bluuebunny/crossref_metadata_2025 huggingface dataset:
Proton-Transfer-Reaction Mass Spectrometry for Rapid Dynamic Measurement of Ethylene Oxide Volatilization from Medical Masks
Authors: Wang Runyu , Zhang Yunhe , Jiao Leizi , Zhao Xiande , Gao Zhen , Dong Daming
Published: January 2024

Sterile medical masks are essential in preventing infectious diseases. However, the ethylene oxide contained within these masks is a class I carcinogen. The standard method for measuring ethylene oxide is gas chromatography-mass spectrometry, which is not fit with the dynamic process of human inhalation. Thus, the amount of ethylene oxide volatilized from masks and inhaled by users is unknown. In this work, ethylene oxide was detected by using proton-transfer-reaction mass spectrometry, which can measure volatile quantities in milliseconds. We found that ethylene oxide was volatilized from masks during use. Within the first minute, the ethylene oxide concentration decreased by 84.65%, and then the rate of reduction gradually slowed. After 5 min, all ethylene oxide was effectively volatilized, and the average mass of ethylene oxide inhaled was 299.02 μg. We investigated three methods to reduce the concentration of ethylene oxide in masks before use: natural airing, shaking the mask, and blowing the mask with a hair dryer. The hair dryer method produced the best results: the ethylene oxide concentration decreased by 88.3% after only 10 s. The natural airing method was the least effective: the ethylene oxide concentration decreased by 60.7% even after 3 h.
Review of Smog Chamber Experiments for Secondary Organic Aerosol Formation
Authors: Kim Hyun , Kang Dahyun , Jung Heon Young , Jeon Jongho , Lee Jae Young
Published: January 2024

In this study, we reviewed smog chamber systems and methodologies used in secondary organic aerosol (SOA) formation studies. Many important chambers across the world have been reviewed, including 18 American, 24 European, and 8 Asian chambers. The characteristics of the chambers (location, reactor size, wall materials, and light sources), measurement systems (popular equipment and working principles), and methodologies (SOA yield calculation and wall-loss correction) are summarized. This review discussed key experimental parameters such as surface-to-volume ratio (S/V), temperature, relative humidity, light intensity, and wall effect that influence the results of the experiment, and how the methodologies have evolved for more accurate simulation of atmospheric processes. In addition, this review identifies the sources of uncertainties in finding SOA yields that are originated from experimental systems and methodologies used in previous studies. The intensity of the installed artificial lights (photolysis rate of NO2 varied from 0.1/min to 0.40/min), SOA density assumption (varied from 1 g/cm3 to 1.45 g/cm3), wall-loss management, and background contaminants were identified as important sources of uncertainty. The methodologies developed in previous studies to minimize those uncertainties are also discussed.
A Self Adaptive Three-Step Numerical Scheme for Variational Inequalities
Authors: Sanaullah Kubra , Ullah Saleem , Aloraini Najla M.
Published: January 2024

In this paper, we introduce a new three-step iterative scheme for finding the common solutions of the variational inequality using the technique of updating the solution. We suggest, iterative algorithms involving three-steps for the predictor-corrector method of variational inequality in real Hilbert spaces H. Our results include the Takahashi and Toyoda, extra gradient, Mann and Noor iterations as special cases. We also investigate the convergence criteria of the three-step iterative scheme. As special cases, the earlier findings are included in our results, which can be seen as an advancement and improvement over the previous investigation. This is a new refinement in our existing literature and previously known algorithms. A numerical example is given to illustrate the efficiency and performance of the proposed self-adaptive scheme.
A Long-Chain Dextran Produced by Weissella cibaria Boosts the Diversity of Health-Related Gut Microbes Ex Vivo
Authors: Tintoré Maria , Cuñé Jordi , Vu Lam Dai , Poppe Jonas , Van den Abbeele Pieter , Baudot Aurélien , de Lecea Carlos
Published: January 2024

Long-chain dextrans are α-glucans that can be produced by lactic acid bacteria. NextDextTM, a specific long-chain dextran with a high degree of polymerisation, produced using Weissella cibaria, was recently shown to exert prebiotic potential in vitro. In this study, the ex vivo SIFR® technology, recently validated to provide predictive insights into gut microbiome modulation down to the species level, was used to investigate the effects of this long-chain dextran on the gut microbiota of six human adults that altogether covered different enterotypes. A novel community modulation score (CMS) was introduced based on the strength of quantitative 16S rRNA gene sequencing and the highly controlled ex vivo conditions. This CMS overcomes the limitations of traditional α-diversity indices and its application in the current study revealed that dextran is a potent booster of microbial diversity compared to the reference prebiotic inulin (IN). Long-chain dextran not only exerted bifidogenic effects but also consistently promoted Bacteroides spp., Parabacteroides distasonis and butyrate-producing species like Faecalibacterium prausnitzii and Anaerobutyricum hallii. Further, long-chain dextran treatment resulted in lower gas production compared to IN, suggesting that long-chain dextran could be better tolerated. The additional increase in Bacteroides for dextran compared to IN is likely related to the higher propionate:acetate ratio, attributing potential to long-chain dextran for improving metabolic health and weight management. Moreover, the stimulation of butyrate by dextran suggests its potential for improving gut barrier function and inflammation. Overall, this study provides a novel tool for assessing gut microbial diversity ex vivo and positions long-chain dextran as a substrate that has unique microbial diversity enhancing properties.
Progress to Clarify How NOTCH3 Mutations Lead to CADASIL, a Hereditary Cerebral Small Vessel Disease
Authors: Mizuta Ikuko , Nakao-Azuma Yumiko , Yoshida Hideki , Yamaguchi Masamitsu , Mizuno Toshiki
Published: January 2024

Notch signaling is conserved in C. elegans, Drosophila, and mammals. Among the four NOTCH genes in humans, NOTCH1, NOTCH2, and NOTCH3 are known to cause monogenic hereditary disorders. Most NOTCH-related disorders are congenital and caused by a gain or loss of Notch signaling activity. In contrast, cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) caused by NOTCH3 is adult-onset and considered to be caused by accumulation of the mutant NOTCH3 extracellular domain (N3ECD) and, possibly, by an impairment in Notch signaling. Pathophysiological processes following mutant N3ECD accumulation have been intensively investigated; however, the process leading to N3ECD accumulation and its association with canonical NOTCH3 signaling remain unknown. We reviewed the progress in clarifying the pathophysiological process involving mutant NOTCH3.
Personal and Psychological Traits of University-Going Women That Affect Opportunities and Entrepreneurial Intentions
Authors: Valdez-Juárez Luis Enrique , Ramos-Escobar Elva Alicia , Ruiz-Zamora José Alonso , Borboa-Álvarez Edith Patricia
Published: January 2024

The purpose of this research is to analyze how personality traits and psychological profiles influence the detection of entrepreneurial opportunities by, and the intentions of, university-going women in the northwest region of Mexico. It also examines how business opportunities are decisive when it comes to awakening entrepreneurial intention. The moderating and mediating effects of the detection of business opportunities and the psychological profile are also examined with respect to the direct relationship between personal traits and entrepreneurial intentions. For this study, information was collected from 1197 students attending the Autonomous University of Baja California and the Technological Institute of Sonora through a digital survey (Google Forms) distributed via email during the second half of 2022. The PLS-SEM statistical technique was used to test the hypotheses of the proposed theoretical model. The results revealed that personality traits have positive and significant effects on the psychological profile and on business opportunities. However, it was clearly observed that one’s personal traits and psychological profile have little or no influence on entrepreneurial intentions. We also found that the psychological profile is the construct that most influences business opportunities. In addition, it was also highlighted that business opportunities contribute to awakening the entrepreneurial intentions of university-going women. On the other hand, it was revealed that business opportunities have a negative moderating effect on the relationship between the psychological profile and entrepreneurial intentions. Likewise, this study has shown that the detection of business opportunities and the psychological profile have indirect effects on the relationship between the personal traits and the entrepreneurial intentions of university-going women. This research contributes to the development and strengthening of trait theory, the theory of reasoned action, and the theory of planned behavior.
Activated Human Adipose Tissue Transplantation Promotes Sensorimotor Recovery after Acute Spinal Cord Contusion in Rats
Authors: Bonnet Maxime , Ertlen Céline , Seblani Mostafa , Brezun Jean-Michel , Coyle Thelma , Cereda Cristina , Zuccotti Gianvincenzo , Colli Mattia , Desouches Christophe , Decherchi Patrick , Carelli Stephana , Marqueste Tanguy
Published: January 2024

Traumatic spinal cord injuries (SCIs) often result in sensory, motor, and vegetative function loss below the injury site. Although preclinical results have been promising, significant solutions for SCI patients have not been achieved through translating repair strategies to clinical trials. In this study, we investigated the effective potential of mechanically activated lipoaspirated adipose tissue when transplanted into the epicenter of a thoracic spinal contusion. Male Sprague Dawley rats were divided into three experimental groups: SHAM (uninjured and untreated), NaCl (spinal cord contusion with NaCl application), and AF (spinal cord contusion with transplanted activated human fat). Pro-inflammatory cytokines (IL-1β, IL-6, TNF-α) were measured to assess endogenous inflammation levels 14 days after injury. Sensorimotor recovery was monitored weekly for 12 weeks, and gait and electrophysiological analyses were performed at the end of this observational period. The results indicated that AF reduced endogenous inflammation post-SCI and there was a significant improvement in sensorimotor recovery. Moreover, activated adipose tissue also reinstated the segmental sensorimotor loop and the communication between supra- and sub-lesional spinal cord regions. This investigation highlights the efficacy of activated adipose tissue grafting in acute SCI, suggesting it is a promising therapeutic approach for spinal cord repair after traumatic contusion in humans.
Microglia-Derived Insulin-like Growth Factor 1 Is Critical for Neurodevelopment
Authors: Rusin Dominika , Vahl Becirovic Lejla , Lyszczarz Gabriela , Krueger Martin , Benmamar-Badel Anouk , Vad Mathiesen Cecilie , Sigurðardóttir Schiöth Eydís , Lykke Lambertsen Kate , Wlodarczyk Agnieszka
Published: January 2024

Insulin-like growth factor 1 (IGF-1) is a peptide hormone essential for the proper development and growth of the organism, as a complete knockout of Igf1 in mice is lethal, causing microcephaly, growth retardation and the defective development of organs. In the central nervous system, neurons and glia have been reported to express Igf1, but their relative importance for postnatal development has not yet been fully defined. In order to address this, here, we obtained mice with a microglia-specific inducible conditional knockout of Igf1. We show that the deficiency in microglial Igf1, starting in the first postnatal week, leads to body and brain growth retardation, severely impaired myelination, changes in microglia numbers, and behavioral abnormalities. These results emphasize the importance of microglial-derived Igf1 for brain development and function and open new perspectives for the investigation of the role of microglial-Igf1 in neurological diseases.
The Inhibition of the Membrane-Bound Transcription Factor Site-1 Protease (MBTP1) Alleviates the p.Phe508del-Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) Defects in Cystic Fibrosis Cells
Authors: Santinelli Raphaël , Benz Nathalie , Guellec Julie , Quinquis Fabien , Kocas Ervin , Thomas Johan , Montier Tristan , Ka Chandran , Luczka-Majérus Emilie , Sage Edouard , Férec Claude , Coraux Christelle , Trouvé Pascal
Published: January 2024

Cystic Fibrosis (CF) is present due to mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene, the most frequent variant being p.phe508del. The CFTR protein is a chloride (Cl-) channel which is defective and almost absent of cell membranes when the p.Phe508del mutation is present. The p.Phe508del-CFTR protein is retained in the endoplasmic reticulum (ER) and together with inflammation and infection triggers the Unfolded Protein Response (UPR). During the UPR, the Activating Transcription Factor 6 (ATF6) is activated with cleavage and then decreases the expression of p.Phe508del-CFTR. We have previously shown that the inhibition of the activation of ATF6 alleviates the p.Phe508del-CFTR defects in cells overexpressing the mutated protein. In the present paper, our aim was to inhibit the cleavage of ATF6, and thus its activation in a human bronchial cell line with endogenous p.Phe508del-CFTR expression and in bronchial cells from patients, to be more relevant to CF. This was achieved by inhibiting the protease MBTP1 which is responsible for the cleavage of ATF6. We show here that this inhibition leads to increased mRNA and p.Phe508del-CFTR expression and, consequently, to increased Cl-efflux. We also explain the mechanisms linked to these increases with the modulation of genes when MBTP1 is inhibited. Indeed, RT-qPCR assays show that genes such as HSPA1B, CEBPB, VIMP, PFND2, MAPK8, XBP1, INSIG1, and CALR are modulated. In conclusion, we show that the inhibition of MBTP1 has a beneficial effect in relevant models to CF and that this is due to the modulation of genes involved in the disease.
Progress on the Properties of Ceramic Phase-Reinforced High-Entropy Alloy Composite Coatings Produced via Laser Cladding
Authors: Zhang Haoran , Yong Yaowei , Wang Fuwei , Liang Yuan , Liu Lin , Liu Hong , Gao Yang
Published: January 2024

The production of ceramic phase-reinforced high-entropy alloy composite coatings with excellent mechanical properties, high-temperature oxidation resistance, and corrosion resistance via laser cladding is a new hotspot in the field of surface engineering. However, as high-entropy alloys have a wide range of constituent systems and different kinds of ceramic particles are introduced in different ways that give the coatings unique microscopic organization, structure, and synthesized performance, it is necessary to review the methods of preparing ceramic phase-reinforced high-entropy alloys composite coatings via laser cladding. In this paper, the latest research progress on laser cladding technology in the preparation of ceramic phase-reinforced high-entropy alloy composite coatings is first reviewed. On this basis, the effects of ceramic particles, alloying elements, process parameters, and the microstructure and properties of the coatings are analyzed with the examples of the in situ generation method and the externally added method. Finally, research gaps and future trends are pointed out, serving as a reference for the subsequent research, application, and development of the preparation of ceramic phase-reinforced high-entropy alloy composite coatings.
Room-Temperature O3 Detection: Zero-Bias Sensors Based on ZnO Thin Films
Authors: Bolli Eleonora , Fornari Alice , Bellucci Alessandro , Mastellone Matteo , Valentini Veronica , Mezzi Alessio , Polini Riccardo , Santagata Antonio , Trucchi Daniele Maria
Published: January 2024

ZnO thin films with a thickness of 300 nm were deposited on Si and Al2O3 substrates using an electron beam evaporation technique with the aim of testing them as low cost and low power consumption gas sensors for ozone (O3). Scanning electron microscopy and atomic force microscopy were used to characterize the film surface morphology and quantify the roughness and grain size, recognized as the primary parameters influencing the gas sensitivity due to their direct impact on the effective sensing area. The crystalline structure and elemental composition were studied through Raman spectroscopy and X-ray photoelectron spectroscopy. Gas tests were conducted at room temperature and zero-bias voltage to assess the sensitivity and response as a function of time of the films to O3 pollutant. The results indicate that the films deposited on Al2O3 exhibit promising characteristics, such as high sensitivity and a very short response time (<2 s) to the gas concentration. Additionally, it was observed that the films display pronounced degradation effects after a significant exposure to O3.
Elliott State Research Forest Timber Cruise, Oregon, 2015–2016
Authors: West Todd , Strimbu Bogdan M.
Published: January 2024

The Elliott State Research Forest comprises 33,700 ha of temperate, Douglas-fir rainforest along North America’s Pacific Coast (Oregon, United States). In 2015, naturally regenerated stands at least 92 years old covered 49% of the research area and sawtimber plantations younger than 68 years another 50%. During the winter of 2015–2016, a forest wide inventory sampled both naturally regenerated and plantation stands, recording 97,424 trees on 17,866 plots in 738 stands. The resulting dataset is atypical for the area as plot locations were not restricted to upland, commercially harvestable timber. Multiage stands and riparian areas were therefore documented along with plantations 2–61 years old and trees retained through clearcut harvests. This dataset constitutes the only open access, stand-based forest inventory currently available for a large area within the Oregon Coast Range. The dataset enables development of suites of models as well as many comparisons across stand ages and types, both at stand level and at the level of individual trees.
Machine Learning Classification Workflow and Datasets for Ionospheric VLF Data Exclusion
Authors: Arnaut Filip , Kolarski Aleksandra , Srećković Vladimir A.
Published: January 2024

Machine learning (ML) methods are commonly applied in the fields of extraterrestrial physics, space science, and plasma physics. In a prior publication, an ML classification technique, the Random Forest (RF) algorithm, was utilized to automatically identify and categorize erroneous signals, including instrument errors, noisy signals, outlier data points, and the impact of solar flares (SFs) on the ionosphere. This data communication includes the pre-processed dataset used in the aforementioned research, along with a workflow that utilizes the PyCaret library and a post-processing workflow. The code and data serve educational purposes in the interdisciplinary field of ML and ionospheric physics science, as well as being useful to other researchers for diverse objectives.
Diagnostic Accuracy of Deep Learning for the Prediction of Osteoporosis Using Plain X-rays: A Systematic Review and Meta-Analysis
Authors: Yen Tzu-Yun , Ho Chan-Shien , Chen Yueh-Peng , Pei Yu-Cheng
Published: January 2024

(1) Background: This meta-analysis assessed the diagnostic accuracy of deep learning model-based osteoporosis prediction using plain X-ray images. (2) Methods: We searched PubMed, Web of Science, SCOPUS, and Google Scholar from no set beginning date to 28 February 2023, for eligible studies that applied deep learning methods for diagnosing osteoporosis using X-ray images. The quality of studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 criteria. The area under the receiver operating characteristic curve (AUROC) was used to quantify the predictive performance. Subgroup, meta-regression, and sensitivity analyses were performed to identify the potential sources of study heterogeneity. (3) Results: Six studies were included; the pooled AUROC, sensitivity, and specificity were 0.88 (95% confidence interval [CI] 0.85–0.91), 0.81 (95% CI 0.78–0.84), and 0.87 (95% CI 0.81–0.92), respectively, indicating good performance. Moderate heterogeneity was observed. Mega-regression and subgroup analyses were not performed due to the limited number of studies included. (4) Conclusion: Deep learning methods effectively extract bone density information from plain radiographs, highlighting their potential for opportunistic screening. Nevertheless, additional prospective multicenter studies involving diverse patient populations are required to confirm the applicability of this novel technique.
Integration of ShuffleNet V2 and YOLOv5s Networks for a Lightweight Object Detection Model of Electric Bikes within Elevators
Authors: Su Jingfang , Yang Minrui , Tang Xinliang
Published: January 2024

The entry of electric bikes into elevators poses safety risks. This article proposes a lightweight object detection model for edge deployment in elevator environments specifically designed for electric bikes. Based on the YOLOv5s network, the backbone network replaces the original CSPDarknet53 with a lightweight multilayer ShuffleNet V2 convolutional neural network, achieving a lightweight backbone network. Swin Transformer modules are introduced between layers to enhance the feature expression capability of images, and a SimAM attention mechanism is applied at the end layer to further improve the feature extraction capability of the backbone network. In the neck network, lightweight and depth-balanced GSConv and VoV-GSCSP modules replace several Conv and C3 basic convolutional modules, reducing the parameter count while enhancing the cross-scale connection and fusion capabilities of feature maps. The prediction network uses the faster-converging and more accurate EIOU error function as the position loss function for iterative training. This article conducts various lightweighting comparison experiments and ablation experiments on the improved object detection model. The experimental results demonstrate that the proposed object detection model, with a model size of only 2.6 megabytes and 1.1 million parameters, achieves a frame rate of 106 frames per second and a detection accuracy of 95.5%. This represents an 84.8% reduction in computational load compared to the original YOLOv5s model. The model’s volume and parameter count are reduced by 81.0% and 84.3%, respectively, with only a 0.9% decrease in mAP. The improved object detection model proposed in this paper can meet the real-time detection requirements for electric bikes in elevator scenarios, providing a feasible technical solution for its deployment on edge devices within elevators.
Multi-Frequency Channel Measurement and Characteristic Analysis in Forested Scenario for Emergency Rescue
Authors: Guo Wei , Yuan Rong , Ma Hui , Yuan Yongxia , Fei Dan , Guan Ke , Chen Haoran , Shen Yufei , Fang Yudong , Cheng Wenchi
Published: January 2024

Wireless communication has been widely used in emergency rescue, including from command vehicles to command vehicles, command vehicles to rescue teams, command vehicles to wireless sensors, and rescue teams to intelligent platforms such as unmanned vehicles. Compared to those used in cities and suburbs, when the same communication equipment is used in forests, its communication performance, such as transmission distance, is entirely different. The main reason for this phenomenon is the extraordinary complexity of wireless signal propagation in forest scenarios. Therefore, in order to accurately and quantitatively describe the wireless channel characteristics in forest scenarios, a frequency channel measurement activity is conducted in a forest and analysis is performed to acquire the channel characteristics in forest scenarios. The measurements are carried out at 380 MHz, 640 MHz, and 1420 MHz in virgin forest. Based on the measurement data, the average power delay profile (APDP) is obtained, and multipath components (MPCs) are extracted. Root mean square (RMS) delay spread and path loss (PL) are analyzed according to MPCs. Furthermore, a new path loss model is proposed. Finally, a new path loss model and relative analysis are provided.
A Repeated Game-Based Distributed Denial of Service Attacks Mitigation Method for Mining Pools
Authors: Liu Xiao , Huang Zhao , Wang Quan , Chen Yin , Cao Yuan
Published: January 2024

A Distributed Denial of Service (DDoS) attack is a prevalent issue in the blockchain network layer, causing significant revenue loss for honest mining pools. This paper introduces a novel method, the Repeated Game-based DDoS attack mitigation (RGD), to address this problem. Unlike traditional methods such as game theory and machine learning-based detection, the RGD method can effectively reflect the changes in mining revenue and strategies under different network-strength environments. In particular, we abstract the problem of DDoS mining pool revenue loss into a game revenue model and propose the subgame perfect equilibrium (SPE) approach to solve the optimal payoffs and pool strategies in various network environments. Furthermore, we address the returns of mining pools in an infinitely repeated game environment using the Two-Stage Repeated Game (TSRG) method, where the strategy varies with different network environments. The Matlab experimental simulation results indicate that as the network environment improves, the optimal mining strategies of mining pools are gradually shifting from honest strategies to launching DDoS attacks against each other. The RGD method can effectively represent the impact of changes in the network environment on the mining pool’s strategy selection and optimal revenue. Consequently, with the changing network environment, the optimal revenue of the mining pool only increases by 10% of the revenue loss during a DDoS attack.
CNB Net: A Two-Stage Approach for Effective Image Deblurring
Authors: Zhang Xiu , Zheng Fengbo , Jiang Lifen , Guo Haoyu
Published: January 2024

Image blur, often caused by camera shake and object movement, poses a significant challenge in computer vision. Image deblurring strives to restore clarity to these images. Traditional single-stage methods, while effective in detail enhancement, often neglect global context in favor of local information. Yet, both aspects are crucial, especially in real-life scenarios where images are typically large and subject to various blurs. Addressing this, we introduce CNB Net, an innovative deblurring network adept at integrating global and local insights for enhanced image restoration. The network operates in two stages, utilizing our specially designed Convolution and Normalization-Based Block (CNB Block) and Convolution and Normalization-Based Plus Block (CNBP Block) for multi-scale information extraction. A progressive learning approach is adopted with a Feature Active Selection (FAS) module at the end of each stage that captures spatial detail information under the guidance of real images. The Two-Stage Feature Fusion (TSFF) module reduces information loss caused by downsampling operations while enriching features across stages for increased robustness. We conduct experiments on the GoPro dataset and the HIDE dataset. On the GoPro dataset, our Peak Signal-to-Noise Ratio (PSNR) result is 32.21 and the Structural Similarity (SSIM) result is 0.950; and on the HIDE dataset, our PSNR result is 30.38 and the SSIM result is 0.932. Our results exceed other similar algorithms. By comparing the generated feature maps, we find that our model takes into account both global and local information well.
New Programmable LFSR Counters with Automatic Encoding and State Extension
Authors: Grymel Martin
Published: January 2024

An efficient method for detecting the end of a count of a linear feedback shift register (LFSR) is presented. We show how this detector can be used to extend a maximum-length LFSR sequence by a number of states up to one less than the degree of the polynomial. In addition, a new algorithm is proposed to encode binary sequence values into their corresponding LFSR sequence states. Based on this algorithm and an alternative second method, two novel programmable, full-range, high-speed LFSR counter designs are proposed. The time complexity of the conversion algorithms ranges from quadratic to exponential time in the degree of the underlying polynomial. The proposed counter solutions are fully synchronous and can be implemented with standard cells, enabling easy portability across technologies. As the levels of logic are independent of the counter size, high scalability is facilitated. For evaluation, all the designs, including the recreated state-of-the-art solution, have been implemented on an FPGA, and also simulated targeting TSMC’s N3 CMOS process node for a wide range of counter sizes. The results confirm the superiority of the proposed solutions over the state of the art in terms of frequency, area and power efficiency as the counter is scaled up.
Effects of CO2 Treatments on Functional Carbon Efficiencies and Growth of Forest Tree Seedlings: A Study of Four Early-Successional Deciduous Species
Authors: Brisebois Axel , Major John E.
Published: January 2024

Atmospheric CO2 levels have been increasing, and these changes may result in differential adaptive responses in both genera and species and highlight the need to increase carbon sequestration. Ecophysiological and morphological responses of four early-successional deciduous species were examined under ambient CO2 (aCO2, 400 ppm) and elevated CO2 (eCO2, 800 ppm) treatments. The four species, all of which are used in restoration, were Alnus viridis subsp. crispa (Ait.) Turrill (green alder), A. incana subsp. rugosa (Du Roi) R.T. Clausen (speckled alder), Betula populifolia (Marshall) (gray birch), and B. papyrifera (Marshall) (white birch); all are from the same phylogenetic family, Betulaceae. We examined biochemical efficiencies, gas exchange, chlorophyll fluorescence, chlorophyll concentrations, foliar nitrogen (N), and growth traits. A general linear model, analysis of variance, was used to analyze the functional carbon efficiency and growth differences, if any, among genera, species, and provenances (only for growth traits). The alders had greater biochemical efficiency traits than birches, and alders upregulated these traits, whereas birches mostly downregulated these traits in response to eCO2. In response to eCO2, assimilation either remained the same or was upregulated for alders but downregulated for birches. Stomatal conductance was downregulated for all four species in response to eCO2. Intrinsic water use efficiency was greater for alders than for birches. Alders exhibited a consistent upregulation of stem dry mass and height growth, whereas birches were somewhat lower in height and stem dry mass in response to eCO2. Foliar N played an important role in relation to ecophysiological traits and had significant effects relative to genus (alders > birches) and CO2 (aCO2 > eCO2), and a significant genus × CO2 interaction, with alders downregulating foliar N less than did birches. Covariate analysis examining carbon efficiency traits in relation to foliar N showed clear functional responses. Both species in both genera were consistent in their ecophysiological and morphological responses to CO2 treatments. There was supporting evidence that assimilation was sink-driven, which is related to a plant organ’s ability to continue to grow and incorporate assimilates. The alders used in this study are actinorhizal, and the additional available foliar N, paired with increased stem dry mass sink activity, appeared to be driving upregulation of the carbon efficiencies and growth in response to eCO2. Alders’ greater carbon efficiencies and carbon sequestration in impoverished soils demonstrate that alders, as opposed to birches, should be used to accelerate ecological restoration in a world of increasing atmospheric CO2.
Identifying Influential Spatial Drivers of Forest Fires through Geographically and Temporally Weighted Regression Coupled with a Continuous Invasive Weed Optimization Algorithm
Authors: Pahlavani Parham , Raei Amin , Bigdeli Behnaz , Ghorbanzadeh Omid
Published: January 2024

Identifying the underlying factors derived from geospatial and remote sensing data that contribute to forest fires is of paramount importance. It aids experts in pinpointing areas and periods most susceptible to these incidents. In this study, we employ the geographically and temporally weighted regression (GTWR) method in conjunction with a refined continuous invasive weed optimization (CIWO) algorithm to assess certain spatially relevant drivers of forest fires, encompassing both biophysical and anthropogenic influences. Our proposed approach demonstrates theoretical utility in addressing the spatial regression problem by meticulously accounting for the autocorrelation and non-stationarity inherent in spatial data. We leverage tricube and Gaussian kernels to weight the GTWR for two distinct temporal datasets, yielding coefficients of determination (R2) amounting to 0.99 and 0.97, respectively. In contrast, traditional geographically weighted regression (GWR) using the tricube kernel achieved R2 values of 0.87 and 0.88, while the Gaussian kernel yielded R2 values of 0.8138 and 0.82 for the same datasets. This investigation underscores the substantial impact of both biophysical and anthropogenic factors on forest fires within the study areas.
Bioeconomic Analysis of Snook Centropomus viridis, C. nigrescens, and C. medius for the Development of Mariculture in Northern Sinaloa
Authors: Montoya Ponce Celeste Osiris , Santamaría Miranda Apolinar , Trigueros Salmerón José Ángel , Apún Molina Juan Pablo , Valenzuela Orduño Francisco Guadalupe , Lugo Gamboa Refugio Riquelmer
Published: January 2024

The bioeconomy offers an opportunity to implement a truly sustainable global economy based on biological resources, which, thanks to biotechnologies, become renewable. In this study, we conducted a bioeconomic analysis of the three most important species of sea snook in northern Sinaloa using fishery and mathematical models to support the selection of the species with the highest growth and feasibility. Our results showed a condition factor lower than 1 (K < 1) for the three species. The size condition factor was higher in younger organisms for the three snook species. The growth rates were K = 0.320, K = 0.160, and K = 0.440 for C. viridis, C. nigrescens, and C. medius, respectively. Individual growth was 1.8 g/day for C. viridis, 1.47 g/day for C. nigrescens, and 0.91 g/day for C. medius. The length-to-weight ratio indicated negative allometric growth (b = 2.82, b = 2.72, and b = 2.73, respectively) for C. viridis, C. nigrescens, and C. medius. The simulation for possible commercial cultivation reflected varied sizes: 600 g for C. viridis and C. nigrescens and 400 g for C. medius. The financial projection of C. viridis produced IRRs of 14% and 48% in captured fishing and aquaculture models, respectively, with positive NPV. However, simulations for C. nigrescens and C. medius were not economically viable. We conclude that, according to the aquaculture model, the most financially feasible species to farm in the north of Sinaloa is C. viridis, which showed the highest growth based on fishery data compared to those for C. nigrescens and C. medius.
Idiopathic Epilepsy Risk Allele Trends in Belgian Tervuren: A Longitudinal Genetic Analysis
Authors: Kinsey Nathan , Belanger Janelle M. , Mandigers Paul J. J. , Leegwater Peter A. , Heinonen Tiina , Hytönen Marjo K. , Lohi Hannes , Ostrander Elaine A. , Oberbauer Anita M.
Published: January 2024

Idiopathic epilepsy (IE) has been known to be inherited in the Belgian Tervuren for many decades. Risk genotypes for IE in this breed have recently been identified on Canis familiaris chromosomes (CFA) 14 and 37. In the current study, the allele frequencies of these loci were analyzed to determine whether dog breeders had employed a purposeful selection against IE, leading to a reduction in risk-associated allele frequency within the breed over time. The allele frequencies of two generational groupings of Belgian Tervuren with and without IE were compared. Allele frequencies for risk-associated alleles on CFA14 were unchanged between 1985 and 2015, whereas those on CFA37 increased during that time in the control population (p < 0.05). In contrast, dogs with IE showed a decrease (p < 0.05) in the IE risk-associated allele frequency at the CFA37 locus. Seizure prevalence in the Belgian Tervuren appears to be increasing. These results suggest that, despite awareness that IE is inherited, selection against IE has not been successful.
Optimizing Analytical Thresholds for Low-Template DNA Analysis: Insights from Multi-Laboratory Negative Controls
Authors: Chen Dezhi , Tan Mengyu , Xue Jiaming , Wu Mengna , Song Jinlong , Wu Qiushuo , Liu Guihong , Zheng Yazi , Xiao Yuanyuan , Lv Meili , Liao Miao , Qu Shengqiu , Liang Weibo
Published: January 2024

When analyzing challenging samples, such as low-template DNA, analysts aim to maximize information while minimizing noise, often by adjusting the analytical threshold (AT) for optimal results. A potential approach involves calculating the AT based on the baseline signal distribution in electrophoresis results. This study investigates the impact of reagent kits, testing quarters, environmental conditions, and amplification cycles on baseline signals using historical records and experimental data on low-template DNA. Variations in these aspects contribute to differences in baseline signal patterns. Analysts should remain vigilant regarding routine instrument maintenance and reagent replacement, as these may affect baseline signals. Prompt analysis of baseline status and tailored adjustments to ATs under specific laboratory conditions are advised. A comparative analysis of published methods for calculating the optimal AT from a negative signal distribution highlighted the efficiency of utilizing baseline signals to enhance forensic genetic analysis, with the exception of extremely low-template samples and high-amplification cycles. Moreover, a user-friendly program for real-time analysis was developed, enabling prompt adjustments to ATs based on negative control profiles. In conclusion, this study provides insights into baseline signals, aiming to enhance genetic analysis accuracy across diverse laboratories. Practical recommendations are offered for optimizing ATs in forensic DNA analysis.
Low-Dose Occupational Exposure to Ionizing Radiation and Cardiovascular Effects: A Narrative Review
Authors: Manenti Guglielmo , Coppeta Luca , Kirev Ivan Valentinov , Verno Greta , Garaci Francesco , Magrini Andrea , Floris Roberto
Published: January 2024

Historically, non-cancer diseases have not been considered a health risk following low-dose exposure to ionizing radiation. However, it is now well known that high-dose ionizing radiation causes cardiovascular disease, and emerging epidemiological evidence suggests an excess risk of non-cancer diseases even following exposure to lower doses of ionizing radiation than previously thought. In fact, the evidence is strongest for cardiovascular disease (CVD). The aim of this review was to report the most representative studies and data on the risk of CVD from low-dose radiation in people with occupational exposure. We reported the results of 27 articles selected from a database search of 1151 studies. The results show a complex evidence landscape on the relationship between radiation exposure and cardiovascular disease. In general, published papers show a positive association between ionizing radiation exposure and dermal microcirculation damage, ischemic heart disease, and cerebrovascular disease. Overall, they highlight the need for comprehensive and detailed research to clarify this relationship. Due to limited statistical power, the dose–risk relationship below 0.5 Gy is inconclusive, but if this relationship is found to have no threshold, it could have a significant impact on current estimates of health risks at low doses.
Chip Digital PCR (cdPCR) to Identify and Quantify Botrytis cinerea Infection in Tomatoes
Authors: Morcia Caterina , Carrara Ilaria , Ghizzoni Roberta , Terzi Valeria , Bolli Giovanni , Chiusa Giorgio
Published: January 2024

Botrytis cinerea is a fungal pathogen present in almost any environment, able to cause a severe postharvest disease on a wide range of crops, resulting in significant economic losses. Furthermore, B. cinerea is frequently found in plant tissues as a latent, asymptomatic infection that, when stimulated by favorable alterations in the environment or the physiology of the host, can swiftly develop into a significant symptomatic infection. In greenhouses, fields, and on propagation materials, the principal strategy adopted to control infection is the use of chemical fungicides or eco-friendly alternative methods. For the optimal success of conventional and biocontrol treatments, it is crucial to monitor the disease development and the fungal infection entity. The aim of this work was to develop a fast new method based on chip digital PCR (cdPCR) to estimate the extent of the B. cinerea infection in tomatoes. To better evaluate the amount of plant infection, a duplex assay able to co-amplify both fungal and host plant DNA was fine-tuned. The cdPCR assays were applied to quantify B. cinerea in tomato seedling samples, both naturally and artificially contaminated. The developed method offers sensitive detection, reliable identification, and precise pathogen quantification. The method can be used for B. cinerea diagnostics along the tomato production chain, starting from the seeds and transplanting seedlings to plants and crop residues in open fields and greenhouses. To the best of our knowledge, this is the first study directed at applying cdPCR to B. cinerea diagnosis in tomatoes.
Quantitative Study on American COVID-19 Epidemic Predictions and Scenario Simulations
Authors: Sun Jingtao , Qi Jin , Yan Zhen , Li Yadong , Liang Jie , Wu Sensen
Published: January 2024

The COVID-19 pandemic has had a profound impact on people’s lives, making accurate prediction of epidemic trends a central focus in COVID-19 research. This study innovatively utilizes a spatiotemporal heterogeneity analysis (GTNNWR) model to predict COVID-19 deaths, simulate pandemic prevention scenarios, and quantitatively assess their preventive effects. The results show that the GTNNWR model exhibits superior predictive capacity to the conventional infectious disease dynamics model (SEIR model), which is approximately 9% higher, and reflects the spatial and temporal heterogeneity well. In scenario simulations, this study established five scenarios for epidemic prevention measures, and the results indicate that masks are the most influential single preventive measure, reducing deaths by 5.38%, followed by vaccination at 3.59%, and social distancing mandates at 2.69%. However, implementing single stringent preventive measures does not guarantee effectiveness across all states and months, such as California in January 2025, Florida in August 2024, and March–April 2024 in the continental U.S. On the other hand, the combined implementation of preventive measures proves 5 to-10-fold more effective than any single stringent measure, reducing deaths by 27.2%. The deaths under combined implementation measures never exceed that of standard preventive measures in any month. The research found that the combined implementation of measures in mask wearing, vaccination, and social distancing during winter can reduce the deaths by approximately 45%, which is approximately 1.5–3-fold higher than in the other seasons. This study provides valuable insights for COVID-19 epidemic prevention and control in America.
Tip of the Iceberg: A New Wave of Iron–Sulfur Cluster Proteins Found in Viruses
Authors: Heffner Audrey L. , Maio Nunziata
Published: January 2024

Viruses rely on host cells to replicate their genomes and assemble new viral particles. Thus, they have evolved intricate mechanisms to exploit host factors. Host cells, in turn, have developed strategies to inhibit viruses, resulting in a nuanced interplay of co-evolution between virus and host. This dynamic often involves competition for resources crucial for both host cell survival and virus replication. Iron and iron-containing cofactors, including iron–sulfur clusters, are known to be a heavily fought for resource during bacterial infections, where control over iron can tug the war in favor of the pathogen or the host. It is logical to assume that viruses also engage in this competition. Surprisingly, our knowledge about how viruses utilize iron (Fe) and iron–sulfur (FeS) clusters remains limited. The handful of reviews on this topic primarily emphasize the significance of iron in supporting the host immune response against viral infections. The aim of this review, however, is to organize our current understanding of how viral proteins utilize FeS clusters, to give perspectives on what questions to ask next and to propose important avenues for future investigations.
Critical Review on the Use of Extractives of Naturally Durable Woods as Natural Wood Protectants
Authors: Kirker Grant , Hassan Babar , Mankowski Mark , Eller Fred
Published: January 2024

Naturally durable wood pre-dates preservative-treated wood and has been demonstrated to offer a suitable service life for certain applications where preservative-treated wood is not feasible. Heartwood extractives have been demonstrated to impart bio-deteriorative resistance to naturally durable wood species. These extractives are typically found in the heartwood of living trees and are produced either by the death of parenchyma cells or as the result of external stimuli. The mechanisms of natural durability are not well understood, as heartwood extractives can be extremely variable in their distribution, composition, and efficacy in both living and harvested trees. The underlying complexity of heartwood extractives has hindered their standardization in residential building codes for use as wood preservatives. The use of naturally durable lumber is not always feasible, as woods with exceptionally durable heartwood do not typically yield lumber with acceptable machining properties. A potential approach to overcome the inherent difficulty in establishing guidelines for the appropriate use of naturally durable wood is to focus solely on the extractives as a source of bioactive protectants based on the strategies used on living and dead wood to repel the agents of biodeterioration. This critical review summarizes the relevant literature on naturally durable woods, their extractives, and their potential use as bio-inspired wood protectants. An additional discussion will be aimed at underscoring the past difficulties in adopting this approach and how to overcome the future hurdles.