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Effects of adding N2-fixing Rhodopseudomonas palustris to stimulate the growth and yield of canary melon (Cucumis melo L.)
This study evaluated the effects of a mixture of four N2-fixing strains of Rhodopseudomonas palustris-VNW64, VNS89, TLS06, and VNS02-(PNSB) on soil properties, nitrogen (N) uptake, plant growth, and yield of canary melon cultivated in alluvial soil. A greenhouse experiment was conducted using a completely randomized block design with eight treatments: (i) 100% N of recommended fertilizer formula (RFF), (ii) 85% N of RFF, (iii) 70% N of RFF, (iv) 100% N of RFF + PNSB, (v) 85% N of RFF + PNSB, (vi) 70% N of RFF + PNSB, (vii) PNSB only, and (viii) no fertilization. The application of PNSB improved soil pH and available N concentrations. The highest N uptake (33.9 kg N ha ⁻ ¹) was recorded in the 100% RFF + PNSB treatment. Notably, the 70% RFF + PNSB treatment achieved comparable N uptake (27.7 kg N ha ⁻ ¹) to the 100% RFF treatment (28.6 kg N ha ⁻ ¹). The 85% RFF + PNSB treatment maintained plant height and yield equivalent to the 100% RFF treatment. These results suggest that supplementing with PNSB can reduce N fertilizer application by up to 15% without compromising crop performance. The PNSB mixture should be further tested under a field trial.
Comparison of negative pressure wound therapy with conventional wound care in the treatment of sternal wound infection after cardiac surgery: A meta-analysis with trial sequential analysis
Background Negative pressure wound therapy (NPWT) has become a popular treatment option for sternal wound infection (SWI). However, it remains uncertain whether the therapeutic benefits of NPWT are superior to conventional wound care. This study aimed to systematically evaluate the therapeutic effects of NPWT on SWI compared to conventional wound care through meta-analysis. Methods A comprehensive search of PubMed, Web of Science, Embase, and the Cochrane Library databases was conducted from inception to April 29, 2024 for all potential studies. The pooling of dichotomous outcome data was achieved using relative risk (RR), with results presented within a 95% confidence interval (CI). We utilized the standard mean difference (SMD) and 95% CI for continuous outcomes. Heterogeneity test, publication bias assessment, sensitivity analysis, and trial sequential analysis (TSA) were conducted. Publication bias was detected through the Begg’s and Egger’s tests. Software R 4.3.1, Stata 12.0, and TSA v0.9.5.10 Beta software were utilized for all analyses. Results Out of 1832 articles identified, 10 were included in this study. The overall results revealed that NPWT significantly decreased the sternal wound reinfection (SWRI) rate (RR [95% CI] = 0.179 [0.099 to 0.323], 95% prediction interval [PI]: 0.082 to 0.442), in-hospital mortality (RR [95% CI] = 0.242 [0.149 to 0.394], 95% PI: 0.144 to 0.461), and shortened the length of intensive care unit (ICU) stay (SMD [95% CI] = −0.601 [−0.820 to −0.382], 95% PI: −1.317 to 0.128) compared with conventional wound care. There was no significant difference in length of hospital stay (SMD [95% CI] = −0.402 [−0.815 to 0.012], 95% PI: −1.801 to 0.998) and treatment duration (SMD [95% CI] = −0.398 [−1.646 to 0.849], 95% PI: −16.340 to 15.543) between the NPWT group and control group. Further subgroup analysis demonstrated the benefits of NPWT in shortening hospitalization length in the European population (p < 0.05). Conclusion The present evidence corroborates that the application of NPWT in the treatment of SWI after cardiac surgery effectively reduces the SWRI incidence and in-hospital mortality while shortening the length of ICU stay.
Improving the prediction of potato yield gaps: Solanum-model parameterization and evaluation in southwestern China
Sustainable agriculture has made significant contributions to both food security and global economic growth. Development of new cultivars and identifying and utilizing the yield potential of existing gemplasms are two key options for sustainable crop production. In this study, field trials and a new plant growth stimulator model developed by the International Potato Center called “Solanum” was used to assess the yield potential and ‘yield gaps’ of three potato cultivars in spring, autumn, and early spring seasons in southwestern China. The results showed that the average potential yield of potato crops in spring, early spring, and autumn seasons was 125.6 t/ha, 56.40 t/ha, and 45.30 t/ha with a gap from the potential yield of 107.30 t/ha, 36.70 t/ha, and 32.10t/ha, respectively. Further analysis revealed that the late blight disease was the main cause of large yield gap in spring season, whereas inadequate rain fall was the the major factor impacting the actual yield of potato crops in autumn and early spring seasons. Therefore, we report for the first time, that the spring potato in Yunnan Province, southwestern China has the highest potential yield in the world, and that extending growing season coupled with managing late blight can increase actual yield by 115%. The high yield potential of spring potato could be very beneficial to the local economy and add an enormous pelf and prosperity to the region.
Instantaneous center of rotation, the first step to build up the digital laboratory of complex motions
Calculating instantaneous centers of rotation to describe combined rotational and translational motions has a long history in many fields of applied science and basic rigid body kinematics. However, only some theoretical studies have explored the fundamental characteristics of this system. This study used digital three-dimensional modeling and computing methods to examine the system’s operation in a controlled in vitro-like environment. The effects of inaccurate registrations on the resulting motion were also analyzed. We registered 28.65, 14.33 and 9.55 EcD_ratios for 2°, 4° and 6° of closure respectively, and described a structured, predictable framework based on a solid mathematical background. Our findings align with previous publications, indicating that the longstanding debate over the pure rotation of the temporomandibular joint arises from misinterpretations of scientific findings due to a lack of fundamental knowledge of the basic characteristics of the system. Our simplified geometrical approach significantly reduces the complexity of the existing complex kinematic model, making it more accessible for practical applications, easier to understand, and potentially more applicable in orthopedics or temporomandibular joint radiology. We identified five fundamental characteristics of the system as we described the effects of the acting translational component in the complex motion. We also presented a detailed model concerning the effects of inaccurate rotation axis registration on the resulting compromised transformation, improving our understanding of the error tolerance level of articulation systems. Our results show that the system might tolerate errors as great as 3–4 cm in some settings in the parallel error direction, while in case of circular and perpendicular error types an approximately 2 mm axis registration error would exceed the clinically desirable 0.1 mm occlusal error level. Our experimental modeling strategy might provide extensive data for machine learning and for analyzing and comprehending the fundamental characteristics of different complex motion systems in the future.
Antibiotic self-medication in Otuke District, Northern Uganda: Prevalence and associated factors
Antibiotic self-medication is a growing public health concern, particularly in low- and middle-income countries where access to healthcare is limited. The practice contributes to antimicrobial resistance which increases health care costs, morbidity, and mortality in the population. This study aimed to investigate the prevalence of antibiotic self-medication and its associated factors in the Otuke District, Northern Uganda. A community-based cross-sectional study was conducted in Otuke. Data was collected among adults aged 18 years and above using semi-structured questionnaire. Collected data was coded and double-entered into SPSS Software version 26 and exported to STATA 14 for analysis of frequencies and percentages. Modified Poisson regression was used to run analysis of the association at a P-value of 0.05. Out of the 385 participants, 261 (67.79%) reported having self-medicated with antibiotics in the past six months. The most commonly self-medicated antibiotics were amoxicillin 134 (51.3%), ampiclox 87 (33.3%) and metronidazole 57 (21.9%). Participants with previous successful treatment were 2.33 times more likely to self-medicate (PR = 2.33, 95% CI: 1.89–2.87, P < 0.001) while poor staff attitude increased the likelihood by 1.53 times (PR = 1.53, 95% CI: 1.38–1.71, P < 0.001). Knowledge about antibiotics was negatively associated with ASM in that those who had knowledge on antibiotics were 25% less likely to self-medicate with antibiotics (PR = 0.75, 95% CI: 0.65–0.86, P < 0.001). The practice of antibiotic self-medication was highly prevalent in Otuke district due to previous successful treatments and poor health care systems. The commonly self-medicated antibiotics were amoxicillin, ampiclox and metronidazole. We recommend public health interventions such as community education on antimicrobial resistance regulation of antibiotic use in the country.
Designing and evaluation of the effect of community-based intervention on breast self-examination among reproductive-aged women in Ethiopia: A Cluster Randomized Controlled Trial
Background Though early intervention saves many lives worldwide, breast cancer remains a leading cause of cancer among women in Ethiopia. This study, therefore, aimed to evaluate community-based interventions promoting breast self-examination using the Health Belief Model. Methods A cluster randomized controlled trial followed by a cross-sectional study lasting six months was used to evaluate the effectiveness of the community-based educational intervention on breast self-examination among reproductive-aged women in Ethiopia. A total of 810 participants were randomly assigned in a 1:1 ratio and assessed at baseline, three months, and six months post-intervention. A general linear model for repeated measures was used to examine the mean differences in study variables across time points. Non-parametric tests (Cochran’s Q) were employed to analyze dichotomous variables related to breast self-examination practices. Path analysis was conducted to examine the interactions among the constructs of the Health Belief Model. Results A total of 810 reproductive-aged women participated in the study, yielding a 100% response rate at baseline. The mean age of participants was 33.2 ± 7.7 years in the intervention group and 33.5 ± 8.1 years in the control group. The proportion of women performing breast self-examinations increased from 33.3% at baseline to 59.9% at the end of the intervention. And the Comprehensive knowledge about breast self-examination rose from 11.7% to 69.1% over the same period. Perceived susceptibility, perceived severity, knowledge, and health motivations had a statistically significant mean difference between the intervention and control groups (p < 0.0001). We registered PACTR database (https://pactr.samrc.ac.za/): “PACTR201802002902886”. Conclusions The study found that there is a strong interplay between the likelihood of performing breast self-examination and perceived susceptibility, perceived severity, knowledge, and health motivations. Field specialists should figure out the problem related to perception and awareness through intensive health promotion interventions. Trial registration Registered in the Pan African Clinical Trial Registry (www.pactr.org) database, and the unique identification number for the registry is PACTR201802002902886.
Virus and viroid diversity in hops, investigating the German hop virome
Germany is worldwide one of the largest hop (Humulus lupulus L.) producers, an essential crop for the brewing industry. However, infections caused by viruses and viroids can severely impact hop yield and quality. In 2019, citrus bark cracking viroid (CBCVd) – a highly aggressive pathogen in hop – was first reported in Germany, raising concerns about its spread and prompting a broader investigation of the German hop virome.To investigate the viro-diversity in German hops, we started with a pilot study in 2021 targeting three hopyards in the Hallertau region (Bavaria), where CBCVd was previously detected. This study was expanded in 2022 and 2023 to include other main hop growing regions of Tettnang (Baden-Wuerttemberg) and Elbe-Saale (Saxony, Saxony-Anhalt, Thuringia). Leaf samples were collected from hop as well as non-hop plants inside and outside the hopyard, pooled, and proceeded for double-stranded RNAs extraction. High-throughput sequencing (HTS) was used as a diagnostic tool, followed by RT-PCR confirmation. Our analysis identified four viruses infecting hops; hop latent virus (HpLV), hop mosaic virus (HpMV), apple mosaic virus (ApMV), arabis mosaic virus (ArMV) – and two viroids; hop latent viroid (HLVd) and CBCVd. HpLV, HpMV, and HLVd were consistently found across all targeted hopyards, while CBCVd was confined to the Hallertau region. ArMV was only detected in one hopyard at one sampling timepoint. ApMV was the only virus detected in both hop and non-hop plants. Additional analysis of hop pool datasets revealed the presence of other potential hop pathogens, i.e., fungi and bacteria. The results showed a low diversity of viruses and viroids infecting hops. However, this study provides a comprehensive overview on the major viruses and viroids in German hopyards. The results may serve as a useful resource for the development of disease management strategies in hop cultivation and highlight the valuable implementation of HTS in plant pathogen surveillance.
Incidence of dry eye symptoms and behavioural-cultural risk factors among university students population in Jordan
Purpose To estimate the incidence of dry eye (DE) symptoms among university students in Jordan and to examine the relationship between behavioral and cultural risk factors and DE symptom severity. Methods A cross-sectional study involving 788 university students was conducted in Jordan. Participants’ mean age was 21.87 years (SD = 3.824; range: 18–45 years). The incidence and severity of DE symptom were assessed using the validated Arabic version of the Ocular Surface Disease Index (ARB-OSDI) questionnaire, administered through Google Forms. The survey included demographic questions and behavioral-cultural risk factors (smoking and eye cosmetic use). One-Way ANOVA and multi-regression analyses were used to investigate the association between OSDI mean scores and behavioral-cultural risk factors. Results The incidence of DE symptoms, defined as an OSDI score ≥ 13, was 74.2% among university students. Higher DE symptom severity was statistically associated with females’ gender (p < 0.001), older age (≥27 years) (p = 0.032), contact lens use (p = 0.001), frequent use of eye cosmetics (p < 0.001), and a history of DED (p < 0.001). Smoking habits, including the use of Dokha or Ajami, smoking in enclosed spaces, and daily smoking, were also associated with increased DE symptom severity (all p < 0.001). Contributing factors to the high incidence and severity of DE symptom included long-term use of eye cosmetics (particularly mascara and internal eyeliner) and sleeping while wearing contact lenses. Conclusion Dry eye symptoms are highly prevalent among university students in Jordan and are significantly associated with factors such as age, gender, contact lens use, cosmetic application, and tobacco consumption.
AI learns from nature to design super-adhesive gels that work underwater
GNN-RMNet: Leveraging graph neural networks and GPS analytics for driver behavior and route optimization in logistics
Logistics networks are becoming increasingly complex and rely more heavily on real-time vehicle data, necessitating intelligent systems to monitor driver behavior and identify route anomalies. Traditional techniques struggle to capture the dynamic spatiotemporal relationships that define driver actions, route deviations, and operational inefficiencies in big fleets. This paper introduces GNN-RMNet, a hybrid deep learning system that combines GNN, ResNet, and MobileNet for interpretable, scalable, and efficient driver behavior profiling and route anomaly detection. GNN-RMNet utilizes spatiotemporal GPS trajectories and vehicle sensor streams to learn contextual and relational patterns from structured driving data in real time, thereby identifying dangerous driving and route violations. On a real-world GPS-vehicle sensor dataset, the proposed model achieves 98% accuracy, 97% recall, an F1-score of 97.5%, and domain-specific measures like Anomaly Detection Precision (96%) and Route Deviation Sensitivity (95%). Modular design offloads ResNet-GNN analytics to edge nodes while preserving MobileNet components for on-vehicle inference, resulting in reduced inference latency (32 ms). Comparing GNN-RMNet against baseline, ensemble, and hybrid models shows its accuracy, efficiency, and generalization advantages. Computational feasibility, anomaly scoring interpretability, and future deployment concerns, including cybersecurity, data privacy, and multimodal sensor integration, are all covered. For real-time fleet safety management and secure, intelligent, and context-aware logistics, GNN-RMNet seems promising. The framework incorporates multimodal, privacy-aware, and scalable driver analytics, enabling its use in intelligent transportation systems and urban logistics infrastructures.
Is your AI benchmark lying to you?
BBDetector: Intelligent border binary detection in IoT device firmware based on a multidimensional feature model
In the field of firmware security analysis for Internet of Things (IoT) devices, border binary detection has become an important research focus. However, the existing methods for border binary detection have problems such as insufficient feature characterization, high false-negative rates, and low intelligence levels. To mitigate these issues, we introduce BBDetector, a border binary detection method based on a multidimensional feature model. First, we constructed the first known set of border binaries at a certain scale by collecting and analyzing a diverse set of real-world firmware. To characterize the features of border binaries comprehensively, we proposed a multidimensional feature model (MDFM). Next, we extracted the feature vectors of binaries via the MDFM and designed a novel stacking method to achieve border binary detection. This method involves ensemble learning, combining extreme gradient boosting, light gradient boosting machine, and categorical boosting as base learners with random forest as the meta-learner. Finally, a border binary detection model (XLC-R) was obtained by training with feature vectors. We tested and evaluated BBDetector on two datasets. The experimental results showed that XLC-R achieved a precision of 94.98%, a recall of 91.02%, and an F1 score of 92.84% for the constructed representative Dataset I. Additionally, BBDetector detected 3.25 times and 2.23 times more border binaries in Dataset II than did the state-of-the-art tools Karonte and SaTC, respectively. BBDetector provides an accurate method for border binary detection in IoT firmware security analysis, significantly enhancing the pertinence of vulnerability detection, dramatically reducing the complexity of firmware security analysis, and providing essential technical support for improving IoT device security.
Evaluation of collapsible deformation of foundation under rectangular load based on the improved binary medium model
The increasing frequency of extreme weather events and climate change can substantially impact the collapse phenomenon and other challenges associated with the deformation of foundation soils. These can also affect soil moisture regimes, particularly soil suction. The global engineering and geotechnical hazards related to the deformation of foundation soil collapsibility require immediate attention from engineers. The differential equations of the collapsible consolidation deformation of a collapsible loess foundation under concentrated force are formulated using an improved two-dimensional medium model in conjunction with the Biot consolidation theory, fracture mechanics, and continuum theory. The equations are solved using the mathematical and physical methodologies of the Laplace transform and the Hankel transform, and boundary conditions are introduced. The mathematical models of lateral displacement, vertical displacement, and pore water pressure of a collapsible loess foundation with vertical depth, radial distance, and saturation under rectangular load are provided. The proposed model was validated through a series of numerical calculations and analyses. It was demonstrated that the deformation of the collapsible loess foundation under the improved binary medium rectangular load is exceedingly similar to the corresponding engineering deformation. The results of the investigation significantly impact the theoretical research of collapsible loess foundations.
Data-driven de novo design of super-adhesive hydrogels
Redox-powered autonomous directional C–C bond rotation under enzyme control
Abstract Living biological systems rely on the continuous operation of chemical reaction networks. These networks sustain out-of-equilibrium regimes in which chemical energy is continually converted into controlled mechanical work and motion 1–3 . Out-of-equilibrium reaction networks have also enabled the design and successful development of artificial autonomously operating molecular machines 4,5 , in which networks comprising pairs of formally—but non-microscopically—reverse reaction pathways drive controlled motion at the molecular level. In biological systems, the concurrent operation of several reaction pathways is enabled by the chemoselectivity of enzymes and their cofactors, and nature’s dissipative reaction networks involve several classes of reactions. By contrast, the reactivity that has been harnessed to develop chemical reaction networks in pursuit of artificial molecular machines is limited to a single reaction type. Only a small number of synthetic systems exhibit chemically fuelled continuous controlled molecular-level motion 6–8 and all exploit the same class of acylation–hydrolysis reaction. Here we show that a redox reaction network, comprising concurrent oxidation and reduction pathways, can drive chemically fuelled continuous autonomous unidirectional motion about a C–C bond in a structurally simple synthetic molecular motor based on an achiral biphenyl. The combined use of an oxidant and reductant as fuels and the directionality of the motor are both enabled by exploiting the enantioselectivity and functional separation of reactivity inherent to enzyme catalysis.
StatModPredict: A user-friendly R-Shiny interface for fitting and forecasting with statistical models
Background Many disciplines, such as public health, rely on statistical time series models for real-time and retrospective forecasting efforts; however, effectively implementing related methods often requires extensive programming knowledge. Therefore, such tools remain largely inaccessible to those with limited programming experience, including students training in modeling, as well as professionals and policymakers seeking to forecast an epidemic’s trajectory. To address the need for accessible and intuitive forecasting applications, we present StatModPredict, an R-Shiny dashboard for conducting robust forecasting analysis utilizing auto-regressive integrated moving average (ARIMA), generalized linear models (GLM), generalized additive models (GAM), and Meta’s Prophet model. Methods StatModPredict supports robust real-time forecasting and retrospective model analysis, including fitting, forecasting, evaluation, visualization, and comparison of results from four popular models. After loading an incident time series data set into the interface, users can easily customize model parameters and forecasting options to obtain the desired output. Additionally, StatModPredict offers multiple editable figures for, but not limited to, the time series data, the forecasts, and model fit and forecast metrics. Users can also upload external forecasts produced elsewhere and evaluate their performance alongside the dashboard’s built-in models, thereby enabling direct comparisons. We provide a detailed demonstration of the dashboard’s features using publicly available annual HIV case data in the US. A video tutorial is available at https://www.youtube.com/watch?v=zgZOvqhvqw8. Conclusions By eliminating programming barriers, StatModPredict facilitates exploration and use by students training in forecasting, as well as professionals and policymakers aiming to forecast epidemic trajectories. Additionally, the flexibility in the required input data structure and parameter specification process extends the application of StatModPredict to any discipline that employs time series data. By offering this open-source interface, we aim to broaden access to forecasting tools, promote hands-on learning, and foster contributions from users across disciplines.
High-accuracy laser spectroscopy of $${{\bf{H}}}_{{\bf{2}}}^{{\boldsymbol{+}}}$$ and the proton–electron mass ratio
Abstract The molecular hydrogen ions (MHI) are three-body systems suitable for advancing our knowledge in several domains: fundamental constants, tests of quantum physics, search for new interparticle forces, tests of the weak equivalence principle1 and, once the anti-molecule $$\overline{p}\,\overline{p}\,{e}^{+}$$ p ¯ p ¯ e + becomes available, new tests of charge–parity–time-reversal invariance and local position invariance1–3. To achieve these goals, high-accuracy laser spectroscopy of several isotopologues, in particular $${{\rm{H}}}_{2}^{+}$$ H 2 + , is required4. Here we present a Doppler-free laser spectroscopy of a $${{\rm{H}}}_{2}^{+}$$ H 2 + rovibrational transition, achieving line resolutions as large as 2.2 × 1013. We accurately determine the transition frequency with 8 × 10−12 fractional uncertainty. We also determine the spin–rotation coupling coefficient with 0.1 kHz uncertainty and its value is consistent with the state-of-the-art theory prediction5. The combination of our theoretical and experimental $${{\rm{H}}}_{2}^{+}$$ H 2 + data allows us to deduce a new value for the proton-electron mass ratio m p/m e. It is in agreement with the value obtained from mass spectrometry and has 2.3 times lower uncertainty. From combined MHI, H/D and muonic H/D data, we determine the baryon mass ratio m d/m p with 1.1 × 10−10 absolute uncertainty. The value agrees with the directly measured mass ratio6. Finally, we present a match between a theoretical prediction and an experimental result, with a fractional uncertainty of 8.1 × 10−12. Both results indicate a notable confirmation of the predictive power of quantum theory and the absence of beyond-the-standard-model effects at these levels.
Biomimetic model for computing missing data imputation and inconsistency reduction in pairwise comparisons matrices
A biomimetic model is presented to compute missing data imputation and reduce inconsistencies in pairwise comparisons matrices. The proposed regeneration method emulates three primary phases of a biological process: identifying the most damaged areas (by identifying inconsistencies in the pairwise comparison matrix), cell proliferation (filling in missing data), and stabilization (optimization of global consistency). An iterative algorithm is employed to correct inconsistencies and compute missing data imputations within the pairwise comparison matrix. The results demonstrate that the biomimetic approach is robust and reliably converges to a consistent solution.
Do monetary incentives encourage local communities to collect and upload mosquito sound data using smartphones? A case study in the Democratic Republic of the Congo
Malaria is one of the deadliest vector borne diseases affecting sub-Saharan Africa. A suite of systems are being used to monitor and manage malaria risk and disease incidence, with an increasing focus on technological interventions that allow private citizens to remotely record and upload data. However, data collected by citizen scientists must be standardised and consistent if it is to be used for scientific analysis. Studies that aim to improve data collection quality and quantity have often included incentivisation, providing citizen scientists with monetary or other benefits for their participation in data collection. We tested whether monetary incentives enhance participation and data collection in a study trialling an acoustic mosquito sensor. Working with the community in two health areas in the Democratic Republic of Congo, we measured data collection participation, completeness, and community responses. Our results showed mixed responses to the incentive, with more participants interested in the social status and monetary value of the technology used than the monetary incentive itself. The effect of incentives on data collection varied over the course of the trial, increasing participation in the start of the trial but with no effect in the latter half of the trial. Feedback from participants showed that opinions on technology, research objectives, and incentives varied between communities, and was associated with differences in data collection quantity and quality, suggesting that differences in community interest in data collection and the incentives may be more important than the incentive value itself. These results suggest that though there is an initial benefit, extrinsic motivations do not override differences in intrinsic motivations over time, and enhanced communication and dialogue with participants may improve citizen science participation and attitudes.