Using Cognitive Work Analysis for Information System Design - a Dashboard for Visualising Liquidity

This paper presents the application of Cognitive Work Analysis (CWA) to create an Abstraction Hierarchy (AH) model that helps users to identify key functional relationships for managing financial systemic risk. Users may include investors, government agencies, policymakers, and financial institutions. The AH model will ultimately lead to an artefact that embeds visual analytics (the science of analytical reasoning facilitated by interactive interfaces) and combines automated analysis with dynamic interaction with the data. Based on the notion that companies with high leverage (total debt/equity) are more likely to become financially distressed than those with low leverage, our approach demonstrates how the CWA approach can be incorporated into a visual analytics system development methodology, and how the resultant prototype can be successfully applied to visualise macroprudential risk

TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval

3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573

A review on chemical constituents of essential oils of Aframomum genus

Aframomum K.Schum is a genus native to Africa belonging to Zingiberaceae. Members of the genus Aframomum are the aromatic and perennial rhizomatous herbs. Many plant parts of Aframomum species contain essential oils, including fruits, seeds, leaves, stems, rhizomes, pods, husks, pericarps, limbos and leaf sheaths. The major constituents of Aframomum plants are mainly composed of sesquiterpene hydrocarbons, oxygenated monoterpenes, oxygenated sesquiterpenes and monoterpene hydrocarbons. The present work provides the comprehensive information regarding the volatile components of various Aframomum species

Longitudinal Analyses of Presenteeism and Its Role as a Predictor of Sick Leave in Patients With Ankylosing Spondylitis

ObjectiveTo investigate, in a cohort of patients with ankylosing spondylitis (AS) adequately treated with infliximab, changes over time in presenteeism and the role of presenteeism relative to that of the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and the Bath Ankylosing Spondylitis Functional Index (BASFI) in predicting sick leave. MethodsData were analyzed from 71 patients with paid work and taking a stable dose of infliximab participating in a 96-week study with 5 assessment points. Covariates included presenteeism, sick leave, time, sex, age, BASDAI, BASFI, Bath Ankylosing Spondylitis Metrology Index, and part- or full-time work. Presenteeism represented the AS impact on productivity (by visual analog scale, range 0-10, where 10=completely unproductive). Sick leave represented the number of days absent from work due to AS in the last 6 months. A linear mixed-effects model for presenteeism, and hurdle and zero-inflated count models for sick leave were explored. ResultsMeanSD presenteeism ranged from 2.2 +/- 2.2 to 3.8 +/- 7.8, and sick leave occurred in 8-17% of the patients during the 6-month period. Presenteeism positively correlated with BASDAI and BASFI, but was not significantly influenced by time. The chance of incurring sick leave was affected by presenteeism but not by BASDAI and BASFI. Conditional on being absent from work, the effect of presenteeism on the length of sick leave (in days) was much stronger than BASDAI and BASFI. For presenteeism 5, an increase of 1 unit in presenteeism yielded an increase by 36-40% (or 2-12 days) in the length of sick leave during the following 6 months. ConclusionPresenteeism, even measured by a simple visual analog scale, was an important factor to explain future sick leave

VIETHERB: a database for Vietnamese herbal species

Vietnam carries a highly diverse practice of traditional medicine in which various combinations of herbs have been widely used as remedies for many types of diseases. Poor hand-written records and current text-based databases, however, perplex the process of conventionalizing and evaluating canonical therapeutic effects. In efforts to reorganize the valuable information, we provide the VIETHERB database (http://vietherb.com.vn/) for herbs documented in Vietnamese traditional medicines. This database is constructed with confidence to provide users with information on herbs and other side information including metabolites, diseases, morphologies, and geographical locations for each individual species. Our data in this release consist of 2,881 species, 10,887 metabolites, 458 geographical locations, and 8,046 therapeutic effects. The numbers of species-metabolite, species-therapeutic effect, species-morphology, and species-distribution binary relationships are 17,602, 2,718, 11,943, and 16,089, respectively. The information on Vietnamese herbal species can be easily accessed or queried using their scientific names. Searching for species sharing side information can be simply done by clicking on the data. The database primarily serves as an open source facilitating users in studies of modernizing traditional medicine, computer-aided drug design, conservation of endangered plants, and other relevant experimental sciences

Metal complexes of benzimidazole-derived as potential anti-cancer agents: synthesis, characterization, combined experimental and computational studies

In this study, a series of 14 Cu (II), Zn (II), Ni (II) and Ag (I) complexes containing bis-benzimidazole derivatives were successfully designed and synthesized from 2-(1H-benzimidazole-2-yl)-phenol derivatives and corresponding metal salt solutions. The compound structures were identified by FT-IR, 1H-NMR, powder X-ray diffraction and ESI-MS analyses, and the presence of the metal in the complexes was confirmed by ultraviolet-visible spectroscopy and ICP optical emission spectrometry. Electronic structure calculations were also carried out to describe the detailed structures in addition to the electronic absorption spectra of the ligands. The cytotoxic activity of the complexes was evaluated against three human cancer cell lines: lung (A549), breast (MDA-MB-231) and prostate (PC3) cancer cells. All complexes inhibited anti-proliferative cancer cells better than free ligands, especially Zn (II) and Ag (I) complexes, which are most sensitive to MDA-MB-231 cells. In addition, showing the growth inhibition of three cancer cell lines with IC50 < 10.4 µM, complexes C1, C3 and C14 could be considered potential multi-targeted anti-cancer agents

Evaluation of a liquid biopsy protocol using ultra-deep massive parallel sequencing for detecting and quantifying circulation tumor DNA in colorectal cancer patients

The identification and quantification of actionable mutations are critical for guiding targeted therapy and monitoring drug response in colorectal cancer. Liquid biopsy (LB) based on plasma cell-free DNA analysis has emerged as a non-invasive approach with many clinical advantages over conventional tissue sampling. Here, we developed a LB protocol using ultra-deep massive parallel sequencing and validated its clinical performance for detection and quantification of actionable mutations in three major driver genes ( and ). The assay showed a 92% concordance for mutation detection between plasma and paired tissues and great reliability in quantification of variant allele frequency

Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening