bluuebunny/crossref_metadata_2025_split huggingface dataset:
Insight into chloroplast genome structural variation of the Mongolian endemic species Adonis mongolica (Ranunculaceae) in the Adonideae tribe
Authors: Nyamgerel Nudkhuu , Baasanmunkh Shukherdorj , Oyuntsetseg Batlai , Bayarmaa Gun-Aajav , Erst Andrey , Park Inkyu , Choi Hyeok Jae 
Published: December 2023

AbstractAdonis mongolicais a threatened species that is endemic to Mongolia. It is a medicinal plant from theAdonisgenus and has been used to treat heart diseases. However, the genomics and evolution of this species have not been thoroughly studied. We sequenced the first complete plastome ofA. mongolicaand compared it with ten Adonideae species to describe the plastome structure and infer phylogenetic relationships. The complete plastome ofA. mongolicawas 157,521 bp long and had a typical quadripartite structure with numerous divergent regions. The plastomes of Adonideae had relatively constant genome structures and sizes, except for those ofAdonis. The plastome structure was consistent acrossAdonis. We identified a 44.8 kb large-scale inversion within the large single-copy region andrpl32 gene loss in theAdonisplastomes compared to other members of the Adonideae tribe. Additionally,Adonishad a smaller plastome size (156,917–157,603 bp) than the other genera within the tribe (159,666–160,940 bp), which was attributed to deletions of intergenic regions and partial and complete gene losses. These results suggested that an intramolecular mutation occurred in the ancestor of theAdonisgenus. Based on the phylogenetic results,Adonisseparated earlier than the other genera within the Adonideae tribe. The genome structures and divergences of specific regions in theAdonisgenus were unique to the Adonideae tribe. This study provides fundamental knowledge for further genomic research in Mongolia and a better understanding of the evolutionary history of endemic plants.
Clinical correlation of cholelithiasis in patients undergoing percutaneous endoscopic gastrostomy
Authors: Cha Boram , Lee Jungnam , Lee Jaehyuk , Park Jin-Seok , Jeong Seok , Lee Don Haeng 
Published: December 2023

AbstractThe risk factor for cholelithiasis include low physical activity. With an aging society, the number of bedridden patients who undergo percutaneous endoscopic gastrostomy (PEG) has increased, and cholelithiasis has often been found in these patients. This study aimed to evaluate the risk factors correlated with cholelithiasis in adults who underwent PEG. This retrospective single-center design study reviewed patients who underwent PEG and were confirmed to have cholelithiasis through imaging from March 1996 to December 2021. The investigated variables were age, sex, body mass index (BMI, kg/m2), cause of PEG insertion, initial physical activity status, laboratory findings on PEG insertion day, and incidence of acute cholecystitis. The differences between categorical and continuous variables were analyzed using Student’s t test and chi-square test. We enrolled 576 eligible patients who underwent PEG insertion. A total of 161 patients were detected with cholelithiasis (28.0%). The overall independent risk factors for cholelithiasis in patients who underwent PEG insertion were increased C-reactive protein (CRP) levels and decreased physical activity status (bedridden state). The incidence of cholelithiasis was increased by up to 30.7%, especially in patients with bedridden status. However, the incidence of acute cholecystitis among cholelithiasis group was only 5.6%. BMI and total cholesterol were positively correlated with the size of gallbladder (GB) stones. One of the major risk factors for cholelithiasis is decreased physical activity, especially in patients who underwent PEG insertion. Abdominal imaging is recommended to confirm the presence of cholelithiasis and to consider prophylaxis for cholelithiasis, especially in bedridden patients with elevated initial CRP levels at the time of PEG insertion.
Prehabilitative high-intensity interval training and resistance exercise in patients prior allogeneic stem cell transplantation
Authors: Kuehl Rea , Feyer Jule , Limbach Matthias , Pahl Antonia , Stoelzel Friederike , Beck Heidrun , Wegner Annika , Rosenberger Friederike , Dreger Peter , Luft Thomas , Wiskemann Joachim 
Published: December 2023

AbstractPhysical capacity prior allogeneic stem cell transplantation (allo-HCT) has been shown as a relevant prognostic factor for survival after transplant. Therefore, we evaluated feasibility and preliminary efficacy of a high-intensity interval training (HIIT) and moderate to high-intensity resistance exercise (RE) to increase physical capacity in patient’s prior allo-HCT. In this multicentre single arm pilot study, a supervised exercise program was performed twice weekly for 4–12 weeks prior allo-HCT, depending on the individual time remaining. Outcomes were feasibility (recruitment, adherence, safety), physical capacity (cardiorespiratory fitness [VO2peak], muscle strength) and patient reported outcomes (physical functioning, fatigue). Thirty patients were intended, 16 could be included, and 14 completed post intervention assessment (75% male, 55 ± 11 years). The study was stopped early due to a low recruitment rate. Nine patients (64%) reached the initial minimum planned number of eight exercise sessions. Individual adherence was high with 92% for HIIT and 85% for RE. 87% of all performed exercise sessions were completed without complaints and VO2peak increased significantly from 20.4 to 23.4 ml/kg/min. The low recruitment rate suggests that initiation of the intervention concept immediately before allo-HCT is feasible only in a small number of patients. In particular, the timeframe directly prior allo-HCT seems too short for exercise interventions, although the exercise program was designed to improve outcomes in a very short time frame. HIIT and RE were feasible, effective and well accepted by the included patients.
Early diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithm
Authors: Wang Wenjing , Liu Yi , Wu Jianan 
Published: December 2023

AbstractOral cancer can occur in different parts of the mouth, including the lips, palate, gums, and inside the cheeks. If not treated in time, it can be life-threatening. Incidentally, using CAD-based diagnosis systems can be so helpful for early detection of this disease and curing it. In this study, a new deep learning-based methodology has been proposed for optimal oral cancer diagnosis from the images. In this method, after some preprocessing steps, a new deep belief network (DBN) has been proposed as the main part of the diagnosis system. The main contribution of the proposed DBN is its combination with a developed version of a metaheuristic technique, known as the Combined Group Teaching Optimization algorithm to provide an efficient system of diagnosis. The presented method is then implemented in the “Oral Cancer (Lips and Tongue) images dataset” and a comparison is done between the results and other methods, including ANN, Bayesian, CNN, GSO-NN, and End-to-End NN to show the efficacy of the techniques. The results showed that the DBN-CGTO method achieved a precision rate of 97.71%, sensitivity rate of 92.37%, the Matthews Correlation Coefficient of 94.65%, and 94.65% F1 score, which signifies its ability as the highest efficiency among the others to accurately classify positive samples while remaining the independent correct classification of negative samples.
Database screening as a strategy to identify endogenous candidate metabolites to probe and assess mitochondrial drug toxicity
Authors: De la Rosa Mery Vet George , Patel Dipali , McCann Marc R. , Stringer Kathleen A. , Rosania Gus R. 
Published: December 2023

AbstractAdverse drug reactions (ADRs) are considered an inherent risk of medication use, and some ADRs have been associated with off-target drug interactions with mitochondria. Metabolites that reflect mitochondrial function may help identify patients at risk of mitochondrial toxicity. We employed a database strategy to identify candidate mitochondrial metabolites that could be clinically useful to identify individuals at increased risk of mitochondrial-related ADRs. This led to l-carnitine being identified as the candidate mitochondrial metabolite. l-carnitine, its acetylated metabolite, acetylcarnitine and other acylcarnitines are mitochondrial biomarkers used to detect inborn errors of metabolism. We hypothesized that changes in l-carnitine disposition, induced by a “challenge test” of intravenous l-carnitine, could identify mitochondrial-related ADRs by provoking variation in l-carnitine and/or acetylcarnitine blood levels. To test this hypothesis, we induced mitochondrial drug toxicity with clofazimine (CFZ) in a mouse model. Following CFZ treatment, mice received an l-carnitine “challenge test”. CFZ-induced changes in weight were consistent with previous work and reflect CFZ-induced catabolism. l-carnitine induced differences in whole blood acetylcarnitine concentrations in a manner that was dependent on CFZ treatment. This supports the usefulness of a database strategy for the discovery of candidate metabolite biomarkers of drug toxicity and substantiates the potential of the l-carnitine “challenge test” as a “probe” to identify drug-related toxicological manifestations.
Automatic diagnosis of true proximity between the mandibular canal and the third molar on panoramic radiographs using deep learning
Authors: Jeon Kug Jin , Choi Hanseung , Lee Chena , Han Sang-Sun 
Published: December 2023

AbstractEvaluating the mandibular canal proximity is crucial for planning mandibular third molar extractions. Panoramic radiography is commonly used for radiological examinations before third molar extraction but has limitations in assessing the true contact relationship between the third molars and the mandibular canal. Therefore, the true relationship between the mandibular canal and molars can be determined only through additional cone-beam computed tomography (CBCT) imaging. In this study, we aimed to develop an automatic diagnosis method based on a deep learning model that can determine the true proximity between the mandibular canal and third molars using only panoramic radiographs. A total of 901 third molars shown on panoramic radiographs were examined with CBCT imaging to ascertain whether true proximity existed between the mandibular canal and the third molar by two radiologists (450 molars: true contact, 451 molars: true non-contact). Three deep learning models (RetinaNet, YOLOv3, and EfficientDet) were developed, with performance metrics of accuracy, sensitivity, and specificity. EfficientDet showed the highest performance, with an accuracy of 78.65%, sensitivity of 82.02%, and specificity of 75.28%. The proposed deep learning method can be helpful when clinicians must evaluate the proximity of the mandibular canal and a third molar using only panoramic radiographs without CBCT.
Analysis of geometric morphometrics and molecular phylogeny for Anopheles species in the Republic of Korea
Authors: Jeon Jiseung , Kim Heung Chul , Klein Terry A. , Choi Kwang Shik 
Published: December 2023

AbstractHuman malaria, transmitted byAnophelesmosquitoes, is the most predominant mosquito-borne disease that is responsible for hundreds of thousands of deaths worldwide each year. In the Republic of Korea (ROK), there are currently several hundred malaria cases annually, mostly near the demilitarized zone (DMZ). Eight species ofAnophelesmosquitoes are currently known to be present in the ROK. Similar to other major malaria vectors in Africa and India, it is very challenging to morphologically differentiateAnophelesmosquitoes in the ROK due to their extremely similar morphology. In this study, wing geometric morphometrics (WGM) were used to differentiate the eightAnophelesspecies collected at six locations near the DMZ, Seoul and Pyeongtaek from April–October 2021. Phylogenetic analysis was also performed using cytochromecoxidase subunit 1 (COI), internal transcribed spacer 2 (ITS2), and tyrosine hydroxylase (TH) genes for comparison with WGM analysis and to infer evolutionary relationships. The results of cross-validation (overall accuracy = 74.8%) demonstrated that species identification using WGM alone was not possible with a high accuracy for all eight species. While phylogenetic analyses based on theCOIregion could not clearly distinguish some species, the analysis based on ITS2 andTHwas more useful for resolving the phylogenetic correlation of the eight species. Our results may improveAnophelesspecies identification strategies for effective identification and control of malaria vectors in the ROK.
AI-based diagnosis of nuclear cataract from slit-lamp videos
Authors: Shimizu Eisuke , Tanji Makoto , Nakayama Shintato , Ishikawa Toshiki , Agata Naomichi , Yokoiwa Ryota , Nishimura Hiroki , Khemlani Rohan Jeetendra , Sato Shinri , Hanyuda Akiko , Sato Yasunori 
Published: December 2023

AbstractIn ophthalmology, the availability of many fundus photographs and optical coherence tomography images has spurred consideration of using artificial intelligence (AI) for diagnosing retinal and optic nerve disorders. However, AI application for diagnosing anterior segment eye conditions remains unfeasible due to limited standardized images and analysis models. We addressed this limitation by augmenting the quantity of standardized optical images using a video-recordable slit-lamp device. We then investigated whether our proposed machine learning (ML) AI algorithm could accurately diagnose cataracts from videos recorded with this device. We collected 206,574 cataract frames from 1812 cataract eye videos. Ophthalmologists graded the nuclear cataracts (NUCs) using the cataract grading scale of the World Health Organization. These gradings were used to train and validate an ML algorithm. A validation dataset was used to compare the NUC diagnosis and grading of AI and ophthalmologists. The results of individual cataract gradings were: NUC 0: area under the curve (AUC) = 0.967; NUC 1: AUC = 0.928; NUC 2: AUC = 0.923; and NUC 3: AUC = 0.949. Our ML-based cataract diagnostic model achieved performance comparable to a conventional device, presenting a promising and accurate auto diagnostic AI tool.
Improving the efficiency of using multivalued logic tools: application of algebraic rings
Authors: Suleimenov Ibragim E. , Vitulyova Yelizaveta S. , Kabdushev Sherniyaz B. , Bakirov Akhat S. 
Published: December 2023

AbstractIt is shown that in order to increase the efficiency of using methods of abstract algebra in modern information technologies, it is important to establish an explicit connection between operations corresponding to various varieties of multivalued logics and algebraic operations. For multivalued logics, the number of variables in which is equal to a prime number, such a connection is naturally established through explicit algebraic expressions in Galois fields. It is possible to define an algebraic δ-function, which allows you to reduce any truth table to an algebraic expression, for the case when the number of values accepted by a multivalued logic variable is equal to an integer power of a prime number. In this paper, we show that the algebraic δ-function can also be defined for the case when the number of values taken by a multivalued logic variable is p − 1, where p is a prime number. This function also allows to reduce logical operations to algebraic expressions. Specific examples of the constructiveness of the proposed approach are presented, as well as electronic circuits that experimentally prove its adequacy.
Linking microbiome and stress hormone responses in wild tropical treefrogs across continuous and fragmented forests
Authors: Neely Wesley J. , Martins Renato A. , Mendonça da Silva Camila M. , Ferreira da Silva Tainá , Fleck Lucas E. , Whetstone Ross D. , Woodhams Douglas C. , Cook W. Harrison , Prist Paula R. , Valiati Victor H. , Greenspan Sasha E. , Tozetti Alexandro M. , Earley Ryan L. , Becker C. Guilherme 
Published: December 2023

AbstractThe amphibian skin microbiome is an important component of anti-pathogen defense, but the impact of environmental change on the link between microbiome composition and host stress remains unclear. In this study, we used radiotelemetry and host translocation to track microbiome composition and function, pathogen infection, and host stress over time across natural movement paths for the forest-associated treefrog, Boana faber. We found a negative correlation between cortisol levels and putative microbiome function for frogs translocated to forest fragments, indicating strong integration of host stress response and anti-pathogen potential of the microbiome. Additionally, we observed a capacity for resilience (resistance to structural change and functional loss) in the amphibian skin microbiome, with maintenance of putative pathogen-inhibitory function despite major temporal shifts in microbiome composition. Although microbiome community composition did not return to baseline during the study period, the rate of microbiome change indicated that forest fragmentation had more pronounced effects on microbiome composition than translocation alone. Our findings reveal associations between stress hormones and host microbiome defenses, with implications for resilience of amphibians and their associated microbes facing accelerated tropical deforestation.
How citizens engage with the social media presence of climate authorities: the case of five Brazilian cities
Authors: Ponciano Lesandro 
Published: December 2023

AbstractCitizen-government communication is essential in preparedness and adaptation to climate events. Local-level government authorities have sought to communicate via social media, but little is known about their communication strategies and citizens’ participation in replying to their publications. This study draws on conceptual frameworks for Social Media Presence and Human Engagement to establish behavioural modelling and topic modelling approaches for assessing citizen-authority communication from a long-term perspective. Empirical analyses focus on official government authorities for the Brazilian cities of São Paulo, Rio de Janeiro, Belo Horizonte, Porto Alegre and Belém, which cover over 25 million people and 500 thousand followers on the X platform (former Twitter). Analyses are based on 10.2 thousand authorities’ publications and their 5.5 thousand received replies from 2.6 thousand people over one year. Findings show that authorities use periodic passive posting, providing meteorological, hydrological, and geological alerts, forecasts, and momentary weather updates. Citizen engagement is short-term, providing corrections, additions, and updates but not connecting weather events with climate change. Practice-oriented implications concern the adequacy of social media for providing citizens awareness, keeping them updated, and building their trust in authorities over climate event developments.
Two-way free-space optics-based interface between fibre and 5G communication employing polarisation-orthogonal modulation
Authors: Lu Hai-Han , Huang Xu-Hong , Li Chung-Yi , Xu Yan-Zhen , Jin Jia-Lian , Chen Wei-Xiang , Lin Chih-Hong , Wu Tsai-Man 
Published: December 2023

AbstractFifth-generation (5G) communication provides a substantial increase in data transmission capacity because of more available bandwidth and advanced communication techniques. It opens the door to a range of new applications, such as 4K/8K video streaming, Internet of Things, autonomous vehicles, unmanned aerial vehicles, and more. However, atmospheric attenuation poses a technical challenge for 5G communication, especially at higher frequencies. Free-space optics (FSO)-based interface between fibre and 5G communication integrates fibre optics, FSO and 5G communications, achieving high-speed and long-distance transmission and providing an alternative for 5G signal transport. Here we report a two-way fibre-FSO-5G wireless communication employing polarisation-orthogonal modulation. After 25-km single-mode fibre, 1-km FSO, and 20-m (millimetre-wave)/10-m (sub-6 GHz) radio-frequency wireless transport, satisfactorily low bit error rates and error vector magnitudes are acquired, meeting the demands of 5G emerging applications.
Approximating complex 3D curves using origami spring structures
Authors: Liu Zuolin , Zhang Zian , Fang Hongbin 
Published: December 2023

AbstractOrigami provides a versatile platform for creating intricate three-dimensional (3D) reconfigurable structures through folding techniques. However, the applications of origami patterns are restricted due to limited deformation modes and complex actuation. Here we explore origami spring structures as a solution to address these limitations by approximating complex 3D curves with an underactuated scheme. By doing so, we showcase the reconfigurability and versatility of origami springs while tackling control complexity. Through the introduction of virtual creases, we simplify non-rigid deformations and enable accurate descriptions of their 3D configurations. Furthermore, we develop inverse kinematics optimization algorithms to determine optimal configurations closely approximating given 3D curves with full actuation and underactuated situations. Experimental realization of various 3D curves demonstrates the feasibility and effectiveness of our proposed approach. This research could find practical utility in soft robotics, flexible mechanisms, and deployable structures.
Adaptive neural network H∞$H_\infty$ control for offshore platform with input delay and nonlinearity
Authors: Zhang Yun , Ma Hui , Wang Shu‐Qing , Xu Jianliang , Su Hao , Zhang Jing 
Published: December 2023

AbstractIn this work, an adaptive learning robust controller is proposed to suppress the vibration of offshore platforms, which are subject to waves, winds, varying control delays and parametric perturbations. To realize nonlinear uncertainty approximation under the bounded performance, the controller incorporates both an online adaptive part and an offline fixed part. The adaptive part constructed by neural networks adjusts online, while the fixed part is obtained by regulating the performance. Importantly, adaptive updating strategy does not require accurate values or upper bounds for real‐time control delay or uncertainty. Several comparable experiments demonstrate the feasibility and effectiveness in vibration‐suppression of the designed adaptive controller in shallow/deep water. This scheme significantly reduces system response variations due to structural and hydrodynamic uncertainty, as well as additional random environmental forces caused by winds.
Medium‐term load forecasting of power system based on BiLSTM and parallel feature extraction network
Authors: Li Fei , Sun Chenjun , Han Wei , Yan Tongyu , Li Gang , Zhao Zhenbing , Sun Yi 
Published: December 2023

AbstractWith the diversification of users’ energy demands, accurate load forecasting is an important prerequisite for optimal scheduling and economic operation of the system, but a single‐load forecasting method cannot effectively predict multi‐energy loads accurately. Therefore, this paper proposes a multi‐energy load forecasting method based on bidirectional long short‐term memory (BiLSTM) and parallel feature extraction networks. Firstly, residual network and convolutional block attention module were used to extract the spatial coupling features of multi‐energy load data. Secondly, BiLSTM is used to capture the temporal features and long‐term dependencies in the load data, and the spatial coupling features are fused to obtain non‐linear prediction results. Finally, the non‐linear prediction results and the linear prediction results obtained by using multi‐energy linear regression were linearly superimposed to obtain the final prediction results. In this paper, IES load data of Tempe Campus of Arizona State University was used to verify and compared with several existing methods, and the results showed that Weighted Mean Absolute Percentage Error decreased by more than 20%.