Browse Articles

Sustainable visions: unsupervised machine learning insights on global development goals

The 2030 Agenda for Sustainable Development of the United Nations outlines 17 goals for countries of the world to address global challenges in their development. However, the progress of countries towards these goal has been slower than expected and, consequently, there is a need to investigate the reasons behind this fact. In this study, we have used a novel data-driven methodology to analyze time-series data for over 20 years (2000–2022) from 107 countries using unsupervised machine learning (ML) techniques. Our analysis reveals strong positive and negative correlations between certain SDGs (Sustainable Development Goals). Our findings show that progress toward the SDGs is heavily influenced by geographical, cultural and socioeconomic factors, with no country on track to achieve all the goals by 2030. This highlights the need for a region-specific, systemic approach to sustainable development that acknowledges the complex interdependencies between the goals and the variable capacities of countries to reach them. For this our machine learning based approach provides a robust framework for developing efficient and data-informed strategies to promote cooperative and targeted initiatives for sustainable progress.

Impact of chemical fertilizer and composts application on growth and yield of rice in Northeast India

Solvent-free processing of lignin into robust room temperature phosphorescent materials

Abstract Producing room temperature phosphorescent (RTP) materials from biomass resources using a solvent free method is essential but hard to achieve. Here, we discovered that lignin dissolved well in the liquid monomer, 2-hydroxyethyl acrylate (HEA), due to extensive hydrogen bonding and non-bonding interactions between lignin and HEA. Motivated by this discovery, we developed a solvent free system consisting of HEA and urethane dimethacrylate (UDMA) for converting lignin into RTP materials. With this design, lignin generated radicals upon UV irradiation, which initiated the polymerization of HEA (as monomer) and UDMA (as crosslinker). The as-obtained polymer network rigidifies lignin and activates the humidity/water-resistant RTP of lignin with a lifetime of 202.9 ms. Moreover, the afterglow color was successfully tuned to red after loading with RhB via energy transfer (TS-FRET). Using these properties, the as-developed material was used as photocured multiple-emission RTP inks, luminescent coatings and a smart anti-counterfeiting logo for a medicine bottle.

Velocity of Sargassum migration in the Caribbean observed with Landsat 8/9 and Sentinel 2 A/B imagery

Imagery from Landsat 8/9 (L89) and Sentinel-2 A/B (S2) was employed to monitor the velocity migration of Sargassum aggregations. The displacement characteristics of these aggregations offer insights that can inform the formulation of preventive strategies and the planning of harvesting operations for the floating biomass. Images L89 and S2 are sometimes acquired the same day and a few minutes apart. Sargassum landmark identification was performed manually on enhanced RGB composite images using quotient indices. A review of images between 2019 and 2023 was performed to select rafts that were distinguishable in both images. Geographic positions were recorded to determine traveled distance, direction, and speed. Pairs of 279 rafts were found on 21 coincident dates. Ninety eight percent of Sargassum rafts traveled between 200 m and 1700 m in a time frame of 14 to 26 minutes with an average speed of 0.63 m/s, a standard deviation of 0.24 m/s, a minimum of 0.15 m/s, and a maximum of 1.40m/s. Dominant directions were 34% NW, 23% WNW, 14% NNW, 14% W and 6% N. HYCOM ocean currents showed a positive correlation with Sargassum drift, and translation rates are also consistent with surface drifter data. The use of L89 and S2 satellite imagery as an early warning system, in conjunction with current and wind data, may help anticipate the arrival of Sargassum in coastal areas.

Conformational ensembles for protein structure prediction

Abstract Acquisition of conformational ensembles for a protein is a challenging task, which is actually involving to the solution for protein folding problem and the study of intrinsically disordered protein. Despite AlphaFold with artificial intelligence acquired unprecedented accuracy to predict structures, its result is limited to a single state of conformation and it cannot provide multiple conformations to display protein intrinsic disorder. To overcome the barrier, a FiveFold approach was developed with a single sequence method. It applied the protein folding shape code (PFSC) uniformly to expose local folds of five amino acid residues, formed the protein folding variation matrix (PFVM) to reveal local folding variations along sequence, obtained a massive number of folding conformations in PFSC strings, and then an ensemble of multiple conformational protein structures is constructed. The P53_HUMAN as a well-known protein and LEF1_HUMAN and Q8GT36_SPIOL as typical disordered proteins are token as the benchmark to evaluate the predicted outcomes. The results demonstrated an effective algorithm and biological meaningful process well to predict protein multiple conformation structures.

Enantioselective Multicomponent Electrochemical Difunctionalization of Terminal Alkynes

Ground-state charge transfer in single-molecule junctions covalent organic frameworks for boosting photocatalytic hydrogen evolution

Analysis of the release pattern of floral aroma components of Rhus chinensis based on HS-SPME-GC-MS technique

Rhus chinensis, a native plant species of China, possesses significant economic value in the ornamental sector. This study investigates the floral fragrance components and release patterns of R. chinensis, thus providing a theoretical foundation for the utilization of its floral fragrance. Headspace-solid phase microextraction (HS-SPME), gas chromatography-mass spectrometry (GC-MS), and chemometrics were used in conjunction with principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to identify the essential components of the floral aroma during the budding, blooming, and withering stages of R. chinensis. The important components of the aroma were also indicated by using the Variable Importance Projections (VIP) and Kruskal-Wallis nonparameters (P). The floral scent components of R. chinensis were abundant; 91 and 84 types of floral compounds were found throughout varying flowering seasons and daily patterns, respectively. The primary compounds responsible for flower odors were terpenes, representing over 70% of the floral aroma. Significant fluctuations were observed in the composition of 18 essential scent components and 21 chemicals, with daily variations observed in various flowering stages. The types of floral scent substances continued to rise during the flowering process; however, the relative concentrations of the floral aroma components of R. chinensis initially climbed and then fell, reaching 3.60μg/g at the full flowering stage and only 2.40μg/g after the withering stage. In the course of the daily shift, the release amount increased during the day compared to the night, peaking at 4.80μg/g. The substance type reached its greatest point at 12:00, making the circadian rhythm change rule evident. This study provides a reference for the further development and utilization of the flower fragrance of R. chinensis.

Elevated urinary phytoestrogens are associated with delayed biological aging: a cross-sectional analysis of NHANES data

Cobalt-Doped Ru@RuO₂ Core–Shell Heterostructure for Efficient Acidic Water Oxidation in Low-Ru-Loading Proton Exchange Membrane Water Electrolyzers

GWAS identifies genetic loci, lifestyle factors and circulating biomarkers that are risk factors for sarcoidosis

Abstract Sarcoidosis is a complex inflammatory disease with a strong genetic component. Here, we perform a genome-wide association study in 9755 sarcoidosis cases to identify risk loci and map associated genes. We then use transcriptome-wide association studies and enrichment analyses to explore pathways involved in sarcoidosis and use Mendelian randomization to examine associations with modifiable factors and circulating biomarkers. We identify 28 genomic loci associated with sarcoidosis, with the C1orf141-IL23R locus showing the largest effect size. We observe gene expression patterns related to sarcoidosis in the spleen, whole blood, and lung, and highlight 75 tissue-specific genes through transcriptome-wide association studies. Furthermore, we use enrichment analysis to establish key roles for T cell activation, leukocyte adhesion, and cytokine production in sarcoidosis. Additionally, we find associations between sarcoidosis and genetically predicted body mass index, interleukin-23 receptor, and eight circulating proteins.

The causal effects of inflammatory bowel disease on its ocular manifestations: A Mendelian randomization study

Background Observational studies have shown that ocular manifestations of inflammatory bowel disease (IBD) are common extraintinal manifestations, among which iridocyclitis, scleritis and episcleritis are the most common. However, whether there is a causal relationship between the two is unclear. The purpose of this study was to evaluate the causality of IBD on ocular manifestations using the mendelian randomization (MR) analysis. Methods We performed a two-sample MR analysis with public genome-wide association studies (GWAS) data. Eligible instrumental variables (IVs) were selected according to the three assumptions of MR analysis. The inverse-variance weighted (IVW) method was the main method. Complementary methods included the MR-Egger regression, the Weighted Median, the Weighted Mode and MR pleiotropy residual sum and outlier (MR-PRESSO) methods. Results After false discovery rate (FDR) correction, genetically predicted IBD (IVW OR =  1.184, 95% CI: 1.125-1.247, P_FDR <  0.001), Crohn’s disease (CD, IVW OR =  1.082, 95% CI: 1.033-1.133, P_FDR =  0.007) and ulcerative colitis (UC, IVW OR =  1.192, 95% CI: 1.114-1.275, P_FDR <  0.001) were associated with an increased risk of iridocyclitis. Moreover, IBD (IVW OR =  1.128, 95% CI: 1.064-1.196, P_FDR =  0.001), CD (IVW OR =  1.077, 95% CI: 1.026-1.131, P_FDR =  0.019) and UC (IVW OR =  1.153, 95% CI: 1.069-1.243, P_FDR = 0.003) were associated with a higher risk of uveitis (uveitis includes iridocyclitis). Further sensitivity analyses validated the robustness of the above associations. However, IBD and its subtypes were not associated with scleritis, episcleritis, optic neuritis and corneal disease. Results of complementary methods were generally consistent with those of the IVW method. Conclusions Our study revealed genetically predicted associations of IBD, CD and UC on iridocyclitis and uveitis in European populations. However, IBD, CD, and UC are not causally related to scleritis, external scleritis, optic neuritis, and corneal disease.

Innovative art selection through neutrosophic hesitant fuzzy partitioned Maclaurin symmetric mean aggregation operator

Nickel-Catalyzed Asymmetric Homobenzylic Hydroamidation of Aryl Alkenes to Access Chiral β-Arylamides

Foxa2-dependent uterine glandular cell differentiation is essential for successful implantation

Combatting antibiotic resistance in Gardnerella vaginalis: A comparative in silico investigation for drug target identification

Gardnerella vaginalis is the most frequently identified bacterium in approximately 95% of bacterial vaginosis (BV) cases. This species often exhibits resistance to multiple antibiotics, posing challenges for treatment. Therefore, there is an urgent need to develop and explore alternative therapeutic strategies for managing bacterial vaginosis. The objective of this study was to identify virulence factors and potential drug targets against Gardnerella vaginalis by utilizing in silico methods, including subtractive and comparative genomics. These methods enabled the systematic comparison of genetic sequences to pinpoint specific features unique to G. vaginalis and crucial for its pathogenicity, which could then inform the development of targeted therapeutic strategies. The analysis of the pathogen's proteomic data aimed to identify proteins that fulfilled specific criteria. These included being non-homologous to human proteins, essential for bacterial survival, amenable to drug targeting, involved in virulence, and contributing to antibiotic resistance. Following these analyses and an extensive literature review, the phospho-2-dehydro-3-deoxyheptonate aldolase enzyme emerged as a promising drug target. To deepen our understanding of the biological function of the identified protein, comprehensive protein structural modeling, validation studies, and network topology analyses were conducted. The subsequent structural analysis, encompassing modeling, validation, and network topology assessment, is aimed at further characterizing the protein. Using a library of around 9,000 FDA-approved compounds from the DrugBank database, a virtual screening was conducted to identify potential compounds that could effectively target the proposed drug target. This approach facilitated the evaluation of existing drugs for their ability to inhibit the target, potentially offering an efficient pathway for developing new treatments against the pathogen. Leveraging the established efficacy, safety, pharmacokinetics, and pharmacodynamics of these compounds, the study suggests repurposing them for Gardnerella vaginalis infections. Among the screened compounds, five specific agents—DB03332, DB07452, DB01262, DB02076, and DB00727—were identified as cost-effective therapeutic options for treating infections related to Gardnerella vaginalis. These compounds were selected based on their efficacy in targeting the pathogen while maintaining economic feasibility. While the results indicate potential efficacy in treating infections caused by the pathogen, further experimental studies are essential to validate these findings.

Activin A affects colorectal cancer progression and immunomodulation in a stage dependent manner

Abstract Advanced colorectal cancer (CRC) continues to present with poor survival and treatment options remain limited. We have shown that increased activin A (activin) expression in the tumor microenvironment (TME) is associated with poor outcome in a cohort of stage III and IV CRC patients. Here, we hypothesized that activin promotes stage specific outcomes in CRC, enhancing metastasis and tolerance in late-stage CRC exclusively. We employed Digital Spatial Profiling (DSP) technology on a cohort of stage II and III CRC patient tissue samples obtained at the time of curative surgery to show that activin co-localization was associated with increased mitogenic signaling, proliferation, and immunosuppression in stage III, but not stage II, CRCs. Furthermore, we found strong linear correlations between markers of immunosuppression and signaling proteins in activin (+) areas, an effect that was not observed in activin (-) areas of tissue. Taken together these data suggest activin exerts pro-metastatic and immunosuppressive effects in stage III, but not stage II, CRC providing an attractive therapeutic target for advanced CRC.

An Operationally Unsaturated Iridium-Pincer Complex That C–H Activates Methane and Ethane in the Crystalline Solid-State

Insights into the compact CRISPR–Cas9d system

The efficacy of the corpus-based error correction method on revision in writing classrooms

Despite the growing interests in investigating the application of data-driven learning (DDL), much existing research remains outcome-oriented. Limited attention has been paid to learners’ interactions with corpora, especially the experiences of consulting corpora and decision-making processes during revision in second language (L2) writing. In this regard, this study investigates how corpora assist language learning during the revision process in a classroom-based foreign language learning context. We recruited 123 non-English major undergraduates from a university in China, learners’ revision behaviours and outcomes were analysed in each draft. To gather the learners’ experiences and perceptions of using the BAWE corpus for error correction, a stimulated recall interview was conducted using the revised drafts as stimuli. Quantitative results indicated that corpus consultation was highly effective for revising word form errors (93.6%), and participants were more likely to use the BAWE corpus to correct collocation and phrase errors (62.7%). Interview results demonstrated that the corpus-based error correction method was effective in enhancing correctness and facilitating language learning. Learners were able to utilise corpora to identify patterns, shape writing habits, and test linguistic hypothesis. The findings and the implications of this study also provide valuable insights for teachers into the potential role and implementation of corpora in designing classroom writing tasks.