bluuebunny/crossref_metadata_2025 huggingface dataset:
Metallothionein Family Proteins as Regulators of Zinc Ions Synergistically Enhance the Anticancer Effect of Cannabidiol in Human Colorectal Cancer Cells
Authors: Kwon In-Seo , Hwang Yu-Na , Park Ju-Hee , Na Han-Heom , Kwon Tae-Hyung , Park Jin-Sung , Kim Keun-Cheol
Published: November 2023

Cannabidiol (CBD) is a chemical obtained from Cannabis sativa; it has therapeutic effects on anxiety and cognition and anti-inflammatory properties. Although pharmacological applications of CBD in many types of tumors have recently been reported, the mechanism of action of CBD is not yet fully understood. In this study, we perform an mRNA-seq analysis to identify the target genes of CBD after determining the cytotoxic concentrations of CBD using an MTT assay. CBD treatment regulated the expression of genes related to DNA repair and cell division, with metallothionein (MT) family genes being identified as having highly increased expression levels induced by CBD. It was also found that the expression levels of MT family genes were decreased in colorectal cancer tissues compared to those in normal tissues, indicating that the downregulation of MT family genes might be highly associated with colorectal tumor progression. A qPCR experiment revealed that the expression levels of MT family genes were increased by CBD. Moreover, MT family genes were regulated by CBD or crude extract but not by other cannabinoids, suggesting that the expression of MT family genes was specifically induced by CBD. A synergistic effect between CBD and MT gene transfection or zinc ion treatment was found. In conclusion, MT family genes as novel target genes could synergistically increase the anticancer activity of CBD by regulating the zinc ions in human colorectal cancer cells.
Nanodiamond Effects on Cancer Cell Radiosensitivity: The Interplay between Their Chemical/Physical Characteristics and the Irradiation Energy
Authors: Varzi Veronica , Fratini Emiliano , Falconieri Mauro , Giovannini Daniela , Cemmi Alessia , Scifo Jessica , Di Sarcina Ilaria , Aprà Pietro , Sturari Sofia , Mino Lorenzo , Tomagra Giulia , Infusino Erminia , Landoni Valeria , Marino Carmela , Mancuso Mariateresa , Picollo Federico , Pazzaglia Simonetta
Published: November 2023

Nanoparticles are being increasingly studied to enhance radiation effects. Among them, nanodiamonds (NDs) are taken into great consideration due to their low toxicity, inertness, chemical stability, and the possibility of surface functionalization. The objective of this study is to explore the influence of the chemical/physical properties of NDs on cellular radiosensitivity to combined treatments with radiation beams of different energies. DAOY, a human radioresistant medulloblastoma cell line was treated with NDs—differing for surface modifications [hydrogenated (H-NDs) and oxidized (OX-NDs)], size, and concentration—and analysed for (i) ND internalization and intracellular localization, (ii) clonogenic survival after combined treatment with different radiation beam energies and (iii) DNA damage and apoptosis, to explore the nature of ND–radiation biological interactions. Results show that chemical/physical characteristics of NDs are crucial in determining cell toxicity, with hydrogenated NDs (H-NDs) decreasing either cellular viability when administered alone, or cell survival when combined with radiation, depending on ND size and concentration, while OX-NDs do not. Also, irradiation at high energy (γ-rays at 1.25 MeV), in combination with H-NDs, is more efficient in eliciting radiosensitisation when compared to irradiation at lower energy (X-rays at 250 kVp). Finally, the molecular mechanisms of ND radiosensitisation was addressed, demonstrating that cell killing is mediated by the induction of Caspase-3-dependent apoptosis that is independent to DNA damage. Identifying the optimal combination of ND characteristics and radiation energy has the potential to offer a promising therapeutic strategy for tackling radioresistant cancers using H-NDs in conjunction with high-energy radiation.
Enhancing Selectivity of Protein Biopharmaceuticals in Ion Exchange Chromatography through Addition of Organic Modifiers
Authors: Duivelshof Bastiaan Laurens , Bouvarel Thomas , Pirner Sebastian , Larraillet Vincent , Knaupp Alexander , Koll Hans , D’Atri Valentina , Guillarme Davy
Published: November 2023

Charge heterogeneity among therapeutic monoclonal antibodies (mAbs) is considered an important critical quality attribute and requires careful characterization to ensure safe and efficacious drug products. The charge heterogeneity among mAbs is the result of chemical and enzymatic post-translational modifications and leads to the formation of acidic and basic variants that can be characterized using cation exchange chromatography (CEX). Recently, the use of mass spectrometry-compatible salt-mediated pH gradients has gained increased attention to elute the proteins from the charged stationary phase material. However, with the increasing antibody product complexity, more and more selectivity is required. Therefore, in this study, we set out to improve the selectivity by using a solvent-enriched mobile phase composition for the analysis of a variety of mAbs and bispecific antibody products. It was found that the addition of the solvents to the mobile phase appeared to modify the hydrate shell surrounding the protein and alter the retention behavior of the studied proteins. Therefore, this work demonstrates that the use of solvent-enriched mobile phase composition could be an attractive additional method parameter during method development in CEX.
Applications of the Order Reduction Optimization of the H-Infinity Controller in Smart Structures
Authors: Moutsopoulou Amalia , Petousis Markos , Vidakis Nectarios , Stavroulakis Georgios E. , Pouliezos Anastasios
Published: November 2023

In this paper, our strategy is to look for locally optimum answers to a non-smooth optimization problem that has been constructed to include minimization goals and restrictions for smart structures’ vibration suppression. In both theoretical analysis and practical implementation, it is widely recognized that designing multi-objective control systems poses a considerable challenge. In this study, we assess the effectiveness of this method by employing the open-source Matlab toolbox Hifoo 2.0 and juxtapose our findings with established industry standards. We start by framing the control problem as a mathematical optimization issue and proceed to identify the controller that effectively addresses this optimization. This approach introduces the potential application of intelligent structures in tackling the challenge of vibration suppression. This study makes use of the most recent version of the freely available application Hifoo which tries to study vibration suppression with the limits outlined above in the context of multi-objective controller design. A controller directive is initially set, allowing for a lower order.
The Impact of Machine Learning and Robot-Assisted Gait Training on Spinal Cord Injury: A Systematic Review and Meta-Analysis
Authors: Wardhana Dewa Putu Wisnu , Maliawan Sri , Mahadewa Tjokorda Gde Bagus , Rosyidi Rohadi Muhammad , Wiranata Sinta
Published: November 2023

Introduction: Spinal cord injury (SCI) is a significant and transforming event, with an estimated annual incidence of 40 cases per million individuals in North America. Considering the significance of accurate diagnosis and effective therapy in managing SCI, Machine Learning (ML) and Robot-Assisted Gait Training (RAGT) technologies hold promise for enhancing optimal practices and elevating the quality of care. This study aims to determine the impact of the ML and RAGT techniques employed on the outcome results of SCI. Methods: We reviewed four databases, including PubMed, Scopus, ScienceDirect, and the Cochrane Central Register of Controlled Trials (CENTRAL), until 20 August 2023. The keywords used in this study encompassed the following: a comprehensive search was executed on research exclusively published in the English language: machine learning, robotics, and spinal cord injury. Results: A comprehensive search was conducted across four databases, identifying 2367 articles following rigorous data filtering. The results of the odd ratio (OR) and confidence interval (CI) of 95% for the ASIA Impairment Scale, or AIS grade A, were 0.093 (0.011–0.754, p = 0.026), for AIS grade B, 0.875 (0.395–1.939, p = 0.743), for AIS grade C, 3.626 (1.556–8.449, p = 0.003), and for AIS grade D, 8.496 (1.394–51.768, p = 0.020). The robotic group exhibited a notable reduction in AS (95% CI = −0.239 to −0.045, p = 0.004) and MAS (95% CI = −3.657 to −1.066, p ≤ 0.001) measures. This study also investigated spasticity and walking ability, which are significant. Conclusions: The ML approach exhibited enhanced precision in forecasting AIS result scores. Implementing RAGT has been shown to impact spasticity reduction and improve walking ability.
CD24 in Head and Neck Malignancies—An Uprising Biomarker?
Authors: Carmel Neiderman Narin N. , Shapira Shiran , Klein Linor , Rafael Dor , Gorelik Gregory , Kampel Liyona , Arber Nadir , Muhanna Nidal
Published: November 2023

Introduction: CD24 is often overexpressed in human tumors as a regulator of cell migration, invasion and proliferation. It has been associated with poor prognosis and chemoresistance in laryngeal cancer. In oral cavity tumors, it was correlated with better overall survival. In this study, we aimed to evaluate the role of CD24 in peripheral blood leukocytes (PBLs) as a potential marker for head and neck malignancies. Materials and Methods: CD24/CD11b expression in peripheral blood leukocytes (PBLs) of head and neck cancer patients and matched healthy controls was analyzed via flow cytometry. Tumors and healthy tissues were immune-stained for CD24 expression and the intensity of stain was ranked. Clinical data including tumor site, size, locoregional or metastatic spread, histopathological characteristics and recurrence events were analyzed. Results: CD24 expression in PBLs was significantly higher in a cohort of 101 head and neck cancer patients compared with 101 matched healthy controls (26.9 ± 12.9 vs. 22.4 ± 13.8; p = 0.02). No significant differences in CD24 levels in PBLs were found between different head and neck subsites involved with malignancy. Higher CD24 levels did not correlate with any adverse feature, i.e., perineural invasion or lymphovascular invasion, advanced T stage or regional spread. Immunohistochemistry analysis demonstrated that CD24 was highly expressed in tumor tissue in comparison to healthy surrounding tissue. Conclusions: CD24 is a possible uprising marker for tumor identification, overexpressed in PBLs and is intensely stained in tumor tissue and pre-malignant lesions. Tumor–PBLs should be further studied.
Mesenchymal Stromal Cell Therapy for Thoracic Surgeons: An Update
Authors: Petrella Francesco , Cassina Enrico Mario , Libretti Lidia , Pirondini Emanuele , Raveglia Federico , Tuoro Antonio
Published: November 2023

Stem cells are undifferentiated cells presenting extensive self-renewal features and the ability to differentiate “in vitro” and “in vivo” into a range of lineage cells, like chondrogenic, osteogenic and adipogenic lineages when cultured in specific inducing media. Two major domains of clinical applications of stem cells in thoracic surgery have been investigated: regenerative medicine, which is a section of translational research in tissue engineering focusing on the replacement, renewal or regeneration of cells, tissues and organs to re-establish damaged physiologic functions; drug loading and delivery, representing a new branch proposing stem cells as carriers to provide selected districts with anti-cancer agents for targeted treatments.
A “Status-Habitat-Potential” Model for the Evaluation of Plant Communities in Underwater Mining Areas via Time Series Remote Sensing Images and GEE
Authors: Mi Jiaxin , Yang Deli , Hou Huping , Zhang Shaoliang
Published: November 2023

Mining activities are the primary human-induced disturbances on plant communities in various ecosystems, and they also are important for implementing strategies of ecological protection and restoration based on them. The effects of underwater mining on plant communities in wetland ecosystems, however, are seldom demonstrated, and it is also difficult to accurately evaluate the state of plant communities’ condition, considering the dynamic and randomness of plant communities under multiple factors, including climate, mining, and other human activities. To address these issues, a “Status-Habitat-Potential” (SHP) model has been developed, with nine indicators from the status, habitat, and potential of plant communities, and the plant communities in the Nansi Lake mining area are evaluated to illustrate the effects of underwater mining. Time series remote sensing images from Sentinel-2 and Google Earth Engine are applied. Comparison analysis, Global Moran’s index, and hot and cold analysis are also used to demonstrate the spatial characteristics of the SHP index. Results show that the SHP index varies between 0 and 0.57 and shows a high aggregation pattern according to the Global Moran’s index (0.41), with high and low values aggregating in the center of the lake and living areas, respectively. The SHP index between subsidence and contrast areas shows no significant difference (at p < 0.05), indicating little effect of mining subsidence on plant communities directly. Overall, underwater mining would not cause as obvious effects on plant communities as underground mining, but human activities accompanied by mining activities will result in the loss of plant communities around lake shores and river channels. This study put forward a new model to evaluate plant communities in terms of their status, habitat, and potential, which could also be used to illustrate other long-term effects of disturbances on plant communities.
Isma‘ili Continuity and Social Change: Chronotopes and Practicing Taqiyya within the Sulaymani Community of Saudi Arabia
Authors: Aljuran Aidah , Brewster Jarred
Published: November 2023

This paper examines discourses around the religious and social practice of taqiyya among members of the Sulaymani Isma‘ili community in Saudi Arabia. Isma‘ilism, in the context of 1200 years of anti-Shi‘a discrimination, cultivated the practice known as taqiyya (Arabic, ‘circumscription’) as a tool for self-preservation, which was then further rationalized and reinforced by the sect’s esoteric theology. Taqiyya consists of concealing religious identity, public avoidance of certain rituals, and, in some instances, claiming to be a member of the unmarked Sunni majority. Sweeping changes in Saudi society in the last several years have meant a growing ambivalence about taqiyya and its continued utility. This is significant since taqiyya for many of our interlocutors in this study is not merely a survival tactic. Instead, it is better understood as an embodied disposition cultivated against the backdrop of household privacy. This disposition intimately links everyday comportment to the esoteric cosmology of Isma‘ilism, which is the distinctive and iconic feature of the faith. Our interlocutors’ narratives demonstrated how the invocation of different spatial and temporal frameworks provides a basic heuristic by which to interpret these individuals’ accounts of taqiyya. For some of these individuals, taqiyya is an essential and timeless practice, while for others, the meaning has been reshaped by the recent socio-political reforms.
Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers
Authors: Mamede Fábio Polola , da Silva Roberto Fray , de Brito Junior Irineu , Yoshizaki Hugo Tsugunobu Yoshida , Hino Celso Mitsuo , Cugnasca Carlos Eduardo
Published: November 2023

Background: Transportation demand forecasting is an essential activity for logistics operators and carriers. It leverages business operation decisions, infrastructure, management, and resource planning activities. Since 2015, there has been an increase in the use of deep learning models in this domain. However, there is a gap in works comparing traditional statistics and deep learning models for transportation demand forecasts. This work aimed to perform a case study of aggregated transportation demand forecasts in 54 distribution centers of a Brazilian carrier. Methods: A computational simulation and case study methods were applied, exploring the characteristics of the datasets through autoregressive integrated moving average (ARIMA) and its variations, in addition to a deep neural network, long short-term memory, known as LSTM. Eight scenarios were explored while considering different data preprocessing methods and evaluating how outliers, training and testing dataset splits during cross-validation, and the relevant hyperparameters of each model can affect the demand forecast. Results: The long short-term memory networks were observed to outperform the statistical methods in ninety-four percent of the dispatching units over the evaluated scenarios, while the autoregressive integrated moving average modeled the remaining five percent. Conclusions: This work found that forecasting transportation demands can address practical issues in supply chains, specially resource planning management.
Effect of Varying the Volume Fractions of Ledeburitic Cementite and Graphite on the Tribological Properties of Commercially Used Cast Irons
Authors: Frocisz Łukasz , Matusiewicz Piotr , Krawczyk Janusz
Published: November 2023

The types and volume fractions of the carbonaceous phases present in the microstructures of cast irons strongly influence their properties. In the case of materials used commercially for tools, an important parameter with regard to their use is the resistance to abrasion wear. Cementite is the main reinforcing phase in cast irons and is present in significant quantities. In addition, cast irons contain graphite precipitates, which also affect wear by interacting with the matrix of the alloys. In this study, abrasive wear tests were carried out on a group of cast irons with different chemical compositions and, consequently, different microstructural morphologies. Due to the wide scatter of the results and the commercial rather than laboratory nature of the alloys studied, it was decided to use analysis of variance (ANOVA) to determine whether there was a statistically significant difference between the volume fractions of the carbonaceous phases. The volume fractions of graphite and ledeburite were then related to the results of the tribological tests. Statistical analysis confirmed significant differences in the results obtained for the alloys tested. A continuous increase in the volume fractions of both graphite and ledeburitic cementite is unfavourable in terms of the wear resistance and friction coefficient values. Optimum results can be obtained by balancing the volume fractions of the two phases observed. In addition, the phase composition of the material matrix plays an important role in wear, as the differences in the matrix of the tested alloys modify the nature of the influence of cementite and graphite on the wear.
Wire Arc Additive Manufacturing: A Study of Process Parameters Using Multiphysics Simulations
Authors: Han You Sung
Published: November 2023

In this work, analyses focus on understanding the effects of the scanning pattern and speed on the thermal profile, phase transformation, and residual stress generation in the WAAM deposition. An FE numerical model is constructed that takes into account the phase evolution and transformation plasticity using the ABAQUS user subroutine, UMAT. The results show that the scanning pattern significantly affects the heat accumulation and the cooling rate during the AM deposition, and, eventually, the generation of residual stresses. According to the simulation results, the highest residual stress is generated in the case of the out–in scanning, while the alternate pattern leads to the lowest residual stress. The influence of the scanning speed on the thermal profiles and residual stress are also examined. The analyses show that an increase in the scan speed leads to a decrease in the peak temperature and an increase in the cooling rate, which result in an increase in the martensite volume fraction of the deposition.
A Novel Sodium–Potassium Anode Supported by Fluorinated Aluminum Foam
Authors: Lou Jin , Zhou Jingan , Ma Xiaosong , Chen Kanghua , Chen Songyi
Published: November 2023

Sodium–potassium (NaK) liquid alloy is a promising candidate for use as an anode material in sodium batteries because of its fluidity, which effectively suppresses the growth of sodium or potassium dendrites. However, the poor wettability of NaK alloy on conventional metal substrates is unfavorable for cell fabrication due to its strong surface tension. In this paper, low-density and low-cost fluorinated aluminum foam is used as a substrate support material for NaK liquid alloy. By combining low-surface-tension NaKC with fluorinated aluminum foam, we obtain a uniformly distributed and structurally stable electrode material. The composite electrode has a cycling stability of more than 3000 h in a symmetrical cell. Furthermore, when coupled with a sulfurized polyacrylonitrile cathode in carbonate electrolyte, it maintains excellent stability even after 800 cycles, with 72% of capacity retention.
Endodontic Radiopacifying Application of Barium Titanate Prepared through a Combination of Mechanical Milling and Heat Treatment
Authors: Lin Hsiu-Na , Chen Wei-Wen , Hsu Chun-Chun , Chen May-Show , Chang Pei-Jung , Chang Wei-Min , Zhang Fang-Hao , Chen Chin-Yi , Lee Pee-Yew , Lin Chung-Kwei
Published: November 2023

Mineral trioxide aggregates (MTA) are commonly used as endodontic filling materials but suffer from a long setting time and tooth discoloration. In the present study, the feasibility of using barium titanate (BTO) for discoloration and a calcium chloride (CaCl2) solution to shorten the setting time was investigated. BTO powder was prepared using high-energy ball milling for 3 h, followed by sintering at 700–1300 °C for 2 h. X-ray diffraction was used to examine the crystallinity and crystalline size of the as-milled and heat-treated powders. MTA-like cements were then prepared using 20–40 wt.% BTO as a radiopacifier and solidified using a 0–30% CaCl2 solution. The corresponding radiopacity, diametral tensile strength (DTS), initial and final setting times, and discoloration performance were examined. The experimental results showed that for the BTO powder prepared using a combination of mechanical milling and heat treatment, the crystallinity and crystalline size increased with the increasing sintering temperature. The BTO sintered at 1300 °C (i.e., BTO-13) exhibited the best radiopacity and DTS. The MTA-like cement supplemented with 30% BTO-13 and solidified with a 10% CaCl2 solution exhibited a radiopacity of 3.68 ± 0.24 mmAl and a DTS of 2.54 ± 0.28 MPa, respectively. In the accelerated discoloration examination using UV irradiation, the color difference was less than 1.6 and significantly lower than the clinically perceptible level (3.7). This novel MTA exhibiting a superior color stability, shortened setting time, and excellent biocompatibility has potential for use in endodontic applications.
Analysis and Design of Small-Impact Magnetoelectric Generator
Authors: Niu Shaohua , Li Bing , Li Bingyang , Wang Pengfei , Song Yuxi
Published: November 2023

For projectile impact penetration experiment, batteries or capacitors are usually used as power sources for projectile-borne recording devices. However, these power sources are easy to fail under high impact. In this paper, a small-impact magnetoelectric generator is introduced, which converts impact force into electrical energy to supply power for devices. The influence of generator structure on force–electricity conversion efficiency is analyzed. Based on the analysis, a small-impact magnetoelectric generator with double springs and two-part coils is designed. A hammer test is carried out on the generator. The test results show that this generator structure would achieve higher force–electricity conversion efficiency under small space.
Multivariate Forecasting Model for COVID-19 Spread Based on Possible Scenarios in Ecuador
Authors: Guamán Juan , Portilla Karen , Arias-Muñoz Paúl , Jácome Gabriel , Cabrera Santiago , Álvarez Luis , Batallas Bolívar , Cadena Hernán , García Juan Carlos
Published: November 2023

So far, about 770.1 million confirmed cases of COVID-19 have been counted by August 2023, and around 7 million deaths have been reported from these cases to the World Health Organization. In Ecuador, the first confirmed COVID-19 case was registered on 19 February 2020, and the country’s mortality rate reached 0.43% with 12986 deaths, suggesting the need to establish a mechanism to show the virus spread in advance. This study aims to build a dynamic model adapted to health and socio-environmental variables as a multivariate model to understand the virus expansion among the population. The model is based on Susceptible-Infected-Recovered (SIR), which is a standard model in which the population is divided into six groups with parameters such as susceptible S(t), transit stage E(t), infected I(t), recovered R(t), deceased Me(t), infected asymptomatic Ia(t), infected symptomatic Is(t) and deceased by other causes M(t) to be considered and adapted. The model was validated by using consistent data from Chile and run by inconsistent data from Ecuador. The forecast error was analyzed based on the mean absolute error between real data and model forecast, showing errors within a range from 6.33% to 8.41% for Chile, with confidence a interval of 6.17%, then 3.87% to 4.70% range for Ecuador with a confidence interval of 2.59% until 23rd December 2020 of the database. The model forecasts exponential variations in biosecurity measures, exposed population, and vaccination.
Integral Models in the Form of Volterra Polynomials and Continued Fractions in the Problem of Identifying Input Signals
Authors: Solodusha Svetlana , Kokonova Yuliya , Dudareva Oksana
Published: November 2023

The paper discusses the prospect of using a combined model based on finite segments (polynomials) of the Volterra integral power series. We consider a case when the problem of identifying the Volterra kernels is solved. The predictive properties of the classic Volterra polynomial are improved by adding a linear part in the form of an equivalent continued fraction. This technique allows us to distinguish an additional parameter—the connection coefficient α, which is effective in adapting the constructed integral model to changes in technical parameters at the input of a dynamic system. In addition, this technique allows us to take into account the case of perturbing the kernel of the linear term of the Volterra polynomial in the metric C[0,T] by a given value δ, implying the ideas of Volterra regularizing procedures. The problem of choosing the connection coefficient is solved using a special extremal problem. The developed algorithms are used to solve the problem of identifying input signals of test dynamic systems, among which, in addition to mathematical ones, thermal power engineering devices are used.
Breast Cancer Detection and Classification Using Hybrid Feature Selection and DenseXtNet Approach
Authors: Alshehri Mohammed
Published: November 2023

Breast Cancer (BC) detection and classification are critical tasks in medical diagnostics. The lives of patients can be greatly enhanced by the precise and early detection of BC. This study suggests a novel approach for detecting BC that combines deep learning models and sophisticated image processing techniques to address those shortcomings. The BC dataset was pre-processed using histogram equalization and adaptive filtering. Data augmentation was performed using cycle-consistent GANs (CycleGANs). Handcrafted features like Haralick features, Gabor filters, contour-based features, and morphological features were extracted, along with features from deep learning architecture VGG16. Then, we employed a hybrid optimization model, combining the Sparrow Search Algorithm (SSA) and Red Deer Algorithm (RDA), called Hybrid Red Deer with Sparrow optimization (HRDSO), to select the most informative subset of features. For detecting BC, we proposed a new DenseXtNet architecture by combining DenseNet and optimized ResNeXt, which is optimized using the hybrid optimization model HRDSO. The proposed model was evaluated using various performance metrics and compared with existing methods, demonstrating that its accuracy is 97.58% in BC detection. MATLAB was utilized for implementation and evaluation purposes.
Integrating a Pareto-Distributed Scale into the Mixed Logit Model: A Mathematical Concept
Authors: Ohdoko Taro , Komatsu Satoru
Published: November 2023

A generalized multinomial logit (G-MNL) model is proposed to alleviate the four challenges inherent to the conditional logit model, including (1) simultaneous unidentifiability, (2) the immediacy of decision-making, (3) the homogeneity of preferences in unobservable variables, and (4) the independence of irrelevant alternatives. However, the G-MNL model has some restrictions that are caused by the assumed logit scale of the lognormal distribution used in the G-MNL model. We propose a mixed logit with integrated Pareto-distributed scale (MIXL-iPS) model to address the restriction of the G-MNL model by introducing a logit scale in accordance with the Pareto distribution type I with an expected value of 1. We have clarified the mathematical properties and examined the distributional properties of the novel MIXL-iPS model. The results suggest that the MIXL-iPS model is a model in which the instability in the estimation of the G-MNL model is modified. Moreover, the apparent preference parameter was confirmed to have a skewed distribution in general in the MIXL-iPS model. In addition, we confirm that in the MIXL-iPS model, bounded rationality is reasonably well represented, as many individuals have below-average choice consistency.
A Novel Class of Separation Axioms, Compactness, and Continuity via C-Open Sets
Authors: Alqahtani Mesfer H. , Saleh Hind Y.
Published: November 2023

In this paper, we originate a new class of open sets, namely C-open sets, and we review its important properties. Then, some separation axioms of C-open sets are introduced and investigated. In addition, we define the so-called C-compact and C′-compact spaces via C-open sets, and the theorems based on them are discussed with counterexamples. Moreover, we entitle the C-continuous and C′-continuous functions by applying C-open sets. In particular, several inferred properties of them and their connection with the other topological spaces are studied theoretically. Many examples are given to explain the concepts lucidly. The results established in this research paper are new in the field of topology.
Composing Diverse Ensembles of Convolutional Neural Networks by Penalization
Authors: Harangi Balazs , Baran Agnes , Beregi-Kovacs Marcell , Hajdu Andras
Published: November 2023

Ensemble-based systems are well known to have the capacity to outperform individual approaches if the ensemble members are sufficiently accurate and diverse. This paper investigates how an efficient ensemble of deep convolutional neural networks (CNNs) can be created by forcing them to adjust their parameters during the training process to increase diversity in their decisions. As a new theoretical approach to reach this aim, we join the member neural architectures via a fully connected layer and insert a new correlation penalty term in the loss function to obstruct their similar operation. With this complementary term, we implement the standard guideline of ensemble creation to increase the members’ diversity for CNNs in a more detailed and flexible way than similar existing techniques. As for applicability, we show that our approach can be efficiently used in various classification tasks. More specifically, we demonstrate its performance in challenging medical image analysis and natural image classification problems. Besides the theoretical considerations and foundations, our experimental findings suggest that the proposed technique is competitive. Namely, on the one hand, the classification rate of the ensemble trained in this way outperformed all the individual accuracies of the state-of-the-art member CNNs according to the standard error functions of these application domains. On the other hand, it is also validated that the ensemble members get more diverse and their accuracies are raised by adding the penalization term. Moreover, we performed a full comparative analysis, including other state-of-the-art ensemble-based approaches recommended for the same classification tasks. This comparative study also confirmed the superiority of our method, as it overcame the current solutions.
Herbal Melanin Inhibits Real-Time Cell Proliferation, Downregulates Anti-Apoptotic Proteins and Upregulates Pro-Apoptotic p53 Expression in MDA-MB-231 and HCT-116 Cancer Cell Lines
Authors: Rajabathar Jothi Ramalingam , Al-Lohedan Hamad , Arokiyaraj Selvaraj , Mohammed Fathima , Al-Dhayan Dhaifallah M. , Faqihi Norah A. , Al-Saigh Hassan
Published: November 2023

Background and Objectives: Cancer is the second-most-important deadly disease in the world, leading to severe socioeconomic consequences and posing a public threat. Consequently, breast and colorectal cancers are significant cancer types that affect women and men more commonly, respectively. Treatment failure or recurrent diseases frequently occur due to resistance, in addition to the side effects of the currently available anticancer agents. Therefore, in this study, herbal melanin anticancer activity was investigated against human breast adenocarcinoma (MDA-MB-231) and human colorectal (HCT 116) cell proliferation and the expression of downregulated anti-apoptotic proteins and upregulated pro-apoptotic p53. Materials and Methods: MDA-MB-231 and HCT 116 cells were monitored for their real-time proliferation properties using Xcelligence. Herbal melanin of various concentrations significantly inhibited MDA-MB-231 and HCT 116 cell proliferation. Then, the expression of proapoptotic and anti-apoptotic proteins such as p53, Bcl-2 and Bcl-xl was studied using Western blotting. Results: The Bcl-2 and Bcl-xl expressions were downregulated, while the p53 expression was upregulated after treatment with herbal melanin. Similarly, the expression of apoptotic proteins such as Bcl-2, Bcl-xl, XIAP, Survivin, Bid, Bax, p53, Cytochrome C, PARP genes and mRNA was studied after herbal melanin treatment using real-time PCR, which revealed the downregulation of Bcl-2, Bcl-xl, XIAP and Survivin and the upregulation of Bid, Bax, p53, Cytochrome C and PARP apoptotic protein expression. Also, caspase 3 and 9 expressions were monitored after the treatment with herbal melanin, which revealed the upregulation of both the MDA-MB-231 and HCT 116 cell types. Conclusions: Overall, herbal melanin can be used as an alternative anticancer agent against the MDA-MB-231 and HCT 116 cell types.
Technical Note on Simplified Free Gingival Graft Using Tack Fixation (sFGG)
Authors: Lee Won-Pyo , You Jae-Seek , Oh Ji-Su
Published: November 2023

Free gingival graft (FGG) is the gold standard procedure for the reliable augmentation of lost keratinized mucosa (KM) around dental implants. This conventional surgical approach has its drawbacks, including limitations in manipulation, the requirement for suturing, postoperative discomfort, and pain. This case report aimed to evaluate the efficacy of a simplified free gingival graft (sFGG) in addressing the issue of inadequate keratinized mucosa around dental implants. Fixation tacks were used to perform the sFGG procedure. Initially, a partial-thickness flap was created and apically repositioned. The gingival graft was harvested from the palate with a narrow profile and securely affixed to the recipient site using 5 mm long fixation tacks. Significant gains in keratinized mucosa were achieved and successfully maintained within 1 year. Consequently, the sFGG technique emerges as a simple and reliable treatment approach for managing inadequate keratinized mucosa around dental implants.
Effects of SMAT Temperature and Stacking Fault Energy on the Mechanical Properties and Microstructure Evolution of Cu-Al-Zn Alloys
Authors: Zhou Zhuangdi , Gong Yulan , Sun Lele , Li Cong , Yang Jingran , Kang Zhuang , Qin Shen , Quan Shuwei , Zhu Xinkun
Published: November 2023

Alloys with a gradient structure (GS) exhibit a superior combination of strength and ductility. However, the effects of treatment temperature and stacking fault energy on the tensile behavior and microstructure evolution of GS alloys have not been systematically investigated. In this study, GS Cu-Al-Zn alloys with different stacking fault energy (SFE, 40/7 mJ/m2) were prepared using surface mechanical attrition treatment (SMAT) at cryogenic and room temperature, respectively. The microstructure results indicate that more stacking faults and deformation twins were activated in the SFE-7 alloys at cryogenic temperature, which led to higher strength compared to that of the alloys SMAT-ed at room temperature. In addition, it was found that the yield strength and hetero-deformation-induced (HDI) stress of the SFE-7 alloy were significantly higher than those of the SFE-40 alloy, resulting in a good combination of strength and ductility. Furthermore, more dispersed strain bands were observed in the SFE-7 sample during whole tensile deformation, which contributes to higher ductility.
Study on the Microstructure and Mechanical Properties of Ultrasonic-Assisted TIG Welded 441 Ferritic Stainless Steel Joints
Authors: Zhao Xiaohui , Chen Yunhao , Zhang Wenqiang , Liu Yu , Zhang Yunhui
Published: November 2023

A self-designed ultrasonic-assisted welding platform was built to improve the poor microstructure and properties of conventional TIG welded ferritic stainless steel. The ultrasonic vibration was transmitted to the weld pool through the base metal in the manner of point–surface contact in the optimal position after calculation. The results show that the coarse columnar grains in the welded joint can be transformed into very fine equiaxed grains under ultrasonic vibration, especially the coarse columnar grains near the fusion line where cliff-like refinement occurs. The maximum grain size in the weld seam is reduced from 420 μm to 260 μm, and the average size is reduced by 60%. At the same time, the grain orientation tends to be harmonized. The microhardness of the welded joint is greatly improved on the whole, and the softening of the heat-affected zone caused by grain coarsening is effectively inhibited. Compared with the welded joints without ultrasonic assistance, the tensile strength and yield strength can be increased by 61 MPa and 47 MPa, respectively, under 130 W ultrasonic vibration. By strengthening the weak part of the welded joint, the weldability and toughness reserve of 441 ferritic stainless steel can be significantly improved.
A Metabolomic Analysis to Assess the Responses of the Male Gonads of Mytilus galloprovincialis after Heavy Metal Exposure
Authors: Lettieri Gennaro , Marinaro Carmela , Brogna Carlo , Montano Luigi , Lombardi Martina , Trotta Alessio , Troisi Jacopo , Piscopo Marina
Published: November 2023

In recent years, metabolomics has become a valuable new resource in environmental monitoring programs based on the use of bio-indicators such as Mytilus galloprovincialis. The reproductive system is extremely susceptible to the effects of environmental pollutants, and in a previous paper, we showed metabolomic alterations in mussel spermatozoa exposed to metal chlorides of copper, nickel, and cadmium, and the mixture with these metals. In order to obtain a better overview, in the present work, we evaluated the metabolic changes in the male gonad under the same experimental conditions used in the previous work, using a metabolomic approach based on GC-MS analysis. A total of 248 endogenous metabolites were identified in the male gonads of mussels. Statistical analyses of the data, including partial least squares discriminant analysis, enabled the identification of key metabolites through the use of variable importance in projection scores. Furthermore, a metabolite enrichment analysis revealed complex and significant interactions within different metabolic pathways and between different metabolites. Particularly significant were the results on pyruvate metabolism, glycolysis, and gluconeogenesis, and glyoxylate and dicarboxylate metabolism, which highlighted the complex and interconnected nature of these biochemical processes in mussel gonads. Overall, these results add new information to the understanding of how certain pollutants may affect specific physiological functions of mussel gonads.
Spectroscopic Reflectometry for Optimizing 3D Through-Silicon-Vias Process
Authors: Ku Yi-Sha , Lo Chun-Wei , Lee Cheng-Kang , Cho Chia-Hung , Cheah Wen-Qii , Chou Po-Wen
Published: November 2023

The main challenges in 3D metrology involve measuring TSVs etched with very high aspect ratios, where the via depth to diameter ratio approaches 10:1–20:1. In this paper, we introduce an innovative approach to enhance our in-house spectroscopic reflectometer module by integrating aperture technology, resulting in a substantial amplification of interference signals. Our system offers the flexibility to conduct measurements on an average number of TSVs, individual TSVs, or specific periodic arrays of TSVs. Additionally, we demonstrate the utility of the spectroscopic reflectometer as a non-destructive, high-speed metrology solution for in-line monitoring of TSV etch uniformity. Through a series of experimental trials in a reactive ion etch (RIE) process, we show that leveraging feedback data from the reflectometer leads to marked improvements in etch depth uniformity.
Microbial Fuel Cells as Effective Tools for Energy Recovery and Antibiotic Detection in Water and Food
Authors: Massaglia Giulia , Spisni Giacomo , Pirri Candido F. , Quaglio Marzia
Published: November 2023

This work demonstrates that microbial fuel cells (MFCs), optimized for energy recovery, can be used as an effective tool to detect antibiotics in water-based environments. In MFCs, electroactive biofilms function as biocatalysts by converting the chemical energy of organic matter, which serves as the fuel, into electrical energy. The efficiency of the conversion process can be significantly affected by the presence of contaminants that act as toxicants to the biofilm. The present work demonstrates that MFCs can successfully detect antibiotic residues in water and water-based electrolytes containing complex carbon sources that may be associated with the food industry. Specifically, honey was selected as a model fuel to test the effectiveness of MFCs in detecting antibiotic contamination, and tetracycline was used as a reference antibiotic within this study. The results show that MFCs not only efficiently detect the presence of tetracycline in both acetate and honey-based electrolytes but also recover the same performance after each exposure cycle, proving to be a very robust and reliable technology for both biosensing and energy recovery.
Contamination of a Water Stream and Water Drainage Reaching Matosinhos Beach by Antibiotic-Resistant Bacteria
Authors: Pereira Matilde A. , Palmeira Josman D. , Ferreira Helena
Published: November 2023

Antibiotic-resistant bacteria represent a major public health concern, especially impacting medical care centers and hospitals, thereby challenging the effectiveness of current infection treatment protocols. The emergence and persistence of antimicrobial resistance in the environment have been thoroughly researched, with a focus on the aquatic environment as a potential reservoir of these bacteria in areas with anthropogenic contamination. Having this in mind, this work aims to investigate the water streams of Riguinha and Brito Capelo Street, both of which ultimately flow into Matosinhos Beach in Portugal, to determine the potential presence of fecal contamination. Six water samples were collected and analyzed within twenty-four hours from these two water streams. A phenotypic characterization was performed in various volumes on MacConkey agar with antibiotics. Randomly selected lactose-fermenting gram-negative bacteria underwent antimicrobial susceptibility tests using the agar diffusion method following EUCAST guidelines, covering β-lactam and non-β-lactam antibiotics. The isolates were analyzed through Polymerase Chain Reaction. The findings of this study confirm that both water streams were contaminated by multidrug-resistant bacteria such as Enterobacteriaceae, including Escherichia coli, the KESC group, and Pseudomonas, exhibiting extended-spectrum β-lactamases (ESBL), AmpC β-lactamases, and carbapenemases. These indicate the presence of fecal contamination with relevant antimicrobial-resistant threats.
Prevalence of Pneumocystis jirovecii Colonization in Non-Critical Immunocompetent COVID-19 Patients: A Single-Center Prospective Study (JiroCOVID Study)
Authors: Buonomo Antonio Riccardo , Viceconte Giulio , Fusco Ludovica , Sarno Marina , di Filippo Isabella , Fanasca Luca , Salvatore Paola , Gentile Ivan
Published: November 2023

Background: Pneumocystis jirovecii pneumonia (PJP) is an invasive fungal infection (IFI) that occurs mainly in immunocompromised hosts. After observing a high prevalence of PJP as a complication of COVID-19 in immunocompetent patients, we conducted a study to evaluate the prevalence of P. jirovecii colonization with PCR on oral washing samples (OWS) among non-immunocompromised and non-critical patients admitted with COVID-19 pneumonia at our university hospital. Methods: All patients over 18 years of age admitted to the Infectious Diseases Unit for SARS-CoV-2 pneumonia between July 2021 and December 2022 were included. Patients undergoing invasive mechanical ventilation or ECMO, those with risk factors for developing PJP, and those receiving prophylaxis for P. jirovecii were excluded. Samples were collected by gargling with 10 mL of 0.9% NaCl on day 14 of the hospital stay or at discharge. Results: Of 290 screened patients, 59 (20%) met the inclusion criteria and were enrolled. Only 1 of 59 patients (1.7%) tested positive for P. jirovecii detection with PCR, and the same patient was the only one to develop PJP in the follow-up period. Conclusions: Our results are in line with the previous findings of other studies that confirmed a very low prevalence of P. jirovecii colonization on OWS in the immunocompetent population. Despite the limitations of the study, the fact that the only patient who tested positive for P. jirovecii was the only one in our cohort to develop PJP leads us to reflect on the role of this non-invasive sample in predicting the risk of PJP in patients with COVID-19.