Miracle-dz/UniFAR-FTUs huggingface dataset:
AXL Mediates Cetuximab and Radiation Resistance Through Tyrosine 821 and the c-ABL Kinase Pathway in Head and Neck Cancer

Purpose: Radiation and cetuximab are therapeutics used in management of head and neck squamous cell carcinoma (HNSCC). Despite clinical success with these modalities, development of both intrinsic and acquired resistance is an emerging problem in the management of this disease. The purpose of this study was to investigate signaling of the receptor tyrosine kinase AXL in resistance to radiation and cetuximab treatment. Experimental Design: To study AXL signaling in the context of treatment-resistant HNSCC, we used patient-derived xenografts (PDXs) implanted into mice and evaluated the tumor response to AXL inhibition in combination with cetuximab or radiation treatment. To identify molecular mechanisms of how AXL signaling leads to resistance, three tyrosine residues of AXL (Y779, Y821, Y866) were mutated and examined for their sensitivity to cetuximab and/or radiation. Furthermore, reverse phase protein array (RPPA) was employed to analyze the proteomic architecture of signaling pathways in these genetically altered cell lines. Results: Treatment of cetuximab- and radiation-resistant PDXs with AXL inhibitor R428 was sufficient to overcome resistance. RPPA analysis revealed that such resistance emanates from signaling of tyrosine 821 of AXL via the tyrosine kinase c-ABL. In addition, inhibition of c-ABL signaling resensitized cells and tumors to cetuximab or radiotherapy even leading to complete tumor regression without recurrence in head and neck cancer models. Conclusions: Collectively, the studies presented herein suggest that tyrosine 821 of AXL mediates resistance to cetuximab by activation of c-ABL kinase in HNSCC and that targeting of both EGFR and c-ABL leads to a robust antitumor response.
Intelligent Beamforming Algorithm with Low Data Quality

In the field of radar digital signal processing, adaptive beamforming technology is a very valuable algorithm. For an actual system, the array may have outliers. These outliers affect the convergence of the adaptive beamforming algorithm, resulting in poor robustness of the algorithm under outliers. Therefore, this thesis applies a Huber-loss function and AdaGrad optimization algorithm in machine learning, and proposes a robust adaptive beamforming algorithm under strong interference. Through the simulation analysis, the Huber-loss loss function is insensitive to outliers, and the AdaGrad optimization algorithm can dynamically adjust the learning rate of each gradient component, making the optimization process converge faster, and the convergence is less sensitive to scale changes. Therefore, the paper applies the both to the adaptive beamforming algorithm, minimizing the influence of outliers on the adaptive algorithm and improving the adaptability of the algorithm to different data scales. And it can make the algorithm more robust.
Investigation of the effect of epicardial adipose tissue thickness on cardiac conduction system in children with type 1 diabetes mellitus

Abstract Objectives Investigation of the association between epicardial adipose tissue thickness (EATT) and P-wave dispersion (Pd), QT dispersion (QTd), corrected QT dispersion (QTcd) and Tp-e interval in children with Type 1 Diabetes Mellitus (T1DM) was aimed. Methods Forty-one children with T1DM and 41 age- and gender-matched healthy children were included in the study. Demographical characteristics of all cases were examined. In echocardiography; in addition to conventional echocardiographic measurements, end-systolic EATT was measured from right ventricular free wall. In electrocardiogram; Pd, QTd, QTcd and Tp-e interval durations, as well as Tp-e/QT and Tp-e/QTc ratios were calculated. Correlation values between EATT and electrocardiographic parameters were also noted. Results Mean age of the patient group was determined to be 12.43 ± 3.04 years and that of the control group was determined to be 12.08 ± 2.56 years. There was no significant difference between the groups in regard to age, gender, body weight, height and body mass index. In the patient group; EATT, Pd, QTd, QTcd and Tp-e interval were determined to be significantly higher compared to the control group. In the patient group, no significant correlation was determined between EATT and Pd, QTd, QTcd and Tp-e. However, when both patient and control groups were evaluated together, a statistically significant positive correlation was determined between EATT and Pd, QTd, QTcd and Tp-e. Conclusions In children with T1DM, an increase in epicardial adipose tissue thickness and in risk of cardiac arrhythmias has been demonstrated. To reveal the possible unfavorable effects of EATT on cardiac conduction system in T1DM patients needs further studies.
Honey Bee Pollination Increases Soybean Yields in the Mississippi Delta Region of Arkansas and Missouri

The effect of foraging honey bees, Apis mellifera L. , on yield of soybeans, Glycine max (L.) Merr. , was evaluated in the Mississippi Delta regions of Arkansas and Missouri. In combined analysis of 2 cage trials, 15% more beans were produced in cages with bees than in cages without bees. In soybean fields with adjacent apiaries, yields were significantly higher at distances of up to 100 m from the apiaries than at greater distances.
Where did the vessels go? An analysis of the EU fishing fleet gravitation between home ports, fishing grounds, landing ports and markets

The mobile nature of fishing activity entails dynamic spatial relations and dependencies between coastal communities and fishing grounds drawn by the movement of fishing vessels. Analysing these spatial relations is essential to allocate the socio-economic impact of the fishing activity into the relevant coastal communities. In addition, such spatial information gives the possibility, on the one hand, to assess the impacts from fisheries on the marine environment and, on the other, to manage competing uses of the sea space between different activities. In this paper, we use AIS data, which is individual vessels’ positioning data, to examine the activity of the EU large-scale fishing fleets, their home ports, high intensity fishing areas (i.e., main fishing grounds), main ports and coastal communities involved.
Depression-level symptoms among Syrian refugees: findings from a Canadian longitudinal study

Abstract Background Canada launched the Syrian Refugee Resettlement Initiative in 2015 and resettled over 40,000 refugees. Aim To evaluate the prevalence of depression-level symptoms at baseline and one year post-resettlement and analyze its predictors. Methods Data come from the Syrian Refugee Integration and Long-term Health Outcomes in Canada study (SyRIA.lth) involving 1924 Syrian refugees recruited through a variety of community-based strategies. Data were collected using structured interviews in 2017 and 2018. Depression symptoms were measured using Patient Health Questionnaire 9 (PHQ-9). Analysis for associated factors was executed using multinomial logistic regression. Results Mean age was 38.5 years (SD 13.8). Sample included 49% males and 51% females settled in Ontario (48%), Quebec (36%) and British Columbia (16%). Over 74% always needed an interpreter, and only 23% were in employment. Prevalence of depression-level symptoms was 15% at baseline and 18% in year-2 (p < 0.001). Significant predictors of depression-level symptoms at year-2 were baseline depression, sponsorship program, province, poor language skills, lack of satisfaction with housing conditions and with health services, lower perceived control, lower perceived social support and longer stay in Canada. Conclusion Increase in depression-level symptoms deserves attention through focusing on identified predictors particularly baseline depression scores, social support, perceived control and language ability.
Aggregating Unstructured Submissions for Reliable Answers in Crowdsourcing Systems

In crowdsourcing systems, requesters assign tasks to multiple workers for obtaining reliable solutions. Task aggregation is performed on submissions received from crowd workers. Existing aggregation methods focus on structured submissions without accounting reliability factor of workers. Hence they are not suitable for aggregating unstructured answers. This paper proposes a novel task aggregation approach for a generic crowdsourcing tasks using an iterative probabilistic model. We make use of the reliability parameter and expertness of workers along with similarity information and requesters feedback. Experiments on the empirical data demonstrate that our scheme yields better results compared to existing state-of-the-art approaches.
Estimation of Knee Angles Based on Thigh Motion: A Functional Approach and Implications for High-Level Controlling of Active Prosthetic Knees

Human locomotion is the result of complex neuromusculoskeletal cooperation between different joints and limbs of the lower extremities. This collaboration (also called synergy [1]) results in an energyefficient gait. Unfortunately, for an above-knee amputee, this cooperation is lost since current prosthetic devices do not emulate the missing functionality in a manner similar to their biological counterparts. Transfemoral amputees can select between passive (Mauch knee [2]), semi-active (Ottobock's C-leg [3] and Ossur's rheo knee [4]), and active prosthetic devices (Ossur's power knee [5]). What distinguishes the active ones from the other two is the injection of positive power for activities such as ascending stairs and standing up from a chair.
Occluded Work-piece Localization via Adversarial Network and Template Matching

Work-piece recognition is a typical application of computer vision in the field of industry. In order to accomplish the task of work-piece assorting and assembling, the position and posture of work-pieces need to be obtained. However, the occlusion between several work-pieces is often occurred in industrial production sites, which brings a great challenge to their recognition. We proposed a novel work-piece recognition and localization method based on adversarial network and template matching, which can defend the much occlusion in the production line. Compared with most of current recognition methods on occlusion work-pieces, the proposed method is more robust to occlusion and light change, and achieves plausible performance.
Bayesian Network Based C2P Risk Assessment for Cyber-Physical Systems

Cyber-Physical Systems (CPS) refer to a new generation of intelligent systems with integrated computational performance and physical capabilities. However, with the expansion of system complexity and the enhancement of system openness, most CPS become not only safety-critical but also security-critical since they involv physical objects, computer networks and communications. In the past decade, it is no longer rare to see safety incidents and security attacks happening in industries. Identifying risks in CPS is critical to ensure the normal operation of these systems. The Cyber-to-Physical (C2P) risk is defined as the impact of cyber threats on physical process safety. In this paper, a C2P risk assessment model based on hierarchical Bayesian Network (BN) is proposed, and the feasibility of this model is verified by constructing two undesired event scenarios on a typical CPS. The quantitative risk values are explained and compared through qualitative risk analysis and assessment.
One-Class Classification in Images and Videos Using a Convolutional Autoencoder With Compact Embedding

In One-Class Classification (OCC) problems, the classifier is trained with samples of a class considered normal, such that exceptional patterns can be identified as anomalies. Indeed, for real-world problems, the representation of the normal class in the feature space is an important issue, considering that one or more clusters can describe different aspects of the normality. For classification purposes, it is important that these clusters be as compact (dense) as possible, for better discriminating anomalous patterns, which is a recurrent problem in OCC tasks. This work introduces a hybrid approach using deep learning and One-Class Support Vector Machine (OC-SVM) methods, named Convolutional Autoencoder with Compact Embedding (CAE-CE), for enhancing the compactness of clusters in the feature space. Such an approach is still underexplored in the literature, being restricted to models within the context of metric learning. Additionally, the absence of anomalous samples during training makes it difficult to determine when to interrupt the learning process, so as to avoid over-compression of the normal examples, thus resulting in overfitting of the model. In this work, we propose a novel sensitivity-based stop criterion, and its suitability for OCC problems was assessed. Using an OC-SVM for the classification task, several experiments were done using publicly available image and video datasets. We also introduce other two new benchmarks, specifically designed for video anomaly detection in highways. The final performance of the proposed method was compared with a baseline Convolutional Autoencoder (CAE). Overall results suggest that the enhanced compactness introduced by the CAE-CE improved the classification performance for most datasets. Also, the qualitative analysis of frames at the visual level indicated that features learned by CAE-CE are closely correlated to the anomalous events.
In Vitro Effect of Dovitinib (TKI258), a Multi-Target Angiokinase Inhibitor on Aggressive Meningioma Cells

Abstract Background: Meningiomas represent ∼30% of primary central nervous system (CNS) tumors. Although advances in surgery and radiotherapy have significantly improved survival, there remains an important subset of patients whose tumors have more aggressive behavior and are refractory to conventional therapy. Recent advances in molecular genetics and epigenetics suggest that this aggressive behavior may be due to the deletion of the DNA repair and tumor suppressor gene, CHEK2, neurofibromatosis Type 2 (NF2) mutation on chromosome 22q12, and genetic abnormalities in multiple RTKs including FGFRs. Management of higher-grade meningiomas, such as anaplastic meningiomas (AM: WHO grade III), is truly challenging and there isn’t an established chemotherapy option. We investigate the effect of active multi tyrosine receptor kinase inhibitor Dovitinib at stopping AM cell growth in in vitro with either frequent codeletion or mutated CHEK2 and NF2 gene. Methods: Treatment effects were assessed using MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay, western blot analysis, caspases assay, and DNA fragmentation assay. Results: Treatment of CH157MN and IOMM-Lee cells with Dovitinib suppressed multiple angiokinases—mainly FGFRs, leading to suppression of downstream signaling by RAS–RAF–MAPK molecules and PI3K–AKT molecules which are involved in cell proliferation, cell survival, and tumor invasion. Furthermore, Dovitinib induced apoptosis via downregulation of survival proteins (Bcl-XL), and over-expression of apoptotic factors (Bax and caspase-3) regardless of CHEK2 and NF2 mutation status. Conclusions: This study establishes the groundwork for the development of Dovitinib as a therapeutic agent for high-grade AM with either frequent codeletion or mutated CHEK2 and NF2, an avenue with high translational potential.
Gender classification from gait silhouette using observation angle-based GEIs

Gender classification is in high demand because of its versatility. It can be used as a surveillance system for security. It can also be used to classify customers in retail establishments. In this work, we propose a new gender classification technique from a gait silhouette using observation angle-based GEIs. The system consists of two main parts: the observation angle classification model and the gender classification model. The observation angle classification generates 10 observation angle-based GEIs. The gender classification model uses angle-based GEIs to predict gender. The proposed methods perform well with freestyle walks which contain a viewpoint issue. The proposed model achieves 90.74% accuracy in the freestyle walk dataset (SIIT-CN-B) and 97.58% on the CASIA-B (fixed-direction walk) dataset. The proposed model outperforms the existing models in both freestyle and fixed-direction walk datasets.
DUST-MASK: A Framework for Preventing Bitcoin’s Dust Attacks

Bitcoin Cryptocurrency is an advanced decentralized electronic payment system which stores all the transaction records in a public ledger connected to the blockchain. Many cyber-attacks including Double-Spending, Distributed denial-of-service (DDOS), Sybil attack and Dust attack, have been targeting Blockchain-based application Cryptocurrency, mainly Bitcoin cryptocurrency. In this paper, the proposed DUST-MASK, a secure Bitcoin system against dust attack, protects Bitcoin’s availability and pseudo-anonymity from attackers sending dust transactions in order to analyze the data and link the transactions to a specific user. The proposed system will inform the user of those kinds of malicious transactions and guarantee the user a choice whether to accept or reject them.
Terahertz Beam Steering Concept Based on a MEMS-Reconfigurable Reflection Grating

With an increasing number of applications of terahertz systems in industrial fields and communications, terahertz beamforming and beam steering techniques are required for high-speed, large-area scanning. As a promising means for beam steering, micro-electro-mechanical system (MEMS)-based reflection gratings have been successfully implemented for terahertz beam control. So far, the diffraction grating efficiency is relatively low due to the limited vertical displacement range of the reflectors. In this paper, we propose a design for a reconfigurable MEMS-based reflection grating consisting of multiple subwavelength reflectors which are driven by 5-bit, high-throw electrostatic actuators. We vary the number of the reflectors per grating period and configure the throw of individual reflectors so that the reflection grating is shaped as a blazed grating to steer the terahertz beam with maximum diffraction grating efficiency. Furthermore, we provide a mathematical model for calculating the radiation pattern of the terahertz wave reflected by general reflection gratings consisting of subwavelength reflectors. The calculated and simulated radiation patterns of the designed grating show that we can steer the angle of the terahertz waves in a range of up to ± 56.4∘ with a maximum sidelobe level of −10 dB at frequencies from 0.3 THz to 1 THz.
Effect of 4% glycerol and low aeration on result of expression in Escherichia coli of Cin3 and three Venturia inaequalis EST's recombinant proteins

The phytopathogenic fungus Venturia inaequalis causes scab of apple. Once this fungus penetrates the plant surface, it forms a specialized body called a stroma between the inner cuticle surface and the epidermal cell wall. A novel Venturia inaequalis 5704 (Cin3) and three expressed sequence tags (ESTs); 38, 6987, and 4010 are strongly up-regulated in the early stages of infection. The CIN3 and three ESTs using two vectors pMAL-c2 and pET 21 were expressed in Escherichia coli. Recombinant proteins expression, solubility and yields were analyzed. 38, 5704 (Cin3) and 6987 re- combinant proteins were expressed in soluble form and while 4010 was expressed in inclusion bodies. Re- solution on native-PAGE, the recombinant proteins; 38, 5704 (Cin3), 6987 were shown to be present in dimmer, tetramer and polymer. A method was de- veloped, consisting of induction of expression at va- rious temperatures, and using enriched broth with 4% glycerol together with slow shaking, led to a decrease in concentration of nascent polypeptide and production of soluble recombinant proteins of; 38, 5704 (Cin3), 6987 and 4010. Resolution on native- PAGE, the recombinant proteins were shown to be present as monomer.
NOMA in Cooperative Underlay Cognitive Radio Networks Under Imperfect SIC

In conventional cognitive radio (CR), the users in secondary network (SN) can only access the idle spectrum when users in primary network (PN) are absent. This novel strategy provides higher spectrum efficiency when detecting the presence of the PN. Hence, spectrum utilization of the traditional scheme can be further improved as exploiting application of non-orthogonal multiple access (NOMA). As combination of CR and NOMA, such CR-NOMA has been proposed to improve spectrum efficiency to adapt to requirements in 5G communications. In this study, the relaying scheme is employed in the SN of the proposed CR-NOMA and the relay is allowed to energy harvesting (EH) from the secondary transmitter to serve signal forwarding to distant secondary users. With this regard, the complex model of EH-assisted CR-NOMA is explored in outage behavior and throughput performance as awareness on imperfect successive interference cancellation (SIC) at the receiver. As most important results, the exact closed-form of the exact outage probability is derived for each NOMA destination by assuming that the channel coefficients among considered links follow Rayleigh distribution. Furthermore, performance gap between two NOMA users can be controlled by various parameters such as transmit power, energy harvesting coefficients and levels of imperfect SIC. Simulation results verify our analytical results.
Pitx2-Sox2-Lef1 interactions specify progenitor oral/dental epithelial cell signaling centers

ABSTRACT Epithelial signaling centers control epithelial invagination and organ development, but how these centers are specified remains unclear. We report that Pitx2 (the first transcriptional marker for tooth development) controls the embryonic formation and patterning of epithelial signaling centers during incisor development. We demonstrate using Krt14Cre/Pitx2flox/flox (Pitx2cKO) and Rosa26CreERT/Pitx2flox/flox mice that loss of Pitx2 delays epithelial invagination, and decreases progenitor cell proliferation and dental epithelium cell differentiation. Developmentally, Pitx2 regulates formation of the Sox2+ labial cervical loop (LaCL) stem cell niche in concert with two signaling centers: the initiation knot and enamel knot. The loss of Pitx2 disrupted the patterning of these two signaling centers, resulting in tooth arrest at E14.5. Mechanistically, Pitx2 transcriptional activity and DNA binding is inhibited by Sox2, and this interaction controls gene expression in specific Sox2 and Pitx2 co-expression progenitor cell domains. We demonstrate new transcriptional mechanisms regulating signaling centers by Pitx2, Sox2, Lef1 and Irx1. Summary: New mechanisms are shown for the role of two early epithelial signaling centers required for tooth development. A transcriptional network controlling stem cell proliferation and patterning is identified involving protein-protein interactions.