Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Materials Decomposition using clinical Dualenergy CT

Dual energy computed tomography(DECT) can give the variation of the attenuation coefficient with energy. Furthermore, the material information of the scanned object can be deduced. In medical application of DECT, the scanned body can be seen as a combination of several materials, such as bone, soft tissue, fat and blood. A two-steps multi-materials decomposition method was proposed to attain the composition information. Firstly, a projection domain decomposition is performed to estimate the attenuation coefficient function of the scanned object. Then the estimated attenuation coefficient function is decomposed with the attenuation coefficient functions of basis materials. There is a problem that in the first step, the spectrum of the x-ray source or additional calibration data is necessary but they may not be available in practice. We proposed an image domain multi-materials decomposition method. The basic framework is the same with the two-steps multi-materials decomposition method. The difference occurs in the first step that a direct reconstruction to dual energy datasets not the projection domain decomposition is conducted. Accordingly, the attenuation coefficients of basis materials for decomposition in the second step should be carefully selected or constructed because the direct reconstructed value is the effective attenuation coefficient. The attenuation coefficients of basis materials are chosen from a density-based clustering to the dual energy reconstruction data. We did some experiments based on clinical DECT data. Without knowing exact spectrum, the material information acquired by the proposed method is relatively accurate.
The Analysis of Quality Indicators on Face Recognition in Video Frames

In this paper we examine the effects of various environmental and camera constraints on the accuracy of deep-learning-based face recognition system. More specifically the paper takes a look on the decline of accuracy of the famous ResNet architecture trained on “pretty” images of celebrities, when it is used on stills from video sequences from real-life television broadcast. It introduces a number of different quality indicators (connected to the sharpness of image, the expression of subject and image contrast) and describes their effect on the results of the convolutional network. It attempts to give recommendations on the ideal values for these indicators in order to choose the best candidate from the video sequence on which to perform the recognition algorithm.
Cluster-based Direct Estimation of Parametric Maps of Dopamine Response in Dynamic PET Data

Advanced kinetic models, such as the linear parametric neurotransmitter PET (lp-ntPET), have been recently developed to model and quantify transient changes in the radiotracer efflux from the target tissue, caused by endogenous neurotransmitter release during an cognitive task or environmental stimulus. Nevertheless, application of these models to voxel-wise time-activity curves is often challenging due to high levels of noise, which substantially increases the likelihood of generating false positive findings. In this paper we propose and a direct reconstruction framework which includes a cluster-based model selection before the parameter fitting step to allow the use of the lp-ntPET model only to those voxels with significant activation response. We use principal component analysis and Gaussian mixture models to determine clusters of voxels which share similar properties (e.g. similar activation magnitude), with the ultimate aim to reduce the false positive responses in brain regions other than the target (e.g. striatum). We used computer simulations based on a digital rat brain phantom to generate dynamic PET data, representing a [11C]raclopride study, with a known activation response whose magnitude was similar to an endogenous dopamine release (50% increase over the baseline). Results showed that the proposed direct reconstruction framework can define the activated voxels with high accuracy, minimising false positive responses outside the target region, compared to its post reconstruction counterpart. In addition, it led to more accurate voxel-wise activation profiles, with clear temporal information. Work is currently in progress to apply this methodology to real rat data of unknown endogenous induced activation response.
Stakeholder specific visualisation from heterogeneous modeling tools.

Our software-intensive and cyber-physical systems are becoming more complex because of added features. Model-Based Systems Engineering helps to manage the complexity by introducing models and analyses in a top-down design of the system. Appropriate formalisms and tools are used at each abstraction level to model requirements, architecture and behaviour. Most of these tools offer different visualisations of the models. However, there is not a lot of support in the visualisation of the design that spans different modelling formalisms and tools. In this paper we propose a solution based on domain-specific languages. Our language defines the operational steps needed to combine (filtered) model elements together from different tools and map the resulting graph or tree to a visualisation template.
Allocation with Traffic Spikes

Motivated by Internet advertising applications, online allocation problems have been studied extensively in various adversarial and stochastic models. While the adversarial arrival models are too pessimistic, many of the stochastic (such as i.i.d. or random-order) arrival models do not realistically capture uncertainty in predictions. A significant cause for such uncertainty is the presence of unpredictable traffic spikes, often due to breaking news or similar events. To address this issue, a simultaneous approximation framework has been proposed to develop algorithms that work well both in the adversarial and stochastic models; however, this framework does not enable algorithms that make good use of partially accurate forecasts when making online decisions. In this article, we propose a robust online stochastic model that captures the nature of traffic spikes in online advertising. In our model, in addition to the stochastic input for which we have good forecasting, an unknown number of impressions arrive that are adversarially chosen. We design algorithms that combine a stochastic algorithm with an online algorithm that adaptively reacts to inaccurate predictions. We provide provable bounds for our new algorithms in this framework. We accompany our positive results with a set of hardness results showing that our algorithms are not far from optimal in this framework. As a byproduct of our results, we also present improved online algorithms for a slight variant of the simultaneous approximation framework.
An Efficient Compact Flexible EM Energy Harvester

In this paper, we present a compact flexible electromagnetic energy harvester systems using a small loop resonator. They are designed and fabricated on a flexible substrate, and their performance are verified through measurement for both flat and curved configurations. The efficiency is investigated with respect to power density. It is observed that the proposed systems can achieve high conversion efficiency for low input power while at the same time occupying a compact footprint areas. In addition, the output DC power by harvester system using multi-resonator array is compered to a system with a single resonator. The measurement results demonstrate that an enhancement in the performance of up to 4 times are achieved.
Immunization of Experimental Dogs With Salivary Proteins From Lutzomyia longipalpis, Using DNA and Recombinant Canarypox Virus Induces Immune Responses Consistent With Protection Against Leishmania infantum

Metacyclic Leishmania promastigotes are transmitted by sand flies that inject parasites and saliva into the host's skin. Previous studies have demonstrated that DNA plasmids encoding Lutzomyia longipalpis salivary proteins LJM17 and LJL143, when used to immunize dogs, resulted in a systemic and local Th1 cell-mediated immunity that interfered in parasite survival in vitro. Here we evaluated the ability of these same salivary antigens to induce anti-Leishmania immunity and to confer protection by immunizing dogs using a novel vaccination strategy more suitable for use in the field. The strategy consisted of a single dose of plasmid followed by two doses of recombinant Canarypoxvirus (rCanarypoxvirus) expressing L. longipalpis salivary proteins (LJM17 or LJL143). Thirty days after the final immunization, dogs were intradermally challenged with 107 Leishmania infantum promastigotes in the presence of L. longipalpis saliva. We followed the experimentally infected dogs for 10 months to characterize clinical, parasitological, and immunological parameters. Upon vaccination, all immunized dogs presented strong and specific humoral responses with increased serum concentrations of IFN-γ, TNF, IL-7, and IL-15. The serum of dogs immunized with LJM17 also exhibited high levels of IL-2, IL-6, and IL-18. L. infantum infection was established in all experimental groups as evidenced by the presence of anti-Leishmania IgG, and by parasite detection in the spleen and skin. Dogs immunized with LJM17-based vaccines presented higher circulating levels of IFN-γ, IL-2, IL-6, IL-7, IL-15, IL-18, TNF, CXCL10, and GM-CSF post-infection when compared with controls. Results demonstrated that relevant Leishmania-specific immune responses were induced following vaccination of dogs with L. longipalpis salivary antigen LJM17 administered in a single priming dose of plasmid DNA, followed by two booster doses of recombinant Canarypox vector. Importantly, a significant increase in pro-inflammatory cytokines and chemokines known to be relevant for protection against leishmaniasis was evidenced after challenging LJM17-vaccinated dogs as compared to controls. Although similar results were observed following immunization with LJL143, the pro-inflammatory response observed after immunization was attenuated following infection. Collectively, these data suggest that the LJM17-based vaccine induced an immune profile consistent with the expected protective immunity against canine leishmaniosis. These results clearly support the need for further evaluation of the LJM17 antigen, using a heterologous prime-boost vaccination strategy against canine visceral leishmaniosis (CVL).
Towards Semantic Model Extensibility in Interoperable IoT Data Exchange Platforms

Data exchange platforms and marketplaces are gaining popularity as next-generation IoT data monetization and discovery solutions. They utilize different information models to represent heterogeneous data in a uniform and interoperable manner. Those platforms have the need to dynamically extend and enrich their semantic models in order to accommodate new data offerings. Using the BIG IoT semantic models and the resulting knowledge graph as the basis, we propose a new approach to incorporate user-defined semantic annotations into the model on the fly, which firstly makes them usable before their incorporation into an official release of the model, and secondly minimizes the efforts required from ontology engineers in the model evolution phase. The process of new annotation inclusion is based on the dynamic generation of user interface elements (e.g. web forms) from annotation patterns stored in the BIG IoT knowledge graph. By filling in such web forms, a cooperative user creates an unambiguous description of a new concept meaning and connects the concept with related ones, thus, preserving knowledge graph integrity and model consistency.
Improvement of Embedding Channel-Wise Activation in Soft-Attention Neural Image Captioning

The paper dives into the topic of image captioning with the soft attention algorithm. We first review relevant works on the captioned topic in terms of background introduction and then explains the original model in details. On top of the plain soft attention model, we propose two approaches for further improvements: SE attention model which adds an extra channel-wise activation layer, and bi-directional attention model that explores two-way attention order feasibility. We implement both methods under limited experiment conditions and in addition swap the original encoder with state-of-art structure. Quantitative results and example demonstrations show that our proposed methods have achieved better performance than baselines. In the end, some suggestions of future work on top of proposed are summarized for a purpose of completeness.
Design of NMPC-Based Framework for Mobile Robot Motion in Unstructured Environments

The paper treats a design of the mobile robot motion framework based on Nonlinear Model Predictive Control (NMPC). This approach relies on laser range-finder measurements and safety regions described around the detected obstacles. The controller optimization involves both actuator and environment constraints excluding safety regions. The safe motion of the mobile robot using the proposed framework is provided in unknown static and dynamic environments. Stability issue of the used closed-loop motion control system is guaranteed employing direct Lyapunov method. The simulation setup was conducted using a 2D simulator (Stage) in the Robot Operating System (ROS) environment. The obtained results through different scenarios demonstrates that proposed NMPC-based framework approach ensures smooth trajectories from start to the goal point.
Treatment of Refractory Adrenocortical Carcinoma with Thalidomide: Analysis of 27 Patients from the European Network for the Study of Adrenal Tumours Registry.

OBJECTIVE Adrenocortical carcinoma (ACC) is a rare malignancy with a dismal prognosis. In advanced stages, tumour control by mitotane and cytotoxic chemotherapy is often temporary and salvage treatments are warranted. METHODS Retrospective cohort study of participants in the prospective European Networks for the Study of Adrenal Tumours (ENSAT) registry. Main outcome measures were best response during treatment, progression-free survival (PFS), both measured according to RECIST 1.1 by two blinded radiologists, and overall survival (OS). RESULTS Twenty-seven patients (13 males; median age 44.1 years) progressing after mitotane and a median of 4 further systemic treatments were included. Thalidomide was administered as tolerated with a starting dose of 50 mg and target dose of 200 mg /d. The median interval between treatment initiation and first imaging was 10.5 (4.4-17.5) weeks. The best response to treatment was stable disease (SD, n=2) and progressive disease (n=25), with a median PFS of 11.2 weeks and a median OS of 36.4 weeks. The first patient with SD discontinued treatment due to mild epistaxis and diarrhea after 22.3 weeks. The second patient had SD at the second treatment evaluation after 25.2 weeks and continued thalidomide but then had clinical progression and deceased after 54.3 weeks. In general, thalidomide induced only mild or moderate adverse effects (mainly fatigue and gastrointestinal complaints). CONCLUSION Thalidomide was overall well tolerated but resulted in disease control in only 2/27 (7.4%) patients. In the absence of predictive response markers, thalidomide should only be considered in exceptional cases as a salvage therapy in ACC.
Epilepsy and Cannabis: A Literature Review

Epilepsy is considered to be one of the most common non-communicable neurological diseases especially in low to middle-income countries. Approximately one-third of patients with epilepsy have seizures that are resistant to antiepileptic medications. Clinical trials for the treatment of medically refractory epilepsy have mostly focused on new drug treatments, and result in a significant portion of subjects whose seizures remain refractory to medication. The off-label use of cannabis sativa plant in treating seizures is known since ancient times. The active ingredients of this plant are delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD), the latter considered safer and more effective in treating seizures, and with less adverse psychotropic effects. Clinical trials prior to two years ago have shown little to no significant effects of cannabis in reducing seizures. These trials seem to be underpowered, with a sample size less than 15. In contrast, more recent studies that have included over 100 participants showed that CBD use resulted in a significant reduction in seizure frequency. Adverse effects of CBD overall appear to be benign, while more concerning adverse effects (e.g., elevated liver enzymes) improve with continued CBD use or dose reduction. In most of the trials, CBD is used in adjunct with epilepsy medication, therefore it remains to be determined whether CBD is itself antiepileptic or a potentiator of traditional antiepileptic medications. Future trials may evaluate the efficacy of CBD in treating seizures due to specific etiologies (e.g., post-traumatic, post-stroke, idiopathic).
Prognostic significance of matrix metalloproteinase 9 expression in osteosarcoma

Background: Matrix metalloproteinase 9 (MMP-9) is significant in the progression of osteosarcoma (OS) via increasing tumor growth, invasion and metastasis. Although previous reports indicate the prognostic value of MMP-9 in OS, there is still a great degree on inconsistency between studies. Here we report a comprehensive evaluation of the value of MMP-9 in metastasis of OS by conducting a meta-analysis of published studies. Methods: The quantity of the studies was evaluated using the Newcastle-Ottawa quality assessment scale (NOS). Sixteen studies with a total of 816 patients with OS were examined and we calculated the pooled odds ratio (OR) with corresponding 95% confidence interval (CI) (95% CI) to evaluate that the positive expression of MMP-9 predicts neoplasm metastasis and poor survival in OS. Results: The results of Meta-analysis indicated that patients with positive expression of MMP-9 were significantly associated with neoplasm metastasis (OR = 4.69, 95% CI: 3.05–7.21, P <.001) and poor survival in OS with the pooled OR of 7.19 (95% CI 4.32–11.98, P <.001) when compared to their counterparts with a negative expression of MMP-9. The results of sensitivity analysis showed that the pooled OR was stable. It doesn’t significantly change when a single study was removed. Conclusions: The results of meta-analysis indicated that MMP-9 may be a prognostic biomarker guiding the clinical therapy for OS.
A Semantic Based Multi-Platform IoT Integration Approach from Sensors to Chatbots

The Internet-of-Things paradigm has brought exciting opportunities to increase productivity and efficiency but also to enhance customer experience. Such things are expected to connect and exchange information enabling applications to propose a value added services. However, due to their heterogeneous nature, things communicate in different semantics making integration a laborious and costly to achieve. We propose in this paper an end to end semantic-based integration approach from sensors to chatbot. Our approach provides integration between two different IoT platforms and offers a coherent view of a building facility.
Efficient Real-Time Coding-Assisted Heterogeneous Data Access in Vehicular Networks

Through the embedded processors and communication technologies, vehicles are increasingly being connected with the Internet of Things. Recently, much attentions have been paid to network-coding-assisted data broadcast in vehicular networks. However, majority of the works consider all the accessed data items are the same size. In this paper, we have studied the network coding-assisted heterogeneous on-demand real-time data access in vehicular networks. First, we have investigated the less efficiency of conventional coding-assisted approach in accessing heterogeneous data items in real-time vehicular environment. Due to ignoring the impact of heterogeneous data items in decoding, the conventional coding does not achieve expected performance in accessing data items with diverse size. Second, based on our observations, for efficiently serving heterogeneous data items, we have proposed a dynamic threshold-based coding-assisted real-time data broadcast approach called earliest deadline first<inline-formula> <tex-math notation="LaTeX">$_{\Theta }$ </tex-math></inline-formula>. Third, we have derived the probabilistic analysis of the system performance of the proposed approach and the state-of-the-art approaches. Fourth, based on our further investigations, we have proposed another approach, called inverse of slack time multiply distance with THRESHOLD (<inline-formula> <tex-math notation="LaTeX">${\Theta }$ </tex-math></inline-formula>) (ISXD<inline-formula> <tex-math notation="LaTeX">$_{\Theta }$ </tex-math></inline-formula>). The proposed network-coding-assisted ISXD<inline-formula> <tex-math notation="LaTeX">$_{\Theta }$ </tex-math></inline-formula> exploits the different modulation and coding scheme of IEEE 802.11p physical layer for leveraging the variable serving rate considering the dynamic positions of vehicles along with the vehicle mobility. The comprehensive simulation results demonstrate the efficacy of the proposed approaches over the state-of-the-art approaches in terms of improving the on-demand requests serving capability and reducing the system response time.
Skutki cyberbullyingu-oskarzenie czy obrona technologii?

Cyberbullying (cyberprzemoc) jest formą przemocy rowieśniczej, ktora pojawila sie ostatnio w naszych spolecznościach. Jak wskazuje nazwa tego zjawiska, cyberbullying polega na wykorzystywaniu mediow elektronicznych w celu krzywdzenia innych osob. Chociaz badania dowiodly, ze cyberprzemoc pociąga za sobą szereg powaznych skutkow, nadal nie znamy odpowiedzi na wiele pytan dotyczących jej oddzialywania. Nie dysponujemy jeszcze wynikami badan podluznych, ktore dostarczylyby danych empirycznych dotyczących dlugofalowych nastepstw cyberprzemocy. Jednak zdaniem wielu badaczy skutki te mogą byc rownie powazne, – a moze nawet gorsze – niz skutki tradycyjnej przemocy rowieśniczej. W dyskusji dotyczącej rzeczywistego oddzialywania cyberbullyingu badacze podnoszą rozmaite kwestie związane z rolą nowych technologii (a zwlaszcza Internetu), takie jak: mozliwośc zachowania anonimowości; mozliwośc ingerowania w zycie rowieśnikow przez dwadzieścia cztery godziny na dobe, siedem dni w tygodniu; mozliwośc pozostawania niezauwazonym przez nauczycieli, rodzicow i wychowawcow; a takze fakt, ze sprawca cyberbullyingu nie odbiera sygnalow niewerbalnych wysylanych przez ofiare. Niniejszy artykul stara sie zaspokoic zapotrzebowanie na bardziej klarowny model teoretyczny cyberprzemocy poprzez zebranie i krytyczne omowienie tych kwestii w ramach aktualnej wiedzy o cyberbullyingu.
Chemical modulation of autophagy as an adjunct to chemotherapy in childhood and adolescent brain tumors

Brain tumors are the leading cause of cancer-related death in children and are the most challenging childhood cancer in relation to diagnosis, treatment, and outcome. One potential novel strategy to improve outcomes in cancer involves the manipulation of autophagy, a fundamental process in all cells. In cancer, autophagy can be thought of as having a “Janus”-like duality. On one face, especially in the early phases of cancer formation, autophagy can act as a cellular housekeeper to eliminate damaged organelles and recycle macromolecules, thus acting as tumor suppressor. On the other face, at later stages of tumor progression, autophagy can function as a pro-survival pathway in response to metabolic stresses such as nutrient depravation, hypoxia and indeed to chemotherapy itself, and can support cell growth by supplying much needed energy. In the context of chemotherapy, autophagy may, in some cases, mediate resistance to treatment. We present an overview of the relevance of autophagy in central nervous system tumors including how its chemical modulation can serve as a useful adjunct to chemotherapy, and use this knowledge to consider how targeting of autophagy may be relevant in pediatric brain tumors.