Intermediate Results Research Articles

Intermediate results of efficacy and safety of combined treatment with brentuximab vedotin and bendamustine for refractory and relapsed forms of peripheral T-cell lymphoma: A retrospective study

Aim. To evaluate the efficacy and safety profile of the BvB combination in the treatment of R/R nPTCL, the complete response (CR) and partial response (PR) rates, overall survival (OS), and progression-free survival (PFS). Materials and methods. The analysis included 39 patients with R/R forms of nPTCL who underwent treatment in state budgetary institutions of the Moscow Department of Health (06.2008–08.2024). The efficacy (CR, PR, OS, PFS) and safety profile of the BvB combination were evaluated. Results. The best efficacy was noted when the BvB regimen was prescribed as a 2nd-line therapy compared to 3rd and subsequent lines. The overall response rate (ORR) when prescribed as 2nd-line therapy was 57% (n=13) of cases, CR – in 39.1% (n=9), PR – in 8.7% (n=2). The use of the BvB regimen as 3rd and subsequent lines of therapy led to ORR in 5 (31.3%) patients, CR – in 2 (12.5%), PR – in 2 (12.5%) patients. Statistically significant differences were obtained for the median PFS of patients who received the BvB regimen in the 2nd vs 3rd and subsequent lines of therapy: 6.5 and 2.5 months, respectively (p=0.0304). The BvB combination was characterized by an acceptable safety profile. Conclusion. The use of the BvB regimen in R/R forms of nPTCL demonstrated high efficacy and an optimal safety profile. The obtained results indicate the need to continue the study on a larger group of patients.

Ranked Enumeration for Database Queries

Ranked enumeration is a query-answering paradigm where the query answers are returned incrementally in order of importance (instead of returning all answers at once). Importance is defined by a ranking function that can be specific to the application, but typically involves either a lexicographic order (e.g., "ORDER BY R.A, S.B" in SQL) or a weighted sum of attributes (e.g., "ORDER BY 3*R.A + 2*S.B"). Recent work has introduced any-k algorithms for (multi-way) join queries, which push ranking into joins and avoid materializing intermediate results until necessary. The top-ranked answers are returned asymptotically faster than the common join-then-rank approach of database systems, resulting in orders-of-magnitude speedup in practice.

Towards Integrating Federated Learning with Split Learning via Spatio-temporal Graph Framework for Brain Disease Prediction.

Functional Magnetic Resonance Imaging (fMRI) is used for extracting blood oxygen signals from brain regions to map brain functional connectivity for brain disease prediction. Despite its effectiveness, fMRI has not been widely used: on the one hand, collecting and labeling the data is time-consuming and costly, which limits the amount of valid data collected at a single healthcare site; on the other hand, integrating data from multiple sites is challenging due to data privacy restrictions. To address these issues, we propose a novel, integrated Federated learning and Split learning Spatio-temporal Graph framework (FS2G). Specifically, we introduce federated learning and split learning techniques to split a spatio-temporal model into a client temporal model and a server spatial model. In the client temporal model, we propose a time-aware mechanism to focus on changes in brain functional states and use an InceptionTime model to extract information about changes in the brain states of each subject. In the server spatial model, we propose a united graph convolutional network to integrate multiple graph convolutional networks. Integrating federated learning and split learning, FS2G can utilize multi-site fMRI data without violating data privacy protection and reduce the risk of overfitting as it is capable of learning from limited training data sets. Moreover, it boosts the extraction of spatio-temporal features of fMRI using spatio-temporal graph networks. Experiments on ABIDE and ADHD200 datasets demonstrate that our proposed method outperforms state-of-the-art methods. In addition, we explore biomarkers associated with brain disease prediction using community discovery algorithms using intermediate results of FS2G. The source code is available at https://github.com/yutian0315/FS2G.

A study of the comparative effectiveness of dual antiplatelet therapy and a combination of acetylsalicylic acid with colchicine in patients in the acute period of “minor” ischemic atherothrombotic stroke during hospitalization. Interim analysis of data from the «COLCHIDA» study

Influence on inflammation is currently becoming a priority target for the prevention of recurrent atherothrombotic events, including stroke. In 2022, we planned the KOLCHIDA study, a two-center, prospective, randomized, open-label, controlled clinical trial with endpoint assessment (ClinicalTrials.gov identifier: NCT06102720). The purpose of this study is to conduct a comparative assessment of the clinical effectiveness of dual antiplatelet therapy with acetylsalicylic acid (ASA) and clopidogrel and the combination of ASA with colchicine in patients in the acute period of “minor” ischemic atherothrombotic stroke. The purpose of this publication is to present the basic concept of the protocol, its design and the first intermediate results. Material and methods. The intervention consisted of colchicine 0.5 mg/day in addition to ASA versus dual antiplatelet therapy (DAPT) in patients with non-severe ischemic stroke. 31 patients were included in the colchicine and ASA group, and 34 patients were included in the DAPT group. Results. In the comparison group, 7 (20.6%) recurrent non-fatal strokes were recorded, in contrast to the colchicine group, where acute vascular events were recorded in only two patients (6.5%) [RRR 2.2, NNT = 7, χ2 = 2, 9, p = 0.09]. There were no clinically significant bleeding or other adverse events in both groups. Conclusion. Interim results of the study indicate possible confirmation of the hypothesis that anti-inflammatory therapy with low doses of colchicine in combination with standard therapy is not inferior to the combination of ASA and clopidogrel, and perhaps more effectively reduces the number of early neurological deteriorations and recurrent ischemic strokes in patients in the acute period of mild atherothrombotic stroke with equal security profile.

Forecasting techniques based on the use of time series models for forecasting expenditures on social protection and social security

In the context of a military conflict, it is growing the number of people who are in difficult life circumstances and need state assistance. Therefore, the issue of forecasting spending on social protection and social security is becoming increasingly relevant. The aim of the paper is to consider modern methods of analyzing and forecasting time series and propose their application to determine the need for spending on social protection and social security. The article provides a brief overview of regression models, their advantages and features of use for analyzing and predicting nonlinear and linear processes described by time series of statistical indicators used in the population social protection system. The paper offers a combination of predictions that can be made based on locally linear and nonlinear models, such as threshold autoregression (TAR) and exponential autoregression (ExAR), which have known quality characteristics. This approach primarily concerns time series that contain data on structural changes in the analyzed processes. Using combined predictions, it is possible to estimate the contribution of each component for both different and selected time points using variable weights. To build forecasts, depending on the scenario of distribution of social protection and social security expenditures, it is proposed to use Bayes networks. Their advantages are flexibility, high quality of results, the possibility of structural and parametric optimization and adaptation to new data and conditions, support for quality assessment by appropriate sets of statistical criteria, which makes it possible to analyze thoroughly intermediate and final results. It helps to increase the adequacy of models and the high quality of the final result of their application.

Velocity prediction program for windsurfing: A hierarchical approach integrating biomechanical insights

Windsurfing sails have three degrees of controllable angular freedoms and rely heavily on the sailor’s gravity to balance the heeling moments. The position of the sailor’s centre of gravity that affect the moment generation is greatly influenced by the posture. These factors result in the complexity of predicting windsurfing velocity during steady sailing, which is shown to be an under-defined problem. To address the challenges, this study proposes a hierarchical approach. At the lower levels of the hierarchy, a mechanical model of windsurfing is constructed by incorporating a tailor-made biomechanical model of the sailor, as well as the aero- and hydrodynamic properties of the sail, board, and sailor. The mechanical model allows the investigation of control variables that lead to different steady states, including sail roll, yaw and hydrofoil roll. At a higher level, these variables are optimized to maximize sailing speed. The proposed approach provides intermediate results that explore factors affecting sailing velocity, such as sail and board orientation angles, the sailor’s center of gravity, and wind conditions. Furthermore, the approach generates a velocity polar chart that illustrates the maximum sailing speed for different travel directions. The chart indicates the maximum VMG and optimal sailing angle for achieving an upwind or downwind mark. This study contributes to a deeper understanding of the critical variables influencing windsurfing velocity and provide insights for improving sailing performance.

Study on Seroprevalence and Leptospiral Antibody Distribution Among Livestock Breeders and Farmers in Golestan Province.

The high prevalence of leptospirosis in humans is of great public health concern, particularly in tropical and subtropical regions. This study aimed to determine the seroprevalence of leptospiral antibodies and the distribution of serovars in livestock breeders and farmers in Golestan province. Seventy samples of serum collected from farmers and ranchers suspected of leptospirosis were examined using an ELISA method for surveying Immunoglobulin M (IgM) anti-leptospiria. Also from samples, DNA was extracted and PCR was performed using by primers for 16s rRNA. Demographic properties of positive patients were analysed. Chi-square statistical test shows a statistically significant difference between the gender and prevalence leptospirosis (p-value = 0.004). Also, by examining the age, it was shown that 68.57% of patients are in the middleaged rang. According to the results obtained from the study and investigation of blood serum IgM-and Immunoglobulin G (IgG) in people suspected of leptospirosis, 3 cases of the patients had IgM higher than 11 and were known to be positive for leptospirosis. Also, by examining the IgG level of patients, 5 cases had intermediate results, and 2 cases were found to be positive for IgG. The PCR results showed that 41.42% of patients tested positive for the 16s rRNA. Leptospirosis is a common disease among farmers, and in Golestan province, considering traditional farming methods, it is considered an important infectious disease. Therefore, health and safety measures should be expanded to control and prevent this disease. Also, by employing mechanised agricultural methods, the prevalence of leptospirosis in this region can be significantly reduced.

A machine learning based regression methods to predicting syngas composition for plasma gasification system

Plasma gasification is considered a promising technology that converts waste into energy through an environmentally friendly process. This research focuses on predicting the outputs of this process, utilizing ML regression techniques. Data from previous studies involving different solid fuels were collected, and four regression techniques Random Forest Regression, Gaussian Process Regression, Decision Tree Regression, and Support Vector Regression were employed to predict the levels of CO2, CO, N2, O2, H2, and CH4 in the plasma gasification process. The experimental dataset was gathered using a microwave gasifier with varying air flow rates (50–100 sL/min) and plasma power (3–6 kW). GPR a coefficient of determination (R2) values exceeding 0.983 for all outputs and low NRMSE and MSLE values (<0.082 and <0.00123, respectively). RFR also performed well, with R2 values >0.98 for all outputs except CH4 and CO2. SVR exhibited the least favorable performance, while DTR showed intermediate results. Plasma gasification Machine Learning modeling has proven to enable the prediction of the high-complexity chemical reaction chain in gasification. These models hold promise for applications in simulation environments and can be integrated into microcontroller-based systems for practical use for optimization and control. By this means, the cost of plasma gasification processes would be reduced, and so the plasma gasification system would be more flexible.

Collaborative Governance dalam Pengembangan Agrowisata di Kota Palembang: Studi Kasus Agrowisata Edukasi Sungai Jawi Kecamatan Kalidoni Kota Palembang

The purpose of this research is to determine and analyze the Collaborative Governance Process in Agrotourism Development in Palembang City (Case Study of Sungai Jawi Educational Agrotourism, Sungai Selincah Village, Kalidoni District in Palembang City). The research method used in this research is a qualitative research method. According to Dermadi (2013:153), the research method is a scientific method for obtaining data with several useful purposes. The scientific method refers to research activities that are based on scientific principles, namely rational, experimental, and systematic. Based on the explanation above, it can be concluded that the research method is a scientific method for obtaining data with certain purposes and applications. Based on the results of research regarding the Collaborative Governance Development Process in Palembang City (case study of Sungai Jawi Educational Agrotourism, Kalidoni District), the Collaborative Governance Process in the development of Jawi River Educational Agrotourism has been carried out through the stages of face to face dialogue, trust building. trust), commitment to the process (commitment to the process), and intermediate outcomes (intermediate results achieved) which have been implemented well.

Digital health and quality of care in Primary Health Care: an evaluation model

IntroductionThe implementation of technologies in Primary Health Care with monitoring and evaluation of the quality of health care is fundamental to direct the access and quality of health care in the context of the Sustainable Development Goals. Our objective was to develop a model for evaluating digital health interventions in primary care, considering its impact on first contact, longitudinality, integrality and coordination in health.MethodologyThis is an exploratory methodological study of a qualitative nature. This study seeks to explore strategic actors’ perceptions of an evaluation model, and was developed in a process between June 2021 and March 2024. The following stages were followed: Identification of previous models available in the literature, model development, model validation, model update. We performed a nominal group consensus technique online with seven experts. Stages taken to define the design of the model: sending the elaborated model, together with an electronic form with 18 subjective questions, such as brainstorming strategy, for recording impressions, judgment of agreement and suggestions; workshop for discussion by videoconference, at the time the objectives and the model diagram were presented, followed by debate with clarification of doubts and suggestions for clarification of the step-by-step design. After incorporating the suggestions, the model agreed upon in the workshop was subjected to another analysis by the same experts, sent in an online Google document, in which possible inconsistencies between the proposed model and the agreed one were verified, as well as the final agreement on the recommended proposal. At the end of this stage, with synchronous and asynchronous remote procedures, consensus was reached.ResultsThe proposed evaluation model presents as primary categories, structure, process and result. The structure encompasses four indicators, namely, employees; financial resources; infrastructure resources; and regulatory and strategic resources. The process is organized into three dimensions, namely: technical dimension, organizational dimension and relational dimension. The results will be evaluated in products; intermediate results; and impacts (short, medium and long term). The results will be measured by the seven pillars of quality: efficacy, effectiveness, efficiency, optimization, acceptability, legitimacy, equity.ConclusionThis study achieved the goal of developing a model to evaluate digital health interventions in Primary Health Care, helping to identify adequate and useful evaluation methods filling the gap of the lack of quality evaluation standards in the brazilian Digital Health Strategy. It presents an important difference in relation to models from different countries, as it considers the impacts on Primary Health Care quality attributes such as first contact care, longitudinality, integrality and coordination of care. The model will be used by managers and health professionals of Primary Health Care in a case study in Brazil to evaluate the quality of health care. It is expected that the proposed model may be used in other evaluation studies and countries through contextual adaptations.

Community gardening: metaphorical reconstruction of transformative health governance

Abstract Transdisciplinary working has been defined by the inclusion of non-academic stakeholders in the process of knowledge production. While theoretical, phenomenological and experimental dimensions have been described in the literature, Rigolot proposes transdisciplinarity as a ‘way of being’ that stretches beyond professional life. The promise of transdisciplinarity however does not always meet the expectations in practice. How to get started, maintain and overcome barriers requires guidance. In an action-oriented evaluation research study we explored this dimension in the Dutch All about Health collaborative platform, which since 2014 facilitates a continuous development of bottom up health initiatives, networks, alliances and other forms of collaboration and learning. From our case studies we developed the Transformative Health Governance model. The model shows the ongoing and reciprocal connections between the actions, the context and intermediate results that were verified in a survey among 103 platform participants. Five types of returns are also building blocks that increase the chance of real (yet hardly measurable) increasing health benefits. They reinforce each other in a continuous process of ideation, integrative approach, innovation, implementation, and impact. The context provides for acceleration or deceleration of that process. With the help of a cartoonist we translated our model into an ongoing cycle of community gardening and maintenance. This metaphor makes the intangible of underlying social processes more tangible, providing a bit more grip on the building blocks, timing and time aspects, as well as deeper understanding the role and necessity of conflict and explicit conflict engagement rather than management. Moreover the cartoon elicits recognition, acknowledgement and spirit to continue their rowing against the systems-invoked tide for better health. It serves as a concrete aid to transdisciplinary working towards planetary health.

Prevention of mechanical valve thrombosis with a combination of the factor Xa inhibitor edoxaban plus aspirin in swine: intermediate results from the EdoValves project

Abstract Background Patients with an implanted mechanical heart valve (MHV) currently have a contraindication to the use of non-vitamin K antagonist oral anticoagulants (NOACs) based on the unfavorable results of the RE-ALIGN trial with dabigatran. We hypothesized that the combination of a Factor Xa inhibitor, acting upstream of thrombin generation, plus aspirin, may effectively prevent valve thrombosis, providing a much more friendly and viable alternative to vitamin K antagonists for such patients. We tested this in a swine model with a recently implanted mechanical valve prosthesis in the mitral position, combining the most unfavorable conditions promoting mechanical valve thrombosis, based on (a) the implanting position; (b) recent surgery; (c) the high thrombogenicity of the animal model. The study was approved by the Animal Ethics Committee of the University of Pisa and the Italian Ministry of Health and conducted according to Good Clinical Practices for Animal Experiments. Methods 17 farm male pigs, weighing ≈30 kg at the beginning, underwent cardiac surgery under general anesthesia and with extracorporeal circulation to receive a 25 mm or 27 mm bileaflet Corcym Bicarbon mechanical heart valve replacement in the mitral position. Pigs were given 1 mg/kg enoxaparin immediately after surgery and started with the oral daily administration of edoxaban 1 mg/kg (30 mg, then escalated to 60 mg with the animal growth) + aspirin 100 mg, both given as crushed tablets with a food pellet since the animal awakening from anesthesia. An initially planned warfarin control group was quickly abandoned because of the extreme difficulty of controlling warfarin activity. Animals were submitted to blood sampling and transthoracic echocardiography at least twice in the follow-up when uneventful for up to three months or in case of health deterioration. The autopsy was performed by a certified pathologist after each spontaneous death or procured sacrifice. Results (Figure 1): Excluding 5 peri-operative deaths, 12 animals survived surgery. Of those, 6 died within the second month: pulmonary edema occurred in 3 cases, due to prosthesis-mismatch in the growing animals; pulmonary infection occurred in 1 subject; heart failure due to valve thrombosis occurred in 2 cases. Six swine were full-term survivors in good health status up to the 3-month planned observational time. No hemorrhagic events were ever documented. Conclusion The combination of edoxaban + aspirin effectively avoided valve thrombosis events in 10 out of 12 (83%) analyzable animals and was safe from bleeding complications. The study is ongoing up to a predicted sample size of ≥20 animals. If further confirmed, these data may open the possibility of an alternative to vitamin K antagonists in humans with the fixed-dose drug combination here used, without the need for continuous anticoagulation monitoring and with expected greater safety.Figure 1

Comparative growth, production, carcass characteristics and reproduction performance of Naked Neck, Black Australorp and their crossbred chicken

This study evaluated the phenotypic and genetic parameters of indigenous Naked Neck (NN), exotic Black Australorp (BA), and their crossbred (NNBA) chickens. A total of 270 day-old chicks (30 per group) were raised under identical conditions from day 1 to 48 weeks of age. Growth performance, including body weight, daily and total gain, feed consumption, and feed conversion ratio (FCR), was highest in BA, followed by NNBA, and lowest in NN. Carcass composition also varied significantly, with BA and NNBA having higher dressing percentages (62.98% and 62.68%, respectively) compared to NN (60.60%). BA produced the heaviest eggs (54.0 g), followed by NNBA (50.9 g) and NN (47.93 g). BA also had the best egg quality, hatchability, and FCR (3.15), with NNBA showing intermediate results. NN had the poorest performance in all traits. Age at sexual maturity was earlier in NNBA (161.23 days) compared to NN and BA. The study concluded that NNBA crossbreds outperformed NN in growth, carcass traits, and egg production, indicating their potential for enhancing rural poultry farming.

Using Continuous Online Assessment of Learning Outcomes for Grading: A Case Study

In recent years, the problem of the number of students enrolled in STEM courses falling below the minimum required to carry out the courses has worsened. This situation, which used to occur primarily in degree programs at the end of their life cycle, is increasingly happening in active STEM degree programs as well. The declining attractiveness of traditional STEM degree programs and negative demographic trends are seen as the main causes of this problem. To assist students and their teachers in this situation, a novel approach to grading student work based on the level at which students achieve learning outcomes in an online system was explored. Since the focus was on STEM students, math-based tasks were used to assess student knowledge within the online system. In addition to the accuracy of the final results of the tasks, the accuracy of the intermediate results was also monitored. Each result was linked to the corresponding concepts, which in turn were linked to the corresponding learning outcomes. By entering correct intermediate and final results, the online system was able to monitor student success in mastering concepts and learning outcomes. The implemented online system utilizes fuzzy inference to calculate the levels at which students have achieved the learning outcomes. These levels are continuously recalculated during the semester and presented to the students through generated recommendations after each solved task. The proposed approach has positively impacted students’ motivation to learn during the semester, as confirmed by an anonymous questionnaire. Additionally, the results have shown that the final level at which students have achieved the learning outcomes within the online system, calculated at the end of the semester, can be used for grading and is comparable to the grades students would achieve in traditional midterm and final exams, thus helping teachers with their workload.

Development of the Web Application to Support the Process of Conducting Intellectual Games

The article discusses technologies for developing the web application designed to accompany the process of conducting an intellectual game in the format of an interactive quiz. An analysis of similar applications was carried out, which showed that none of them satisfies all the criteria essential in this situation. Requirements for the web application have been determined: the ability to create a library of quizzes, various types of questions, edit ready-made quizzes, participants enter answers through their devices online, award points for the correctness and speed of answers, display questions on the presenter's screen and on team devices, view intermediate and final results in the form of a list, returning participants to the game if necessary, saving the points received. The process of developing an optimized user interface using HTML, CSS, JavaScript, which allows users to interact with the web application, is described, the structure of the web application, the main forms of the administrator and game participants are presented. The server side of the application was developed using Node.JS technologies, including the Socket.io library, and the MongoDB database, the resulting collection of non-relational database is shown. To implement the functionality of the application pages, JavaScript was used; examples of codes are presented that implement the mechanism for connecting the presenter to the game, retrieving information from the database, recording the team's response, and awarding points for the correctness and speed of the response. The web application allows you to reduce the time spent by event organizers and participants and makes it possible to improve the organization of work in general. The presented web application for accompanying intellectual games was successfully approbated during events at the Nizhny Tagil State Social-Pedagogical Institute (branch) of the Russian State Vocational Pedagogical University. The materials of the article can serve as practical recommendations for the development of such web applications to accompany intellectual games held in the format of interactive quizzes.

A Cascaded ReRAM-based Crossbar Architecture for Transformer Neural Network Acceleration

Emerging resistive random-access memory (ReRAM) based processing-in-memory (PIM) accelerators have been increasingly explored in recent years because they can efficiently perform in-situ matrix-vector multiplication (MVM) operations involved in a wide spectrum of artificial neural networks. However, there remain significant challenges to apply existing ReRAM-based PIM accelerators to the most popular Transformer neural networks. Since Transformers involve a series of matrix-matrix multiplication (MatMul) operations with data dependencies, they should write intermediate results of MatMuls to ReRAM crossbar arrays for further processing. Conventional ReRAM-based PIM accelerators often suffer from high latency of ReRAM writes and intra-layer pipeline stalls. In this paper, we propose ReCAT, a ReRAM-based PIM accelerator designed particularly for Transformers. ReCAT exploits transimpedance amplifiers (TIAs) to cascade a pair of crossbar arrays for MatMul operations involved in the self-attention mechanism. The intermediate result of a MatMul generated by one crossbar array can be directly mapped to another crossbar array, avoiding costly analog-to-digital conversions. In this way, ReCAT allows MVM operations to overlap with the corresponding data mapping, hiding the high latency of ReRAM writes. Furthermore, we propose an analog-to-digital converter (ADC) virtualization scheme to dynamically share scarce ADCs among a group of crossbar arrays, and thus significantly improve the utilization of ADCs to eliminate the performance bottleneck of MVM operations. Experimental results show that ReCAT achieves 207.3 ×, 2.11 ×, and 3.06 × performance improvement on average compared with other Transformer acceleration solutions—GPUs, ReBert, and ReTransformer, respectively.

Multi task deep learning phase unwrapping method based on semantic segmentation

Abstract Phase unwrapping is a key step to obtain continuous phase distribution in optical phase measurement. When the wrapped phase obtained from the interference pattern is unclear and noisy, estimating the unwrapped phase becomes more challenging. As deep learning advances in optical image processing, it will enhance processing efficiency and accuracy, bringing broader possibilities for various applications. This paper introduces an innovative phase unwrapping method based on multi-task learning, aiming to simultaneously enhancing denoised images and predicting wrap count. The proposed network, named ICER-Net, comprises an encoder and two decoders, transforming the input low-luminance, noisy wrapped phase into two intermediate outputs: enhanced wrapped phase and wrap count. Finally, these two intermediate results are fused to obtain the unwrapped phase. Experimental results demonstrate that ICER-Net not only enhances the accuracy of phase unwrapping, particularly when facing challenges of various noise levels and luminance sizes but also exhibits outstanding performance in actual collected speckle phase images. This indicates that ICER-Net holds significant superiority in addressing complex issues in optical image processing.

Remote Sensing and Field Data Analysis to Evaluate the Impact of Stone Bunds on Rainfed Agriculture in West Africa

This study evaluates the effectiveness of stone bunds in enhancing soil moisture, vegetation health, and crop yields in Ghana’s semi-arid Upper East Region, an important area for agricultural productivity in West Africa. In this region, agricultural practices are heavily impacted by erratic rainfall and poor soil moisture retention, threatening food security. Despite the known benefits of traditional soil conservation practices like stone bunds, their effectiveness in this context has not been fully quantified. Field and remote sensing data were used to evaluate the influence of stone bunds on soil moisture dynamics, vegetation growth, and crop yield. Experimental plots with and without stone bunds were monitored for climate, soil water infiltration, and soil moisture and analyzed using the NDVI from Sentinel-2 satellite imagery over two growing seasons under sorghum production (2022–2023). The results indicated that stone bunds enhanced soil moisture retention and increased infiltration rates. The NDVI analysis consistently revealed higher vegetation health and growth in the plots with stone bunds, particularly during critical growth periods. The intermediate results of the conducted experiment indicated that stone bunds increased sorghum yields by over 35% compared to the control plots. The substantial agronomic benefits of stone bunds as a soil and water conservation strategy were evident, improving soil water infiltration, water retention, vegetation health, and crop yields. The findings support the broader adoption of stone bunds in semi-arid regions to enhance agricultural productivity and resilience against climate variability. Further research is recommended to explore the long-term impacts and the integration of stone bunds with other sustainable farming practices to optimize rainfed agricultural outcomes.

The High Estradiol Environment after IVF Causes the Increased Risk of Glucose Metabolic Dysfunction in Offspring.

Assisted reproductive technology (ART) is associated with increased metabolic risks in offspring. The effect of high maternal estradiol (E2) levels during early pregnancy on the glucose metabolism of offspring remains unclear. To evaluate glucose metabolism in in vitro fertilization (IVF)-conceived children and assess whether high E2 exposure during early pregnancy is associated with metabolic alterations. This retrospective analysis included 500 singletons aged 3-10 years born after fresh embryo transfer (ET) (n=200), frozen ET (n=100), and natural conception (NC) (n=200) from a university hospital. Children underwent anthropometric measurements and examinations for fasting glucose, insulin, and lipid levels. A mouse model of high E2 exposure during early pregnancy was established to study glucose and insulin tolerance, and insulin secretion. Compared with NC, children born after fresh ET showed higher fasting glucose/insulin levels, increased insulin resistance, and higher incidence of impaired fasting glucose, which might be associated with a higher maternal E2 levels. Frozen ET showed intermediate results. In mice, offspring exposed to high E2 levels during gestation exhibited impaired glucose/insulin tolerance and defects in insulin secretion. High maternal E2 levels in early pregnancy are associated with altered glucose metabolism and increased metabolic risks in IVF-conceived children. Further studies are needed to elucidate the underlying mechanisms.

Smart-KG: Partition-Based Linked Data Fragments for querying knowledge graphs

RDF and SPARQL provide a uniform way to publish and query billions of triples in open knowledge graphs (KGs) on the Web. Yet, provisioning of a fast, reliable, and responsive live querying solution for open KGs is still hardly possible through SPARQL endpoints alone: while such endpoints provide a remarkable performance for single queries, they typically can not cope with highly concurrent query workloads by multiple clients. To mitigate this, the Linked Data Fragments (LDF) framework sparked the design of different alternative low-cost interfaces such as Triple Pattern Fragments (TPF), that partially offload the query processing workload to the client side. On the downside, such interfaces still come with the expense of unnecessarily high network load due to the necessary transfer of intermediate results to the client, leading to query performance degradation compared with endpoints. To address this problem, in the present work, we investigate alternative interfaces, refining and extending the original TPF idea, which also aims at reducing server-resource consumption, by shipping query-relevant partitions of KGs from the server to the client. To this end, first, we align formal definitions and notations of the original LDF framework to uniformly present existing LDF implements and such “partition-based” LDF approaches. These novel LDF interfaces retrieve, instead of the exact triples matching a particular query pattern, a subset of pre-materialized, compressed, partitions of the original graph, containing all answers to a query pattern, to be further evaluated on the client side. As a concrete representative of partition-based LDF, we present smart-KG+, extending and refining our prior work (In WWW ’20: The Web Conference 2020 (2020) 984–994 ACM / IW3C2) in several respects. Our proposed approach is a step forward towards a better-balanced share of the query processing load between clients and servers by shipping graph partitions driven by the structure of RDF graphs to group entities described with the same sets of properties and classes, resulting in significant data transfer reduction. Our experiments demonstrate that the smart-KG+ significantly outperforms existing Web SPARQL interfaces on both pre-existing benchmarks for highly concurrent query execution as well as an accustomed query workload inspired by query logs of existing SPARQL endpoints.