bluuebunny/crossref_metadata_2025 huggingface dataset:
Involvement of Pyocyanin in Promoting LPS-Induced Apoptosis, Inflammation, and Oxidative Stress in Bovine Mammary Epithelium Cells
Authors: Zhu Hao , Cao Wendi , Huang Yicai , Karrow Niel A. , Yang Zhangping
Published: November 2023

Pyocyanin (PCN) is an extracellular toxin secreted by Pseudomonas aeruginosa (PA), which has redox capacity and disrupts the redox balance of host cells, affecting cell function and leading to cell death. The aim of this experiment was to compare the degree of apoptosis, inflammation, and oxidative stress of bovine mammary epithelium cells (bMECs) induced by lipopolysaccharide (LPS) and pyocyanin (PCN) and to examine whether PCN can promote the apoptosis, inflammation, and oxidative stress of bMECs induced by LPS. In this study, 1 µg/mL LPS and 1 µg/mL PCN were finally selected for subsequent experiments through dose-dependent experiments. In this study, cells were not given any treatment and were used as the control group (NC). The cells were treated with PCN or LPS individually for 6 h as the PCN group (PCN) or the LPS group (LPS), and the combination of LPS and PCN challenge for 6 h as the LPS + PCN (LPS + PCN) group. Compared with the control and LPS groups, PCN resulted in a significantly upregulated expression of genes related to pro-inflammatory (IL-6, TNF-α, MyD88), apoptotic (Bax, Caspase3, Caspase9), as well as protein expression of components in the TLR4/NF-κB signaling pathway (TLR4, p-p65, p65), and p53 signaling pathway (p-p53, p53, Caspase9) (p < 0.05). Moreover, the expression of genes and proteins was significantly upregulated after PCN treatment combined with LPS compared to either LPS or PCN challenge alone (p < 0.05). The stimulation of PCN combined with LPS significantly increased reactive oxygen species (ROS) and malondialdehyde (MDA) production in bovine mammary epithelial cells (bMECs), as well as decreased glutathione peroxidase (GSH-Px) and total antioxidant capacity (T-AOC). Moreover, cells in the LPS + PCN group aggravated oxidative stress and antioxidant inhibition in cells. In addition, the expression of the corresponding genes and proteins related to the Nrf2 pathway (Nrf2, HO-1) was significantly down-regulated in the PCN group as compared to the control group (p < 0.05). Altogether, PCN stimulation exacerbates inflammatory reactions, apoptosis, and oxidative stress reactions, as well as when combined with LPS challenge in bMECs. Therefore, this study indicates that PCN manifests a role in promoting apoptosis, inflammation, and oxidative stress and interacting with LPS to enhance more serious biological stress responses.
HMFN-FSL: Heterogeneous Metric Fusion Network-Based Few-Shot Learning for Crop Disease Recognition
Authors: Yan Wenbo , Feng Quan , Yang Sen , Zhang Jianhua , Yang Wanxia
Published: November 2023

The high performance of deep learning networks relies mainly on massive data. However, collecting enough samples of crop disease is impractical, which significantly limits the intelligent diagnosis of diseases. In this study, we propose Heterogeneous Metric Fusion Network-based Few-Shot Learning (HMFN-FSL), which aims to recognize crop diseases with unseen categories using only a small number of labeled samples. Specifically, CBAM (Convolutional Block Attention Module) was embedded in the feature encoders to improve the feature representation capability. Second, an improved few-shot learning network, namely HMFN-FSL, was built by fusing three metric networks (Prototypical Network, Matching Network, and DeepEMD (Differentiable Earth Mover’s Distance)) under the framework of meta-learning, which solves the problem of the insufficient accuracy of a single metric model. Finally, pre-training and meta-training strategies were optimized to improve the ability to generalize to new tasks in meta-testing. In this study, two datasets named Plantvillage and Field-PV (covering 38 categories of 14 crops and containing 50,403 and 665 images, respectively) are used for extensive comparison and ablation experiments. The results show that the HMFN-FSL proposed in this study outperforms the original metric networks and other state-of-the-art FSL methods. HMFN-FSL achieves 91.21% and 98.29% accuracy for crop disease recognition on 5way-1shot, 5way-5shot tasks on the Plantvillage dataset. The accuracy is improved by 14.86% and 3.96%, respectively, compared to the state-of-the-art method (DeepEMD) in past work. Furthermore, HMFN-FSL was still robust on the field scenes dataset (Field-PV), with average recognition accuracies of 73.80% and 85.86% on 5way-1shot, 5way-5shot tasks, respectively. In addition, domain variation and fine granularity directly affect the performance of the model. In conclusion, the few-shot method proposed in this study for crop disease recognition not only has superior performance in laboratory scenes but is also still effective in field scenes. Our results outperform the existing related works. This study provided technical references for subsequent few-shot disease recognition in complex environments in field environments.
Regional NDVI Attribution Analysis and Trend Prediction Based on the Informer Model: A Case Study of the Maowusu Sandland
Authors: Hou Hongfei , Li Ruiping , Zheng Hexiang , Tong Changfu , Wang Jun , Lu Haiyuan , Wang Guoshuai , Qin Ziyuan , Wang Wanning
Published: November 2023

Terrestrial ecosystems depend heavily on their vegetation; it is possible to forecast future growth trends of regional vegetation by keeping an eye on changes in vegetation dynamics. To circumvent the potential reduction in prediction accuracy caused by the non-stationarity of meteorological changes, we analyzed the characteristics of NDVI (Normalized Difference Vegetation Index) spatial and temporal changes and the influencing factors over the past 20 years in the Maowusu Sandland of China via attribution analysis. We also constructed a comprehensive analysis system for vegetation pre-restoration. Moreover, we combined meteorological data from 2000 to 2018 and presented a deep-learning NDVI-Informer prediction model with a self-attentive mechanism. We also used distillation operation and fusion convolutional neural network for NDVI prediction. Incorporating a probsparse self-attention method successfully overcomes Transformer weaknesses by lowering the memory use and complexity of large time series. It significantly accelerates the inference speed of long time series prediction and works well with non-smooth data. The primary findings were: (1) the Maowusu Sandland’s 20-year average showed a consistent increasing trend in the NDVI at 0.0034 a−1, which was mostly caused by climate change, with a relative contribution rate of 55.47%; (2) The Informer-based model accurately forecasted the NDVI in the research region based on meteorological elements and conducted a thorough analysis of the MAPE (mean absolute percentage error) (2.24%). This suggests that it can effectively lower the data’s volatility and increase prediction accuracy. The anticipated outcomes indicate that the trend will stabilize during the following ten years. To attain more sustainable and efficient agricultural production, the results of this study may be used to accurately estimate future crop yields and NDVI using previous data.
Invisible Invertebrates: The Welfare of Invertebrates in Public Aquaria
Authors: Perkins Kerry
Published: November 2023

Awareness of welfare issues within animal collections is increasing as information becomes more accessible for staff and the public. A knowledge gap remains when considering the welfare of invertebrates, particularly when housed in public aquaria. TripAdvisor comments were analyzed for 485 worldwide aquariums. The public focused on anthropogenic features or charismatic organisms within collections. Invertebrate welfare was only presented in 18% of negative welfare comments compared with the 51% of represented vertebrates and 31% of negative general welfare comments. The UK and USA reported a greater number of perceived invertebrate welfare issues. Greater dissemination of information between aquarists and scientists should be encouraged to drive welfare standards and improve husbandry. In addition, incorporating input from invertebrate aquarists while utilizing welfare toolkits are vital for improving overall standards if we are to have greater representation of invertebrate welfare in public aquaria.
Hyperbaric Oxygen Therapy Alleviates Memory and Motor Impairments Following Traumatic Brain Injury via the Modulation of Mitochondrial-Dysfunction-Induced Neuronal Apoptosis in Rats
Authors: Sakas Reem , Dan Katya , Edelman Doron , Abu-Ata Saher , Ben-Menashe Aviv , Awad-Igbaria Yaseen , Francois-Soustiel Jean , Palzur Eilam
Published: November 2023

Traumatic brain injury (TBI) is a leading cause of morbidity and mortality in young adults, characterized by primary and secondary injury. Primary injury is the immediate mechanical damage, while secondary injury results from delayed neuronal death, often linked to mitochondrial damage accumulation. Hyperbaric oxygen therapy (HBOT) has been proposed as a potential treatment for modulating secondary post-traumatic neuronal death. However, the specific molecular mechanism by which HBOT modulates secondary brain damage through mitochondrial protection remains unclear. Spatial learning, reference memory, and motor performance were measured in rats before and after Controlled Cortical Impact (CCI) injury. The HBOT (2.5 ATA) was performed 4 h following the CCI and twice daily (12 h intervals) for four consecutive days. Mitochondrial functions were assessed via high-resolution respirometry on day 5 following CCI. Moreover, IHC was performed at the end of the experiment to evaluate cortical apoptosis, neuronal survival, and glial activation. The current result indicates that HBOT exhibits a multi-level neuroprotective effect. Thus, we found that HBOT prevents cortical neuronal loss, reduces the apoptosis marker (cleaved-Caspase3), and modulates glial cell proliferation. Furthermore, HBO treatment prevents the reduction in mitochondrial respiration, including non-phosphorylation state, oxidative phosphorylation, and electron transfer capacity. Additionally, a superior motor and spatial learning performance level was observed in the CCI group treated with HBO compared to the CCI group. In conclusion, our findings demonstrate that HBOT during the critical period following the TBI improves cognitive and motor damage via regulating glial proliferation apoptosis and protecting mitochondrial function, consequently preventing cortex neuronal loss.
Nanophotonics and Integrated Photonics
Authors: Lu Fanfan , Yang Zhiqiang
Published: November 2023

Nanophotonics, a field combining photonics and nanotechnology, focuses on the mechanism and application of light–matter interactions at the nanoscale [...]
Integrated Dynamic Model for Numerical Modeling of Complex Landslides: From Progressive Sliding to Rapid Avalanche
Authors: Qiao Cheng , Wang Chunrong
Published: November 2023

Landslides are one of the most common catastrophic mass flows in mountainous areas. The occurrence of fragmentation leads to the evolution of the integrity and stiffness of the sliding mass. The changes in internal composition caused by basal erosion and entrainment make the dynamic evolution of landslides more complex. To consider these complex processes, physics-based dynamic models are often used to analyze the dynamic characteristics of landslides. However, the proprietary assumptions of dynamic models often limit their application to complex events. A single dynamic model is often not competent for the analysis of landslides with evolving dynamic characteristics. In this study, two dynamic models are effectively integrated according to the evolving characteristics of the landslide. The common effects of basal erosion and entrainment are also considered. The maximum sliding velocity, accumulation range, and erosion depth characteristics of this integrated dynamic model are more consistent with the field than those of the single dynamic model. Under the terrain conditions of this study, within a few seconds of the triggering stage, if the occurrence of disintegration is advanced by 2 s, the maximum impact area will increase by about 3.1% to 4.1%, and the maximum kinetic energy will increase by more than 20%. Simulation results indicate that the changes in the integrity of the landslide body significantly affect the evolution of subsequent landslide dynamic characteristics.
Evaluating the Effectiveness of Designs for Low-Cost Digital Manufacturing Systems
Authors: Kaiser Jan , Hawkridge Gregory , Mukherjee Anandarup , McFarlane Duncan
Published: November 2023

There are many well-known systematic approaches to design the digital systems used in manufacturing. However, there are only a few approaches that specifically deal with low-cost components. Such components may not provide the same level of completeness as more expensive industrial alternatives and may need to be combined with other components to become comparable. Consequently, common design challenges for systems comprising such low-cost components revolve around extendability and interface standardisation. There is a need for analysing the capability of the existing approaches to design these systems. This study aims to evaluate the effectiveness of designs for low-cost digital manufacturing systems that have been derived from a particular design approach. The proposed evaluation methodology is used for the special case of designs that are directly based on reference architectures and for the development of specific metrics for that purpose. To quantify the effectiveness, these metrics are applied to a number of design scenarios. Although focusing on reference-architecture-based designs, the proposed methodology can also be used for other design approaches. The evaluation and structured implementation comparison helps practitioners in selecting an effective design approach to low-cost digital manufacturing systems and provides insights into how a particular design approach can become more effective.
Seismic Resilience Evaluation of Reinforced Concrete Frame Considering the Effect of Mainshock-Aftershock Sequences
Authors: Li Peiran , Li Xueqian , Wang Xin , Wang Ding
Published: November 2023

The effect of aftershocks on the function recovery process of damaged structures after large earthquakes cannot be ignored in the resilience evaluation of buildings and communities. Based on the Monte Carlo simulation method, this paper proposes an approach to evaluate the seismic resilience of reinforced concrete frame considering the effect of mainshock-aftershock sequences. The findings of this study can provide a reference for the seismic performance evaluation of reinforced concrete frame structures under aftershocks.
Short-Circuit Damage Diagnosis in Transformer Windings Using Quaternions: Severity Assessment through Current and Vibration Signals
Authors: Contreras-Hernandez Jose-Luis , Almanza-Ojeda Dora-Luz , Ibarra-Manzano Mario-Alberto , Amezquita-Sanchez Juan Pablo , Valtierra-Rodriguez Martin , Camarena-Martinez David
Published: November 2023

Short circuits occurring between turns within the windings are widely known as one of the primary causes of damage in electrical transformers; as a result, early detection plays a fundamental role in preventing further and more serious damage. This study introduces a novel approach that relies on the analysis of current and vibration signals, specifically employing the analysis of quaternion signals, to effectively detect short circuits within electrical transformers., offering an identification of conditions ranging from a healthy state to six levels of short circuit turns. in a no-load transformer, i.e., 0, 5, 10, 15, 20, 25 and 30 SCT. This proposed method employs quaternion rotation to extract statistical features that can be used to classify the condition of the transformer. To evaluate the effectiveness of the proposed methodology, an experimental validation is carried out using a 1.5 kVA transformer, comparing its performance against other existing methods. The results demonstrate the feasibility of the proposal, accurately identifying various levels of SCT, achieving an accuracy of 97.5%, using only 100 samples with the k nearest neighbors method.
An Overview of Hydrogen’s Application for Energy Purposes in Lithuania
Authors: Pranckevičius Darius , Marčiukaitis Mantas , Perednis Eugenijus , Masaitis Sigitas
Published: November 2023

Hydrogen has emerged as a promising climate-neutral energy carrier able to facilitate the processes of the European Union (EU) energy transition. Green hydrogen production through the electrolysis process has gained increasing interest recently for application in various sectors of the economy. As a result of the increasing renewable energy developments in the EU, hydrogen is seen as one of the most promising solutions for energy storage challenges; therefore, the leading countries in the energy sector are heavily investing in research of the technical obstacles for hydrogen applications and assessment of the current hydrogen market, which in turn leads to the acceleration of the upscaling of hydrogen production. The main objective of this article was to provide a comprehensive overview of various green hydrogen production, transportation and industrial application technologies and challenges in Europe, with a separate analysis of the situation in Lithuania. Various water electrolysis technologies and their production costs are investigated, along with recent developments in storage and transportation solutions. In addition, the performances and limitations of electrochemical processes are presented and analysed, research trends in the field are discussed and possible solutions for performance and cost improvements are overviewed. This paper proposes a discussion of perspectives in terms of future applications and research directions.
Design and Experiment of an Electric Control Spiral-Pushing Feed Mechanism for Field Fertilizer Applicator
Authors: Dun Guoqiang , Wu Xingpeng , Ji Xinxin , Wei Yuhan
Published: November 2023

Due to the unique structural characteristics of the traditional spiral fertilizer applicator, the instantaneous filling coefficient cannot be determined, which is not conducive to achieving precise control of the fertilizer discharge rate. Therefore, a spiral-pushing fertilizer applicator has been designed. By using a structure of variable diameter and variable spiral pitch to squeeze fertilizer gradually, precise control of the fertilizer discharge is achieved. The study analyzes the effects of screw pitch, screw diameter, and rotational speed on the filling coefficient; it uses spiral pitch elongation percentage, spiral diameter elongation percentage, and rotational speed as experimental factors, and filling coefficient and particle axial velocity coefficient as experimental indicators. Through quadratic orthogonal rotation combination design experiments, the fertilizer discharge performance of the spiral-pushing fertilizer applicator was optimized. The experimental results indicate that for the filling coefficient, x1x2 has an extremely significant impact, while for the axial velocity coefficient of particles, x1 and x3 have an extremely significant impact. When the rotational speed x3 is 30 r/min, the optimized spiral pitch elongation percentage x1 is 189.82–200%, the spiral diameter elongation percentage x2 is 102.75–106.76, the filling coefficient is greater than 95%, and the particle axial velocity coefficient is less than 10%, achieving the best fertilizer discharge performance. An electrically controlled fertilizer discharge system was also designed, and bench tests were conducted on it. The results show that the average deviation between the fertilizer discharge performance of the spiral-pushing fertilizer applicator driven by the electrically controlled fertilizer discharge system and the preset value is 2.14%. This proves that, when the fertilizer demand changes, the fertilizer discharge flow can be adjusted through the electrically controlled fertilizer discharge system to achieve precise fertilization. This study provides a reference for the design of spiral fertilizer applicators.
Improving the Model Performance of the Ecosystem Carbon Cycle by Integrating Soil Erosion–Related Processes
Authors: Zhang Jinliang , Zhang Chao , Ma Wensi , Wang Wei , Li Haofei
Published: November 2023

Soil erosion is a key factor in soil quality degradation and carbon balance in arid ecosystems. However, many models ignore the soil erosion process in arid regions, which may lead to limits in our understanding of ecosystem processes in arid regions. In this study, we added the soil erosion process according to field observed data of soil hydrothermal regimes and carbon flux. We validated this coupling version of IBIS (Integrated Biosphere Simulator) and RUSLE (RU–IBIS) by examining four different vegetation types and the carbon budget in the arid region on the Loess Plateau (LP). Our results indicated that the coupling model (RU–IBIS) produced more reliable simulations of the soil water content (with the r from 0.23–0.90 to 0.71–0.97) and evaporation (ET) (the average r was 0.76) and significantly improved the simulation of the leaf area index (LAI) (the average r was 0.95) and net primary production (NPP) (the average r was 0.95). We also conducted sensitivity experiments to determine how soil texture and aerodynamic roughness (Z0m) affect the soil water content. Moreover, it was revealed that specific leaf area (SLA) plays a key role in the simulation of NPP and NEE. Our study suggests that the coupled soil erosion process and parameterization can effectively improve the performance of IBIS in arid regions. These results need to be considered in future Earth system models.
Robert J. Gorlin: Personal Memory of a Friend and Mentor in Clinical Genetics
Authors: Dallapiccola Bruno , Mingarelli Rita
Published: November 2023

Robert J [...]
Optimizing Energy Conversion in a Piezo Disk Using a Controlled Supply of Electrical Load
Authors: Boryseiko Oleksandr , Laptiev Oleksandr , Perehuda Oleh , Ryzhov Anton
Published: November 2023

Piezoceramic products are actively used in modern technical devices and appliances. Disk piezoelectric devices are widely used in elements of information systems: in wireless communication, elements of satellite communication, global positioning systems. Among such devices, microwave piezo motors on traveling waves of various types are distinguished, including a motor built on an electromechanical disk converter, which performs non-axisymmetric oscillations under the action of a harmonic electric load. In this study, we perform separation of the electrode coating of the plate with thin diametrical dielectric sections and introduce a time-varying electric potential difference of different amplitude or phase to individual parts of the plate. We study the problem of non-axisymmetric planar oscillations of a piezoceramic disk with thickness polarization and the problem of optimization of the electromechanical coupling coefficient (EMCO) value as a function of an infinite number of parameters in space l2 of square summable sequences.
Utilizing Deep Learning Algorithms for Signal Processing in Electrochemical Biosensors: From Data Augmentation to Detection and Quantification of Chemicals of Interest
Authors: Esmaeili Fatemeh , Cassie Erica , Nguyen Hong Phan T. , Plank Natalie O. V. , Unsworth Charles P. , Wang Alan
Published: November 2023

Nanomaterial-based aptasensors serve as useful instruments for detecting small biological entities. This work utilizes data gathered from three electrochemical aptamer-based sensors varying in receptors, analytes of interest, and lengths of signals. Our ultimate objective was the automatic detection and quantification of target analytes from a segment of the signal recorded by these sensors. Initially, we proposed a data augmentation method using conditional variational autoencoders to address data scarcity. Secondly, we employed recurrent-based networks for signal extrapolation, ensuring uniform signal lengths. In the third step, we developed seven deep learning classification models (GRU, unidirectional LSTM (ULSTM), bidirectional LSTM (BLSTM), ConvGRU, ConvULSTM, ConvBLSTM, and CNN) to identify and quantify specific analyte concentrations for six distinct classes, ranging from the absence of analyte to 10 μM. Finally, the second classification model was created to distinguish between abnormal and normal data segments, detect the presence or absence of analytes in the sample, and, if detected, identify the specific analyte and quantify its concentration. Evaluating the time series forecasting showed that the GRU-based network outperformed two other ULSTM and BLSTM networks. Regarding classification models, it turned out signal extrapolation was not effective in improving the classification performance. Comparing the role of the network architectures in classification performance, the result showed that hybrid networks, including both convolutional and recurrent layers and CNN networks, achieved 82% to 99% accuracy across all three datasets. Utilizing short-term Fourier transform (STFT) as the preprocessing technique improved the performance of all datasets with accuracies from 84% to 99%. These findings underscore the effectiveness of suitable data preprocessing methods in enhancing neural network performance, enabling automatic analyte identification and quantification from electrochemical aptasensor signals.
Open Hardware Implementation of Real-Time Phase and Amplitude Estimation for Neurophysiologic Signals
Authors: Ochoa José Ángel , Gonzalez-Burgos Irene , Nicolás María Jesús , Valencia Miguel
Published: November 2023

Adaptive deep brain stimulation (aDBS) is a promising concept in the field of DBS that consists of delivering electrical stimulation in response to specific events. Dynamic adaptivity arises when stimulation targets dynamically changing states, which often calls for a reliable and fast causal estimation of the phase and amplitude of the signals. Here, we present an open-hardware implementation that exploits the concepts of resonators and Hilbert filters embedded in an open-hardware platform. To emulate real-world scenarios, we built a hardware setup that included a system to replay and process different types of physiological signals and test the accuracy of the instantaneous phase and amplitude estimates. The results show that the system can provide a precise and reliable estimation of the phase even in the challenging scenario of dealing with high-frequency oscillations (~250 Hz) in real-time. The framework might be adopted in neuromodulation studies to quickly test biomarkers in clinical and preclinical settings, supporting the advancement of aDBS.
Single-Cell Measurements and Modeling and Computation of Decision-Making Errors in a Molecular Signaling System with Two Output Molecules
Authors: Emadi Ali , Lipniacki Tomasz , Levchenko Andre , Abdi Ali
Published: November 2023

A cell constantly receives signals and takes different fates accordingly. Given the uncertainty rendered by signal transduction noise, a cell may incorrectly perceive these signals. It may mistakenly behave as if there is a signal, although there is none, or may miss the presence of a signal that actually exists. In this paper, we consider a signaling system with two outputs, and introduce and develop methods to model and compute key cell decision-making parameters based on the two outputs and in response to the input signal. In the considered system, the tumor necrosis factor (TNF) regulates the two transcription factors, the nuclear factor κB (NFκB) and the activating transcription factor-2 (ATF-2). These two system outputs are involved in important physiological functions such as cell death and survival, viral replication, and pathological conditions, such as autoimmune diseases and different types of cancer. Using the introduced methods, we compute and show what the decision thresholds are, based on the single-cell measured concentration levels of NFκB and ATF-2. We also define and compute the decision error probabilities, i.e., false alarm and miss probabilities, based on the concentration levels of the two outputs. By considering the joint response of the two outputs of the signaling system, one can learn more about complex cellular decision-making processes, the corresponding decision error rates, and their possible involvement in the development of some pathological conditions.
Functional and Transcriptomic Characterization of Postnatal Maturation of ENS and SIP Syncytium in Mice Colon
Authors: Wu Zhihao , Wang Qianqian , Yang Fan , Wang Jiaxuan , Zhao Yuying , Perrino Brian A. , Chen Jie
Published: November 2023

The interplay of the enteric nervous system (ENS) and SIP syncytium (smooth muscle cells–interstitial cells of Cajal–PDGFRα+ cells) plays an important role in the regulation of gastrointestinal (GI) motility. This study aimed to investigate the dynamic regulatory mechanisms of the ENS-SIP system on colon motility during postnatal development. Colonic samples of postnatal 1-week-old (PW1), 3-week-old (PW3), and 5-week-old (PW5) mice were characterized by RNA sequencing, qPCR, Western blotting, isometric force recordings (IFR), and colonic motor complex (CMC) force measurements. Our study showed that the transcriptional expression of Pdgfrα, c-Kit, P2ry1, Nos1, and Slc18a3, and the protein expression of nNOS, c-Kit, and ANO1 significantly increased with age from PW1 to PW5. In PW1 and PW3 mice, colonic migrating movement was not fully developed. In PW5 mice, rhythmic CMCs were recorded, similar to the CMC pattern described previously in adult mice. The inhibition of nNOS revealed excitatory and non-propulsive responses which are normally suppressed due to ongoing nitrergic inhibition. During postnatal development, molecular data demonstrated the establishment and expansion of ICC and PDGFRα+ cells, along with nitrergic and cholinergic nerves and purinergic receptors. Our findings are important for understanding the role of the SIP syncytium in generating and establishing CMCs in postnatal, developing murine colons.
Beyond the Cardiovascular Effects of Glucagon-like Peptide-1 Receptor Agonists: Body Slimming and Plaque Stabilization. Are New Statins Born?
Authors: Lecis Dalgisio , Prandi Francesca Romana , Barone Lucy , Belli Martina , Sergi Domenico , Longo Susanna , Muscoli Saverio , Romeo Francesco , Federici Massimo , Lerakis Stamatios , Barillà Francesco
Published: November 2023

Atherosclerosis is a chronic inflammatory disease characterized by lipid and inflammatory cell deposits in the inner layer of large- and medium-sized elastic and muscular arteries. Diabetes mellitus (DM) significantly increases the risk of cardiovascular diseases and the overall and cardiovascular mortality, and it is a pro-atherogenic factor that induces atherosclerosis development and/or accelerates its progression through a multifactorial process. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are a new class of drugs, belonging to the armamentarium to fight type 2 DM, that have shown robust reductions in atherosclerotic events and all-cause mortality in all studies. Preclinical studies have shown that GLP-1RAs play a role in the immunomodulation of atherosclerosis, affecting multiple pathways involved in plaque development and progression. In this review, we wanted to explore the translational power of such preclinical studies by analyzing the most recent clinical trials investigating the atheroprotective effect of GLP-1RAs.
The Art of Designing Work: Work/Family Interface as a Mediator in the Relationship between Work Design, Burnout, and Performance
Authors: Giancaspro Maria Luisa , Gemmano Cataldo Giuliano , Manuti Amelia
Published: November 2023

Because of the massive changes experienced within work contexts over the last decades, work design has received renewed attention both from scholars and practitioners interested in carefully balancing job demands with employees’ needs, aiming to boost performance. Hence, work design, meant as a strategic human resource management tool to craft job context and content, has been proven to impact on work performance and burnout. However, despite this evidence, the literature clearly explaining the paths through which work design might lead to positive or negative organizational outcomes is still scarce. To address this gap, the present study investigated the contribution of work–family interface aspects (i.e., work–family conflict and work–family enrichment) as mediators in this relationship. The participants were 160 white-collar employees, invited to fill in an online survey encompassing socio-demographical information (e.g., age, gender, education, and professional role) and individual self-report responses on the study variables (i.e., work design, work/family conflict, work/family enrichment, burnout, and work performance). The path analyses were conducted to investigate the direct and indirect relationships among constructs. The results showed that work–family conflict mediated the relationships between some work design characteristics and burnout, whereas work–family enrichment had a mediating role in the paths leading both to burnout and to work performance. The implications for research and practice were discussed with respect to an evidence-based human resource management perspective.
Scenario-Based Seismic Risk Assessment for the Reykjavik Capital Area
Authors: Bessason Bjarni , Rupakhety Rajesh , Bjarnason Jón Örvar
Published: November 2023

About two-thirds of the population in Iceland lives in the Reykjavik capital area (RCA), which is close to active volcanoes and seismic zones. In the period 1900–2019, a total of 53 earthquakes of Mw≥5.0 struck in these zones. The two largest events in the Reykjanes Peninsula, Mw6.36 and Mw6.12, occurred in 1929 and 1968, respectively. Both events were less than 20 km from the outskirts of the RCA. Late in the year 2020, the seismicity on the peninsula greatly increased due to magma intrusion and volcanic activity, which has so far resulted in three eruptions, in 2021, 2022, and 2023, and six earthquakes of Mw≥5.0. Based on historical and geological data, the ongoing activity is probably the initial phase of an active period ahead that could continue for many decades, and has the potential to trigger larger earthquakes like those in 1929 and 1968. Further east, in the South Iceland Seismic Zone, two earthquakes of Mw6.52 and 6.44 struck in June 2000, and in May 2008, a Mw6.31 earthquake occurred. In both cases, around 5000 buildings were affected. Insurance loss data from these events have been used to develop empirical vulnerability models for low-rise buildings. In this study, the loss data are used to calibrate seismic vulnerability models in terms of the source-site distance. For a given magnitude scenario, this provides a simpler representation of seismic vulnerability and is useful for emergency planning and disaster management. These models are also used to compute different types of scenario risk maps for the RCA for a repeat of the 1929 earthquake.
Measurement of Water Retention Ratio in Rust Layer by Electrical Resistance
Authors: Hasuike Rina , Yoneyama Ryotaro , Aso Toshihiko
Published: November 2023

One significant form of deterioration in weathering steel bridges is corrosion, and steel requires water and oxygen to corrode. As a measurement method for the wetness time of the rust layer on weathering steel, measuring electrical resistance has been proposed. In this research, the fundamental data have been collected as preliminary considerations to develop this method of measuring water retention in the rust layer. Based on the measurement of specimens, it is revealed that measuring the exact amount of water retention is difficult because electrical resistance depends on the thickness of the rust layer and the supplied amount of NaCl. Thus, the water retention ratio is calculated by dividing the mass of the water-retained specimen by the mass of the full water-retained specimen. These measurement results suggest a potential method for predicting water retention ratio by measuring electrical resistance and rust thickness. The approximate water retention ratio is predicted by plotting electrical resistance and rust thickness in the proposed diagram.
Application of Unconventional External-Field Treatments in Air Pollutants Removal over Zeolite-Based Adsorbents/Catalysts
Authors: Cheng Haodan , Ren Xiaoning , Yao Yuan , Tang Xiaolong , Yi Honghong , Gao Fengyu , Zhou Yuansong , Yu Qingjun
Published: November 2023

Zeolite-based materials are widely used as adsorbents and catalysts for purifying air pollutants like NOx and VOCs due to abundant pore structure, regular pore distribution, and numerous ion exchange sites. Thermal treatment is a necessary procedure for both removing impurities in pores and promoting the metal active dispersed evenly before the zeolite-based adsorbents/catalysts were applied for purifying the NOx/VOCs. Nevertheless, the conventional thermal field treatment (i.e., high-temperature calcination, high-temperature purging, etc.) takes large energy consumption. In contrast, unconventional external-field treatments such as non-thermal plasma and microwave show significant advantages of high efficiency, low energy consumption as well and low pollution, which were used to substitute the traditional thermal treatment in many fields. In this paper, the roles of non-thermal plasma or microwave in the adsorption/catalysis of the NOx/VOCs are reviewed from three aspects assisting activation of materials, cooperative catalysis process, and assisting zeolites synthesis. The reasons for unconventional treatments in improving textural properties, active sites, performance, etc. of zeolite-based materials were illuminated in detail. Moreover, the influences of various parameters (i.e., power, time, temperature, etc.) on the above aspects are elaborated. It is hoped that this review could provide some advanced guidance for the researchers to develop highly efficient materials.
The Roles of Four Important Contexts in Japan’s Carbon Neutrality Policy and Politics, 1990–2020
Authors: Odeyemi Christo , Sekiyama Takashi
Published: November 2023

This study answers four research questions by contextualising the background to Japan’s “carbon neutrality and net-zero” (CNN) policy, which was announced in October 2020, and identifying important changes in Japanese climate policy between 1990 and 2020. What is the link between the problem of fairness under the Kyoto targets and the Japanese government’s initial reluctance towards ambitious carbon emission reductions? Why did the Japanese business sector initially resist the possibility of ambitious carbon emission reductions? How has the term “climate crisis” contributed to the need for CNN policy? Why did the Japanese government change its reluctant stance and announce the CNN policy in October 2020? Four main findings were extracted from a narrative technique-based analysis of Japan’s policy documents related to CNN. The following are the findings: [i] the framing of climate change as a “climate crisis” by influential Japanese climate stakeholders was a key motivation for Japan to formally announce its CNN policy in October 2020; [ii] pressure from the international community and the political leadership of the Yoshihide Suga administration are essential factors that led the Japanese government to change its stance and announced this policy; [iii] it is possible that the policy could have been announced sooner, but concern among Japanese climate stakeholders about the problem of fairness in the Kyoto Protocol’s emission reduction targets likely impeded such an announcement; and [iv] this concern underpinned Keidanren’s (or the business sector’s) consistent opposition to the introduction of regulatory schemes. These results emerge for the first time in a study of Japan’s carbon neutrality, particularly in terms of the broader context of climate politics. Finally, we offer a possible explanation for Suga’s deliberate announcement of the CNN policy. This opens up space for future research to complement our study by providing important indicators on the trajectory of this important policy.
Update on Locoregional Therapies for Liver Cancer: Radiation Segmentectomy
Authors: Dadrass Farnaz , Sher Alex , Kim Edward
Published: November 2023

Over 900,000 people worldwide were diagnosed with liver cancer in 2022 alone, with hepatocellular carcinoma (HCC) accounting for 75–85% of cases. Treatment for HCC includes some combination of systemic therapies, surgery, liver transplantation, ablation, and intra-arterial therapies with transarterial chemoembolization (TACE) or transarterial radioembolization (TARE). Currently, the Barcelona Clinic Liver Cancer (BCLC) guidelines have acknowledged liver transplantation, surgical resection, and thermal ablation as curative therapies in very early to early stage HCC (BCLC-0 and BCLC-A). While these modalities are the preferred curative treatments for a very early to early stage disease, there are challenges associated with these options, such as organ availability and patient eligibility. Current data shows the role of radiation segmentectomy as a curative therapeutic option for very early to early stage HCC that is unresectable and not amenable to ablation. As future data continues to elucidate the ability for radiation segmentectomy to achieve complete pathologic necrosis, the goal is for the BCLC staging model to acknowledge its role as a curative treatment in this patient population and incorporate it into the ever-evolving guidelines.
No Association between SARS-CoV-2 Infection and the Polymorphism of the Toll-like Receptor 7 (TLR7) Gene in Female Population
Authors: Zayed Mohammed , Kim Yong-Chan , Lee Chang-Seop , Jeong Byung-Hoon
Published: November 2023

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a single-stranded RNA virus. Toll-like receptor 7 (TLR7) recognizes single-stranded RNA viruses. The TLR7 gene plays a critical role in the human innate and adaptive immune response to SARS-CoV-2 infections. Genetic factors probably affect SARS-CoV-2 infection susceptibility. In the current study, our aim was to search for genetic variations associated with COVID-19 patients in the TLR7 gene of a Korean population. We designed five gene-specific primers to cover the coding region of the human TLR7 gene. Using amplicon sequencing, we screened the genetic polymorphisms in the coding region of the TLR7 gene in COVID-19 patients and healthy controls. The genotype frequencies, allele frequencies, and Hardy–Weinberg equilibrium (HWE) were examined. We identified a low-frequency synonymous single nucleotide polymorphism (SNP) (rs864058) in the coding region of the TLR7 gene. There were no significant differences in the genotype or allele frequencies of the TLR7 rs864058 polymorphism between COVID-19 female patients and healthy controls (p = 1.0). In conclusion, TLR7 (rs864058) polymorphism is low frequency in Korean populations and is not associated with SARS-CoV-2 infection.
Unpredictable Metastasis in the Head and Neck Region: A Diagnostic Immunohistochemical Challenge
Authors: Closca Raluca-Maria , Nicoara Adrian , Rakitovan Marina , Mot Ion Cristian , Baderca Flavia
Published: November 2023

Metastatic disease is a complex and sequential process that involves the migration of tumor cells from the primary site to distant areas. This metastatic pathway is not always predictable. Therefore, this paper presents three rare cases of unusual metastases, due to their primary site: two metastases of a clear cell renal cell carcinoma, one gingival, and one nasal, as well as a mandibular metastasis of a hepatocellular carcinoma. In all cases, an incisional biopsy was performed in order to find out the diagnosis. After microscopical examination of morphological Hematoxylin and Eosin-stained slides, for all cases, immunohistochemical reactions were performed to support the primary tumor site. Two cases had a previous histopathological diagnosis of a primary tumor, while for the third case, the metastatic lesion represented the first manifestation of the neoplastic disease, with an unfavorable prognosis.
Measurement of Retrobulbar Blood Flow and Vascular Reactivity—Relevance for Ocular and Cardiovascular Diseases
Authors: Böhm Elsa Wilma , Grauhan Nils F. , Pfeiffer Norbert , Gericke Adrian
Published: November 2023

Abnormal retrobulbar hemodynamics have been linked to the development of various ocular diseases, including glaucoma, age-related macular degeneration, and diabetic retinopathy. Additionally, altered retrobulbar blood flow has been observed in patients with severe cardiovascular diseases, including carotid artery occlusion, stroke, heart failure, and acute coronary syndrome. Due to the complex and intricate anatomy of retrobulbar blood vessels and their location behind the eyeball, measurement of retrobulbar blood flow and vascular reactivity, as well as the interpretation of the findings, are challenging. Various methods, such as color Doppler imaging, computed tomography angiography or magnetic resonance imaging, have been employed to assess retrobulbar blood flow velocities in vivo. Color Doppler imaging represents a fast and non-invasive method to measure retrobulbar blood flow velocities in vivo. While no information about vessel diameter can be gained performing this method, computed tomography angiography and magnetic resonance imaging provide information about vessel diameter and detailed information on the anatomical course. Additionally, ex vivo studies, such as myography, utilizing genetically modified animal models may provide high optical resolution for functional vascular investigations in these small vessels. To our best knowledge, this is the first review, presenting a detailed overview of methods aiming to evaluate retrobulbar blood flow and vascular reactivity in both humans and laboratory animals. Furthermore, we will summarize the disturbances observed in retrobulbar blood flow in retinal, optic nerve, and cardiovascular diseases.
Evolving Aspects of Prognostic Factors for Pediatric Cancer
Authors: Kourti Maria , Hatzipantelis Emmanouel
Published: November 2023

Advances in risk-directed therapy based on prognostic factors that include clinical, biologic, and genetic features of cancer in children have yielded improved and prolonged responses [...]