Miracle-dz/UniFAR-FTUs huggingface dataset:
Bone sarcomas: ESMO-PaedCan-EURACAN Clinical Practice Guidelines for diagnosis, treatment and follow-up.

Primary bone tumours are rare, accounting for < 0.2% of malignant neoplasms registered in the EUROCARE (European Cancer Registry based study on survival and care of cancer patients) database. Different bone tumour subtypes have distinct patterns of incidence, and each has no more than 0.3 incident cases per 100 000 per year. Osteosarcoma (OS) and Ewing sarcoma (ES) have a relatively high incidence in the second decade of life, whereas chondrosarcoma (CS) is more common in older age.
Transfection of choanoflagellates illuminates their cell biology and the ancestry of animal septins

As the closest living relatives of animals, choanoflagellates offer unique insights into animal origins and core mechanisms underlying animal cell biology. However, unlike traditional model organisms, such as yeast, flies and worms, choanoflagellates have been refractory to DNA delivery methods for expressing foreign genes. Here we report the establishment of a robust method for expressing transgenes in the choanoflagellate Salpingoeca rosetta, overcoming barriers that have previously hampered DNA delivery and expression. To demonstrate how this method accelerates the study of S. rosetta cell biology, we engineered a panel of fluorescent protein markers that illuminate key features of choanoflagellate cells. We then investigated the localization of choanoflagellate septins, a family of GTP-binding cytoskeletal proteins that are hypothesized to regulate the multicellular rosette development in S. rosetta. Fluorescently tagged septins localized to the basal pole of S. rosetta single cells and rosettes in a pattern resembling septin localization in animal epithelia. The establishment of transfection in S. rosetta and its application to the study of septins represent critical advances in the growth of S. rosetta as an experimental model for investigating choanoflagellate cell biology, core mechanisms underlying animal cell biology, and the origin of animals.
A comparative study of cybersickness during exposure to virtual reality and "classic" motion sickness: are they different?

Existing evidence suggest that cybersickness may be clinically different from "classic", motion-induced motion sickness; this evidence was however obtained in separate studies that focussed on just one of the two conditions. Our aim was to bring clarity to this issue, by directly comparing subjective symptoms and physiological effects of motion sickness induced by physical motion (Coriolis cross-coupling) and by immersion in virtual reality (ride on a roller coaster) in the same subjects. A cohort of 30 young healthy volunteers was exposed to both stimulations in a counter-balance order on two separate days at least one week apart. Nausea scores were recorded during the exposure, and Motion Sickness Assessment Questionnaire (MSAQ) was used to profile subjective symptoms post-experiment. Tonic and phasic forehead skin conductance level (SCL) was measured before and during exposure in both stimulation methods. We found that nausea onset times and maximum nausea ratings were significantly correlated during both provocations (r=0.40, p=0.03 and r=0.56, p=0.0012, respectively). Symptom profiling with the MSAQ revealed substantial and significant correlations between total symptom scores (r=0.69, p<0.0001), between each of four symptom clusters and between 15/18 individual symptoms assessed in both conditions. Both provocations caused increase in tonic SCL associated with nausea, with a close correlation between the conditions (r=0.48, p=0.04). This was accompanied by a significant increase in the amplitude of phasic skin conductance transients in both experiments. We conclude that symptoms and physiological changes occurring during cybersickness and "classical" motion sickness are quite similar, at least during advanced stages of these malaises.
Reconfiguration of Adaptors Based on Trace Compliance in Cloud Service Composition

Aiming at the problem that exsiting approaches for run-time adaptation can not support reconfiguration of adaptors in evolving cloud computing environments, an automatic approach for adaptor reconfiguration in the running phase is proposed. By introducing the notions of history trace compliance and future trace compliance, the validity of an adaptor instance under the definition of a new adaptor version is determined. Then, the algorithms for reconfiguration of adaptors are presented and can be used for determining the migratability of an adaptor instance and the corresponding target state of reconfiguration. The effectiveness and feasibility of the approach is illustrated by a case study.
CELLO-3D: Estimating the Covariance of ICP in the Real World

The fusion of Iterative Closest Point (ICP) registrations in existing state estimation frameworks relies on an accurate estimation of their uncertainty. In this paper, we study the estimation of this uncertainty in the form of a covariance. First, we scrutinize the limitations of existing closed-form covariance estimation algorithms over 3D datasets. Then, we set out to estimate the covariance of ICP registrations through a data-driven approach, with over 5100000 registrations on 1020 pairs from real 3D point clouds. We assess our solution upon a wide spectrum of environments, ranging from structured to unstructured and indoor to outdoor. The capacity of our algorithm to predict covariances is accurately assessed, as well as the usefulness of these estimations for uncertainty estimation over trajectories. The proposed method estimates covariances better than existing closed-form solutions, and makes predictions that are consistent with observed trajectories.
Battery Currents Limitation in Passivity Based Controlled Battery/Supercapacitor Hybrid Energy Storage System

In stand-alone power supply systems, due to fluctuations of electric energy generation and consumption, special devices are used for energy storage, most often batteries. In order to remove stress from batteries during sudden load change, it is advisable to use hybrid energy storage system (HESS), by adding a supercapacitor module to the battery. In this work, stand-alone power supply system with battery/supercapacitor HESS of active configuration are investigated in two modes with different structures – at the low load and at the high one. In the latter battery current should be limited due to improve the battery lifetime. Both HESS structures are represented as port-controlled Hamiltonian systems, and the synthesis of control systems is carried out using the IDA-PBC method. Given the nonlinearity of the system and its propensity to oscillations, the transition to the battery current limitation is accomplished by implementing the sliding mode of switching of the designed controllers. The conducted simulation studies in the Matlab/Simulink environment showed the workability and effectiveness of the proposed solution.
Causality Analysis and Cell Network Modeling of Spatial Calcium Signaling Patterns in Liver Lobules

Dynamics as well as localization of Ca2+ transients plays a vital role in liver function under homeostatic conditions, repair, and disease. In response to circulating hormonal stimuli, hepatocytes exhibit intracellular Ca2+ responses that propagate through liver lobules in a wave-like fashion. Although intracellular processes that control cell autonomous Ca2+ spiking behavior have been studied extensively, the intra- and inter-cellular signaling factors that regulate lobular scale spatial patterns and wave-like propagation of Ca2+ remain to be determined. To address this need, we acquired images of cytosolic Ca2+ transients in 1300 hepatocytes situated across several mouse liver lobules over a period of 1600 s. We analyzed this time series data using correlation network analysis, causal network analysis, and computational modeling, to characterize the spatial distribution of heterogeneity in intracellular Ca2+ signaling components as well as intercellular interactions that control lobular scale Ca2+ waves. Our causal network analysis revealed that hepatocytes are causally linked to multiple other co-localized hepatocytes, but these influences are not necessarily aligned uni-directionally along the sinusoids. Our computational model-based analysis showed that spatial gradients of intracellular Ca2+ signaling components as well as intercellular molecular exchange are required for lobular scale propagation of Ca2+ waves. Additionally, our analysis suggested that causal influences of hepatocytes on Ca2+ responses of multiple neighbors lead to robustness of Ca2+ wave propagation through liver lobules.
Machine Learning in Healthcare: A Review

Machine Learning is modern and highly sophisticated technological applications became a huge trend in the industry. Machine Learning is Omni present and is widely used in various applications. It is playing a vital role in many fields like finance, Medical science and in security. Machine learning is used to discover patterns from medical data sources and provide excellent capabilities to predict diseases. In this paper, we review various machine learning algorithms used for developing efficient decision support for healthcare applications. This paper helps in reducing the research gap for building efficient decision support system for medical applications.
IL-2/CD25: A Long-Acting Fusion Protein That Promotes Immune Tolerance by Selectively Targeting the IL-2 Receptor on Regulatory T Cells

Low-dose IL-2 represents an immunotherapy to selectively expand regulatory T cells (Tregs) to promote tolerance in patients with autoimmunity. In this article, we show that a fusion protein (FP) of mouse IL-2 and mouse IL-2Rα (CD25), joined by a noncleavable linker, has greater in vivo efficacy than rIL-2 at Treg expansion and control of autoimmunity. Biochemical and functional studies support a model in which IL-2 interacts with CD25 in the context of this FP in trans to form inactive head-to-tail dimers that slowly dissociate into an active monomer. In vitro, IL-2/CD25 has low sp. act. However, in vivo IL-2/CD25 is long lived to persistently and selectively stimulate Tregs. In female NOD mice, IL-2/CD25 administration increased Tregs within the pancreas and reduced the instance of spontaneous diabetes. Thus, IL-2/CD25 represents a distinct class of IL-2 FPs with the potential for clinical development for use in autoimmunity or other disorders of an overactive immune response.
Reduced-code static linearity test of SAR ADCs using a built-in incremental ∑Δ converter

This work presents a strategy for static linearity self-testing of successive-approximation analog to digital converters (SAR ADCs) with the goal of reducing test time. The proposed test technique takes advantage of the SAR ADC architecture to drastically reduce the number of necessary measurements for a complete static linearity characterization. Moreover, we show that static linearity measurements can be performed on-chip without the need of a test stimulus generator, by generating the major carrier transitions of the SAR ADC and acquiring them with a low resolution ADC. The proposed test circuitry is reduced to a simple incremental ∑Δ ADC. The technique is validated with behavioral simulations and the design trade-offs of the proposed test circuitry are explored.
International Conference on Electronics and Nanotechnology ( ELNANO ) Spectra , Thresholds , and Modal Fields of a Triangular Microcavity Laser

We study the modes of a triangular microcavity laser, namely the frequencies of emission, threshold values of gain, and mode field patterns, within the classical electro-magnetics approach. Our instrument is the accurate formalism of Lasing Eigenvalue Problem (LEP), i.e. boundary-value problem with full set of the boundary and radiation conditions. The LEP is reduced to a nonlinear eigenvalue problem for the Muller integral equation on the cavity’s boundary. Discretizing it with a Nyström approximation of weakly singular integral operators, we obtain a characteristic equation for the mode frequencies and thresholds. Keywords—microcavity laser; lasing eigenvalue problem; Muller boundary integral equation; Nyström method.
Extreme Augmentation : Can deep learning based medical image segmentation be trained using a single manually delineated scan?

Yes, it can. Data augmentation is perhaps the oldest preprocessing step in computer vision literature. Almost every computer vision model trained on imaging data uses some form of augmentation. In this paper, we use the inter-vertebral disk segmentation task alongside a deep residual U-Net as the learning model, to explore the effectiveness of augmentation. In the extreme, we observed that a model trained on patches extracted from just one scan, with each patch augmented 50 times; achieved a Dice score of 0.73 in a validation set of 40 cases. Qualitative evaluation indicated a clinically usable segmentation algorithm, which appropriately segments regions of interest, alongside limited false positive specks. When the initial patches are extracted from nine scans the average Dice coefficient jumps to 0.86 and most of the false positives disappear. While this still falls short of state-of-the-art deep learning based segmentation of discs reported in literature, qualitative examination reveals that it does yield segmentation, which can be amended by expert clinicians with minimal effort to generate additional data for training improved deep models. Extreme augmentation of training data, should thus be construed as a strategy for training deep learning based algorithms, when very little manually annotated data is available to work with. Models trained with extreme augmentation can then be used to accelerate the generation of manually labelled data. Hence, we show that extreme augmentation can be a valuable tool in addressing scaling up small imaging data sets to address medical image segmentation tasks.
Top-down attentional factors modulate action priming in reach-to-grasp action

Previous studies report that viewing exaggerated, high-lifting reaches (versus direct reaches) primes higher vertical deviation in wrist trajectory in the observer’s subsequent reaches (trajectory priming), but it is unclear to what extent this effect depends upon task instructions relevant to top-down attention. In two experiments, participants were instructed to gaze at a dot presented on a large monitor for a colour-change go signal that cued them to execute a direct reach to a target. In the background, the monitor also displayed life-sized films of a human model. The films were of the model either remaining still or reaching to grasp a target with either a direct trajectory or an exaggerated, high-lifting trajectory. When the dot traced the human model’s wrist throughout her movement, a robust trajectory priming effect emerged. When the dot remained stationary in a central location but the human model reached in the background, the human model’s trajectory did not alter the participants’ trajectories. Finally, when the dot traced exaggerated and direct trajectories and the human model remained stationary, the dot’s movement produced an attenuated, non-significant trajectory priming effect. These findings show that top-down attentional factors modulate trajectory priming. In addition, a moving non-human stimulus does not produce the same degree of action priming when contextual factors make salient its independence of human agency and/or intention.
Human-Centered Autonomous Vehicle Systems: Principles of Effective Shared Autonomy

Building e�ffective, enjoyable, and safe autonomous vehicles is a lot harder than has historically been considered. �The reason is that, simply put, an autonomous vehicle must interact with human beings. �This interaction is not a robotics problem nor a machine learning problem nor a psychology problem nor an economics problem nor a policy problem. It is all of these problems put into one. It challenges our assumptions about the limitations of human beings at their worst and the capabilities of artificial intelligence systems at their best. Th�is work proposes a set of principles for designing and building autonomous vehicles in a human-centered way that does not run away from the complexity of human nature but instead embraces it. We describe our development of the Human-Centered Autonomous Vehicle (HCAV) as an illustrative case study of implementing these principles in practice.
Video Solar Plant Monitoring

In order to ensure efficient monitoring of solar plants, aerial video inspection using drones is unavoidable. The usually encountered problem is the shadow caused by drone on solar panels which may cause a false image interpretation. In this paper, we propose a technique of live shadow detection in video frames based on Otsu thresholding algorithm. To avoid Shaded Frames, the supervisor can automatically detect shaded captured frames of solar panels then acquire frames of the same area from another drone position with corrected angle of view of camera.
Application of Low Harmonic 18-Pulse Rectifier Power Supply for Radar Power System

Due to its low harmonic, high efficiency, and high power factor (PF), the multipulse autotransformer has been frequently used in the aviation sector. Many studies have been carried out on the theoretical calculation and simulation analysis of the autotransformer. However, the practical application of the multipulse rectifier in the practical engineering field is inevitable. This paper investigates an 18-pulse rectifier power supply of 11 kW being constructed using a multipulse autotransformer to suppress the harmonics of a radar power system, in which the parameters of the various parts are designed in detail, and test results are simulated using MATLAB/Simulink software. The actual test suggests that, at the rated condition, the PF of the rectifier is 0.99, the total harmonic distortion of the input current is below 6.7%, and the efficiency is more than 96%. The power supply is successfully applied to the radar power supply system of a large transport aircraft, and meets the requirements of an environmental test. Furthermore, the results also provide powerful application support for the power supplies and distribution systems in other fields.
The implant surface and its role in affecting the dynamic processes of bone remodeling by means of distance osteogenesis: A comparative in vivo study.

PURPOSE This study aimed to evaluate whether different surface modifications affect the dynamics of bone remodeling at the implant and the adjacent local bone. MATERIALS AND METHODS Seventy-two dental implants with different surfaces (smooth and rough control [smCtrl; rCtrl], smooth and rough + O2-plasma spray [smPlas; rPlas], smooth and rough + nanocrystalline SiO2-hydroxyapatite coating [ncSiO2HA] + O2-plasma spray [smNB-C; rNB-C]; each n = 12) were bilaterally inserted into the femora of 36 New Zealand white rabbits. Intravital fluorochrome labeling was performed to visualize the dynamics of bone formation. The objectives were quantification of bone-to-implant contact (BIC [%]) at 2 and 4 weeks and the dynamic bone formation (dbf [%]) at the implants' adjacent local bone within 1, 2, and 3 weeks. RESULTS After 2 weeks, BIC was significantly higher for both smNB-C (BIC: 59% ± 2% SEM) and rNB-C (BIC: 66% ± 3% SEM) compared with controls (BIC: 42% ± 1% SEM; P < .005). After 4 weeks, BIC for rNB-C (65% ± 2%) was superior to all test groups (BIC: 39% ± 2% SEM; P = .012). Regarding dbf (%), neither within 1 (P = .88), 2 (P = .48), nor after 3 weeks (P = .36) did any differences occur among the groups, even in accordance to the implant level. CONCLUSION Although distance osteogenesis seems crucial for the development of secondary stability and thus of osseointegration, it apparently does not get affected by a bioactive ncSiO2HA surface coating. Changing the surfaces' release kinetics and composition may increase distance osteogenesis.
A toolkit for rapid CRISPR-SpCas9 assisted construction of hexose-transport-deficient Saccharomyces cerevisiae strains

Abstract Hexose transporter-deficient yeast strains are valuable testbeds for the study of sugar transport by native and heterologous transporters. In the popular Saccharomyces cerevisiae strain EBY.VW4000, deletion of 21 transporters completely abolished hexose transport. However, repeated use of the LoxP/Cre system in successive deletion rounds also resulted in major chromosomal rearrangements, gene loss and phenotypic changes. In the present study, CRISPR/SpCas9 was used to delete the 21 hexose transporters in an S. cerevisiae strain from the CEN.PK family in only three deletion rounds, using 11 unique guide RNAs. Even upon prolonged cultivation, the resulting strain IMX1812 (CRISPR-Hxt0) was unable to consume glucose, while its growth rate on maltose was the same as that of a strain equipped with a full set of hexose transporters. Karyotyping and whole-genome sequencing of the CRISPR-Hxt0 strain with Illumina and Oxford Nanopore technologies did not reveal chromosomal rearrangements or other unintended mutations besides a few SNPs. This study provides a new, ‘genetically unaltered’ hexose transporter-deficient strain and supplies a CRISPR toolkit for removing all hexose transporter genes from most S. cerevisiae laboratory strains in only three transformation rounds.
The struggle for existence in the world market ecosystem

The global trade system can be viewed as a dynamic ecosystem in which exporters struggle for resources: the markets in which they export. We can think that the aim of an exporter is to gain the entirety of a market share (say, car imports from the United States). This is similar to the objective of an organism in its attempt to monopolize a given subset of resources in an ecosystem. In this paper, we adopt a multilayer network approach to describe this struggle. We use longitudinal, multiplex data on trade relations, spanning several decades. We connect two countries with a directed link if the source country’s appearance in a market correlates with the target country’s disappearing, where a market is defined as a country-product combination in a given decade. Each market is a layer in the network. We show that, by analyzing the countries’ network roles in each layer, we are able to classify them as out-competing, transitioning or displaced. This classification is a meaningful one: when testing the future export patterns of these countries, we show that out-competing countries have distinctly stronger growth rates than the other two classes.
Diagnosis of skin diseases using Convolutional Neural Networks

Dermatology is one of the most unpredictable and difficult terrains to diagnose due its complexity. In the field of dermatology, many a times extensive tests are to be carried out so as to decide upon the skin condition the patient may be facing. The time may vary from practitioner to practitioner. This is also based on the experience of that person too. So, there is a need of a system which can diagnose the skin diseases without any of these constraints. We propose an automated image based system for recognition of skin diseases using machine learning classification. This system will utilize computational technique to analyze, process, and relegate the image data predicated on various features of the images. Skin images are filtered to remove unwanted noise and also process it for enhancement of the image. Feature extraction using complex techniques such as Convolutional Neural Network (CNN), classify the image based on the algorithm of softmax classifier and obtain the diagnosis report as an output. This system will give more accuracy and will generate results faster than the traditional method, making this application an efficient and dependable system for dermatological disease detection. Furthermore, this can also be used as a reliable real time teaching tool for medical students in the dermatology stream.