Literature Datasets Research Articles

Contrasting three diet datasets for the African brown house snake, Boaedon capensis

ABSTRACT Data on the dietary preferences of species can provide important information that deepens our knowledge of their evolutionary history and contemporary ecology. The combination of low detectability and irregular feeding frequency of snakes has limited the resolution of available diet data for many of these species. A variety of methodologies exist for collecting snake feeding data, however, each of these varies in their strengths and biases. Here, we contrast characterisations of the diet of the brown house snake, Boaedon capensis, based on three common approaches to collecting feeding data: 1) examining gut contents from preserved museum specimens, 2) reviewing published literature, and 3) crowdsourcing feeding records from community science platforms. Gut contents included only mammal and reptile prey, presenting a significantly narrower diet breadth (Levins’ Ba = 0.12) than the community science (Levins’ Ba = 0.66) and literature (Levins’ Ba = 0.49) datasets, which both also included amphibian and avian prey. Interpretation of museum data alone conveys that B. capensis has a relatively specialised, mammal-dominated diet whereas the community science and literature datasets indicate a much wider dietary breadth. Our findings reiterate the importance of utilising multiple methods to assess the dietary composition of snakes to provide a more comprehensive understanding of their feeding ecology.

Unveiling and advancing grassland degradation research using a BERTopic modelling approach

Grassland degradation presents overwhelming challenges to biodiversity, ecosystem services, and the socio-economic sustainability of dependent communities. However, a comprehensive synthesis of global knowledge on the frontiers and key areas of grassland degradation research has <pg=>not been achieved due to the limitations of traditional scientometrics methods. The present synthesis of information employed BERTopic, an advanced natural language processing tool, to analyze the extensive ecological literature on grassland degradation. We compiled a dataset of 4,504 publications from the Web of Science core collection database and used it to evaluate the geographic distribution and temporal evolution of different grassland types and available knowledge on the subject. Our analysis identified key topics in the global grassland degradation research domain, including the effects of grassland degradation on ecosystem functions, grassland ecological restoration and biodiversity conservation, erosion processes and hydrological models in grasslands, and others. The BERTopic analysis significantly outperforms traditional methods in identifying complex and evolving topics in large datasets of literature. Compared to traditional scientometrics analysis, BERTopic provides a more comprehensive perspective on the research areas, revealing not only popular topics but also emerging research areas that traditional methods may overlook, although scientometrics offers more specificity and detail. Therefore, we argue for the simultaneous use of both approaches to achieve more systematic and comprehensive assessments of specific research areas. This study represents an emerging application of BERTopic algorithms in ecological research, particularly in the critical research focused on global grassland degradation. It also highlights the need for integrating advanced computational methods in ecological research in this era of data explosion. Tools like the BERTopic algorithm are essential for enhancing our understanding of complex environmental problems, and it marks an important stride towards more sophisticated, data-driven analysis in ecology.

Exploring the potential cost-effectiveness of a new computerised decision support tool for identifying fetal compromise during monitored term labours: an early health economic model

BackgroundAround 60% of term labours in the UK are continuously monitored using cardiotocography (CTG) to guide clinical labour management. Interpreting the CTG trace is challenging, leading to some babies suffering adverse outcomes and others unnecessary expedited deliveries. A new data driven computerised tool combining multiple clinical risk factors with CTG data (attentive CTG) was developed to help identify term babies at risk of severe compromise during labour. This paper presents an early health economic model exploring its potential cost-effectiveness.MethodsThe model compared attentive CTG and usual care with usual care alone and simulated clinical events, healthcare costs, and infant quality-adjusted life years over 18 years. It was populated using data from a cohort of term pregnancies, the literature, and administrative datasets. Attentive CTG effectiveness was projected through improved monitoring sensitivity/specificity and potential reductions in numbers of severely compromised infants. Scenario analyses explored the impact of including litigation costs.ResultsNationally, attentive CTG could potentially avoid 10,000 unnecessary alerts in labour and 2400 emergency C-section deliveries through improved specificity. A reduction of 21 intrapartum stillbirths amongst severely compromised infants was also predicted with improved sensitivity. Attentive CTG could potentially lead to cost savings and health gains with a probability of being cost-effective at £25,000 per QALY ranging from 70 to 95%. Potential exists for further cost savings if litigation costs are included.ConclusionsAttentive CTG could offer a cost-effective use of healthcare resources. Prospective patient-level studies are needed to formally evaluate its effectiveness and economic impact in routine clinical practice.

Gene Expression and Pathway Activation Biomarkers of Breast Cancer Sensitivity to Taxanes.

Taxanes are one of the most widely used classes of breast cancer (BC) therapeutics. Despite the long history of clinical usage, the molecular mechanisms of their action and cancer resistance are still not fully understood. Here we aimed to identify gene expression and molecular pathway activation biomarkers of BC sensitivity to taxane drugs paclitaxel and docetaxel. We used to our knowledge the biggest collection of clinically annotated publicly available literature BC gene expression data (12 datasets, n=1250) and the experimental clinical BC cohort (n=12). Seven literature datasets were used for biomarker discovery (n=914), and the remaining five literature plus one experimental datasets (n=336) - for the validation. We totally found 34 genes and 29 molecular pathways which could strongly discriminate good and poor responders to taxane treatments. The biomarker genes and pathways were associated with molecular processes related to formation of mitotic spindle and centromeres, and with the spindle assembly mitotic checkpoint. Furthermore, we created gene expression and pathway activation signatures predicting BC response to taxanes. These signatures were tested on the validation BC cohort and demonstrated strong biomarker potential reflected by mean AUC values of 0.76 and 0.77, respectively, which outperforms previously reported analogs. Taken together, these findings can deepen our understanding of mechanism of action of taxanes and potentially improve personalization of treatment in BC.

Self-guided Knowledge-Injected Graph Neural Network for Alzheimer's Diseases.

Graph neural networks (GNNs) are proficient machine learning models in handling irregularly structured data. Nevertheless, their generic formulation falls short when applied to the analysis of brain connectomes in Alzheimer's Disease (AD), necessitating the incorporation of domain-specific knowledge to achieve optimal model performance. The integration of AD-related expertise into GNNs presents a significant challenge. Current methodologies reliant on manual design often demand substantial expertise from external domain specialists to guide the development of novel models, thereby consuming considerable time and resources. To mitigate the need for manual curation, this paper introduces a novel self-guided knowledge-infused multimodal GNN to autonomously integrate domain knowledge into the model development process. We propose to conceptualize existing domain knowledge as natural language, and devise a specialized multimodal GNN framework tailored to leverage this uncurated knowledge to direct the learning of the GNN submodule, thereby enhancing its efficacy and improving prediction interpretability. To assess the effectiveness of our framework, we compile a comprehensive literature dataset comprising recent peer-reviewed publications on AD. By integrating this literature dataset with several real-world AD datasets, our experimental results illustrate the effectiveness of the proposed method in extracting curated knowledge and offering explanations on graphs for domain-specific applications. Furthermore, our approach successfully utilizes the extracted information to enhance the performance of the GNN.

A lumped kinetic model and experimental investigation of poly(ethylene terephthalate) condensed-phase pyrolysis

In a circular economy perspective, plastic waste (PW) is a valuable source of chemicals and energy vectors. Understanding the effect of poly(ethylene terephthalate) (PET) in thermochemical valorisation of complex and contaminated PW mixtures requires definition of suitable kinetic models. This work proposes a condensed-phase semi-detailed kinetic model for PET pyrolysis based on a consolidated functional group approach already validated for other polymers. The reaction network is built considering studies on thermal degradation of PET, model compounds, and small gas-phase esters. Reaction pathways proposed in the scientific literature are critically assessed through analogy with high accuracy gas-phase calculation and are complemented by new proposed pathways. The resulting model couples molecular and radical mechanism and consists of 85 gas and liquid species with 700 liquid-phase reactions, being suitable for CFD simulations of PW pyrolysis upon further reduction. This work also presents new experimental data including TG analysis coupled with GC × GC speciation measurements and elemental characterization of the solid residue. The model is validated by comparison with the new experimental data and a comprehensive set of literature data in terms of characteristic degradation times and detailed product yields. The present work expands the relevant data available for chemistry models development and extends the CRECK kinetic framework for thermochemical recycling of PW mixtures. The proposed kinetic model is attached as Supplementary material and freely available as an open GitHub repository.

Investigating the impact of clinical and genetic factors on the post-surgery prognosis of sinonasal squamous cell carcinoma

Sinonasal squamous cell carcinoma (SNSCC) is an aggressive cancer affecting the nasal and sinus regions, with its progression factors, particularly genetic ones, not yet fully understood. Here, we first conducted a retrospective study with 219 SNSCC patients to identify clinical factors affecting SNSCC prognosis. Additionally, we mined a vast literature dataset to uncover genetic factors associated with SNSCC progression. Based on this data, we constructed SNSCC prognosis pathways and performed a gene set enrichment analysis (GSEA). Clear operative margins were linked to a 73.5–86.3% improvement in overall survival and a 73.5–88.9% lower risk of recurrence. Nasal cavity-originated cases exhibited a 67.6–97.4% decrease in mortality and an 80.7–96.7% lower recurrence rate. Patients at T1-2 staging had a 65.0–80.6% reduced risk of death and recurrence compared to those at T3 stage. Additionally, we identified 53 genes associated with SNSCC, with 14 also implicated in primary tumor site, T stage, and operative margin. These genes, including EGFR, PIK3CA, ERBB2, PTEN, BCL2, BRAF, KRAS, and PRL, form a complex SNSCC-prognosis pathway and were significantly enriched in 42 KEGG pathways and Gene Ontology (GO) terms (FDR-corrected p-value < 0.001), influencing cell growth, apoptosis, and oncogenic signaling pathways. Our study suggests that three clinical parameters (operative margin type, primary tumor site, and T-stage) and 14 genetic factors may influence SNSCC prognosis post-surgery. These findings deepen our understanding of SNSCC and offer potential avenues to enhance its treatment and outcomes.

An assignment-based decomposition approach for the vehicle routing problem with backhauls

In the context of the Vehicle Routing Problem with Backhauls, which involves delivering to linehaul and picking up from backhaul customers, we propose a novel mathematical model that can decompose the main problem into three sub-problems: two Open Vehicle Routing Problems and one Assignment Problem. In our proposed model, Open Vehicle Routing Problems optimize routes for homogeneous vehicles serving linehaul/backhaul customers, while the Assignment Problem matches linehaul and backhaul routes. We utilize Lagrangian decomposition approach and solve subproblems in parallel and sequential layouts. We measure the performance of the foregoing arrangements (in terms of solution quality and computational efficiency) by testing our model on two benchmark datasets, proposed by Goetschalckx and Jacobs-Blecha (1989) and Toth and Vigo (1997) and are known as GJ and TV datasets in the extant literature, respectively. Our model matches best known solutions in 35 % and 33 %, with most within 2 % deviation, for GJ and TV instances, respectively. We also showcase our model on a real-world transportation network containing 100, 250, and 500 customers and geographically located in Lansing, Michigan. To reduce the computational burden of solving the Vehicle Routing Problem with Backhauls on the Lansing dataset, we present a cluster first-route second algorithm and then analyze the impact of vehicle capacity on the solution quality of our proposed algorithm.

GooseDetectlion: A Fully Annotated Dataset for Lion-head Goose Detection in Smart Farms

Large datasets are required to develop Artificial Intelligence (AI) models in AI powered smart farming for reducing farmers’ routine workload, this paper contributes the first large lion-head goose dataset GooseDetectlion, which consists of 2,660 images and 98,111 bounding box annotations. The dataset was collected with 6 cameras deployed in a goose farm in Chenghai district of Shantou city, Guangdong province, China. Images sampled from videos collected during July 9 -10 in 2022 were fully annotated by a team of fifty volunteers. Compared with another 6 well known animal datasets in literature, our dataset has higher capacity and density, which provides a challenging detection benchmark for main stream object detectors. Six state-of-the-art object detectors have been selected to be evaluated on the GooseDetectlion, which includes one two-stage anchor-based detector, three one-stage anchor-based detectors, as well as two one-stage anchor-free detectors. The results suggest that the one-stage anchor-based detector You Only Look Once version 5 (YOLO v5) achieves the best overall performance in terms of detection precision, model size and inference efficiency.

Imazalil and prochloraz toxicokinetics in fish probed by a physiologically based kinetic (PBK) model.

Azole fungicides are highly suspected endocrine disruptors (EDs) and are frequently detected in surface water. Among them, there are prochloraz (PCZ), a commonly used moleculefor ED studies, and imazalil (IMZ), a highly suspected ED. Little is known about their toxicokinetic (TK) behavior in fish. Hence, research suggested that an improved risk assessment could be achieved by gaining insight into their TK behavior. The aim of this study is to understand and model the TK of both substances in different fish species, irrespective of the scheme of exposure. TK data from the literature were retrieved including different modes of exposure (per os and waterborne). In addition, two experiments on zebrafish exposed to either IMZ or PCZ were performed to address the lack of in vivo TK data. A physiologically based kinetic (PBK) model applied to IMZ and PCZ was developed, capable of modeling different exposure scenarios. The parameters of the PBK model were simultaneously calibrated on datasets reporting internal concentration in several organs in three fish species (original and literature datasets) by Bayesian methods (Monte Carlo Markov Chain). Model predictions were then compared to other experimental data (i.e., excluded from the calibration step) to assess the predictive performance of the model. The results strongly suggest that PCZ and IMZ are actively transported across the gills, resulting in a small fraction being effectively absorbed by the fish. The model's results also confirm that both molecules are extensively metabolized by the liver into mainly glucuronate conjugates. Overall, the model performances were satisfying, predicting internal concentrations in several key organs. On average, 90% of experimental data were predicted within a two-fold range. The PBK model allows the understanding of IMZ and PCZ kinetics profiles by accurately predicting internal concentrations in three different fish species regardless of the exposure scenario. This enables a proper understanding of the mechanism of action of EDs at the molecular initiating event (MIE) by predicting bioaccumulation in target organs, thus linking this MIE to a possible adverse outcome.

Breeding biology of mimetic species based on secondary data sources should address misidentifications: the case of the lesser kiskadee Philohydor lictor (Aves: Tyrannidae)

ABSTRACT Philohydor lictor is a Neotropical tyrant flycatcher that belongs to a kiskadee-like mimetic system. This system encompasses seven genera with at least one species with strikingly similar plumage patterns, in which smaller species mimic larger species to deceive predators. The reproduction of P. lictor is poorly known, and misidentification of its nests and eggs with other species with similar plumage may be common, creating confusion between them. Here, we aim to summarise the knowledge about its reproductive biology and highlight the common pitfalls in gathering information on mimetic species in citizen science, literature, and museum databases. We assembled 106 reproductive records from between 1898 and 2023, of which 49 were from citizen science, 37 were from the literature, and 20 were from museum egg collections. Most reproductive records were from Panama (31.3%, N = 26) and Brazil (25.3%, N = 21). Egg-laying mainly occurred in the first half of the year and peaked when monthly total precipitation was between 150 and 300 mm (40%, N = 16). Nests were loose cups placed on vegetation near water and built with dry twigs, roots, and tendrils lined with fine twigs. Eggs from museum collections measured 20.9 × 16.1 mm (N = 23). Misidentified records comprised 21.7% (N = 23) of total breeding records and mainly occurred in <1935 museum egg collections. Misidentifications included records of domed nests, eggs larger than expected for the species’ body size, and records from outside the species’ range. These results stress that studies of reproductive biology of mimetic species based on citizen science, literature, and museum datasets are not straightforward, and data should be refined by validation procedures. These validations should include verification that records fall inside the species’ geographic range, include correct description of nests, and match egg size with adult body size.

Machine learning-based analysis of heavy metal contamination in Chinese lake basin sediments: Assessing influencing factors and policy implications

Sediments are important heavy metal sinks in lakes, crucial for ensuring water environment safety. Existing studies mainly focused on well-studied lakes, leaving gaps in understanding pollution patterns in specific basins and influencing factors.We compiled comprehensive sediment contamination data from literature and public datasets, including hydro-geomorphological, climatic, soil, landscape, and anthropogenic factors. Using advanced machine learning, we analyzed typical pollution factors to infer potential sources and migration pathways of pollutants and predicted pollution levels in basins with limited data availability. Our analysis of pollutant distribution data revealed that Cd had the most extensive pollution range, with the most severe pollution occurring in the Huaihe and Yangtze River basins. Furthermore, we identified distinct groups of driving factors influencing various heavy metals. Cd, Cr, and Pb were primarily influenced by human activities, while Cu and Ni were affected by both anthropogenic and natural factors, and Zn tended more towards natural sources. Our predictions indicated that, in addition to the typical highly polluted areas, the potential risk of Cd, Cu and Ni is higher in Xinjiang, and in Tibet and Qinghai, the potential risk of Cd, Cr, Cu and Ni is higher. Pb and Zn presented lower risks, except in the Huaihe and Yangtze River Basins. Temperature, wind, precipitation, precipitation rate, and the cation exchange capacity of soil significantly impacted the predictions of heavy metal pollution in sediments, suggesting that particulate migration, rainfall runoff, and soil erosion are likely the main pathways for pollutant migration into sediments. Considering the migration, pathways, and sources of pollutants, we propose strategies such as low-impact development and promoting sustainable transportation to mitigate pollution. This study provides the latest insights into heavy metal pollution in Chinese lake sediments, offering references for policy-making and water resource management.

Influence of Performance Packages on Fuel Consumption and Exhaust Emissions of Passenger Cars and Commercial Vehicles under WLTP

The transportation sector is responsible for about 16% of worldwide greenhouse gas emissions. Despite efforts for a sensible reduction by means of new technologies’ development, the average age of a vehicle fleet is 12.3 years in the European Union. In light of this, actions aiming at improving the efficiency of circulating vehicles can prove effective in the short to mid-term. Introducing performance packages in standard fuels could allow a reduction in the CO2 emissions of whole vehicle fleets without any modification to powertrain. Such a kind of additive is generally used in premium fuels; deposit control additives can reduce or control the deposits at intake valves and at nozzle holes with benefits for the fuel efficiency and exhaust emissions. Further improvements in combustion phasing can be achieved with cetane/octane improver. This paper aims to assess the influence of two performance packages on the exhaust emissions and fuel consumption of five vehicles set to be as representative as possible of circulating Italian passenger cars and light commercial fleet vehicles (LCVs). Based on the literature datasets, three Euro 4 vehicles were selected with a mileage representative of each single vehicle class: two passenger cars (one spark ignition and one diesel) and an LCV. Further, two diesel Euro 6 vehicles, a passenger car and an LCV, were tested to investigate the effect of fuel additives on the combustion of vehicles compliant with current homologation regulation. Exhaust emissions and fuel consumption were experimentally estimated on a chassis dynamometer over a worldwide harmonized light vehicles test cycle (WLTC) in a climate-controlled laboratory. Each vehicle was preliminarily tested when running with base fuel, then a 3000 km clean-up stage was performed using the additive package. Finally, WLTC tests were repeated. Results demonstrated the efficiency of the performance packages with a reduction between 1.2% (diesel Euro 6 passenger car) and 8.1% (diesel Euro 4 passenger car) in fuel consumption. Similar trends were found for CO2 emissions. Further, a sensible reduction in THCs, CO and PM was found for each vehicle class.

Bridging the Gap in Cancer Research: Sulfur Metabolism of Leukemic Cells with a Focus on L-Cysteine Metabolism and Hydrogen Sulfide-Producing Enzymes.

Leukemias are cancers of the blood-forming system, representing a significant challenge in medical science. The development of leukemia cells involves substantial disturbances within the cellular machinery, offering hope in the search for effective selective treatments that could improve the 5-year survival rate. Consequently, the pathophysiological processes within leukemia cells are the focus of critical research. Enzymes such as cystathionine beta-synthase and sulfurtransferases like thiosulfate sulfurtransferase, 3-mercaptopyruvate sulfurtransferase, and cystathionine gamma-lyase play a vital role in cellular sulfur metabolism. These enzymes are essential to maintaining cellular homeostasis, providing robust antioxidant defenses, and supporting cell division. Numerous studies have demonstrated that cancerous processes can alter the expression and activity of these enzymes, uncovering potential vulnerabilities or molecular targets for cancer therapy. Recent laboratory research has indicated that certain leukemia cell lines may exhibit significant changes in the expression patterns of these enzymes. Analysis of the scientific literature and online datasets has confirmed variations in sulfur enzyme function in specific leukemic cell lines compared to normal leukocytes. This comprehensive review collects and analyzes available information on sulfur enzymes in normal and leukemic cell lines, providing valuable insights and identifying new research pathways in this field.

Can GPT embeddings enhance visual exploration of literature datasets? A case study on isostatic pressing research

Can GPT embeddings enhance visual exploration of literature datasets? A case study on isostatic pressing research

VELOcities of CEpheids (VELOCE)

We present the first data release of VELOcities of CEpheids (VELOCE), dedicated to measuring the high-precision radial velocities (RVs) of Galactic classical Cepheids (henceforth, Cepheids). The first data release (VELOCE DR1) comprises 18 225 RV measurements of 258 bona fide classical Cepheids on both hemispheres collected mainly between 2010 and 2022, along with 1161 observations of 164 stars, most of which had previously been misclassified as Cepheids. The median per-observation RV uncertainty for Cepheids is 0.037 km s−1 and reaches 2 m s−1 for the brightest stars observed with Coralie. Non-variable standard stars were used to characterize RV zero-point stability and to provide a base for future cross-calibrations. We determined zero-point differences between VELOCE and 31 literature data sets using template fitting, which we also used to investigate linear period changes of 146 Cepheids. In total, 76 spectroscopic binary Cepheids and 14 candidate binary Cepheids were identified using VELOCE data alone, which are investigated in detail in a companion Paper (VELOCE II). VELOCE DR1 provides a number of new insights into the pulsational variability of Cepheids, most importantly: a) the most detailed description of the Hertzsprung progression based on RVs to date; b) the identification of double-peaked bumps in the pulsation curve; and c) clear evidence that virtually all Cepheids feature spectroscopic variability signals that lead to modulated RV variability at the level of tens to hundreds of m s−1 and that cannot be satisfactorily modeled using single-periodic Fourier series. We identified 36 stars exhibiting such modulated variability, of which 4 also exhibit orbital motion. Linear radius variations depend strongly on pulsation period and a steep increase in slope of the ΔR/p vs. log P-relation is found near 10 days. This effect, combined with significant RV amplitude differences at fixed period, challenges the existence of a tight relation between Baade-Wesselink projection factors and pulsation periods. We investigated the accuracy of RV time series measurements, υγ, and RV amplitudes published by Gaia’s third data release (Gaia DR3) and determined an offset of 0.65 ± 0.11 km s−1 relative to VELOCE. Whenever possible, we recommend adopting a single set of template correlation parameters for distinct classes of large-amplitude variable stars to avoid systematic offsets in υγ among stars belonging to the same class. The peak-to-peak amplitudes of Gaia RVs exhibit significant (16%) dispersion. Potential differences of RV amplitudes require further inspection, notably in the context of projection factor calibration.

Localization of stuttering based on causal brain lesions.

Stuttering affects approximately 1 in 100 adults and can result in significant communication problems and social anxiety. It most often occurs as a developmental disorder but can also be caused by focal brain damage. These latter cases may lend unique insight into the brain regions causing stuttering. Here, we investigated the neuroanatomical substrate of stuttering using three independent datasets: (i) case reports from the published literature of acquired neurogenic stuttering following stroke (n = 20, 14 males/six females, 16-77 years); (ii) a clinical single study cohort with acquired neurogenic stuttering following stroke (n = 20, 13 males/seven females, 45-87 years); and (iii) adults with persistent developmental stuttering (n = 20, 14 males/six females, 18-43 years). We used the first two datasets and lesion network mapping to test whether lesions causing acquired stuttering map to a common brain network. We then used the third dataset to test whether this lesion-based network was relevant to developmental stuttering. In our literature dataset, we found that lesions causing stuttering occurred in multiple heterogeneous brain regions, but these lesion locations were all functionally connected to a common network centred around the left putamen, including the claustrum, amygdalostriatal transition area and other adjacent areas. This finding was shown to be specific for stuttering (PFWE < 0.05) and reproducible in our independent clinical cohort of patients with stroke-induced stuttering (PFWE < 0.05), resulting in a common acquired stuttering network across both stroke datasets. Within the common acquired stuttering network, we found a significant association between grey matter volume and stuttering impact for adults with persistent developmental stuttering in the left posteroventral putamen, extending into the adjacent claustrum and amygdalostriatal transition area (PFWE < 0.05). We conclude that lesions causing acquired neurogenic stuttering map to a common brain network, centred to the left putamen, claustrum and amygdalostriatal transition area. The association of this lesion-based network with symptom severity in developmental stuttering suggests a shared neuroanatomy across aetiologies.

Enhancing College Access in Zimbabwe: Impact of Student Loans and Grants

This study explores how government interventions, grants, and loans influence college access in Zimbabwe, focusing on understanding the factors affecting college access and retention rates. The research aims to inform evidence-based decision-making regarding college access through a mixed research approach integrating qualitative and quantitative methods. Combining both qualitative and quantitative methods to ensure the validity and reliability of the findings. It analyses secondary data sources, such as literature, reports, and statistical datasets, to explore aspects like college access, enrollment trends, poverty rates, financial aid programs, and student loan systems in Zimbabwe. Triangulation techniques enhance the credibility of the study's findings by integrating evidence from various sources and employing diverse data collection and analysis methods, enriching perspectives and strengthening the validity and reliability of the study's conclusions. Key findings suggest that grants and loans significantly contribute to increasing college enrollment rates, particularly among economically disadvantaged groups. However, enrollment disparities persist, especially among marginalized populations like women. Despite a modest increase in government spending on education, concerns arise due to a slowdown in overall enrollment growth. Barriers to student loan uptake include complex application processes, concerns about debt burdens, and uncertainties regarding post-graduation employment prospects. The study emphasizes the importance of comprehensive policy interventions to simplify eligibility criteria, enhance financial literacy, and promote employment pathways. Using human capital and open systems theories, the research lays a groundwork for evidence-based decision-making to enhance access to higher education in Zimbabwe.

Perspectives on Conservation Impacts of the Global Primate Trade

The global trade in nonhuman primates represents a substantial threat to ecosystem health, human health, and primate conservation worldwide. Most of the primate trade involves trade for pet-keeping, consumption, or biomedical experimentation. We present an overview of international primate trade through five case studies; each describes a different facet of this trade. We draw on published scientific literature, media outlets, and open access datasets, including the CITES Trade Database to build these case studies. Case study 1 describes the role of introduced island populations of Macaca and Chlorocebus in trade for biomedical experimentation; case study 2 covers the global health threats posed by the primate trade, including zoonotic disease transmission once animals enter the trade pipeline; case study 3 addresses the ways that changing patterns of primate trade, from local markets to online, have increased the demand for primates as pets; case study 4 recognizes the role that local environmental activism can play in mitigating trade; and case study 5 shows variation between global regions in their contribution to the primate trade. We recommend greater oversight of primate trade, especially domestic trade within primate range countries, and real-time reporting to CITES to accurately track primate trade. Effective conservation-focused regulations that can minimise the negative effects of primate trade must be tailored to specific regions and species and require transparency, careful regulation, field research, and an understanding of the magnitude of this trade.

The rise of Indo-German collaborative research: 1990–2022

Purpose This study aims to highlight the growth and development of Indo-German collaborative research over the past three decades. Moreover, this study encompasses an in-depth examination of funding acknowledgements to gain valuable insights into the financial support that underpins these collaborative endeavours. Together with this paper, the authors provide an openly accessible data set of Indo-German research papers for further and reproducible research activities (the “Indo-German Literature Dataset”). Design/methodology/approach The data were retrieved from the Web of Science (WoS) database from the year 1990 till the 30th of November 2022. A total of 36,999 records were retrieved against the used query. Acknowledged entities were extracted using a named entity recognition (NER) model specifically trained for this task. Interrelations between the extracted entities and scientific domains, lengths of acknowledgement texts, number of authors and affiliations, number of citations and gender of the first author, as well as collaboration patterns between Indian and German funders were examined. Findings The study reveals a consistent and increasing growth in the publication trend over the years. The study brings to light that Physics, Chemistry, Materials Science, Astronomy and Astrophysics and Engineering prominently dominate the Indo-German collaborative research. The USA, followed by England and France, are the most active collaborators in Indian and German research. Largely, research was funded by major German and Indian funding agencies, international corporations and German and American universities. Associations between the first author’s gender and acknowledged entity were observed. Additionally, relations between entity, entity type and scientific domain were discovered. Practical implications The study paves the way for enhanced collaboration, optimized resource utilization and societal advantages by offering a profound comprehension of the intricacies inherent in research partnerships between India and Germany. Implementation of the insights gleaned from this study holds the promise of cultivating a more resilient and influential collaborative research ecosystem between the two nations. Originality/value The study highlights a deeper understanding of the composition of the Indo-German collaborative research landscape of the past 30 years and its significance in advancing scientific knowledge and fostering international partnerships. Furthermore, the authors provide an open version of the original WoS data set. The Indo-German Literature Data set consists of 22,844 papers from OpenAlex and is available for related studies like literature studies and scientometrics.