Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinically Significant Dysregulation of hsa-miR-30d-5p and hsa-let-7b Expression in Patients with Surgically Resected Non-Small Cell Lung Cancer

Background: The cyclin E2 (CYCE2) is an important regulator in the progression and development of NSCLC, and its ectopic expression promoted the proliferation, invasion, and migration in several tumors, including Non-Small Cell Lung Cancer (NSCLC). However, the upregulation of CYCE2 in NSCLC cells suggested that it has a key role in tumorigenicity. In addition, the RAS family proteins as oncoproteins were activated in many major tumor types and its suitability as the therapeutic target in NSCLC was proposed. Considering the crucial role of microRNAs, it was hypothesized that altered expression of hsa-miR-30d-5p and hsa-let-7b might provide a reliable diagnostic tumor marker for diagnosis of NSCLC. Method: Real-time RT-PCR approach could evaluate the expression alteration of hsa-miR-30d-5p and hsa-let-7b and it was related to the surgically resected tissue of 24 lung cancer patients and 10 non-cancerous patients. The miRNAs expression was associated with clinicopathological features of the patients. Results: Hsa-miR-30d showed a significant downregulation (p=0.0382) in resected tissue of NSCLC patients compared with control group. Its expression level could differentiate different stages of malignancies from each other. The ROC curve analysis gave it an AUC=0.73 (p=0.037) which was a good score as a reliable biomarker. In contrast, hsa-let-7b was significantly overexpressed in tumor samples (p=0.03). Interestingly, our findings revealed a significant association of hsa-let-7b in adenocarcinoma tumors, compared to Squamous Cell Carcinomas (SCC) (p<0.05). Also, analysis of ROC curve of hsa-let-7b (AUC=0.74, p-value=0.042) suggests that it could be as a suitable biomarker for NSCLC. Conclusion: Together, these results suggest a possible tumor suppressor role for hsa-miR-30d in lung tumor progression and initiation. Moreover, upregulation of hsa-let-7b was associated with the tumor type.
Identifying and Categorising Profane Words in Hate Speech

This study attempts to explore the different types of Hate Speech appearing in social media by identifying profane words used in hate speech. This study also compares the profane words used in different generations to assist in identifying the user's profile. Five-hundred (500) comments posted on YouTube on the abusive topics were collected. Profane words are classified into eight different types of hate speech. The finding shows 35% of profane words found in our sample are words related to sexual orientation. Comparison of the terms between 1970 and 2017 also show a high percentage of profane words are sexual orientation. Though the results are found based on only 500 comments collected from YouTube link in the current study, they are useful in establishing the list of profane words which will serve as the base for automatic hate speech identification in our future study. The originality of this research is the development of a training list of profane words for each category and comparison of the type of the words used in 1970 century with today's social media platform.
Robust Control Contraction Metrics: A Convex Approach to Nonlinear State-Feedback  ${H}^\infty$  Control

This letter proposes a new method for robust state-feedback control design for nonlinear systems. We introduce robust control contraction metrics (RCCM), extending the method of control contraction metrics from stabilization to disturbance attenuation and robust control. An RCCM is a Riemannian metric that verifies differential $L^{2}$ -gain bounds in closed-loop, and guarantees robust stability of arbitrary trajectories via small gain arguments. Numerical search for such a metric can be transformed to a convex optimization problem. We also show that the associated Riemannian energy can be used as a robust control Lyapaunov function. A simple computational example based on jet-engine surge illustrates the approach.
Public HMDs: Modeling and Understanding User Behavior around Public Head-Mounted Displays

Head-Mounted Displays (HMDs) are becoming ubiquitous; we are starting to see them deployed in public for different purposes. Museums, car companies and travel agencies use HMDs to promote their products. As a result, situations arise where users use them in public without experts supervision. This leads to challenges and opportunities, many of which are experienced in public display installations. For example, similar to public displays, public HMDs struggle to attract the passer-by's attention, but benefit from the honeypot effect that draws attention to them. Also passersby might be hesitant to wear a public HMD, due to the fear that its owner might not approve, or due to the perceived need for a prior permission. In this work, we discuss how public HMDs can benefit from research in public displays. In particular, based on the results of an in-the-wild deployment of a public HMD, we propose an adaptation of the audience funnel flow model of public display users to fit the context of public HMD usage. We discuss how public HMDs bring in challenges and opportunities, and create novel research directions that are relevant to both researchers in HMDs and researchers in public displays.
Sense Space for Word Sense Disambiguation

Word sense disambiguation is essential for semantic analysis in many natural language-related applications, such as information retrieval, data mining, and machine translation. One of the effective models for word sense disambiguation is the word space model that represents context vectors and sense vectors in a word vector space. In this paper, we extend the word vector space model to reflect a more finegrained meaning in context vectors by incorporating embedded senses. Using a large Korean sense-tagged corpus, we build an embedded sense space with supervised learning and evaluate the effectiveness of the sense embedding for word sense disambiguation.
Sedimentary mechanisms of a modern banded iron formation on Milos Island, Greece

Abstract. An early Quaternary shallow submarine hydrothermal iron formation (IF) in the Cape Vani sedimentary basin (CVSB) on Milos Island, Greece, displays banded rhythmicity similar to Precambrian banded iron formation (BIF). Field-wide stratigraphic and biogeochemical reconstructions show two temporal and spatially isolated iron deposits in the CVSB with distinct sedimentological character. Petrographic screening suggests the presence of a photoferrotrophic-like microfossil-rich IF (MFIF), accumulated on a basement consisting of andesites in a ∼ 150 m wide basin in the SW margin of the basin. A banded nonfossiliferous IF (NFIF) sits on top of the Mn-rich sandstones at the transition to the renowned Mn-rich formation, capping the NFIF unit. Geochemical data relate the origin of the NFIF to periodic submarine volcanism and water column oxidation of released Fe(II) in conditions predominated by anoxia, similar to the MFIF. Raman spectroscopy pairs hematite-rich grains in the NFIF with relics of a carbonaceous material carrying an average δ13Corg signature of ∼ −25‰. A similar δ13Corg signature in the MFIF could not be directly coupled to hematite by mineralogy. The NFIF, which postdates large-scale Mn deposition in the CVSB, is composed primarily of amorphous Si (opal-SiO2 ⋅ nH2O) while crystalline quartz (SiO2) predominates the MFIF. An intricate interaction between tectonic processes, changing redox, biological activity, and abiotic Si precipitation are proposed to have collectively formed the unmetamorphosed BIF-type deposits in a shallow submarine volcanic center. Despite the differences in Precambrian ocean–atmosphere chemistry and the present geologic time, these formation mechanisms coincide with those believed to have formed Algoma-type BIFs proximal to active seafloor volcanic centers.
Evaluating bilingual word embeddings on the long tail

Bilingual word embeddings are useful for bilingual lexicon induction, the task of mining translations of given words. Many studies have shown that bilingual word embeddings perform well for bilingual lexicon induction but they focused on frequent words in general domains. For many applications, bilingual lexicon induction of rare and domain-specific words is of critical importance. Therefore, we design a new task to evaluate bilingual word embeddings on rare words in different domains. We show that state-of-the-art approaches fail on this task and present simple new techniques to improve bilingual word embeddings for mining rare words. We release new gold standard datasets and code to stimulate research on this task.
Large-Scale Distance Metric Learning with Uncertainty

Distance metric learning (DML) has been studied extensively in the past decades for its superior performance with distance-based algorithms. Most of the existing methods propose to learn a distance metric with pairwise or triplet constraints. However, the number of constraints is quadratic or even cubic in the number of the original examples, which makes it challenging for DML to handle the large-scale data set. Besides, the real-world data may contain various uncertainty, especially for the image data. The uncertainty can mislead the learning procedure and cause the performance degradation. By investigating the image data, we find that the original data can be observed from a small set of clean latent examples with different distortions. In this work, we propose the margin preserving metric learning framework to learn the distance metric and latent examples simultaneously. By leveraging the ideal properties of latent examples, the training efficiency can be improved significantly while the learned metric also becomes robust to the uncertainty in the original data. Furthermore, we can show that the metric is learned from latent examples only, but it can preserve the large margin property even for the original data. The empirical study on the benchmark image data sets demonstrates the efficacy and efficiency of the proposed method.
Group Sparse Representation Based on Nonlocal Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification

Spectral-spatial classification has been widely applied for remote sensing applications, especially for hyperspectral imagery. Traditional methods mainly focus on local spatial similarity and neglect nonlocal spatial similarity. Recently, nonlocal self-similarity (NLSS) has gradually gained support since it can be used to support spatial coherence tasks. However, these methods are biased towards the direct use of spatial information as a whole, while discriminative spectral information is not well exploited. In this paper, we propose a novel method to couple both nonlocal spatial and local spectral similarity together in a single framework. In particular, the proposed approach exploits nonlocal spatial similarities by searching non-overlapped patches, whereas spectral similarity is analyzed locally within the locally discovered patches. By fusion of nonlocal and local information, we then apply group sparse representation (GSR) for classification based on a group structured prior. Experimental results on three real hyperspectral data sets demonstrate the efficiency of the proposed approach, and the improvements are significant over the methods that consider either nonlocal or local similarity.
Coalitional Game for the Creation of Efficient Virtual Core Network Slices in 5G Mobile Systems

Many ongoing research activities relevant to 5G mobile systems concern the virtualization of the mobile core network, including the evolved packet core (EPC) elements, aiming for system scalability, elasticity, flexibility, and cost-efficiency. Virtual EPC (vEPC)/5G core will principally rely on some key technologies, such as network function virtualization, software defined networking, and cloud computing, enabling the concept of mobile carrier cloud. The key idea beneath this concept, also known as core network as a service, consists in deploying virtual instances (i.e., virtual machines or containers) of key core network functions [i.e., virtual network functions (VNF) of 4G or 5G], such as the mobility management entity (MME), Serving GateWay (SGW), Packet Data network gateWay (PGW), access and mobility management function (AMF), session management function (SMF), authentication server function (AUSF), and user plane functions, over a federated cloud. In this vein, an efficient VNF placement algorithm is highly needed to sustain the quality of service (QoS) while reducing the deployment cost. Our contribution in this paper is twofold. First, we devise an algorithm that derives the optimal number of virtual instances of 4G (MME, SGW, and PGW) or 5G (AMF, SMF, and AUSF) core network elements to meet the requirements of a specific mobile traffic. Second, we propose an algorithm for the placement of these virtual instances over a federated cloud. While the first algorithm is based on mixed integer linear programming, the second is based on coalition formation game, wherein the aim is to build coalitions of cloud networks to host the virtual instances of the vEPC/5G core elements. The obtained results clearly indicate the advantages of the proposed algorithms in ensuring QoS given a fixed cost for vEPC/5G core deployment, while maximizing the profits of cloud operators.
Knowledge representation for image-content analysis in medical image database

The object-oriented knowledge representation is considered as a natural and effective approach. Nevertheless, the use of object-oriented within complex image analysis has not undergone a rapid growth as it happened in other fields. We argue that one of the major problems comes from the difficulty of conceiving a comprehensive framework for coping with the different abstraction levels and the vision task operations. With the goal to overcome such a drawback, we present a new knowledge model for medical image content analysis based on the object-oriented paradigm. The new model abstracts common model for medical image content analysis based on the object-oriented paradigm. The new model abstracts common properties from different types of medical images by using three attribute parts: description, component, and semantic graph, and also specifies its actions to schedule the detection procedure, properly deform the shape of model components to match the corresponding anatomies in images, select the best match candidates, and verify combination graphs from detected candidates with the semantic graph defined in the model. The performance of the proposed model has been tested on pelvis digital radiographs. Initial results are encouraging.
Image Captioning with Scene-graph Based Semantic Concepts

Different from existing approaches for image captioning, in this paper, we explore the co-occurrence dependency of high-level semantic concepts and propose a novel method with scene-graph based semantic representation for image captioning. To embed scene graph as an intermediate state, we divide the task of image captioning into two phases, called concept cognition and sentence construction respectively. We build a vocabulary of semantic concepts and propose a CNN-RNN-SVM framework to generate the scene-graph-based sequence, which is then transformed into a bit vector, as the input of RNN in the next phase. We evaluate our method on MS COCO dataset. Experimental results show that our approaches obtain a competitive or superior result to the state-of-the-arts.
Efficient k-Regret Query Algorithm with Restriction-free Bound for any Dimensionality

Extracting interesting tuples from a large database is an important problem in multi-criteria decision making. Two representative queries were proposed in the literature: top- k queries and skyline queries. A top- k query requires users to specify their utility functions beforehand and then returns k tuples to the users. A skyline query does not require any utility function from users but it puts no control on the number of tuples returned to users. Recently, a k-regret query was proposed and received attention from the community because it does not require any utility function from users and the output size is controllable, and thus it avoids those deficiencies of top- k queries and skyline queries. Specifically, it returns k tuples that minimize a criterion called the maximum regret ratio . In this paper, we present the lower bound of the maximum regret ratio for the k -regret query. Besides, we propose a novel algorithm, called SPHERE, whose upper bound on the maximum regret ratio is asymptotically optimal and restriction-free for any dimensionality, the best-known result in the literature. We conducted extensive experiments to show that SPHERE performs better than the state-of-the-art methods for the k -regret query.
Vitamin K Antagonists and Cognitive Decline in Older Adults: A 24-Month Follow-Up

Vitamin K participates in brain physiology. This study aimed to determine whether using vitamin K antagonists (VKAs), which interfere with the vitamin K cycle, were (i) cross-sectionally associated with altered cognitive performance, and (ii) independent predictors of cognitive changes in older adults over 24 months. Information was collected on the use of VKAs (i.e., warfarin, acenocoumarol, and fluindione) among 378 geriatric outpatients (mean, 82.3 ± 5.6 years; 60.1% female). Global cognitive performance and executive functions were assessed with Mini-Mental State Examination (MMSE) and Frontal Assessment Battery (FAB) scores, respectively, at baseline and after 12 and 24 months of follow-up. Age, gender, body mass index, mean arterial pressure, disability, gait speed, comorbidities, atrial fibrillation, stroke, carotid artery stenosis, leukoaraiosis grade on computed tomography (CT) scan, psychoactive drugs, antidementia drugs, blood-thinning drugs (i.e., anticoagulants other than VKAs, antiplatelet medications), serum creatinine levels, and vitamin B12 concentrations were considered as potential confounders. Using VKAs was associated with lower (i.e., worse) FAB score at baseline (adjusted β = −2.1, p = 0.026), and with a decrease in FAB score after 24 months (adjusted β = −203.6%, p = 0.010), but not after 12 months (p = 0.659). Using VKAs was not associated with any change in MMSE score at baseline (p = 0.655), after 12 months (p = 0.603), or after 24 months (p = 0.201). In conclusion, we found more severe executive dysfunction at baseline and incident executive decline over 24 months among geriatric patients using VKAs, when compared with their counterparts.
Structural and biochemical analysis of the dual-specificity Trm10 enzyme from Thermococcus kodakaraensis prompts reconsideration of its catalytic mechanism

tRNA molecules get heavily modified post-transcriptionally. The N-1 methylation of purines at position 9 of eukaryal and archaeal tRNA is catalyzed by the SPOUT methyltranferase Trm10. Remarkably, while certain Trm10 orthologs are specific for either guanosine or adenosine, others show a dual specificity. Structural and functional studies have been performed on guanosine- and adenosine-specific enzymes. Here we report the structure and biochemical analysis of the dual-specificity enzyme from Thermococcus kodakaraensis (TkTrm10). We report the first crystal structure of a construct of this enzyme, consisting of the N-terminal domain and the catalytic SPOUT domain. Moreover, crystal structures of the SPOUT domain, either in the apo form or bound to S-adenosyl-l-methionine or S-adenosyl-l-homocysteine reveal the conformational plasticity of two active site loops upon substrate binding. Kinetic analysis shows that TkTrm10 has a high affinity for its tRNA substrates, while the enzyme on its own has a very low methyltransferase activity. Mutation of either of two active site aspartate residues (Asp206 and Asp245) to Asn or Ala results in only modest effects on the N-1 methylation reaction, with a small shift toward a preference for m1G formation over m1A formation. Only a double D206A/D245A mutation severely impairs activity. These results are in line with the recent finding that the single active-site aspartate was dispensable for activity in the guanosine-specific Trm10 from yeast, and suggest that also dual-specificity Trm10 orthologs use a noncanonical tRNA methyltransferase mechanism without residues acting as general base catalysts.
Therapeutic effects of various therapeutic strategies on non-exudative age-related macular degeneration

Purpose: Age-related macular degeneration (AMD) is a chronic progressive central retinal disease. Geographic atrophy (GA) is a late stage of dry AMD (DAMD) and is a slowly but inexorably progressive disease that causes irreversible blindness over time. We aimed to assess various therapeutic strategies for DAMD and GA treatment by network meta-analysis. Methods: We searched PubMed, Embase, and the Cochrane Library to identify randomized controlled trials (RCTs) of atrophic AMD treatments published prior to December 16, 2017. Best-corrected visual acuity (BCVA) and change in GA area were evaluated to reflect therapeutic effects. A random-effects network meta-analysis, with a frequentist framework, was used to assess the effectiveness of therapeutic strategies for DAMD treatment. Results: We included 22 articles that assessed 16 types of regimens and 2482 patients in our meta-analysis. The network meta-analysis results showed that zinc-monocysteine (98.1%) was the most likely to improve BCVA (logMAR), followed by alprostadil (84.0%), eculizumab (70.5%), and rheohemapheresis (67.3%). In BCVA (letters) outcomes, rheohemapheresis (99.6%), lampalizumab (69.5%), and the antioxidant complex (67.9%) showed marked benefits in visual function recovery. Regarding the outcome of GA area change, isopropyl unoprostone (IU) (88.6%) might have the best GA area reduction; however, there was no significant difference between IU and the blank control. Conclusions: Zinc-monocysteine and rheohemapheresis showed significantly better effects on BCVA (logMAR) improvement, and compared with the blank control, rheohemapheresis and the antioxidant complex showed better effects on BCVA (letters) improvement. Other treatments have potential effects on DAMD, including alprostadil, eculizumab, and lampalizumab. However, there is no effective treatment for GA area reduction.
Resting-State Pallidal-Cortical Oscillatory Couplings in Patients With Predominant Phasic and Tonic Dystonia

Pallidal deep brain stimulation (DBS) improves the symptoms of dystonia. The improvement processes of dystonic movements (phasic symptoms) and tonic symptoms differ. Phasic symptoms improve rapidly after starting DBS treatment, but tonic symptoms improve gradually. This difference implies distinct neuronal mechanisms for phasic and tonic symptoms in the underlying cortico-basal ganglia neuronal network. Phasic symptoms are related to the pallido–thalamo–cortical pathway. The pathway related to tonic symptoms has been assumed to be different from that for phasic symptoms. In the present study, local field potentials of the globus pallidus internus (GPi) and globus pallidus externus (GPe) and electroencephalograms from the motor cortex (MCx) were recorded in 19 dystonia patients to analyze the differences between the two types of symptoms. The 19 patients were divided into two groups, 10 with predominant phasic symptoms (phasic patients) and 9 with predominant tonic symptoms (tonic patients). To investigate the distinct features of oscillations and functional couplings across the GPi, GPe, and MCx by clinical phenotype, power and coherence were calculated over the delta (2–4 Hz), theta (5–7 Hz), alpha (8–13 Hz), and beta (14–35 Hz) frequencies. In phasic patients, the alpha spectral peaks emerged in the GPi oscillatory activities, and alpha GPi coherence with the GPe and MCx was higher than in tonic patients. On the other hand, delta GPi oscillatory activities were prominent, and delta GPi–GPe coherence was significantly higher in tonic than in phasic patients. However, there was no significant delta coherence between the GPi/GPe and MCx in tonic patients. These results suggest that different pathophysiological cortico-pallidal oscillations are related to tonic and phasic symptoms.