bluuebunny/crossref_metadata_2025 huggingface dataset:
Antimicrobial Resistance in Enterococcus spp. Isolates from Red Foxes (Vulpes vulpes) in Latvia
Authors: Terentjeva Margarita , Ķibilds Juris , Avsejenko Jeļena , Cīrulis Aivars , Labecka Linda , Bērziņš Aivars
Published: January 2024

Antimicrobial resistance (AMR) is an emerging public health threat and is one of the One Health priorities for humans, animals, and environmental health. Red foxes (Vulpes vulpes) are a widespread predator species with great ecological significance, and they may serve as a sentinel of antimicrobial resistance in the general environment. The present study was carried out to detect antimicrobial resistance, antimicrobial resistance genes, and genetic diversity in faecal isolates of red foxes (Vulpes vulpes). In total, 34 Enterococcus isolates, including E. faecium (n = 17), E. faecalis (n = 12), E. durans (n = 3), and E. hirae (n = 2), were isolated. Antimicrobial resistance to 12 antimicrobial agents was detected with EUVENC panels using the minimum inhibitory concentration (MIC). The presence of antimicrobial resistance genes (ARGs) was determined using whole-genome sequencing (WGS). Resistance to tetracycline (6/34), erythromycin (3/34), ciprofloxacin (2/34), tigecycline (2/34), and daptomycin (2/34) was identified in 44% (15/34) of Enterococcus isolates, while all the isolates were found to be susceptible to ampicillin, chloramphenicol, gentamicin, linezolid, teicoplanin, and vancomycin. No multi-resistant Enterococcus spp. were detected. A total of 12 ARGs were identified in Enterococcus spp., with the presence of at least 1 ARG in every isolate. The identified ARGs encoded resistance to aminoglycosides (aac(6′)-I, ant(6)-Ia, aac(6′)-Iih and spw), tetracyclines (tet(M), tet(L) and tet(S)), and macrolide–lincosamide–streptogramin AB (lnu(B,G), lsa(A,E), and msr(C)), and their presence was associated with phenotypical resistance. Core genome multilocus sequence typing (cgMLST) revealed the high diversity of E. faecalis and E. faecium isolates, even within the same geographical area. The distribution of resistant Enterococcus spp. in wild foxes in Latvia highlights the importance of a One Health approach in tackling AMR.
Therapeutic Potential and Mechanisms of Rosmarinic Acid and the Extracts of Lamiaceae Plants for the Treatment of Fibrosis of Various Organs
Authors: Boo Yong Chool
Published: January 2024

Fibrosis, which causes structural hardening and functional degeneration in various organs, is characterized by the excessive production and accumulation of connective tissue containing collagen, alpha-smooth muscle actin (α-SMA), etc. In traditional medicine, extracts of medicinal plants or herbal prescriptions have been used to treat various fibrotic diseases. The purpose of this narrative review is to discuss the antifibrotic effects of rosmarinic acid (RA) and plant extracts that contain RA, as observed in various experimental models. RA, as well as the extracts of Glechoma hederacea, Melissa officinalis, Elsholtzia ciliata, Lycopus lucidus, Ocimum basilicum, Prunella vulgaris, Salvia rosmarinus (Rosmarinus officinalis), Salvia miltiorrhiza, and Perilla frutescens, have been shown to attenuate fibrosis of the liver, kidneys, heart, lungs, and abdomen in experimental animal models. Their antifibrotic effects were associated with the attenuation of oxidative stress, inflammation, cell activation, epithelial–mesenchymal transition, and fibrogenic gene expression. RA treatment activated peroxisomal proliferator-activated receptor gamma (PPARγ), 5′ AMP-activated protein kinase (AMPK), and nuclear factor erythroid 2-related factor 2 (NRF2) while suppressing the transforming growth factor beta (TGF-β) and Wnt signaling pathways. Interestingly, most plants that are reported to contain RA and exhibit antifibrotic activity belong to the family Lamiaceae. This suggests that RA is an active ingredient for the antifibrotic effect of Lamiaceae plants and that these plants are a useful source of RA. In conclusion, accumulating scientific evidence supports the effectiveness of RA and Lamiaceae plant extracts in alleviating fibrosis and maintaining the structural architecture and normal functions of various organs under pathological conditions.
Establishment of a Pressure Variation Model for the State Estimation of an Underwater Vehicle
Authors: Kim Ji-Hye , Mai Thi Loan , Cho Aeri , Heo Namug , Yoon Hyeon Kyu , Park Jin-Yeong , Byun Sung-Hoon
Published: January 2024

This study presents a pressure variation model (PVM) derived from the regression analysis of dynamic pressure computed through numerical analysis to estimate the velocity of underwater vehicles. Furthermore, the drift angle estimation algorithm was developed using predicted velocities from PVM and pressure sensor differences. This approach estimates the single-motion states of underwater vehicles, such as straight, turning, and gliding. Furthermore, it confirms the viability of state estimation even in multiple motions involving turning and gliding motion with a drift angle and spiral motion. The comparison with numerical analysis results validated prediction accuracy within 15%.
Linguistic Interval-Valued Spherical Fuzzy Soft Set and Its Application in Decision Making
Authors: Hou Tie , Yang Zheng , Wang Yanling , Zheng Hongliang , Zou Li , Martínez Luis
Published: January 2024

Under uncertain environments, how to characterize individual preferences more naturally and aggregate parameters better have been hot research topics in multiple attribute decision making (MADM). Fuzzy set theory provides a better mathematical tool to deal with uncertain data, which promotes substantial extended studies. In this paper, we propose a hybrid fuzzy set model by combining a linguistic interval-valued spherical fuzzy set with a soft set for MADM. The emergence of a linguistic interval-valued spherical fuzzy soft set (LIVSFSS) not only handles qualitative information and provides more freedom to decision makers, but also solves the inherent problem of insufficient parameterization tools for fuzzy set theory. To tackle the application challenges, we introduce the basic concepts and define some operations of LIVSFSS, e.g., the “complement”, the “AND”, the “OR”, the “necessity”, the “possibility” and so on. Subsequently, we prove De Morgan’s law, associative law, distribution law for operations on LIVSFSS. We further propose the linguistic weighted choice value and linguistic weighted overall choice value for MADM by taking parameter weights into account. Finally, the MADM algorithm and parameter reduction algorithm are provided based on LIVSFSS, together with examples and comparisons with some existing algorithms to illustrate the rationality and effectiveness of the proposed algorithms.
Active Strategies Based on Parametric Design for Applying Shading Structures
Authors: Choi Ho-Soon
Published: January 2024

This study aimed to increase the energy independence of buildings by utilizing solar energy to produce renewable energy. The subject of this study was a shading structure installed in an outdoor space to provide solar energy. Solar panels were applied to the shaded structures to generate energy actively. The solar panels were designed to be moved according to the optimal tilt angle each month to produce the optimal amount of renewable energy. The architectural design of the shading structure and the energy simulation of the solar panels were conducted using a parametric design. The results of the energy simulation showed the generation of 31,570 kWh· year−1 of renewable energy. This amount of energy is 10% higher than that produced by fixed solar panels. Thus, the moving solar panel system developed in this study not only increases the energy independence of buildings, but also has the advantage of higher renewable energy production compared with fixed solar panels. Additionally, various types of shading structures can be designed depending on the combination of solar panel modules; in particular, moving solar panels have the potential to facilitate ecofriendly designs when applied to the exterior of buildings.
A Model-Free Online Learning Control for Attitude Tracking of Quadrotors
Authors: Tan Lining , Jin Guodong , Zhou Shuhua , Wang Lianfeng
Published: January 2024

This paper investigates the problem of attitude tracking in quadrotor unmanned aerial vehicles (UAVs) using a model-free online learning control (MFOLC) scheme. The attitude system, which is represented by unit quaternions, is considered in the presence of uncertain and unknown inertia parameters, time-varying external disturbances, and angular velocity measurement noise. A computationally low-cost control scheme consisting of a model-free baseline controller and a module capable of learning from previous control input is designed. The proposed controller does not require precise inertial parameters and does not involve feedforward terms that use these parameters and true system states. This ensures that the approach can protect the control effort from sensor noise as well as parameter uncertainty. We also show that all the signals in the closed-loop system are uniformly ultimately bounded. Comparative simulations and real-world experiments are conducted for validation, which demonstrate the effectiveness and fine performance of the proposed scheme.
Fire Detection and Flame-Centre Localisation Algorithm Based on Combination of Attention-Enhanced Ghost Mode and Mixed Convolution
Authors: Liu Jiansheng , Yin Jiahao , Yang Zan
Published: January 2024

This paper proposes a YOLO fire detection algorithm based on an attention-enhanced ghost mode, mixed convolutional pyramids, and flame-centre detection (AEGG-FD). Specifically, the enhanced ghost bottleneck is stacked to reduce redundant feature mapping operations in the process for achieving lightweight reconfiguration of the backbone, while attention is added to compensate for accuracy loss. Furthermore, a feature pyramid built using mixed convolution is introduced to accelerate network inference speed. Finally, the local information is extracted by the designed flame-centre detection (FD) module for furnishing auxiliary information in effective firefighting. Experimental results on both the benchmark fire dataset and the video dataset show that the AEGG-FD performs better than the classical YOLO-based models such as YOLOv5, YOLOv7 and YOLOv8. Specifically, both the mean accuracy (mAP0.5, reaching 84.7%) and the inferred speed (FPS) are improved by 6.5 and 8.4 respectively, and both the number of model parameters and model size are compressed to 72.4% and 44.6% those of YOLOv5, respectively. Therefore, AEGG-FD achieves an effective balance between model weight, detection speed, and accuracy in firefighting.
ChatGPT Translation of Program Code for Image Sketch Abstraction
Authors: Kumar Yulia , Gordon Zachary , Alabi Oluwatunmise , Li Jenny , Leonard Kathryn , Ness Linda , Morreale Patricia
Published: January 2024

In this comprehensive study, a novel MATLAB to Python (M-to-PY) conversion process is showcased, specifically tailored for an intricate image skeletonization project involving fifteen MATLAB files and a large dataset. The central innovation of this research is the adept use of ChatGPT-4 as an AI assistant, pivotal in crafting a prototype M-to-PY converter. This converter’s capabilities were thoroughly evaluated using a set of test cases generated by the Bard bot, ensuring a robust and effective tool. The culmination of this effort was the development of the Skeleton App, adept at image sketching and skeletonization. This live and publicly available app underscores the enormous potential of AI in enhancing the transition of scientific research from MATLAB to Python. The study highlights the blend of AI’s computational prowess and human ingenuity in computational research, making significant strides in AI-assisted scientific exploration and tool development.
An Experimental Demonstration of 2D-Multiple-Input-Multiple-Output-Based Deep Learning for Optical Camera Communication
Authors: Le Duy Tuan Anh , Nguyen Huy , Jang Yeong Min
Published: January 2024

Currently, radio frequency (RF) waveforms are widely used in wireless communication systems and are widely used in many fields to improve human quality of life. In Internet of Things (IoT) systems and satellite systems, the installation and deployment of wireless communication systems have become easier and offer many advantages compared to wired communication. However, high RF frequencies can have detrimental effects on the human body. Therefore, the visible light bandwidth is being researched and used as a replacement for RF in certain wireless communication systems. Several strategies have been explored: free-space optics, light fidelity, visible light communication, and optical camera communication. By leveraging time-domain on–off keying, this article presents a multiple-input-multiple-output (MIMO) modulation technique using a light-emitting diode (LED) array designed for IoT applications. The proposed scheme is versatile and suitable for both roller shutter and global shutter cameras commonly found on the market, including CCTV cameras commonly found in factories and buildings. By using deep learning for threshold prediction, the proposed scheme could achieve better performance compared to the traditional scheme. Despite the compact size of the LED array, the precise control of the exposure time, camera focal length, and channel encoding enabled the successful implementation of this scheme and supported four links at various positions within a communication distance of 22 m, taking into account the mobility effect (3 m/s).
Suitable Integral Sampling for Bandpass-Sampling Time-Modulated Fourier Transform Spectroscopy
Authors: Chen Xinwen , Tan Zheng , Zhao Na , Wang Jianwei , Liu Yangyang , Tang Yinhui , He Peidong , Li Weiyan , Sun Jianying , Si Jia , Lv Qunbo
Published: January 2024

For traditional Fourier transform (FTS), its integral sampling usually meets the Spectral Modulation Transfer Function (SMTF) criterion. However, for bandpass-sampling Fourier transform spectroscopy (BPS-FTS), based on our analysis, the integral sampling condition derived from the Spectral Modulation Transfer Function (SMTF) is excessively stringent. In other words, the interval of the integral sampling time that fulfills the tolerance requirements for the reconstructed spectrum is very narrow. There are numerous integration sampling time intervals outside this range that still meet the tolerance requirements for the reconstructed spectrum. In this paper, through theoretical modeling, we propose a method based on average |SMTF| as the selection criterion for the integration sampling time. Through simulation analysis, it is evident that the intervals and range of the integral sampling time obtained via this method are more accurate, ensuring the tolerance requirements of the reconstructed spectrum. Under these intervals, when conducting integral sampling on the interferogram, the spectral deviation of the reconstructed spectrum is minimal, and the Spectral Correlation Mapper (SCM) is nearly equal to one. This indicates that compared with the SMTF criterion in traditional FTS, this method is more suitable for the characteristics of BPS-FTS. The analysis in this paper can provide theoretical and simulation support for the implementation of BPS-FTS.
Osteogenic Potential of a Biomaterial Enriched with Osteostatin and Mesenchymal Stem Cells in Osteoporotic Rabbits
Authors: Luengo-Alonso Gonzalo , Bravo-Gimenez Beatriz , Lozano Daniel , Heras Clara , Sanchez-Salcedo Sandra , Benito-Garzón Lorena , Abella Monica , Vallet-Regi María , Cecilia-Lopez David , Salinas Antonio J.
Published: January 2024

Mesoporous bioactive glasses (MBGs) of the SiO2–CaO–P2O5 system are biocompatible materials with a quick and effective in vitro and in vivo bioactive response. MBGs can be enhanced by including therapeutically active ions in their composition, by hosting osteogenic molecules within their mesopores, or by decorating their surfaces with mesenchymal stem cells (MSCs). In previous studies, our group showed that MBGs, ZnO-enriched and loaded with the osteogenic peptide osteostatin (OST), and MSCs exhibited osteogenic features under in vitro conditions. The aim of the present study was to evaluate bone repair capability after large bone defect treatment in distal femur osteoporotic rabbits using MBGs (76%SiO2–15%CaO–5%P2O5–4%ZnO (mol-%)) before and after loading with OST and MSCs from a donor rabbit. MSCs presence and/or OST in scaffolds significantly improved bone repair capacity at 6 and 12 weeks, as confirmed by variations observed in trabecular and cortical bone parameters obtained by micro-CT as well as histological analysis results. A greater effect was observed when OST and MSCs were combined. These findings may indicate the great potential for treating critical bone defects by combining MBGs with MSCs and osteogenic peptides such as OST, with good prospects for translation to clinical practice.
An Investigation of Metabolic Risk Factors and Gut Microbiota in Unexplained Syncope
Authors: Longo Susanna , Del Chierico Federica , Scanu Matteo , Toto Francesca , Legramante Jacopo M. , Rizza Stefano , Putignani Lorenza , Federici Massimo
Published: January 2024

Background: The pathogenesis of many syncopal episodes remains unexplained. Intestinal dysbiosis could be involved in the pathophysiological mechanisms of syncope due to its connection with the central nervous system via the microbiota–gut–brain axis. This pilot study aimed to explore the specific cardiometabolic risk factors and gut microbiota in unexplained syncope (US), compared to other types of syncope, to assess their similarity or verify their different origins. Methods: We studied 86 participants with syncope, who were divided into four groups: an orthostatic syncope group (OH, n = 24), a neuromediated syncope group (NMS, n = 26), a cardiological syncope group (CS, n = 9), and an unexplained syncope group (US, n = 27). We evaluated the anthropometric, clinical, and metabolic characteristics of the four groups; the α- and β-diversity; and the differences in the abundance of the microbial taxa. Results: The US group had a lower incidence of systolic hypertension at the first visit and a lower frequency of patients with nocturnal hypertension than the CS group. Compared to the OH and NMS groups, the US group had a higher incidence of carotid plaques and greater carotid intima–media thickness, respectively. The microbiota differed significantly between the US and CS groups, but not between the US group and the OH or NMS group. Conclusions: We observed significant differences in the gut microbiota between CS and US. Future studies are necessary to evaluate the involvement of the gut microbiota in the complex pathogenesis of syncope and whether its analysis could support the interpretation of the pathophysiological mechasnisms underlying some episodes classifiable as US.
Impact Resistance of Rubberized Alkali-Activated Concrete Incorporating Recycled Aggregate and Recycled Steel Fiber
Authors: Che Weixian , Li Lei , Chen Zhongmin , Liang Donghua , Guo Yongchang
Published: January 2024

Alkali-activated concrete (AAC) features excellent mechanical properties and sustainability. The incorporation of crumb rubber (CR), recycled concrete aggregates (RCAs), and recycled steel fibers (RSFs) can further enhance environmental sustainability. This paper mainly investigated the dynamic behaviors of a novel rubberized AAC incorporating RCAs and RSFs (RuAAC) through Split-Hopkinson Pressure Bar (SHPB) tests. The variables included three types of RSF content (1%, 2% and 3%), five types of rubber content (0%, 5%, 20%, 35% and 50%) and five impact pressures (0.5 MPa, 0.6 MPa, 0.7 MPa, 0.8 MPa and 0.9 MPa). Dynamic stress–strain curves, dynamic strength, the dynamic increase factor (DIF), impact toughness and the synergistic effects of RSF and CR were discussed. The results show that increasing RSF and CR contents could improve the impact resistance of RuAAC under impact loading. The RuAAC exhibited significant strain rate sensitivity, and the sensitivity increased with larger contents of RSF and CR. The increase in strain rate sensitivity was more pronounced with higher CR contents, which was reflected in larger dynamic increase factor (DIF) values. Under high impact pressure, the impact toughness was obviously enhanced with higher RSF contents, while the contribution of increased CR content to impact toughness was not apparent, which may be attributed to the fact that this study only calculated the integral under the dynamic stress–strain curve before the peak stress to determine impact toughness, neglecting the potential contribution of CR particles after the peak point. The obvious strain sensitivity exhibited by the RuAAC in the SHPB tests indicated superior impact performance, making it particularly suitable for architectural structures prone to seismic or explosive impacts.
Equivalent SDOF Models for Estimating Isolation-Layer Energy Dissipation in Base-Isolated Tall Buildings under Strong Winds
Authors: Qian Xiaoxin , Sato Daiki , Osabel Dave Montellano
Published: January 2024

An isolation layer composed of isolators and steel dampers in base-isolated tall buildings can dissipate wind-induced energy via repeated elasto-plastic deformation. Its energy dissipation can be used to estimate its wind-induced responses and the fatigue damage in the steel dampers. Computationally costly time history analyses using multi-degree-of-freedom (MDOF) models suggest that some structural parameters influence the isolation-layer energy dissipation. However, using common single-degree-of-freedom (CS) models cannot fully capture such influences (e.g., those caused by the damping ratio and the natural period of the upper structure). Hence, this paper proposes a more accurate new equivalent single-degree-of-freedom (ES) model to estimate the isolation-layer energy dissipation in base-isolated tall buildings under strong winds. The ES model considers the influence of structural parameters and uses the first mode shapes of the MDOF models. It is as computationally efficient as, but is more accurate than, the CS model. The results indicate that it can estimate the isolation-layer energy dissipation as closely as MDOF models of base-isolated tall-building under strong winds.
Does Genotype-Specific Phenotype in Patients with Multiple Endocrine Neoplasia Type 2 Occur as Current Guidelines Predict?
Authors: Binter Teresa , Baumgartner-Parzer Sabina , Schernthaner-Reiter Marie Helene , Arikan Melisa , Hargitai Lindsay , Niederle Martin Bruno , Niederle Bruno , Scheuba Christian , Riss Philipp
Published: January 2024

The clinical manifestation of multiple endocrine neoplasia type 2 (MEN2) in terms of developing medullary thyroid cancer (MTC), pheochromocytoma (PCC), and/or primary hyperparathyroidism (PHPT) is related to the respective pathogenic variant of the RET proto-oncogene. The aim of this study is to retrospectively analyze the individual, genotype-dependent clinical manifestations of a large cohort of MEN2 patients. By comparing their clinical profile with currently existing evidence-based knowledge, an optimal therapy and prevention strategy in terms of prophylactic thyroidectomy and clinical follow-up could be ensured. This is a retrospective single-center study of 158 MEN2 patients who were diagnosed and/or surgically treated at a tertiary referral care center between 1990 and 2022. All participants were categorized according to their pathogenic variant of the RET proto-oncogene. Subsequently, the clinical manifestation of the disease and its time of occurrence was documented. Our analysis showed results in line with existing studies, except for a considerably lower-than-predicted occurrence of PCC in patients with V804M/L mutations. This study supports the current recommendation regarding the pathogenic variant-dependent management of this rare cancer-associated syndrome.
The Short- and Long-Term Anticipation of Prostate Cancer Incidence in Korea: Based on Social Aging Trends and Prostate-Specific Antigen Testing Rate during the Last Decade
Authors: Pyun Jong Hyun , Ko Young Hwii , Kim Sang Won , Son Nak-Hoon
Published: January 2024

The current incidence of prostate-specific antigen (PSA) testing, which plays a crucial role in detecting prostate cancer (PCa) in an aged population, is low in Korea. Reflecting these epidemiologic characteristics, we estimated the short- and long-term incidences of PCa. A regression equation model was extracted based on two critical pieces of information: (1) the distribution of newly detected PCa cases in each age group of the 50s, 60s, 70s, and over 80s from a recent period (2006–2020), and (2) the PSA testing rate (PSAr) from the previous decade (2006–2016) for each age subgroup. The incidence increased fourfold (4533 in 2006 to 16,815 in 2020), with each age subgroup accounting for 7.9% (50s), 31.4% (60s), 43.0% (70s), and 17.1% (over 80s) of cases in 2020. PSAr increased by an average of 1.08% annually. If these trends are maintained, 28,822 new cases will be diagnosed in 2030 (expected PSAr: 14.4%) and 40,478 cases in 2040 (expected PSAr: 26.4%). If a public PSA screening were implemented for men only in their 60s (assuming a PSAr of 60% in the 60s) and 70s (assuming a PSAr of 80% in the 70s) in 2030, 37,503 cases in 2030 (expected PSAr: 23.1%) and 43,719 cases in 2040 (expected PSAr: 29.9%) would be estimated. According to the projection, the incidence of PCa will increase twofold by 2034 compared to 2020. If national screening were only conducted in the 60s and 70s, a higher detection of almost threefold would be expected by 2040.
Methylated Cell-Free Tumor DNA in Sputum as a Tool for Diagnosing Lung Cancer—A Systematic Review and Meta-Analysis
Authors: Wen Sara Witting Christensen , Borg Morten , Timm Signe , Hansen Torben Frøstrup , Hilberg Ole , Andersen Rikke Fredslund
Published: January 2024

Lung cancer is the leading cause of cancer-related mortality worldwide. Early diagnosis is pivotal for the prognosis. There is a notable overlap between lung cancer and chronic bronchitis, and the potential use of methylated tumor DNA in sputum as a biomarker for lung cancer detection is appealing. This systematic review and meta-analysis followed the PRISMA 2020 statement. A comprehensive search was conducted in Embase, Medline, Web of Science, and the Cochrane Library, using these search strings: Lung cancer, sputum, and methylated tumor DNA. A total of 15 studies met the eligibility criteria. Studies predominantly utilized a case–control design, with sensitivity ranging from 10 to 93% and specificity from 8 to 100%. A meta-analysis of all genes across studies resulted in a summary sensitivity of 54.3% (95% CI 49.4–59.2%) and specificity of 79.7% (95% CI 75.0–83.7%). Notably, two less explored genes (TAC1, SOX17) demonstrated sensitivity levels surpassing 85%. The study’s findings highlight substantial variations in the sensitivity and specificity of methylated tumor DNA in sputum for lung cancer detection. Challenges in reproducibility could stem from differences in tumor site, sample acquisition, extraction methods, and methylation measurement techniques. This meta-analysis provides a foundation for prioritizing high-performing genes, calling for a standardization and refinement of methodologies before potential application in clinical trials.
Progress in Oligometastatic Prostate Cancer: Emerging Imaging Innovations and Therapeutic Approaches
Authors: Oka Ryo , Utsumi Takanobu , Noro Takahide , Suzuki Yuta , Iijima Shota , Sugizaki Yuka , Somoto Takatoshi , Kato Seiji , Endo Takumi , Kamiya Naoto , Suzuki Hiroyoshi
Published: January 2024

Prostate cancer (PCa) exhibits a spectrum of heterogeneity, from indolent to highly aggressive forms, with approximately 10–20% of patients experiencing metastatic PCa. Oligometastatic PCa, characterized by a limited number of metastatic lesions in specific anatomical locations, has gained attention due to advanced imaging modalities. Although patients with metastatic PCa typically receive systemic therapy, personalized treatment approaches for oligometastatic PCa are emerging, including surgical and radiotherapeutic interventions. This comprehensive review explores the latest developments in the field of oligometastatic PCa, including its biological mechanisms, advanced imaging techniques, and relevant clinical studies. Oligometastatic PCa is distinct from widespread metastases and presents challenges in patient classification. Imaging plays a crucial role in identifying and characterizing oligometastatic lesions, with new techniques such as prostate-specific membrane antigen positron emission tomography demonstrating a remarkable efficacy. The management strategies encompass cytoreductive surgery, radiotherapy targeting the primary tumor, and metastasis-directed therapy for recurrent lesions. Ongoing clinical trials are evaluating the effectiveness of these approaches. Oligometastatic PCa occupies a unique position between locally advanced and high-volume metastatic diseases. While a universally accepted definition and standardized diagnostic criteria are still evolving, emerging imaging technologies and therapeutic strategies hold promise for improving the patient outcomes in this intermediate stage of PCa.
The Catalytic Mechanism of [Bmim]Cl-Transition Metal Catalysts for Hydrochlorination of Acetylene
Authors: Shao Hui , Lu Yingzhou , Liang Xin , Li Chunxi
Published: January 2024

Ionic liquids (ILs) are green solvents involved in chemical reaction and separation processes. In this paper, four ILs-based metal catalysts were prepared by dissolving four transition metal chlorides into 1-butyl-3-methylimidazolium chloride ([Bmim]Cl). Their catalytic performance was measured, and the catalytic mechanism was studied via density functional theory (DFT) based on the analysis of the Mayer bonding order, Mulliken charge, molecular electrostatic potential (ESP), electron localization function (ELF), and partial density of states (PDOS). The results show that the catalytic activity follows the order [Bmim]Cl-RuCl3 > [Bmim]Cl-AgCl > [Bmim]Cl-CuCl2 > [Bmim]Cl-CuCl. [Bmim]Cl helps to dissolve and activate HCl, and the metal chlorides can greatly reduce the activation energy of the reaction. This study provides new insights into the catalytic mechanism of IL, transition metals, and their synergistic effect from a microscopic point of view and sheds light on the development of new catalysts for acetylene hydrochlorination.
Scalability and Investigation of the Geometrical Features and Shapes of a Tandem Photo-Electrolysis Cell Based on Non-Critical Raw Materials
Authors: Lo Vecchio Carmelo , Giacoppo Giosuè , Barbera Orazio , Carbone Alessandra , Baglio Vincenzo , Aricò Antonino Salvatore , Monforte Giuseppe , Trocino Stefano
Published: January 2024

Tandem photoelectrochemical cells (PECs) are devices useful for water splitting (WS) with the production of oxygen at the photoanode (PA) and hydrogen at the photocathode (PC) by adsorbing more than 75% of the solar irradiation; a portion of the UV/Vis direct solar irradiation is captured by the PA and a diffused or transmitted IR/Vis portion by the PC. Herein, Ti-doped hematite (PA) and CuO (PC) were employed as abundant and non-critical raw semiconductors characterised by proper band gap and band edge banding for the photoelectrochemical WS and absorption of sunlight. The investigation of inexpensive PEC was focused on the scalability of an active area from 0.25 cm2 to 40 cm2 with a rectangular or square shape. For the first time, this study introduces the novel concept of a glass electrode membrane assembly (GEMA), which was developed with an ionomeric glue to improve the interfacial contact between the membrane and photoelectrodes. On a large scale, the electron–hole recombination and the non-optimal photoelectrodes/electrolyte interface were optimized by inserting a glass support at the photocathode and drilled fluorine tin oxide (FTO) at the photoanode to ensure the flow of reagents and products. Rectangular 40 cm2 PEC showed a larger maximum enthalpy efficiency of 0.6% compared to the square PEC, which had a value of 0.37% at a low bias-assisted voltage (−0.6 V). Furthermore, throughput efficiency reached a maximum value of 1.2% and 0.8%, demonstrating either an important effect of the PEC geometries or a non-significant variation of the photocurrent within the scalability.
Moderate Elevation of Homocysteine Induces Endothelial Dysfunction through Adaptive UPR Activation and Metabolic Rewiring
Authors: Chatterjee Barun , Fatima Fabeha , Seth Surabhi , Sinha Roy Soumya
Published: January 2024

Elevation of the intermediate amino acid metabolite Homocysteine (Hcy) causes Hyperhomocysteinemia (HHcy), a metabolic disorder frequently associated with mutations in the methionine-cysteine metabolic cycle as well as with nutritional deficiency and aging. The previous literature suggests that HHcy is a strong risk factor for cardiovascular diseases. Severe HHcy is well-established to correlate with vascular pathologies primarily via endothelial cell death. Though moderate HHcy is more prevalent and associated with an increased risk of cardiovascular abnormalities in later part of life, its precise role in endothelial physiology is largely unknown. In this study, we report that moderate elevation of Hcy causes endothelial dysfunction through impairment of their migration and proliferation. We established that unlike severe elevation of Hcy, moderate HHcy is not associated with suppression of endothelial VEGF/VEGFR transcripts and ROS induction. We further showed that moderate HHcy induces a sub-lethal ER stress that causes defective endothelial migration through abnormal actin cytoskeletal remodeling. We also found that sub-lethal increase in Hcy causes endothelial proliferation defect by suppressing mitochondrial respiration and concomitantly increases glycolysis to compensate the consequential ATP loss and maintain overall energy homeostasis. Finally, analyzing a previously published microarray dataset, we confirmed that these hallmarks of moderate HHcy are conserved in adult endothelial cells as well. Thus, we identified adaptive UPR and metabolic rewiring as two key mechanistic signatures in moderate HHcy-associated endothelial dysfunction. As HHcy is clinically associated with enhanced vascular inflammation and hypercoagulability, identifying these mechanistic pathways may serve as future targets to regulate endothelial function and health.
A Hybrid Approach to Improve the Video Anomaly Detection Performance of Pixel- and Frame-Based Techniques Using Machine Learning Algorithms
Authors: Tutar Hayati , Güneş Ali , Zontul Metin , Aslan Zafer
Published: January 2024

With the rapid development in technology in recent years, the use of cameras and the production of video and image data have similarly increased. Therefore, there is a great need to develop and improve video surveillance techniques to their maximum extent, particularly in terms of their speed, performance, and resource utilization. It is challenging to accurately detect anomalies and increase the performance by minimizing false positives, especially in crowded and dynamic areas. Therefore, this study proposes a hybrid video anomaly detection model combining multiple machine learning algorithms with pixel-based video anomaly detection (PBVAD) and frame-based video anomaly detection (FBVAD) models. In the PBVAD model, the motion influence map (MIM) algorithm based on spatio–temporal (ST) factors is used, while in the FBVAD model, the k-nearest neighbors (kNN) and support vector machine (SVM) machine learning algorithms are used in a hybrid manner. An important result of our study is the high-performance anomaly detection achieved using the proposed hybrid algorithms on the UCF-Crime data set, which contains 128 h of original real-world video data and has not been extensively studied before. The AUC performance metrics obtained using our FBVAD-kNN algorithm in experiments were averaged to 98.0%. Meanwhile, the success rates obtained using our PBVAD-MIM algorithm in the experiments were averaged to 80.7%. Our study contributes significantly to the prevention of possible harm by detecting anomalies in video data in a near real-time manner.
The Bear Truth: Analyzing Genetic Variability and Population Structure in Sloth Bear across the Vidarbha Landscape Using Microsatellite Markers
Authors: Gomes Lynette , Modi Shrushti , Nigam Parag , Habib Bilal
Published: January 2024

Endemic to the Indian subcontinent, the sloth bear (Melursus ursinus) is a threatened species, present in fragmented habitats across India. Field techniques such as direct observation and camera trapping alone are not sufficient and may not be explicit enough to understand a monomorphic species like the sloth bear at larger spatial scales. In this study, we looked into the genetic structure, variability and population demographics amongst the extant sloth bear populations in the highly fragmented Vidarbha landscape, using a panel of 13 microsatellite markers with a cumulative PID value of 1.48 × 10−5 PIDsibs. Our results revealed genetic clustering (K = 5) and moderate structuring amongst the study populations. Despite being geographically distant and placed in two different genetic clusters, sloth bears from the Melghat Tiger Reserve and Sahyadri Tiger Reserve shared genetic signatures, indicating connectivity, while migration was detected amongst other study areas as well. The findings from this study can serve as baseline assessment for future genetic monitoring of the species in the human-dominated landscape and assist in managerial decisions to step up protection of fragmented forest patches and reduce human–bear conflicts without compromising on the genetic connectivity.
Forced Straightening of the Back Does Not Improve Body Shape
Authors: Kiebzak Wojciech Piotr , Ha Sun-Young , Kosztołowicz Michał , Żurawski Arkadiusz
Published: January 2024

Background: Sitting for a long time causes various postural problems, such as slump sitting. It has been reported that employing a corrected sitting position while lifting the sternum is effective in improving this form of posture. We investigated how a corrected sitting posture with the lifting of the sternum is different from a forced position that is applied through the command and passive positions. Materials and methods: The postural angle of 270 subjects aged 19–23 years was measured in the passive, forced, and corrected positions using a Saunders inclinometer and a Formetric 4D system. Results: As a result, the corrected position had a small range (min–max) at all angles, but the forced position and passive position had a large range (min–max). The lumbar lordosis angle in the corrected position showed positive values throughout its range (min–max), while the other groups showed negative values, which indicates the kyphotic position of the lumbar section. In addition, the percentage error in the corrected position was small, but it presented high values in the other groups. When comparing the average angles between the groups, there were substantial changes observed between the corrected position and the other groups. It was found that the corrected position with the sternum lifted, which is applied to improve slump sitting in the clinical environment, exhibited an angle that differed from that of the forced position and the passive position. Conclusions: Our results suggest that a forced position on the command “scapular retraction” does not meet the clinical assumptions of posture correction, in contrast to the corrected position with the lifting of the sternum for the improvement of slump sitting. The accurate correction of the position of the sternum and sacrum improves the position of the spine in the sagittal plane, enabling physiological values for the kyphosis and lordosis angle parameters to be obtained. This approach combines the ease of execution and precision of the effect. The fact that this method does not require complex tools to accurately correct the body encourages the implementation of this solution in clinical practice.
Adversarial Robustness with Partial Isometry
Authors: Shi-Garrier Loïc , Bouaynaya Nidhal Carla , Delahaye Daniel
Published: January 2024

Despite their remarkable performance, deep learning models still lack robustness guarantees, particularly in the presence of adversarial examples. This significant vulnerability raises concerns about their trustworthiness and hinders their deployment in critical domains that require certified levels of robustness. In this paper, we introduce an information geometric framework to establish precise robustness criteria for l2 white-box attacks in a multi-class classification setting. We endow the output space with the Fisher information metric and derive criteria on the input–output Jacobian to ensure robustness. We show that model robustness can be achieved by constraining the model to be partially isometric around the training points. We evaluate our approach using MNIST and CIFAR-10 datasets against adversarial attacks, revealing its substantial improvements over defensive distillation and Jacobian regularization for medium-sized perturbations and its superior robustness performance to adversarial training for large perturbations, all while maintaining the desired accuracy.
The Thermomajorization Polytope and Its Degeneracies
Authors: vom Ende Frederik , Malvetti Emanuel
Published: January 2024

Drawing inspiration from transportation theory, in this work, we introduce the notions of “well-structured” and “stable” Gibbs states and we investigate their implications for quantum thermodynamics and its resource theory approach via thermal operations. It is found that, in the quasi-classical realm, global cyclic state transfers are impossible if and only if the Gibbs state is stable. Moreover, using a geometric approach by studying the so-called thermomajorization polytope, we prove that any subspace in equilibrium can be brought out of equilibrium via thermal operations. Interestingly, the case of some subsystem being in equilibrium can be witnessed via the degenerate extreme points of the thermomajorization polytope, assuming that the Gibbs state of the system is well structured. These physical considerations are complemented by simple new constructions for the polytope’s extreme points, as well as for an important class of extremal Gibbs-stochastic matrices.
Internet of Underwater Things: A Survey on Simulation Tools and 5G-Based Underwater Networks
Authors: Nkenyereye Lewis , Nkenyereye Lionel , Ndibanje Bruce
Published: January 2024

The term “Internet of Underwater Things (IoUT)” refers to a network of intelligent interconnected underwater devices designed to monitor various underwater activities. The IoUT allows for a network of autonomous underwater vehicles (AUVs) to communicate with each other, sense their surroundings, collect data, and transmit them to control centers on the surface at typical Internet speeds. These data serve as a valuable resource for various tasks, including conducting crash surveys, discovering shipwrecks, detecting early signs of tsunamis, monitoring animal health, obtaining real-time aquatic information, and conducting archaeological expeditions. This paper introduces an additional set of alternative simulation tools for underwater networks. We categorize these tools into open-source and licensed simulator options and recommend that students consider using open-source simulators for monitoring underwater networks. There has not been widespread deployment or extensive research on underwater 5G-based networks. However, simulation tools provide some general insights into the challenges and potential issues associated with evaluating such networks, based on the characteristics of underwater communication and 5G, by surveying 5G-based underwater networks and 5G key aspects addressed by the research community in underwater network systems. Through an extensive review of the literature, we discuss the architecture of both Internet of Underwater application-assisted AUVs and Internet of Underwater Things communications in the 5G-based system.
A Tracking-Based Two-Stage Framework for Spatio-Temporal Action Detection
Authors: Luo Jing , Yang Yulin , Liu Rongkai , Chen Li , Fei Hongxiao , Hu Chao , Shi Ronghua , Zou You
Published: January 2024

Spatio-temporal action detection (STAD) is a task receiving widespread attention and has numerous application scenarios, such as video surveillance and smart education. Current studies follow a localization-based two-stage detection paradigm, which exploits a person detector for action localization and a feature processing model with a classifier for action classification. However, many issues occur due to the imbalance between task settings and model complexity in STAD. Firstly, the model complexity of heavy offline person detectors adds to the inference overhead. Secondly, the frame-level actor proposals are incompatible with the video-level feature aggregation and Region-of-Interest feature pooling in action classification, which limits the detection performance under diverse action motions and results in low detection accuracy. In this paper, we propose a tracking-based two-stage spatio-temporal action detection framework called TrAD. The key idea of TrAD is to build video-level consistency and reduce model complexity in our STAD framework by generating action track proposals among multiple video frames instead of actor proposals in a single frame. In particular, we utilize tailored tracking to simulate the behavior of human cognitive actions and used the captured motion trajectories as video-level proposals. We then integrate a proposal scaling method and a feature aggregation module into action classification to enhance feature pooling for detected tracks. Evaluations in the AVA dataset demonstrate that TrAD achieves SOTA performance with 29.7 mAP, while also facilitating a 58% reduction in overall computation compared to SlowFast.
Asynchronous Consensus Quorum Read: Pioneering Read Optimization for Asynchronous Consensus Protocols
Authors: Dong He , Liu Shengyun
Published: January 2024

In the era of cloud computing, the reliability and efficiency of distributed systems, particularly in cloud-based databases and applications, are important. State Machine Replication (SMR), underpinning these distributed architectures, commonly utilizes consensus protocols to ensure linearizable operations. These protocols are critical in cloud environments as they maintain data consistency across geographically dispersed data centers. However, the inherent latency in cloud infrastructures poses a challenge to the performance of consensus-based systems, especially for read operations that do not alter the system state and are frequently executed. This paper addresses this challenge by proposing “Asynchronous Consensus Quorum Read” (ACQR), a novel read optimization method specifically designed for asynchronous consensus protocols in cloud computing scenarios. We have incorporated ACQR into Rabia, an advanced asynchronous consensus protocol, to show its effectiveness. The experimental results are encouraging, they demonstrate that ACQR improves Rabia’s performance, achieving up to a 1.7× increase in throughput and a 40% reduction in optimal latency. This advancement represents a critical step in enhancing the efficiency of read operations in asynchronous consensus protocols within cloud computing environments.