Primary Dataset Research Articles

Determinants of household participation in the cassava value-chain in South Africa

Cassava’s adaptability to different agroecological conditions, high yield, as well as its ability to thrive under harsh climatic conditions, makes it an essential food security crop. In South Africa, the cassava value chain is currently uncoordinated and underdeveloped, with a couple of smallholder farmers growing the crop for household consumption and as a source of income. Other farmers regard it as a secondary crop and hardly any producers grow it for industrial purposes. Hence, this study sought to analyze the determinants of household participation in the cassava value chain in South Africa. The study employed the multivariate probit model to analyze the determinants of household participation in the cassava value chain in South Africa, using a primary dataset collected through a simple sample method from smallholder farmers in KwaZulu-Natal, Mpumalanga, and Limpopo provinces. Results show that livestock ownership has a positive and significant effect on the likelihood of farmers participating in the value chain by growing cassava for household food consumption. Also, findings reveal that hiring labour in cassava production and an increase in the yield during the previous season increases the probability of farmers’ interest in selling cassava tubers along the value chain. Hence, the positive and statistically significant influence of hiring labour during cassava production in driving the farmers’ interest in selling cassava tubers and cuttings implies that the development of the cassava value chain presents great opportunities for creating jobs (employment) in the country. Also, policy interventions that ensure land tenure security and empower farmers to increase their cassava yields are bound to encourage further participation in the value chain with an interest in selling fresh tubers, among other derived products to generate income. Lastly, programmes that empower and encourage youth participation in the cassava value chain can increase the number of farmers interested in selling cassava products.

Inclusion of Veterans Health Administration hospitals in Centers for Medicare & Medicaid Services Overall Hospital Quality Star Ratings.

The Centers for Medicare & Medicaid Services (CMS) Overall Hospital Quality Star Rating, established in 2016, is a summary of publicly available quality information for acute care hospitals. In July 2023, Veterans Health Administration (VHA) hospitals became eligible to receive a CMS Overall Hospital Quality Star Rating for the first time. Our objective was to compare performance in quality ratings among VHA and non-VHA hospitals. We used the hospital quality measure scores posted to Care Compare on Medicare.gov as of January 2023 as our primary data set. We conducted a pair of analyses to characterize the performance of VHA hospitals compared to non-VHA hospitals: an overall analysis including all rated hospitals, and a matched analysis in which only a single nearby hospital was included for each VHA hospital. Of the 4518 non-VHA hospitals, 2962 (65.6%) received a Star Rating, compared to 114 (84%) of 136 VHA hospitals. VHA hospitals tended to receive higher ratings overall (one-star: 8%; two-star: 11%; three-star: 14%; four-star: 35%; five-star: 32%) than non-VHA (one-star: 8%; two-star: 22%; three-star: 29%; four-star: 26%; five-star: 15%). A similar pattern was observed in the matched analysis. VHA hospitals tended to perform better on the Overall Star Rating compared to non-VHA hospitals, as evidenced by being more likely to receive a four- or five-star rating. The eligibility of VHA hospitals to receive an Overall Star Rating signifies an important addition to the program that will allow Veterans to make more informed healthcare decisions.

KEGG: biological systems database as a model of the real world.

KEGG (https://www.kegg.jp/) is a database resource for representation and analysis of biological systems. Pathway maps are the primary dataset in KEGG representing systemic functions of the cell and the organism in terms of molecular interaction and reaction networks. The KEGG Orthology (KO) system is a mechanism for linking genes and proteins to pathway maps and other molecular networks. Each KO is a generic gene identifier and each pathway map is created as a network of KO nodes. This architecture enables KEGG pathway mapping to uncover systemic features from KO assigned genomes and metagenomes. Additional roles of KOs include characterization of conserved genes and conserved units of genes in organism groups, which can be done by taxonomy mapping. A new tool has been developed for identifying conserved gene orders in chromosomes, in which gene orders are treated as sequences of KOs. Furthermore, a new dataset called VOG (virus ortholog group) is computationally generated from virus proteins and expanded to proteins of cellular organisms, allowing gene orders to be compared as VOG sequences as well. Together with these datasets and analysis tools, new types of pathway maps are being developed to present a global view of biological processes involving multiple organism groups.

GeriNOT in the Surgical Inpatient Setting.

The guideline of the Federal Joint Committee (G-BA) on quality assessment measures for the care of patients with hip fracture makes it mandatory for hospitals to use an appropriate geriatric screening instrument in the context of acute inpatient care. After systematic application of GeriNOT and data collection in the admission process with integration into the Hospital Information System (HIS), it is possible to identify potential risks in geriatric patients with other diagnoses as well.With the integration of GeriNOT into the acute inpatient admission process, it was examined whether vulnerable geriatric patients with other diagnoses could benefit from the early initiation of risk identification.The data base for the present study was a retrospective bicentric collection of electronic case records (May 2014 to April 2015, n=3,443). From this primary data set, the subgroup of inpatient acute admissions (n=821) in the orthopaedic/trauma surgery of a study centre was analysed and evaluated with respect to the endpoints "utilisation of needs-based post-inpatient care services" and "new admission to inpatient permanent/short-term care". The predictive power and classification accuracy of GeriNOT of these patients who were 70 years and older to the endpoints were assessed for several groups: total acute admissions, total fractures, hip fracture, and spinal disorders including spinal fractures.A total of 821 patients were admitted as acute inpatients during the study period. The mean age of the patients was 81.4±6.8 years (n=821; 68.1% women, 31.9% men). The following subgroups were formed and analysed: total fractures (n=490), spinal disorders (n=265) including spinal fractures (n=174), and hip fracture (n=108). Both in the overall group (n=821; M=4.279; SD=2.180) and in the subgroups, the mean GeriNOT score was above the threshold ≥4. The highest score was found in the hip fracture group (M=4.852; SD=2.022), and the lowest in the spine fracture group (M=4.177; SD=2.171). At admission, if the terms of variables for requiring treatment were "polypharmacy" and "nursing services already used as needed", there were only slight differences in the diagnostic groups. Admissions from short-term and long-term care occurred in the total group in 16.44% of cases, most frequently with 31.48% in the group of hip fractures, compared to spinal diseases with 6.79%. For this group, GeriNOT detected an elevated risk with respect to the defined endpoints. However, only 4.26% of all patients with identified geriatric risk potential received further geriatric care.The results showed increased geriatric risk in all analysed groups, but most pronouncedly within the group of spinal diseases. The HIS-supported use of GeriNOT offers the possibility of systematic risk identification in acute inpatient admission management. The continuous visualisation of results at HIS workstations throughout the workflow could be used as a starting point for the subsequent application of standardized assessment tools and risk-adjusted treatment pathways. These findings could potentially improve outcomes.

Learning to match patients to clinical trials using large language models

Objective:This study investigates the use of Large Language Models (LLMs) for matching patients to clinical trials (CTs) within an information retrieval pipeline. Our objective is to enhance the process of patient-trial matching by leveraging the semantic processing capabilities of LLMs, thereby improving the effectiveness of patient recruitment for clinical trials. Methods:We employed a multi-stage retrieval pipeline integrating various methodologies, including BM25 and Transformer-based rankers, along with LLM-based methods. Our primary datasets were the TREC Clinical Trials 2021–23 track collections. We compared LLM-based approaches, focusing on methods that leverage LLMs in query formulation, filtering, relevance ranking, and re-ranking of CTs. Results:Our results indicate that LLM-based systems, particularly those involving re-ranking with a fine-tuned LLM, outperform traditional methods in terms of nDCG and Precision measures. The study demonstrates that fine-tuning LLMs enhances their ability to find eligible trials. Moreover, our LLM-based approach is competitive with state-of-the-art systems in the TREC challenges.The study shows the effectiveness of LLMs in CT matching, highlighting their potential in handling complex semantic analysis and improving patient-trial matching. However, the use of LLMs increases the computational cost and reduces efficiency. We provide a detailed analysis of effectiveness-efficiency trade-offs. Conclusion:This research demonstrates the promising role of LLMs in enhancing the patient-to-clinical trial matching process, offering a significant advancement in the automation of patient recruitment. Future work should explore optimising the balance between computational cost and retrieval effectiveness in practical applications.

Quantifying Creep on the Laohushan Fault Using Dense Continuous GNSS

The interseismic behavior of faults (whether they are locked or creeping) and their quantitative kinematic constraints are critical for assessing the seismic hazards of faults and their surrounding areas. Currently, the creep of the eastern segment of the Laohushan Fault in the Haiyuan Fault Zone at the northeastern margin of the Tibetan Plateau, as revealed by InSAR observations, lacks confirmation from other observational methods, particularly high-precision GNSS studies. In this study, we utilized nearly seven years of observation data from a dense GNSS continuous monitoring profile (with a minimum station spacing of 2 km) that crosses the eastern segment of the Laohushan Fault. This dataset was integrated with GNSS data from regional continuous stations, such as those from the Crustal Movement Observation Network of China, and multiple campaign measurements to calculate GNSS baseline change time series across the Laohushan Fault and to obtain a high spatial resolution horizontal crustal velocity field for the region. A comprehensive analysis of this primary dataset indicates that the Laohushan Fault is currently experiencing left-lateral creep, characterized by a partially locked shallow segment and a deeper locked segment. The fault creep is predominantly concentrated in the shallow crustal region, within a depth range of 0–5.7 ± 3.4 km, exhibiting a creep rate of 1.5 ± 0.7 mm/yr. Conversely, at depths of 5.7 ± 3.4 km to 16.8 ± 4.2 km, the fault remains locked, with a loading rate of 3.9 ± 1.1 mm/yr. The shallow creep is primarily confined within 3 km on either side of the fault. Over the nearly seven-year observation period, the creep movement within approximately 5 km of the fault’s near field has shown no significant time-dependent variation, instead demonstrating a steady-state behavior. This steady-state creep appears unaffected by postseismic effects from historical large earthquakes in the adjacent region, although the deeper (far-field) tectonic deformation of the Laohushan Fault may have been influenced by the postseismic effects of the 1920 Haiyuan M8.5 earthquake.

Is seed aid distribution still justified in South Sudan?

Seed aid—or free distribution of seeds to farmers—is a popular intervention to simultaneously reduce food insecurity and dependency on food aid in fragile countries. However, seed aid distribution also has the potential to hinder or distort the development of local seed markets. In this study we analyze the targeting and impact of seed aid across the green belt (cutting across the southern/equatorial states) of South Sudan. Using a primary and unique dataset on 1,990 farm households, we find that seed aid is widely rather than selectively distributed. Almost a third of farm households receive seed aid despite the general availability of locally recycled seed varieties. Seed aid distribution does not seem to favor particularly poor, vulnerable and food insecure households, but those that are embedded in community networks, organizations and institutions. Using a double robust methodology based on Inverse Probability Weighted Regression Adjustment (IPWRA), we also find that the adoption of seed aid by farm households does not result in increased maize production, as it is neither associated with agricultural intensification nor with the expansion of cultivated land. Seed aid seems to substitute rather than supplement local seed varieties. These findings emphasize a lack of intentionality in seed aid distribution. Still, it must be noted that the effectiveness of seed aid distribution may be greater outside our study area, above the green belt, where conflicts and natural disasters remain more frequent and intense, and where farmers are more likely to be seed insecure. But overall, this study supports the widespread perception that South Sudan is ready for a transition towards a market-based seed distribution system.

A taxonomy for human social perception: Data-driven modeling with cinematic stimuli.

Every day, humans encounter complex social situations that need to be encoded effectively to allow interaction with others. Yet, principles for organizing the perception of social features from the external world remain poorly characterized. In this large-scale study, we investigated the principles of social perception in dynamic scenes. In the primary data set, we presented 234 movie clips (41 min) containing various social situations to 1,140 participants and asked them to evaluate the presence of 138 social features in each clip. Analyses of the social feature ratings revealed that some features are perceived categorically (present or absent) and others continuously (intensity) and simple social features requiring immediate response are perceived most consistently across participants. To establish the low-dimensional perceptual organization for social features based on movies, we used principal coordinate analysis and consensus clustering for the feature ratings. These dimension reduction analyses revealed that the social perceptual structure can be modeled with eight main dimensions and that behaviorally relevant perceptual categories emerge from these main dimensions. This social perceptual structure generalized from the perception of unrelated Hollywood movie clips to the perception of a full Finnish movie (70 min) and to the perception of static images (n = 468) and across three independent sets of participants (n = 2,254). Based on the results, we propose eight basic dimensions of social perception as a model for rapid social perception where social situations are perceived along eight orthogonal perceptual dimensions (most importantly emotional valence, empathy vs. dominance, and cognitive vs. physical behavior). (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Green voices in digital spaces: exploring the impact of knowledge sharing on sustainable engagement and eWOM in tourism

PurposeThis study explores the dynamics of companies’ green knowledge sharing (GKS) within the tourism sector, focusing on its influence on green electronic word-of-mouth (eWOM) intention via the mediation of tourists’ green engagement behaviour. Additionally, the study considers the moderating effects of green destination psychological ownership and perceived injunctive norms of green self-expression online on the pathway from green knowledge sharing to tourists’ green engagement behaviour to green eWOM.Design/methodology/approachThe primary dataset, consisting of 902 valid responses obtained through time-lagged surveys administered to environmentally conscious tourists, was analysed using structural equation modelling (SEM) by PROCESS package for R.FindingsFindings indicate that green destination psychological ownership enhances the mediation effect of tourists’ green engagement behaviour on green eWOM intention, reinforcing the sense of personal investment and belonging among tourists. Conversely, perceived injunctive norms of green self-expression online moderate this pathway by shaping the social norms and acceptability of green behaviours online.Practical implicationsTo effectively spread green knowledge, tourism businesses should focus on enhancing tourists' psychological ownership of green destinations and align their communications with the perceived norms of green expression online. This strategy not only deepens tourists' environmental commitment but also stimulates active participation in spreading sustainable practices.Originality/valueThis study contributes to the green marketing literature by revealing how intrinsic and extrinsic motivational factors interact to influence green engagement and eWOM intention in the tourism industry, aiding marketers in strategically fostering green value co-creation and enhancing sustainable practices through targeted green knowledge sharing.

Data-driven neuroanatomical subtypes of primary progressive aphasia.

The primary progressive aphasias are rare, language-led dementias, with three main variants: semantic, non-fluent/agrammatic, and logopenic. Whilst semantic variant has a clear neuroanatomical profile, the non-fluent/agrammatic and logopenic variants are difficult to discriminate from neuroimaging. Previous phenotype-driven studies have characterised neuroanatomical profiles of each variant on MRI. In this work we used a machine learning algorithm known as SuStaIn to discover data-driven neuroanatomical "subtype" progression profiles and performed an in-depth subtype-phenotype analysis to characterise the heterogeneity of primary progressive aphasia. Our study included 270 participants with primary progressive aphasia seen for research in the UCL Queen Square Institute of Neurology Dementia Research Centre, with follow-up scans available for 137 participants. This dataset included individuals diagnosed with all three main variants (semantic: n=94, non-fluent/agrammatic: n=109, logopenic: n=51) as well as individuals with un-specified primary progressive aphasia (n=16). A data set of 66 patients (semantic n=37, non-fluent/agrammatic: n=29) from the ALLFTD North American cohort study, was used to validate our results. MRI scans were segmented and SuStaIn was employed on 19 regions of interest to identify neuroanatomical profiles independent of the diagnosis. We assessed the assignment of subtypes and stages, as well as their longitudinal consistency. We discovered four neuroanatomical subtypes of primary progressive aphasia, labelled S1 (left temporal), S2 (insula), S3 (temporoparietal), S4 (frontoparietal), exhibiting robustness to statistical scrutiny. S1 correlated strongly with semantic variant, while S2, S3, and S4 showed mixed associations with the logopenic and non-fluent/agrammatic variants. Notably, S3 displayed a neuroanatomical signature akin to a logopenic only signature, yet a significant proportion of logopenic cases were allocated to S2. The non-fluent/agrammatic variant demonstrated diverse associations with S2, S3, and S4. No clear relationship emerged between any of the neuroanatomical subtypes and the unspecified cases. At first follow up 84% of patients' subtype assignment was stable, and 91.9% of patients' stage assignment was stable. We partially validated our findings in the ALLFTD dataset, finding comparable qualitative patterns. Our study, leveraging machine learning on a large primary progressive aphasia dataset, delineated four distinct neuroanatomical patterns. Our findings suggest that separable spatio-temporal neuroanatomical phenotypes do exist within the PPA spectrum, but that these are noisy, particularly for nfvPPA and lvPPA. Furthermore, these phenotypes do not always conform to standard formulations of clinico-anatomical correlation. Understanding the multifaceted profiles of the disease, encompassing neuroanatomical, molecular, clinical, and cognitive dimensions, holds potential implications for clinical decision support.

A 2001–2022 global gross primary productivity dataset using an ensemble model based on the random forest method

Abstract. Advancements in remote sensing technology have significantly contributed to the improvement of models for estimating terrestrial gross primary productivity (GPP). However, discrepancies in the spatial distribution and interannual variability within GPP datasets pose challenges to a comprehensive understanding of the terrestrial carbon cycle. In contrast to previous models that rely on remote sensing and environmental variables, we developed an ensemble model based on the random forest method (denoted ERF model). This model used GPP outputs from established models: Eddy Covariance Light Use Efficiency (EC-LUE), GPP estimate model based on Kernel Normalized Difference Vegetation Index (GPP-kNDVI), GPP estimate model based on Near-Infrared Reflectance of Vegetation (GPP-NIRv), Revised-EC-LUE, Vegetation Photosynthesis Model (VPM), and GPP estimate model based on the Moderate Resolution Imaging Spectroradiometer (MODIS). These outputs were used as inputs to estimate GPP. The ERF model demonstrated superior performance, explaining 85.1 % of the monthly GPP variations at 170 sites and surpassing the performance of selected GPP estimate models (67.7 %–77.5 %) and an independent random forest model using remote sensing and environmental variables (81.5 %). Additionally, the ERF model improved accuracy across each month and with various subranges, mitigating the issue of “high-value underestimation and low-value overestimation” in GPP estimates. Over the period from 2001 to 2022, the global GPP estimated by the ERF model was 132.7 PgC yr−1, with an increasing trend of 0.42 PgC yr−2, which is comparable to or slightly better than the accuracy of other mainstream GPP datasets in terms of validation results of GPP observations independent of FLUXNET (i.e., ChinaFLUX). Importantly, for a growing number of GPP datasets, our study provides a way to integrate these GPP datasets, which may lead to a more reliable estimate of global GPP.

Basal metabolic rate mediates the causal relationship between gut microbiota and osteoarthritis: a two-step bidirectional Mendelian randomization study.

The relationship between gut microbiota and osteoarthritis (OA) occurrence remains unclear. Existing research needs to clearly understand how basal metabolic rate (BMR) regulates this relationship. Therefore, using a two-step bidirectional Mendelian Randomization approach, our study aims to investigate whether BMR levels mediate the causal relationship between gut microbiota and OA. In this study, we examined publicly available summary statistics from Genome-Wide Association Studies (GWAS) to determine the correlation between gut microbiota and OA. The analysis included one primary dataset and two secondary datasets. Initially, a two-step, two-sample, and reverse MR analysis was performed to identify the causal relationship between gut microbiota and OA. Subsequently, a two-step MR analysis revealed that the relationship between microbiota and OA is mediated by BMR. Sensitivity analyses confirmed the robustness of the study results. In our analysis of the primary dataset, we discovered a positive correlation between three taxa and the outcome of OA, and eight taxa exhibited a negative correlation with the OA outcome. Through comparisons with the secondary dataset and multiple testing corrections, we found a negative association between the class Actinobacteria (OR=0.992886277, p-value = 0.003) and the likelihood of OA occurrence. Notably, knee osteoarthritis (KOA) and hip osteoarthritis (HOA) had a strong negative correlation (OR = 0.927237553/0.892581219). Our analysis suggests that BMR significantly mediates the causal pathway from Actinobacteria to OA, with a mediated effect of 2.59%. Additionally, BMR mediates 3.98% of the impact in the path from the order Bifidobacteriales and the family Bifidobacteriaceae to OA. Besides these findings, our reverse analysis did not indicate any significant effect of OA on gut microbiota or BMR. Our research results indicate that an increase in the abundance of specific gut microbial taxa is associated with a reduced incidence of OA, and BMR levels mediate this causal relationship. Further large-scale randomized controlled trials are necessary to validate the causal impact of gut microbiota on the risk of OA. This study provides new insights into the potential prevention of OA by modulating the gut microbiota.

Pseudo-label refinement via hierarchical contrastive learning for source-free unsupervised domain adaptation

Source-free unsupervised domain adaptation aims to adapt a source model to an unlabeled target domain without accessing the source data due to privacy considerations. Existing works mainly solve the problem by self-training methods and representation learning. However, these works typically learn the representation on a single semantic level and barely exploit the rich hierarchical semantic information to obtain clear decision boundaries, which makes it hard for these methods to achieve satisfactory generalization performance. In this paper, we propose a novel hierarchical contrastive domain adaptation algorithm that exploits self-supervised contrastive learning on both fine-grained instances and coarse-grained cluster semantics. On the one hand, we propose an adaptive prototype pseudo-labeling strategy to obtain much more reliable labels. On the other hand, we propose hierarchical contrastive representation learning on both fine-grained instance-wise level and coarse-grained cluster level to reduce the negative effect of label noise and stabilize the whole training procedure. Extensive experiments are conducted on primary unsupervised domain adaptation benchmark datasets, and the results demonstrate the effectiveness of the proposed method.

A 30-m gross primary production dataset from 2016 to 2020 in China

Estimating gross primary production (GPP) of terrestrial ecosystems is important for understanding the terrestrial carbon cycle. However, existed nationwide GPP datasets are primarily driven by coarse spatial resolutions (≥500 m) remotely sensed data, which fails to capture the spatial heterogeneity of GPP across different ecosystem types at land surface. This paper introduces a new GPP dataset, Hi-GLASS GPP v1, with a fine spatial resolution (30-m) and monthly temporal resolution from 2016 to 2020 in China. The Hi-GLASS GPP v1 dataset is generated from 30-m Landsat data using a process based light use efficiency model. The Hi-GLASS GPP v1 model integrates a detailed map of maize plantations, a crucial C4 crop in China known for its higher photosynthetic efficiency compared to C3 crops. This inclusion helps correct the underestimation of GPP that typically occurs when all croplands are categorized as C3. The Hi-GLASS GPP v1 dataset demonstrates a robust correlation with GPP data derived from eddy covariance towers, thereby enabling a more accurate assessment of terrestrial carbon sequestration across China.

Brain tumor detection and classification in MRI using hybrid ViT and GRU model with explainable AI in Southern Bangladesh

Brain tumor, a leading cause of uncontrolled cell growth in the central nervous system, presents substantial challenges in medical diagnosis and treatment. Early and accurate detection is essential for effective intervention. This study aims to enhance the detection and classification of brain tumors in Magnetic Resonance Imaging (MRI) scans using an innovative framework combining Vision Transformer (ViT) and Gated Recurrent Unit (GRU) models. We utilized primary MRI data from Bangabandhu Sheikh Mujib Medical College Hospital (BSMMCH) in Faridpur, Bangladesh. Our hybrid ViT-GRU model extracts essential features via ViT and identifies relationships between these features using GRU, addressing class imbalance and outperforming existing diagnostic methods. We extensively processed the dataset, and then trained the model using various optimizers (SGD, Adam, AdamW) and evaluated through rigorous 10-fold cross-validation. Additionally, we incorporated Explainable Artificial Intelligence (XAI) techniques-Attention Map, SHAP, and LIME-to enhance the interpretability of the model’s predictions. For the primary dataset BrTMHD-2023, the ViT-GRU model achieved precision, recall, and F1-score metrics of 97%. The highest accuracies obtained with SGD, Adam, and AdamW optimizers were 81.66%, 96.56%, and 98.97%, respectively. Our model outperformed existing Transfer Learning models by 1.26%, as validated through comparative analysis and cross-validation. The proposed model also shows excellent performances with another Brain Tumor Kaggle Dataset outperforming the existing research done on the same dataset with 96.08% accuracy. The proposed ViT-GRU framework significantly improves the detection and classification of brain tumors in MRI scans. The integration of XAI techniques enhances the model’s transparency and reliability, fostering trust among clinicians and facilitating clinical application. Future work will expand the dataset and apply findings to real-time diagnostic devices, advancing the field.

Methaemoglobin as a surrogate marker of primaquine antihypnozoite activity in Plasmodium vivax malaria: A systematic review and individual patient data meta-analysis.

The 8-aminoquinolines, primaquine and tafenoquine, are the only available drugs for the radical cure of Plasmodium vivax hypnozoites. Previous evidence suggests that there is dose-dependent 8-aminoquinoline induced methaemoglobinaemia and that higher methaemoglobin concentrations are associated with a lower risk of P. vivax recurrence. We undertook a systematic review and individual patient data meta-analysis to examine the utility of methaemoglobin as a population-level surrogate endpoint for 8-aminoquinoline antihypnozoite activity to prevent P. vivax recurrence. We conducted a systematic search of Medline, Embase, Web of Science, and the Cochrane Library, from 1 January 2000 to 29 September 2022, inclusive, of prospective clinical efficacy studies of acute, uncomplicated P. vivax malaria mono-infections treated with radical curative doses of primaquine. The day 7 methaemoglobin concentration was the primary surrogate outcome of interest. The primary clinical outcome was the time to first P. vivax recurrence between day 7 and day 120 after enrolment. We used multivariable Cox proportional-hazards regression with site random-effects to characterise the time to first recurrence as a function of the day 7 methaemoglobin percentage (log base 2 transformed), adjusted for the partner schizonticidal drug, the primaquine regimen duration as a proxy for the total primaquine dose (mg base/kg), the daily primaquine dose (mg/kg), and other factors. The systematic review protocol was registered with PROSPERO (CRD42023345956). We identified 219 P. vivax efficacy studies, of which 8 provided relevant individual-level data from patients treated with primaquine; all were randomised, parallel arm clinical trials assessed as having low or moderate risk of bias. In the primary analysis data set, there were 1,747 patients with normal glucose-6-phosphate dehydrogenase (G6PD) activity enrolled from 24 study sites across 8 different countries (Indonesia, Brazil, Vietnam, Thailand, Peru, Colombia, Ethiopia, and India). We observed an increasing dose-response relationship between the daily weight-adjusted primaquine dose and day 7 methaemoglobin level. For a given primaquine dose regimen, an observed doubling in day 7 methaemoglobin percentage was associated with an estimated 30% reduction in the risk of P. vivax recurrence (adjusted hazard ratio = 0.70; 95% confidence interval [CI] [0.57, 0.86]; p = 0.0005). These pooled estimates were largely consistent across the study sites. Using day 7 methaemoglobin as a surrogate endpoint for recurrence would reduce required sample sizes by approximately 40%. Study limitations include the inability to distinguish between recrudescence, reinfection, and relapse in P. vivax recurrences. For a given primaquine regimen, higher methaemoglobin on day 7 was associated with a reduced risk of P. vivax recurrence. Under our proposed causal model, this justifies the use of methaemoglobin as a population-level surrogate endpoint for primaquine antihypnozoite activity in patients with P. vivax malaria who have normal G6PD activity.

Prediction of Nernst coefficient of S-containing compounds between fuel and ionic liquid phases in the extractive desulfurization using linear and supported vector machine (SVM) methods: QSPR-based machine learning

BackgroundThe presence of sulfur-containing compounds (SCCs) in the refinery streams has significant economic and environmental implications. Great attention has been focused on finding the proper green solvents like ionic liquids (ILs11Ionic liquids) with efficient extraction performance as a matter of concern of environmental issues. MethodsThe research aimed to develop a predictive model using QSPR to forecast the partition coefficient of dibenzothiophene by ILs from n-dodecane. Utilizing a dataset of 54 ILs and their partition coefficients for DBT, the study employed two methods to optimize ILs structures and compared linear (GA-MLR) and non-linear (LS-SVM) models, with non-linear models showing higher accuracy. After initial modeling and assessing the primary dataset of 54 ILs, yielding an R2 parameter of 0.39 for the test set, the dataset was divided into smaller clusters for further analysis. Three additional clusters were investigated. The second cluster comprised 14 ILs with identical cations and varying anions, modeled with two descriptors. The third cluster, consisting of 21 ILs with imidazolium cations and diverse anions, was modeled with three descriptors. Lastly, the fourth cluster, comprising 26 ILs with different cations but the same anion, was also modeled with three descriptors. Significant findingsThe MLR model yielded R2 values of 0.98, 0.85, and 0.93 for the test sets of the second, third, and fourth clusters respectively. Effective descriptors, including cation polarizability and alkyl branch length, were examined for their impact on partition coefficient and desulfurization efficiency. This research aids in enhancing EDS processes with ILs, advancing more efficient desulfurization technologies.

Analysis of interest expense deduction in transfer pricing dispute in Indonesia

This study provides a comprehensive examination of interest expense disputes within the context of transfer pricing in Indonesia. Multinational enterprises frequently employ transfer pricing mechanisms to reduce their tax obligations, often leading to legal challenges in tax courts. The research employs a qualitative methodology, focusing on a detailed analysis of several tax court verdicts related to interest expense disputes. The primary dataset comprises appellate verdicts available from the official tax court repository. The study identifies key factors influencing the outcomes of such disputes, including the strength of the evidence presented, the consistency between recorded contracts and actual economic conditions, coherence between transfer pricing documentation and real-world practices, the legitimacy and rationality of the loan arrangements, utilization of reliable comparability data, and substantive arguments concerning the applicability of the Debt-to-Equity Ratio (DER) for assessing interest expense reasonableness. Furthermore, the research underscores crucial considerations for taxpayers, such as the role of DER in determining expense reasonableness, the regulatory framework governing interest-free loans, methodologies for applying the arm’s length principle in interest expense transactions, and the importance of meticulous documentation in transfer pricing files (TP Documentation).

The Impact of Investor Sentiment on Corporate Tax Avoidance from a Behavioral Finance Perspective

ABSTRACT This paper delves into the intersection of investor behaviors and corporate strategies from a corporate finance perspective, thus broadening the scope of research on how investor sentiment influences corporate tax avoidance. Focusing on A-share listed companies as its primary dataset, the study explores the relationship between investor sentiment and corporate tax avoidance strategies. It reveals a notable finding: heightened investor sentiment significantly escalates corporate tax avoidance activities. Companies tend to indulge in more tax avoidance practices to align with investor expectations and secure stakeholder support as investor sentiment rises. Moreover, the research uncovers that this positive correlation between investor sentiment and tax avoidance is particularly pronounced in private sector industries and companies experiencing substantial financing constraints. This insight offers a novel perspective on behavioral finance by examining it through the lens of micro-level corporate behavior. It also provides valuable guidance for future research in corporate finance, suggesting new pathways for integrating and enhancing theoretical frameworks in this field.

Evidence from an Applied Health Research Question: Health Equity Profiles of Community Health Centre (CHC) and non-CHC clients

Objective/ApproachThe Black Health Equity Working Group's Applied Health Research Question aimed to compare cancer screening rates and surgical wait times between community health centre (CHC) clients based on race-related data and non-CHC clients. CHC client data was categorized by self-identified racial groups, with Black self-identification compared to non-Black racialized, White, and missing racial self-identification, and non-CHC clients. Health card numbers were encrypted to link individuals to the Primary Care Population dataset for breast, cervical, and colorectal cancer screening rates. Surgical wait time indicators, such as the number of patients undergoing surgery and average wait times for initial consultation and completed surgeries, were derived from the Wait Time Information System. Assessments were conducted semi-annually from fiscal year 2018 to 2021. ResultsFollowing the onset of COVID-19, CHC clients self-identifying as Black experienced the most significant decrease (6.8%) in colorectal screenings compared to other groups. Mammogram screenings remained consistently higher for CHC clients self-identifying as Black pre- and post-pandemic. Average cervical cancer screening rates were approximately 8% higher among CHC clients compared to non-CHC clients, irrespective of racial self-identification. However, due to small sample sizes and missing data among self-identified racial groups, trends in surgical wait times for both CHC and non-CHC clients were unstable. Conclusion/ImplicationsTailored interventions targeting Black CHC clients can enhance cancer screening rates, particularly colorectal screenings. The analysis of CHC data offers valuable insights into race-based disparities in health outcomes. Improved data completeness is essential for accurately assessing health outcome variations among different racial groups.