Traditional Analysis Research Articles

The Atypical Transport System: Natural Gas Pipeline System

The natural gas pipeline system (NGPS) is complex with a diverse network topology. This study elucidates the network characteristics of the NGPS, revealing that it shares commonalities with small-world networks, but with differences caused by its structural and operational characteristics. The combination of the low aggregation effect caused by branching structure and the offsetting effect caused by economic and geographical constraints results in a network with small world characteristics. NGPS has longer mean path than common small world network, but the aggregation factor is close to that of a standard small world network. With respect to its small-world characteristics and correlation with self-organized criticality (SOC), an in-depth study of the SOC characteristics of the NGPS was conducted utilizing a combination of traditional logarithm analysis and R/S analysis. The findings establish that there are atypical SOC characteristics, characterized by a weakened power law within a specific functional range, thereby encouraging the advancement of SCADA and other safety technologies. Because of the line-pack, which induces time lags in the NGPS, NGPS can prevent the cascade failures. The reasons for the periodicity and centrality of NGPS data are analyzed by the affecting components, with the R/S analysis verifying a weak positive correlation of the function data across different time series. The interlinking of the SOC and R/S analysis illustrates the connection between critical status, accident type, and operation index. The results prove the reasonability and importance of insurance measures, such as regular internal detection, higher grade steel pipes change and staff training et al., in a novel view of SOC. The future works in building accidents database and human factors study are proposed.

Research of active compounds from Allii Macrostemonis Bulbus and potential targets against Non-Hodgkin's Lymphoma based on Network pharmacology.

Non-Hodgkin's Lymphoma (NHL) is a series of lymphoid malignancies in some aggressive subtypes with unsatisfactory treatment effects. Allii Macrostemonis Bulbus (Xie Bai) is a traditional Chinese medicine with anti-cancer activities, which may potentially suppress aggressive NHL. This study tries to discover active components and targets of Xie Bai in treating NHL by network pharmacology-based approaches. Compounds and related targets of Xie Bai were collected from the Traditional Chinese Medicine Database and Analysis Platform. Target genes associated with NHL were searched by GeneCards and DisGeNET, then the overlapped targets were further analyzed by STRING tool, GO, and KEGG pathway enrichment analysis. Molecular docking was employed to verify the interaction between compounds and targets. 11 bioactive compounds were successfully identified, with 30 targets that were screened out for the treatment of NHL. Functional enrichment analysis suggested that Xie Bai exerted its potential effects against NHL via pathways in cancer, such as PI3K/ AKT, p53, and MAPK signaling pathways. Molecular docking results showed that 3 active compounds (quercetin, beta-sitosterol, and naringenin) had good affinity with selected 6 targets（TP53, AKT1, CASP3, CCND1, HPK1, and NLRP3). Identifying six potential genes could accurately be docked with Xie Bai and had close interactions with NHL, which may provide insight into further research and new treatment strategy.

Yantiao Formula intervention in Rats with Sepsis: Network Pharmacology and Experimental Analysis.

Traditional Chinese Medicine prescribes Yantiao Formula (YTF; peach kernel, mirabilite, Angelica sinensis, Radix Scrophulariae, raw rhubarb, Radix Paeoniae, Flos Lonicerae, Forsythia, and Ophiopogon japonicus) to treat sepsis. Clinically, it reduced the inflammatory response of sepsis. It also reduced lung damage by decreasing the level of TNF-α in septic rats' serum. Using network pharmacology analysis, we investigated the efficacy network and mechanism of YTF in treating sepsis. We used the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and a Bioinformatics Analysis Tool for Molecular Mechanisms of Traditional Chinese Medicine (BATMAN-TCM) combined with literature to collect the main components in YTF and their targets. DisGeNET and GENECARDS databases were used for sepsis-related targets. Cytoscape 3.7.1 software was used to construct the herbcomponent- target and ingredient-target-disease interaction protein-protein interaction networks of YTF. The jvenn was used to perform the intersection of YTF targets and sepsis targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed． We also created a sepsis rat model using cecal ligation and perforation and stimulated alveolar macrophages (NR8383) with endotoxin to investigate the mechanisms of YTF. GO, and KEGG enrichment analysis revealed that these targets involved mineralocorticoid secretion, aldosterone secretion, active regulation of chronic inflammatory response, the exogenous coagulation pathway, and other pathophysiology. It was linked to various inflammatory factors and the MAPK pathway. YTF inhibits the p38MAPK pathway and decreases TNF- α, IL-6, and CXCL8 levels. YTF has a multi-component, multi-target, and multi-channel role in treating sepsis. The primary mechanisms may involve inhibiting the p38MAPK pathway to reduce the inflammatory response.

Unruly Truth: On Parrhesia and/as Political Refusal

This article employs Bonnie Honig’s concepts of refusal and intensification to conceptualize the ancient practice of ‘parrhesia’ as a form of conflictual, political truth-telling. This entails envisaging political truth-telling as an intense, agonal practice that does not establish unalterable foundations but takes part in world-building practices. To this end, I first reconstruct parrhesia as an agonistic practice of truth-telling. Against this background, I take up Honig’s concept of intensification to make sense of parrhesia’s intricate political stakes with reference to Euripides’s Ion tragedy. Finally, I reenvisage the bacchants’ secession to Cithaeron against the backdrop of Michel Foucault’s analysis of the cynic tradition, where parrhesia turns into a subversive political practice of displaying and prefiguring other forms of existence and social relations.

Investigating the Shared Genetic Etiology Between Parkinson's Disease and Depression.

Depression is a common symptom in Parkinson's disease (PD), resulting from underlying neuropathological processes and psychological factors. However, the extent to which shared genetic risk factors contribute to the relationship between depression and PD is poorly understood. To examine the effects of common genetic variants influencing the etiology of PD and depression risk at the genome-wide and local genomic regional level. We comprehensively investigated the genetic relationship between PD and depression using genome-wide association studies data. First, we estimated the genetic correlation at the genome-wide level using linkage-disequilibrium score regression, followed by local genetic correlation analysis using the GWAS-pairwise method and functional annotation to identify genes that may jointly influence the risk for both traits. Also, we performed Latent Causal Variable, Latent Heritable Confounder Mendelian Randomization, and traditional Mendelian Randomization analyses to investigate the potential causal relationship. Although the genetic correlation between PD and depression was not statistically significant at the genome-wide level, GWAS-pairwise analyses identified 16 genomic segments associated with PD and depression, implicating nine genes. Further analyses revealed distinct patterns within individual genes, suggesting an intricate pattern. These genes involve various biological processes, including neurotransmitter regulation, senescence, and nucleo-cytoplasmic transport mechanisms. We did not observe genetic evidence of causality between PD and depression. Our findings did not support a genome-wide genetic correlation or a causal association between both conditions. However, we identified genomic segments but identified genomic segments linked to distinct biological pathways influencing their etiology.Further research is needed to understand their functional consequences.

Reflection of Aesthetic Concepts of Russian Realism in Chinese Visual Art

This paper examines the impact of Russian realism on Chinese visual art, focusing on how Chinese artists have adapted and reinterpreted Russian aesthetic concepts. The study highlights the role of cross-cultural exchanges in enriching both traditional Chinese art and the broader understanding of realism. Through a detailed analysis, the author explores the reflection of Russian realism's social criticism and humanistic ideals in Chinese artistic practices, and how these influences have fostered new artistic forms and themes within China.The research employs comparative analysis of artworks and artistic traditions alongside historical and cultural contexts of both Russia and China. This approach underscores the importance of international artistic exchanges in creating a more diverse and multicultural art world. The study's novelty lies in uncovering the mechanisms of cultural integration and innovation through artistic exchanges between the two countries, detailing how Russian realism has not only shaped Chinese art but also spurred the creation of unique forms that reflect contemporary societal and cultural demands.Significantly, the fusion of Russian realism and traditional Chinese aesthetics has led to a new artistic direction in China that melds global artistic trends with local cultural values. This synthesis has facilitated deeper cultural dialogues and mutual understanding, enriching the artistic traditions of both Russia and China. Artists have been provided with new opportunities to explore innovative ideas and forms, thus strengthening cross-cultural ties and expanding the boundaries of artistic interaction. The paper emphasizes the transformative power of this artistic merger in enriching the global art space and fostering a more inclusive and varied artistic milieu.

Deep Learning Approaches and Biomarkers in Medical Diagnosis

Abstract: The size of the medical information system is growing gradually. Due to this, traditional data analysis for extracting helpful information for any disease has become inefficient in providing accurate real-time valid information. Traditional data mining and statistical learning techniques, which lack sufficient domain knowledge on a complicatedly colossal amount of data, need to stop adopting new advances in deep learning technologies. Thus, the use of sophisticated machines with in-depth understanding and analysis capability is vital to provide real-time information in detecting and diagnosing diseases in the healthcare system. In this paper, we study recent deep learning approaches which are capable of working on high-dimensional and multi-dimensional data. These approaches have been deployed to identify the root cause of various diseases like Cancer, Lung Diseases, Heart Diseases, Diabetes, Hepatitis, Alzheimer’s, Dengue, Parkinson’s, etc. : In this study, our key contributions are: 1: A decent overview of the deep learning techniques. 2: Several modalities in the diagnosis of various diseases. 3: Set out recent trends in various applications of deep learning in healthcare, some of which are analysis and diagnosis of medical images, precision medicine, drug discovery, predictive analysis to support clinical decisions, and sustainable public health. 4: Several deep learning approaches with their performance are described in detail. 5: Although deep learning has achieved notable performance in detecting AD, there are several limitations, especially the availability of the patient’s data for training deep learning models on a particular disease is comparatively much less than required.

Evaluation of therapeutic mechanism of Hedyotis diffusa Willd (HDW)‒Scutellaria barbata (SB) in clear cell renal cell carcinoma via single-cell RNA sequencing and network pharmacology.

The present study aimed to investigate the therapeutic mechanism of Hedyotis diffusa Willd (HDW) and Scutellaria barbata (SB) in ccRCC using a combination of single-cell RNA sequencing (scRNA-seq) and network pharmacology. The active ingredients and potential molecular targets of HDW-SB were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. Gene expression data (GSE53757) were obtained from the Gene Expression Omnibus database. The hub genes of HDW-SB against ccRCC were identified via the protein-protein interaction network, and further analyzed by molecular complex detection. The roles of these genes in the diagnosis and immune infiltration of ccRCC were analyzed. The clinical significance of hub genes was verified using scRNA-seq data (GSE121638) and molecular docking. Following the PPI network analysis, 29 hub genes of HDW-SB against ccRCC were identified. All hub genes, except for CENPE, had significantly different expressions in tumor tissue and a more accurate diagnosis of ccRCC. Fifteen cell clusters were defined based on the scRNA-seq dataset, and the clusters were annotated as six cell types using marker genes. TYMS and KIAA0101 from hub genes were highly expressed in NK cells. Three active compounds, quercetin, luteolin, and baicalein, were found to target TYMS and KIAA0101 from the compound-target interaction network. 29 hub genes of HDW-SB against ccRCC were identified and showed good performance in terms of diagnosis and prognosis. Moreover, among these hub genes docking with the main ingredients of HDW-SB, TYMS and KIAA0101 exerted anti-ccRCC effects through NK cells.

Network pharmacology and experimental validation to reveal the pharmacological mechanisms of Sini decoction against renal fibrosis.

To investigate the mechanism by which Sini decoction (, SND) improves renal fibrosis (Rf) in rats based on transforming growth factor β1/Smad (TGF-β1/Smad) signaling pathway. Network pharmacology was applied to obtain potentially involved signaling pathways in SND's improving effects on Rf. The targets of SND drug components and the targets of Rf were obtained by searching databases, such as the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCSMP) and GeenCard. The intersection targets of two searches were obtained and underwent signaling pathway analysis using a Venn diagram. Then experimental pharmacology was utilized to prove and investigate the effects of SND on target proteins in the TGF-β1/Smad signaling pathway. The Rf rat model was established by unilateral ureteral occlusion (UUO). The expression levels of transforming growth factor, matrix metalloproteinase-9 (MMP-9), matrix metal protease-2 (MMP-2), connective tissue growth factor (CTGF), and tissue inhibitor of metalloproteinase-1 (TIMP-1) were determined by Masson staining of rat renal tissue, and immunohistochemical methods. The expression levels of Smad3, Smad2, and Smad7 in renal tissue were determined by Western blotting (WB). The mechanism of the improving effects of SND on Rf was investigated based on TGF-β1/Smad signaling pathway. A total of 12 drug components of Fuzi (Radix Aconiti Lateralis Preparata), 5 drug components of Ganjiang (Rhizoma Zingiber), and 9 drug components of Gancao (Radix Glycy et Rhizoma) were obtained from the database search, and 207 shared targets were found. A total of 1063 Rf targets were found in the database search. According to the Venn diagram, in total, 96 intersection targets were found in two database searches. The metabolic pathways involved included TGF-β signaling pathway, phosphatidylinositol-3-kinase/serine-threonine protein kinase signaling (PI3K/Akt) pathway, and hypoxia-inducible factor-1 (HIF-1) signaling pathway. Masson staining analysis showed that compared with the model group, the renal interstitial collagen deposition levels in the SSN and SND groups were significantly lower (P < 0.05). Immunohistochemical analysis, compared with the control group, the positive cell area expression levels of MMP-9/TIMP-1 and MMP-2/TIMP-1 in the kidney tissue of the model group were significantly decreased (P < 0.05, P < 0.01), and the positive cell area expression levels of CTGF and TGF-β1 were significantly increased (P < 0.01). Compared with the model group, the positive cell area expression levels of MMP-9/TIMP-1 and MMP-2/TIMP-1 in the kidney tissue of the SSN and SND groups were significantly increased (P < 0.05, P < 0.01), and the positive cell area expression levels of CTGF and TGF-β1 in the kidney tissue were significantly decreased (P < 0.05, P < 0.01). WB results showed that the SSN group and the SND group could reduce the expression of Smad2 and Smad3 (P < 0.05) and increase the expression of Smad7 (P < 0.05).

Paleoanthropological Analysis of Osteological Material from the Myntobe Burial Ground

The article deals with the paleoanthropological analysis of the skeletons unearthed from the Myntobe necropolis and dated to the II – IV centuries AD. Myntobe necropolis is located 2 km south of Gani Muratbayev village in the Keles district of Turkestan province. The burial ground consists of more than 600 randomly located mounds of various sizes. All mounds have a dirt embankment. Archaeological excavations were carried out at the burial ground in 2017 and 2022, as a result of which burials in the catacombs and naus were unearthed. An analysis of the burial tradition and recovered material allows researchers to speak about the belonging of these burials to the Kangli tribes. Since the burials date back to the Kangli period, the theoretical part reveals the issues of the location and political structure of the tribes and, most importantly, the determination of the paleopathology of the Myntobints by conducting a macroscopic analysis of the discovered skeletons. The results of the analysis allowed for drawing preliminary conclusions about lifestyle, social life, and nutritional status. When writing the theoretical part of the article, electronic textbooks and resources from the book fund of the National Library of Kazakhstan and the library of the International Kazakh-Turkish University named after Kozh Ahmet Yasawi were used. In total, 9 skeletons were selected for paleoanthropological and paleopathological analysis, from mounds No. 3, No. 6, and No. 7. Paleoanthropological reasoning in the main section is written on the basis of materials in Turkish and English from the collections of Turkish libraries. The skeletons found were analyzed macroscopically and many diseases were identified, such as osteoarthritis (joint deformity), osteopathy, ankylosing spondylitis, heel spurs, thickening of the cranial bone, and deformity of the mandibular joint. Preliminary conclusions about the paleopathology of the discovered skeletons are made.

Analysis method of explosive electromagnetic radiation energy

AbstractThe energy of electromagnetic radiation from explosions is coupled to electronic equipment circuits, disrupting the initiation sequence or causing failure in an increasing number of cases, which seriously affects the stability of weapon systems. There is a significant difference between the characteristics of the explosive electromagnetic radiation signals and modulated electromagnetic signals. The electric field intensity and signal power cannot directly represent the magnitude of the explosive electromagnetic radiation energy, and traditional electromagnetic signal analysis methods are unsuitable for explosion electromagnetic signal analysis. To solve this problem, the mechanism of explosive electromagnetic radiation was first analyzed. Through verification experiments of the explosion electromagnetic intensity and temperature, it was concluded that there is a strong correlation between the explosion plasma temperature and electromagnetic intensity. The temperature of the explosive plasma is derived based on the measured surface temperature of the explosive fireball, a functional relationship is established between the energy of the explosive plasma and the temperature of the plasma, and plasma energy is introduced as a parameter for electric field intensity correction. The interference signal analysis method based on eye diagram is used to calibrate the electromagnetic radiation damage ability, achieving quantitative analysis of the interference degree of electromagnetic radiation energy on the signal, and providing a new approach for the analysis of explosive electromagnetic radiation energy.

A Triplet Multimodel Transfer Learning Network for Speech Disorder Screening of Parkinson’s Disease

Deterioration in the quality of a person’s voice and speech is an early sign of Parkinson’s disease (PD). Although a number of computer-based methods have been invested to use patients’ speech for early diagnosis of Parkinson’s disease, they only focus on a fixed pronunciation test, such as the subjects’ monosyllabic pronunciation is analyzed to determine whether they have potential possibility of PD. Moreover, only using traditional speech analysis methods to extract single-view speech features cannot provide a comprehensive feature representation. This paper is dedicated to the study of various pronunciation tests for patients with PD, including the pronunciation of five monosyllabic vowels and a spontaneous dialogue. A triplet multimodel transfer learning network is designed and proposed for identifying subjects with PD in these two groups of tests. First, multisource data extract mel frequency cepstrum coefficient (MFCC) features of speech for preprocessing. Subsequently, a pretrained triplet model represents features from three dimensions as the upstream task of the transfer learning framework. Finally, the pretrained model is reconstructed as a novel model that integrates the triplet model, temporal model, and auxiliary layer as the downstream task, and weights are updated through fine-tuning to identify abnormal speech. Experimental results show that the highest PD detection rates in the two groups of tests are 99% and 90% , respectively, which outperform a large number of internationally popular pattern recognition algorithms and serve as a baseline for other academic researchers in this field.

Mechanical response of monolayer graphene via a multi-probe approach

Push-to-pull (PTP) testing is employed to probe the uniaxial tensile response of freestanding monolayer graphene. Various analytical approaches are employed to estimate the elastic modulus of end-clamped graphene samples, combining in-situ Raman spectroscopy and scanning electronic microscope (SEM) measurements. The utilization of spatially resolved Raman-derived strains for assessing the elastic properties of monolayer graphene leads to results consistent with previous experimental and theoretical values of the elastic modulus (approximately 1 TPa). Molecular dynamics (MD) simulations of (pristine and defective) freestanding graphene sheets uniaxially loaded under varying clamping conditions are performed to support the experimental observations. The computational results indicate that the mechanical responses of the sheets are affected by the type, the spatial profile, and the heterogeneity of the clamping. When uniaxial pulling of end-clamped graphene is applied by a substrate adhering to the graphene sheet through van der Waals forces (as in PTP testing), the elastic modulus may be highly underestimated due to often inhomogeneous stress distribution and slippage processes. The MD simulations predict that the elastic modulus of pristine monolayer graphene is approximately 1 TPa, whereas its fracture strength can reach values of up to 110 GPa. Overall, this study underscores the limitations of traditional analyses of PTP experiments (utilizing indentation readouts and SEM imaging) and proposes new potential avenues (involving Raman measurements) for future research on the elastic properties of 2D materials.

How Sticky Is Retirement Behavior in the United States?

Abstract We study how increases in the U.S. Social Security full retirement age (FRA) affect benefit-claiming behavior and retirement behavior separately. Using long panels of Social Security administrative data, we implement complementary research designs of a traditional cohort analysis and a regression-discontinuity design. We find that while claiming ages strongly and immediately shift in response to increases in the FRA, retirement ages exhibit persistent “stickiness” at the old FRA of 65. We use several strategies to explore the likely mechanisms behind the stickiness in retirement, and we find suggestive evidence that employers play a role in workers’ responses to the FRA.

Nonunion scaphoid bone shape prediction using iterative kernel principal polynomial shape analysis.

The scaphoid is an important mechanical stabilizer for both the proximal and distal carpal columns. The precise estimation of the complete scaphoid bone based on partial bone geometric information is a crucial factor in the effective management of scaphoid nonunion. Statistical shape model (SSM) could be utilized to predict the complete scaphoid shape based on the defective scaphoid. However, traditional principal component analysis (PCA) based SSM is limited by its linearity and the inability to adjust the number of modes used for prediction. This study proposes an iterative kernel principal polynomial shape analysis (iKPPSA)-based SSM to predict the pre-morbid shape of the scaphoid, aiming at enhancing the accuracy as well as the robustness of the model. Sixty-five sets of scaphoid images were used to train SSM and nine sets of scaphoid images were used for validation. For each validation image set, three defect types (tubercle, proximal pole, and avascular necrosis) were virtually created. The predicted shapes of the scaphoid by PCA, PPSA, KPCA, and iKPPSA-based SSM were evaluated against the original shape in terms of mean error, Hausdorff distance error, and Dice coefficient. The proposed iKPPSA-based scaphoid SSM demonstrates significant robustness, with a generality of 0.264mm and a specificity of 0.260mm. It accounts for 99% of variability with the first seven principal modes of variation. Compared to the traditional PCA-based model, the iKPPSA-based scaphoid model prediction demonstrated superior performance for the proximal pole type fracture, with significant reductions of 25.2%, 24.7%, and 24.6% in mean error, Hausdorff distance, and root mean square error (RMSE), respectively, and a 0.35% improvement in Dice coefficient. This study showed that the iKPPSA-based SSM exploits the nonlinearity of data features and delivers high reconstruction accuracy. It can be effectively integrated into preoperative planning for scaphoid fracture management or morphology-based biomechanical modeling of the scaphoid.

Nanoscale Methods for Longitudinal Extraction of Intracellular Contents.

Longitudinal analysis of intracellular contents including gene and protein expression is crucial for deciphering the fundamentally dynamic nature of cells. This offers invaluable insights into complex tissue composition and behavior, and drives progress in disease diagnosis, biomarker discovery, and drug development. Traditional longitudinal analysis workflows, involving the destruction of cells at various timepoints, limit insights to singular moments and fail to account for cellular heterogeneity. Current non-destructive approaches, like temporal modeling with single-cell ribonucleic acid sequencing (RNA-seq) and live-cell fluorescence imaging, either rely on biological assumptions or possess the risk of cellular perturbation. Recent advances in nanoscale technologies for non-destructive intracellular content extraction offer a promising solution to these challenges. These novel methods work at the nanoscale to non-destructively access cellular membranes and can be broadly classified into three mechanisms: tip-facilitated aspiration, membrane-based, and probe-based methods. This perspective focuses on these emerging nanotechnologies for repeated intracellular content extraction. Their potential in longitudinal analysis is discussed, the critical requirements for effective repeated sampling are addressed, and the suitability of each technique for various applications is explored. Furthermore, unresolved challenges in repeated sampling are highlighted to encourage further research in this growing field.

Estimating Time-Dependent Structures in a Multivariate Causality for Land–Atmosphere Interactions

Abstract The land surface and atmosphere interaction forms an integral part of the climate system. However, this intricate relationship involves many complicated interactions and feedback effects between multiple variables. As a result, relying solely on traditional linear regression analysis and correlation analysis to distinguish between multivariate complex “driver–response” relations can be challenging, since they do not have the needed asymmetry to establish causality. The Liang–Kleeman (LK) information flow theory provides a strict nonparametric causality measurement for identifying the causality between any given time series, and its recent extension from bivariate to multivariate form provides a powerful tool for causal inference in complex multivariate systems. However, the multivariate LK information flow also assumes stationarity in time and requires a sufficiently long time series to ensure statistical sufficiency. To remedy this challenge, we rely on the square-root Kalman filter to estimate the time-varying form of the multivariate LK information flow causality. The results from theoretical and real-world applications show that the new algorithm provides a valuable tool for characterizing time-varying causal relationships in land–atmosphere interactions, even when the time series are short and highly correlated. Significance Statement Causality in land–atmosphere interactions is generally characterized by seasonal and intraseasonal changes that are usually not captured with commonly used approaches, because most approaches assume the time series are stationary. In this study, we extend the recently proposed multivariate Liang–Kleeman information flow causality (MtvLK) to handle nonstationary systems such as those in land–atmosphere interactions. By considering nonstationarity, we aim to unravel time-varying causal structures that are usually masked out in commonly used methods. Validating the MtvLK with synthetic models showed that the MtvLK is able to obtain the expected causal structures. Furthermore, real-world applications reveal novel findings of the time-varying causal structures between soil moisture, vapor pressure deficit, and the gross primary product.

The Interplay among Mythology, Culture, and the English Language

Mythology refers to traditional stories often involving gods and heroes. Many of these tales have been widely popularized, and in turn, have been found to be of high significance in investigating the evolution of the English language and culture. As mythologies are often passed down through cultures, these tales heavily influence modern traditions and beliefs. They also play an important role in the evolution and development of certain languages. This paper discusses the relationship between mythology, language, and culture through the help of a comparative analysis of mythologies, vocabularies, literary works, and traditions regarding death. The analysis reveals that many English words and phrases originate from Greek and Roman mythology, and that an individual's belief is closely connected to the mythology of the culture they grew up in. This, in turn, proves that mythology cannot be ignored when investigating language and culture, and that an understanding of mythological tales will allow individuals to better appreciate both literature and cultural traditions.

LinChemIn: Route Arithmetic─Operations on Digital Synthetic Routes.

Computational tools are revolutionizing our understanding and prediction of chemical reactivity by combining traditional data analysis techniques with new predictive models. These tools extract additional value from the reaction data corpus, but to effectively convert this value into actionable knowledge, domain specialists need to interact easily with the computer-generated output. In this application note, we demonstrate the capabilities of the open-source Python toolkit LinChemIn, which simplifies the manipulation of reaction networks and provides advanced functionality for working with synthetic routes. LinChemIn ensures chemical consistency when merging, editing, mining, and analyzing reaction networks. Its flexible input interface can process routes from various sources, including predictive models and expert input. The toolkit also efficiently extracts individual routes from the combined synthetic tree, identifying alternative paths and reaction combinations. By reducing the operational barrier to accessing and analyzing synthetic routes from multiple sources, LinChemIn facilitates a constructive interplay between artificial intelligence and human expertise.

Enhanced Calculation of Property Distributions in Chemical Fragment Spaces.

Chemical fragment spaces exceed traditional virtual compound libraries by orders of magnitude, making them ideal search spaces for drug design projects. However, due to their immense size, they are not compatible with traditional analysis and search algorithms that rely on the enumeration of molecules. In this paper, we present SpaceProp2, an evolution of the SpaceProp algorithm, which enables the calculation of exact property distributions for chemical fragment spaces without enumerating them. We extend the original algorithm by the capabilities to compute distributions for the TPSA, the number of rotatable bonds, and the occurrence of user-defined molecular structures in the form of SMARTS patterns. Furthermore, SpaceProp2 produces example molecules for every property bin, enabling a detailed interpretation of the distributions. We demonstrate SpaceProp2 on six established make-on-demand chemical fragment spaces as well as BICLAIM, the in-house fragment space of Boehringer Ingelheim. The possibility to search multiple SMARTS patterns simultaneously as well as the produced example molecules offers previously impossible insights into the composition of these vast combinatorial molecule collections, making it an ideal tool for the analysis and design of chemical fragment spaces.