Miracle-dz/UniFAR-FTUs huggingface dataset:
Pheophytin Derived Near-Infrared-Light Responsive Carbon Dot Assembly as a New Phototheranotic Agent for Bioimaging and Photodynamic Therapy.

Carbon dots (CDs), a kind of phototheranostic agent with the capability of simultaneous bioimaging and phototherapy [i.e., photodynamic therapy (PDT) or photothermal therapy (PTT)], have received considerable attention because of their remarkable properties, including flexibility for surface modification, high biocompatibility, low toxicity and photo-induced activity for malignant tumor cells. Among numerous carbon sources, it has been found that natural biomass are good candidates for the preparation of CD phototheranostic agents. In this study, pheophytin, a type of Mg-free chlorophyll derivative and also a natural product with low toxicity, was used as a raw carbon source for the synthesis of CDs by using a microwave method. The obtained hydrophobic CDs exhibited a maximum near-infrared (NIR) emission peak at approximately 680 nm, and high singlet oxygen (1 O2 ) generation with a quantum yield of 0.62. The self-assembled CDs from the as-prepared CDs with DSPE-mPEG2000 retained efficient 1 O2 generation. The obtained carbon dot assembly was not only an efficient fluorescence (FL) imaging agent but also a smart PDT agent. Our studies indicated that the obtained hydrophilic CD assembly holds great potential as a new phototheranostic agent for cancer therapy. This work provides a new route for synthesis of CDs and proposes a readily available candidate for tumor treatment.
Trust Prediction with Propagation and Similarity Regularization

Online social networks have been used for a variety of rich activities in recent years, such as investigating potential employees and seeking recommendations of high quality services and service providers. In such activities, trust is one of the most critical factors for the decision-making of users. In the literature, the state-of-the-art trust prediction approaches focus on either dispositional trust tendency and propagated trust of the pair-wise trust relationships along a path or the similarity of trust rating values. However, there are other influential factors that should be taken into account, such as the similarity of the trust rating distributions. In addition, tendency, propagated trust and similarity are of different types, as either personal properties or interpersonal properties. But the difference has been neglected in existing models. Therefore, in trust prediction, it is necessary to take all the above factors into consideration in modeling, and process them separately and differently. In this paper we propose a new trust prediction model based on trust decomposition and matrix factorization, considering all the above influential factors and differentiating both personal and interpersonal properties. In this model, we first decompose trust into trust tendency and tendency-reduced trust. Then, based on tendency-reduced trust ratings, matrix factorization with a regularization term is leveraged to predict the tendency-reduced values of missing trust ratings, incorporating both propagated trust and the similarity of users' rating habits. In the end, the missing trust ratings are composed with predicted tendency-reduced values and trust tendency values. Experiments conducted on a real-world dataset illustrate significant improvement delivered by our approach in trust prediction accuracy over the state-of-the-art approaches.
An Iterative Electrical Capacitance Tomography Image Reconstruction Algorithm

Conventional electrical capacitance tomography (ECT) image reconstruction relies on the sensitivity matrix of ECT sensor. The linear back-projection algorithm using sensitivity matrix, (LBP_SM for short) and the Landweber Iterative algorithm using sensitivity matrix (I_SM for short) are the most widely used algorithms. An iterative ECT reconstruction algorithm is researched in this paper. Its ECT forward model is obtained by using multivariate linear regression, and its solution of the inverse problem is obtained by iteration, so we call this algorithm I_MLR for short. Capacitance values of a 16-electrode ECT sensor under 10 permittivity distributions are calculated using ECT finite element analysis software. Reconstruction images and reconstruction errors generated by above three algorithms are compared. Comparison results show that I_MLR algorithm can produce much better images than can the LBP_SM algorithm; better images than can the I_SM algorithm in most cases.
A cuttable multi-touch sensor

We propose cutting as a novel paradigm for ad-hoc customization of printed electronic components. As a first instantiation, we contribute a printed capacitive multi-touch sensor, which can be cut by the end-user to modify its size and shape. This very direct manipulation allows the end-user to easily make real-world objects and surfaces touch-interactive, to augment physical prototypes and to enhance paper craft. We contribute a set of technical principles for the design of printable circuitry that makes the sensor more robust against cuts, damages and removed areas. This includes novel physical topologies and printed forward error correction. A technical evaluation compares different topologies and shows that the sensor remains functional when cut to a different shape.
Motor Cortex Is Functionally Organized as a Set of Spatially Distinct Representations for Complex Movements

There is a long-standing debate regarding the functional organization of motor cortex. Intracortical microstimulation (ICMS) studies have provided two contrasting views depending on the duration of stimulation. In the rat, short-duration ICMS reveals two spatially distributed forelimb movement representations, the rostral forelimb area (RFA) and caudal forelimb area (CFA), eliciting identical movements. In contrast, long-duration ICMS reveals spatially distributed, complex, multijoint movement areas, with grasping found exclusively in the rostral area and reach-shaping movements of the arm located in the caudal area. To provide corroboration for which interpretation is correct, we selectively inactivated the RFA/grasp area during the performance of skilled forelimb behaviors using a reversible cortical cooling deactivation technique. A significant impairment of grasping in the single-pellet retrieval task and manipulations of pasta was observed during cooling deactivation of the RFA/grasp area, but not the CFA/arm area. Our results indicate a movement-based, rather than a muscle-based, functional organization of motor cortex, and provide evidence for a conserved homology of independent grasp and reach circuitry shared between primates and rats.
Face Recognition System Based on Sparse Codeword Analysis

In recent times, large-scale content-based face image retrieval has grown up with rapid improvement and it is an enabling technology for many emerging applications. Content based face image retrieval is done by computing the similarity between images in the databases and the input/query face image. Content based face image retrieval systems retrieves the image only using low level features therefore the retrieval rate is low in this system. To improve the retrieval rate sparse codeword based scalable face image retrieval system is developed. This system uses both low level features and high level human attributes. The proposed system has several stages to retrieve the images; 1. Low level features are extracted using LTP descriptor and utilize the automatically detected high level human attributes such as hair, Gender and race. 2. Sparse codeword techniques are applied on the low level features and attributes to generate the codeword. 3. The third stage is an indexing; in the indexing attribute embedded inverted indexing method is used. Using the methods mentioned above, face image retrieval system has achieved promising retrieval result. Experiment is conducted on different dataset such as pub fig, LFW and FERET. Among those dataset LFW dataset achieve higher performance.
Image reconstruction algorithm for a SPECT system with a convergent rotating slat collimator

In this paper, we suggest the use of a convergent rotating slat collimator in a SPECT system that contains strip-shaped CdZnTe detectors. This CdZnTe imaging device is able to provide better energy resolution than a conventional NaI(Tl) detector. A convergent imaging geometry has a higher detection sensitivity than a parallel imaging geometry when imaging a small object. A novel design method and reconstruction technique are proposed for use in this system.
A Matter of the Mix: Integration of Compile and Runtime Variability

While dynamic software product lines focus on runtime variability, traditional software product lines typically aim at development-time variability. In this paper, we argue that integrating both kinds of binding times into a single variability model can be beneficial for modeling the adaptation space as well as for controlling runtime decision making. We achieve this by a mix of modeling and constraint capabilities. We illustrate the integration of compile time and runtime variability using a general-purpose variability modeling language and an example from the field of adaptive data stream processing. We also discuss advantages and disadvantages of our approach.
STRUCTURE AND DEVELOPMENT OF VIRUSES OBSERVED IN THE ELECTRON MICROSCOPE

Vaccinia and fowl pox viruses were visualized by the electron microscope in sections of infected chorioallantoic membrane of chicken embryos. The viruses were of similar structure and size, averaging 200 x 300 mµ with considerable individual variation. Intracytoplasmic viral particles contained a dense, nucleus-like body (nucleoid) separated from granular material (viroplasm) by a zone of lesser density. They were enclosed by a single membrane. Near the surface of the host cell and in the extracellular space the particles consisted of a central body of variable shape and density enclosed by a double membrane. The initial sites of development were confined to the cytoplasm of the host cell. Before release from the host cell the viral nucleoids appeared to enlarge and to occupy a central position within the particle, which became enclosed by a double limiting membrane. The brick-shaped forms found after removal of the embedding plastic from thick sections indicated that drying caused characteristic distortion of certain viral particles.
Assessing Trauma Exposure and Posttraumatic Morbidity

his chapter is intended as a resource for the mental health clinician who assesses clients for a history of psychological trauma exposure, posttraumatic stress disorder (PTSD), and associated psychopathology. We have aimed this chapter toward both clinicians and clinical trainees who are relatively unfamiliar with the technical aspects of trauma/PTSD assessment (regardless of level of general clinical experience), or who are experienced but interested in an update on key assessment issues and resources. We will cover three topics in this chapter: (a) assessing trauma exposure history, (b) assessing PTSD symptoms to establish a PTSD diag-nosis, and (c) assessing other mental health problems associated with trauma exposure. For each area, we will consider important issues and controversies in assessment, discuss general assessment and diagnostic strategies, and sample available instruments. At the end of the chapter, we include a checklist to assist in assessing each of these areas.
Large-Scale Spiking Neural Networks using Neuromorphic Hardware Compatible Models

Neuromorphic engineering is a fast growing field with great potential in both understanding the function of the brain, and constructing practical artifacts that build upon this understanding. For these novel chips and hardware to be useful, hardware compatible applications and simulation tools are needed. We argue that the neural circuit approach, in which networks of neuronal elements model brain circuitry are constructed, allows the development of practical applications and the exploration of brain function. At this level of abstraction, networks of 105 neurons or larger can be efficiently simulated, but still preserve the neuronal and synaptic dynamics that appear to be important for brain function. Because the neural circuit level supports spiking neural networks and the prevalent Addressable Event Representation (AER) communication scheme, it fits well with many existing neuromorphic hardware and simulation tools. To show how this approach can be applied, we present case studies of spiking neural networks in vision and recognition tasks based on one instantiation of a simulation environment. However, there are now many hardware options, simulation environments, and applications in this emerging field. These approaches and other considerations are discussed.
Optimal Lévy-flight foraging in a finite landscape

We present a simple model to study Lévy-flight foraging with a power-law step-size distribution in a finite landscape with countable targets. We find that different optimal foraging strategies characterized by a wide range of power-law exponent μopt, from ballistic motion (μopt → 1) to Lévy flight (1 < μopt < 3) to Brownian motion (μopt ≥ 3), may arise in adaptation to the interplay between the termination of foraging, which is regulated by the number of foraging steps, and the environmental context of the landscape, namely the landscape size and number of targets. We further demonstrate that stochastic returning can be another significant factor that affects the foraging efficiency and optimality of foraging strategy. Our study provides a new perspective on Lévy-flight foraging, opens new avenues for investigating the interaction between foraging dynamics and the environment and offers a realistic framework for analysing animal movement patterns from empirical data.
Classification of Aortic Stenosis by Flow and Gradient Patterns Provides Insights into the Pathophysiology of Disease

Different patterns of flow and valve gradients can lead to diagnostic uncertainty about the severity of aortic stenosis (AS). Consecutive patients with severe AS (valve area <1 cm2) underwent echocardiography and computed tomography. Patients were classified into 4 groups (high-gradient/normal flow [HGNF], high-gradient/low flow [HGLF], low-gradient/normal flow [LGNF], and low-gradient/low flow [LGLF]). Low flow was defined as stroke volume index <35 mL/m2 and low gradient as a mean aortic gradient <40 mm Hg. Aortic valve calcification (AVC) was calculated using the Agatston score. Of 181 patients, 56, 30, 46, and 49 had HGNF, HGLF, LGNF and LGLF with median AVC of 2048, 2015, 1366, and 1178 AU/m2 (P < .0001) and valvuloarterial impedance of 4.5, 6.4, 4.2, and 5.9, respectively (P < .0001). Among those with LGLF, AVC was lower in patients with preserved compared to reduced left ventricular ejection fraction (1018 vs 2550 AU/m2; P < .0001), but valvuloarterial impedance was similar (P = .33). The LGLF AS with preserved ejection fraction is associated with lower AVC and may identify patients with less severe AS in association with an adaptive ventricular response to high afterload.
Wireless Sensor Network simulators relevance compared to a real IEEE 802.15.4 Testbed

The diversity of research topics in Wireless Sensor Networks (WSNs) is attracting more and more researchers from different fields. The common point of all proposed solutions and protocols for WSNs is the evaluation usually done with network simulators. In this paper, we focus on the results relevance of wireless sensor network simulators in different scenarios based on indoor and outdoor environments. We propose a comparative study between 3 usual simulators (NS2, OPNET and QualNet) while using as reference a real testbed based on recent Imote2 sensors. The simulators give different results even in similar environments. NS2 and Qualnet give results close to those of the experimentation in the case of an indoor environment, but in the outdoor environment Opnet gives results closer to the reality. In addition, the impact of different MAC protocols (B-MAC and TKN15.4 MAC) which observe or do not observe the IEEE 802.15. 4 standard is illustrated by real experimentations. They show that TKN15.4 protocol gives a better throughput than B-MAC.
The effect of anti-TNF-α vs. DMARDs on fatigue in rheumatoid arthritis patients

Objectives: Fatigue is experienced frequently by patients with rheumatoid arthritis (RA). Fatigue may be caused by high levels of pain and disease activity in RA but can remain present while disease activity is moderate to low. It is not clear whether RA patients receiving anti-tumour necrosis factor (TNF) treatment reach lower levels of acute fatigue than RA patients receiving disease-modifying anti-rheumatic drug (DMARD) treatment. The aim of our study was to analyse whether, in patients with RA, the effect of anti-TNF on fatigue is greater than the effect of DMARD treatment. Method: Sixty-seven RA patients receiving anti-TNF treatment and 104 RA patients receiving DMARDs were included. All patients were on stable treatment for at least 6 months prior to baseline measurement. Fatigue was measured monthly over 1 year with the fatigue severity subscale of the Checklist Individual Strength (CIS-fatigue). The association between persistent severe fatigue and medication group was analysed using multiple linear regression including confounders. Results: In the anti-TNF group the mean (SD) level of persistent fatigue was significantly higher than in the DMARD group [32.2 (11.4) vs. 28.3 (10.9), p = 0.025] and more patients experienced persistent severe (CIS-fatigue score ≥ 35) fatigue (42% and 27% respectively, p = 0.043). However, when correcting for age, disease activity, haemoglobin, treatment duration, pain, physical disability, and clinical depression, medication type seemed to influence neither the mean level of persistent fatigue (p = 0.251) nor the percentage of patients with persistent severe fatigue (p = 0.745). Conclusions: When taking into account probable confounders including disease activity, medication type did not influence persistent fatigue in RA patients. It seems that, besides its anti-inflammatory effect, anti-TNF has no complementary effect on persistent fatigue.
TR-IIS-16-002 Model-Based Anomaly Detection on Network Services

The key hypothesis to anomaly detection assumes anomalous behaviors are suspicious from a normality point of view. This work provides a new perspective, network service, to model network activity for detecting anomalies. Past models often suffer from lacking of model normality verification, only including particular behavior aspect, and focusing on individual model. To confront them, we propose a framework based on the perspective of multiple network service models. For normality verification, we show how to construct underlying protocol models by static and dynamic approach to guarantee the usability. For the latter two problems, we show how to construct composite service model with protocol interaction and correlation. Classes of attacks, such as worm and botnet, illustrate that our composite model detects the symptom of attack that individual ones cannot. This work provides a promising perspective from network service and a new construction method for service modeling.
Multichannel Blind Source Separation Using Convolution Kernel Compensation

This paper studies a novel decomposition technique, suitable for blind separation of linear mixtures of signals comprising finite-length symbols. The observed symbols are first modeled as channel responses in a multiple-input-multiple-output (MIMO) model, while the channel inputs are conceptually considered sparse positive pulse trains carrying the information about the symbol arising times. Our decomposition approach compensates channel responses and aims at reconstructing the input pulse trains directly. The algorithm is derived first for the overdetermined noiseless MIMO case. A generalized scheme is then provided for the underdetermined mixtures in noisy environments. Although blind, the proposed technique approaches Bayesian optimal linear minimum mean square error estimator and is, hence, significantly noise resistant. The results of simulation tests prove it can be applied to considerably underdetermined convolutive mixtures and even to the mixtures of moderately correlated input pulse trains, with their cross-correlation up to 10% of its maximum possible value.
High-Temperature Core Flood Investigation of Nanocellulose as a Green Additive for Enhanced Oil Recovery

Recent studies have discovered a substantial viscosity increase of aqueous cellulose nanocrystal (CNC) dispersions upon heat aging at temperatures above 90 °C. This distinct change in material properties at very low concentrations in water has been proposed as an active mechanism for enhanced oil recovery (EOR), as highly viscous fluid may improve macroscopic sweep efficiencies and mitigate viscous fingering. A high-temperature (120 °C) core flood experiment was carried out with 1 wt. % CNC in low salinity brine on a 60 cm-long sandstone core outcrop initially saturated with crude oil. A flow rate corresponding to 24 h per pore volume was applied to ensure sufficient viscosification time within the porous media. The total oil recovery was 62.2%, including 1.2% oil being produced during CNC flooding. Creation of local log-jams inside the porous media appears to be the dominant mechanism for additional oil recovery during nano flooding. The permeability was reduced by 89.5% during the core flood, and a thin layer of nanocellulose film was observed at the inlet of the core plug. CNC fluid and core flood effluent was analyzed using atomic force microscopy (AFM), particle size analysis, and shear rheology. The effluent was largely unchanged after passing through the core over a time period of 24 h. After the core outcrop was rinsed, a micro computed tomography (micro-CT) was used to examine heterogeneity of the core. The core was found to be homogeneous.
Data memory optimization in LTE downlink

Optimizations related to memory accesses and data storage make a significant difference to the performance and energy of a wide range of data-intensive applications. Such strategies need to evolve with modern SoC and processor architectures, which lead to new optimization opportunities. In this paper, we focus on data memory optimization for LTE downlink receiver as this is a data- and computation-intensive part of the LTE application with tight energy and latency constraints. We study the data dependencies globally and conclude that by providing data samples from the antennas in interleaved form at the FFT input, we can achieve 7-15% reduction in memory access energy over an optimized implementation without any performance overhead.
Improved Spatio-temporal Salient Feature Detection for Action Recognition

Spatio-temporal salient features can localize the local motion events and are used to represent video sequences for many computer vision tasks such as action recognition. The robust detection of these features under geometric variations such as affine transformation and view/scale changes is however an open problem. Existing methods use the same filter for both time and space and hence, perform an isotropic temporal filtering. A novel anisotropic temporal filter for better spatio-temporal feature detection is developed. The effect of symmetry and causality of the video filtering is investigated. Based on the positive results of precision and reproducibility tests, we propose the use of temporally asymmetric filtering for robust motion feature detection and action recognition.