bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effectiveness of a breastfeeding promotion intervention model based on Society ecosystems Theory for maternal women: a study protocol of randomized controlled trial
Authors: Ouyang Yan-Qiong , Zhou Jie , Guo Jin-Yi , Wang Shi-Yun , Wang Xin , Zhou-Chen Yi-Bei , Redding Sharon R. , Chen Hui-Jun 
Published: December 2023

Abstract
Background
Breastfeeding is recognized internationally as the most scientific and effective way to feed infants and young children. According to the World Health Organization in 2022, the exclusive breastfeeding rate within 6 months is 34.1% in China, which is still far from the goal of “more than 60% exclusive breastfeeding rate of infants within 6 months” by 2030 required by China’s State Council. It is necessary to promote breastfeeding and provide maternal breastfeeding guidance to increase exclusive breastfeeding. Factors influencing breastfeeding can be explained by the society ecosystems theory, distributed in macro, mezzo and micro systems. The interventions focused on breastfeeding promotion are mainly carried out in the health systems and services, home and family environment, community environment, work environment, policy environment or a combination of these facilities. But there is sparse research on integrating resources in the macro, mezzo and micro systems of maternal breastfeeding processes to promote breastfeeding behavior. A randomized controlled trial will test the effect of a breastfeeding promotion intervention model based on the society ecosystems theory versus usual prenatal and postnatal care on maternal and infant health and the exclusive breastfeeding rate at 6 months.

Methods/design
The study is a single-blind, parallel design, randomized controlled trial with an intervention group (n = 109) and a control group (n = 109) that compares the effect of a breastfeeding promotion intervention model based on the society ecosystems theory with usual prenatal and postnatal care. The intervention covers macro- (policy, culture), mezzo- (family-hospital-community) and micro- (biological, psychological and social) systems of the maternal breastfeeding process. Infant feeding patterns, neonatal morbidity and physical and mental health of antenatal and postpartum women will be collected at baseline (28 to 35 weeks of gestation), 1-, 4-, and 6-month postpartum.

Discussion
This is a multifaceted, multifactorial, and multi-environmental breastfeeding promotion strategy to help mothers and their families learn breastfeeding knowledge and skills. The study provides a new modality for adding breastfeeding interventions to prenatal and postnatal care for healthcare providers in the hospital and the community.
Trial registration: Chinese Clinical Trial Registry at www.chictr.org.cn, ChiCTR2300075795.
Using fuzzy logic to compare species distribution models developed on the basis of expert knowledge and sampling records
Authors: Romero David , Maneyro Raúl , Guerrero José Carlos , Real Raimundo 
Published: December 2023

Abstract
Background
Experts use knowledge to infer the distribution of species based on fuzzy logical assumptions about the relationship between species and the environment. Thus, expert knowledge is amenable to fuzzy logic modelling, which give to propositions a continuous truth value between 0 and 1. In species distribution modelling, fuzzy logic may also be used to model, from a number of records, the degree to which conditions are favourable to the occurrence of a species. Therefore, fuzzy logic operations can be used to compare and combine models based on expert knowledge and species records. Here, we applied fuzzy logic modelling to the distribution of amphibians in Uruguay as inferred from expert knowledge and from observed records to infer favourable locations, with favourability being the commensurable unit for both kinds of data sources. We compared the results for threatened species, species considered by experts to be ubiquitous, and non-threatened, non-ubiquitous species. We calculated the fuzzy intersection of models based on both knowledge sources to obtain a unified prediction of favourable locations.

Results
Models based on expert knowledge involved a larger number of variables and were less affected by sampling bias. Models based on experts had the same overprediction rate for the three types of species, whereas models based on species records had a lower prediction rate for ubiquitous species. Models based on expert knowledge performed equally as well or better than corresponding models based on species records for threatened species, even when they had to discriminate and classify the same set of records used to build the models based on species records. For threatened species, expert models predicted more restrictive favourable territories than those predicted based on records. Observed records generated the best-fitted models for non-threatened non-ubiquitous species, and ubiquitous species.

Conclusions
Fuzzy modelling permitted the objective comparison of the potential of expert knowledge and incomplete distribution records to infer the territories favourable for different species. Distribution of threatened species was able to be better explained by subjective expert knowledge, while for generalist species models based on observed data were more accurate. These results have implications for the correct use of expert knowledge in conservation planning.
Pre-trained protein language model sheds new light on the prediction of Arabidopsis protein–protein interactions
Authors: Zhou Kewei , Lei Chenping , Zheng Jingyan , Huang Yan , Zhang Ziding 
Published: December 2023

AbstractBackgroundProtein–protein interactions (PPIs) are heavily involved in many biological processes. Consequently, the identification of PPIs in the model plant Arabidopsis is of great significance to deeply understand plant growth and development, and then to promote the basic research of crop improvement. Although many experimental Arabidopsis PPIs have been determined currently, the known interactomic data of Arabidopsis is far from complete. In this context, developing effective machine learning models from existing PPI data to predict unknown Arabidopsis PPIs conveniently and rapidly is still urgently needed.ResultsWe used a large-scale pre-trained protein language model (pLM) called ESM-1b to convert protein sequences into high-dimensional vectors and then used them as the input of multilayer perceptron (MLP). To avoid the performance overestimation frequently occurring in PPI prediction, we employed stringent datasets to train and evaluate the predictive model. The results showed that the combination of ESM-1b and MLP (i.e., ESMAraPPI) achieved more accurate performance than the predictive models inferred from other pLMs or baseline sequence encoding schemes. In particular, the proposed ESMAraPPI yielded an AUPR value of 0.810 when tested on an independent test set where both proteins in each protein pair are unseen in the training dataset, suggesting its strong generalization and extrapolating ability. Moreover, the proposed ESMAraPPI model performed better than several state-of-the-art generic or plant-specific PPI predictors.ConclusionProtein sequence embeddings from the pre-trained model ESM-1b contain rich protein semantic information. By combining with the MLP algorithm, ESM-1b revealed excellent performance in predicting Arabidopsis PPIs. We anticipate that the proposed predictive model (ESMAraPPI) can serve as a very competitive tool to accelerate the identification of Arabidopsis interactome.
Exosomes from bone marrow mesenchymal stem cells ameliorate glucocorticoid-induced osteonecrosis of femoral head by transferring microRNA-210 into bone microvascular endothelial cells
Authors: Zheng Che , Wu Yuangang , Xu Jiawen , Liu Yuan , Ma Jun 
Published: December 2023

AbstractObjectivesBone microvascular endothelial cells (BMECs) played an important role in the pathogenesis of glucocorticoid-induced osteonecrosis of femoral head (GCS-ONFH), and exosomes derived from bone marrow mesenchymal stem cells (BMSC-Exos) may provide an effective treatment. This study aimed to evaluate the effects of BMSC-Exos and internal microRNA-210-3p (miRNA-210) on GCS-ONFH in an in vitro hydrocortisone-induced BMECs injury model and an in vivo rat GCS-ONFH model.MethodsBMECs, BMSCs and BMSC-Exos were isolated and validated. BMECs after the treatment of hydrocortisone were cocultured with different concentrations of BMSC-Exos, then proliferation, migration, apoptosis and angiogenesis of BMECs were evaluated by CCK-8, Annexin V-FITC/PI, cell scratch and tube formation assays. BMSCs were transfected with miRNA-210 mimics and miRNA-210 inhibitors, then BMSC-ExosmiRNA−210 mimicand BMSC-ExosmiRNA−210 inhibitorsecreted from such cells were collected. The differences between BMSC-Exos, BMSC-ExosmiRNA−210 mimicand BMSC-ExosmiRNA−210 inhibitorin protecting BMECs against GCS treatment were analyzed by methods mentioned above. Intramuscular injections of methylprednisolone were performed on Sprague–Dawley rats to establish an animal model of GCS-ONFH, then tail intravenous injections of BMSC-Exos, BMSC-ExosmiRNA−210 mimicor BMSC-ExosmiRNA−210 inhibitorwere conducted after methylprednisolone injection. Histological and immunofluorescence staining and micro-CT were performed to evaluate the effects of BMSC-Exos and internal miRNA-210 on the in vivo GCS-ONFH model.ResultsDifferent concentrations of BMSC-Exos, especially high concentration of BMSC-Exos, could enhance the proliferation, migration and angiogenesis ability and reduce the apoptosis rates of BMECs treated with GCS. Compared with BMSC-Exos, BMSC-ExosmiRNA−210 mimiccould further enhance the proliferation, migration and angiogenesis ability and reduce the apoptosis rates of BMECs, while BMECs in the GCS + BMSC-ExosmiRNA−210 inhibitorgroup showed reduced proliferation, migration and angiogenesis ability and higher apoptosis rates. In the rat GCS-ONFH model, BMSC-Exos, especially BMSC-ExosmiRNA−210 mimic, could increase microvascular density and enhance bone remodeling of femoral heads.ConclusionsBMSC-Exos containing miRNA-210 could serve as potential therapeutics for protecting BMECs and ameliorating the progression of GCS-ONFH.
Polygenic risk scores for cervical HPV infection, neoplasia and cancer show potential for personalised screening: comparison of two methods
Authors: Tisler Anna , Uusküla Anneli , Ojavee Sven Erik , Läll Kristi ,   Estonian Biobank research team, Metspalu Andres , Milani Lili , Esko Tõnu , Mägi Reedik , Nelis Mari , Hudjashov Georgi , Laisk Triin 
Published: December 2023

AbstractThe era of precision medicine requires the achievement of accurate risk assessment. Polygenic risk scores (PRSs) have strong potential for increasing the benefits of nationwide cancer screening programs. The current pool of evidence on the role of a PRS as a risk stratification model in actual practice and implementation is limited. To better understand the impact of possible method-induced variance, we constructed and validated two PRSs for cervical cancer (CC) using the Estonian Biobank female population (691 CC cases and 13,820 controls) and evaluated their utility in predicting incident cervical neoplasia (CIN), cancer, and human papillomavirus (HPV) infection using two methods (LDPred and BayesRR-RC). This study demonstrated that two genetic risk scores were significantly associated with CIN, CC, and HPV infection incidence. Independent of the method, we demonstrated that women with elevated PRS values reached the observed cumulative risk levels of CIN or CC much earlier. Our results indicated that the PRS-based discrimination rules could differ substantially when the PRSs contain similar predictive information. In summary, our analysis indicated that PRSs represent a personalized genetic component that could be an additional tool for cervical cancer risk stratification, and earlier detection of abnormalities provides invaluable information for those at high risk.
Patterns of surgical complications after delayed fixation of peripartum pubic symphysis rupture: a report of 5 cases
Authors: Doroszewski Grzegorz , Wasielewski Jan , Bartosz Paweł , Caban Adam , Scholz Anna , Białecki Jerzy 
Published: December 2023

Abstract
Background
The disruption of the pubic symphysis during the peripartum period is a rare injury to the pelvic ring. In most cases, conservative treatment is successful. Nonetheless, there are cases where surgical intervention is necessary. We analyzed five surgical cases treated in our department and performed a literature review.

Case presentations
Five women, ranging in age from 25 to 38, who experienced peripartum symphysis rupture were primarily treated with a conservative approach. Patients who did not show improvement and met certain criteria, such as experiencing pain starting from childbirth, having a separation in the pubic bone of more than 10 mm, and/or having a vertical instability greater than 5 mm, were recommended to undergo surgery. The average length of time between childbirth and surgery was 5.6 months, ranging from 1 to 14 months. One patient was treated with an external fixator, another patient received a combination of an external fixator and an anterior plate, and three patients were treated with anterior plates. In four cases, we observed a failure in fixation and a partial or complete loss of reduction. The plate and screws were removed in one case, and in three cases, revision surgery was performed. One case involved using a larger plate, while the other used 90-90 plating, known as “box plate fixation.” The mean follow-up was 7.4 years. Two cases had good results, and two had excellent results on the Lindahl scale.

Conclusion
For patients with peripartum pubic symphyseal dislocation, our case series and literature review demonstrated that early reduction and fixation correlate with improved clinical outcomes and lower implant failure. For patients with subacute/chronic injuries, there was a higher incidence of implant failure. Orthogonal plate fixation and/or pubic symphysiodesis was associated with improved clinical outcomes.
Pathological progress and remission strategies of osteoarthritic lesions caused by long-term joint immobilization
Authors: Gan Donghao , Jin Xiaowan , Wang Xiangpeng , Tao Chu , Yan Qinnan , Jia Qingyun , Huo Shaochuan , Chen Di , Yao Qing , Xiao Guozhi 
Published: December 2023

Abstract
Objective
While joint immobilization is a useful repair method for intra-articular ligament injury and periarticular fracture, prolonged joint immobilization can cause multiple complications. A better understanding how joint immobilization and remobilization impact joint function and homeostasis will help clinicians develop novel strategies to reduce complications.

Design
We first determined the effects of long-term immobilization on joint pain and osteophyte formation in patients after an extraarticular fracture or ligament injury. We then developed a mouse model of joint immobilization and harvested the knee joint samples at 2, 4, and 8 weeks. We further determined the effects of remobilization on recovery of the osteoarthritis (OA) lesions induced by immobilization in mice.

Results
We found that the long-term (6 weeks) joint immobilization caused significant joint pain and osteophytes in patients. In mice, 2-week immobilization already induced moderate sensory innervation and increased pain sensitivity and infiltration in synovium without inducing marked osteophyte formation and cartilage loss. Long-term immobilization (4 and 8 weeks) induced more severe sensory innervation and inflammatory infiltration in synovium, massive osteophyte formation on both sides of the femoral condyle, and the edge of the tibial plateau and significant loss of the articular cartilage in mice. Remobilization, which ameliorates normal joint load and activity, restored to certain extent some of the OA lesions and joint function in mice.

Conclusions
Joint immobilization caused multiple OA-like lesions in both mice and humans. Joint immobilization induced progressive sensory innervation, synovitis, osteophyte formation, and cartilage loss in mice, which can be partially ameliorated by remobilization.
The sensitivity of mTORC1 signaling activation renders tissue regenerative capacity
Authors: Dou Hanyu , Li Jianzhou , Huang Taomin , Ding Xiaolei 
Published: December 2023

AbstractA better understanding of how and why the regenerative capacity differs among species will not only provide insights into the regeneration process but also hold value for the development of regenerative medicine and the improvement of healing procedures. In a recent Nature article, Zhulyn et al. identify a critical role played by the activation of mechanistic target of rapamycin complex 1 (mTORC1) signaling in enhancing tissue regenerative capacity in animals.
Inter-rater reliability of risk of bias tools for non-randomized studies
Authors: Kalaycioglu Isabel , Rioux Bastien , Briard Joel Neves , Nehme Ahmad , Touma Lahoud , Dansereau Bénédicte , Veilleux-Carpentier Ariane , Keezer Mark R. 
Published: December 2023

Abstract
Purpose
There is limited knowledge on the reliability of risk of bias (ROB) tools for assessing internal validity in systematic reviews of exposure and frequency studies. We aimed to identify and then compare the inter-rater reliability (IRR) of six commonly used tools for frequency (Loney scale, Gyorkos checklist, American Academy of Neurology [AAN] tool) and exposure (Newcastle–Ottawa scale, SIGN50 checklist, AAN tool) studies.

Methods
Six raters independently assessed the ROB of 30 frequency and 30 exposure studies using the three respective ROB tools. Articles were rated as low, intermediate, or high ROB. We calculated an intraclass correlation coefficient (ICC) for each tool and category of ROB tool. We compared the IRR between ROB tools and tool type by inspection of overlapping ICC 95% CIs and by comparing their coefficients after transformation to Fisher’s Z values. We assessed the criterion validity of the AAN ROB tools by calculating an ICC for each rater in comparison with the original ratings from the AAN.

Results
All individual ROB tools had an IRR in the substantial range or higher (ICC point estimates between 0.61 and 0.80). The IRR was almost perfect (ICC point estimate > 0.80) for the AAN frequency tool and the SIGN50 checklist. All tools were comparable in IRR, except for the AAN frequency tool which had a significantly higher ICC than the Gyorkos checklist (p = 0.021) and trended towards a higher ICC when compared to the Loney scale (p = 0.085). When examined by category of ROB tool, scales, and checklists had a substantial IRR, whereas the AAN tools had an almost perfect IRR. For the criterion validity of the AAN ROB tools, the average agreement between our raters and the original AAN ratings was moderate.

Conclusion
All tools had substantial IRRs except for the AAN frequency tool and the SIGN50 checklist, which both had an almost perfect IRR. The AAN ROB tools were the only category of ROB tools to demonstrate an almost perfect IRR. This category of ROB tools had fewer and simpler criteria. Overall, parsimonious tools with clear instructions, such as those from the AAN, may provide more reliable ROB assessments.
PDLB: Path Diversity-aware Load Balancing with adaptive granularity in data center networks
Authors: Gao Weimin , Huang Jiawei , Li Zhaoyi , Zou Shaojun , Wang Jianxin 
Published: December 2023

AbstractModern data center topologies often take the form of a multi-rooted tree with rich parallel paths to provide high bandwidth. However, various path diversities caused by traffic dynamics, link failures and heterogeneous switching equipment widely exist in the production datacenter network. Therefore, the multi-path load balancer in data center should be robust to these diversities. Although prior fine-grained schemes such as RPS and Presto make the best use of available paths, However, they are prone to experiencing packet reordering problem under the asymmetric topology. The coarse-grained solutions such as ECMP and LetFlow effectively avoid packet reordering, but easily lead to under-utilization of multiple paths. To cope with these inefficiencies, we propose a load balancing mechanism called PDLB, which adaptively adjusts flowcell granularity according to path diversity. PDLB increases flowcell granularity to alleviate packet reordering under large degrees of topology asymmetry, while reducing flowcell granularity to obtain high link utilization under small degrees of topology asymmetry. PDLB is only deployed on the sender without any modification on switch. We evaluate PDLB through large-scale NS2 simulations. The experimental results show that PDLB reduces the average flow completion time by up to $$\sim$$
∼
8-53% over the state-of-the-art load balancing schemes.
Reinforcement learning based task scheduling for environmentally sustainable federated cloud computing
Authors: Wang Zhibao , Chen Shuaijun , Bai Lu , Gao Juntao , Tao Jinhua , Bond Raymond R. , Mulvenna Maurice D. 
Published: December 2023

AbstractThe significant energy consumption within data centers is an essential contributor to global energy consumption and carbon emissions. Therefore, reducing energy consumption and carbon emissions in data centers plays a crucial role in sustainable development. Traditional cloud computing has reached a bottleneck, primarily due to high energy consumption. The emerging federated cloud approach can reduce the energy consumption and carbon emissions of cloud data centers by leveraging the geographical differences of multiple cloud data centers in a federated cloud. In this paper, we propose Eco-friendly Reinforcement Learning in Federated Cloud (ERLFC), a framework that uses reinforcement learning for task scheduling in a federated cloud environment. ERLFC aims to intelligently consider the state of each data center and effectively harness the variations in energy and carbon emission ratios across geographically distributed cloud data centers in the federated cloud. We build ERLFC using Actor-Critic algorithm, which select the appropriate data center to assign a task based on various factors such as energy consumption, cooling method, waiting time of the task, energy type, emission ratio, and total energy consumption of the current cloud data center and the details of the next task. To demonstrate the effectiveness of ERLFC, we conducted simulations based on real-world task execution data, and the results show that ERLFC can effectively reduce energy consumption and emissions during task execution. In comparison to Round Robin, Random, SO, and GJO algorithms, ERLFC achieves respective reductions of 1.09, 1.08, 1.21, and 1.26 times in terms of energy saving and emission reduction.