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Utilizing an explanatory case method approach to explore alternative recruitment strategies for a longitudinal randomized clinical trial of insomnia treatment in cancer survivors amid COVID-19

COVID-19 was a barrier to meeting recruitment goals in clinical trials particularly for behavioral interventions requiring innovative and evolving strategies. This paper explores recruitment approaches prior to, during, and after the in-person recruitment pause in a longitudinal randomized controlled trial (RCT) in which cancer survivors received one of two interventions to self-manage insomnia. An explanatory case study method was used to investigate pre, during, and post COVID-19 recruitment during a longitudinal RCT. Data analysis included descriptive frequencies of enrollment approaches and outcomes obtained from the research team’s weekly documented recruitment activities, and qualitative analysis of post-recruitment focus group of clinical partner experiences within the environmental context of the clinic settings. Team analysis included data triangulation between research team’s recruitment data and clinical staff experiences, and times series analysis with explanation building with team consensus on the final product. A total of 136 heterogenous cancer survivor participants were recruited utilizing both in-person and virtual strategies with an 87.5% retention rate. Variability in success of recruitment approaches over time was demonstrated within the environmental contexts. Overall, in-person recruitment was the most effective strategy (55.1%) followed by passive strategies of print outreach and social media (36.8%). A creative and persistent research team was needed to achieve the recruitment target with a high retention rate. Recruiting in-person post COVID-19 was challenging due to clinical staff barriers. The explanatory case study method offers insight into the complex recruitment process and potential approaches that could be implemented for future public health insomnia treatment studies.

Public-private partnerships for seed industry development in developing countries: Lessons from MasAgro maize in Mexico

Public-private partnerships (PPPs) are globally recognized for their potential to accelerate genetic improvement and delivery of new high-yielding seed varieties in developing countries. However, despite the strong advocacy for PPPs in crop improvement, there is little empirical evidence about their performance, capacities, and contribution to the development of seed industries and the promotion of competitive seed markets. This paper uses the experience of the MasAgro maize consortium, a PPP in Mexico, to examine crop variety innovation and delivery through PPPs, assess PPPs’ capacities to commercialize public germplasm-based varieties, and derive lessons for the design and implementation of future PPPs. Drawing on a combination of multiple data sources, we examined the PPP’s performance in the generation, dissemination, and commercialization of new maize hybrids. Our examination over the period 2011–2019 shows that the consortium was successful in maintaining a substantial flow of agronomically competitive maize hybrids, which compared favourably with the number of new varieties generated by national and international seed companies and the public sector. The partnership also contributed to refreshing and rejuvenating the variety portfolios of the consortium companies, which appear to have succeeded in bringing MasAgro varieties quickly into the market. However, seed sales achieved by MasAgro hybrids over this period remained small and multinational companies consistently maintained their leadership in the maize seed market. Our analysis shows that PPPs have strong capacities for the development of competitive seed varieties, but they face significant challenges in scaling up the uptake and adoption of these innovations in highly concentrated markets. To succeed in their objective of delivering affordable, high-quality seed on a large scale to smallholder farmers in developing countries, PPPs need to urgently incorporate a commercial and market-oriented perspective along all steps of the plant breeding and dissemination process.

Evaluation of therapeutic agent selection based on comprehensive genomic profiling in gastroenteropancreatic neuroendocrine neoplasms

Introduction Comprehensive genomic profiling (CGP) is increasingly being integrated into standard clinical practice as a strategy to guide subsequent treatment decisions by identifying novel therapeutic options based on tumor-specific mutations. However, its clinical utility in neuroendocrine neoplasms (NENs) remains to be determined. We conducted a cross-sectional analysis of genomic alterations, including tumor mutational burden (TMB) and microsatellite instability (MSI), in gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs), comparing neuroendocrine tumors (NETs) and neuroendocrine carcinomas (NECs). Results CGP was performed on 50 patients with advanced GEP-NENs between August 2017 and October 2023. Of these, 38 were diagnosed with NETs and 12 with NECs. Primary tumor sites included the pancreas (n = 24), gastrointestinal tract (n = 25), and one case of unknown origin. In our study, CGP analysis comparing NETs and NECs documented different genetic profiles. In addition, median TMB was significantly higher in NECs (5.0 vs. 1.9 mutations/megabase; p = 0.0101). High TMB was identified in 4 of 12 NECs (33.3%) and in only 1 of 38 NETs (2.6%) (p = 0.0093). NECs also harbored a significantly greater number of mutations per case than NETs (5.5 vs. 2.0; p = 0.0014). Actionable mutations were identified in 18 of 50 patients, and 7 patients received mutation-guided therapy: 2 with NETs and 5 with NECs. The frequency of treatment initiation based on CGP findings was significantly higher in patients with NECs (p = 0.0059). Conclusions NECs are genetically distinct from NETs, with a higher prevalence of actionable mutations and greater therapeutic relevance of CGP findings. These results suggest that CGP may be particularly valuable in NECs, where therapeutic options are limited, supporting its proactive implementation in this subgroup.

Automatic detection of foreign object intrusion along railway tracks based on MACENet

Ensuring high accuracy and efficiency in foreign object intrusion detection along railway lines is critical for guaranteeing railway operational safety under limited resource conditions. However, current visual detection methods generally exhibit limitations in effectively handling diverse object shapes, scales, and varying environmental conditions, while typically incurring substantial computational overhead. To overcome these limitations, this study proposes a multi-level feature aggregation and context enhancement network (MACE-Net). The network architecture integrates the GOLD-YOLO module, an advanced object detection approach, alongside the updated deformable convolutional networks (DCNv3). The incorporation of DCNv3 allows the model to dynamically adapt its sampling positions according to actual object shapes, significantly enhancing feature extraction accuracy, especially for irregularly shaped intrusions. Additionally, the convolutional block attention module (CBAM) is employed to refine spatial and channel-wise feature representation, enabling the model to emphasize crucial object characteristics without substantially increasing computational complexity. Meanwhile, to improve localization robustness, the generalized intersection over union (GIoU) loss function is implemented, offering more reliable detection across various object sizes and shapes. Furthermore, to address the shortage of domain-specific datasets, we created a railway intrusion dataset comprising 7,200 images. Experimental results demonstrate that MACE-Net achieves superior detection performance, improving mAP@0.5 from 78.9% (baseline YOLOv8) to 83.8%—a notable increase of 4.9%. Meanwhile, the F1-score also rises by 5.2%. Importantly, despite significant accuracy gains, MACE-Net maintains computational efficiency similar to that of the baseline, affirming its suitability for real-time railway foreign object detection tasks under constrained energy and computational environments.

Expression of Concern: Priorities for intervention of childhood stunting in northeastern Ethiopia: A matched case-control study

Assessment of liquefaction resistance of soil with fines using cyclic hollow cylinder testing

Soil liquefaction is a devastating effect of earthquakes. It occurs when saturated granular soils lose their shear strength because of a sudden increase in pore water pressure under dynamic loads. Over the last six decades, considerable focus has been placed on understanding the mechanisms and phenomena associated with liquefaction, making it a critical area of research. Evaluating soil liquefaction accurately is crucial for maintaining the seismic safety of construction. To investigate how fines content affects soil liquefaction resistance under cyclic simple shear loads and gradual principal stress rotation, a series of cyclic hollow cylinder tests (CHCT) were carried out under isotropic consolidation and undrained conditions. The experiments were conducted using medium Monterey No. 0/30 Sand (MS), where five varying percentages of fine content were analyzed under two confining pressures (σ3’ = 103 kPa and 206 kPa) and at two relative densities (Dr = 30%, 45% and 60%). The results of CHCT tests contributed to the development of liquefaction-potential evaluation curves. The findings demonstrated that increasing the acceptable fines content up to 15% reduces liquefaction resistance. However, when fines content exceeds 15%, further increases lead to enhanced liquefaction resistance. Based on all laboratory test results, back propagation neural network (BPNN) was applied to predict cyclic stress ratios leading to initial liquefaction after cyclic loading cycles. The BPNN model can give superior precision with mean absolute percentage error (MAPE) values of 1.05%, and also can help engineering better understand liquefaction potential of soil samples with different fines content.

Prognostic factors associated with recurrence in patients undergoing surgery for hepatic cystic echinococcosis: A systematic review protocol

Background Cystic echinococcosis (CE) is a zoonotic disease caused by Echinococcus granulosus, with hepatic localization being the most frequent presentation in humans (70–80%). Surgical intervention remains the most common therapeutic option, despite an associated recurrence rate of 8% (95% CI: 6%–10%). CE recurrence increases postoperative morbidity, hospital stay, and healthcare costs. Several prognostic factors (PFs), such as cyst size, presence of multiple cysts, and personal or family history of CE, have been linked to recurrence. However, associations with cyst location, type, and biliary complications remain uncertain due to conflicting evidence. This systematic review (SR) aims to identify PFs associated with recurrence in patients undergoing surgery for hepatic CE. Methods We will conduct a systematic review of primary longitudinal observational, and experimental studies evaluating the association between clinical, parasitic, and surgical PFs and CE recurrence. We will search Medline, Scopus, Embase, Web of Science, BIREME-BVS, and SciELO using a sensitive search strategy. Two reviewers will independently screen studies, extract data, and assess risk of bias using the QUIPS tool. Methodological quality will be assessed using MInCir-Pr2, and data extraction will follow the CHARMS-PF checklist. A qualitative synthesis will be performed, and where appropriate, random-effects meta-analyses will be conducted using the inverse-variance and restricted maximum likelihood methods. Heterogeneity will be assessed using Chi², Tau², I², and 95% prediction intervals. Publication bias will be explored via funnel plots and statistical tests (Egger’s). Certainty of evidence will be assessed using GRADE. Discussion The results of this SR will support early risk stratification and inform clinical decision-making in the management of hepatic CE, a neglected tropical disease. Registration PROSPERO, CRD42024538005.

Dynamic principal modeling of cemented phosphogypsum stabilized soil under dry and wet cycles

Aiming at the influence of dynamic loading and wet/dry cycles during the operation of roadbed and in response to the proposal of Guizhou Provincial Government to promote the efficient utilization of phosphogypsum to solve the current situation of oversupply of phosphogypsum, the dynamic triaxial experiment was carried out to explore the dynamic constitutive model of phosphogypsum-stabilized soil with different numbers of wet/dry cycles, different peripheral pressures, and different consolidation ratios. The test results show that: (1) the effect of wet and dry cycles has a greater impact on the dynamic constitutive model of the mix, Monismith exponential model is suitable for the dynamic stress-strain curves in the case of wet and dry cycling; (2) The dynamic shear modulus-dynamic shear strain constitutive model of phosphogypsum stabilized soil was established based on dynamic soil mechanics, and the fitted correlation coefficients were all higher than 0.85, and the maximum values of MAE and RMSE were 4.827 and 5.990, respectively; (3) The prediction of the dynamic resilience modulus of phosphogypsum stabilized soil can be fitted by Ni model and power exponential model; (4) By comparing the dynamic and static modulus of rebound, it is concluded that the value of dynamic modulus of rebound for phosphogypsum-stabilized soil under dry and wet cycles is 1.1–1.5 times of the static modulus of rebound, and the recommended value of dynamic modulus of rebound is 90–100 MPa.

Adaptation of retired older adult return migrants in their place of origin in the Yogyakarta Special Region, Indonesia

Migrants must adapt to the social life of their destination, including those who return to their place of origin. One such destination for older adult return migrants in Indonesia is the Yogyakarta Special Region (Daerah Istimewa Yogyakarta DIY), located in the centre of Java island. The majority of residents in this province, which is under the jurisdiction of the Sultanate of Yogyakarta, are of Javanese ethnicity and continue to uphold traditional Javanese cultural values in their daily social interactions. This study examines the adaptation strategies employed by retired older adults who return to their place of origin, defined as either their place of birth or previous residence, as their primary DIY location. Using a qualitative method, data were collected through open-ended interviews with 27 retired older adult return migrants, selected through snowball sampling. The findings indicate that these return migrants primarily rely on Javanese cultural values to navigate social reintegration, regardless of the length of time they lived outside the DIY region. Embracing these values facilitates smoother adaptation to the local social environment, where such traditions remain strong. This strategy could apply to migrants of other ethnicities who internalize their cultural values, some of which may share similarities with Javanese values, when migrating to a new destination or returning to their place of origin.

Mangrove species classification using a proposed ensemble U-Net model and Planet satellite imagery: A case study in Ngoc Hien district, Ca Mau province, Vietnam

Land cover and plant species identification using satellite images and deep learning approaches have recently been a widely addressed area of research. However, mangroves, a specific species that have significantly declined in quantity and quality worldwide despite their numerous benefits, have not been the subject of attention. The novelty of this research is to deal with this species based on an advanced deep learning solution (a proposed ensemble U-Net model) and a high-resolution Planet satellite imagery (5 m x 5 m) in a case study of Ngoc Hien district, Ca Mau province, Vietnam. Twelve single U-Net backbone models were trained, and three quantitative metrics (Intersection over Union, F1-score, and Overall Accuracy) were used to evaluate. The findings indicate that three out of twelve models (MobileNet, SEResNeXt-101 and Efficientnet-B7) experienced the most efficient assessment results for identifying all classes, in which the MobileNet model was the best. These models were applied for the ensemble model’s development. The ensemble model’s quantitative assessment metrics increased considerably by about 3–10% compared to the single-component models. The IoU, F1-score, and OA values of this model were 80.08%, 95.82%, and 95.90%, respectively. Three classes of mangrove species (Avicennia alba, Rhizophora apiculate, and mixed mangroves) in the ensemble model had more uniform assessment results. In conclusion, to achieve optimal classification outcomes, a land-cover map comprising mangrove species is possibly established using the proposed ensemble model, while a distribution map of mangrove species enables to be developed using the MobileNet model.

Spotlight on Mechanosterics: A Bulky Macrocycle Promotes Functional Group Reactivity in a [2]Rotaxane

An Expedient Synthesis of the Antimitotic Natural Products Sarcodictyin and Eleutherobin, and Carbohydrate Analogues

Phosphorus-Driven Dual d-Band Harmonization for Reversible Electrocatalysis

NMR-Guided Studies to Establish the Binding Interaction between a Peptoid and Protein

Real-world super-resolution with VLM-based degradation prior learning

Comparative effect of dietary patterns on selected cardiovascular risk factors: A network study

Sex-specific differences in performance and pacing in the world’s longest triathlon in history

Sipeimine reduces ethanol-induced gastric ulcer in mice by suppressing Jak-Stat activation and restoring gut microbiota balance

Abstract Long-term excessive alcohol intake can directly injure the gastroduodenal mucosa, causing gastric erosions, gastric ulcers, and gastrorrhagia. Fritillaria ussuriensis Maxim is a famous traditional Chinese medicine and health food produced in China. Sipeimine is an alkaloidal component of Fritillaria ussuriensis Maxim. This research aimed to investigate the protective effects of sipeimine on ethanol-induced gastric ulcers in mice. The results displayed that sipeimine could alleviate gastric tissue damage and decrease the levels of SOD, MDA, IL-6, IFN-γ, TNF-α, and IL-1β. Sipeimine treatment also adjusted macrophage polarization and the balance of Th17/Treg cell by reducing the expression of Jak1/2, p-Jak1/2, Stat1/3, and p-Stat1/3. Moreover, sipeimine could increase the abundance of Lactobacillus_johnsonii and decrease the abundance of Bacteroides_vulgatus in the gut microbiota. Meanwhile, sipeimine treatment significantly decreased the abundance of Rodentibacter_heylii and Streptococcus_cuniculi in the gastric microbiota. In conclusion, sipeimine can improve gastric ulcers by suppressing the Jak-Stat pathway, reversing gut-gastro microbiota dysbiosis, inhibiting macrophage M1 polarization, maintaining the balance of Th17/Treg cell, and lessening sustained inflammatory injury.

Identification of new selective CD36 inhibitors to potentiate HER2-targeted therapy in HER2-positive breast cancer

Deep learning based localisation and classification of gamma photon interactions in thick nanocomposite and ceramic monolithic scintillators

Abstract Accurate localisation of the first point of interaction (FPoI) of incident gamma photons in monolithic scintillators is crucial for many radiation-based imaging applications - in particular, accurate estimation of the lines of response in positron emission tomography (PET). This is particularly challenging in thick nanocomposite and ceramic scintillator materials, which exhibit high levels of Rayleigh scattering compared to monocrystalline scintillators. In this work, we evaluate deep neural network-based approaches for (1) classifying the mode of photon interaction using an InceptionNet-based classifier and (2) accurately estimating the location of the FPoI based on scintillation photon distributions in several monolithic nanocomposite and ceramic scintillators using both CNN- and InceptionNet-based regression networks. The classifier was able to correctly categorise single-energy deposition events with an accuracy $$\ge$$  90.1%, two-deposition interactions with an accuracy $$\ge$$  77.6% and three-plus deposition interactions with an accuracy $$\ge$$  66.7%. Across the evaluated materials, median total localisation error ranged from 0.58 mm to 2.91 mm with the CNN and 0.59 mm to 2.10 mm with InceptionNet, assuming 50% detector quantum efficiency. Localisation in nanocomposites using the InceptionNet-based regression network improved the most relative to previously-reported results based on classical techniques, in some cases approaching the accuracy achieved with ceramic scintillators.