bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cerium oxide-based nanozyme suppresses kidney calcium oxalate crystal depositions via reversing hyperoxaluria-induced oxidative stress damage
Authors: Deng Jiwang , Yu Bangxian , Chang Zhenglin , Wu Sicheng , Li Guanlin , Chen Wenzhe , Li Shujue , Duan Xiaolu , Wu Wenqi , Sun Xinyuan , Zeng Guohua , Liu Hongxing 
Published: December 2022

AbstractOxidative stress damage to renal epithelial cells is the main pathological factor of calcium oxalate calculi formation. The development of medicine that could alleviate oxidative damage has become the key to the prevention and treatment of urolithiasis. Herein, porous nanorods CeO2 nanoparticles (CNPs) were selected from CeO2 with different morphologies as an antioxidant reagent to suppress kidney calcium oxalate crystal depositions with excellent oxidation resistance due to its larger specific surface area. The reversible transformation from Ce3+ to Ce4+ could catalyze the decomposition of excess free radicals and act as a biological antioxidant enzyme basing on its strong ability to scavenge free radicals. The protection capability of CNPS against oxalate-induced damage and the effect of CNPS on calcium oxalate crystallization were studied. CNPS could effectively reduce reactive oxygen species production, restore mitochondrial membrane potential polarity, recover cell cycle progression, reduce cell death, and inhibit the formation of calcium oxalate crystals on the cell surface in vitro. The results of high-throughput sequencing of mRNA showed that CNPs could protect renal epithelial cells from oxidative stress damage caused by high oxalate by suppressing the expression gene of cell surface adhesion proteins. In addition, CNPS can significantly reduce the pathological damage of renal tubules and inhibit the deposition of calcium oxalate crystals in rat kidneys while having no significant side effect on other organs and physiological indicators in vivo. Our results provide a new strategy for CNPS as a potential for clinical prevention of crystalline kidney injury and crystal deposition.
PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time
Authors: Daviet Benoit , Fernandez Romain , Cabrera-Bosquet Llorenç , Pradal Christophe , Fournier Christian 
Published: December 2022

Abstract
Background
High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs.

Results
We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10–355 plants.

Conclusions
We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.
A green and efficient synthetic methodology towards the synthesis of 1-allyl-6-chloro-4-oxo-1,4-dihydroquinoline-3-carboxamide derivatives
Authors: Gill Muhammad Shoaib Ali , Azzman Nursyuhada , Hassan Sharifah Syed , Shah Syed Adnan Ali , Ahemad Nafees 
Published: December 2022

AbstractQuinolone is a privileged scaffold in medicinal chemistry and 4-Quinolone-3-Carboxamides have been reported to harbor vast therapeutic potential. However, conversion of N-1 substituted 4-Quinolone 3-Carboxylate to its corresponding carbamates is highly restrictive. This motivated us to adopt a much simpler, scalable and efficient methodology for the synthesis of highly pure N-1 substituted 4- Quinolone-3-Carboxamides with excellent yields. Our adopted methodology not only provides a robust pathway for the convenient synthesis of N-1 substituted 4- Quinolone-3-Carboxamides which can then be explored for their therapeutic potential, this may also be adaptable for the derivatization of other such less reactive carboxylate species.
Task offloading in hybrid-decision-based multi-cloud computing network: a cooperative multi-agent deep reinforcement learning
Authors: Chen Juan , Chen Peng , Niu Xianhua , Wu Zongling , Xiong Ling , Shi Canghong 
Published: December 2022

AbstractMulti-cloud computing is becoming a promising paradigm to provide abundant computation resources for Internet-of-Things (IoT) devices. For a multi-device multi-cloud network, the real-time computing requirements, frequently varied wireless channel gains and changeable network scale, make the system more dynamic. It is critical to satisfy the dynamic nature of network with different constraints of IoT devices in multi-cloud environment. In this paper, we establish a continuous-discrete hybrid decision offloading model, each device should learn to make coordinated actions, including cloud server selection, offloading ratio and local computation capacity. Therefore, both continuous-discrete hybrid decision and coordination among IoT devices are challenging. To this end, we first develop a probabilistic method to relax the discrete action (e.g. cloud server selection) to a continuous set. Then, by leveraging a centralized training and distributed execution strategy, we design a cooperative multi-agent deep reinforcement learning (CMADRL) based framework to minimize the total system cost in terms of the energy consumption of IoT device and the renting charge of cloud servers. Each IoT device acts as an agent, which not only learns efficient decentralized policies, but also relieves IoT devices’ computing pressure. Experimental results demonstrate that the proposed CMADRL could efficiently learn dynamic offloading polices at each IoT device, and significantly outperform the four state-of-the-art DRL based agents and two heuristic algorithms with lower system cost.
No “carry-over” effects of tracking devices on return rate and parameters determining reproductive success in once and repeatedly tagged common swifts (Apus apus), a long-distance migratory bird
Authors: Wellbrock Arndt H. J. , Witte Klaudia 
Published: December 2022

AbstractBackgroundTo understand life-history strategies in migratory bird species, we should focus on migration behaviour and possible carry-over effects on both population and individual level. Tracking devices are useful tools to directly investigate migration behaviour. With increased use of tracking devices, questions arise towards animal welfare and possible negative effects of logger on birds. Several studies were conducted to address this question in birds that were tagged and tracked for one complete non-breeding season including migration but with mixed results. To detect individual-based decisions regarding migration strategy, we need to track the same individuals several times. So far, there are no studies investigating effects of repeatedly tagging on reproduction and life-history traits in individual migratory birds, especially in small birds.MethodsWe used long-term data of 85 tagged common swifts (Apus apus), a long-distance migratory bird, of a breeding colony in Germany to test whether carrying a geolocator or GPS logger once or repeatedly during non-breeding season affected return rate, apparent survival, and parameters determining reproductive success. Additionally, we checked for individual differences in arrival date and breeding parameters when the same individuals were tagged and when they were not tagged in different years. Further, we calculated the individual repeatability in arrival at the breeding colony and date of egg laying in repeatedly tagged swifts.ResultsOnce and repeatedly tagged birds returned to the colony at a similar rate as non-logger birds and arrived earlier than non-logger birds. We found no effect of logger-type on return rate in logger birds. We detected no differences in apparent survival, time lag to clutch initiation, date of clutch initiation, clutch size, number of chicks and fledglings between logger and non-logger birds. We found neither an effect of loggers nor of logger-types on the arrival date and breeding parameter on individual-level. Arrival date was highly repeatable and date of clutch initiation was moderately repeatable within repeatedly tagged individuals.
Inferring the temporal evolution of synaptic weights from dynamic functional connectivity
Authors: Celotto Marco , Lemke Stefan , Panzeri Stefano 
Published: December 2022

AbstractHow to capture the temporal evolution of synaptic weights from measures of dynamic functional connectivity between the activity of different simultaneously recorded neurons is an important and open problem in systems neuroscience. Here, we report methodological progress to address this issue. We first simulated recurrent neural network models of spiking neurons with spike timing-dependent plasticity mechanisms that generate time-varying synaptic and functional coupling. We then used these simulations to test analytical approaches that infer fixed and time-varying properties of synaptic connectivity from directed functional connectivity measures, such as cross-covariance and transfer entropy. We found that, while both cross-covariance and transfer entropy provide robust estimates of which synapses are present in the network and their communication delays, dynamic functional connectivity measured via cross-covariance better captures the evolution of synaptic weights over time. We also established how measures of information transmission delays from static functional connectivity computed over long recording periods (i.e., several hours) can improve shorter time-scale estimates of the temporal evolution of synaptic weights from dynamic functional connectivity. These results provide useful information about how to accurately estimate the temporal variation of synaptic strength from spiking activity measures.
A calcitonin-producing pancreatic neuroendocrine neoplasm treated with distal pancreatectomy a lengthy time after a left trisectionectomy for liver metastases: a case report
Authors: Yamamoto Ryusei , Yamaguchi Ryuzo , Yoshida Katsushi , Ando Masataka , Toyoda Yoshitaka , Tanaka Aya , Kato Kenji 
Published: December 2022

Abstract
Background
Calcitonin-producing pancreatic neuroendocrine neoplasms (PanNENs) are extremely rare. There have been no reports of a patient in whom liver metastases were the presenting finding, and a calcitonin-producing PanNEN was subsequently detected after a lengthy period.

Case presentation
A 53-year-old man had diarrhea for several years. Computed tomography (CT) revealed multiple liver tumors. We performed a left trisectionectomy with a bile duct resection. The histologic examination showed neuroendocrine tumors G1. Immunohistochemistry was positive for calcitonin and the serum calcitonin level was elevated. Neuroendocrine neoplasms of hepatic origin are extremely rare, so a systemic exploration was performed, but no tumor was detected. CT showed a 4-mm calcification in the pancreatic body, but no contrast-enhanced mass was noted. Although the liver tumors were resected, the diarrhea and high serum calcitonin level persisted. Serial examinations were performed for 6 years, but no tumor was identified; however, 6.5 years after the hepatectomy the serum calcitonin level increased. CT showed a 10-mm contrast-enhanced mass in the calcified area of the pancreatic body. A distal pancreatectomy was performed. The histologic examination showed a neuroendocrine tumor G1, which mimicked the liver tumors. Immunohistochemistry was positive for calcitonin. After the distal pancreatectomy, the serum calcitonin level decreased and diarrhea resolved. The calcitonin-producing neuroendocrine neoplasm was considered the pancreatic primary and the hepatic tumors were metastases.

Conclusions
Calcitonin-producing PanNENs may be initially recognized as liver tumors and may become evident after a lengthy period, thus long-term observation is recommended. Aggressive surgeries may contribute to long-term survival.
Investigating the impact of explicit and implicit instruction on the use of interactional metadiscourse markers
Authors: El-Dakhs Dina Abdel Salam , Yahya Noorchaya , Pawlak Miroslaw 
Published: December 2022

AbstractThe current study aimed to compare the influence of explicit versus implicit instruction on EFL learners’ use of interactional metadiscourse markers in their writing. The study also aimed to explore the perceptions of EFL learners on the instruction of these markers in their writing classes. 120 female undergraduates who are Arab EFL learners took part in the investigation. A mixed-methods research design was adopted with an explicit instruction group, an implicit instruction group and a control group. The experimental groups were introduced to a set of interactional metadiscourse markers as per Hyland’s (2005) interaction model in two 70-minute sessions. Additionally, the experimental groups completed two post-experimental questionnaires. The results of analysis of variance showed a positive, albeit very limited, influence for the explicit/implicit teaching with the markers of self-mentions, appeals to shared knowledge, directives and questions. As for the participants’ perception of the instructional intervention, the participants viewed both explicit and implicit instruction as helpful but could not always apply what they had learned due to task demands. The results are discussed in light of the existing literature and the specific context in which the study was implemented. Furthermore, implications for writing pedagogy are proposed.
Co-learner presence and praise alters the effects of learner-generated explanation on learning from video lectures
Authors: Pi Zhongling , Liu Caixia , Meng Qian , Yang Jiumin 
Published: December 2022

AbstractLearning from video lectures is becoming a prevalent learning activity in formal and informal settings. However, relatively little research has been carried out on the interactions of learning strategies and social environment in learning from video lectures. The present study addresses this gap by examining whether learner-generated explanations and co-learner presence with or without nonverbal praise independently and interactively affected learning from a self-paced video lecture about infectious diseases. University students were randomized into viewing either the video with instructor-generated explanations or the same video but generating explanations themselves. Outcomes were assessed by the quality of explanations, learning performance, mental effort, attention allocation, and behavioral patterns. Between-group comparisons showed that, in the absence of a peer co-learner, learning performance was similar in both the instructor-generated and learner-generated explanation groups. However, in the presence of a peer, learner-generated explanation facilitated learning performance. Furthermore, learner-generated explanation in the presence of a co-learner also reduced learners’ mental effort and primed more behaviors related to self-regulation and monitoring. The results lead to the following strong recommendation for educational practice when using video lectures: if students learn by generating their own explanations in the presence of a co-learner, they will show better learning performance even though the learning is not necessarily more demanding, and will engage in more behaviors related to explanation adjustment and self-regulation.
Coronary angiography using spectral detector dual-energy CT: is it the time to assess myocardial first-pass perfusion?
Authors: D’Angelo Tommaso , Martin Simon , Micari Antonino , Booz Christian , Steyer Alexandra , Blandino Alfredo , Lanzafame Ludovica R. , Koch Vitali , Ascenti Giorgio , Mazziotti Silvio 
Published: December 2022

AbstractCoronary computed tomography angiography (CCTA) represents a common approach to the diagnostic workup of patients with suspected coronary artery disease. Technological development has recently allowed the integration of conventional CCTA information with spectral data. Spectral CCTA used in clinical routine may allow for improving CCTA diagnostic performance by measuring myocardial iodine distribution as a marker of first-pass perfusion, thus providing additional functional information about coronary artery disease.
Non-motor manifestation of Parkinson's disease: a cross-sectional study in a teaching hospital in Jordan
Authors: Dahbour Said Salah , Al Murr Mohammad J. , Oweis Liyan H. , Al Antary Nada T. , Mohsen Maram , Al Fegi Safa 
Published: December 2022

Abstract
Background
Parkinson's disease (PD) is the most common degenerative movement disorder. It is featured by motor manifestations and up till now the clinical diagnosis is based on them. Since the progress in the symptomatic treatment of PD and the longer survival of patients, non-motor manifestations (NMM) were more recognized and considered to be significant. The importance of NMM is that they reflect the more diffuse pathology of PD and may represent an opportunity of earlier diagnosis and treatment. Here in this cross-sectional study, we try to estimate the frequency of such manifestations in PD patients in the country. Using slightly modified PD non-motor (28 of 30 responses) questionnaire (NMS Quest), we studied the incidence of NMM in 100 PD patients attending one major teaching hospital and compared their occurrence in 130 age- and gender-matched non-PD controls.

Results
Out of 100 PD patients (40% females) mean age 67.4 ± 12 with disease duration of 7.3 ± 5.8, range < 1–33.2 years), and 130 control subjects (48.5% females), mean age 65.0 ± 7.0. PD patients had 8.6 ± 5.3 NMM while controls had 3.4 ± 3.3 NMM, respectively (p < 0.00001 t test). Constipation, urgency, insomnia, sad feeling, panic, light headedness and recent memory impairment were the most prevalent NMM in PD compared to controls, while nocturia, restless legs, encopresis and falling were not different in the two groups. The number of NMM ranged from 0 to 21 in PD patients with 50% having ≥ 8 manifestations. The number of NMM did not correlate with age, gender, or disease duration as defined by the classical motor symptoms. Frequency of 23 of these 28 manifestations differed significantly in PD patients compared to controls.

Conclusions
This study confirms that NMM in Jordanian PD patients are very common as reported in other populations. This signifies the universal prevalence of such NMM reflecting their important impact on their daily life and their relevant contribution to better understanding of this disease.
Assessment of the diagnostic performance of various screening tools for bipolar disorder: a meta-analysis
Authors: Rahim Fakher , Rouhani Kianmehr , Delirrooyfard Ali , Sayyah Mehdi 
Published: December 2022

Abstract
Background
Various studies have shown that about 40–50% of patients with bipolar disorder (BD) are undiagnosed at the time of referral and are often treated as depression. The present meta-analysis was conducted to determine the diagnostic accuracy of psychometric properties of the bipolarity index (BI) in people with BD.

Methods
We systematically searched databases including, Scopus, ISI Web of Sciences (WOS), Pubmed/Medline, Embase, and PsycINFO using standard search terms.

Results
Two hundred and ninety-six records were found through the initial search. Of 679 articles, 25 duplicated studies were found and 70 were omitted due to the irrelevant titles and abstracts. The rest 450 were entered the full-text screening, of which 186 were excluded due to pre-defined inclusion criteria. Ultimately, 15 studies on 6525 patients were included. Our findings showed that the pooled sensitivity of BI in the diagnosis of BD was 0.82 (95%CI: 0.81–0.83, P < 0.0001, I2 = 99%), while the pooled specificity also was 0.73 (95%CI: 0.72–0.74, P = 0.000, I2 = 99%). The BI was significantly more accurate than the other tests with a pooled DOR of 47.2 (95%CI: 12.01–85.52, P = 0.0000, I2 = 99.2%).

Conclusion
BI appears to be a useful screening instrument with suitable psychometric properties to identify BD compared to both the MDQ and the HCL-32. Consequently, patients detected by the BI should be confirmed through diagnostic interviews. Thus, more studies are needed to explore the optimal cut-off values of BI among screened populations during long-term follow-up, since a considerable portion of individuals primarily diagnosed with major depressive disorders could have BD.
Exploring the role of microfinance in women’s empowerment and entrepreneurial development: a qualitative study
Authors: Khursheed Ambreen 
Published: December 2022

AbstractIn developing countries, women’s empowerment is a major concern. Several efforts were made to tackle this issue as the aims of poverty reduction and development cannot be achieved without giving attention to women’s empowerment. Over the past decades, microfinance institutions (MFIs) have appeared as crucial tools not only to address the issue of poverty but also particularly to empower women. Resultantly, a huge number of studies focus on the relationships between MFI and women empowerment. However, in the context of rural areas of Pakistan, the research is limited. Therefore, the objective of this study is to investigate the role of MFI in women’s empowerment in Pakistan so that the research will facilitate MFIs and policymakers in strengthening the link between MFIs and women entrepreneurship. We have used a qualitative methodology, using primary data collected through in-depth interviews and a focus group discussion with six female borrowers of Rural Community Development Programs (RCDP). The empirical results provide valuable insights into the efforts made by RCDP to empower women and combat poverty by encouraging women’s entrepreneurship. Hence, this paper not only examines empowerment, which women are attaining from microfinance but also assists MFIs to know about their significance in developing the economy. The paper is significant for MFI practitioners to develop policies for boosting women’s entrepreneurship and to help their existing women clients with efficient training and supervision.