Miracle-dz/UniFAR-FTUs huggingface dataset:
GAMS: Graph Augmentation with Module Swapping

Data augmentation is a widely adopted approach to solve the large-data requirements of modern deep learning techniques by generating new data instances from an existing dataset. While there is a huge literature and experience on augmentation for vectorial or image-based data, there is relatively little work on graph-based representations. This is largely due to complex, non-Euclidean structure of graphs, which limits our abilities to determine operations that do not modify the original semantic grouping. In this paper, we propose an alternative method for enlarging the graph set of graph neural network datasets by creating new graphs and keeping the properties of the originals. The proposal starts from the assumptions that the graphs compose a set of smaller motifs into larger structures. To this end, we extract modules by grouping nodes in an unsupervised way, and then swap similar modules between different graphs reconstructing the missing connectivity based on the original edge statistics and node similarity. We then test the performance of the proposed augmentation approach against state-of-the-art approaches, showing that on datasets, where the information is dominated by structure rather than node labels, we obtain a significant improvement with respect to alternatives.
Multielectrode Arrays for Functional Phenotyping of Neurons from Induced Pluripotent Stem Cell Models of Neurodevelopmental Disorders

Simple Summary Multielectrode array technology allows researchers to record the spontaneous firing activity of cultured neurons over a period of multiple weeks or months. These data can be valuable for understanding how the functional relationships between neurons evolve as they begin to form connections and wire into a functional network. This technology has been adopted by researchers using stem cells to produce human neurons in culture to study neurodevelopmental disorders. However, the dizzying complexity and scale of the data generated have posed some challenges with the analysis and interpretation of experimental results. Here, we first provide historical context as to why multielectrode array platforms were originally developed, and use this perspective to explore some of the challenges currently facing the field. We then highlight new analysis methods, provide some guidance for improving the analysis of multielectrode array data, and discuss standardizing how these findings are communicated in scientific publications. Abstract In vitro multielectrode array (MEA) systems are increasingly used as higher-throughput platforms for functional phenotyping studies of neurons in induced pluripotent stem cell (iPSC) disease models. While MEA systems generate large amounts of spatiotemporal activity data from networks of iPSC-derived neurons, the downstream analysis and interpretation of such high-dimensional data often pose a significant challenge to researchers. In this review, we examine how MEA technology is currently deployed in iPSC modeling studies of neurodevelopmental disorders. We first highlight the strengths of in vitro MEA technology by reviewing the history of its development and the original scientific questions MEAs were intended to answer. Methods of generating patient iPSC-derived neurons and astrocytes for MEA co-cultures are summarized. We then discuss challenges associated with MEA data analysis in a disease modeling context, and present novel computational methods used to better interpret network phenotyping data. We end by suggesting best practices for presenting MEA data in research publications, and propose that the creation of a public MEA data repository to enable collaborative data sharing would be of great benefit to the iPSC disease modeling community.
Compact, Accurate and Low-cost Hand Tracking System based on LEAP Motion Controllers and Raspberry Pi

: The large diffusion of low-cost computer vision (CV) hand tracking sensors used for hand gesture recognition, has allowed the development of precise and low cost touchless tracking systems. The main problem with CV solutions is how to cope with occlusions, very frequent when the hand has to grasp a tool, and self-occlusions occurring when some joint obscures some other. In most cases occlusions are solved by using synchronized multiple stereo sensors. Virtual Glove (VG) is one of the CV-based systems that uses two orthogonal LEAP sensors integrated into a single system. The VG system is driven by a Personal Computer in which both a master operating system (OS) and a virtual machine have to be installed in order to drive the two sensors (just one sensor at a time can be driven by a single OS instance). This is a strong limitation because VG has to run on a powerful PC, thus resulting in a not properly low-cost and portable solution. We propose a VG architecture based on three Raspberry Pi (RP), each consisting of a cheap single board computer with Linux OS. The proposed architecture assigns an RPi to each LEAP and a third RP to collect data from the other two. The third RP merges, in real time, data into a single hand model and makes it available, through an API, to be rendered in a web application or inside a Virtual Reality (VR) interface. The detailed design is proposed, the architecture is implemented and experimental benchmark measurements, demonstrating the RPi-based VG real-time behaviour while containing costs and power consumption, are presented and discussed. The proposed architecture could open the way to develop modular hand tracking systems based on more than two LEAPs, each associated to one RP, in order to further improve robustness.
Genetic Variation Interacts with Selenium Exposure Regarding Breast Cancer Risk: Assessing Dietary Intake, Serum Levels and Genetically Elevated Selenium Levels

Selenium has been suggested to be protective regarding breast cancer risk but no overall effect has been established. Genetics may modify the effect. This study compares the effect of selenium exposure on breast cancer risk between women with different alleles in single-nucleotide polymorphisms (SNPs). The Malmö Cancer and Diet Study, a cohort including 17,035 women and >25 years of follow-up on breast cancer diagnosis, was used. Five promising SNPs regarding interaction with selenium exposure were selected from the literature: rs1050450, rs4880, rs3877899, rs7579, and rs71304. Selenium exposure was assessed in three ways: genetically elevated (n = 16,429), dietary intake (n = 15,891) and serum levels (n = 2037) at baseline. Cox regression and logistic regression analyses evaluated breast cancer risk from selenium exposure, stratified for the SNPs and adjusted for risk factors. A total of 1946 women were diagnosed with breast cancer. Women with T/T alleles in rs1050450 had lower breast cancer risk compared with C/C, HR 0.81 (0.68–0.96). Interaction by rs1050450 limited a protective effect of higher selenium intake to T/T carriers, HR 0.68 (0.43–1.08) for intermediate intake and HR 0.63 (0.40–1.00) for high intake. No interactions or risk differences were seen for other SNPs or for serum selenium or genetically elevated selenium. The results indicate that genetic variation in rs1050450 might affect breast cancer risk and that selenium exposure could be a possible modifiable risk factor for breast cancer among women with that variation.
Lower Limits of Contact Resistance in Phosphorene Nanodevices with Edge Contacts

Edge contacts are promising for improving carrier injection and contact resistance in devices based on two-dimensional (2D) materials, among which monolayer black phosphorus (BP), or phosphorene, is especially attractive for device applications. Cutting BP into phosphorene nanoribbons (PNRs) widens the design space for BP devices and enables high-density device integration. However, little is known about contact resistance (RC) in PNRs with edge contacts, although RC is the main performance limiter for 2D material devices. Atomistic quantum transport simulations are employed to explore the impact of attaching metal edge contacts (MECs) on the electronic and transport properties and contact resistance of PNRs. We demonstrate that PNR length downscaling increases RC to 192 Ω µm in 5.2 nm-long PNRs due to strong metallization effects, while width downscaling decreases the RC to 19 Ω µm in 0.5 nm-wide PNRs. These findings illustrate the limitations on PNR downscaling and reveal opportunities in the minimization of RC by device sizing. Moreover, we prove the existence of optimum metals for edge contacts in terms of minimum metallization effects that further decrease RC by ~30%, resulting in lower intrinsic quantum limits to RC of ~90 Ω µm in phosphorene and ~14 Ω µm in ultra-narrow PNRs.
Factors Influencing Intention of Greek Consumers to Use Smart Home Technology

New technologies’ advances offer innovative automations to people’s daily lives. More and more devices are continuously connected to the internet allowing people to control them remotely. The smart home is such a technological development. However, it is uncertain whether and to what extent the average consumer will accept smart home technology. The purpose of this study is to investigate the factors that affect the intention of Greek consumers to use smart home technology. The results of this study show that Greek consumers are beginning to have a positive attitude towards smart home technology. Important factors that contribute to their intention to use smart home technology include their perceived usefulness, compatibility, and ease of use of smart home technology. On the contrary, they do not think that they are influenced by their social environment regarding their intention to use smart home technology. Finally, they think that the major benefits of using smart home technology include the health monitoring, home security, and cost savings.
Modeling Soil Temperature for Different Days Using Novel Quadruplet Loss-Guided LSTM

Soil temperature (Ts), a key variable in geosciences study, has generated growing interest among researchers. There are many factors affecting the spatiotemporal variation of Ts, which poses immense challenges for the Ts estimation. To enrich processing information on loss function and achieve better performance in estimation, the paper designed a new long short-term memory model using quadruplet loss function as an intelligence tool for data processing (QL-LSTM). The model in this paper combined the traditional squared-error loss function with distance metric learning between the sample features. It can zoom analyze the samples accurately to optimize the estimation accuracy. We applied the meteorological data from Laegern and Fluehli stations at 5, 10, and 15 cm depth on the 1st, 5th, and 15th day separately to verify the performance of the proposed soil temperature estimation model. Meanwhile, this paper inputs the variables into the proposed model including radiation, air temperature, vapor pressure deficit, wind speed, air pressure, and past Ts data. The performance of the model was tested by several error evaluation indices, including root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NS), Willmott Index of Agreement (WI), and Legates and McCabe index (LMI). As the test results at different soil depths show, our model generally outperformed the four existing advanced estimation models, namely, backpropagation neural networks, extreme learning machines, support vector regression, and LSTM. Furthermore, as experiments show, the proposed model achieved the best performance at the 15 cm depth of soil on the 1st day at Laegern station, which achieved higher WI (0.998), NS (0.995), and LMI (0.938) values, and got lower RMSE (0.312) and MAE (0.239) values. Consequently, the QL-LSTM model is recommended to estimate daily Ts profiles estimation on the 1st, 5th, and 15th days.
Localized surface plasmon resonance induced temperature enhancement by MWCNT-HfTe2 van der Waals heterostructure

Localized surface plasmon resonance (LSPR) shows great promise in optoelectronic devices, solar steam generation, and medical treatment owing to its strong enhancement of light-matter interactions. Herein, for the first time, 1D-2D metallic MWCNTs and HfTe2 van der Waals (vdW) heterostructure are used for demonstrating the LSPR to enhance the temperature of a solar absorber. The proposed vdW heterostructure is synthesized by a facile self-grown hydrothermal method and grown on top of a copper (Cu) foam. The HRTEM image and EDS spectrum confirm the formation of the vdW heterostructure on the Cu foam. The synergic effect of Te-based TMD with MWCNT provides a broadband absorbance of approximately 92% weighted by the standard air mass 1.5 global solar spectrum and takes full advantage of LSPR to confine heat in a small area. Moreover, the ultrathin nature of MWCNT endows them with the super permeability of water vapor. The solar-driven steam generation performance of the prepared vdW heterostructure demonstrates an excellent evaporation efficiency of 87.43% and an increment of the surface temperature to 79.8 °C in less than 20 mins under 1 kWm–2 solar illumination. Therefore, the proposed vdW heterostructure can be realized in high-temperature steam generation applications.
View-invariant 3D Skeleton-based Human Activity Recognition based on Transformer and Spatio-temporal Features

: With the emergence of depth sensors, real-time 3D human skeleton estimation have become easier to accom-plish. Thus, methods for human activity recognition (HAR) based on 3D skeleton have become increasingly accessible. In this paper, we introduce a new approach for human activity recognition using 3D skeletal data. Our approach generates a set of spatio-temporal and view-invariant features from the skeleton joints. Then, the extracted features are analyzed using a typical Transformer encoder in order to recognize the activity. In fact, Transformers, which are based on self-attention mechanism, have been successful in many domains in the last few years, which makes them suitable for HAR. The proposed approach shows promising performance on different well-known datasets that provide 3D skeleton data, namely, KARD, Florence 3D, UTKinect Action 3D and MSR Action 3D.
Association and mechanism of garlic consumption with gastrointestinal cancer risk: A systematic review and meta-analysis

Gastrointestinal cancer is one of the most commonly diagnosed cancer type worldwide, with millions of cases per year. The aim of this review was to investigate the relationship between garlic intake and the risk reduction of gastrointestinal cancer. We performed saturated data mining on various public domain databases, including PubMed (https://pubmed.ncbi.nlm.nih.gov/), Embase (https://www.embase.com/landing? status=grey), and Cochrane Library (https://www.cochranelibrary.com/), with key terms including: ‘garlic’, ‘allium’, ‘stomach’, ‘gastric’, ‘colon’, ‘neoplasms’, ‘cancer’ and ‘tumor’. Furthermore, we identified additional references through expert manual curation. Finally, a meta-analysis was conducted to determine whether garlic intake reduces the risk of gastric and/or colorectal cancer. The association between garlic intake and reduction in the risk of gastric cancer [odds ratio (OR)=0.65, 95% confidence interval (CI)=0.49-0.87, P<0.001] were clear. Nine studies on garlic intake and colorectal cancer showed that garlic reduced cancer risk with a statistical significance (OR=0.75, 95% CI=0.65-0.87, P<0.001). We summarized that four main organic sulfides in garlic, diallyl disulfide (DADS), diallyl trisulfide (DATS), S-allylmercaptocysteine (SAMC) and allicin, may contribute to the regulation of tumor cell apoptosis, migration and the cell cycle. We identified the association between garlic intake and reduced risk of gastric and colorectal cancers and hypothesized that the active ingredients in garlic may act on multiple pathways to reduce the risk of gastrointestinal tumors according to published papers. Importantly, the potential tumor-preventing effect of these garlic ingredients warrants further investigation in regards to the specific mechanism of the underlying antitumor activities.
YOBO Online Music Player Based on Android

According to the market demand,in order to satisfy the consumer for online listening music in their own mobile phone,and didn'tspend too much traffic,a YOBOonline music player was developed for using Android mobile operating system.The core technology is introduced,stated the design of the framework and the achieve results of the player's important module,used Java language、XML language and YOBO API technology,designed and realized five functional modules which included user authorized,user personal information display,my music box,my music radio and songs search,and gived part of the test results.So people can make use of mobile phone to realize online listening music,it is an effective way to break through online listening music of space constraints.
Risk perception and behaviour change after personal vaccination for COVID-19 in the USA

Although vaccines are crucial for giving pandemic-stricken societies the confidence to return to socioeconomic normalcy, vaccination may also induce laxity in personal protective behaviours (e.g., handwashing, facemask use). We use the quasi-experimental context of the COVID-19 vaccine rollout across the United States to quantify the impact of different stages of personal vaccination on people’s risk perceptions, daily activities, and risk mitigation behaviours, which we measure in a three-wave national panel study (N wave-1 = 7,358, N wave-2 = 3,000, N wave-3 = 2,345) from March to June, 2021, and validate using vaccination, infection, and human mobility data. Socializing rebounded after only partial vaccination. After full vaccination, communal activities recovered; however, the propensity for protective behaviours declined. The effects were heterogenous depending on vaccination level, demographics, and infection history. We further use a utility theory framework to model risk-value trade-offs and risk-construction for different behaviours.
Knowledge And Awareness on Oral Care Among Mothers of Children Less Than Five Years Old: A Survey

Introduction: Health-related behaviors are influenced by knowledge and awareness, with oral health being no exception. It is well-known that oral diseases are influenced by social determinants. Children depend on their parents and caretakers for maintenance of their oral health so oral health knowledge of mothers could be associated with the status of their children’s oral health. Aim: The aim of this survey was to evaluate and create awareness on oral care among mothers of children less than 5 years old. Materials and Methods: An online survey was conducted among mothers of children less than five years old using a questionnaire in google forms. The Questionnaire was framed which mainly targeted awareness on dental attitude, knowledge and practice of mothers regarding their children’s oral health. The survey was done in January 2021. The results were obtained and statistically analysed through SPSS software, chi square test was done and represented as pie-chart and bar graphs. Results: Out of 100 mothers who participated in the study, 66.09% have studied up to primary/high school level and 33.91% have qualified university level. About 54.78% of the participants agreed that the importance of oral health to general health, 28.70% disagreed. The association of mothers' education and their opinion on children's age on brushing their teeth shows that the majority of primary/high school mothers agreed that children should brush their teeth older than 5 years than other participants and it was statistically significant p value=0.04(>0.05)) Conclusion: Mothers who are graduates are more aware of the importance of oral health in children, treatment of dental caries, and brushing technique than mothers who qualified with school education.
Turmeric oleoresin encapsulated by spray drying in maltodextrin/gelatin and starch/gelatin blends: storage stability and water sorption

Turmeric oleoresin is a widely used a flavoring agent and a food color with relevant nutraceutical properties. It is obtained by organic solvent extraction of turmeric (Curcuma longa L.). Microencapsulation is a good alternative to transform liquid food flavorings free-flowing powders and to improve stability of protection of compounds of interest. Thus, the aim of this study was to assess microencapsulation by spray drying of turmeric oleoresin using blends of maltodextrin-gelatin and starch-gelatin, evaluating its sorption isotherms and the storage stability at different temperatures. Turmeric oleoresin encapsulated was stored at - 20, 25 and 60°C, in the absence of light, and at 25ºC in the presence of light, and analyzed over a period of 35 days for curcumin and total phenolic contents and color. The encapsulation efficiency was 72.3% for capsules with starch-gelatin blends and 52.1% with maltodextrin-gelatin blends. Besides the greater encapsulation efficiency, starch-gelatin blends also showed greater stability during storage and retention of curcumin and phenolic compounds. Encapsulated materials were more stable during storage at - 20ºC and less stable at 25°C in the presence of light. Water adsorption of turmeric oleoresin microcapsules were well described by GAB model and the microcapsules produced with starch-gelatin blends showed high hygroscopic behavior.
Towards a More Reliable and Reproducible Protocol of Source Camera Recognition

: Source digital camera recognition is an important branch of digital image forensics, which aims at authenti-cating cameras from the captured images. By analysing the noise artifacts left on the images, it is possible to recognize the label: brand, model and device of the camera (e.g. Nikon - NikonD70 - NikonD70 of Alice). Camera recognition is increasingly difﬁcult as the label become more precise. In the speciﬁc case of source camera recognition based on deep learning, literature has widely addressed recognition of the camera model, while the recognition of the instance of the camera (i.e. device) is currently under-studied. Moreover, we have identiﬁed a lack of protocols for performance assessment: state-of-the-art methods are usually assessed on databases that have speciﬁc compositions, such as the Dresden Image database (74 cameras of 27 models). However, using only one database for evaluation does not reﬂect reality, where it may be necessary to analyse different sets of devices that are more or less difﬁcult to classify. Also, for some scenarios, veriﬁcation (1-to-1) is better suited to camera recognition than identiﬁcation (1-to-N). Based on these elements, we propose a more reliable and reproducible protocol for veriﬁcation of the source camera made of three different levels ( basic , intermediate and advanced ) of increasing difﬁculty, based on camera labels (brand, model and device). State-of-the-art methods are tested with the proposed protocol on the Dresden Image Database and on SOCRatES. The obtained results prove our assumptions, with a relative drop in performance, up to 49.08% between the basic and advanced difﬁculty levels. Our protocol is able to assess the robustness of methods for source camera recognition, as it tests whether they are really able to correctly classify cameras in realistic contexts.