bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Mindfulness on Functional Brain Connectivity and Peripheral Inflammation in Breast Cancer Survivors with Cognitive Complaints
Authors: Melis Michelle , Schroyen Gwen , Blommaert Jeroen , Leenaerts Nicolas , Smeets Ann , Van Der Gucht Katleen , Sunaert Stefan , Deprez Sabine
Published: July 2023

Background: Cancer-related cognitive impairment (CRCI) has been linked to functional brain changes and inflammatory processes. Hence, interventions targeting these underlying mechanisms are needed. In this study, we investigated the effects of a mindfulness-based intervention on brain function and inflammatory profiles in breast cancer survivors with CRCI. Methods: Female breast cancer survivors reporting cognitive complaints (n = 117) were randomly assigned to a mindfulness-based intervention (n = 43), physical training (n = 36), or waitlist control condition (n = 38). Region-of-interest (ROI) and graph theory analyses of resting state functional MRI data were performed to study longitudinal group differences in functional connectivity and organization in the default mode, dorsal attention, salience, and frontoparietal network. Additionally, bead-based immunoassays were used to investigate the differences in inflammatory profiles on serum samples. Measures were collected before, immediately after and three months post-intervention. Results: No ROI-to-ROI functional connectivity changes were identified. Compared to no intervention, graph analysis showed a larger decrease in clustering coefficient after mindfulness and physical training. Additionally, a larger increase in global efficiency after physical training was identified. Furthermore, the physical training group showed a larger decrease in an inflammatory profile compared to no intervention (IL-12p70, IFN-γ, IL-1β, and IL-8). Conclusion: Both mindfulness and physical training induced changes in the functional organization of networks related to attention, emotion processing, and executive functioning. While both interventions reduced functional segregation, only physical training increased functional integration of the neural network. In conclusion, physical training had the most pronounced effects on functional network organization and biomarkers of inflammation, two mechanisms that might be involved in CRCI.
A Highly Efficient Composite Catalyst (Au/Ta3N5)/CdS for Photocatalytic Hydrogen Production
Authors: Tian Jinfeng , Wen Xing , Hu Wenfeng , Luo Li , Wang Wei , Lin Keying , Zhan Haijuan , Ma Baojun
Published: July 2023

As non-noble metal co-catalysts, Nitrides have received extensive attention for their high efficiency and low cost in photocatalytic hydrogen production. In this study, we develop a novel composite co-catalyst of Au/Ta3N5 that significantly enhanced the photocatalytic hydrogen production activity of CdS. The activity of photocatalyst (Au/Ta3N5)/CdS is 7.3 times of bare CdS and 2.2 times of Ta3N5/CdS. The proper capacitance and enhanced activity of Ta3N5 demonstrates the capacitance catalysis effect of Ta3N5, which improves the charges separation and storing of the photoexcited electrons from CdS. Au acts as an active site for proton reduction to further improve the activity.
Structured Semiconductors in Photocatalysis
Authors: Belver Carolina , Bedia Jorge
Published: July 2023

Photocatalysis appears as an interesting approach for different applications, with the possibility of using sunlight as a sustainable and renewable source of energy [...]
Reverse-ChIP Techniques for Identifying Locus-Specific Proteomes: A Key Tool in Unlocking the Cancer Regulome
Authors: MacKenzie Tim M. G. , Cisneros Rocío , Maynard Rajan D. , Snyder Michael P.
Published: July 2023

A phenotypic hallmark of cancer is aberrant transcriptional regulation. Transcriptional regulation is controlled by a complicated array of molecular factors, including the presence of transcription factors, the deposition of histone post-translational modifications, and long-range DNA interactions. Determining the molecular identity and function of these various factors is necessary to understand specific aspects of cancer biology and reveal potential therapeutic targets. Regulation of the genome by specific factors is typically studied using chromatin immunoprecipitation followed by sequencing (ChIP-Seq) that identifies genome-wide binding interactions through the use of factor-specific antibodies. A long-standing goal in many laboratories has been the development of a ‘reverse-ChIP’ approach to identify unknown binding partners at loci of interest. A variety of strategies have been employed to enable the selective biochemical purification of sequence-defined chromatin regions, including single-copy loci, and the subsequent analytical detection of associated proteins. This review covers mass spectrometry techniques that enable quantitative proteomics before providing a survey of approaches toward the development of strategies for the purification of sequence-specific chromatin as a ‘reverse-ChIP’ technique. A fully realized reverse-ChIP technique holds great potential for identifying cancer-specific targets and the development of personalized therapeutic regimens.
Baseline Factors Associated with Pain Intensity, Pain Catastrophizing, and Pain Interference in Intensive Interdisciplinary Pain Treatment for Youth
Authors: Long Rob D. , Walker Andrew , Pan Si Chen , Miller Jillian Vinall , Rayner Laura , Vallely Joanne , Rasic Nivez
Published: July 2023

Background: More could be known about baseline factors related to desirable Intensive Interdisciplinary Pain Treatment (IIPT) outcomes. This study examined how baseline characteristics (age, gender, child pain catastrophizing (PCS-C), pain interference, pain intensity, anxiety, depression, paediatric health-related quality of life (PedsQLTM), and parent catastrophizing (PCS-P)) were associated with discharge and 3-month follow-up scores of PCS-C, pain intensity, and pain interference. Methods: PCS-C, pain intensity, and pain interference T-scores were acquired in 45 IIPT patients aged 12–18 at intake (baseline), discharge, and 3-month follow-up. Using available and imputed data, linear mixed models were developed to explore associations between PCS-C, pain intensity, and pain interference aggregated scores at discharge and follow-up with baseline demographics and a priori selected baseline measures of pain, depression, anxiety, and PCS-C/P. Results: PCS-C and pain interference scores decreased over time compared to baseline. Pain intensity did not change significantly. Baseline PCS-C, pain interference, anxiety, depression, and PedsQLTM were associated with discharge/follow-up PCS-C (available and imputed data) and pain interference scores (available data). Only baseline pain intensity was significantly associated with itself at discharge/follow-up. Conclusions: Participants who completed the IIPT program presented with reduced PCS-C and pain interference over time. Interventions that target pre-treatment anxiety and depression may optimize IIPT outcomes.
Colloidal Synthesis and Ultraviolet Luminescence of Rb2AgI3 Nanocrystals
Authors: Deng Yuan , Zeng Yicheng , Gu Wanying , Huang Pan , Jin Geyu , Liu Fangze , Wei Jing , Li Hongbo
Published: July 2023

Semiconductor nanocrystals (NCs) hold immense potential as luminescent materials for various optoelectronic applications. While significant progress has been made in developing NCs with outstanding optical properties in the visible range, their counterparts emitting in the ultraviolet (UV) spectrum are less developed. Rb2AgI3 is a promising UV-emitting material due to its large band gap and high stability. However, the optical properties of low-dimensional Rb2AgI3 NCs are yet to be thoroughly explored. Here, we synthesized Rb2AgI3 NCs via a hot injection method and investigated their properties. Remarkably, these NCs exhibit UV luminescence at 302 nm owing to self-trapped excitons. The wide-bandgap nature of Rb2AgI3 NCs, combined with their intrinsic UV luminescence, offers considerable potential for applications in UV photonic nanodevices. Our findings contribute to the understanding of Rb2AgI3 NCs and pave the way for exploiting their unique properties in advanced optoelectronic systems.
A Preprocessing Manifold Learning Strategy Based on t-Distributed Stochastic Neighbor Embedding
Authors: Shi Sha , Xu Yefei , Xu Xiaoyang , Mo Xiaofan , Ding Jun
Published: July 2023

In machine learning and data analysis, dimensionality reduction and high-dimensional data visualization can be accomplished by manifold learning using a t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm. We significantly improve this manifold learning scheme by introducing a preprocessing strategy for the t-SNE algorithm. In our preprocessing, we exploit Laplacian eigenmaps to reduce the high-dimensional data first, which can aggregate each data cluster and reduce the Kullback–Leibler divergence (KLD) remarkably. Moreover, the k-nearest-neighbor (KNN) algorithm is also involved in our preprocessing to enhance the visualization performance and reduce the computation and space complexity. We compare the performance of our strategy with that of the standard t-SNE on the MNIST dataset. The experiment results show that our strategy exhibits a stronger ability to separate different clusters as well as keep data of the same kind much closer to each other. Moreover, the KLD can be reduced by about 30% at the cost of increasing the complexity in terms of runtime by only 1–2%.
Microlearning for the Development of Teachers’ Digital Competence Related to Feedback and Decision Making
Authors: Betancur-Chicué Viviana , García-Valcárcel Muñoz-Repiso Ana
Published: July 2023

The assessment and feedback area of the European Framework for the Digital Competence of Educators (DigCompEdu) establishes a specific competence related to the ability to use digital technologies to provide feedback and make decisions for learning. According to the literature, this particular competence is one of the least developed in the teaching profession. As there are few specialised training strategies in the field of information and communication technology (ICT)-mediated feedback, this study aims to validate a microlearning proposal for university teachers, organised in levels of progression following the DigCompEdu guidelines. To validate the proposal, a literature analysis was carried out and a training proposal was developed and submitted to a peer review process to assess its relevance. This study identifies the elements that should be included in a training strategy in the area of feedback and decision making for university contexts. Finally, it is concluded that this type of training requires a combination of agile and self-managed strategies (characteristics of microlearning), which can be complemented by the presentation of evidence and collaborative work with colleagues.
Scratch as an Environment for Learning the Coordinate System by Elementary School Students
Authors: Anabousy Ahlam , Daher Wajeeh , Bassan-Cincinatus Ronit
Published: July 2023

The focal point of mathematics education research is to comprehend how students develop and consolidate their abstract mathematical knowledge. As goal of this research, the focus was on exploring the processes that sixth-grade students used to construct mathematical concepts linked to the coordinate system. In this study, a group of eight students was selected for research purposes. Video recordings were used to collect data through observations. The Abstraction in Context (AiC) framework was utilized to analyze the students’ processes that took place within the Scratch environment. The research findings suggested that the grade six students successfully constructed most of the anticipated knowledge components, consolidated their previous knowledge related to the zero concept, and began to develop new ones. Specifically, the students were able to construct the concept of zero as a number that is neither positive nor negative, as well as comprehend the X and Y axes.
D-UAP: Initially Diversified Universal Adversarial Patch Generation Method
Authors: Sun Lei , Wang Xiaoqin , Yang Youhuan , Mao Xiuqing
Published: July 2023

With the rapid development of adversarial example technologies, the concept of adversarial patches has been proposed, which can successfully transfer adversarial attacks to the real world and fool intelligent object detection systems. However, the real-world environment is complex and changeable, and the adversarial patch attack technology is susceptible to real-world factors, resulting in a decrease in the success rate of attack. Existing adversarial-patch-generation algorithms have a single direction of patch initialization and do not fully consider the impact of initial diversification on its upper limit of adversarial patch attack. Therefore, this paper proposes an initial diversified adversarial patch generation technology to improve the effect of adversarial patch attacks on the underlying algorithms in the real world. The method uses YOLOv4 as the attack model, and the experimental results show that the attack effect of the adversarial-patch-attack method proposed in this paper is higher than the baseline 8.46%, and it also has a stronger attack effect and fewer training rounds.
RAdam-DA-NLSTM: A Nested LSTM-Based Time Series Prediction Method for Human–Computer Intelligent Systems
Authors: Liu Banteng , Chen Wei , Wang Zhangquan , Pouriyeh Seyedamin , Han Meng
Published: July 2023

At present, time series prediction methods are widely applied for Human–Computer Intelligent Systems in various fields such as Finance, Meteorology, and Medicine. To enhance the accuracy and stability of the prediction model, this paper proposes a time series prediction method called RAdam-Dual stage Attention mechanism-Nested Long Short-Term Memory (RAdam-DA-NLSTM). First, we design a Nested LSTM (NLSTM), which adopts a new internal LSTM unit structure as the memory cell of LSTM to guide memory forgetting and memory selection. Then, we design a self-encoder network based on the Dual stage Attention mechanism (DA-NLSTM), which uses the NLSTM encoder based on the input attention mechanism, and uses the NLSTM decoder based on the time attention mechanism. Additionally, we adopt the RAdam optimizer to solve the objective function, which dynamically selects Adam and SGD optimizers according to the variance dispersion and constructs the rectifier term to fully express the adaptive momentum. Finally, we use multiple datasets, such as PM2.5 data set, stock data set, traffic data set, and biological signals, to analyze and test this method, and the experimental results show that RAdam-DA-NLSTM has higher prediction accuracy and stability compared with other traditional methods.
Text3D: 3D Convolutional Neural Networks for Text Classification
Authors: Wang Jinrui , Li Jie , Zhang Yirui
Published: July 2023

Convolutional Neural Networks (CNNs) have demonstrated promising performance in many NLP tasks owing to their excellent local feature-extraction capability. Many previous works have made word-level 2D CNNs deeper to capture global representations of text. Three-dimensional CNNs perform excellently in CV tasks through spatiotemporal feature learning, though they are little utilized in text classification task. This paper proposes a simple, yet effective, approach for hierarchy feature learning using 3D CNN in text classification tasks, named Text3D. Text3D efficiently extracts rich information through text representations structured in three dimensions produced by pretrained language model BERT. Specifically, our Text3D utilizes word order, word embedding and hierarchy information of BERT encoder layers as features of three dimensions. The proposed model with 12 layers outperforms the baselines on four benchmark datasets for sentiment classification and topic categorization. Text3D with a different hierarchy of output from BERT layers demonstrates that the linguistic features from different layers have varied effects on text classification.
Research on Improved YOLOv5 for Low-Light Environment Object Detection
Authors: Wang Jing , Yang Peng , Liu Yuansheng , Shang Duo , Hui Xin , Song Jinhong , Chen Xuehui
Published: July 2023

Object detection in low-light scenarios has been widely acknowledged as a significant research area in the field of computer vision, presenting a challenging task. Aiming at the low detection accuracy of mainstream single-stage object detection models in low-light scenarios, this paper proposes a detection model called DK_YOLOv5 based on YOLOv5, specifically designed for such scenarios. First, a low-light image enhancement algorithm with better results is selected to generate enhanced images that achieve relatively better visual effects and amplify target features. Second, the SPPF layer is improved to an R-SPPF module with faster inference speed and stronger feature expression ability. Next, we replace the C3 module with the C2f module and incorporate an attention mechanism to develop the C2f_SKA module, enabling richer gradient information flow and reducing the impact of noise features. Finally, the model detection head is replaced with a decoupled head suitable for the object detection task in this scenario to improve model performance. Additionally, we expand the Exdark dataset to include low-light data of underground mine scenario targets, named Mine_Exdark. Experimental results demonstrate that the proposed DK_YOLOv5 model achieves higher detection accuracy than other models in low-light scenarios, with an mAP0.5 of 71.9% on the Mine_Exdark dataset, which is 4.4% higher than that of YOLOv5.
Multiscale Local and Global Feature Fusion for the Detection of Steel Surface Defects
Authors: Zhang Li , Fu Zhipeng , Guo Huaping , Sun Yange , Li Xirui , Xu Mingliang
Published: July 2023

Steel surface defects have a significant impact on the quality and performance of many industrial products and cause huge economic losses. Therefore, it is meaningful to detect steel surface defects in real time. To improve the detection performance of steel surface defects with variable scales and complex backgrounds, in this paper, a novel method for detecting steel surface defects through a multiscale local and global feature fusion mechanism is proposed. The proposed method uses a convolution operation with a downsampling mechanism in the convolutional neural network model to obtain rough multiscale feature maps. Then, a context-extraction block (CEB) is proposed to adopt self-attention learning on the feature maps extracted by the convolution operation at each scale to obtain multiscale global context information to make up for the shortcomings of convolutional neural networks (CNNs), thus forming a novel multiscale self-attention mechanism. Afterwards, using the feature pyramid structure, multiscale feature maps are fused to improve multiscale object detection. Finally, the channel and spatial attention module and the WIOU (Wise Intersection over Union) loss function are introduced. The model achieved 78.2% and 71.9% mAP respectively on the NEU-DET and GC10-DET dataset. Compared to algorithms such as Faster RCNN and EDDN, this method is effective in improving the detection performance of steel surface defects.
Enhancing Energy Power Quality in Low-Voltage Networks Integrating Renewable Energy Generation: A Case Study in a Microgrid Laboratory
Authors: Villa-Ávila Edisson , Arévalo Paul , Aguado Roque , Ochoa-Correa Danny , Iñiguez-Morán Vinicio , Jurado Francisco , Tostado-Véliz Marcos
Published: July 2023

Nowadays, energy decarbonization due to integrating renewable energy sources presents important challenges to overcome. The intermittent nature of photovoltaic systems reduces power quality by producing voltage variations and frequency deviations in electrical system networks, especially in weak and isolated distribution systems in developing countries. This paper presents a power smoothing method for improving the low-pass filter and moving average for grid-connected photovoltaic systems. This novel method includes state-of-charge monitoring control of the supercapacitor’s energy storage system to reduce the fluctuations of photovoltaic power at the point of common coupling. A case study for a microgrid in a high-altitude city in Ecuador is presented with exhaustive laboratory tests using real data. This research aims to improve energy power quality in electrical distribution systems to cope with the growth of renewable penetration. The results demonstrate significant power quality and stability improvements achieved through the proposed method. For instance, the power smoothing method effectively reduced power fluctuations by 16.7% with the low-pass filter, 14.05% with the ramp-rate filter, and 9.7% with the moving average filter.
Intelligent Backstepping Control of Permanent Magnet-Assisted Synchronous Reluctance Motor Position Servo Drive with Recurrent Wavelet Fuzzy Neural Network
Authors: Lin Faa-Jeng , Huang Ming-Shi , Chien Yu-Chen , Chen Shih-Gang
Published: July 2023

An intelligent servo drive system for a permanent magnet-assisted synchronous reluctance motor (PMASynRM) that can adapt to the control requirements considering the motor’s nonlinear and time-varying natures is developed in this study. A recurrent wavelet fuzzy neural network (RWFNN) with intelligent backstepping control is proposed to achieve this. In this study, first, a maximum torque per ampere (MTPA) controlled PMASynRM servo drive is introduced. A lookup table (LUT) is created, which is based on finite element analysis (FEA) results by using ANSYS Maxwell-2D dynamic model to determine the current angle command of the MTPA. Next, a backstepping control (BSC) system is created to accurately follow the desired position in the PMASynRM servo drive system while maintaining robust control characteristics. However, designing an efficient BSC for practical applications becomes challenging due to the lack of prior uncertainty information. To overcome this challenge, this study introduces an RWFNN as an approximation for the BSC, aiming to alleviate the limitations of the traditional BSC approach. An enhanced adaptive compensator is also incorporated into the RWFNN to handle potential approximation errors effectively. In addition, to ensure the stability of the RWFNN, the Lyapunov stability method is employed to develop online learning algorithms for the RWFNN and to guarantee its asymptotic stability. The proposed intelligent backstepping control with recurrent wavelet fuzzy neural network (IBSCRWFNN) demonstrates remarkable effectiveness and robustness in controlling the PMASynRM servo drive, as evidenced by the experimental results.
Enhanced Photocatalytic CO2 Reduction to CH4 Using Novel Ternary Photocatalyst RGO/Au-TNTAs
Authors: Hossen Md. Arif , Khatun Fatema , Ikreedeegh Riyadh Ramadhan , Muhammad Aamina Din , Abd Aziz Azrina , Leong Kah Hon , Sim Lan Ching , Lihua Wu , Monir Minhaj Uddin
Published: July 2023

Photocatalytic CO2 reduction into hydrocarbon fuels is one of the most efficient processes since it serves as a renewable energy source while also lowering atmospheric CO2 levels. The development of appropriate materials and technology to attain greater yield in CO2 photoreduction is one of the key issues facing the 21st century. This study successfully fabricated novel ternary reduced graphene oxide (RGO)/Au-TiO2 nanotube arrays (TNTAs) photocatalysts to promote CO2 photoreduction to CH4. Visible light-responsive RGO/Au-TNTAs composite was synthesized by facile electrochemical deposition of Au nanoparticles (NPs) and immersion of RGO nanosheets onto TNTAs. The synthesized composite has been thoroughly investigated by FESEM, HR-TEM, XRD, XPS, FT-IR, UV-Vis DRS, and PL analyzer to explain structural and functional performance. Under the source of visible light, the maximum yield of CH4 was attained at 35.13 ppm/cm2 for the RGO/Au-TNTAs composite photocatalyst after 4 h, which was considerably higher by a wide margin than that of pure TNTAs, Au-TNTAs and RGO-TNTAs. The CO2 photoreduction of the RGO/Au-TNTAs composite has been improved due to the combined effects of Au NPs and RGO. Due to its surface plasmonic resonance (SPR) mechanism, Au NPs play a crucial role in the absorption of visible light. Additionally, the middle RGO layers serve as effective electron transporters, facilitating better separation of electron-hole pairs. The newly constructed composite would be a promising photocatalyst for future photocatalytic applications in other fields.
Sustainable Utilization of Biowaste Resources for Biogas Production to Meet Rural Bioenergy Requirements
Authors: Singh Akhilesh Kumar , Pal Priti , Rathore Saurabh Singh , Sahoo Uttam Kumar , Sarangi Prakash Kumar , Prus Piotr , Dziekański Paweł
Published: July 2023

Since the impending warning of fossil fuel inadequacy, researchers’ focus has shifted to alternative fuel generation. This resulted in the use of a wide variety of renewable biomass sources for making biofuels. Biofuels made from biomass are seen as the most promising long-term strategy for addressing issues associated with conventional energy sources, atypical climate change, and greenhouse gas emissions. Hydrocarbons may be efficiently extracted from biomass, which contains a lot of sugars. Biofuels including bioethanol, biodiesel, biohydrogen, and biogas can be produced from biomass for widespread usage in transportation, industry, and households. In recent years, there have been numerous reports of breakthroughs in the manufacturing of biofuels and biogas. This paper examines the big picture of biogas generation, with an emphasis on the many forms of biomass utilization in both commercial and residential settings in rural areas.
Using Central Composite Design to Improve Methane Production from Anaerobic Digestion of Tomato Plant Waste
Authors: Ruiz-Aguilar Graciela M. L. , Martínez-Martínez Juan H. , Costilla-Salazar Rogelio , Camarena-Martínez Sarai
Published: July 2023

Tomato plant waste (TPW) is a significant agricultural byproduct that has been often underutilized. Recent studies have shown that its use to obtain methane in an anaerobic digestion (AD) process is viable. However, there is not much information available on studies to improve methane production from this substrate using statistical methods for optimization processes such as central composite design (CCD). For this investigation, CCD was adopted to analyze the effect of S/I ratio (substrate/inoculum ratio) (0.32–1.12), temperature (27–43 °C), and inoculum concentration (10.35–20.95 g VS/L) on methane generation and volatile solids (VS) removal in a batch AD system mono-digestion of TPW. The highest average value of methane yield was found to be 210.8 mL CH4/g VS (S/I ratio 0.48, 40 °C, and 18.80 g VS/L), and the highest average value of VS removal was found to be 36.9% (S/I ratio 1.12, 35 °C, and 15.65 g VS/L). We obtain a model with a better fit for the VS removal (R2 = 0.9587) than for the methane production (R2 = 0.9156). Temperature and S/I ratio were the factors most important for methane production and VS removal, respectively.
The Role of Microorganisms in the Isolation of Nanocellulose from Plant Biomass
Authors: Yahya Esam Bashir , Elarbash Suhail Salem , Bairwan Rahul Dev , Mohamed Montaha Mohamed Ibrahim , Khan Niaz Bahadur , Harlina Putri Widyanti , Abdul Khalil H. P. S.
Published: July 2023

The isolation and bottom-up assembly of nano-cellulose by using microorganisms offers unique advantages that fine-tune and meet the main key design criteria of sustainability, rapid renewability, low toxicity and scalability for several industrial applications. As a biomaterial, several properties are required to maintain the quality and functional period of any product. Thus, researchers nowadays are extensively using microorganisms to enhance the yield and properties of plant nanocellulose. A microbial process requires approximately 20%–50% less energy compared to the chemical isolation process that consumes high energy due to the need for intense mechanical processing and harsh chemical treatments. A microbial process can also reduce production costs by around 30%–50% due to the use of renewable feedstocks, fewer chemical additives, and simplified purification steps. A chemical isolation process is typically more expensive due to the extensive use of chemicals, complex processing steps, and higher energy requirements. A microbial process also offers higher yields of nanocellulose with well-defined and uniform dimensions, leading to improved mechanical properties and enhanced performance in various applications, compared with the chemical isolation process, which may result in a wider range of nanocellulose sizes, potentially leading to variations in properties and performance. The present review discusses the role of different microorganisms (bacteria, yeasts and fungi) in the isolation and production of nanocellulose. The types and properties of nanocellulose from different sources are also discussed to show the main differences among them, showing the use of microorganisms and their products to enhance the yield and properties of nanocellulose isolation. Finally, the challenges and propositions regarding the isolation, production and enhancement the quality of nanocellulose are addressed.
Hybrid Cheeses—Supplementation of Cheese with Plant-Based Ingredients for a Tasty, Nutritious and Sustainable Food Transition
Authors: Genet Blandine M. L. , Sedó Molina Guillermo Eduardo , Wätjen Anders Peter , Barone Giovanni , Albersten Kristian , Ahrné Lilia M. , Hansen Egon Bech , Bang-Berthelsen Claus H.
Published: July 2023

With increasing awareness of the impact of food on the climate, consumers are gradually changing their dietary habits towards a more plant-based diet. While acceptable products have been developed in meat analogues and non-fermented dairy products, alternative fermented dairy products such as yogurt and particularly ripened hard and semi-soft cheese products are not yet satisfactory. Since the cheese category has such a broad range of flavors and applications, it has proven complicated to find plant-based sources able to mimic them in terms of texture, meltability, ripening and flavor. Moreover, plant-based dairy alternatives do not provide the same nutritional supply. New technological approaches are needed to make cheese production more sustainable, which should be integrated in the already existing conventional cheese production to ensure a fast and cost-efficient transition. This can be tackled by incorporating plant-based components into the milk matrix, creating so-called “hybrid cheeses”. This review will discuss the challenges of both animal- and plant-based cheese products and highlight how the combination of both matrices can associate the best properties of these two worlds in a hybrid product, reviewing current knowledge and development on the matter. Emphasis will be drawn to the selection and pre-processing of raw materials. Furthermore, the key challenges of removing the off-flavors and creating a desirable cheese flavor through fermentation will be discussed.
Sea Bass Side Streams Extracts Obtained by Pulsed Electric Fields: Nutritional Characterization and Effect on SH-SY5Y Cells
Authors: Martí-Quijal Francisco J. , Castagnini Juan Manuel , Ruiz María-José , Barba Francisco J.
Published: July 2023

Fish side streams are an environmental and economic problem. In this work, pulsed electric fields (PEF) extraction was optimized and used as a new method for their valorization. Sea bass head, skin, viscera, and backbone were used. PEF technology (123–300 kJ/kg, 1–3 kV/cm) improved the extraction of proteins and antioxidant compounds from head and skin, but was not successful for viscera. SDS-PAGE showed that the protein molecular weight distribution was affected by the extraction process, revealing differences between the control and PEF extraction conditions. In addition, the extraction of macro-minerals and micro-minerals were also evaluated. The effect of PEF differed according to the matrix and the mineral studied. Heavy metals were also taken into account, studying the presence of As, Cd, Hg, and Pb in the extracts. PEF pre-treatment reduced the presence of As in skin, viscera, and backbone, ranging from 18.25 to 28.48% according to the matrix evaluated. The analysis of potential antioxidant bioactive peptides showed that the treatment of the sample directly influenced their variety. Additionally, the extracts obtained from the head were found to increase cell viability when tested on SH-SY5Y cells. In conclusion, PEF extraction can be a useful tool for the valorization of fish side streams.
Phylogenetic Analysis of Some Species of the Anopheles hyrcanus Group (Diptera: Culicidae) in China Based on Complete Mitochondrial Genomes
Authors: Dong Haowei , Yuan Hao , Yang Xusong , Shan Wenqi , Zhou Qiuming , Tao Feng , Zhao Chunyan , Bai Jie , Li Xiangyu , Ma Yajun , Peng Heng
Published: July 2023

Some species of the Hyrcanus group are vectors of malaria in China. However, the member species are difficult to identify accurately by morphology. The development of sequencing technologies offers the possibility of further studies based on the complete mitochondrial genome. In this study, samples of mosquitoes of the Hyrcanus group were collected in China between 1997 and 2015. The mitochondrial genomes of ten species of the Hyrcanus group were analyzed, including the structure and base composition, codon usage, secondary structure of tRNA, and base difference sites in protein coding regions. Phylogenetic analyses using maximum-likelihood and Bayesian inference were performed based on mitochondrial genes and complete mitochondrial genomes The mitochondrial genome of 10 Hyrcanus group members ranged from 15,403 bp to 15,475 bp, with an average 78.23% (A + T) content, comprising of 13 PCGs (protein coding genes), 22 tRNAs, and 2 rRNAs. Site differences between some closely related species in the PCGs were small. There were only 36 variable sites between Anopheles sinensis and Anopheles belenrae for a variation ratio of 0.32% in all PCGs. The pairwise interspecies distance based on 13 PCGs was low, with an average of 0.04. A phylogenetic tree constructed with the 13 PCGs was consistent with the known evolutionary relationships. Some phylogenetic trees constructed by single coding regions (such as COI or ND4) or combined coding regions (COI + ND2 + ND4 + ND5 or ND2 + ND4) were consistent with the phylogenetic tree constructed using the 13 PCGs. The phylogenetic trees constructed using some coding genes (COII, ND5, tRNAs, 12S rRNA, and 16S rRNA) differed from the phylogenetic tree constructed using PCGs. The difference in mitochondrial genome sequences between An. sinensis and An. belenrae was very small, corresponding to intraspecies difference, suggesting that the species was in the process of differentiation. The combination of all 13 PCG sequences was demonstrated to be optimal for phylogenetic analysis in closely related species.
In Situ Cosmogenic 10Be Dating of Laurentide Ice Sheet Retreat from Central New England, USA
Authors: Drebber Jason S. , Halsted Christopher T. , Corbett Lee B. , Bierman Paul R. , Caffee Marc W.
Published: July 2023

Constraining the timing and rate of Laurentide Ice Sheet (LIS) retreat through the northeastern United States is important for understanding the co-evolution of complex climatic and glaciologic events that characterized the end of the Pleistocene epoch. However, no in situ cosmogenic 10Be exposure age estimates for LIS retreat exist through large parts of Connecticut or Massachusetts. Due to the large disagreement between radiocarbon and 10Be ages constraining LIS retreat at the maximum southern margin and the paucity of data in central New England, the timing of LIS retreat through this region is uncertain. Here, we date LIS retreat through south-central New England using 14 new in situ cosmogenic 10Be exposure ages measured in samples collected from bedrock and boulders. Our results suggest ice retreated entirely from Connecticut by 18.3 ± 0.3 ka (n = 3). In Massachusetts, exposure ages from similar latitudes suggest ice may have occupied the Hudson River Valley up to 2 kyr longer (15.2 ± 0.3 ka, average, n = 2) than the Connecticut River Valley (17.4 ± 1.0 ka, average, n = 5). We use these new ages to provide insight about LIS retreat timing during the early deglacial period and to explore the mismatch between radiocarbon and cosmogenic deglacial age chronologies in this region.
The Prevalence of Xerostomia in Older Thai Individuals with Type II Diabetes Mellitus and Its Association with Type of Toothpaste and Oral Functions: A Cross-Sectional Study Using Questionnaires
Authors: Sonpanao Panitan , Janebodin Kajohnkiart , Namvichaisirikul Niwatchai , Thongjit Supattarayan , Jitprasertwong Paiboon
Published: July 2023

Aim: To investigate the prevalence of xerostomia in older people with diabetes mellitus and its impacts on oral functions, as well as to determine potential risk factors for xerostomia. Methods: An analytical cross-sectional study was conducted on 623 older type 2 diabetes mellitus (T2DM) Thai people using valid structural questionnaires. Patients were interviewed, and data were recorded. Xerostomia was assessed using subjective symptom questionnaires. Risk factors for xerostomia were analyzed using bivariate and multiple logistic regression analyses. Results: Among the study participants, 38.4% of the older T2DM people had xerostomia, which is associated with sex, age, type of toothpaste, years of diabetes, hemoglobin A1c level, other systemic diseases, medication, smoking, alcohol consumption, and denture wearing. It was significant that xerostomia was associated with toothpaste containing spicy herbal extracts (OR: 9.32 [3.46 to 15.25]), while toothpaste containing artificial sweeteners tended to lower the risk of xerostomia. In addition, older T2DM adults with xerostomia had greater impaired oral functions, which include difficulties in speaking (OR: 3.31 [1.11 to 9.80]), tasting (OR: 5.12 [3.26 to 8.06]), swallowing (OR: 3.59 [2.32 to 5.53]), and chewing (OR: 3.34 [1.15 to 5.82]). Conclusions: Xerostomia is prevalent in older Thai people with T2DM. The results suggest that toothpaste containing spicy herbal extracts might increase the risk of xerostomia, resulting in various oral function problems. Therefore, greater awareness of xerostomia in this group should be raised to monitor dental health, and professionals should work in parallel with other aspects of oral health promotion.
Characterizing Employees with Primary and Secondary Caregiving Responsibilities: Informal Care Provision in Malaysia
Authors: Chan Caryn Mei Hsien , Siau Ching Sin , Wong Jyh Eiin , Yahya Noorazrul , Azmi Nor Aniza , Chu Shin Ying , Ahmad Mahadir , Chong Agnes Shu Sze , Wee Lei Hum , Tan Jo Pei
Published: July 2023

There is a need to determine the extent to which Malaysian employees reconcile both paid employment and informal care provision. We examined data from the Malaysia’s Healthiest Workplace via AIA Vitality Online Survey 2019 (N = 17,286). A multivariate multinomial regression was conducted to examine characteristics for the following groups: primary caregiver of a child or disabled child, primary caregiver of a disabled adult or elderly individual, primary caregiver for both children and elderly, as well as secondary caregivers. Respondent mean age ± SD was 34.76 ± 9.31, with 49.6% (n = 8573), identifying as either a primary or secondary caregiver to at least one child under 18 years, an elderly individual, or both. Males (n = 6957; 40.2%) had higher odds of being primary caregivers to children (OR 2.06; 95% CI 1.85–2.30), elderly (OR 1.24; 95% CI 1.09–1.41) and both children and elderly (OR 1.87; 95% CI 1.57–2.22). However, males were less likely to be secondary caregivers than females (OR 0.61; 95% CI 0.53–0.71). Our results highlight the differences in characteristics of employees engaged in informal care provision, and to a lesser degree, the extent to which mid-life individual employees are sandwiched into caring for children and/or the elderly.
Efficacy and Safety of Woohwangchungsimwon Combined with Donepezil in Behavioral and Psychological Symptoms of Dementia in Patients with Probable Alzheimer’s Disease: Study Protocol for a Randomized Controlled Trial
Authors: Kim Man Gi , Hyun Mi-Suk , Park Sang Gu , Cho Eun , Kim Jinsik , Choi Hyung-Kyoon , Son Kyung-Lak , Lim Chi-Yeon , Kim Kwang Ki , Koo Byung Soo
Published: July 2023

Behavioral and psychological symptoms of dementia are a major factor in the burden of care and medical expenses. Conventional pharmacological treatments do not exert a distinct effect on the benefits versus the risks. The herbal medicine woohwangchungsimwon is frequently prescribed for neuropsychiatric disorders. An effect of woohwangchungsimwon on behavioral and psychological symptoms of dementia has been previously reported; however, no clinical studies have been conducted. We aim to evaluate the efficacy and safety of woohwangchungsimwon combined with donepezil for alleviating these symptoms in probable Alzheimer’s disease. In this randomized, assessor-blinded, parallel-group clinical trial, 74 participants with probable Alzheimer’s disease will be divided via block randomization into a woohwangchungsimwon + donepezil combination group (n = 37) or a donepezil single group (n = 37). Participants will include patients under donepezil treatment for at least a month. We will perform the study for 24 weeks. The Neuro-Psychiatric Inventory subscale scores will be the primary outcome. Secondary outcomes will include cognitive function, dementia severity, physical function, quality of life, depression, anxiety, and insomnia. For safety evaluation, we will assess adverse reactions, measure vital signs, and conduct laboratory tests. This is the first trial aiming to confirm the efficacy and safety of woohwangchungsimwon combined with donepezil for alleviating behavioral and psychological symptoms of dementia. Its findings could provide a basis for their co-administration to control these symptoms in probable Alzheimer’s disease.
Formation of Calcium Oxalate Patinas as Protective Layer on Basaltic Stone Surfaces of 17th Century Raigad Hill Fort, India
Authors: Singh Manager Rajdeo , Yadav Rajendra
Published: July 2023

This work reports calcium oxalate film formation on basaltic stone surfaces of the 17th-century western India Raigad Hill Fort. Nine stone samples extracted from the exterior surfaces of different historical structures of the fort were investigated under FTIR, optical microscopy, XRD, and SEM-EDX. The FTIR spectroscopy revealed intense peaks for Ca-oxalate patinas on basaltic stone surfaces. Observation under optical microscopy clearly showed milky white oxalate films, and peaks for crystalline calcium oxalate, including rock silicates, were prominently observed through XRD investigations. The surface morphology, the origin of the oxalate film, and the state of conservation of the basalt rock were investigated through SEM-EDX. The massive structures at Raigad, at a height of about 800 m, have hardly been chemically cleaned or coated with preservatives in the past. The presence of organic filaments in SEM photomicrographs indicated the biological origin of the oxalate patina due to the thick growth of microbiota on the monument stone during very heavy monsoons. The oxalic acid secreted by microbes dislodged the Ca-rich plagioclase of the stone, ensuring Ca-ions’ availability for film formation. The optical and mineralogical analyses suggest that the film is not the result of simple deposition but of the surface transformation of basaltic stone.
Community Fire Risk Reduction: Longitudinal Assessment for HomeSafe Fire Prevention Program in Canada
Authors: Al-Hajj Samar , Thomas Larry , Morris Shelley , Clare Joseph , Jennings Charles , Biantoro Chris , Garis Len , Pike Ian
Published: July 2023

(1) Background: Residential fires represent the third leading cause of unintentional injuries globally. This study aims to offer an overview and a longitudinal evaluation of the HomeSafe program implemented in Surrey in 2008 and to assess its effectiveness in mitigating fire-related outcomes. (2) Methods: Data were collected over a 12-year period (2008–2019). Assessed outcomes comprised frequency of fire incidents, residential fires, casualties, functioning smoke alarms, and contained fires. The effectiveness of each initiative was determined by comparing the specific intervention group outcome and the city-wide outcome to the pre-intervention period. (3) Results: This study targeted 120,349 households. HomeSafe achieved overwhelming success in decreasing fire rates (−80%), increasing functioning smoke alarms (+60%), increasing the percentage of contained fires (+94%), and decreasing fire casualties (−40%). The study findings confirm that the three most effective HomeSafe initiatives were firefighters’ visits of households, inspections and installations of smoke alarms, and verifications of fire crew alarms at fire incidents. Some initiatives were less successful, including post-door hangers (+12%) and package distribution (+15%). (4) Conclusions: The HomeSafe program effectively decreased the occurrence and magnitude of residential fires. Lessons learned should be transferred to similar contexts to implement an evidence-based, consistent, and systematic approach to sustainable fire prevention initiatives.
The Eyewitness Community Survey: An Engaging Citizen Science Tool to Capture Reliable Data while Improving Community Participants’ Environmental Health Knowledge and Attitudes
Authors: Butsch Kovacic Melinda , Elshaer Shereen , Baker Theresa A. , Hill Vincent , Morris Edith , Mabisi Keren , Snider Ian , Gertz Susan , Hershberger Susan , Martin Lisa J.
Published: July 2023

Many youths and young adults have variable environmental health knowledge, limited understanding of their local environment’s impact on their health, and poor environmentally friendly behaviors. We sought to develop and test a tool to reliably capture data, increase environmental health knowledge, and engage youths as citizen scientists to examine and take action on their community’s challenges. The Eyewitness Community Survey (ECS) was developed through several iterations of co-design. Herein, we tested its performance. In Phase I, seven youths audited five 360° photographs. In Phase II, 27 participants works as pairs/trios and audited five locations, typically 7 days apart. Inter-rater and intra-rater reliability were determined. Changes in participants’ knowledge, attitudes, behaviors, and self-efficacy were surveyed. Feedback was obtained via focus groups. Intra-rater reliability was in the substantial/near-perfect range, with Phase II having greater consistency. Inter-rater reliability was high, with 42% and 63% of Phase I and II Kappa, respectively, in the substantial/near-perfect range. Knowledge scores improved after making observations (p ≤ 0.032). Participants (85%) reported the tool to be easy/very easy to use, with 70% willing to use it again. Thus, the ECS is a mutually beneficial citizen science tool that rigorously captures environmental data and provides engaging experiential learning opportunities.