bluuebunny/crossref_metadata_2025 huggingface dataset:
Explainable Multimodal Graph Isomorphism Network for Interpreting Sex Differences in Adolescent Neurodevelopment
Authors: Patel Binish , Orlichenko Anton , Patel Adnan , Qu Gang , Wilson Tony W. , Stephen Julia M. , Calhoun Vince D. , Wang Yu-Ping
Published: May 2024

Background: A fundamental grasp of the variability observed in healthy individuals holds paramount importance in the investigation of neuropsychiatric conditions characterized by sex-related phenotypic distinctions. Functional magnetic resonance imaging (fMRI) serves as a meaningful tool for discerning these differences. Among deep learning models, graph neural networks (GNNs) are particularly well-suited for analyzing brain networks derived from fMRI blood oxygen level-dependent (BOLD) signals, enabling the effective exploration of sex differences during adolescence. Method: In the present study, we introduce a multi-modal graph isomorphism network (MGIN) designed to elucidate sex-based disparities using fMRI task-related data. Our approach amalgamates brain networks obtained from multiple scans of the same individual, thereby enhancing predictive capabilities and feature identification. The MGIN model adeptly pinpoints crucial subnetworks both within and between multi-task fMRI datasets. Moreover, it offers interpretability through the utilization of GNNExplainer, which identifies pivotal sub-network graph structures contributing significantly to sex group classification. Results: Our findings indicate that the MGIN model outperforms competing models in terms of classification accuracy, underscoring the benefits of combining two fMRI paradigms. Additionally, our model discerns the most significant sex-related functional networks, encompassing the default mode network (DMN), visual (VIS) network, cognitive (CNG) network, frontal (FRNT) network, salience (SAL) network, subcortical (SUB) network, and sensorimotor (SM) network associated with hand and mouth movements. Remarkably, the MGIN model achieves superior sex classification accuracy when juxtaposed with other state-of-the-art algorithms, yielding a noteworthy 81.67% improvement in classification accuracy. Conclusion: Our model’s superiority emanates from its capacity to consolidate data from multiple scans of subjects within a proven interpretable framework. Beyond its classification prowess, our model guides our comprehension of neurodevelopment during adolescence by identifying critical subnetworks of functional connectivity.
Intelligent Diagnosis of Concrete Defects Based on Improved Mask R-CNN
Authors: Huang Caiping , Zhou Yongkang , Xie Xin
Published: May 2024

With the rapid development of artificial intelligence, computer vision techniques have been successfully applied to concrete defect diagnosis in bridge structural health monitoring. To enhance the accuracy of identifying the location and type of concrete defects (cracks, exposed bars, spalling, efflorescence and voids), this paper proposes improvements to the existing Mask Region Convolution Neural Network (Mask R-CNN). The improvements are as follows: (i) The residual network (ResNet101), the backbone network of Mask R-CNN which has too many convolution layers, is replaced by the lightweight network MobileNetV2. This can solve the problem that the large number of parameters leads to a slow training speed of the model, and improve the ability to extract features of smaller targets. (ii) Embedding attention mechanism modules in Feature Pyramid Networks (FPNs) to better extract the target features. (iii) A path aggregation network (PANet) is added to solve the problem that the model Mask R-CNN lacks the ability to extract shallow layer feature information. To validate the superiority of the proposed improved Mask R-CNN, the multi-class concrete defect image dataset was constructed, and using the K-means clustering algorithm to determine the aspect ratio of the most suitable prior bounding box for the dataset. Following, the identification results of improved Mask-RCNN, original Mask-RCNN and other mainstream deep learning networks on five types of concrete defects (cracks, exposed bars, spalling, efflorescence and voids) in the dataset were compared. Finally, the intelligent identification system for concrete defects has been established by innovatively combining images taken by unmanned aerial vehicles (UAVs) with our improved defect identification model. The reinforced concrete bridge defects images collected by UAVs were used as test set for testing. The result is the improved Mask R-CNN with superior accuracy, and the identification accuracy is higher than the original Mask-RCNN and other deep learning networks. The improved Mask-RCNN can identify the new untrained concrete defects images taken by UAVs, and the identification accuracy can meet the requirements of bridge structural health monitoring.
Integration of Building Services in Modular Construction: A PRISMA Approach
Authors: Kazeem Kayode O. , Olawumi Timothy O. , Adam Jibrin J. , Lam Edmond Wai-Ming
Published: May 2024

Modular construction is gaining worldwide attention. Building services are the systems that make buildings comfortable and efficient. Mechanical, electrical, and plumbing (MEP) systems provide heating, cooling, lighting, energy distribution, and water services. Modular construction integrates building services into prefabricated modules at the manufacturing site, which are then transported to the construction site. A systematic review (SR) of building services integration within modular construction is thus necessary. Prior to the SR, a quantitative analysis of the retrieved 115 publications from the Scopus database was explored. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guide, 13 relevant publications from the Scopus database and via the Research Rabbit application were profiled. MEP systems integration approaches and research needs were examined. Recent literature emphasizes technology integration and sustainability, while early studies laid the framework for modular approaches. The findings highlight the potential of digital technologies like building information modeling (BIM) and recommend a holistic framework for the entire building’s lifetime, from design to operation. Future research directions include performance studies, modular building service adaptation, and industry-wide standards building. Researchers and practitioners seeking to improve modular construction methods and integrate complex building services will gain insights from this study.
A Hybrid Strategy Improved SPEA2 Algorithm for Multi-Objective Web Service Composition
Authors: Wang Hanting , Du Yugen , Chen Fan
Published: May 2024

Service-oriented architectures have become increasingly prevalent in internet application design, leading to a proliferation of services. Managing and composing these web services pose a classic non-deterministic polynomial-time hard problem. The web service composition problem (WSCP) based on the input–output model (IOM) involves the automatic composition of services without relying on a pre-defined workflow. Multi-objective evolutionary algorithms offer a solution that goes beyond simple weighted average calculations for quality of service, catering to diverse user needs in service composition. This paper introduces a multi-objective heuristic algorithm based on the SPEA2 called MSPEA2+. Specific modifications are incorporated in MSPEA2+ to support the breeding, crossover, and mutation processes, tailored for IOM-based WSCP. Validating the algorithm performance through finding suitable combination results for each task in the WSC-08 dataset, our experiments showed that MSPEA2+ has better iterative efficiency than existing multi-objective methods and outperforms them in terms of quality of service. MSPEA2+ achieved the lowest IGD value across all seven tasks, an improvement of 81.7% IGD value compared with the SPEA2 algorithm in WSC-0804.
Influence of Subsoil and Building Material Properties on Mine-Induced Soil–Structure Interaction Effect
Authors: Zajac Maciej , Kuzniar Krystyna , Tatara Tadeusz
Published: May 2024

Soil–structure interaction (SSI) refers to the dynamic interaction between a structure and the surrounding soil on which it rests. The behavior of the soil can significantly affect the response of the building structure. In the context of civil engineering and structural analysis, SSI becomes particularly important when considering the response of structures to dynamic loads such as earthquakes or so-called paraseismic loads, e.g., mining tremors. Several factors contribute to SSI. Soil and building structure material properties, foundation type, and loading conditions are the most important parameters. The article concerns SSI in the case of mining rock bursts in Poland. The influence of changes in site material conditions and building material properties on the SSI phenomenon was investigated. A few variants of different properties of typical construction materials (brick, reinforced concrete, and cellular concrete) in the case of selected representative building structure were considered. The subsoil material properties from the wide range were also taken into account. Numerical three-dimensional finite element method (FEM) analysis was applied. The adopted models of the soil-structure system were verified by data from in situ experimental vibration measurements. A significant influence of the subgrade material and the building structure material on the SSI was demonstrated.
Development of a Refrigerant-Free Cryotrap Unit for Pre-Concentration of Biogenic Volatile Organic Compounds in Air
Authors: Ding Xiaoxiao , Gong Daocheng , Li Qinqin , Liu Shiwei , Deng Shuo , Wang Hao , Li Hongjie , Wang Boguang
Published: May 2024

Biogenic volatile organic compounds (BVOCs) are key compounds in atmospheric chemistries, but difficult to measure directly. In this study, a pre-concentration unit combined with gas chromatography-mass spectrometry (GC-MS) was developed for the quantitative analysis of 18 BVOCs in ambient air. The analytes are trapped on an empty silonite-coated tube, which is cooled by a thermoacoustic cooler to cryotrap at −150 °C, and then desorbed by rapid heating to 200 °C. The set-up involves neither the exchange of solid adsorbents nor any further condensation or refocusing steps. Reliable operation is ensured by the thermoacoustic cooler, which neither contains a liquid refrigerant nor requires refilling a cryogen. The pre-concentration unit parameters such as water removal temperature, desorption temperature and desorption time were optimized. All compounds had correlation coefficients that were better than 0.95, and the detection limits were 0.005–0.009 ppbv when the injection volume is 400 mL. The repeatability ranges were 0.9–5.8%. The recoveries were ranged from 81.8% to 93.2%. This new method was applied for the first time to measure ambient BVOCs in suburb Guangzhou in summer 2022. Isoprene concentrations ranged from 0.375 ppbv to 2.98 ppbv. In addition, several extremely low-level monoterpenes (e.g., α-pinene, β-pinene, and D-limonene) were also detected by the method.
Protective Effects of Resveratrol on Cytotoxicity of Mouse Hepatic Stellate Cells Induced by PM2.5
Authors: Zhang Mei , Chen Shanshan , Bai Lirong , Chen Wenqi , Li Ruijin
Published: May 2024

The atmosphere’s fine particulate matter (PM2.5) can enter the liver through the circulatory system, leading to hepatic inflammation and fibrosis. As a non-flavonoid polyphenolic compound, resveratrol (RES) has anti-oxidant, anti-inflammatory and hepatoprotective effects, but the molecular mechanisms of liver fibrosis induced by PM2.5 exposure are still limited. In this study, we established an in vitro cell model to investigate the intervention effect of RES with different concentrations (5 and 20 μmol/mL) on mouse hepatic stellate cells (mHSCs) injury induced by PM2.5 (100 μg/mL). We determined the cell viability in mHSCs after treatment with PM2.5 or/and RES for 24 h. We investigated the intracellular oxidative stress by detecting the changes in reactive oxygen species (ROS), malondialdehyde (MDA), superoxide dismutase (SOD) and lactate dehydrogenase (LDH) levels. We also measured the protein expressions of fibrosis-related genes (α-SMA, Collagen I and Collagen III) and key genes (SIRT1, NF-κB, NLRP3, Cleaved-Caspase1, IL-1β) in the NLRP3 pathway in mHSCs exposed to PM2.5 with or without RES. The results showed that (1) PM2.5 has cytotoxic effects on mHSCs, whereas RES (5 μmol/L and 20 μmol/L) inhibited PM2.5-induced cytotoxicity and LDH leakage; (2) RES effectively reduces ROS and MDA production caused by PM2.5 while concurrently enhancing SOD levels, thereby improving cellular anti-oxidant capacity; (3) the expression of α-SMA, Collagen I and Collagen III were notably downregulated in the PM2.5 plus RES treatment group compared to the PM2.5-exposed group; (4) RES significantly increased SIRT1 expression and decreased the expression of NF-κB, NLRP3, Cleaved-Caspase1 and IL-1β in mHSCs exposure to PM2.5 compared to the PM2.5 group. These results demonstrate that RES can up-regulate SIRT1 and mitigate PM2.5-induced fibrosis by suppressing oxidative stress in mHSCs and the SIRT1/NF–κB/NLRP3 pathway activated by PM2.5.
A Field Survey on Indoor Climate in Land Transport Cabins of Buses and Trains
Authors: Ogundiran John , Nyembwe Jean-Paul , Ribeiro Anabela , da Silva Manuel
Published: May 2024

Assessing indoor environmental quality (IEQ) is fundamental to ensuring health, well-being, and safety. A particular type of indoor compartment, land transport cabins (LTCs), specifically those of trains and buses, was surveyed. The global rise in commute and in-cabin exposure time gives relevance to the current study. This study discusses indoor climate (IC) in LTCs to emphasize the risk to the well-being and comfort of exposed occupants linked to poor IEQ, using objective assessment and a communication method following recommendations of the CEN-EN16798-1 standard. The measurement campaign was carried out on 36 trips of real-time travel on 15 buses and 21 trains, mainly in the EU region. Although the measured operative temperature, relative humidity, CO2, and VOC levels followed EN16798-1 requirements in most cabins, compliance gaps were found in the indoor climate of these LTCs as per ventilation requirements. Also, the PMV-PPD index evaluated in two indoor velocity ranges of 0.1 and 0.3 m/s showed that 39% and 56% of the cabins, respectively, were thermally inadequate. Also, ventilation parameters showed that indoor air quality (IAQ) was defective in 83% of the studied LTCs. Therefore, gaps exist concerning the IC of the studied LTCs, suggesting potential risks to well-being and comfort and the need for improved compliance with the IEQ and ventilation criteria of EN16798-1.
COVID-19 Lockdown Air Pollution Reduction: Did It Impact the Number of COPD Hospitalizations?
Authors: Javorac Jovan , Živanović Dejan , Ilić Miroslav , Mijatović Jovin Vesna , Stojkov Svetlana , Smuđa Mirjana , Minaković Ivana , Kolarš Bela , Ćućuz Veljko , Jevtić Marija
Published: May 2024

In addition to the detrimental health consequences, the early stages of the COVID-19 pandemic have yielded unforeseen benefits in terms of reducing air pollution emissions. This study investigated air pollution changes in Novi Sad, Serbia, during the COVID-19 lockdown (March–June 2020) and their correlation with acute exacerbations of chronic obstructive pulmonary disease (AECOPD) hospitalizations. Using quasi-Poisson generalized linear models (GLM) and distributed lag non-linear models (DLNM), we examined the relationship between the number of AECOPD hospitalizations and the concentrations of selected air pollutants (PM10, PM2.5, SO2, and NO2) from March to June of 2019, 2020, and 2021. During the COVID-19 lockdown, significant reductions in most air pollutant concentrations and the number of AECOPD hospitalizations were observed. However, neither the study year nor its interaction with air pollutant concentration significantly predicted AECOPD hospitalizations (p > 0.05). The 95% confidence intervals of the relative risks for the occurrence of AECOPD hospitalizations at each increase in the examined air pollutant by 10 μg/m3 overlapped across years, suggesting consistent effects of air pollution on the risk of AECOPD hospitalizations pre-pandemic and during lockdown. In conclusion, reduced air pollution emissions during the COVID-19 lockdown did not lead to a statistically significant change in the number of AECOPD hospitalizations.
Forecasting the Exceedances of PM2.5 in an Urban Area
Authors: Logothetis Stavros-Andreas , Kosmopoulos Georgios , Panagopoulos Orestis , Salamalikis Vasileios , Kazantzidis Andreas
Published: May 2024

Particular matter (PM) constitutes one of the major air pollutants. Human exposure to fine PM (PM with a median diameter less than or equal to 2.5 μm, PM2.5) has many negative and diverse outcomes for human health, such as respiratory mortality, lung cancer, etc. Accurate air-quality forecasting on a regional scale enables local agencies to design and apply appropriate policies (e.g., meet specific emissions limitations) to tackle the problem of air pollution. Under this framework, low-cost sensors have recently emerged as a valuable tool, facilitating the spatiotemporal monitoring of air pollution on a local scale. In this study, we present a deep learning approach (long short-term memory, LSTM) to forecast the intra-day air pollution exceedances across urban and suburban areas. The PM2.5 data used in this study were collected from 12 well-calibrated low-cost sensors (Purple Air) located in the greater area of the Municipality of Thermi in Thessaloniki, Greece. The LSTM-based methodology implements PM2.5 data as well as auxiliary data, meteorological variables from the Copernicus Atmosphere Monitoring Service (CAMS), which is operated by ECMWF, and time variables related to local emissions to enhance the air pollution forecasting performance. The accuracy of the model forecasts reported adequate results, revealing a correlation coefficient between the measured PM2.5 and the LSTM forecast data ranging between 0.67 and 0.94 for all time horizons, with a decreasing trend as the time horizon increases. Regarding air pollution exceedances, the LSTM forecasting system can correctly capture more than 70.0% of the air pollution exceedance events in the study region. The latter findings highlight the model’s capabilities to correctly detect possible WHO threshold exceedances and provide valuable information regarding local air quality.
Study and Modelling of the Impact of June 2015 Geomagnetic Storms on the Brazilian Ionosphere
Authors: Afolabi Oladayo O. , Candido Claudia Maria Nicoli , Becker-Guedes Fabio , Amory-Mazaudier Christine
Published: May 2024

This study investigated the impact of the June 2015 geomagnetic storms on the Brazilian equatorial and low-latitude ionosphere by analyzing various data sources, including solar wind parameters from the advanced compositional explorer satellite (ACE), global positioning satellite vertical total electron content (GPS-VTEC), geomagnetic data, and validation of the SAMI2 model-VTEC with GPS-VTEC. The effect of geomagnetic disturbances on the Brazilian longitudinal sector was examined by applying multiresolution analysis (MRA) of the maximum overlap discrete wavelet transform (MODWT) to isolate the diurnal component of the disturbance dynamo (Ddyn), DP2 current fluctuations from the ionospheric electric current disturbance (Diono), and semblance cross-correlation wavelet analysis for local phase comparison between the Sq and Diono currents. Our findings revealed that the significant fluctuations in DP2 at the Brazilian equatorial stations (Belem, dip lat: −0.47° and Alta Floresta, dip lat: −3.75°) were influenced by IMF Bz oscillations; the equatorial electrojet also fluctuated in tandem with the DP2 currents, and dayside reconnection generated the field-aligned current that drove the DP2 current system. The short-lived positive ionospheric storm during the main phase on 22 June in the Southern Hemisphere in the Brazilian sector was caused by the interplay between the eastward prompt penetration of the magnetospheric convection electric field and the westward disturbance dynamo electric field. The negative ionospheric storms that occurred during the recovery phase from 23 to 29 June 2015, were attributed to the westward disturbance dynamo electric field, which caused the downward E × B drift of the plasma to a lower height with a high recombination rate. The comparison between the SAMI2 model-VTEC and GPS-VTEC indicates that the SAMI2 model underestimated the VTEC within magnetic latitudes of −9° to −24° in the Brazilian longitudinal sector from 6 to 17 June 2015. However, it demonstrated satisfactory agreement with the GPS-VTEC within magnetic latitudes of −9° to 10° from 8 to 15 June 2015. Conversely, the SAMI2 model overestimated the VTEC between ±10° magnetic latitudes from 16 to 28 June 2015. The most substantial root mean square error (RMSE) values, notably 10.30 and 5.48 TECU, were recorded on 22 and 23 June 2015, coinciding with periods of intense geomagnetic disturbance.
Spatiotemporal Characteristics and Rainfall Thresholds of Geological Landslide Disasters in ASEAN Countries
Authors: Lu Weiping , Xiao Zhixiang , Chen Yuhang , Sun Jingwen , Chen Feisheng
Published: May 2024

Drawing upon a comprehensive global database of landslides and utilizing high-resolution IMERG satellite precipitation data, this study investigates the spatial and temporal variations of landslide occurrences across the member states of the Association of Southeast Asian Nations (ASEAN). This study constructs a region-specific, graded warning system by formulating an average effective intensity–duration (I–D) rainfall threshold curve for each ASEAN member. Examination of 1747 landslide events spanning from 2006 to 2018 illustrates a significant association between the frequency of landslides in ASEAN regions and the latitudinal movement of local precipitation bands. Incidences of landslides hit their lowest in March and April, while a surge is observed from October to January, correlating with the highest mortality rates. Geographical hotspots for landslide activity, characterized by substantial annual rainfall and constrained landmasses, include the Philippine archipelago, Indonesia’s Java Island, and the Malay Peninsula, each experiencing an average of over 2.5 landslides annually. Fatalities accompany approximately 41.4% of ASEAN landslide events, with the Philippines and Indonesia registering the most substantial numbers. Myanmar stands out for the proportion of large-scale landslide incidents, with an average casualty rate of 10.89 deaths per landslide, significantly surpassing other countries in the region. The I–D rainfall threshold curves indicate that the Philippines experienced the highest precipitation levels before landslide initiation, whereas Myanmar has the threshold set at a considerably lower level.
A Vector Representation of Multicomplex Numbers and Its Application to Radio Frequency Signals
Authors: Borio Daniele
Published: May 2024

Hypercomplex numbers, which are multi-dimensional extensions of complex numbers, have been proven beneficial in the development of advanced signal processing algorithms, including multi-dimensional filter design, linear regression and classification. We focus on multicomplex numbers, sets of hypercomplex numbers with commutative products, and introduce a vector representation allowing one to isolate the hyperbolic real and imaginary parts of a multicomplex number. The orthogonal decomposition of a multicomplex number is also discussed, and its connection with Hadamard matrices is highlighted. Finally, a multicomplex polar representation is provided. These properties are used to extend the standard complex baseband signal representation to the multi-dimensional case. It is shown that a set of 2n Radio Frequency (RF) signals can be represented as the real part of a single multicomplex signal modulated by several frequencies. The signal RFs are related through a Hadamard matrix to the modulating frequencies adopted in the multicomplex baseband representation. Moreover, an orthogonal decomposition is provided for the obtained multicomplex baseband signal as a function of the complex baseband representations of the input RF signals.
AI-Driven Spectral Decomposition: Predicting the Most Probable Protein Compositions from Surface Enhanced Raman Spectroscopy Spectra of Amino Acids
Authors: Srivastava Siddharth , Sandhu Nehmat , Liu Jun , Xie Ya-Hong
Published: May 2024

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for elucidating the molecular makeup of materials. It possesses the unique characteristics of single-molecule sensitivity and extremely high specificity. However, the true potential of SERS, particularly in capturing the biochemical content of particles, remains underexplored. In this study, we harnessed transformer neural networks to interpret SERS spectra, aiming to discern the amino acid profiles within proteins. By training the network on the SERS profiles of 20 amino acids of human proteins, we explore the feasibility of predicting the predominant proteins within the µL-scale detection volume of SERS. Our results highlight a consistent alignment between the model’s predictions and the protein’s known amino acid compositions, deepening our understanding of the inherent information contained within SERS spectra. For instance, the model achieved low root mean square error (RMSE) scores and minimal deviation in the prediction of amino acid compositions for proteins such as Bovine Serum Albumin (BSA), ACE2 protein, and CD63 antigen. This novel methodology offers a robust avenue not only for protein analytics but also sets a precedent for the broader realm of spectral analyses across diverse material categories. It represents a solid step forward to establishing SERS-based proteomics.
Updated Toolbox for Assessing Neuronal Network Reconstruction after Cell Therapy
Authors: Gonzalez-Ramos Ana , Puigsasllosas-Pastor Claudia , Arcas-Marquez Ainhoa , Tornero Daniel
Published: May 2024

Cell therapy has proven to be a promising treatment for a range of neurological disorders, including Parkinson Disease, drug-resistant epilepsy, and stroke, by restoring function after brain damage. Nevertheless, evaluating the true effectiveness of these therapeutic interventions requires a deep understanding of the functional integration of grafted cells into existing neural networks. This review explores a powerful arsenal of molecular techniques revolutionizing our ability to unveil functional integration of grafted cells within the host brain. From precise manipulation of neuronal activity to pinpoint the functional contribution of transplanted cells by using opto- and chemo-genetics, to real-time monitoring of neuronal dynamics shedding light on functional connectivity within the reconstructed circuits by using genetically encoded (calcium) indicators in vivo. Finally, structural reconstruction and mapping communication pathways between grafted and host neurons can be achieved by monosynaptic tracing with viral vectors. The cutting-edge toolbox presented here holds immense promise for elucidating the impact of cell therapy on neural circuitry and guiding the development of more effective treatments for neurological disorders.
TD Swin-UNet: Texture-Driven Swin-UNet with Enhanced Boundary-Wise Perception for Retinal Vessel Segmentation
Authors: Li Angran , Sun Mingzhu , Wang Zengshuo
Published: May 2024

Retinal vessel segmentation plays a crucial role in medical image analysis, aiding ophthalmologists in disease diagnosis, monitoring, and treatment guidance. However, due to the complex boundary structure and rich texture features in retinal blood vessel images, existing methods have challenges in the accurate segmentation of blood vessel boundaries. In this study, we propose the texture-driven Swin-UNet with enhanced boundary-wise perception. Firstly, we designed a Cross-level Texture Complementary Module (CTCM) to fuse feature maps at different scales during the encoding stage, thereby recovering detailed features lost in the downsampling process. Additionally, we introduced a Pixel-wise Texture Swin Block (PT Swin Block) to improve the model’s ability to localize vessel boundary and contour information. Finally, we introduced an improved Hausdorff distance loss function to further enhance the accuracy of vessel boundary segmentation. The proposed method was evaluated on the DRIVE and CHASEDB1 datasets, and the experimental results demonstrate that our model obtained superior performance in terms of Accuracy (ACC), Sensitivity (SE), Specificity (SP), and F1 score (F1), and the accuracy of vessel boundary segmentation was significantly improved.
Applying a Random Encounter Model to Estimate the Asiatic Black Bear (Ursus thibetanus) Density from Camera Traps in the Hindu Raj Mountains, Pakistan
Authors: Ahmad Faizan , Mori Tomoki , Rehan Muhammad , Bosso Luciano , Kabir Muhammad
Published: May 2024

Estimating the population density of vulnerable species, such as the elusive and nocturnal Asiatic black bear (Ursus thibetanus), is essential for wildlife conservation and management. We used camera traps and a Random Encounter Model (REM) to estimate the population density of U. thibetanus during the autumn and winter seasons in the Hindu Raj Mountains. We installed 23 camera traps from October to December 2020 and acquired 66 independent pictures of Asiatic black bears over 428 trap nights. Our results showed that the bears preferred lowland areas with the presence of Quercus spp. We estimated, using the REM, a population density of U. thibetanus of 1.875 (standard error = 0.185) per square kilometer, which is significantly higher than that in other habitats. Our results showed that during autumn and winter, the bear population density tends to concentrate at lower elevations. Forest cover showed a positive correlation with the rates of bear encounters unlike the Euclidean distance to human settlements, altitude, and aspect variables. The approaches used here are cost-effective for estimating the population density of rare and vulnerable species such as U. thibetanus, and can be used to estimate their population density in Pakistan. Population density estimation can identify areas where the bears live and human–bear conflicts occurred and use this information in future wildlife management plans.
Astrogliosis in the GFAP-CreERT2:Rosa26iDTR Mouse Model Does Not Exacerbate Retinal Microglia Activation or Müller Cell Gliosis under Hypoxic Conditions
Authors: Rorex Colin , Cardona Sandra M. , Church Kaira A. , Rodriguez Derek , Vanegas Difernando , Saldivar Reina , Faz Brianna , Cardona Astrid E.
Published: May 2024

Diabetic retinopathy (DR) affects over 140 million people globally. The mechanisms that lead to blindness are still enigmatic but there is evidence that sustained inflammation and hypoxia contribute to vascular damage. Despite efforts to understand the role of inflammation and microglia in DR’s pathology, the contribution of astrocytes to hypoxic responses is less clear. To investigate the role of astrocytes in hypoxia-induced retinopathy, we utilized a 7-day systemic hypoxia model using the GFAP-CreERT2:Rosa26iDTR transgenic mouse line. This allows for the induction of inflammatory reactive astrogliosis following tamoxifen and diphtheria toxin administration. We hypothesize that DTx-induced astrogliosis is neuroprotective during hypoxia-induced retinopathy. Glial, neuronal, and vascular responses were quantified using immunostaining, with antibodies against GFAP, vimentin, IBA-1, NeuN, fibrinogen, and CD31. Cytokine responses were measured in both the brain and serum. We report that while both DTx and hypoxia induced a phenotype of reduced microglia morphological activation, DTx, but not hypoxia, induced an increase in the Müller glia marker vimentin. We did not observe that the combination of DTx and hypoxic treatments exacerbated the signs of reactive glial cells, nor did we observe a significant change in the expression immunomodulatory mediators IL-1β, IL2, IL-4, IL-5, IL-6, IL-10, IL-18, CCL17, TGF-β1, GM-CSF, TNF-α, and IFN-γ. Overall, our results suggest that, in this hypoxia model, reactive astrogliosis does not alter the inflammatory responses or cause vascular damage in the retina.
The Third-Generation Sequencing Challenge: Novel Insights for the Omic Sciences
Authors: Scarano Carmela , Veneruso Iolanda , De Simone Rosa Redenta , Di Bonito Gennaro , Secondino Angela , D’Argenio Valeria
Published: May 2024

The understanding of the human genome has been greatly improved by the advent of next-generation sequencing technologies (NGS). Despite the undeniable advantages responsible for their widespread diffusion, these methods have some constraints, mainly related to short read length and the need for PCR amplification. As a consequence, long-read sequencers, called third-generation sequencing (TGS), have been developed, promising to overcome NGS. Starting from the first prototype, TGS has progressively ameliorated its chemistries by improving both read length and base-calling accuracy, as well as simultaneously reducing the costs/base. Based on these premises, TGS is showing its potential in many fields, including the analysis of difficult-to-sequence genomic regions, structural variations detection, RNA expression profiling, DNA methylation study, and metagenomic analyses. Protocol standardization and the development of easy-to-use pipelines for data analysis will enhance TGS use, also opening the way for their routine applications in diagnostic contexts.
Risk and Resilience Variants in the Retinoic Acid Metabolic and Developmental Pathways Associated with Risk of FASD Outcomes
Authors: McKay Leo , Petrelli Berardino , Pind Molly , Reynolds James N. , Wintle Richard F. , Chudley Albert E. , Drögemöller Britt , Fainsod Abraham , Scherer Stephen W. , Hanlon-Dearman Ana , Hicks Geoffrey G.
Published: May 2024

Fetal Alcohol Spectrum Disorder (FASD) is a common neurodevelopmental disorder that affects an estimated 2–5% of North Americans. FASD is induced by prenatal alcohol exposure (PAE) during pregnancy and while there is a clear genetic contribution, few genetic factors are currently identified or understood. In this study, using a candidate gene approach, we performed a genetic variant analysis of retinoic acid (RA) metabolic and developmental signaling pathway genes on whole exome sequencing data of 23 FASD-diagnosed individuals. We found risk and resilience alleles in ADH and ALDH genes known to normally be involved in alcohol detoxification at the expense of RA production, causing RA deficiency, following PAE. Risk and resilience variants were also identified in RA-regulated developmental pathway genes, especially in SHH and WNT pathways. Notably, we also identified significant variants in the causative genes of rare neurodevelopmental disorders sharing comorbidities with FASD, including STRA6 (Matthew–Wood), SOX9 (Campomelic Dysplasia), FDG1 (Aarskog), and 22q11.2 deletion syndrome (TBX1). Although this is a small exploratory study, the findings support PAE-induced RA deficiency as a major etiology underlying FASD and suggest risk and resilience variants may be suitable biomarkers to determine the risk of FASD outcomes following PAE.
The Emerging Role of Ubiquitin-Specific Protease 36 (USP36) in Cancer and Beyond
Authors: Niu Meng-Yao , Liu Yan-Jun , Shi Jin-Jin , Chen Ru-Yi , Zhang Shun , Li Chang-Yun , Cao Jia-Feng , Yang Guan-Jun , Chen Jiong
Published: May 2024

The balance between ubiquitination and deubiquitination is instrumental in the regulation of protein stability and maintenance of cellular homeostasis. The deubiquitinating enzyme, ubiquitin-specific protease 36 (USP36), a member of the USP family, plays a crucial role in this dynamic equilibrium by hydrolyzing and removing ubiquitin chains from target proteins and facilitating their proteasome-dependent degradation. The multifaceted functions of USP36 have been implicated in various disease processes, including cancer, infections, and inflammation, via the modulation of numerous cellular events, including gene transcription regulation, cell cycle regulation, immune responses, signal transduction, tumor growth, and inflammatory processes. The objective of this review is to provide a comprehensive summary of the current state of research on the roles of USP36 in different pathological conditions. By synthesizing the findings from previous studies, we have aimed to increase our understanding of the mechanisms underlying these diseases and identify potential therapeutic targets for their treatment.
Apigenin Provides Structural Protection to Human Fibrinogen against Nitrosative Stress: Biochemical and Molecular Insights
Authors: Farhana Aisha , Alsrhani Abdullah , Khan Yusuf Saleem , Salahuddin Mohammad , Sayeed Mohammed Ubaidullah , Rasheed Zafar
Published: May 2024

Background: Peroxynitrite (ONOO−) is an oxidant linked with several human pathologies. Apigenin, a natural flavonoid known for its health benefits, remains unexplored in relation to ONOO− effects. This study investigated the potential of apigenin to structurally protect fibrinogen, an essential blood clotting factor, from ONOO−-induced damage. Methods: Multi-approach analyses were carried out where fibrinogen was exposed to ONOO− generation while testing the efficacy of apigenin. The role of apigenin against ONOO−-induced modifications in fibrinogen was investigated using UV spectroscopy, tryptophan or tyrosine fluorescence, protein hydrophobicity, carbonylation, and electrophoretic analyses. Results: The findings demonstrate that apigenin significantly inhibits ONOO−-induced oxidative damage in fibrinogen. ONOO− caused reduced UV absorption, which was reversed by apigenin treatment. Moreover, ONOO− diminished tryptophan and tyrosine fluorescence, which was effectively restored by apigenin treatment. Apigenin also reduced the hydrophobicity of ONOO−-damaged fibrinogen. Moreover, apigenin exhibited protective effects against ONOO−-induced protein carbonylation. SDS-PAGE analyses revealed that ONOO−treatment eliminated bands corresponding to fibrinogen polypeptide chains Aα and γ, while apigenin preserved these changes. Conclusions: This study highlights, for the first time, the role of apigenin in structural protection of human fibrinogen against peroxynitrite-induced nitrosative damage. Our data indicate that apigenin offers structural protection to all three polypeptide chains (Aα, Bβ, and γ) of human fibrinogen. Specifically, apigenin prevents the dislocation or breakdown of the amino acids tryptophan, tyrosine, lysine, arginine, proline, and threonine and also prevents the exposure of hydrophobic sites in fibrinogen induced by ONOO−.
NEDD4 and NEDD4L: Ubiquitin Ligases Closely Related to Digestive Diseases
Authors: Xu Jiafan , Jiang Wang , Hu Tian , Long Yan , Shen Yueming
Published: May 2024

Protein ubiquitination is an enzymatic cascade reaction and serves as an important protein post-translational modification (PTM) that is involved in the vast majority of cellular life activities. The key enzyme in the ubiquitination process is E3 ubiquitin ligase (E3), which catalyzes the binding of ubiquitin (Ub) to the protein substrate and influences substrate specificity. In recent years, the relationship between the subfamily of neuron-expressed developmental downregulation 4 (NEDD4), which belongs to the E3 ligase system, and digestive diseases has drawn widespread attention. Numerous studies have shown that NEDD4 and NEDD4L of the NEDD4 family can regulate the digestive function, as well as a series of related physiological and pathological processes, by controlling the subsequent degradation of proteins such as PTEN, c-Myc, and P21, along with substrate ubiquitination. In this article, we reviewed the appropriate functions of NEDD4 and NEDD4L in digestive diseases including cell proliferation, invasion, metastasis, chemotherapeutic drug resistance, and multiple signaling pathways, based on the currently available research evidence for the purpose of providing new ideas for the prevention and treatment of digestive diseases.
Effect of Flavonols of Aronia melanocarpa Fruits on Morphofunctional State of Immunocompetent Organs of Rats under Cyclophosphamide-Induced Immunosuppression
Authors: Bushmeleva Kseniya , Vyshtakalyuk Alexandra , Terenzhev Dmitriy , Belov Timur , Nikitin Evgeniy , Zobov Vladimir
Published: May 2024

Aronia melanocarpa berries contain many compounds with potential benefits for human health. The food flavonoids quercetin and rutin, found in significant amounts in the fruits of A. melanocarpa, are known to have favourable effects on animal and human organisms. However, data on the effect of flavonols isolated from black chokeberry on immune functions during immunosuppression are not available in the literature. Thus, the aim of this study was to evaluate the effect of flavonol fraction isolated from A. melanocarpa fruits, in comparison with pure quercetin and rutin substances, on the dysfunctional state of rat thymus and spleen in immunodeficiency. The study was performed on Wistar rats. The animals were orally administered solutions of the investigated substances for 7 days: water, a mixture of quercetin and rutin and flavonol fraction of A. melanocarpa. For induction of immunosuppression, the animals were injected once intraperitoneally with cyclophosphamide. Substance administration was then continued for another 7 days. The results showed that under the influence of flavonols, there was a decrease in cyclophosphamide-mediated reaction of lipid peroxidation enhancement and stimulation of proliferation of lymphocytes of thymus and spleen in rats. At that, the effect of the flavonol fraction of aronia was more pronounced.
BSA Binding and Aggregate Formation of a Synthetic Amino Acid with Potential for Promoting Fibroblast Proliferation: An In Silico, CD Spectroscopic, DLS, and Cellular Study
Authors: Simonyan Hayarpi , Palumbo Rosanna , Petrosyan Satenik , Mkrtchyan Anna , Galstyan Armen , Saghyan Ashot , Scognamiglio Pasqualina Liana , Vicidomini Caterina , Fik-Jaskólka Marta , Roviello Giovanni N.
Published: May 2024

This study presents the chemical synthesis, purification, and characterization of a novel non-natural synthetic amino acid. The compound was synthesized in solution, purified, and characterized using NMR spectroscopy, polarimetry, and melting point determination. Dynamic Light Scattering (DLS) analysis demonstrated its ability to form aggregates with an average size of 391 nm, extending to the low micrometric size range. Furthermore, cellular biological assays revealed its ability to enhance fibroblast cell growth, highlighting its potential for tissue regenerative applications. Circular dichroism (CD) spectroscopy showed the ability of the synthetic amino acid to bind serum albumins (using bovine serum albumin (BSA) as a model), and CD deconvolution provided insights into the changes in the secondary structures of BSA upon interaction with the amino acid ligand. Additionally, molecular docking using HDOCK software elucidated the most likely binding mode of the ligand inside the BSA structure. We also performed in silico oligomerization of the synthetic compound in order to obtain a model of aggregate to investigate computationally. In more detail, the dimer formation achieved by molecular self-docking showed two distinct poses, corresponding to the lowest and comparable energies, with one pose exhibiting a quasi-coplanar arrangement characterized by a close alignment of two aromatic rings from the synthetic amino acids within the dimer, suggesting the presence of π-π stacking interactions. In contrast, the second pose displayed a non-coplanar configuration, with the aromatic rings oriented in a staggered arrangement, indicating distinct modes of interaction. Both poses were further utilized in the self-docking procedure. Notably, iterative molecular docking of amino acid structures resulted in the formation of higher-order aggregates, with a model of a 512-mer aggregate obtained through self-docking procedures. This model of aggregate presented a cavity capable of hosting therapeutic cargoes and biomolecules, rendering it a potential scaffold for cell adhesion and growth in tissue regenerative applications. Overall, our findings highlight the potential of this synthetic amino acid for tissue regenerative therapeutics and provide valuable insights into its molecular interactions and aggregation behavior.
Affibody PET Imaging of HER2-Expressing Cancers as a Key to Guide HER2-Targeted Therapy
Authors: Eissler Nina , Altena Renske , Alhuseinalkhudhur Ali , Bragina Olga , Feldwisch Joachim , Wuerth Guido , Loftenius Annika , Brun Nikolai , Axelsson Rimma , Tolmachev Vladimir , Sörensen Jens , Frejd Fredrik Y.
Published: May 2024

Human epidermal growth factor receptor 2 (HER2) is a major prognostic and predictive marker overexpressed in 15–20% of breast cancers. The diagnostic reference standard for selecting patients for HER2-targeted therapy is based on the analysis of tumor biopsies. Previously patients were defined as HER2-positive or -negative; however, with the approval of novel treatment options, specifically the antibody–drug conjugate trastuzumab deruxtecan, many breast cancer patients with tumors expressing low levels of HER2 have become eligible for HER2-targeted therapy. Such patients will need to be reliably identified by suitable diagnostic methods. Biopsy-based diagnostics are invasive, and repeat biopsies are not always feasible. They cannot visualize the heterogeneity of HER2 expression, leading to a substantial number of misdiagnosed patients. An alternative and highly accurate diagnostic method is molecular imaging with radiotracers. In the case of HER2, various studies demonstrate the clinical utility and feasibility of such approaches. Radiotracers based on Affibody® molecules, small, engineered affinity proteins with a size of ~6.5 kDa, are clinically validated molecules with favorable characteristics for imaging. In this article, we summarize the HER2-targeted therapeutic landscape, describe our experience with imaging diagnostics for HER2, and review the currently available clinical data on HER2-Affibody-based molecular imaging as a novel diagnostic tool in breast cancer and beyond.