bluuebunny/crossref_metadata_2025 huggingface dataset:
Microbial Consortia and Mixed Plastic Waste: Pangenomic Analysis Reveals Potential for Degradation of Multiple Plastic Types via Previously Identified PET Degrading Bacteria
Authors: Edwards Sabrina , Leon-Zayas Rosa , Ditter Riyaz , Laster Helen , Sheehan Grace , Anderson Oliver , Beattie Toby , Mellies Jay L
Published: May 2022

Global use of single-use non-biodegradable plastics, like bottles made of polyethylene tereph-thalate (PET), have contributed to catastrophic levels of plastic pollution. Fortunately, microbi-al communities are adapting to assimilate plastic waste. Previously, our work showed a full consortium of five bacteria capable of synergistically degrading PET. Using omics approaches we identified key genes implicated in PET degradation within the consortium’s pangenome and transcriptome. This analysis led to the discovery of a novel PETase, EstB, discovered to hydrolyze oligomer BHET, and polymer PET. Besides genes implicated in PET degradation, many other biodegradation genes were discovered. Over 200 plastic and plasticizer degrada-tion related genes were discovered through the Plastic Microbial Biodegradation Database (PMBD). Diverse carbon source utilization was observed by a microbial community-based as-say, which paired with an abundant number of plastic and plasticizer degrading enzymes in-dicates a promising possibility for mixed plastic degradation. Using RNAseq differential analysis, several genes were predicted to be involved in PET degradation including aldehyde dehydrogenases and several classes of hydrolases. Active transcription of PET monomer me-tabolism was also observed, including the generation of polyhydroxyalkanoate (PHA) bi-opolymers. These results present an exciting opportunity for the bio-recycling of mixed plastic waste with upcycling potential.
Is There Any Importance of Brain Tomography and Diffusion Magnetic Resonance Imaging in COVID-19 Patients?
Authors: Türkdoğan Figen Tunalı , Coskun Abuzer
Published: May 2022

Background: As coronavirus (COVID-19) continues to pose a threat to the entire world, it is critical to developing strategies for containing its spread. The purpose of this study was to assess the demographic characteristics, brain tomography characteristics, and diffusion magnetic resonance findings of COVID-19 positive individuals. Material and Method: Between January 1 and December 31, 2021, 317 patients over the age of 18 were admitted to the emergency department with symptoms consistent with COVID-19. Three groups were formed based on clinical findings in patients, divided into light, medium, and severe, and four groups were formed based on radiological imaging findings. Results: The mean age of the 317 patients included in the study was 67.28±12.06 years, with a range of 28-91 years. The clinical classification of the patients was based on laboratory parameters and radiological imaging, not on their age or gender. Mild cases were classified as CO-RADS 0-4; moderate and severe cases were classified as CO-RADS 5-6 (p=0.001). While 60 (18.9%) of patients were followed outpatient, 144 (45.4%) were admitted to the hospital, 73 (23%) were admitted to the intensive care unit, and 40 (12.7%) died (p=0.001). Direct radiographs revealed bilateral involvement in 224 (70.7%) cases, peripheral involvement in 259 (81.7%) cases, and mid-lower lobe lung involvement in 194 (61.2%) cases (p=0.001). Brain tomography revealed infarction in 42 (13.2%) of the patients who were followed and hospitalized and hemorrhage in 22 (6.9%) of the patients. Magnetic resonance imaging revealed diffusion involvement in 68 (21.5%) of the cases (p=0.001). Conclusion: A standardized reporting system for COVID-19 data is required, and it must be simple to use, quick to understand, and focused on determining the risk of pneumonia. Additionally, the diagnostic role of radiological imaging, prognosis prediction, and severity scoring of lung involvement should be included.
A novel ALDH1A1 Inhibitor Blocks Platinum-Induced Senescence and Stemness in Ovarian Cancer
Authors: Muralikrishnan Vaishnavi , Fang Fang , Given Tyler C , Podicheti Ram , Chtcherbinine Mikhail , Sriramkumar Shruthi , O'Hagan Heather M , Hurley Thomas D. , Nephew Kenneth P.
Published: May 2022

Ovarian cancer is a deadly disease attributed to late-stage detection as well as recurrence and development of chemoresistance. Ovarian cancer stem cells (OCSCs) are hypothesized to be largely responsible for emergence of chemoresistant tumors. Although chemotherapy may initially succeed at decreasing the size and number of tumors, it leaves behind residual malignant OCSCs. In this study, we demonstrate that Aldehyde dehydrogenase 1A1 (ALDH1A1) is essential for the survival of OCSCs. We identified a novel ALDH1A1 inhibitor, compound 974, and used 974 as a tool to decipher the mechanism of stemness regulation by ALDH1A1. Treatment of OCSCs with 974 significantly inhibited ALDH activity, expression of stemness genes, spheroid, and colony formation. In vivo limiting dilution assay demonstrated that 974 significantly inhibited CSC frequency. Transcriptomic sequencing of cells treated with 974 revealed significant downregulation of genes related to stemness and chemoresistance as well as senescence and senescence associated secretory phenotype (SASP). We confirmed that 974 inhibited senescence and stemness induced by platinum-based chemotherapy in functional assays. Overall, these data establish that ALDH1A1 is essential for OCSCs survival and ALDH1A1 inhibition sup-presses chemotherapy induced senescence and stemness. Targeting ALDH1A1 using small molecule inhibitors in combination with chemotherapy therefore presents a promising strategy to pre-vent ovarian cancer recurrence and has potential for clinical translation.
Using Compass (Context Optimisation Model for Person-Centred Analysis and Systematic Solutions) Theory to Augment Implementation of Digital Health Solutions
Authors: Mather Carey , Almond Helen
Published: May 2022

Digital health research is an emerging discipline that requires easy-to-understand theoretical frameworks and implementation models for digital health providers in health and social care settings. The COVID-19 pandemic has heightened demand for digital health discipline-specific instruction on ‘how to’ manage evidence-based digital health transformation. Access to the use of these models guarantees that digital health providers can investigate phenomena using safe and suitable approaches and methods to conduct research and identify answers to challenges and problems that arise in health and social care settings. The COMPASS theory is designed to aid transformation of health and social care environments. A navigational rose of primary quadrants is divided by four main compass points, with person-centred care being central to the philosophy. Two axes produce Cartesian planes that intersect to form a box plot, which can be used to discover human and physical resource weightings to augment digital health research design and implementation. A third continuum highlights stakeholders' capabilities, critical for any multidisciplinary study. The COMPASS mnemonic guides end-users through the process of design, development, implementation, evaluation, and communication of digital health transformations. The theory's foundations are presented and explained in context of the 'new normal' of health and social care delivery.
Digital Tools Integration and Human Resources Development for Smart Factories
Authors: Sawada Hiroyuki , Nakabo Yoshihiro , Furukawa Yoshiyuki , Ando Noriaki , Okuma Takashi , Komoto Hitoshi , Masui Keijiro ,   Industrial Cyber-Physical Systems Research Center, National Institute of Advanced Industrial Science and Technology (AIST) 2-3-26 Aomi, Koto-ku, Tokyo 135-0064, Japan,   Industrial Cyber-Physical Systems Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan,   Human Augmentation Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Kashiwa, Japan
Published: May 2022

Promoting digital transformation (DX) and realizing smart factories have become critical for manufacturing companies to meet increasing demands such as short-term delivery, quality assurance, and environmental, social, and corporate governance (ESG) as well as to improve productivity and quality of work (QoW). To this end, digital tools should be provided for practical application in the preparation of the environments in which the companies can learn and study how to use digital technologies and tools by trial and error, while developing human resources for utilizing them for their own problem solving. In this paper, we describe the activities we used to develop various digital tools in the fields of manufacturing, robotics, and service engineering. We integrated these into a cyber physical system (CPS) developed for our model factory and offered a course for the company workers to learn these digital technologies. We also planned to develop our activities in collaboration with companies, universities, and other research institutes.
Application of Metaheuristics to Packing Formation Support Systems of Pre-Cut Lumber Factory
Authors: Tanizaki Takashi ,   Kindai University 1 Takaya-Umenobe, Higashi-Hiroshima, Hiroshima 739-2116, Japan, Yamashita Ryohei
Published: May 2022

In recent years, wood has been considered as a building material from the perspective of SDGs. Pre-cut lumber is often used for wooden houses, including roofs, floors, and pillars. It is preprocessed into a certain shape for the joint parts and has joint brackets if necessary. The use of them has the advantages of shortening the construction period and reducing the construction cost. The number of pre-cut lumber production factories in Japan decreased from 757 in 2001 to 659 in 2011 and then increased to 730 in 2016. Compared with 2011, the number of factories with sales of <500 million yen in 2016 decreased by approximately 30%, while the number of factors with sales of ≥500 million yen increased by 80%, indicating a trend toward a larger scale [1]. Therefore, improving the productivity of these factories is imperative. We have been researching the conversion of factories into smart factories to improve the productivity of pre-cut lumber manufacturing company A. In this company, employees take 1.2 h to prepare packing plans every day, and the company faces the challenge of improving the efficiency of planning operations. Herein, we propose an algorithm using an iterative local search (ILS) with Or-opt (ILS + Or-opt) for packing formation support systems to improve the efficiency of planning operations. This algorithm has the following two features. First, it uses Or-opt to create a neighborhood for a local search. Second, uses exchange neighborhoods when repeating the ILS to achieve diversity in the search.
Production System Evaluation Method Considering Worker Taking Childcare
Authors: Hibino Hironori , Watanabe Yoshihiko ,   College of Economics, Nihon University 1-3-2 Misaki-cho, Chiyoda-ku, Tokyo 101-8360, Japan,   Izugiken, Co., Ltd., Mishima, Japan
Published: May 2022

It is becoming increasingly important for companies to accommodate childcare leave to support childbirth and childcare activities. It is important for them to raise the utilization rate of the maternity and childcare leave mechanisms. On the premise of the use of maternity and childcare leave, production planning and management are realizing that childbirth and childcare are important. We first analyzed female workers’ current situation in the manufacturing industry. Next, we proposed the production system simulation model while considering the production system in relation to maternity and childcare leave, and implemented the simulation while considering this premise. In addition, we proposed the cost evaluation method. To confirm the effectiveness of the proposed method, we carried out a case study that targeted an automobile parts production line.
Reverse Engineering Algorithm for Cutting of Ruled Geometries by Wire
Authors: Beaucamp Anthony T. H. , Takeuchi Yoshimi ,   Department of Micro-Engineering, Kyoto University Cluster C3, Kyotodaigaku-katsura, Nishikyo-ku, Kyoto 615-8540, Japan,   Department of Mechanical Engineering, Chubu University, Kasugai, Japan
Published: May 2022

Abrasive wire cutting (AWC) and wire electric discharge machining (WEDM) are efficient and economical processes for the fabrication of precision parts from bulk material. Operating costs and manufacturing lead times are low compared to more general methods such as 5-axis CNC milling, turning, or electro-discharge machining. In this paper, an algorithm based on differential geometry in Euclidean space is proposed for reverse engineering of ruled geometries. The algorithm can determine whether a given geometry is producible by wire cutting, and can also derive the associated wire trajectories. Implementation is demonstrated by producing complex turbine blade geometries on 4-axis wire cutting machines with an overall shape accuracy of 20–40 μm peak-to-valley.
Improved Synchronous Motion of Linear and Rotary Axes While Avoiding Torque Saturation Under a Constant Feed Speed Vector at the Endmilling Point – Investigation of Motion Error Under Numerical Control Commanded Motion –
Authors: Suzuki Takamaru , Yoshikawa Kazuki , Hirogaki Toshiki , Aoyama Eiichi , Ikegami Takakazu ,   Machine Systems Engineering Course, Department of Creative Engineering, National Institute of Technology, Kitakyushu College 5-20-1 Shii, Kokuraminami-ku, Kitakyushu, Fukuoka 802-0985, Japan,   Department of Mechanical Engineering and Science, Faculty of Science and Engineering, Doshisha University, Kyotanabe, Japan,   DMG MORI Co., Ltd., Yamatokoriyama, Japan
Published: May 2022

A five-axis machining center is known for its synchronous control capability, allowing complicated three-dimensional surfaces, such as propellers and hypoid gears, to be quickly created. Prior research has shown that it is necessary to improve not only the machined shape accuracy but also the machined surface roughness of free-form surfaces. Therefore, in this research, we aimed to maintain the feed speed vector at the endmilling point by controlling two linear axes and a rotary axis with a five-axis machining center to improve the machined surface quality. In previous research, we suggested reducing the shape error of machined workpieces (referred to as shape error in this research) by considering the differences in the servo characteristics of the three axes in the machining method. The shape error was significantly decreased by applying the proposed method, which uses a parameter (referred to as precedent control coefficient in this research) determined by calculation, rather than trial and error. Moreover, to maintain the feed speed vector at the endmilling point when machining complex shapes, a rapid velocity change in each axis is required, causing inaccuracy owing to torque saturation. The results of the experiments and simulations of previous research indicated that torque saturation can be evaluated via simulation. In this research, to reduce the shape error while avoiding torque saturation when movement has high angular velocity, we developed a theoretical method to obtain the most suitable precedent control coefficient of each axis by using a block diagram that considers torque saturation. Therefore, both shape error reduction and torque saturation avoidance can be realized by using the proposed method.
Forming of Multiple Straight Convex Shapes on Aluminum Sheet Using Impulsive Water Pressure
Authors: Yamashita Minoru , Saito Haruki , Nikawa Makoto ,   Department of Mechanical Engineering, Gifu University  1-1 Yanagido, Gifu-shi, Gifu 501-1193, Japan,   Graduate School of Natural Science and Technology, Gifu University, Gifu, Japan
Published: May 2022

Several types of multiple straight convex shapes were formed on a thin aluminum sheet with a grooved die using impulsive water pressure. The maximum pressure was 160 MPa in the high-speed forming, wherein a drop hammer testing machine was used, whereas it was 100 MPa in the low-speed forming because of the limitations of the press machine. The effects of the forming speed, cross-sectional shape, and pitch of the grooves on the deformation behavior were investigated. The increase in the impulsive water pressure was found to be affected significantly by the compressibility of water. The symmetricity of the convex shape in the cross-section decreased at both ends for a smaller pitch because of the imbalance of the material flow at both peripheries of the groove. The concave surface profile of the pressure side was more rounded in the high-speed forming than that in the low-speed forming when semicircular and rectangular grooved dies were used. This may be attributed to the fact that the plastic deformation becomes more uniform owing to the positive strain rate sensitivity of the test material. In the forming with rectangular grooves, fracture occurred under the low- and high-speed conditions, wherein the maximum pressure was set to 100 MPa. However, the material did not fracture during high-speed forming with a pressure of 160 MPa, where the convex shape was higher and the material contacted the bottom of the groove. This behavior may be because the dislocation density of the material did not increase rapidly owing to the strain rate being maintained high until the material suddenly stopped deforming in the latter condition. In forming with a trapezoidal grooved die, the formed profiles were considerably similar under all conditions because the strain was considerably smaller than that with the other grooves.
Evaluation of Different Machine Learning Frameworks to Estimate CO2 Solubility in NaCl Brines: Implications for CO2 Injection into Low-Salinity Formations
Authors: Mohammadian Erfan , Liu Bo , Riazi Amin
Published: May 2022

An accurate estimation of carbon dioxide (CO2) solubility in brine is of great significance for industrial applications such as quantifying CO2 sequestration in subsurface formations, CO2 surface mixing, and different CO2-based enhanced recovery methods (EOR). In this research, four different data-driven/machine learning techniques, extreme gradient boosting (XGB), multilayer perceptron (MLP), K-nearest neighbor (KNN), and in-house genetic algorithm (GA), were used to estimate solubility in terms of pressure, temperature, and salinity. Pressure, temperature, and salinity were used as model inputs, while CO2 solubility was the output. The experimental database used in this study was collected by dissolving CO2 into NaCl brines at salinity ranging from 0 to 15000 ppm, temperature ranging from 298 to 373 K, and pressures up to 200 atm. All data-driven models accurately estimated solubility through a coefficient of correlation (R2) ranging from 0.95 to 0.99, and a precise simple-to-use empirical solubility equation was developed using GA. The performance of the models was analyzed using proper model metrics (such as mean absolute error and relative error). A detailed feature importance analysis was conducted using feature importance, permutation, and Shapley values to clarify the correlation between the input and output parameters. The pressure was found to be the most impactful feature, followed by temperature and salinity. The model’s accuracy was compared to a well-established solubility model from the literature, and a good agreement between the two models’ results was observed. Lastly, conducting sensitivity analysis on the model revealed that the model’s estimations were still accurate when pressure and salinity were beyond the scopes of the original dataset.
Development of an equation free surrogate model using deep learning algorithm for heat transfer simulation
Authors: afzali Somayeh , Moayyedi Mohammad Kazem , Fotouhi Faranak
Published: May 2022

The Partial differential equations are one of the main tools in modeling many phenomena in real life. Since the formation and solving of the governing equations requires high processing time and computational costs. This study seeks to provide a method based on deep learning algorithms that can solve the equations independently of direct and numerical solution methods only by applying the boundary conditions of the problem on the neural network. This work explores the application of this paradigm on two-dimensional steady-state heat transfer equation. Due to the challenges of preparation large data with large dimensions, a novel method has been proposed to reduce the necessity of gathering data in large dimensions and size. In this method, first the steady-state heat transfer pattern encrypted in a kernel using thermal data in small size and dimensions. It is then used to train the steady-state predictor network without observing any steady-state heat transfer data simply by imposing restrictions on its outputs. This model was compared with a supervised model that trained by the large size of labeled data in the original dimensions. The result signifies that the proposed model has more accuracy, learning capability, and higher speed during the training stage.
Physical and Biological dosimetric margin according to prescription method for Stereotactic Body Radiation Therapy
Authors: Kawahara Daisuke , Saito Akito , Nagata Yasushi
Published: May 2022

ObjectiveTo expand the BCF model which covert physical dosimetric margin (PDM) to biological dosimetric margin (BDM) for point prescription with 3DCRT and the marginal prescription method with volumetric-modulated radiotherapy (VMAT).MaterialsCoplanar VMAT of marginal prescription and non-coplanar 3D-CRT of point prescription with lung stereotactic body radiation therapy (SBRT) were planned in RayStation. The biological equivalent dose (BED) for the dose per fraction (DPF) of 3–20 Gy/fr was calculated from these plans. The dose was perturbed with the calculation by 1-mm steps isocenter shift. The dose covering 95% of the target was greater than or equal to 90% of the prescribed physical dose and BED were defined as PDM and BDM, respectively. The BCF was created as a function of the DPF.ResultsThe PDM and BDM for all DPFs were larger with the point prescription method than the marginal prescription method. The marginal prescription method, that with 60% isodose line, had a larger PDM and BDM. The BCF with the point prescription was smaller than for the marginal prescription in left-right, anterior-posterior, and cranio-caudal direction. For the marginal prescription method, that with 60% isodose line had a larger BCF. Conclusion: The improved BCF method could convert to the BDM for point prescription with 3DCRT and the marginal prescription method with VMAT.Advances in knowledgeWe improved the biological conversion factor that converts the physical dosimetric margin to the biological dosimetric margin, which is needed to be used for stereotactic radiation therapy in radio-biological based treatment planning.
Resource Allocation for UAV-assisted 5G mMTC Slicing Networks using Deep Reinforcement Learning
Authors: Gupta Rohit Kumar , Kumar Saubhik , Misra Rajiv
Published: May 2022

The Internet of Things (IoT) application scenarios is becoming extensive due to the quick evolution of smart devices with fifth-generation (5G) network slicing technologies and hence IoT becoming significantly important beyond fifth-generation (B5G) networks. However, communication with IoT devices is more sensitive in disasters because the network depends on the main power supply and devices are fragile. In this paper, we consider Unmanned Aerial Vehicles (UAV) as a flying base station (BS) for the emergency communication system with 5G mMTC Network Slicing to improve the quality of service. The UAV-assisted mMTC creates a base station selection method with the aim of maximizing the system energy efficiency. Then, the system model is reduced into the stochastic optimization based problem using Markov Decision Process (MDP) theory. We propose a Dueling-Deep-Q-Networks (DDQN) based approach based on Reinforcement Learning (RL) technique for maximization of energy efficiency to solve the resource allocation problem. We compare the proposed model with DQN and Q-Learning models and found that the proposed DDQN based model performs better for resource allocation in terms of low transmission power and maximum energy efficiency.
Does green financial reform pilot policy promote green technology innovation？—— Empirical evidence from China
Authors: Wang Xueyang , Sun Xiumei , Zhang Haotian , Xue Chaokai
Published: May 2022

As a new financial model that balances economic and ecological benefits, green finance (GF) plays an important role in promoting green economic development and ecological environmental protection. Based on the panel data set of 30 provinces in China from 2010 to 2020, this paper uses the synthetic control method (SCM) to explore the impact of the green financial reform pilot policy (GFRP) on the green technology innovation (GTI) capabilities of pilot areas and evaluate the policy effects. The specific research conclusions are as follows: (1) On the whole, the GFRP has a positive role in promoting the GTI capability of the pilot areas, but this role is different due to the different resources, environment, and economic development levels of each region. The areas with economic development levels in the middle and head are obviously affected by the policy, and the less developed areas are less affected by the policy or even have a restraining effect. (2) Although the pilot policy has improved the GTI capability of the pilot area, the promotion effect is unstable, that is, the implementation effect of the policy is unstable. In the early stage of policy implementation, the promotion effect of the policy on the regional green innovation capacity is the most obvious, and this promotion effect begins to show a downward or stable trend in the 2–3 years after the policy is implemented. Based on the above conclusions, it can provide some reference for the revision and improvement of GFRP.
Functional identification of bHLH transcription factor MdSAT1 in the ammonium response in apple
Authors: Li Tong , Feng Zi-Quan , Zhu Bai-Hui , Li Ming-Li , Li Guo-Dong , Gao Wen-Sheng , You Chun-Xiang , Wang Xiao‐Fei
Published: May 2022

Plants mainly uptake inorganic nitrogen from soil as ammonium and nitrate. Less energy is required to assimilate ammonium compared to nitrates, and plants prefer to take up ammonium when the external nitrogen concentration is low. Investigating the patterns and mechanisms of ammonium absorption can help improve crop nitrogen utilization. In this study of apple, we isolated MdSAT1, a gene encoding an ammonium-responsive bHLH transcription factor. MdSAT1 promoted the growth and development of lateral roots and root hairs. Overexpression of MdSAT1 increased the transcript levels of genes related to ammonium uptake and assimilation and promoted the activities of ammonium assimilation-related enzymes, indicating that MdSAT1 can enhance ammonium uptake and utilization. MdSAT1 also can modulate ROS accumulation to ultimately regulate plant growth. Taken together, these findings provide insight into the mechanisms by which MdSAT1 controls ammoniun utilization as well as plant growth and development in apple.
Antagonist activation exercises elicit higher post-activation performance enhancement than agonist activities on throwing performance
Authors: Pisz Anna , Blazek Dusan , Jebavy Radim , Kolinger Dominik , Wilk MIchal , Krzysztofik Michal , Stastny Petr
Published: May 2022

Background: This study aimed to determine the acute effect of agonist and antagonist conditioning activities (CA) on medicine ball throw performance among female softball players. Methods: Thirteen national level female softball players (age 22.2 ± 3.1 years; body mass 68.3 ± 11.3 kg; softball experience 7.3 ± 2.4 years) performed 3 medicine ball chest throws before CA and after CA respectively in 3rd, 6th, and 9th minute. CA was the bench press (BP) and bent-over barbell row with 2 sets of 4 repetitions at 60% and 80% of one-repetition maximum, and 2 sets 4 repetition bodyweight push up.Results: Two-way ANOVA revealed an increase in throwing distance (p < 0.001) after bent over barbell row exercise and in throwing speed (m/s) (p < 0.001) after BP and bent over barbell row. The throwing distance was highest after bent over barbell row (Cohen’s d 0.41). Although the BP had the highest effect (Cohen’s d 0.41) to increase the throwing speed, its effect on throwing distance was not significant. Conclusions: We conclude that upper body throwing performance is higher after antagonist exercise than agonist CA and both agonist and antagonist CA increases power. In the resistance training practice, we recommend the interchange of agonist and antagonist PAPE stimulation at push-up bodyweight or BP and bent over raw submaximal (80% of 1RM) intensity to succeed PAPE on upper limbs.
Attitudes and adoption related to technology and active and healthy ageing - A national survey with a generational perspective
Authors: Offerman Jens , Fristedt Sofi , Schmidt Steven , Löfqvist Charlotte , Iwarsson Susanne
Published: May 2022

Research is needed to understand attitudes and adoption of the broad range of technologies currently available to support active and healthy ageing, in different generations. Previous and current research tends to neglect the diversity of products and services, as well as typically focuses on current older people rather than those representing future generations. Research-based knowledge is warranted to support development of future technologies that successfully promote active and healthy ageing. The present study aims to give an overview of the (Blinded) survey and sample, followed by a description of attitudes and acceptance related to technology in general and as a means to support active and healthy ageing. A national cross-sectional survey was conducted with a randomised sample (N = 2,121) including men and women from three age cohorts (30–39, 50–59 and 70–79 year-old persons) in Sweden, including questions on attitudes and acceptance related to a broad perspective of technology. The results show that perspectives were shared across generations rather than just within, especially in the younger and middle-aged generations. However, there were significant generational differences regarding what kind of technologies are preferred to support active and healthy ageing as well as reasons for using certain technologies. Our study described generational perspectives rarely displayed in research and shows that attitudes toward and acceptance of technologies for active and healthy ageing are shared across generations. Notable differences were displayed concerning choosing and adopting new technology, which deserve attention when developing new products and services. The study shows that research applying a comparative generational perspective has potential to nurture the understanding of acceptance and adoption throughout the ageing process, and guide development and implementation of technologies for active and healthy ageing.
Cardiovascular risk reduction with integrated care: results of 8 years follow up
Authors: Smits Geert H.J.M. , Doorn Sander van , Bots Michiel L. , Hollander Monika
Published: May 2022

BackgroundCare groups organize integrated cardiovascular risk management programs in primary care for high risk patients. Results of risk reduction with long-term participation are scarce. The aim was to evaluate the effects of participation in integrated CVRM care on LDL cholesterol, systolic blood pressure and smoking between 2011 and 2018.MethodsA protocol was developed for delegated practice nurse activities. A multidisciplinary data registry was used for uniform registration. The care group organized annual education, feedback meetings and intervision for general practitioners and practice nurses. From 2015 onwards, the care group has started with practice visitations to discuss performance and support practices with improving integrated CVRM care. Results Participants grew from 34,628 patients (mean age 67 years, 53% women, 29% with a previous CVD) in 2011 to 48,397 patients (mean age 70 years, 51% women, 44% with a previous CVD) in 2018. In patients eligible for primary prevention as well as for secondary prevention similar trends were observed: lipid modifying and blood pressure lowering medication increased, mean LDL-cholesterol and mean systolic blood pressure decreased, patients on target for LDL cholesterol and systolic blood pressure increased and the proportion of non-smokers with both LDL-cholesterol and SBP on target increased.Conclusion In patients participating in an integrated CVRM care program, we saw considerable improvements in 3 important cardiovascular risk factors between 2011 and 2018.
Sensitivity Analysis of River Chlorophyll-a to Environmental Factors Based on Support Vector Machine Regression Algorithm in Northern China
Authors: Xu Li , Hao Guizhen , Li Simin , Guo Peiran , Song Fengzhi , Zhao Yong
Published: May 2022

The outbreak of planktonic algae seriously affects the water environmental quality of the artificially reformed rivers in the north of China, and it is difficult to control. In this study, taking a typical small river dam area in the north of China—Qingshui River dam area in Zhangjiakou City as an example, the spatiotemporal variation characteristics of environmental factors were analyzed firstly. On this basis, the Chlorophyll-a (Chl-a) prediction model was established by using the Support Vector Regression (SVR). Further, the sensitivity of Chl-a was analyzed, moreover, we confirmed the main limiting factors of Chl-a. The results showed that in 2018, the average content of Chl-a was 126.25ug/L, while the content of DO and pH was also totally high. The maximum content of TN was 16.68mg/L and high in the whole year; the maximum value of CODmn was 50.00mg/L, which was slightly higher in summer; the average value of NH4+-N and TP was only 0.78mg/L and 0.18mg/L. The eutrophication evaluation showed that the proportion of areas with hyper eutrophication is 73%, and the most severe eutrophication was in summer. We used the RBF kernel function SVR model and 10-fold cross-validation method to optimize the parameters. The penalty parameter c is 1.4142, the kernel function parameter g is 1, and the training and verification errors were only 0.032 and 0.067. The application effect was good. Based on the sensitivity analysis of SVR prediction model, the sensitivity coefficients of Chl-a were TP, WT, DO, pH, TN, NH+ 4-N in turn. According to the analysis of the present water pollution situation, TP is the limiting factor of Chl-a in Qingshui River with 0.571 sensitive coefficient and 33% contribution rate, and it is also the main prevention and control factor of planktonic algae outbreak.
Back and forth: day-night alternation between cover types reveals complementary use of habitats in a large herbivore
Authors: De Groeve Johannes , Cagnacci Francesca , de Weghe Nico Van , Ranc Nathan , Morellet Nicolas , Bonnot Nadège C. , Gehr Benedikt , Heurich Marco , Hewison Mark A.J. , Kröschel Max , Linnell John D.C. , Mysterud Atle , Sandfort Robin
Published: May 2022

Context
The Complementary Habitat Hypothesis posits that animals access resources for different needs by moving between complementary habitats that can be seen as ‘resource composites’. These movements can occur on a range of temporal scales, from diurnal to seasonal, responding to multiple drivers, such as access to food, weather constraints, risk avoidance, human disturbance. Within this framework, we hypothesised that large herbivores may cope with human-altered landscapes through the alternate use of complementary habitats at both daily and seasonal scales.
Objectives
We tested the Complementary Habitat Hypothesis in European roe deer (Capreolus capreolus) by classifying 3900 habitat-annotated movement trajectories of 154 GPS-monitored individuals across contrasting landscapes.
Methods
We considered day-night alternation between food-rich open and closed refuge habitats as a proxy for complementary habitat use. We first identified day-night alternation by using IM-SAM, then we modelled the proportion of day-night alternation over the year in relation to population and individual characteristics.
Results
We found that day-night alternation is a widespread behaviour in roe deer, even across markedly different landscapes. Day-night alternation followed seasonal trends in all populations, partly linked to vegetation phenology. Within populations, seasonal patterns of open/closed habitat alternation differed between male and female adults, but not in juveniles.
Conclusion
Our results support the Complementary Habitat Hypothesis by showing that roe deer adjust the alternate use of habitats in response to changes in resource availability and individual needs. By repeatedly crossing habitat boundaries large herbivores may “engineer” their landscape by transporting seeds, parasites and nutrients between those habitats.
The Transcription Factor Zfp503 Promotes the D1 MSN Identity and Represses the D2 MSN Identity
Authors: Zhang Zhuangzhi , Shang Zicong , Yang Lin , Wang Ziwu , Tian Yu , Gao Yanjing , Su Zihao , Guo Rongliang , Li Weiwei , Liu Guoping , Li Xiaosu , Yang Zhengang , Li Zhenmeiyu
Published: May 2022

The striatum is primarily composed of two types of medium spiny neurons (MSNs) expressing either D1- or D2-type dopamine receptors. However, the fate determination of these two types of neurons is not fully understood. Here, we found that D1 MSNs undergo fate switching to D2 MSNs in the absence of Zfp503. Furthermore, scRNA-seq revealed that the transcription factor Zfp503 affects the differentiation of these progenitor cells in the lateral ganglionic eminence (LGE). More importantly, we found that the transcription factors Sp8/9, which are required for the differentiation of D2 MSNs, are repressed by Zfp503. Finally, sustained Zfp503 expression in LGE progenitor cells promoted the D1 MSN identity and repressed the D2 MSN identity. Overall, Zfp503 promotes the D1 MSN identity and represses the D2 MSN identity by regulating Sp8/9 expression during striatal MSN development.
Relationship between the coronavirus 2019 disease (COVID-19) vaccines and the development of new lower urinary tract symptoms: A cross-sectional study on medical students.
Authors: AlSmadi Jad Khaled , Alswiti Adnan , Hamdan Zaid , Sharkas Layan , Abualnadi Mohammad , Alnsour Mass
Published: May 2022

Background: We created an Internet-based survey of a COVID-19 vaccinated young population to evaluate the occurrence of urinary tract symptoms after getting vaccinated. Methods: From February to April 2022 a cross-sectional survey was conducted among medical students at six medical colleges in Jordan using a self-administered questionnaire. The questionnaire consisted of four sections: respondents’ basic and medical information, COVID-19 vaccines information, development of new symptoms, and IPSS scores. The difference between groups was assessed using Kruskal-Wallis-test, Pearson’s chi-squared test, and Mann-Whitney-U test. The increase in IPSS-scores pre- and post-vaccines was tested by Wilcoxon-Signed-Ranks-test.Results: A total of 341 valid responses were collected, from whom 340 had got a COVID-19 vaccine. The incidence of occurrence of new dysuria was 6.2%, gross hematuria 1.8%, and LUTS 7.6%; without significant difference between vaccine types. Demographic data, the number of vaccine doses, previous medical and surgical conditions, and being on immunosuppressant medications did not significantly increase the incidence of symptoms after taking the vaccine. Conclusions: There was no relationship between taking COVID-19-vaccine and the development of new lower urinary tract symptoms. In addition, vaccine types did not differ in terms of dysuria, gross hematuria, and other lower urinary tract symptoms (LUTS). COVID-19 vaccines may increase IPSS-scores, especially for urgency, but without affecting the bothersome scores. Thus COVID-19 vaccines are safe from a urological point of view and the development of a new lower urinary tract symptom should be investigated appropriately without presuming that it could be from the vaccine.
Predictive Factors for Relapse in Triple-negative Breast Cancer Patients Without Pathologic Complete Response After Neoadjuvant Chemotherapy
Authors: Toss Angela , Venturelli Marta , Civallero Monica , Piombino Claudia , Domati Federica , Ficarra Guido , Combi Francesca , Cabitza Eleonora , Caggia Federica , Barbieri Elena , Barbolini Monica , Moscetti Luca , Omarini Claudia , Piacentini Federico , Tazzioli Giovanni , Dominici Massimo , Cortesi Laura
Published: May 2022

Triple-negative breast cancer (TNBC) patients who do not obtain pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) present higher rate of relapse and worse overall survival. Risk factors for relapse in this subset of patients are poorly characterized. This study aimed to identify predictive factors for relapse in TNBC patients without pCR after NACT. Women with TNBC treated with NACT from January 2008 to May 2020 at the Modena Cancer Center were included in the analysis. We identified 142 patients with median follow-up of 55 months. After NACT, 62 patients obtained pCR (43.9%). Young age at diagnosis (< 50 years) and high ki-67 (>20%) were significantly associated to pCR. Lack of pCR after NACT resulted in worse 5-year EFS and OS. Factors independently predicting EFS in patients without pCR were the presence of multifocal disease (HR 3.22; 95% CI, 1.28–8.11; P 0.01) and Residual Cancer Burden (RCB) III (HR 3.8; 95% CI, 1.12–12; P 0.03). These data can be used to stratify patients and potentially guide treatment decision-making, identifying appropriate candidates for treatment intensification especially in neo/adjuvant setting.
Structural equation modeling of the effects of psychological distress and a fear of coronavirus disease 2019 on diabetes care in Japan
Authors: Minoura Akira , Sugiyama Takehiro , Koyama Teruhide , Yoshioka Takashi , Tabuchi Takahiro
Published: May 2022

This study aimed to examine the effects of psychological distress and a fear of coronavirus disease 2019 (COVID-19) on diabetes care in Japan. We used data from a 2020 nationwide internet survey in Japan involving 28,000 respondents aged 15–79 years. The question items included psychological factors (Kessler psychological distress scale and fear of COVID-19), employment, trust in neighbors, informal caregiving, and history of diabetes care. After excluding respondents with comorbidities and those who had not visited the hospital, 625 patients with diabetes were analyzed. Statistical mediation was then examined through a path analysis using structural equation modeling. Discontinued diabetes care was independently associated with psychological distress (risk ratio = 1.44, 95% confidence interval [1.01–2.06]) and a fear of COVID-19 (1.41 [1.01–1.95]). The results of the structural equation modeling indicated that a fear of COVID-19, employment, trust in neighbors, and informal caregiving were indirectly associated with continued diabetes care via psychological distress. These findings suggest that a fear of COVID-19 is associated with continued diabetes care via psychological distress. COVID-19 pandemic may affect psychological distress and lead to refrained diabetes care among Japanese patients with diabetes. Moreover, employment, trust in neighbors, and family caregiving were also found to buffer refrained diabetes care. As psychological factors and socioeconomic status can affect diabetes care, a fear of COVID-19 should be considered in patients with diabetes to help ensure the continuation of diabetes treatment.
Design, Development and Performance Analysis of Intelligent Virtual Assistive aid with Multi Sensor Fused Navigation for Visually Impaired People
Authors: Bala Myneni Madhu , Vasundhara D. N. , CH V K N S N Moorthy
Published: May 2022

Wearable auxiliary devices’ research and innovations for visually impaired elderly people and blind are playing an attractive role. These inventions attempt to improve the quality of visually impaired people life. However, in spite of the promising research outcomes, the existing wearable aids has numerous weaknesses in terms of weight, feature limitations and cost. The main objective of this manuscript is to provide the detailed design of a novel lightweight wearable aid for visually impaired and blind people. The proposed research aims to design an intelligent virtual assistant that will guide for walking by detecting the environment around them. The framework proposed to develop the virtual assistant is a fusion of sensor based, vision based and smart application. The visual design components include obstacle detection, uneven surface detection, slope and downward steps detection, pothole detection and hallow object detection; location tracking, walking guide, image capturing and video recording. Based on the appearance of this prototype named as Blind's Apron. The invention focusses on parameters like reduction on size (quite handy) and light weight (comfortable to wear), and minimum user intervention (high end operations like switching on and off). All user interactions are friendly and affordable to everyone. The results obtained in this research assures a high end technical intervention with ease of use while comparing with the current working products in this field. These are demonstrated with many blindfolded subjects on visually impaired participants. Finally, the performance of the proposed virtual assistant tested in real-time and targeted people. The results are assured the proposed invention is light weight and in easily understandable instructions.
Energy cascades in polaritonic systems with energetic disorder observed by ultrafast two-dimensional white-light spectroscopy
Authors: Son Minjung , Armstrong Zachary , Allen Ryan , Dhavamani Abitha , Arnold Michael , Zanni Martin
Published: May 2022

Exciton-polaritons are hybrid states formed when molecular excitons are strongly coupled to photons trapped in an optical cavity. These systems have many attractive properties, including large delocalization lengths, but questions regarding the role of energetic disorder remain unanswered. Here, we fabricate microcavities with two different layers of semiconducting carbon nanotubes as a way of controlling the energetic disorder and exploring its impact on energy transfer. Using ultrafast two-dimensional white-light spectroscopy, we observe a delayed growth of a cross peak between the upper- and lower-polariton states. Using Redfield theory, we assign the growth to cascading energy transfer down a manifold of new electronic states created by energetic disorder that is of comparable magnitude to the light-matter coupling. These results broaden our understanding of energy transfer dynamics in exciton-polariton systems beyond the Rabi contraction picture and enable control over how energy is transported in polaritonic systems.
Dynamic Graph Representation Based on Temporal and Contextual Contrasting
Authors: Huang Jin , Zhu Wentai , Xiao Jing , Lu Tian , Yu Weihao
Published: May 2022

Dynamic graph representation learning is critical for graph-based downstream tasks such as link prediction, node classification, and graph reconstruction. Many graph-neural-network–based methods have emerged recently, but most are incapable of tracing graph evolution patterns over time. To solve this problem, we propose a continuous-time dynamic graph framework: dynamic graph temporal contextual contrasting (DGTCC) model, which integrates both the temporal and topology information and mines the latent evolution trend of graph representation. In this model, the node representation is first generated by a self-attention–based temporal encoder, which measures the importance weights of neighbor nodes in temporal sub-graphs and then stores them in the contextual memory module. After sampling the node representation from the memory module, the model maximizes the mutual information of the same node that occurred in two nearby temporal views by the contrastive learning mechanism, which helps track the evolutional trend of nodes. In inductive learning settings, the results on four real datasets demonstrate the superiority of the proposed DGTCC model.