bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing Productivity of Buffel and Sudan Grasses Using Optimal Nitrogen Fertilizer Application under Arid Conditions
Authors: Alhammad Bushra Ahmed , Mohamed Aden , Raza Muhammad Ali , Ngie Mwende , Maitra Sagar , Seleiman Mahmoud F. , Wasonga Daniel , Gitari Harun I.
Published: August 2023

Most arid and semiarid areas are bare and greatly infested with intrusive wild species. Buffel grass (Cenchrus ciliaris L.) and Sudan grass (Sorghum sudanense Piper.) are the supreme drought-tolerant grasses that are commonly grown in dry regions. Besides water, nitrogen (N) is a vital element limiting the growth, yield, and herbage quality of such grasses since it has key roles in physiological and biochemical functions in plants. Hence, this study aimed at assessing the performance of Buffel and Sudan grasses under nitrogen fertilization in Mandera County. This study comprised a field trial laid as a split plot in a randomized complete block design with grasses being assigned to the main plots, whereas N rates (0, 35, 70, 105, and 140 kg N ha−1) occupied the subplots. Data were collected on growth parameters (plant height, leaf length, number of leaves per plant, leaf width, stem girth, and tiller number), yield parameters (shoot weight, root weight, and aboveground biomass yield—DBY), and quality attributes (acid detergent fiber—ADF, crude protein—CP, and neutral detergent fiber—NDF). Across the seasons, plant height progressively increased with increasing N rates up to a maximum of 141 and 246 cm for Buffel grass and Sudan grass, respectively, which were associated with a 105 kg N ha−1 rate. Nonetheless, there was no noteworthy enhancement in plant height (142 and 246 cm) with an additional upsurge in N fertilizer rate to 140 kg N ha−1 relative to the one of 105 kg N ha−1. Regarding the leaf length, Sudan grass had longer and wider leaves at all treatment levels than the Buffel grass. Plant height, leaf length, width, and the number of leaves per plant increased with increasing N level up to the rate of 140 kg N ha−1, though this was not statistically different from the rate of 105 kg N ha−1 for both kinds of grass. The results revealed that Sudan grass contained higher crude protein than Buffel grass at all levels of treatments (10.33 and 8.80% at the rate of 105 kg N ha−1, respectively). More so, crude protein content was found to be higher in plots where N application was performed than in the control plots. There were great associations between the dependent variables (DBY, ADF, NDF, and CP) and independent variables (plant height, leaf length, No. of leaves, leaf width, stem girth, and No. of tillers), with the coefficient of regression ranging from 0.56 to 0.96 for Buffel grass and 0.59 to 0.96 for Sudan grass. Findings from this study indicate that for optimal growth, yield, and nutrient content benefits, Buffel and Sudan grasses ought to be grown using nitrogen fertilizer at a 105 kg N ha−1 rate.
Open-Source Wearable Sensors for Behavioral Analysis of Sheep Undergoing Heat Stress
Authors: Reis Barbara Roqueto dos , Nguyen Tien , Sujani Sathya , White Robin R.
Published: August 2023

Heat stress (HS) negatively affects animal productivity and welfare. The usage of wearable sensors to detect behavioral changes in ruminants undergoing HS has not been well studied. This study aimed to investigate changes in sheep’s behavior using a wearable sensor and explore how ambient temperature influenced the algorithm’s capacity to classify behaviors. Six sheep (Suffolk, Dorset, or Suffolk × Dorset) were assigned to 1 of 2 groups in a cross-over experimental design. Groups were assigned to one of two rooms where they were housed for 20d prior to switching rooms. The thermal environment within the rooms was altered five times per period. In the first room, the temperature began at a thermoneutral level and gradually increased before decreasing. Simultaneously, in the second room, the temperature began at hot temperatures and gradually decreased before increasing again. Physiological responses (respiratory rate, heart rate, and rectal temperature) were analyzed using a linear mixed-effects model. A random forest algorithm was developed to classify lying, standing, eating, and ruminating (while lying and standing). Thermal stress shifted daily animal behavior budgets, increasing total time spent standing in hot conditions (p = 0.036). Although models had a similar capacity to classify behaviors within a temperature range, their accuracy decreased when applied outside that range. Although wearable sensors may help classify behavioral shifts indicative of thermal stress, algorithms must be robustly derived across environments.
A Multi Brain Tumor Region Segmentation Model Based on 3D U-Net
Authors: Li Zhenwei , Wu Xiaoqin , Yang Xiaoli
Published: August 2023

Accurate segmentation of different brain tumor regions from MR images is of great significance in the diagnosis and treatment of brain tumors. In this paper, an enhanced 3D U-Net model was proposed to address the shortcomings of 2D U-Net in the segmentation tasks of brain tumors. While retaining the U-shaped characteristics of the original U-Net network, an enhanced encoding module and decoding module were designed to increase the extraction and utilization of image features. Then, a hybrid loss function combining the binary cross-entropy loss function and dice similarity coefficient was adopted to speed up the model’s convergence and to achieve accurate and fast automatic segmentation. The model’s performance in the segmentation of brain tumor’s whole tumor region, tumor core region, and enhanced tumor region was studied. The results showed that the proposed 3D U-Net model can achieve better segmentation performance, especially for the tumor core region and enhanced tumor region tumor regions.
Crop Prediction Model Using Machine Learning Algorithms
Authors: Elbasi Ersin , Zaki Chamseddine , Topcu Ahmet E. , Abdelbaki Wiem , Zreikat Aymen I. , Cina Elda , Shdefat Ahmed , Saker Louai
Published: August 2023

Machine learning applications are having a great impact on the global economy by transforming the data processing method and decision making. Agriculture is one of the fields where the impact is significant, considering the global crisis for food supply. This research investigates the potential benefits of integrating machine learning algorithms in modern agriculture. The main focus of these algorithms is to help optimize crop production and reduce waste through informed decisions regarding planting, watering, and harvesting crops. This paper includes a discussion on the current state of machine learning in agriculture, highlighting key challenges and opportunities, and presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, incorporating online IoT sensor data that were obtained in a real-time manner, farmers can make more informed verdicts about factors that affect crop growth. Eventually, integrating these technologies can transform modern agriculture by increasing crop yields while minimizing waste. Fifteen different algorithms have been considered to evaluate the most appropriate algorithms to use in agriculture, and a new feature combination scheme-enhanced algorithm is presented. The results show that we can achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99.46% using Naïve Bayes Classifier and Hoeffding Tree algorithms. These results will indicate an increase in production rates and reduce the effective cost for the farms, leading to more resilient infrastructure and sustainable environments. Moreover, the findings we obtained in this study can also help future farmers detect diseases early, increase crop production efficiency, and reduce prices when the world is experiencing food shortages.
Plasmonic Nanomaterials for Micro- and Nanoplastics Detection
Authors: Schiavi Serena , Parmigiani Miriam , Galinetto Pietro , Albini Benedetta , Taglietti Angelo , Dacarro Giacomo
Published: August 2023

Detecting and quantifying micro- and nanoplastics (MNPs) in the environment is a crucial task that needs to be addressed as soon as possible by the scientific community. Many analytical techniques have been proposed, but a common agreement on analytical protocols and regulations still has to be reached. Nanomaterial-based techniques have shown promising results in this field. In this review, we focus on the recent results published on the use of plasmonic noble metal materials for the detection of MNPs. Plasmonic materials can be exploited in different ways due to their peculiar optical end electronic properties. Surface plasmon resonance, plasmon enhanced fluorescence, UV–Vis spectroscopy, and surface enhanced Raman scattering (SERS) will be considered in this review, examining the advantages and drawbacks of each approach.
Visual Programming as Modern and Effective Structural Design Technology—Analysis of Opportunities, Challenges, and Future Developments Based on the Use of Dynamo
Authors: Kossakowski Paweł Grzegorz
Published: August 2023

This study is dedicated to technology based on visual programming as a modern tool to support the design of building structures. It provides a brief background describing the computerization of design and discusses design-aided systems and the concept and tools of visual programming. The principles of the Visual Programming Language (VPL) are presented in detail. The programming and design environments used in the architecture and construction industry are presented. Dynamo, one of the VPL-based programs applicable to structural design, was used in the study. In order to best demonstrate and explore the capabilities of this environment, examples of the practical application of visual programming in structural design are presented. Both simple and more complex structures have been designed and discussed in detail. The integration of Dynamo with computational systems is also presented. A broad discussion was held on the possibilities of using visual programming in structural design, the problems and challenges, and the directions of its development in the architecture and construction industry.
Alleviating Luminescence and Quenching toward Discrimination of Ballpoint Pen Inks Using Spectroscopy and Chromatography Techniques
Authors: Gawad Ahmed A. , Salama Tarek M. , Ibrahim Islam , Meshref Mohamed , Mohamed Gehad G. , Zedan Abdallah F.
Published: August 2023

The phenomenon of luminescence and quenching is widely applied in the forensic examination of various ink materials. Here, we introduce a hybrid spectroscopic and chromatography approach to gain insights into the underlying cause of infrared luminescence (IRL) in blue ballpoint (BP) pen inks. A total of thirty BP pen ink samples from the Egyptian market were employed in this study. Insights into the origin of luminescence and quenching in the studied samples were gained based on video spectral comparator (VSC), thin layer chromatography (TLC), ultraviolet–visible (UV–Vis) spectrophotometry, and photoluminescence (PL) spectroscopy. Results showed that some of the studied inks possessed IRL due to the presence of crystal violet and some other triaryl methane dyes. Nevertheless, some ink samples did not possess IRL, despite the presence of the dyes responsible for IRL in their matrix. Interestingly, the inclusion of phthalocyanine dye in those non-luminescent ink matrices resulted in luminescence quenching, mainly due to the overlap between the absorption of phthalocyanine dye and the luminescence of the triarylmethane dyes. The IRL behavior of the ink sample under the first illumination wave band (400–485 nm) exerted control over the IRL activity across subsequent illumination wavebands, and the most effective differentiation was achieved by utilizing the first and second preset filters in VSC. The results revealed the luminescent components present in studied inks and unraveled their distinct luminescence behavior present within the ink matrix. The combination of optical spectroscopy and chromatography techniques could provide a distinctive tool to reveal the luminescence and quenching behaviors of ink dyes for the successful forensic discrimination of several BP writing pens.
Resizer Swin Transformer-Based Classification Using sMRI for Alzheimer’s Disease
Authors: Huang Yihang , Li Wan
Published: August 2023

Structural magnetic resonance imaging (sMRI) is widely used in the clinical diagnosis of diseases due to its advantages: high-definition and noninvasive visualization. Therefore, computer-aided diagnosis based on sMRI images is broadly applied in classifying Alzheimer’s disease (AD). Due to the excellent performance of the Transformer in computer vision, the Vision Transformer (ViT) has been employed for AD classification in recent years. The ViT relies on access to large datasets, while the sample size of brain imaging datasets is relatively insufficient. Moreover, the preprocessing procedures of brain sMRI images are complex and labor-intensive. To overcome the limitations mentioned above, we propose the Resizer Swin Transformer (RST), a deep-learning model that can extract information from brain sMRI images that are only briefly processed to achieve multi-scale and cross-channel features. In addition, we pre-trained our RST on a natural image dataset and obtained better performance. We achieved 99.59% and 94.01% average accuracy on the ADNI and AIBL datasets, respectively. Importantly, the RST has a sensitivity of 99.59%, a specificity of 99.58%, and a precision of 99.83% on the ADNI dataset, which are better than or comparable to state-of-the-art approaches. The experimental results prove that RST can achieve better classification performance in AD prediction compared with CNN-based and Transformer models.
Enhancing Lane-Tracking Performance in Challenging Driving Environments through Parameter Optimization and a Restriction System
Authors: Lee Seung-Hwan , Kwon Hyuk-Ju , Lee Sung-Hak
Published: August 2023

The autonomous driving market has experienced rapid growth in recent times. From systems that assist drivers in keeping within their lanes to systems that recognize obstacles using sensors and then handle those obstacles, there are various types of systems in autonomous driving. The sensors used in autonomous driving systems include infrared detection devices, lidar, ultrasonic sensors, and cameras. Among these sensors, cameras are widely used. This paper proposes a method for stable lane detection from images captured by camera sensors in diverse environments. First, the system utilizes a bilateral filter and multiscale retinex (MSR) with experimentally optimized set parameters to suppress image noise while increasing contrast. Subsequently, the Canny edge detector is employed to detect the edges of the lane candidates, followed by utilizing the Hough transform to make straight lines from the land candidate images. Then, using a proposed restriction system, only the two lines that the current vehicle is actively driving within are detected from the candidate lines. Furthermore, the lane position information from the previous frame is combined with the lane information from the current frame to correct the current lane position. The Kalman filter is then used to predict the lane position in the next frame. The proposed lane-detection method was evaluated in various scenarios, including rainy conditions, low-light nighttime environments with minimal street lighting, scenarios with interfering guidelines within the lane area, and scenarios with significant noise caused by water droplets on the camera. Both qualitative and quantitative experimental results demonstrate that the lane-detection method presented in this paper effectively suppresses noise and accurately detects the two active lanes during driving.
A Probable Approach to Reliability Assessment of Reinforced Plates
Authors: Pukach Petro , Kvit Roman , Salo Tetyana , Vovk Myroslava
Published: August 2023

A scheme for solving the problem of determining the probability of failure and probabilistic (statistical) characteristics of the failure loading magnitude of a composite material plate is considered. The plate structure is a flat homogeneous matrix with stochastically distributed rigid rod inclusions. The geometric parameters of inclusions are considered independent random variables with given probability distribution laws. The expressions for the failure loading distribution function, the probability of failure, the mean value, and the dispersion of the failure loading were received and presented. Their dependence on the type of stress state, the inclusion number, and matrix Poisson’s ratio were studied graphically.
Rainfall Variability and Teleconnections with Large-Scale Atmospheric Circulation Patterns in West-Central Morocco
Authors: Boughdadi Sara , Ait Brahim Yassine , El Alaoui El Fels Abdelhafid , Saidi Mohamed Elmehdi
Published: August 2023

Morocco is characterized by a semi-arid climate influenced by the Mediterranean, Atlantic, and Saharan environments, resulting in high variability in rainfall and hydrological conditions. Certain regions suffer from insufficient understanding concerning the spatiotemporal patterns of precipitation, along with facing recurrent periods of drought. This study aims to characterize the current trends and periodicities of precipitation in west-central Morocco at monthly and annual scales, using data from six rain gauges. The link between monthly precipitation and both the North Atlantic Oscillation (NAO) and the Western Mediterranean Oscillation (WeMO) indices was tested to identify potential teleconnections with large-scale variability modes. The results reveal interannual variability in precipitation and climate indices, while showing decreasing insignificant trends in annual precipitation. On a monthly scale, temporal precipitation patterns are similar to the annual scale. Furthermore, a remarkably robust and significant component with a periodicity of 6–8 years emerges consistently across all monitoring stations. Intriguingly, this band exhibits a more pronounced presence on the plains as opposed to the mountainous stations. Additionally, it is noteworthy that the NAO modulated winter precipitation, whereas the influence of the WeMO extends until March and April. This mode could be linked to the fluctuations of the WeMO from 1985 to 2005 and, subsequently, to NAO variations. Indeed, this is consistent with the strong significant correlations observed between rainfall and the NAO/WeMO. This study serves as a baseline for future research aiming to understand the influence of climate indices on rainfall in the North African region.
Extropy Based on Concomitants of Order Statistics in Farlie-Gumbel-Morgenstern Family for Random Variables Representing Past Life
Authors: Irshad Muhammed Rasheed , Archana Krishnakumar , Al-Omari Amer Ibrahim , Maya Radhakumari , Alomani Ghadah
Published: August 2023

In this paper, we refined the concept of past extropy measure for concomitants of order statistics from Farlie-Gumbel-Morgenstern family. In addition, cumulative past extropy measure and dynamic cumulative past extropy measure for concomitant of rth order statistic are also conferred and their properties are studied. The problem of estimating the cumulative past extropy is investigated using empirical technique. The validity of the proposed estimator has been emphasized using simulation study.
Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model
Authors: Arif Muhammad Shoaib , Mukheimer Aiman , Asif Daniyal
Published: August 2023

Clinical decision-making in chronic disorder prognosis is often hampered by high variance, leading to uncertainty and negative outcomes, especially in cases such as chronic kidney disease (CKD). Machine learning (ML) techniques have emerged as valuable tools for reducing randomness and enhancing clinical decision-making. However, conventional methods for CKD detection often lack accuracy due to their reliance on limited sets of biological attributes. This research proposes a novel ML model for predicting CKD, incorporating various preprocessing steps, feature selection, a hyperparameter optimization technique, and ML algorithms. To address challenges in medical datasets, we employ iterative imputation for missing values and a novel sequential approach for data scaling, combining robust scaling, z-standardization, and min-max scaling. Feature selection is performed using the Boruta algorithm, and the model is developed using ML algorithms. The proposed model was validated on the UCI CKD dataset, achieving outstanding performance with 100% accuracy. Our approach, combining innovative preprocessing steps, the Boruta feature selection, and the k-nearest neighbors algorithm, along with a hyperparameter optimization using grid-search cross-validation (CV), demonstrates its effectiveness in enhancing the early detection of CKD. This research highlights the potential of ML techniques in improving clinical support systems and reducing the impact of uncertainty in chronic disorder prognosis.
MIL-CT: Multiple Instance Learning via a Cross-Scale Transformer for Enhanced Arterial Light Reflex Detection
Authors: Gao Yuan , Ma Chenbin , Guo Lishuang , Zhang Xuxiang , Ji Xunming
Published: August 2023

One of the early manifestations of systemic atherosclerosis, which leads to blood circulation issues, is the enhanced arterial light reflex (EALR). Fundus images are commonly used for regular screening purposes to intervene and assess the severity of systemic atherosclerosis in a timely manner. However, there is a lack of automated methods that can meet the demands of large-scale population screening. Therefore, this study introduces a novel cross-scale transformer-based multi-instance learning method, named MIL-CT, for the detection of early arterial lesions (e.g., EALR) in fundus images. MIL-CT utilizes the cross-scale vision transformer to extract retinal features in a multi-granularity perceptual domain. It incorporates a multi-head cross-scale attention fusion module to enhance global perceptual capability and feature representation. By integrating information from different scales and minimizing information loss, the method significantly improves the performance of the EALR detection task. Furthermore, a multi-instance learning module is implemented to enable the model to better comprehend local details and features in fundus images, facilitating the classification of patch tokens related to retinal lesions. To effectively learn the features associated with retinal lesions, we utilize weights pre-trained on a large fundus image Kaggle dataset. Our validation and comparison experiments conducted on our collected EALR dataset demonstrate the effectiveness of the MIL-CT method in reducing generalization errors while maintaining efficient attention to retinal vascular details. Moreover, the method surpasses existing models in EALR detection, achieving an accuracy, precision, sensitivity, specificity, and F1 score of 97.62%, 97.63%, 97.05%, 96.48%, and 97.62%, respectively. These results exhibit the significant enhancement in diagnostic accuracy of fundus images brought about by the MIL-CT method. Thus, it holds potential for various applications, particularly in the early screening of cardiovascular diseases such as hypertension and atherosclerosis.
Circulating H3K27 Methylated Nucleosome Plasma Concentration: Synergistic Information with Circulating Tumor DNA Molecular Profiling
Authors: Grolleau Emmanuel , Candiracci Julie , Lescuyer Gaelle , Barthelemy David , Benzerdjeb Nazim , Haon Christine , Geiguer Florence , Raffin Margaux , Hardat Nathalie , Balandier Julie , Rabeuf Rémi , Chalabreysse Lara , Wozny Anne-Sophie , Rommelaere Guillaume , Rodriguez-Lafrasse Claire , Subtil Fabien , Couraud Sébastien , Herzog Marielle , Payen-Gay Lea
Published: August 2023

The molecular profiling of circulating tumor DNA (ctDNA) is a helpful tool not only in cancer treatment, but also in the early detection of relapse. However, the clinical interpretation of a ctDNA negative result remains challenging. The characterization of circulating nucleosomes (carrying cell-free DNA) and associated epigenetic modifications (playing a key role in the tumorigenesis of different cancers) may provide useful information for patient management, by supporting the contributive value of ctDNA molecular profiling. Significantly elevated concentrations of H3K27Me3 nucleosomes were found in plasmas at the diagnosis, and during the follow-up, of NSCLC patients, compared to healthy donors (p-value < 0.0001). By combining the H3K27Me3 level and the ctDNA molecular profile, we found that 25.5% of the patients had H3K27Me3 levels above the cut off, and no somatic alteration was detected at diagnosis. This strongly supports the presence of non-mutated ctDNA in the corresponding plasma. During the patient follow-up, a high H3K27Me3-nucleosome level was found in 15.1% of the sample, despite no somatic mutations being detected, allowing the identification of disease progression from 43.1% to 58.2% over molecular profiling alone. Measuring H3K27Me3-nucleosome levels in combination with ctDNA molecular profiling may improve confidence in the negative molecular result for cfDNA in lung cancer at diagnosis, and may also be a promising biomarker for molecular residual disease (MRD) monitoring, during and/or after treatment.
Thermokinetic Study of Catalytic Pyrolysis of Medium-Density Fiberboards over Beta-Zeolite-Supported Platinum
Authors: Carvalho Mateus da Silva , Mayer Francieli Martins , de Oliveira Ana Paula Stelzer , Ruiz Doris , das Virgens Cesário Francisco , Rangel Maria do Carmo
Published: August 2023

Catalytic pyrolysis is an attractive alternative for converting biomass into energy and chemicals, replacing fossil sources. Efficient catalysts can be used to remove compounds containing oxygen during pyrolysis, improving the bio-oil properties and thus being an important route towards sustainability. Catalytic pyrolysis of medium-density fiberboard (MDF) residues over platinum (1%) supported on beta zeolite was carried out using a biomass/catalyst ratio of 1.0/0.2. The catalysts were characterized via Fourier transform infrared spectroscopy, flame atomic absorption spectrometry, X-ray diffraction, nuclear magnetic resonance, temperature-programmed reduction, and temperature-programmed desorption of ammonia. The thermokinetic and thermodynamic parameters were determined using the isoconversional and non-isothermal methods of Friedman, Flynn-Wall-Ozawa (FWO), and Kissinger-Ahakira-Sunose (KAS). The Friedman method was the most adequate to describe the reaction and thermodynamic parameters. The results show that the catalysts promote the reduction in activation energy compared to non-catalytic pyrolysis. Non-impregnated and impregnated catalysts showed different activation energies and thus different reactions. The addition of platinum slightly increased the activation energy due to the promotion of reactions that require more energy, for example, cracking and coke deposition.
Brain-Derived Neurotrophic Factor Dysregulation as an Essential Pathological Feature in Huntington’s Disease: Mechanisms and Potential Therapeutics
Authors: Speidell Andrew , Bin Abid Noman , Yano Hiroko
Published: August 2023

Brain-derived neurotrophic factor (BDNF) is a major neurotrophin whose loss or interruption is well established to have numerous intersections with the pathogenesis of progressive neurological disorders. There is perhaps no greater example of disease pathogenesis resulting from the dysregulation of BDNF signaling than Huntington’s disease (HD)—an inherited neurodegenerative disorder characterized by motor, psychiatric, and cognitive impairments associated with basal ganglia dysfunction and the ultimate death of striatal projection neurons. Investigation of the collection of mechanisms leading to BDNF loss in HD highlights this neurotrophin’s importance to neuronal viability and calls attention to opportunities for therapeutic interventions. Using electronic database searches of existing and forthcoming research, we constructed a literature review with the overarching goal of exploring the diverse set of molecular events that trigger BDNF dysregulation within HD. We highlighted research that investigated these major mechanisms in preclinical models of HD and connected these studies to those evaluating similar endpoints in human HD subjects. We also included a special focus on the growing body of literature detailing key transcriptomic and epigenetic alterations that affect BDNF abundance in HD. Finally, we offer critical evaluation of proposed neurotrophin-directed therapies and assessed clinical trials seeking to correct BDNF expression in HD individuals.
Skin and Gut Microbiome in Hidradenitis Suppurativa: A Systematic Review
Authors: Lelonek Edyta , Bouazzi Dorra , Jemec Gregor B. E. , Szepietowski Jacek C.
Published: August 2023

Hidradenitis suppurativa (HS) is a chronic skin disease that significantly impairs the quality of life of affected individuals. The disease is characterized by persistent purulent lesions in specific anatomical areas, and its pathophysiology involves multiple factors, including inflammation, genetics, the microbiome, and environmental components. Recent research suggests a potential role for pathogenic bacteria in HS, highlighting the importance of the communication between the human host and the microbiome in maintaining homeostasis and immune system reactivity. However, the exact mechanisms underlying the gut–skin microbial interactions in HS remain unclear. This systematic review aims to examine the existing literature on the differences in skin and gut microbiome composition between HS patients and healthy controls. The review identifies methodological inconsistencies and calls for further research to elucidate the microbiome’s role in HS pathogenesis and to explore new therapeutic interventions. The review highlights the need for advancements in microbiome research methodologies, such as metataxonomics and metagenomics, to improve our understanding of the microbiota’s impact on health and disease.
Numerical Simulation of the Ventilation and Fire Conditions in an Underground Garage with an Induced Ventilation System
Authors: Wang Zhitao , Zhou Xue , Zhu Xiangyuan , Liu Jiying
Published: August 2023

The increasing quantity of air pollutants generated by automobiles can cause significant harm in relatively enclosed indoor environments. Studying the distribution of pollutants under different conditions in underground parking garages is of great significance for improving indoor air quality and reducing casualties in the event of a fire. This article presents a geometric model of an underground parking garage based on PHOENICS modeling. The related results of CO concentration distribution and fire temperature distribution under ventilation and fire conditions are obtained. Based on the CO concentration and velocity distribution as well as the temperature distribution during a fire, reasonable suggestions are proposed to improve indoor air quality and reduce casualty rates in fire incidents. The results show that under ventilation conditions, adjusting the position of the induced ventilation fan can maintain CO concentrations below 30 ppm in partitions one to three and below 37 ppm in partitions four to six. The temperature of smoke gases remained below 50 °C during the evacuation time, and only a small area exhibited CO levels exceeding 2000 ppm. The existing ventilation exhaust system provides effective fire protection, as it minimally affects personnel evacuation due to the relatively lower smoke temperature.
Cost-Effectiveness of Surveillance after Metastasectomy of Stage IV Colorectal Cancer
Authors: Ding Philip Q. , Au Flora , Cheung Winson Y. , Heitman Steven J. , Lee-Ying Richard
Published: August 2023

Surveillance of stage IV colorectal cancer (CRC) after curative-intent metastasectomy can be effective for detecting asymptomatic recurrence. Guidelines for various forms of surveillance exist but are supported by limited evidence. We aimed to determine the most cost-effective strategy for surveillance following curative-intent metastasectomy of stage IV CRC. We performed a decision analysis to compare four active surveillance strategies involving clinic visits and investigations elicited from National Comprehensive Cancer Network (NCCN) recommendations. Markov model inputs included data from a population-based cohort and literature-derived costs, utilities, and probabilities. The primary outcomes were costs (2021 Canadian dollars) and quality-adjusted life years (QALYs) gained. Over a 10-year base-case time horizon, surveillance with follow-ups every 12 months for 5 years was most economically favourable at a willingness-to-pay threshold of CAD 50,000 per QALY. These patterns were generally robust in the sensitivity analysis. A more intensive surveillance strategy was only favourable with a much higher willingness-to-pay threshold of approximately CAD 425,000 per QALY, with follow-ups every 3 months for 2 years then every 12 months for 3 additional years. Our findings are consistent with NCCN guidelines and justify the need for additional research to determine the impact of surveillance on CRC outcomes.
Menin Maintains Cholesterol Content in Colorectal Cancer via Repression of LXR-Mediated Transcription
Authors: Nyul Thomas E. , Beyries Keely , Hojnacki Taylor , Glynn Rebecca , Paulosky Kayla E. , Gedela Anitej , Majer Ariana , Altman Lily , Buckley Kole H. , Feng Zijie , Sun Kunfeng , Peng Zhicheng , Tobias John W. , Hua Xianxin , Katona Bryson W.
Published: August 2023

Background and Aims: Menin is a nuclear scaffold protein that regulates gene transcription in an oftentimes tissue-specific manner. Our previous work showed that menin is over-expressed in colorectal cancer (CRC); however, the full spectrum of menin function in colonic neoplasia remains unclear. Herein, we aimed to uncover novel menin-regulated pathways important for colorectal carcinogenesis. Methods: RNA-Seq analysis identified that menin regulates LXR-target gene expressions in CRC cell lines. Isolated colonic epithelium from Men1f/f;Vil1-Cre and Men1f/f mice was used to validate the results in vivo. Cholesterol content was quantified via an enzymatic assay. Results: RNA-Seq analysis in the HT-29 CRC cell line identified that menin inhibition upregulated LXR-target genes, specifically ABCG1 and ABCA1, with protein products that promote cellular cholesterol efflux. Similar results were noted across other CRC cell lines and with different methods of menin inhibition. Consistent with ABCG1 and ABCA1 upregulation, and similarly to LXR agonists, menin inhibition reduced the total cellular cholesterol in both HT-29 and HCT-15 cells. To confirm the effects of menin inhibition in vivo, we assessed Men1f/f;Vil1-Cre mice lacking menin expression in the colonic epithelium. Men1f/f;Vil1-Cre mice were found to have no distinct baseline phenotype compared to control Men1f/f mice. However, similarly to CRC cell lines, Men1f/f;Vil1-Cre mice showed an upregulation of Abcg1 and a reduction in total cellular cholesterol. Promoting cholesterol efflux, either via menin inhibition or LXR activation, was found to synergistically suppress CRC cell growth under cholesterol-depleted conditions and when administered concomitantly with small molecule EGFR inhibitors. Conclusions: Menin represses the transcription of LXR-target genes, including ABCA1 and ABCG1 in the colonic epithelium and CRC. Menin inhibition conversely upregulates LXR-target genes and reduces total cellular cholesterol, demonstrating that menin inhibition may be an important mechanism for targeting cholesterol-dependent pathways in colorectal carcinogenesis.
Uncovering Novel Roles of miR-122 in the Pathophysiology of the Liver: Potential Interaction with NRF1 and E2F4 Signaling
Authors: Paluschinski Martha , Schira-Heinen Jessica , Pellegrino Rossella , Heij Lara R. , Bednarsch Jan , Neumann Ulf P. , Longerich Thomas , Stuehler Kai , Luedde Tom , Castoldi Mirco
Published: August 2023

MicroRNA miR-122 plays a pivotal role in liver function. Despite numerous studies investigating this miRNA, the global network of genes regulated by miR-122 and its contribution to the underlying pathophysiological mechanisms remain largely unknown. To gain a deeper understanding of miR-122 activity, we employed two complementary approaches. Firstly, through transcriptome analysis of polyribosome-bound RNAs, we discovered that miR-122 exhibits potential antagonistic effects on specific transcription factors known to be dysregulated in liver disease, including nuclear respiratory factor-1 (NRF1) and the E2F transcription factor 4 (E2F4). Secondly, through proteome analysis of hepatoma cells transfected with either miR-122 mimic or antagomir, we discovered changes in several proteins associated with increased malignancy. Interestingly, many of these proteins were reported to be transcriptionally regulated by NRF1 and E2F4, six of which we validated as miR-122 targets. Among these, a negative correlation was observed between miR-122 and glucose-6-phosphate dehydrogenase levels in the livers of patients with hepatitis B virus-associated hepatocellular carcinoma. This study provides novel insights into potential alterations of molecular pathway occurring at the early stages of liver disease, driven by the dysregulation of miR-122 and its associated genes.
Photodynamic Stromal Depletion in Pancreatic Ductal Adenocarcinoma
Authors: Lintern Nicole , Smith Andrew M. , Jayne David G. , Khaled Yazan S.
Published: August 2023

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest solid malignancies, with a five-year survival of less than 10%. The resistance of the disease and the associated lack of therapeutic response is attributed primarily to its dense, fibrotic stroma, which acts as a barrier to drug perfusion and permits tumour survival and invasion. As clinical trials of chemotherapy (CT), radiotherapy (RT), and targeted agents have not been successful, improving the survival rate in unresectable PDAC remains an urgent clinical need. Photodynamic stromal depletion (PSD) is a recent approach that uses visible or near-infrared light to destroy the desmoplastic tissue. Preclinical evidence suggests this can resensitise tumour cells to subsequent therapies whilst averting the tumorigenic effects of tumour–stromal cell interactions. So far, the pre-clinical studies have suggested that PDT can successfully mediate the destruction of various stromal elements without increasing the aggressiveness of the tumour. However, the complexity of this interplay, including the combined tumour promoting and suppressing effects, poses unknowns for the clinical application of photodynamic stromal depletion in PDAC.
Human and Pig Pluripotent Stem Cells: From Cellular Products to Organogenesis and Beyond
Authors: Xuan Yiyi , Petersen Björn , Liu Pentao
Published: August 2023

Pluripotent stem cells (PSCs) are important for studying development and hold great promise in regenerative medicine due to their ability to differentiate into various cell types. In this review, we comprehensively discuss the potential applications of both human and pig PSCs and provide an overview of the current progress and challenges in this field. In addition to exploring the therapeutic uses of PSC-derived cellular products, we also shed light on their significance in the study of interspecies chimeras, which has led to the creation of transplantable human or humanized pig organs. Moreover, we emphasize the importance of pig PSCs as an ideal cell source for genetic engineering, facilitating the development of genetically modified pigs for pig-to-human xenotransplantation. Despite the achievements that have been made, further investigations and refinement of PSC technologies are necessary to unlock their full potential in regenerative medicine and effectively address critical healthcare challenges.
Study of the Influence of Tool Wear of Two Drill Bits Manufactured with Different Coating Processes in Drilling Carbon/Glass Fiber Hybrid Composite Bounded with Epoxy Polymer
Authors: Gutiérrez Santiago Carlos , Meseguer María Desamparados , Muñoz-Sánchez Ana , Feito Norberto
Published: August 2023

Fiber Reinforced Polymer (FRP) laminates have been widely used in engineering applications in recent decades. This is mainly due to their superior mechanical properties compared to single-phase materials. High strength-to-weight ratio, high stiffness, and excellent corrosion and fatigue resistance are some of the attractive properties of these materials. In large structures, drilling composite panels is a typical operation to assemble different parts with mechanical fasteners. This operation severely threatens the quality of the holes and, therefore, the joint strength. This study aims to study the wear evolution of two drill bits manufactured with different coatings processes (chemical vapor deposition and physical vapor deposition) and their influence on the quality of the holes. A carbon/glass fiber sandwich structure was selected as the workpiece, and a high-speed machine center was used to drill 1403 holes per tool in the laminates. The wear analysis of the tool was characterized in terms of flank wear and crater wear. For the delamination analysis caused by drilling, two types of delamination are identified (type I and II), and their values were quantified through the equivalent delamination factor (Fed). The results showed that, in general, the process used to apply the coating to the tool influences the wear mode and the delamination damage. The first tool, diamond coated with Chemical Vapor Deposition (CVD), showed more severe crater wear in the flank face and coating loss at the end of the cutting edges. However, with a Physical Vapor Deposition (PVD) coating process, the second tool presented flank wear more controlled but a more severe coating loss and edge rounding near the tip, producing further delamination. Using a supporting plate showed a reduction of delamination type I but not for delamination type II, which is related to edge rounding.
Erosion Behavior of Stellite-6 and WC-12Co Coatings on SA213-T22 Boiler Steel
Authors: Kiatisereekul Aumpava , Siripongsakul Thamrongsin , Fakpan Kittichai
Published: August 2023

At Mae-Moh power plant, Thailand, superheater tubes, which are exposed to a fly ash environment, often degrade due to solid particle erosion. To extend the service lifetime of the superheater tubes, the high velocity oxy-fuel (HVOF) thermal spray technique is used to deposit a protective coating on the material, SA213-T22 steel. In this work, the solid particle erosion of Stellite-6 and WC-12Co coatings was investigated using erodent particle impingement at angles of 30 and 90°. This was carried out with an average particle size of 60 μm. The erosion behavior of SA213-T22 with and without Stellite-6 and WC-12Co coatings was examined using ductile and brittle erosion modes. The erosion testing resulted in the brittle mode for both Stellite-6 and WC-12Co coatings, while the SA213-T22 without coating indicated the ductile mode. On investigation of the surface morphology, the SA213-T22 steel showed ploughing and microcutting. The Stellite-6 coating showed some evidence of ductile erosion such as lips on the coating surface, different from the WC-12Co coating which showed microcracks and deep cavities. The erosion resistance of the Stellite-6 coating was higher than the WC-12Co coating. This was due to the strength and toughness of the metal matrix composite structure and the low porosity of the coating.
Eighteen-Month Orthodontic Bracket Survival Rate with the Conventional Bonding Technique versus RMGIC and V-Prep: A Split-Mouth RCT
Authors: Ghoubril Victor , Ghoubril Joseph , Abboud Maher , Bou Sakr Tatiana , Hardan Louis , Khoury Elie
Published: August 2023

The association of the V-prep and a resin-modified glass ionomer cement (RMGIC) has shown to be a suitable alternative for the orthodontic bracket bonding procedure in vitro. The aim of this study was to evaluate over eighteen months the clinical bonding failure and survival rates of the conventional bonding technique using the Transbond XT (3M Unitek, Monrovia, CA, USA) and the RMGIC Fuji Ortho LC (GC Corporation, Tokyo, Japan) prepared with the V-prep. Therefore, one operator using the straight-wire technique bonded two hundred metallic brackets to upper and lower premolars of twenty-five patients requiring an orthodontic treatment. The randomized trial was a single-blind design in a split-mouth comparison. Each patient was randomly allocated one of the two bonding systems for each premolar on each side of the mouth. The bonding and rebonding techniques were standardized throughout the trial and bond failure was recorded each month for a period of eighteen months. The survival rates of the brackets were estimated by Kaplan–Meier and log-rank test (p < 0.05). A total of 200 orthodontic brackets were included in the study with a significant lower failure rate of 9.0% for the V-prep and RMGIC compared to 25.0% for the conventional bonding technique (p < 0.05). A higher survival rate was observed for the V-prep and RMGIC (16.36 months) over the conventional bonding technique (13.95 months) (p < 0.05). Lower premolar bonding failure was higher than upper premolar for both bonding techniques. The V-prep followed by RMGIC, with enamel surface protection abilities, can be used as an alternative bonding technique in an orthodontic treatment.
Mammographic Breast Density Model Using Semi-Supervised Learning Reduces Inter-/Intra-Reader Variability
Authors: Watanabe Alyssa T. , Retson Tara , Wang Junhao , Mantey Richard , Chim Chiyung , Karimabadi Homa
Published: August 2023

Breast density is an important risk factor for breast cancer development; however, imager inconsistency in density reporting can lead to patient and clinician confusion. A deep learning (DL) model for mammographic density grading was examined in a retrospective multi-reader multi-case study consisting of 928 image pairs and assessed for impact on inter- and intra-reader variability and reading time. Seven readers assigned density categories to the images, then re-read the test set aided by the model after a 4-week washout. To measure intra-reader agreement, 100 image pairs were blindly double read in both sessions. Linear Cohen Kappa (κ) and Student’s t-test were used to assess the model and reader performance. The model achieved a κ of 0.87 (95% CI: 0.84, 0.89) for four-class density assessment and a κ of 0.91 (95% CI: 0.88, 0.93) for binary non-dense/dense assessment. Superiority tests showed significant reduction in inter-reader variability (κ improved from 0.70 to 0.88, p ≤ 0.001) and intra-reader variability (κ improved from 0.83 to 0.95, p ≤ 0.01) for four-class density, and significant reduction in inter-reader variability (κ improved from 0.77 to 0.96, p ≤ 0.001) and intra-reader variability (κ improved from 0.89 to 0.97, p ≤ 0.01) for binary non-dense/dense assessment when aided by DL. The average reader mean reading time per image pair also decreased by 30%, 0.86 s (95% CI: 0.01, 1.71), with six of seven readers having reading time reductions.