRD42022350407 (22/08/2022)