bluuebunny/crossref_metadata_2025 huggingface dataset:
Gut Microbiota Are a Novel Source of Biomarkers for Immunotherapy in Non-Small-Cell Lung Cancer (NSCLC)
Authors: Del Giudice Teresa , Staropoli Nicoletta , Tassone Pierfrancesco , Tagliaferri Pierosandro , Barbieri Vito
Published: May 2024

Despite the recent availability of immune checkpoint inhibitors, not all patients affected by Non-Small-Cell Lung Cancer (NSCLC) benefit from immunotherapy. The reason for this variability relies on a variety of factors which may allow for the identification of novel biomarkers. Presently, a variety of biomarkers are under investigation, including the PD1/PDL1 axis, the tumor mutational burden, and the microbiota. The latter is made by all the bacteria and other microorganisms hosted in our body. The gut microbiota is the most represented and has been involved in different physiological and pathological events, including cancer. In this light, it appears that all conditions modifying the gut microbiota can influence cancer, its treatment, and its treatment-related toxicities. The aim of this review is to analyze all the conditions influencing the gut microbiota and, therefore, affecting the response to immunotherapy, iRAEs, and their management in NSCLC patients. The investigation of the landscape of these biological events can allow for novel insights into the optimal management of NSCLC immunotherapy.
Driver Mutations in Pancreatic Cancer and Opportunities for Targeted Therapy
Authors: Olaoba Olamide T. , Adelusi Temitope I. , Yang Ming , Maidens Tessa , Kimchi Eric T. , Staveley-O’Carroll Kevin F. , Li Guangfu
Published: May 2024

Pancreatic cancer is the sixth leading cause of cancer-related mortality globally. As the most common form of pancreatic cancer, pancreatic ductal adenocarcinoma (PDAC) represents up to 95% of all pancreatic cancer cases, accounting for more than 300,000 deaths annually. Due to the lack of early diagnoses and the high refractory response to the currently available treatments, PDAC has a very poor prognosis, with a 5-year overall survival rate of less than 10%. Targeted therapy and immunotherapy are highly effective and have been used for the treatment of many types of cancer; however, they offer limited benefits in pancreatic cancer patients due to tumor-intrinsic and extrinsic factors that culminate in drug resistance. The identification of key factors responsible for PDAC growth and resistance to different treatments is highly valuable in developing new effective therapeutic strategies. In this review, we discuss some molecules which promote PDAC initiation and progression, and their potential as targets for PDAC treatment. We also evaluate the challenges associated with patient outcomes in clinical trials and implications for future research.
Zebrafish Avatars: Toward Functional Precision Medicine in Low-Grade Serous Ovarian Cancer
Authors: Fieuws Charlotte , Bek Jan , Parton Bram , De Neef Elyne , De Wever Olivier , Hoorne Milena , Estrada Marta , Van Dorpe Jo , Denys Hannelore , Van de Vijver Koen , Claes Kathleen
Published: May 2024

Ovarian cancer (OC) is an umbrella term for cancerous malignancies affecting the ovaries, yet treatment options for all subtypes are predominantly derived from high-grade serous ovarian cancer, the largest subgroup. The concept of "functional precision medicine" involves gaining personalized insights on therapy choice, based on direct exposure of patient tissues to drugs. This especially holds promise for rare subtypes like low-grade serous ovarian cancer (LGSOC). This study aims to establish an in vivo model for LGSOC using zebrafish embryos, comparing treatment responses previously observed in mouse PDX models, cell lines and 3D tumor models. To address this goal, a well-characterized patient-derived LGSOC cell line with the KRAS mutation c.35 G>T (p.(Gly12Val)) was used. Fluorescently labeled tumor cells were injected into the perivitelline space of 2 days’ post-fertilization zebrafish embryos. At 1 day post-injection, xenografts were assessed for tumor size, followed by random allocation into treatment groups with trametinib, luminespib and trametinib + luminespib. Subsequently, xenografts were euthanized and analyzed for apoptosis and proliferation by confocal microscopy. Tumor cells formed compact tumor masses (n = 84) in vivo, with clear Ki67 staining, indicating proliferation. Zebrafish xenografts exhibited sensitivity to trametinib and luminespib, individually or combined, within a two-week period, establishing them as a rapid and complementary tool to existing in vitro and in vivo models for evaluating targeted therapies in LGSOC.
Unlocking the Transformative Potential of Outdoor Office Work—A Constructivist Grounded Theory Study
Authors: Petersson Troije Charlotte , Lisberg Jensen Ebba , Redmalm David , Wiklund Gustin Lena
Published: May 2024

White-collar workers around the world are reconfiguring their ways of working. Some have found their way out, performing office work outdoors, through walk-and-talks, outdoor meetings, or reading sessions. Working outdoors has proved both invigorating and challenging. This qualitative interview study aims to develop a conceptual framework concerning the implications of white-collar workers incorporating the outdoors into their everyday work life. Applying a constructivist grounded theory approach, 27 interviews with a total of 15 participants were systematically analyzed. Findings evolved around the following categories: practicing outdoor office work, challenging the taken-for-granted, enjoying freedom and disconnection, feeling connected and interdependent, promoting health and well-being, enhancing performance, and finally adding a dimension to work. These categories were worked into a conceptual model, building on the dynamic relationship between the practice of working outdoors on one hand, and how this challenges the system in which office work traditionally takes place on the other. Interviews reflected the profound learning process of the employees. Drawing on the concepts of free space and resonance, we demonstrate how performing office work outdoors may unlock a transformative potential by opening up connectedness and interdependence and contribute to a sustainable work life as well as overall sustainable development.
An Efficient Attribute-Based Participant Selecting Scheme with Blockchain for Federated Learning in Smart Cities
Authors: Yin Xiaojun , Qiu Haochen , Wu Xijun , Zhang Xinming
Published: May 2024

In smart cities, large amounts of multi-source data are generated all the time. A model established via machine learning can mine information from these data and enable many valuable applications. With concerns about data privacy, it is becoming increasingly difficult for the publishers of these applications to obtain users’ data, which hinders the previous paradigm of centralized training through collecting data on a large scale. Federated learning is expected to prevent the leakage of private data by allowing users to train models locally. The existing works generally ignore architectures designed in real scenarios. Thus, there still exist some challenges that have not yet been explored in federated learning applied in smart cities, such as avoiding sharing models with improper parties under privacy requirements and designing satisfactory incentive mechanisms. Therefore, we propose an efficient attribute-based participant selecting scheme to ensure that only someone who meets the requirements of the task publisher can participate in training under the premise of high privacy requirements, so as to improve the efficiency and avoid attacks. We further extend our scheme to encourage clients to take part in federated learning and provide an audit mechanism using a consortium blockchain. Finally, we present an in-depth discussion of the proposed scheme by comparing it to different methods. The results show that our scheme can improve the efficiency of federated learning by enabling reliable participant selection and promote the extensive use of federated learning in smart cities.
Assessing the Impact of Various Decontamination Instruments on Titanium and Zirconia Dental Implants: An In Vitro Study
Authors: Vierling Louisa , Liu Chun Ching , Wiedemeier Daniel , Gubler Andrea , Schmidlin Patrick R.
Published: May 2024

This study investigates the impact of various instrumentation techniques on material removal and surface changes in titanium (Ti)- and zirconia (Zr) implant discs. Ti- and Zr discs were subjected to standardized experiments using various instruments including airflow, ultrasound, carbide, and diamond burs. Instrumentation was performed for 60 s with continuous automatic motion. Abrasion and changes in surface roughness were assessed using profilometry, while scanning electron microscopy was used to examine morphological changes and particle size. Carbide burs predominantly caused abrasion on Ti discs, while diamond burs caused more abrasion on Zr discs. The Ti discs were more susceptible to surface changes. However, among the materials tested, machined Zr discs treated with diamond burs produced the largest particle. In certain cases, a statistical significance (p < 0.05) was observed between the groups, while in others, there was no considerable difference among the means (p > 0.05). These results highlighted the statistical significance of our findings. These results found diverse alterations in surface characteristics of Ti- and Zr discs due to different instruments, with carbide and diamond burs causing notable effects. The findings highlight the need for a careful balance between promoting healing and minimizing harm during implantoplasty.
Asymmetric Exchange Rate Effects on Trade Flows in India
Authors: Sohrabji Niloufer
Published: May 2024

This paper examines the role of exchange rate changes on India’s trade. The drivers of exports and imports (income, exchange rate including sectoral differences, and exchange rate variability) are estimated for the short and long run including a structural break. Using annual data from 1994 to 2022, the results of dynamic fixed effects estimation show that both exports and imports are income-elastic in the short and long run, but income elasticity is far stronger for exports. Moreover, exports are responsive to the real effective exchange rate in the short run but not in the long run, and the reverse is true for imports. Furthermore, exchange rates have asymmetric effects for high-volume and primary sectors for exports and imports. The combined impacts show the ineffectiveness of using currency depreciation to address trade imbalances.
Correction: Chen et al. Temporal Convolutional Network-Enhanced Real-Time Implicit Emotion Recognition with an Innovative Wearable fNIRS-EEG Dual-Modal System. Electronics 2024, 13, 1310
Authors: Chen Jiafa , Yu Kaiwei , Wang Fei , Zhou Zhengxian , Bi Yifei , Zhuang Songlin , Zhang Dawei
Published: May 2024

In the original publication [...]
Application of Computational Electromagnetics Techniques and Artificial Intelligence in the Engineering
Authors: Li Rui , Xu Le
Published: May 2024

Since the establishment of Maxwell’s equations in the 19th century, computational electromagnetics has undergone a century of stable development [...]
A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting
Authors: Fu Hua , Zhang Junnan , Xie Sen
Published: May 2024

Photovoltaic (PV) power forecasting plays a crucial role in optimizing renewable energy integration into the grid, necessitating accurate predictions to mitigate the inherent variability of solar energy generation. We propose a novel forecasting model that combines improved variational mode decomposition (IVMD) with the temporal convolutional network-gated recurrent unit (TCN-GRU) architecture, enriched with a multi-head attention mechanism. By focusing on four key environmental factors influencing PV output, the proposed IVMD-TCN-GRU framework targets a significant research gap in renewable energy forecasting methodologies. Initially, leveraging the sparrow search algorithm (SSA), we optimize the parameters of VMD, including the mode component K-value and penalty factor, based on the minimum envelope entropy principle. The optimized VMD then decomposes PV power, while the TCN-GRU model harnesses TCN’s proficiency in learning local temporal features and GRU’s capability in rapidly modeling sequence data, while leveraging multi-head attention to better utilize the global correlation information within sequence data. Through this design, the model adeptly captures the correlations within time series data, demonstrating superior performance in prediction tasks. Subsequently, the SSA is employed to optimize GRU parameters, and the decomposed PV power mode components and environmental feature attributes are inputted into the TCN-GRU neural network. This facilitates dynamic temporal modeling of multivariate feature sequences. Finally, the predicted values of each component are summed to realize PV power forecasting. Validation using real data from a PV station corroborates that the novel model demonstrates a substantial reduction in RMSE and MAE of up to 55.1% and 54.5%, respectively, particularly evident in instances of pronounced photovoltaic power fluctuations during inclement weather conditions. The proposed method exhibits marked improvements in accuracy compared to traditional PV power prediction methods, underscoring its significance in enhancing forecasting precision and ensuring the secure scheduling and stable operation of power systems.
Analysis and Prevention of AI-Based Phishing Email Attacks
Authors: Eze Chibuike Samuel , Shamir Lior
Published: May 2024

Phishing email attacks are among the most common and most harmful cybersecurity attacks. With the emergence of generative AI, phishing attacks can be based on emails generated automatically, making it more difficult to detect them. That is, instead of a single email format sent to a large number of recipients, generative AI can be used to send each potential victim a different email, making it more difficult for cybersecurity systems to identify the scam email before it reaches the recipient. Here, we describe a corpus of AI-generated phishing emails. We also use different machine learning tools to test the ability of automatic text analysis to identify AI-generated phishing emails. The results are encouraging, and show that machine learning tools can identify an AI-generated phishing email with high accuracy compared to regular emails or human-generated scam emails. By applying descriptive analytics, the specific differences between AI-generated emails and manually crafted scam emails are profiled and show that AI-generated emails are different in their style from human-generated phishing email scams. Therefore, automatic identification tools can be used as a warning for the user. The paper also describes the corpus of AI-generated phishing emails that are made open to the public and can be used for consequent studies. While the ability of machine learning to detect AI-generated phishing emails is encouraging, AI-generated phishing emails are different from regular phishing emails, and therefore, it is important to train machine learning systems also with AI-generated emails in order to repel future phishing attacks that are powered by generative AI.
Study of Acoustic Emission from the Gate of Gallium Nitride High Electron Mobility Transistors
Authors: Paszkiewicz Bartłomiej K. , Paszkiewicz Bogdan , Dziedzic Andrzej
Published: May 2024

Nitrides are the leading semiconductor material used for the fabrication of high electron mobility transistors (HEMTs). They exhibit piezoelectric properties, which, coupled with their high mechanical stiffness, expand their versatile applications into the fabrication of piezoelectric devices. Today, due to advances in device technology that result in a reduction in the size of individual transistor elements and due to increased structural complexity (e.g., multi-gate transistors), the integration of piezoelectric materials into HEMTs leads to an interesting occurrence, namely acoustic emission from the transistor gate due to piezoelectric effects. This could affect the device’s performance, reliability, and durability. However, this phenomenon has not yet been comprehensively described. This paper aims to examine this overlooked aspect of AlGaN/GaN HEMT operation, that is, the acoustic emission from the gate region of the device induced by piezoelectric effects. For this purpose, dedicated test structures were designed, consisting of two narrow 1.7 μm-wide metallization strips placed at distances ranging from 5 μm to 200 μm fabricated in AlGaN/GaN heterostructures to simulate and examine the gate behavior of the HEMT transistor. For comparison, the test device structures were also fabricated on sapphire, which is not a piezoelectric material. Measurements of acoustic and electrical interactions in the microwave range were carried out using the “on wafer” method with Picoprobe’s signal–ground–signal (SGS)-type microwave probes. The dependence of reflectance |S11| and transmittance |S21| vs. frequency was investigated, and the coupling capacitance was determined. An equivalent circuit model of the test structure was developed, and finite element method simulation was performed to study the distribution of the acoustic wave in the nitride layers and substrate for different frequencies using Comsol Multiphysics software. At frequencies up to 2–3 GHz, the formation of volume waves and a surface wave, capable of propagating over long distances (in the order of tens of micrometers) was observed. At higher frequencies, the resulting distribution of displacements as a result of numerous reflections and interferences was more complicated. However, there was always the possibility of a surface wave occurrence, even at large distances from the excitation source. At small gate distances, electrical interactions dominate. Above 100 µm, electrical interactions are comparable to acoustic ones. With further increases in distance, weakly attenuated surface waves will dominate.
Intelligent Geo-Tour Route Recommendation Algorithm Based on Feature Text Mining and Spatial Accessibility Model
Authors: Zhou Xiao , Zhang Zheng , Liang Xinjian , Su Mingzhan
Published: May 2024

In view of the problems in planning and recommending tour routes, this paper constructs a feature text mining (FTM) method and spatial accessibility model (SAM) as the key factors for scenic spot recommendation (SSR) and tour route recommendation (TRR). The scenic spot clustering algorithm (SSCA) based on FTM was constructed by tourists’ text evaluation data mining. Considering the spatial attributes of scenic spots, the scenic spot topology tree algorithm (SSTTA) based on dynamic buffer spatial accessibility (DBSA) was constructed. The optimal scenic spots were recommended based on interest matching and spatial accessibility optimization. As to the recommended scenic spots, this paper proposes an optimal tour route recommendation algorithm (TRRA) based on SSTTA, which aims to determine the optimal adjacent section path structure tree (ASPST) with the lowest cost under travel constraints and transportation modes. The experiment verifies that the proposed algorithm can recommend scenic spots that match tourists’ interests and have optimal spatial accessibility, and the optimal tour routes with the lowest costs under certain travel constraints. Compared with the searched sub-optimal tour routes, the optimal tour route recommended by the proposed algorithm produces the lowest travel costs, and all the scenic spots in the tour route meet the tourists’ interests. Compared with the commonly used BDMA and GDMA methods, the proposed algorithm can determine the optimal routes with lower travel costs.
Joint Transmit and Receive Beamforming Design for DPC-Based MIMO DFRC Systems
Authors: Yang Chenhao , Wang Xin , Ni Wei
Published: May 2024

This paper proposes an optimal beamforming strategy for a downlink multi-user multi-input–multi-output (MIMO) dual-function radar communication (DFRC) system with dirty paper coding (DPC) adopted at the transmitter. We aim to achieve the maximum weighted sum rate of communicating users while adhering to a predetermined transmit covariance constraint for radar performance assurance. To make the intended problem trackable, we leverage the equivalence of the weighted sum rate and the weighted minimum mean squared error (MMSE) to reframe the issue and devise a block coordinate descent (BCD) approach to iteratively calculate transmit and receive beamforming solutions. Through this methodology, we demonstrate that the optimal receive beamforming aligns with the traditional MMSE approach, whereas the optimal transmit beamforming design can be cast into a quadratic optimization problem defined on a complex Stiefel manifold. Based on the majorization–minimization (MM) method, an iterative algorithm is then developed to compute the optimal transmit beamforming design by solving a series of orthogonal Procrustes problems (OPPs) that admit closed-form optimal solutions. Numerical findings serve to validate the efficacy of our scheme. It is demonstrated that our approach can achieve at least 73% higher spectral efficiency than the existing methods in a high signal-to-noise ratio (SNR) regime.
Bio-Inspired Intelligent Swarm Confrontation Algorithm for a Complex Urban Scenario
Authors: Cai He , Luo Yaoguo , Gao Huanli , Wang Guangbin
Published: May 2024

This paper considers the confrontation problem for two tank swarms of equal size and capability in a complex urban scenario. Based on the Unity platform (2022.3.20f1c1), the confrontation scenario is constructed featuring multiple crossing roads. Through the analysis of a substantial amount of biological data and wildlife videos regarding animal behavioral strategies during confrontations for hunting or food competition, two strategies are been utilized to design a novel bio-inspired intelligent swarm confrontation algorithm. The first one is the “fire concentration” strategy, which assigns a target for each tank in a way that the isolated opponent will be preferentially attacked with concentrated firepower. The second one is the “back and forth maneuver” strategy, which makes the tank tactically retreat after firing in order to avoid being hit when the shell is reloading. Two state-of-the-art swarm confrontation algorithms, namely the reinforcement learning algorithm and the assign nearest algorithm, are chosen as the opponents for the bio-inspired swarm confrontation algorithm proposed in this paper. Data of comprehensive confrontation tests show that the bio-inspired swarm confrontation algorithm has significant advantages over its opponents from the aspects of both win rate and efficiency. Moreover, we discuss how vital algorithm parameters would influence the performance indices.
Interleaved High Voltage Gain DC-DC Converter with Winding-Cross-Coupled Inductors and Voltage Multiplier Cells for Photovoltaic Systems
Authors: Chen Shin-Ju , Yang Sung-Pei , Huang Chao-Ming , Li Sin-Da , Chiu Cheng-Hsuan
Published: May 2024

An interleaved high voltage gain DC-DC converter with winding-cross-coupled inductors (WCCIs) and voltage multiplier cells is proposed for photovoltaic systems. The converter configuration is based on the interleaved boost converter integrating the diode-capacitor clamp circuits, the winding-cross-coupled inductors, and voltage multiplier cells to increase the voltage gain and reduce the semiconductor voltage stresses. The equal current sharing of two phases is achieved with the help of the winding-cross-coupled inductors. The converter achieves high voltage gain while operating at a proper duty ratio. The low-voltage-rated MOSFETs with low on-resistance are available to reduce the conduction losses due to the low switch voltage stress. The leakage energy of the coupled inductors is recycled such that the voltage spikes on the power switches are avoided. The input current ripple is decreased due to the interleaved operation. The operating principle and steady-state analysis of the proposed converter are proposed in detail. The design guidelines of the proposed converter are given. In addition, the closed-loop controlled system of the proposed converter is designed to diminish the effect of the variations in input voltage and load on the output voltage. Finally, the experimental results of a 1000 W converter prototype with 36 V input and 400 V output are given to validate the theoretical analysis and the converter performance.
Efficient Design of Battery Thermal Management Systems for Improving Cooling Performance and Reducing Pressure Drop
Authors: Chen Kai , Yang Ligong , Chen Yiming , Wu Bingheng , Song Mengxuan
Published: May 2024

The air-cooled system is one of the most widely used battery thermal management systems (BTMSs) for the safety of electric vehicles. In this study, an efficient design of air-cooled BTMSs is proposed for improving cooling performance and reducing pressure drop. Combining with a numerical calculation method, a strategy with a varied step length of adjustments (∆d) is developed to optimize the spacing distribution among battery cells for temperature uniformity improvement. The optimization results indicate that the developed strategy reduces the optimization time by about 50% compared with a strategy using identical ∆d values while maintaining good performance of the optimized system. Furthermore, the system’s pressure drop does not increase after the spacing optimization. Based on this characteristic, a structural design strategy is proposed to improve the cooling performance and reduce the pressure drop simultaneously. First, the appropriate flow pattern is arranged and the secondary outlet is added to reduce the pressure drop of the system. The results show that the BTMS with U-type flow combined with a secondary outlet against the original outlet can effectively reduce the pressure drop of the system. Subsequently, this BTMS is further improved using the developed cell spacing optimization strategy with varied ∆d values while the pressure drop is fixed. It is found that the final optimized BTMS achieves a battery temperature difference below 1 K for different inlet airflow rates, with the pressure drop being reduced by at least 45% compared with the BTMS before the optimization.
Electrical Machine Winding Performance Optimization by Multi-Objective Particle Swarm Algorithm
Authors: Martins François S. , Alvarenga Bernardo P. , Paula Geyverson T.
Published: May 2024

The present work aims to optimize the magnetomotive force and the end-winding leakage inductance from a discrete distribution of conductors in electrical machines through multi-objective particle swarm heuristics. From the development of an application capable of generating the conductor distribution for different machine configurations (single or poly-phase, single or double layer, integral or fractional slots, full or shortened pitch, with the presence of empty slots, etc.) the curves of magnetomotive force and the end-winding leakage inductance associated with the winding are computed. Taking as an optimal winding the one that presents, simultaneously, less harmonic distortion of the magnetomotive force and less leakage inductance, optimization by multi-objective particle swarm was used to obtain the optimal electrical machine configuration and the results are presented.
A Predictive Energy Management Strategy for Heavy Hybrid Electric Vehicles Based on Adaptive Network-Based Fuzzy Inference System-Optimized Time Horizon
Authors: Lin Benxiang , Wei Chao , Feng Fuyong , Liu Tao
Published: May 2024

Energy management strategies play a crucial role in enhancing the fuel efficiency of hybrid electric vehicles (HEVs) and mitigating greenhouse gas emissions. For the current commonly used time horizon optimization methods that only target the trend curve of the optimal battery state of charge (SOC) trajectory obtained offline, which are only suitable for buses with known future driving conditions, this paper proposed an energy management strategy based on an adaptive network-based fuzzy inference system (ANFIS) that optimizes the time horizon length and enhances adaptability to driving conditions by integrating historical vehicle velocity, accelerations, and battery SOC trajectory. First, the vehicle velocity prediction model based on the radial basis function (RBF) neural network is used to predict the future velocity sequence. After that, ANFIS was used to optimize and update the length of the forecast time horizon based on the historical vehicle velocity sequence. Finally, compared with the fixed time horizon energy management strategy, which is based on model predictive control (MPC), the average calculation time of the energy management strategy is reduced by about 23.5%, and the fuel consumption per 100 km is reduced by about 6.12%.
Mine Site Restoration: The Phytoremediation of Arsenic-Contaminated Soils
Authors: Huslina Feizia , Khudur Leadin S. , Shah Kalpit , Surapaneni Aravind , Netherway Pacian , Ball Andrew S.
Published: May 2024

Arsenic (As) is considered one of the most toxic chemicals to both human and environmental health. Mining activities represent one of the main anthropogenic sources of As; the concentration of As in mine soil can reach 9300 mg kg−1. To overcome the major issue of soil As pollution, soil restoration is required. Biological restoration approaches are generally more cost-effective and environmentally sustainable than physical and chemical methods. In particular, phytoremediation, an environmentally friendly technique based on the use of plants to uptake contaminants from soil, has been successfully implemented to restore As-contaminated soils at mine sites. However, mine soils are generally depleted in essential plant nutrients, such as nitrogen (N). Recent research suggests that phytoremediation can be combined with other techniques (physical, chemical, and biological) to enhance the N content and plant biomass. The aim of this review is to assess the current state of knowledge in the field of the restoration of arsenic-impacted mine site soils, focusing on phytoremediation. We critically assess recent work examining the potential of the co-application of amendments with phytoremediation and identify promising technologies and key research gaps. More studies are required to test the effectiveness of using various soil additives to enhance the phytoremediation of As, not only in pot-scale experiments but also in the field, to enable an improved management strategy for mine site restoration in the future.
Exploring Consumer Preferences and Challenges in Hybrid Meat Products: A Conjoint Analysis of Hotdogs
Authors: Salgaonkar Kashmira , Nolden Alissa A.
Published: May 2024

Plant-based meat has been the primary strategy to reducing meat consumption. While this category has demonstrated success, with the market value estimated to reach USD 20 billion by 2023, the subsequent reduction in meat consumption has not been proportionate. An alternative approach is hybrid products, which are thought to produce products that more closely resemble meat products. However, whether consumers will be willing to purchase hybrid products remains uncertain. Therefore, the present study uses a conjoint analysis approach to assess the product features driving the selection of a hybrid hotdog. This approach uncovers factors driving consumers’ purchase intentions for hybrid meat products when offered as a choice against 100% plant-based and 100% beef products. In an online survey, participants (n = 454; 45.6% female) were asked to select the product they would be most willing to purchase, varying in four characteristics: protein source, price, fat content, and price. Following this task, participants answered questions related to meat attachment, food neophobia, health, ecological, social, and moral motives regarding food consumption. The results revealed that protein source was the most important factor driving product selection, followed by price, fat, and packaging claims (35%, 24%, 21%, and 20% relative importance, respectively). In this study, hybrid hotdogs were the least preferred to beef and plant-based (−16, −2.5, and 18 part-worth utility, respectively). These product-specific attributes (protein, fat, and price) had distinct relationships with the choices of hybrid, plant-based, and hybrid hotdogs, with these factors together explaining slightly more variability in the selection of hybrid (9%) compared to plant-based (7%) and beef hotdogs (4%). For hybrid hotdogs, protein had the greatest influence (B = −1.2) followed by fat (B = −0.8) and price (B = −0.5). Interestingly, person-related parameters (health, meat attachment, ethics, and food neophobia) had no relationship with the selection of hybrid hotdogs, contrary to plant-based (7%) and beef hotdogs (5%). This influence of the different parameters on the selection of hybrid meat is thought to be due to the lack of consumer knowledge and familiarity with hybrid products. The current understanding of plant-based products may not correspond to hybrid products. Engaging with consumers during the development of these products is critical to ensure consumer acceptance and thus support the transition to a more sustainable diet.
Potential of Subcritical Water Hydrolysis to Valorize Low-Valued Ray-Finned Fish (Labeobarbus nedgia): Effects of Hydrolysis Temperature and Pressurization Agent
Authors: Tadesse Solomon Abebaw , Emire Shimelis Admassu , Barea Pedro , Illera Alba Ester , Melgosa Rodrigo , Beltrán Sagrario , Sanz María Teresa
Published: May 2024

Subcritical water (SCW) hydrolysis was applied to valorize the low-valued ray-finned fish (Labeobarbus nedgia) into valuable protein hydrolysates, employing N2 and CO2 as pressurization agents at varying temperatures (140, 160, 180, and 200 °C). The degree of hydrolysis (DH) and total free amino acid content increased with temperature for both pressurizing agents. The highest DH (54.5 ± 0.4%) and total free amino acid content (210 ± 1 mg/gprot) were observed at 200 °C when CO2 gas was used as the pressurizing agent. Predominantly, glycine and alanine were released for both pressurizing agents. The antioxidant activity, evaluated through three different assays, increased with temperature and was found to be the highest at 200 °C. This study illustrated the advantages of the intensified SCW technology by using CO2 as a pressurization agent in valorizing low-valued ray-finned fish (Labeobarbus nedgia), as animal residue rich in proteins, for the production of valuable protein hydrolysates with a high fraction of valuable free amino acids, which could offer potential applications as a functional ingredient in the food industry.
Application of CO2-Laser Micro-Perforation Technology to Freeze-Drying Whole Strawberry (Fragaria ananassa Duch.): Effect on Primary Drying Time and Fruit Quality
Authors: Pinto Marlene , Kusch Cynthia , Belmonte Karyn , Valdivia Silvana , Valencia Pedro , Ramírez Cristian , Almonacid Sergio
Published: May 2024

Freeze-drying (FD) processing preserves foods by combining the most effective traditional technologies. FD conserves the structure, shape, freshness, nutritional/bioactive value, color, and aroma at levels similar to or better than those of refrigerated and frozen foods while delivering the shelf-stable convenience of canned/hot-air-dehydrated foods. The mass transfer rate is the essential factor that can slow down the FD process, resulting in an excessive primary drying time and high energy consumption. The objective of this study was to reduce the FD processing time using CO2 laser technology to improve product competitiveness in the preservation of whole strawberries. The research process consisted of the selection and characterization of fresh strawberries, followed by preparation, pre-treatment, freeze-drying, a primary drying time assessment, and a quality comparison. Experiments were carried out using strawberries without micro-perforation and with five and eight micro-perforations. Quality parameters were determined for fresh, frozen/thawed, and freeze-dried/rehydrated strawberries. It was found that the primary drying time can be significantly reduced by 20% (95% CI) from 26.7 h for non-perforated fruits to 22.3 h when five micro-perforations are made on each strawberry. The quality parameters used to evaluate the strawberries did not show significant differences when comparing frozen/thawed fruits with freeze-dried/rehydrated fruits. The experiments conducted in this study showed that freeze-drying may efficiently compete with freezing technology when processing whole strawberries.
Liquid Chromatography–Mass Spectrometry-Based Metabolomics Reveals Dynamic Metabolite Changes during Early Postmortem Aging of Donkey Meat
Authors: Chai Wenqiong , Wang Liyuan , Li Tong , Wang Tianqi , Wang Xinrui , Yan Miao , Zhu Mingxia , Gao Jingrong , Wang Changfa , Ma Qiugang , Qu Honglei
Published: May 2024

Background: Metabolic changes in donkey meat during the early postmortem period have not been previously reported. Methods: The LC–MS-based metabolomics technique was conducted to understand the metabolic profiles and identify the key metabolites of donkey meat in the first 48 h postmortem. Results: The pH values showed a decreasing trend followed by an increasing trend. Shear force was the lowest at 4 h and the highest at 24 h (p < 0.05). For the metabolome, some candidate biomarker metabolites were identified, such as adenine, inosine, n-acetylhistidine, citric acid, isocitrate, and malic acid. Predominant metabolic pathways, such as citrate cycle (TCA cycle), alanine, aspartate and glutamate metabolism, and purine metabolism, were affected by aging time. Overabundant n-acetylhistidine was identified in LT, declined at 12 h postmortem aging, and then increased. This may explain the significantly lower pH at 12 h postmortem. Adenine was higher at 4 h postmortem, then declined. Decreased ADP may indicate a fast consumption of ATP and subsequent purine metabolism in donkey meat. Conclusions: The results of this study provided new insights into early postmortem aging of donkey meat quality.
Transcriptional Profiling of Early Defense Response to White Pine Blister Rust Infection in Pinus albicaulis (Whitebark Pine)
Authors: Figueroa-Corona Laura , Baesen Kailey , Bhattarai Akriti , Kegley Angelia , Sniezko Richard A. , Wegrzyn Jill , De La Torre Amanda R.
Published: May 2024

Pathogen perception generates the activation of signal transduction cascades to host defense. White pine blister rust (WPBR) is caused by Cronartium ribicola J.C. Fisch and affects a number of species of Pinus. One of the most severely affected species is Pinus albicaulis Engelm (whitebark pine). WPBR resistance in the species is a polygenic and complex trait that requires an optimized immune response. We identified early responses in 2-year-old seedlings after four days of fungal inoculation and compared the underlying transcriptomic response with that of healthy non-inoculated individuals. A de novo transcriptome assembly was constructed with 56,796 high quality-annotations derived from the needles of susceptible and resistant individuals in a resistant half-sib family. Differential expression analysis identified 599 differentially expressed transcripts, from which 375 were upregulated and 224 were downregulated in the inoculated seedlings. These included components of the initial phase of active responses to abiotic factors and stress regulators, such as those involved in the first steps of flavonoid biosynthesis. Four days after the inoculation, infected individuals showed an overexpression of chitinases, reactive oxygen species (ROS) regulation signaling, and flavonoid intermediates. Our research sheds light on the first stage of infection and emergence of disease symptoms among whitebark pine seedlings. RNA sequencing (RNA-seq) data encoding hypersensitive response, cell wall modification, oxidative regulation signaling, programmed cell death, and plant innate immunity were differentially expressed during the defense response against C. ribicola.
Women Acute Myocardial Infarction—Identifying and Understanding the Gender Gap (WAMy-GAP): A Study Protocol
Authors: Giordano Vincenza , Guillari Assunta , Sansone Vincenza , Catone Maria , Rea Teresa
Published: May 2024

Barriers to accessing care and misinterpretations of ischemic heart disease symptoms due to lack of awareness contribute to women’s delay in seeking care. Women may delay seeking treatment for up to 3 h or even up to 5 days. They often perceive themselves to be at low risk of cardiovascular disease (CVD) and prioritize family responsibilities or household chores. The causes of this delay are multifactorial and influence the decision-making process, particularly in the pre-hospital phase. The objective of this study protocol is to evaluate prodromal symptoms and identify risk behaviors in women with acute myocardial infarction (AMI). This is a protocol for a multicenter study that will be conducted using the mixed-method methodology using the McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey (MAPMISS) to evaluate symptoms and semi-structured interviews to investigate behaviors. This study protocol is intended to fill an important knowledge gap on premonitory and acute symptoms of AMI in women in Italy, as well as to understand the causes and mechanisms underlying delays in accessing healthcare services during an acute event such as AMI. The investigation of this issue will facilitate the removal of gender-related inequalities in the diagnosis and treatment of acute myocardial infarction while also fostering dialogue on the barriers to behavior change.
Impact of the COVID-19 Pandemic on Swedish Adolescents’ Mental Health, Psychosocial Functioning, Risk Behaviours, and Victimisation: Gender Differences and Implications
Authors: Johansson Catrin , Hedman Ahlström Britt , Barac Marijana , Berglund Therese , Bador Kourosh , Kerekes Nóra
Published: May 2024

The COVID-19 pandemic has shown varying effects on adolescents’ mental health, psychosocial functioning, risk behaviours, and victimisation. This study aims to examine the changes reported by a sample of Swedish adolescents (N = 1607) at the end of the first year of the pandemic in relation to these factors. Data were collected with an electronic survey between September 2020 and February 2021, targeting upper-secondary high school students (aged 15–19 years). The results indicate a relatively low overall impact of the pandemic on Swedish upper-secondary school students, with notable gender differences. Compared to adolescent women, a higher percentage of adolescent men reported experiencing elevated levels of anxiety, depression, sleep disturbances, anger, and increased illicit drug use as consequences of the pandemic. In contrast, women demonstrated an increase in several salutogenic behaviours. Victimisation rates generally decreased during this period. These findings underscore the importance of heightened awareness among professionals within schools, social services, and healthcare settings regarding the distinct challenges encountered by a larger portion of adolescent men during the COVID-19 pandemic in Sweden.
Qualitative Study to Explore the Occupational and Reproductive Health Challenges among Women Tobacco Farm Laborers in Mysore District, India
Authors: Ravi Priyanka , Muralidhar Kiranmayee , Ngaybe Maiya G. Block , Nanjaiah Shivamma , Jayakrishna Poornima , Lowe Ashley A. , Krupp Karl , Wilson Amanda M. , von Hippel Frank A. , Chen Zhao , Gerald Lynn B. , Madhivanan Purnima
Published: May 2024

Tobacco farm laborers are primarily women and children working for very low wages. The aim of this study was to explore occupational and reproductive health challenges faced by women tobacco farm laborers in Mysore District, India. We conducted interviews and six focus group discussions among 41 women tobacco farm laborers. Codes and themes were generated based on deductive and inductive approaches using the socioecological model. Participants reported symptoms of green tobacco sickness including headaches, back pain, gastric problems, weakness, and allergies during menstruation, pre-natal, and post-natal periods. Participants had poor awareness about the health effects of tobacco farming, and there were gender inequalities in wages and the use of personal protective equipment. Participants received support from family and community health workers during their pregnancy and post-natal period. Women reported wanting maternity benefits from the tobacco board, as well as monetary support and nutritional supplements. There is a need for health education about the environmental dangers of tobacco among farm laborers, and more supportive policies for women farmworkers during pregnancy and post-natal periods.
Towards Quality Management Procedures in 3D Cadastre
Authors: Višnjevac Nenad , Šoškić Mladen , Mihajlović Rajica
Published: May 2024

The 3D cadastre presents a modern approach to the development of cadastral information systems, with the role of improving current cadastral systems and overcoming the challenges of a 2D-based approach. Technological advancements, standardization, and scientific research in recent decades have contributed to the development and definition of the 3D cadastre. This positioned the 3D cadastre as an integral part of the future of land administration. However, every country needs to define a solution for itself based on its own legal system and cadastral tradition, while at the same time relying on international standardization and research. Once a 3D cadastral system is developed, it is crucial to ensure the monitoring, evaluation, and maintenance of both the quality of the cadastral data and the system itself throughout its lifecycle. Since 3D cadastres involve geometric data, quality management procedures must address both geometric and alphanumeric data. In this paper, we analyze and present the quality management procedures that should be included during designing, implementing, and maintaining a 3D cadastral system. Some examples based on real cadastral data were used to emphasize the need for improvement in quality management. The presented quality management procedures require further development in order to meet country-specific requirements and to fully support the 3D cadastre information systems.
Recent Advances in Genome Maintenance Processes
Authors: Tessmer Ingrid
Published: May 2024

Given life’s dependence on genome maintenance, unsurprisingly, investigations of the molecular processes involved in protecting the genome or, failing this, repairing damages to and alterations introduced into genetic material are at the forefront of current research [...]