Miracle-dz/UniFAR-FTUs huggingface dataset:
Treating iPSC-Derived β Cells with an Anti-CD30 Antibody–Drug Conjugate Eliminates the Risk of Teratoma Development upon Transplantation

Insulin-producing cells derived from induced pluripotent stem cells (iPSCs) are promising candidates for β cell replacement in type 1 diabetes. However, the risk of teratoma formation due to residual undifferentiated iPSCs contaminating the differentiated cells is still a critical concern for clinical application. Here, we hypothesized that pretreatment of iPSC-derived insulin-producing cells with an anti-CD30 antibody–drug conjugate could prevent in vivo teratoma formation by selectively killing residual undifferentiated cells. CD30 is expressed in all human iPSCs clones tested by flow cytometry (n = 7) but not in iPSC-derived β cells (iβs). Concordantly, anti-CD30 treatment in vitro for 24 h induced a dose-dependent cell death (up to 90%) in human iPSCs while it did not kill iβs nor had an impact on iβ identity and function, including capacity to secrete insulin in response to stimuli. In a model of teratoma assay associated with iβ transplantation, the pretreatment of cells with anti-CD30 for 24 h before the implantation into NOD-SCID mice completely eliminated teratoma development (0/10 vs. 8/8, p < 0.01). These findings suggest that short-term in vitro treatment with clinical-grade anti-CD30, targeting residual undifferentiated cells, eliminates the tumorigenicity of iPSC-derived β cells, potentially providing enhanced safety for iPSC-based β cell replacement therapy in clinical scenarios.
Sensing of Muscular Mouse Cells C2C12 from Seed Out to Electroporation - A Conceptional Study

In this paper, a sensing concept for the monitoring of electroporation of biological cells is presented. This concept includes sensing of all relevant steps during the electroporation of cells, that are the attachment process of the cells, monitoring temperature change during the poration and the differentiation between porated and unporated cells. Due to this, a biocompatible microwave sensor is presented, which is calibrated by using three liquids with known relative permittivities ε r. Moreover, the influence of noise are analyzed on the sensor measurements. A relative permittivity change of 2% to 6% reveals the dropping and attachment of C2C12 cells on the sensor surface. Further, a difference between unporated and porated cells of 0.4% is detected.
The Impact of Microwave Drying on the Structure of Exemplary Soils—Insights Using X-ray Microtomography

In the field of soil drying methods, rapid microwave heating is progressively replacing conventional techniques. Due to the specific heat transport caused by microwaves, the drying process can significantly modify soil structure, which, in turn, can influence mechanical and filtration characteristics. In this study, we compared structural changes of exemplary non-cohesive (medium quartz sand (MSa)) and cohesive soil (silty clay mainly composed of kaolinite (siCl)). The sample materials were subjected to three different drying methods: air-drying, conventional oven (CO) drying, and microwave oven (MO) drying (MO). Soil structure was studied using X-ray microtomography (XµCT) and described in detail by image analysis methods. The study showed that the analyzed types of heating had a negligible effect on the structure of the sands, but a significant impact in the case of silty clay. Such a phenomenon is discussed and explained in this paper. The study advances the testing of soils microwave drying in a geotechnical laboratory.
Off-State Stability of Phase-Change Material RF-Switches

This paper presents experimental results on the Off-State stability in indirectly heated GeTe RF switches. The time before GeTe recrystallization is different between devices fabricated in Off-state and turned-off switches. The effect of post fabrication thermal annealing on the Off-State stability is shown, as well as the strong temperature dependence of the time before the GeTe recrystallization occurs. Off-State could be maintained up to 7 Hours at 95°C, without significant degradation of the switch.
Who doesn't believe their memories? Development and validation of a new Memory Distrust Scale.

Many people distrust their memories, because they believe themselves prone to omission errors such as forgetfulness, and/or commission errors such as misremembering. However, the most popular psychometric measure of memory distrust — the Squire Subjective Memory Questionnaire (SSMQ) —only probes people’s beliefs about omissio n errors, not commission errors. The present research describes a new memory distrust measure that assesses the latter kind of metamemorial belief. Two studies (combined N = 797) showed that our 20-item Memory Distrust Scale (MDS) has good psychometric properties, and is correlated with — but distinct from — the SSMQ. Participants in Study 2 described eight childhood events, and rated their recollection of and belief in the occurrence of each. MDS scores were associated with the spontaneous reporting of nonbelieved memories, and predicted belief in occurrence better than did SSMQ scores. Our data suggest that the MDS and SSMQ in combination could better predict individual susceptibility to certain memory errors.
Hyper-parameter optimization in deep learning and transfer learning: applications to medical imaging. (Optimisation d'hyper-paramètres en apprentissage profond et apprentissage par transfert: applications en imagerie médicale)

In the last few years, deep learning has changed irrevocably the field of computer vision. Faster, giving better results, and requiring a lower degree of expertise to use than traditional computer vision methods, deep learning has become ubiquitous in every imaging application. This includes medical imaging applications. At the beginning of this thesis, there was still a strong lack of tools and understanding of how to build efficient neural networks for specific tasks. Thus this thesis first focused on the topic of hyper-parameter optimization for deep neural networks, i.e. methods for automatically finding efficient neural networks on specific tasks. The thesis includes a comparison of different methods, a performance improvement of one of these methods, Bayesian optimization, and the proposal of a new method of hyper-parameter optimization by combining two existing methods: Bayesian optimization and Hyperband.From there, we used these methods for medical imaging applications such as the classification of field-of-view in MRI, and the segmentation of the kidney in 3D ultrasound images across two populations of patients. This last task required the development of a new transfer learning method based on the modification of the source network by adding new geometric and intensity transformation layers.Finally this thesis loops back to older computer vision methods, and we propose a new segmentation algorithm combining template deformation and deep learning. We show how to use a neural network to predict global and local transformations without requiring the ground-truth of these transformations. The method is validated on the task of kidney segmentation in 3D US images.
Strategic Orientation and New Product Development Performance of Academic Spin-Offs: The Importance of Team Cohesion and Team Heterogeneity

Prior studies have highlighted the importance of team heterogeneity and team cohesion in supporting academic spin-offs during their commercialization activities. However, there has been little insight into the role of strategic orientation, namely speed to market and product performance, in moderating the relationship between team characteristics and market performance in the new product development process. From the analysis of 105 academic spin-offs, this article suggests that team heterogeneity is more likely to support a strategic orientation focusing on product performance, whereas team cohesion supports both speed to market and product performance. This article provides both theoretical and practical recommendations for managing academic spin-offs for the development of new products, especially highlighting the importance of building a heterogeneous team while at the same time maintaining cohesion among team members.
Application of the Task Technology Fit Model to evaluate the implementation Electronic File Tracking Prototype

Locating files in an exact time is considered one of the greatest problems and the tedious process in universities nowadays. This problem becomes greater when the university has a large number of departments and transactions, as well as the documents are moving from one department to another. Especially, developing countries that have many problems and unstable environment and that may lead to lost or damage the important documents that influence on the decision making. Furthermore, the traditional manner not only wasted the time and energy, but also the paper cost for printing copies of required file. Therefore, this study sought to design and evaluate the prototype system which tracks movements of the documents from one department to another as well as check the completion rate for each department. For providing opportunities to assess how well the use of e-file tracking system meets the needs of management units in universities. The systems implementation research notes the need to fit between tasks, technologies and users. Thus, this empirical study utilized the task technology fit model for this purpose. The results from selected participants indicated that all the factors significant effect on the employee's performance in E-file Tracking System, excepted, task characteristics. This study will be contributed to reduce the corruption and enhance the transparency and help the decision-makers make the right decision at the right time.
Path Loss Characteristics for Urban Street Canyon Environments from 0.75 to 24 GHz

This paper proposes path loss characteristics for urban high-rise environments based on 0.75, 1, 2, 3, 4.5, 6, 10, 18, and 24 GHz measurements. We conducted a measurement campaign using 10- and 2-m-high omnidirectional antennas for transmitting (Tx) and receiving (Rx) sites, respectively. This paper reports the path loss and the parameters of the alpha-beta (AB). The proposed model would be useful for predicting electromagnetic fields located in real environments, and for planning cellular communication services.
An Optimized Comparative Proteomic Approach as a Tool in Neurodegenerative Disease Research

Recent advances in proteomic technologies now allow unparalleled assessment of the molecular composition of a wide range of sample types. However, the application of such technologies and techniques should not be undertaken lightly. Here, we describe why the design of a proteomics experiment itself is only the first step in yielding high-quality, translatable results. Indeed, the effectiveness and/or impact of the majority of contemporary proteomics screens are hindered not by commonly considered technical limitations such as low proteome coverage but rather by insufficient analyses. Proteomic experimentation requires a careful methodological selection to account for variables from sample collection, through to database searches for peptide identification to standardised post-mass spectrometry options directed analysis workflow, which should be adjusted for each study, from determining when and how to filter proteomic data to choosing holistic versus trend-wise analyses for biologically relevant patterns. Finally, we highlight and discuss the difficulties inherent in the modelling and study of the majority of progressive neurodegenerative conditions. We provide evidence (in the context of neurodegenerative research) for the benefit of undertaking a comparative approach through the application of the above considerations in the alignment of publicly available pre-existing data sets to identify potential novel regulators of neuronal stability.
Deep Learning for Super-Resolution DOA Estimation in Massive MIMO Systems

The requirement of the increasing capacity of the communication networks promotes the massive multiple input multiple output (MIMO), which has attracted a lot of attention among academic and industry communities. Due to the inherent sparsity features of channel structure in uplink massive MIMO systems, conventional methods often bring about high computational complexity and also fail to make full use of the structural information. In order to solve this problem, this paper proposes a novel deep learning (DL) based super-resolution direction of arrivals (DOA) estimation method. Specifically, it is realized with the aids of the well-designed deep neural network (DNN). Then we employ the DNN to carry out offline learning and online deployment procedures. This learning mechanism can learn the features of the wireless channel and the spacial structures efficiently. Finally, simulation results are provided to show that the proposed DL based scheme can achieve better performance in terms of the DOA estimation compared with conventional methods.
Music and Odor in Harmony: A Case of Music-Odor Synaesthesia

We report an individual with music-odor synaesthesia who experiences automatic and vivid odor sensations when she hears music. S’s odor associations were recorded on two days, and compared with those of two control participants. Overall, S produced longer descriptions, and her associations were of multiple odors at once, in comparison to controls who typically reported a single odor. Although odor associations were qualitatively different between S and controls, ratings of the consistency of their descriptions did not differ. This demonstrates that crossmodal associations between music and odor exist in non-synaesthetes too. We also found that S is better at discriminating between odors than control participants, and is more likely to experience emotion, memories and evaluations triggered by odors, demonstrating the broader impact of her synaesthesia.
Actual Status Assessment and Prediction of the Musi River Water Quality, Palembang, South Sumatra, Indonesia

Water pollution in rivers is still a crucial problem for the countries that use river water as the primary water source. This study aimed to determine the water quality of the Musi river and the content of heavy metals in water, sediment and mussels as well as to predict the water quality of the Musi river in the next five years. The water samples were taken from 18 stations (sampling points) from upstream to downstream to be checked physically, chemically and biologically. Prediction of the river water quality was made using the QUAL2Kw software. The research results show that the Musi River water is categorized as lightly polluted with concentrations of TSS and DO that have passed the threshold. The heavy metals in river water, such as Pb and Cr have also passed the quality standard. The Fe, Mn, and Zn concentrations are pretty high in the sediment. In turn, in mussels, the metals measured were Fe, Cu, and Zn. The prediction results reveal that several values of the river quality parameters will pass the threshold value with the same pattern tendency from each station.
Assessing the Potential of Qormino Processor for Embedded AI on Board a CubeSat

The launch of small satellites, CubeSats among others, is skyrocketing easing the development of Earth observation missions and the implementation of Space applications. At the same time, artificial intelligence (AI) and deep neural networks algorithms are enjoying impressive success for their results and the diverse applications they enable. The idea of combining nanosatellite standards with AI capabilities arises, thus, from a will to push the boundaries of Space systems further. Led by the Grenoble University Space Center (CSUG), the QlevEr Sat mission aims at embedding such an algorithm on board a CubeSat, to process data directly and send only the relevant results to the ground station. From the images taken with the Emerald sensor, forest/nonforest and cloud/noncloud segmentation maps are inferred with the Teledyne Qormino QLS1046-Space processor to monitor deforestation. A detailed benchmark of this processor is presented in this article and confirms its suitability for the mission. The specifically designed pixel-wise classification algorithm leverages the Qormino processor computing power to produce accurate high-resolution binary maps.
Feedforward Neural Network Based Case Prediction in Turkish Higher Courts

Thanks to natural language processing (NLP) methods, legal texts can be processed by computers and decision prediction applications can be developed in the legal tech field. Increase in the available data sources in the Turkish legal system provides an opportunity to develop NLP applications as well. In order to develop these applications, the necessary corpora and datasets should be created. In this work, legal case texts from the Turkish Higher Courts that are open to public access and free from personal data are used to develop decision prediction methods. Feedforward neural networks (FFNN) are deployed using word embeddings and the features extracted from texts via the Principal Component Analysis (PCA) algorithm. %85.4 Macro F1 score level is achieved.
Analysis and Experiments of the Impact of Frequency Ramp Nonlinearity on Range Resolution and Accuracy in LFMCW Radars

This paper characterizes the impact of the frequency ramp nonlinearity (FRN) on the range resolution and accuracy in linear-frequency-modulated continuous-wave (LFMCW) radars. A nonlinearity index that adapts to different types of FRN is proposed for rigid analysis. Based on the theoretical analysis and experimental evaluations, a new range-dependent resolution model and a new range-dependent accuracy model are proposed for the LFMCW radars under the influence of different types of FRN. Moreover, a novel design methodology is also proposed which might guide the design of LFMCW radars under the influence of the FRN. Both simulations and experiments are performed to validate the proposed theory and methodology.
Sweet Potato Symptomless Virus 1: First Detection in Europe and Generation of an Infectious Clone

Sweet potato (Ipomoea batatas), a staple food for people in many of the least developed countries, is affected by many viral diseases. In 2017, complete genome sequences of sweet potato symptomless virus 1 (SPSMV-1, genus Mastrevirus, family Geminiviridae) isolates were reported, although a partial SPSMV-1 genome sequence had previously been identified by deep sequencing. To assess the presence of this virus in Spain, sweet potato leaf samples collected in Málaga (southern continental Spain) and the Spanish Canary Islands of Tenerife and Gran Canaria were analyzed. SPSMV-1 was detected in samples from all the geographical areas studied, as well as in plants of several entries obtained from a germplasm collection supposed to be virus-free. Sequence analysis of full-length genomes of isolates from Spain showed novel molecular features, i.e., a novel nonanucleotide in the intergenic region, TCTTATTAC, and a 24-nucleotide deletion in the V2 open reading frame. Additionally, an agroinfectious clone was developed and infectivity assays showed that the virus was able to asymptomatically infect Nicotiana benthamiana, Ipomoea nil, I. setosa, and sweet potato, thus confirming previous suggestions derived from observational studies. To our knowledge, this is the first report of the presence of SPSMV-1 in Spain and Europe and the first agroinfectious clone developed for this virus.
AI-Enabled UAV Communications: Challenges and Future Directions

Recently, unmanned aerial vehicles (UAVs) communications gained significant concentration as a talented technology for future wireless communications using its remarkable advantages and broad applicability. Furthermore, UAV networks’ high complex configurations and designs encourage researchers to leverage relevant artificial intelligence (AI) techniques for better beyond fifth-generation (B5G)/sixth-generation (6G) services. This article summarizes AI-aided UAV solutions designated for forthcoming wireless networks. Besides, we deliver a comprehensive summary of machine learning (ML) approaches, including their applications and valuable contributions towards effective UAV network implementations, particularly advanced ML ones like bandits, federated learning (FL), meta-learning, etc. Finally, detailed UAV communication-related future research scopes and challenges is highlighted.
(Re)learning to Teach Science with a Hearing Impairment: An Auto-ethnographic Study

ABSTRACT Despite growing interest in inclusive education, there has been little attention to the inclusion of students with disabilities in teacher education. Research by educators with disabilities about teachers with disabilities can provide vital information that may help improve support services for teachers with disabilities. As a teacher educator who recently became single-sided deaf, and a preservice teacher who has had hearing loss since childhood, we contribute our voices to this gap in the literature. Through autoethnography, we draw on our lived experiences to identify critical incidents in our (re)learning to teach science, contextualizing these within the broader issue of inclusion in science teacher education. By identifying points of intersection and difference in our stories, we generated a list of salient themes related to arenas, levels, and degrees of inclusion. While our specific experiences may not be generalizable, they highlight broader themes that have implications for creating inclusive science teacher education programs.
Partial Offloading Schemes for Latency and Computation Sensitive Tasks

Computational Offloading is a revolutionary technology for enhancing mobile devices' performance. Computation offloading solves resource limitations of mobile devices by providing lower energy consumption, lower latency, and improved application performance. There are two types of offloading to transfer computational workload in the cloud: 1. Full offloading and 2. Partial offloading. This paper provides a complete overview of past developments and recent achievements of partial offloading modeling in edge computing and cloud computing. In particular, we differentiate how our survey work is unique from other offloading related surveys. We mainly concentrate on the most prominent models (Markov-based, graph-based, and deep learning-based) frequently used for partial offloading, and we compare algorithms that follow these models. Finally, we summarize each methodology with a concise explanation and highlight some crucial open research problems from our findings.
High Resolution Ultra-Violet Radiation Detection Using TNT-integrated Wireless Passive Microwave Resonator

This work presents high-resolution detection and quantification of ultraviolet (UV) radiation using a passive split-ring resonator (SRR) tag integrated with TiO2 nanotube (TNT) membrane. The passive tag was electromagnetically coupled to an active feedback-assisted SRR reader, which compensated the dielectric and radiation losses of the coupled resonant sensor and increased the quality factor (Q) from 87 (passive) to 700 (active). The proposed sensor operated at the frequency span of 2.5 GHz to 3.2 GHz, where the passive tag was placed 3 cm above the SRR reader. UV exposure of TNT changed the dielectric properties (permittivity and conductivity) of the membrane and consequently was reflected as variation in the resonant amplitude and frequency of the sensor. Measured results demonstrated five times higher sensitivity for active sensor (0.22 dB/Wcm-2) compared to the passive counterpart (0.04 dB/Wcm-2). Due to improved sensitivity, this robust microwave-based UV sensor is suitable for various industrial and biomedical applications.