Miracle-dz/UniFAR-FTUs huggingface dataset:
Selective heat generation in cancer cells using a combination of 808 nm laser irradiation and the folate-conjugated Fe2O3@Au nanocomplex

Abstract Photothermal therapy (PTT) is a nanotechnology-assisted cancer hyperthermia approach in which the interaction between laser light and plasmonic nanoparticles (NPs) generates localized heating. The exploitation of plasmonic NPs in association with active targeting moieties causes the preferential accumulation of NPs inside cancer cells, thereby providing targeted PTT. Herein, we evaluate the effect of folic acid (FA) as an active targeting agent in enhancing the photothermal efficiency of multifunctional Iron (III) Oxide (Fe2O3)@Au core- shell NPs. Fe2O3@Au NPs were synthesized, modified with FA and then characterized. Human nasopharyngeal (KB) cancer cells were treated with different concentrations of Fe2O3@Au, with and without FA modification and the temperature rise profiles of the cells were measured upon administration of the near-infrared (NIR) laser (808 nm, 6 W/cm2, 10 min). The recorded temperature profiles of the cells were used for thermal dose calculation. Finally, the level of induced apoptosis was determined by flow cytometry using an annexin V-fluorescein isothiocyanate/propidium iodide apoptosis detection kit. The characterization data showed that the Fe2O3@Au NPs are spherical, with a hydrodynamic size of 33 nm. The data corroborated the successful conjugation of the NPs with FA. The thermometry results indicated the superior temperature elevation rate of the cells in the presence of the NPs upon NIR irradiation. Meanwhile, the higher heating rate and the higher thermal dose were obtained for the cells exposed to FA-targeted Fe2O3@Au rather than the non-targeted nanocomplex. Flow cytometry studies revealed that FA-targeted Fe2O3@Au induced higher level of apoptosis than non-targeted Fe2O3@Au NPs. In conclusion, our findings suggest that the synthesized FA-targeted Fe2O3@Au NP has high potentials to be considered as an efficient thermosensitizer in the process of targeted cancer hyperthermia.
A Marker-less Augmented Reality System using Image Processing Techniques for Architecture and Urban Environment

In this study, Augmented Reality (AR) system is proposed to be used for outdoor renovation and maintenance projects of build- ings. The research proposes an outdoor marker-less AR system that considers the mobility of users and their long relative distance to tar- get buildings where 3D virtual objects should be augmented on. The proposed system uses local feature-based image registration technolo- gy and Structure from Motion (SfM) which reconstructs 3DCG mod- els using photographs from multiple viewpoints. A case study has been performed for a research building renovation scenario at Osaka University. The case study verified the performance of image registra- tion and tracking, and confirmed the applicability of the method.
Fast Pattern Matching in Strings

An algorithm is presented which finds all occurrences of one given string within another, in running time proportional to the sum of the lengths of the strings. The constant of proportionality is low enough to make this algorithm of practical use, and the procedure can also be extended to deal with some more general pattern-matching problems. A theoretical application of the algorithm shows that the set of concatenations of even palindromes, i.e., the language $\{\alpha \alpha ^R\}^*$, can be recognized in linear time. Other algorithms which run even faster on the average are also considered.
A model for speech reverberation and intelligibility restoring filters

The problem of removing channel effects from speech has generally been attacked by attempting to recover a time-varying ﬁlter which inverts the entire channel impulse response. We show that human listeners are insensitive to many channel conditions and that the human ear seems to respond primarily to discontinuities of the channel. As a result of these observations, a partial equalization is proposed in which the channel effects to which the ear is sensitive may be removed, without full inversion of the channel. In addition, it is shown that it is possible to build ﬁlters of arbitrary length which do not reduce speech intelligibility and do not produce annoying artifacts.
Relationship of inhaled long-acting bronchodilators with cardiovascular outcomes among patients with stable COPD: a meta-analysis and systematic review of 43 randomized trials

Background: Long-acting muscarinic antagonists (LAMAs) and long-acting β2–agonists (LABAs) are the mainstay of maintenance therapy for chronic obstructive pulmonary disease (COPD). Although previous studies have supported inhaled long-acting bronchodilators (ILABs) for overall cardiovascular safety, the risk of specific cardiovascular outcomes such as arrhythmia, heart failure and stroke is still unknown. Materials and methods: We systematically searched from PubMed, the Embase database and the Cochrane Library for published studies on ILABs and COPD, from its inception to November 10, 2018, with no language restrictions. The RRs and corresponding 95% CIs were pooled to evaluate ILAB/placebo. Results: Finally, 43 randomized controlled trials were included. Compared with placebo, ILABs do not increase the risk of overall and specific cardiovascular adverse events (AEs); on the contrary, they can reduce the incidence of hypertension (RR 0.73, 95% CI 0.55–0.98;I219.9%; P= 0.221). However, when stratified according to the specific agents of ILABs, olodaterol might reduce the risk of overall cardiovascular adverse events (OCAEs) (RR 0.65, 95% CI 0.49–0.88;I227.5%; P= 0.000), and the protective effect of lowing blood pressure disappeared. Similarly, the use of inhaled LABA might increase the risk of cardiac failure (RR 1.71, 95% CI 1.04–2.84;I20%; P= 0.538), but this risk disappeared when stratified according to the specific agents of LABA. Besides, formoterol might decrease the risk of cardiac ischemia (RR 0.53, 95% CI 0.32–0.91; I20%; P= 0.676). Conclusions: Overall, the use of ILABs was not associated with overall cardiovascular AEs in patients with stable COPD. When stratified according to the specific agents of LABA, olodaterol might reduce the risk of OCAE; and formoterol might decrease the risk of cardiac ischemia. LABA might reduce the incidence of hypertension, but might increase the risk of heart failure. Therefore, COPD patients with a history of heart failure should use it with caution.
The Novel Cdc 42 Guanine Nucleotide Exchange Factor , Zizimin 1 , Dimerizes via the Cdc 42-binding CZH 2 Domain *

Rho family small GTPases are critical regulators of multiple cellular processes and activities. Dbl homology domain-containing proteins are the classical guanine nucleotide exchange factors (GEFs) responsible for activation of Rho proteins. Recently another group of mammalian Rho-GEFs was discovered that includes CDM (Ced-5, DOCK180, Myoblast city) proteins that activate Rac and zizimin1 that activates Cdc42 via a nonconventional GEF module that we named the CZH2 domain. We report here that zizimin1 dimerizes via the CZH2 domain and that dimers are the only form detected. Dimerization was mapped to a 200-amino acid region that overlaps but is distinct from the Cdc42binding sequences. Rotary shadowing electron microscopy revealed zizimin1 to be a symmetric, V-shaped molecule. Experiments with DOCK180 and homology analysis suggest that dimerization may be a general feature of CZH proteins. Deletion and mutation analysis indicated existence of individual Cdc42-binding sites in the zizimin1 monomers. Kinetic measurements demonstrated increased binding affinity of Cdc42 to zizimin1 at higher Cdc42 concentration, suggesting positive cooperativity. These features are likely to be critical for Cdc42 activation.
Classification of Alzheimer's Disease using fMRI Data and Deep Learning Convolutional Neural Networks

Over the past decade, machine learning techniques especially predictive modeling and pattern recognition in biomedical sciences from drug delivery system to medical imaging has become one of the important methods which are assisting researchers to have deeper understanding of entire issue and to solve complex medical problems. Deep learning is power learning machine learning algorithm in classification while extracting high-level features. In this paper, we used convolutional neural network to classify Alzheimer's brain from normal healthy brain. The importance of classifying this kind of medical data is to potentially develop a predict model or system in order to recognize the type disease from normal subjects or to estimate the stage of the disease. Classification of clinical data such as Alzheimer's disease has been always challenging and most problematic part has been always selecting the most discriminative features. Using Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified functional MRI data of Alzheimer's subjects from normal controls where the accuracy of test data on trained data reached 96.85%. This experiment suggests us the shift and scale invariant features extracted by CNN followed by deep learning classification is most powerful method to distinguish clinical data from healthy data in fMRI. This approach also enables us to expand our methodology to predict more complicated systems.
Automatic Vehicle Detection and Classification

This paper presents a proposal for an automatic vehicle detection and classification (AVDC) system. The proposed AVDC should classify vehicles accordingly to the Portuguese legislation (vehicle height over the first axel and number of axels), and should also support profile based classification. The AVDC should also fulfill the needs of the Portuguese motorway operator, Brisa. For the classification based on the profile we propose the use of Eigenprofiles, a technique based on Principal Components Analysis. The system should also support multi-lane free flow for future integration in this kind of environments.
Bilingual deaf readers’ use of semantic and syntactic cues in the processing of English relative clauses*

Eye fixation measures were used to examine English relative clause processing by adult ASL–English bilingual deaf readers. Participants processed subject relative clauses faster than object relative clauses, but expected animacy cues eliminated processing difficulty in object relative clauses. This brings into question previous claims that deaf readers’ sentence processing strategies are qualitatively different from those of hearing English native speakers. Measures of English comprehension predicted reading speed, but not differences in syntactic processing. However, a trend for ASL self-ratings to predict the ability to handle syntactic complexity approached significance. Results suggest a need to explore how objective ASL proficiency measures might provide insights into deaf readers’ ability to exploit syntactic cues in English.
Complete mitochondrial genome of the Sepiella maindroni (Sepioidea: Sepiidae)

Abstract The complete mitochondrial genome of the Sepiella maindroni has been sequenced. The mitochondrial genome is 16 170 bp in length, containing 13 protein-coding genes, two ribosomal RNA genes, and 22 transfer RNA genes. The gene order and composition of S. maindroni mitochondrial genome was similar to that of most other invertebrates. The overall nucleotides base composition of the heavy strand is A (39.91%), G (8.06% of), C (16.52%), and T (35.50%), with highly A + T bias of 75.41%. Seen from the phylogenetic tree, S. maindroni, Sepiella japonica, and Sepiella inermis from the same genus (Sepiella) clustered into one branch.
Vertical-Aware Click Model-Based Effectiveness Metrics

Today's web search systems present users with heterogeneous information coming from sources of different types, also known as verticals. Evaluating such systems is an important but complex task, which is still far from being solved. In this paper we examine the hypothesis that the use of models that capture user search behavior on heterogeneous result pages helps to improve the quality of offline metrics. We propose two vertical-aware metrics based on user click models for federated search and evaluate them using query logs of the Yandex search engine. We show that depending on the type of vertical, the proposed metrics have higher correlation with online user behavior than other state-of-the-art techniques.
Performance monitoring of service-level agreements for utility computing using the event calculus

Utility computing (UC) is concerned with the provisioning of computational resources (compute-power, storage, network bandwidth), on a per-need basis, to corporate businesses. Service-level agreements (SLAs) - contracts between a provider and a customer - are a sine qua non in the deployment of UC. A crucial stage in the life-cycle of contracts (such as SLAs) is their automated performance monitoring while active; a significant aspect of which concerns the tracking of contract state. In this work, we define an ontology to capture aspects of SLAs that are pertinent to the tracking of state for performance monitoring, and generalise these aspects so that the ontology may be applicable to other contract domains. The ontology is formalised as an XML-based language, called CTXML (contract tracking XML). The semantics for CTXML are presented in terms of a computational model based on the event calculus.
Two-stage Optimization Approach to Robust Model Predictive Control with a Joint Chance Constraint

This report proposes a new two-stage optimization method for robust Model Predictive Control (RMPC) with Gaussian disturbance and state estimation error. Since the disturbance is unbounded, it is impossible to achieve zero probability of violating constraints. Our goal is to optimize the expected value of a objective function while limiting the probability of violating any constraints over the planning horizon (joint chance constraint). Prior arts include constraint tightening with ellipsoidal relaxation [8] and Particle Control [1], but the former yields very conservative result and the latter is computationally intensive. Our new approach divide the optimization problem into two stages; the upper-stage that optimizes risk allocation, and the lower-stage that optimizes control sequence with tightened constraints. The lower-stage is a regular convex optimization, such as Linear Programming or Quadratic Programming. The upper-stage is also a convex optimization under practical assumption, but the objective function is not always differentiable, and computation of its gradient or subgradient is expensive for large-scale problem. A necessary condition for optimality, which does not explicitly use gradient and hence easy to compute, is discussed. A descent algorithm for the upper-stage called Iterative Risk Allocation (IRA), which does not require the computation of gradient, is proposed. Although the algorithm is not guaranteed to converge to the optimal, empirical results show that it converges quickly to a point that is close to the optimal. Suboptimality is much smaller than ellipsoidal relaxation method while achieving a substantial speedup compared to Particle Control.
Efficient SON handover scheme for enterprise Femtocell networks

Femtocells are used in indoors to improve the coverage and data rates of the mobile users. In Enterprise building having high deployment of femtocells, users keep moving from place to place inside the building. As a result existing handover schemes lead to a lot of handovers. These handovers are happen to provide seamless connectivity. However, some of the handovers can happen at locations where user mobility is constrained and therefore unnecessary. These unnecessary handovers can even lead to considerable packet loss, decrease in system throughput and high signaling cost. Mitigating these unnecessary handovers requires the notion of location and user mobility in indoor environment. In this paper, we propose an efficient self-organizing network (SON) based handover scheme to mitigate unnecessary handovers with the help of estimated user position information. Simulation carried out in NS-3 show that our proposed scheme achieves 31% improvement in reducing handover delay compared to an existing scheme.
On the (in)security of some smart-card-based password authentication schemes for WSN

In this study, we investigate a temporal-credential-based password authentication scheme introduced by Xue et al. in 2012. This protocol only involves hash and XOR operations and thus is suitable for the resource-constrained WSN environments where an external user wants to obtain real-time data from the sensor nodes inside WSN. However, notwithstanding their security arguments, we point out that Xue et al.’s protocol is still vulnerable to smart card security breach attack and privileged insider attack, and fails to preserve user anonymity. The proposed cryptanalysis discourages any practical use of the scheme under investigation and reveals some subtleties and challenges in designing this type of schemes. Remarkably, using Xue et al.’s scheme as a case study, we further put forward a principle: public-key techniques are indispensable to passwordbased authentication schemes using non-tamper-resistant smart cards for WSN. We hope that, by following this principle, similar mistakes repeated in the past can be avoided in the future.
Shortages of Math and Science Teachers and the Feminization of Teachers : A Common Reason

Two concerns in US education are shortages of qualified math and science teachers and the feminization of teaching. I draw on the National Education Longitudinal Study of 1988 to identify a common reason for the two phenomena. Controlling for self-selection by a propensity score matching estimator and the Heckit, I find that the rigid pay structure for teachers, rather than the pay level, appears to generate both problems. Specifically, the returns to math skills in non-teaching are twice as high as those in teaching. The higher returns to math skills in non-teaching attract individuals with high math skills to the non-teaching sector (shortages of qualified math and science teachers). An earnings gap, potentially from discrimination, occurs against women in non-teaching. Along with this earnings gap, the lower average math skills of women than those of men provide women with an incentive to teach (the feminization of teaching). These findings imply that if teachers are paid commensurate with their math skills, individuals with high math skills would be attracted to teaching. The majority of them are expected to be men considering their current advantage over women in math.
Primary Magmas at Mid-Ocean Ridges, 'Hotspots,' and Other Intraplate Settings: Constraints on Mantle Potential Temperature

Intraplate, “hotspot,” and mid-ocean ridge basalts (MORB) are characteristic of the modern Earth, and within the plate tectonics interpretation of the Earth’s behavior become the basis for a petrological model of the lithosphere and asthenosphere. This model is based on experimental studies of various primitive magmas and of peridotite + (C-H-O), including melting and phase relationships under variable oxygen fugacities ( fO 2 ). The deep mantle plume hypothesis requires a large potential temperature difference (� T p 200‐250 °C) between the upwelling plume and normal ambient mantle as sampled by mid-ocean ridge upwelling and by nonplume intraplate basalts. This core tenet of the deep mantle plume hypothesis is testable by comparison of primitive hotspot magmas with intraplate magmas and particularly with primitive MORB. We have examined picrites and olivine phenocryst compositions from Hawaiian volcanoes and a large new dataset of glass compositions from the Hawaiian Scientific Drilling Project, sampling deep into the main cone-building phase of Mauna Kea volcano. By incremental addition of olivine (reversing the crystallization of magmas saturated only in olivine + Cr-Al spinel) we infer the parental or primitive picrite magma compositions for distinctive compositional suites and individual volcanoes. We then calculate liquidus temperatures (1 atm) and infer conditions of melt separation
The top speed of flash worms

Flash worms follow a precomputed spread tree using prior knowledge of all systems vulnerable to the worm's exploit. In previous work we suggested that a flash worm could saturate one million vulnerable hosts on the Internet in under 30 seconds[18]. We grossly over-estimated. In this paper, we revisit the problem in the context of single packet UDP worms (inspired by Slammer and Witty). Simulating a flash version of Slammer, calibrated by current Internet latency measurements and observed worm packet delivery rates, we show that a worm could saturate 95% of one million vulnerable hosts on the Internet in 510 milliseconds. A similar worm using a TCP based service could 95% saturate in 1.3 seconds. The speeds above are achieved with flat infection trees and packets sent at line rates. Such worms are vulnerable to recently proposed worm containment techniques [12, 16, 25]. To avoid this, flash worms should slow down and use deeper, narrower trees. We explore the resilience of such spread trees when the list of vulnerable addresses is inaccurate. Finally, we explore the implications of flash worms for containment defenses: such defenses must correlate information from multiple sites in order to detect the worm, but the speed of the worm will defeat this correlation unless a certain fraction of traffic is artificially delayed in case it later proves to be a worm.
Contextual Recommendation based on Text Mining

The potential benefit of integrating contextual information for recommendation has received much research attention recently, especially with the ever-increasing interest in mobile-based recommendation services. However, context based recommendation research is limited due to the lack of standard evaluation data with contextual information and reliable technology for extracting such information. As a result, there are no widely accepted conclusions on how, when and whether context helps. Additionally, a system often suffers from the so called cold start problem due to the lack of data for training the initial context based recommendation model. This paper proposes a novel solution to address these problems with automated information extraction techniques. We also compare several approaches for utilizing context based on a new data set collected using the proposed solution. The experimental results demonstrate that 1) IE-based techniques can help create a large scale context data with decent quality from online reviews, at least for restaurant recommendations; 2) context helps recommender systems rank items, however, does not help predict user ratings; 3) simply using context to filter items hurts recommendation performance, while a new probabilistic latent relational model we proposed helps.
Fast, Lightweight Virtual Machine Checkpointing

Virtual machine checkpoints provide a clean encapsulation of the full state of an executing system. Due to the large nature of state involved, the process of VM checkpoints can be slow and costly. We describe the implementation of a a fast and lightweight mechanism of VM checkpointing for the Xen virtualization machine monitor that utilizes copy-on-write techniques to reduce the VM’s downtime and performance overhead incurred by other forms of VM checkpointing.