bluuebunny/crossref_metadata_2025_split huggingface dataset:
Seagrass and epiphyte seasonality in a biogeographic transition zone
Authors: Wheeler Mike , Jarvis Jessie , Freshwater Wilson D. 
Published: January 2024

Abstract
North Carolina, USA is an ecotone that includes two seagrass species located at the edge of their distributional ranges: Zostera marina, a temperate species, and Halodule wrightii, a tropical species. Both support dynamic epiphyte communities, yet seagrass and epiphyte species composition and biomass relationships are not well documented in mixed-species transition zones. This study investigates the seagrass and epiphyte biomass and epiphyte community composition by seagrass species by using monthly collections over one year from Topsail Sound, NC. H. wrightii biomass peaked in the fall and declined to seasonal minimums in winter. Z. marina biomass peaked in the spring and was lowest in fall. Maximum epiphyte biomass per leaf area occurred during periods of thermal stress for both species, summer for Z. marina and winter for H. wrightii. Epiphyte community composition differed between seagrass species within all seasons except for spring, which is a period of concurrent growth for both species, while there was no difference in community across the entire year. Variation in epiphyte communities within NC can be explained by divergent periods of thermal stress, leaf growth, and staggered periods of peak biomass between H. wrightii and Z. marina. Mixed temperate and tropical species meadows ensured continuous substrate for macroalgal epiphytes throughout the year with the seasonality of the epiphyte community closely tied to the growth cycles of their seagrass hosts. This research contributes to our understanding of mixed-species seagrass meadows in biogeographic transition zones and informs conservation and management strategies needed for a rapidly changing climate.
Cryoablation for malignant bone and soft tissue tumors and histological assessment of ablated tumors
Authors: Asanuma Kunihiro , Nakatsuka Atsuhiro , Nakamura Tomoki , Fujimori Masashi , Yamanaka Takashi , Hagi Tomohito , Iino Takahiro , Sudo Akihiro 
Published: January 2024

Abstract
Background: Recurrent or metastatic disease after completion of initial therapy for primary sarcoma represents a substantial problem, along with as well as metastasis of cancer. Cryoablation is a new, minimally invasive option for local antitumor therapy that is attracting attention regarding relationships with the immune system. The purpose of this study was to evaluate the efficacy of cryoablation for local control of bone and soft tissue lesions, to elucidate risk factors for recurrence, and to clarify histological changes of necrosis and immune cell invasion after cryoablation.
Methods: Participants comprised 25 patients who underwent cryoablation for 53 discrete lesions of bone or soft tissue recurrence after resection or as metastases of cancer or sarcoma. Local progression-free survival was evaluated after completion of cryoablation. The histology of tumor tissues resected after cryoablation was assessed for 7 cases. Tumor tissues after cryoablation was resected for 7 cases. H&E staining and immunostaining for CD4, CD8, CD68, CD16, CD204, IDO, and CD47 were performed.
Results: Local progression-free survival rates were 88.1% at 1 year and 79.7% at 2 and 3 years. Risk of local progression was significantly higher for recurrent lesions after resection, and for lesions ≥4.0 cm in diameter than for metastatic lesions, or lesions <4.0 cm, respectively (p<0.05 each). In subgroup analysis of bone lesions, lesions with an extraskeletal component tended to be associated with worse local recurrence-free survival than those without an extraskeletal component (p=0.135). On histological examination, tissue in the ablated area was completely necrotic. In the border area between ablated and non-ablated areas, CD68-positive cells were more frequently observed than T cells. CD16-M1-like and CD204-positive M2-like cells were observed.
Conclusions: Cryoablation demonstrated good anti-tumor efficacy without distinction of tumor types and bone. Local control for recurrent and lesions ≥4.0 cm in diameter was inadequate with cryoablation. Further analysis for the relation between macrophage and cryoablation is needed and may provide critical information about a superior anti-tumor effect.
GIC-Flow: Appearance Flow Estimation Via Global Information Correlation for Virtual Try-On under large deformation
Authors: Zhang Peng , Zhan Jiamei , Sun Kexin , Zhang Jie , Wei Meng , Wang Kexin 
Published: January 2024

Abstract
The primary aim of image-based virtual try-on is to seamlessly deform the target garment image to align with the human body. Due to the inherent non-rigid nature of garments, current methods prioritize flexible deformation through appearance flow with high degrees of freedom. However, existing appearance flow estimation methods focus solely on the correlation of local feature information. While this strategy successfully avoids the extensive computational effort associated with directly computing the global information correlation of feature maps, it leads to challenges in garments adapting to large deformation scenarios. To address these limitations, we propose the GIC-Flow framework, which obtains appearance flow by calculating the global information correlation while reducing computational regression. Specifically, our proposed Global Streak Information Matching Module is designed to decompose the appearance flow into horizontal and vertical vectors, effectively propagating global information in both directions. This innovative approach significantly diminishes 1 computational requirements, contributing to an enhanced and efficient process. Additionally, to ensure the accurate deformation of local texture in garments, we propose the Global Aggregate Information Matching Module to aggregate information from nearest neighbors before computing the global correlation, and to enhance weak semantic information. Comprehensive experiments conducted with our method on the VITON and VITON-HD datasets demonstrate GIC-Flow outperforms existing state-of-the-art algorithms, particularly in cases involving complex clothing deformation.
Deep reinforcement learning-based contention window optimization for IEEE 802.11 networks
Authors: Tu Yi-Hao , Ma Yi-Wei , Ke Chih-Heng 
Published: January 2024

Abstract
This study focuses on optimizing the contention window (CW) in IEEE 802.11 networks using deep reinforcement learning (DRL) to enhance the effectiveness of the contention mechanism. Recent research has employed a deep Q-learning network (DQN) as one type of DRL for CW size selection tasks to maximize network throughput. However, a notable limitation of DQN is the substantial overestimation error, which means the predicted reward value significantly deviates from the actual value. To address this issue, our study introduces the smart exponential-threshold-linear with double deep Q-learning network (SETL-DDQN) in a wireless networks scenario, with the aim to mitigate the overestimation error via the CW threshold size optimization with the help of a DDQN-based approach during the learning phase. We experimented with our proposed SETL-DDQN in both static and dynamic scenarios and conducted an analysis to solve the overestimation problem, then enhance the long-term simulation stability. Our experimental results demonstrate that SETL-DDQN achieves more efficient packet transmissions than related existing mechanisms.
A predictive model based on non-contrast CMR and texture analysis for the prognostic evaluation of cardiac amyloidosis
Authors: She Jia-qi , Guo Jia-jun , Sun Yi , Chen Yin-yin , Zeng Meng-su , Ge Mei-ying , Jin Hang 
Published: January 2024

Abstract
We aimed to develop a predictive model based on texture features with non-contrast cardiac magnetic resonance (CMR) imaging for risk stratification toward adverse events in cardiac amyloidosis (CA) patients. A cohort of 78 CA patients was classified into a training set (n = 54) and a validation set (n = 24) at a ratio of 7:3. A total of 275 texture features were extracted from CMR images. MaZda and the support vector machine (SVM) were utilized for feature selection and model construction. A SVM model incorporating radiological and texture features was built for prediction of endpoint events by evaluating area under curve (AUC). In the whole cohort, 52 patients were major adverse cardiovascular events (MACE) occurred and 26 patients were No MACE occurred. By combining 2 radiological features and 8 texture features extracted from cine and T2-weighted imaging (T2WI) images, the SVM model achieved AUCs of receiver operating characteristic (ROC) and precision-recall (PR) of 0.930 and 0.962 in the training cohort and that of 0.867 and 0.941 in the validated cohort. The Kaplan–Meier curve of this SVM model criteria excellently stratified CA outcomes (Log rank test, P < 0.0001). The SVM model based on radiological and texture features derived from non-contrast CMR images can be a reliable biomarker for adverse events prognostication in CA patients.
A Robust Deep-learning System for Detecting and Classifying Cyber-attacks in Cloud Computing
Authors: E saranya , M Chinnadurai 
Published: January 2024

Abstract
The Internet of Things (IoT) has emerged as the benchmark for low-power lossy networks (LLNs) due to the rapid proliferation of low-power, high-throughput technology. Still, IoT systems are open to cyber attacks because the base devices have limited computing, storage, and communication capabilities. As people rely more and more on cloud computers to store and handle important data, cyber dangers that target cloud settings have become more complex and widespread. The identification and categorization of harmful network activity is crucial in safeguarding the integrity and protection of these systems. Here, we present the full design and implementation of a deep-learning-based system called Enhanced battle royal optimized convolutional neural network (EBRO-CNN) for detecting and classifying cyber attacks in cloud-based telecommunications systems. This optimization method creates tiers for network traffic data depending on characteristics of the cloud. Each layer undergoes its own analysis and optimization to meet its own requirements. This optimization across tiers not only increases detection precision but also makes the system more scalable and flexible in the face of ever-changing cloud workloads. We used the NSL-KDD dataset, which contains demonstrations of all the major cloud computing threats, to gauge the effectiveness of the solution we designed. With an accuracy of up to 99% in various configurations, the findings show that the suggested distributed architecture is useful for detecting various cyber attacks.
Comparison between endobronchial-guided transbronchial biopsy and computed tomography-guided transthoracic lung biopsy for the diagnosis of central pulmonary lesions with a diameter ≥3 cm
Authors: Zhang Cheng , Zhu Senlin , Yuan Yanliang , Dai Shenhui 
Published: January 2024

Abstract

BackgroundLung cancer is one of the most common malignant tumors at present. This study aimed to compare the diagnostic accuracy, complication rates, and predictive values of computed tomography (CT)-guided percutaneous transthoracic needle biopsy (PTNB) and endobronchial-guided transbronchial lung biopsy (TBLB) for patients with central pulmonary lesions (CPLs) with a diameter ≥ 3 cm.MethodsWe retrospectively included 110 patients with CPLs with a diameter ≥ 3 cm who underwent preoperative PTNB and TBLB examinations, and ultimately underwent surgery to remove CPLs and obtained pathological results. Detailed information was collected, including age, gender, tumor location, postoperative hospital stay, techniques involved, and pathologic diagnosis. Data were processed using SPSS software (version 26.0; IBM Corp). Data were compared byt-test or chi-square test.P-value < 0.05 was considered statistically significant.ResultsAll patients underwent surgical treatment at the department of thoracic surgery and obtained a final pathological diagnosis. The rate of positive predictive value (PPV) was comparable between the two methods, and the negative predictive value (NPV) was significantly higher in the PTNB group compared with the TBLB group. In addition, PTNB was more sensitive and accurate than TBLB. However, the PTNB group had a higher probability of complications and TBLB was a relatively safer examination method.ConclusionPTNB demonstrated a higher accuracy and sensitivity than TBLB in the treatment of CPLs with a diameter ≥ 3 cm, but the complication rates of PTNB are relatively high. These methods exhibited different diagnostic accuracies and therefore should be selected based on different medical conditions.
Modified -Vehicle Detection and Localization model for Autonomous Vehicle Traffic System
Authors: Juyal Amit , Sharma Sachin , Bhadula Shuchi 
Published: January 2024

Abstract
The modification of vehicles for financial gain is an evolving tendency observed in India. These modified vehicles are riskier than other automobiles that are allowed. Recognizing and detecting of these modified illicit cars is an important but critical task in autonomous vehicles. In a real-world traffic scenario, it is always possible for a cyclist or pedestrian to traverse obstacles or other fixed objects that appear in front of any moving vehicle. Vehicles that are autonomous or self-driving require a different system to quickly identify both stationary and moving objects. A deep learning model named YOLOv5-CBAM is proposed here for the Indian traffic System which is based on YOLOv5m. The algorithm is applied to the vehicle dataset collected from different regions of Uttarakhand. The proposed algorithm, YOLOv5-CBAM, has three major components. The first module, the backbone module is employed for feature extraction. The second module is to detect static as well as dynamic objects at the same time and the third CBAM module is adopted in the backbone and neck part, which mainly focuses on the more prominent features. Two CSP modules were used after every convolutional layer resulting in an additional head to the proposed model. Four head modules equipped with anchor boxes performed the final detection. For the present dataset, the proposed model showed 98.2% mAP, 98.4% precision, and 94.8% recall as compared to the original YOLOv5m.
LCSMIBR: Design of a Low complexity Sleep-scheduling Model with Iterative Bioinspired learning for improved Routing efficiency under heterogeneous network condition
Authors: Kuthe Annaji , Khandelwal Sarika 
Published: January 2024

Abstract
One of the best methods for increasing energy efficiency in large-scale wireless networks is sleep scheduling. The scalability of existing sleep scheduling algorithms is limited in real-time applications due to their higher complexity or lack of coordination. Furthermore, these models' routing performance needs to be adjusted for various use scenarios. This essay suggests designing a low complexity sleep-scheduling model with iterative bioinspired learning for better routing efficiency in the context of diverse networks in order to integrate these qualities. At first, the suggested model gathers progressively larger data sets on node location, energy usage, fault frequency, and additional quality of service measures. These parameter sets are used to train a set of scalar nodes (nodes with low efficiency levels), that decide initial sleep scheduling cycles for high-efficiency multimedia nodes. The sleep schedules are co-ordinated by multimedia nodes during their inter-node communications, which assists the scalar nodes to iteratively improve sleep-and-wake cycles. These cycles are decided by a Bacterial Foraging Optimization (BFO) process, which assists in improving energy efficiency without compromising on QoS performance levels. The BFO process is cascaded with a fault-tolerant Grey Wolf Particle Swarm Optimizer (GWPSO), that evaluates high-efficiency routing paths that can provide low delay, low energy consumption, with lower probability of faults. Due to these operations, the proposed model is able to reduce communication delay by 4.9%, and energy needed for communication by 8.5%, while improving throughput by 3.4%, and consistency of routing-performance by 1.9% when compared with existing routing techniques. This performance was observed to be consistent even under node-level faults for heterogenous network scenarios.
Coexistence of roof rats and carnivores in barns on an urban livestock farm in Japan
Authors: Koizumi Ryoko , Endo Tomohiko , Tanikawa Tsutomu , Hirata Shigeki , Kiyokawa Yasushi 
Published: January 2024

Abstract
Brown rats (Rattus norvegicus), roof rats (Rattus rattus), and house mice (Mus musculus) are three of the most important rodent pest species in the world. Because these rodents infest livestock farms, rodent control on such farms is an important issue for the livestock industry. Although the diel activity patterns of rodents are key to their control, information on this aspect of their ecology is limited. Furthermore, the effect of carnivores on rodent activity patterns, as well as the carnivore species present, on urban livestock farms is unclear. Here, we set camera traps in an open-type cow barn and in an enclosed pig barn on the same urban livestock farm in Japan from August through October 2021. The only rodents observed in both barns were roof rats, and the carnivore species observed were dogs (Canis familiaris), cats (Felis catus), and Japanese weasels (Mustela itatsi). The relative abundance index of cats was similar between the barns, while dogs and Japanese weasels were observed only in the cow and pig barns, respectively. These results suggest that cats and Japanese weasels could easily enter the enclosed barn. We also found that roof rats showed different patterns of activity and behavior between the barns. However, because the pattern in both barns was nocturnal, the activity patterns of roof rats and carnivores showed a moderate to high degree of overlap. Therefore, roof rats did not appear to shift their activity patterns to avoid nocturnal carnivores. Taken together, the present study provides valuable information for rodent control in urban livestock farms.
Whole Genome Sequenced-based Characterization and Determination of quinolone Resistance Among Methicillin-resistant and Methicillin-susceptible S. aureus isolates from Patients Attended Regional Referral Hospitals in Tanzania
Authors: Juma Masoud , Kumburu Happiness , Wadugu Boaz , Kuchaka Davis , Shayo Mariana , Kimu Patrick , Kanje Livin , Beti Melkiory , Zwetselaar Marco Van , Mmbaga Blandina , Sonda Tolbert 
Published: January 2024

Abstract

Background
The emergency of multidrug-resistant S. aureus (MRSA) strains, driven by acquisition of resistance gene in mecA imposes a substantial challenge in the treatment and control of their related infections. Despite the fact that Quinolones have historically been effective against both MRSA and methicillin-susceptible S. aureus (MSSA) strains, the escalating rising of quinolones resistance among S. aureus isolates particularly in MRSA, has severely curtailed their potency and further narrow down the therapeutic options. This study aimed at determining the burden of MRSA among isolates, their resistance profile, genotypic characterization and determining the molecular relatedness through construction of phylogenetic tree.
Material and Methods
Archieved clinical S. aureus isolates from a descriptive cross-sectional study involving six regional referral hospitals in Dodoma, Songea, Kigoma, Kitete, and Morogoro in mainland and Mnazi Mmoja in Zanzibar were analyzed. Bacterial identification was performed using both classical microbiology and whole genome sequencing on Illumina Nextseq 550 Sequencer. Species identification was done using KmerFinder 3.2, Multilocus Sequence Types using MLST 2.0, SCCmec type using SCCmecFinder 1.2, resistance genes using ResFinder 4.1, and phylogenetic relatedness using CSI Phylogeny 1.4.
Results
Out of the 140 isolates analyzed, 69 (49.3%) were identified as MRSA, with 57 (82.6%) exhibiting quinolone resistance. Conversely, 71 isolates were identified as MSSA, and none of them exhibited resistance to quinolone. Spa-typing revealed 6 spa types, with t355, t1476, and t498 being most common. Moreover, all (69) MRSA were found to carry SCCmec type IV. The isolates exhibited 14 different sequence types (ST). Notably, ST152 was prevalent 50(70%) among MSSA while ST8 was the predominant 58(84%) sequence type among MRSA. The antimicrobial resistance profile revealed at least three horizontally acquired resistance genes, with blaZ, dfrG, tet(K), and aac (6’)-aph (2’’) genes being highly prevalent.
Conclusion
There is a high genetic diversity among the S. aureus isolates existing in Tanzania regional hospitals, with a concerning burden of quinolone resistance in MRSA isolates. The diversity in resistance genes among MRSA lineages emphasizes the necessity for development of sustainable antimicrobial stewardship and surveillance to support evidence-based guidelines for the management and control of MRSA infections in both community and hospital settings.
Effects of Zhengqing Fengtongning in treatment Knee osteoarthritis based on TLR4/NLRP3/Caspase-3 signalling pathway
Authors: Rong Kuan , Zhang Ziming , Chen Lin , Ou Liang , Li Zhenyu , Zhao Haoming , Liu Ke , Kuang Haoming , Yang Hui , Kuang Jianjun 
Published: January 2024

Abstract
Introduction
Knee osteoarthritis (KOA) is a degenerative joint disease that leads to joint deformity and loss of function in severe cases. Its primary symptoms include joint pain, swelling, and impaired mobility. It has been reported that more than 80% of patients with knee osteoarthritis are over 60 years old, constituting a significant proportion of hospitalizations in developed countries. This imposes a substantial burden on patients, their families, and society. Currently, the drugs of choice for KOA in clinical practice are non-steroidal anti-inflammatory drugs (NSAIDs). However, their efficacy is suboptimal as they primarily alleviate symptoms, and long-term use can lead to serious adverse reactions. The focus and hotspot of orthopedic research lie in understanding the mechanism of effectively inhibiting cartilage degeneration, protecting cartilage cells and tissues, and slowing down or even reversing the progression of KOA.
Methods
The composition of Zhengqingfengtongning (ZQFTN) was verified using mass spectrometry. Fifteen female Wistar rats were randomly assigned to the normal group (control), KOA model group (model), Zhengqing group (ZQFTN Sustained Release Tablets), TLR4-inhibitor group (TLR4-inhibitor), and Zhengqing + TLR4-inhibitor group (ZQFTN Sustained Release Tablets and TLR4-inhibitor), with three rats in each group. Histological staining, Western blotting, and Elisa were employed to compare histomorphology and data of IL-6, IL-1β, TNF-α, TAK1, TAB1, iKBα, IKKβ, Bax, Bcl-2, and Caspase-3 in rats from each group.
Results
Histological staining, Western blotting, and Elisa assay results demonstrated the effectiveness of ZQFTN in improving the condition of KOA model rats. Furthermore, the expression of IL-6, IL-1β, TNF-α, TAB1, iKBα, IKKβ, and Caspase-3 levels in chondrocytes was suppressed, while Bcl-2 levels were increased.
Conclusions
ZQFTN ameliorated the condition of KOA model rats, and its therapeutic mechanism may involve the regulation of inflammatory factors and cellular autophagy, along with the inhibition of the TLR4/NLRP3/Caspase-3 signaling pathway.