Miracle-dz/UniFAR-FTUs huggingface dataset:
Ophthalmic Drug Delivery Systems for Antibiotherapy—A Review

The last fifty years, ophthalmic drug delivery research has made much progress, challenging scientists about the advantages and limitations of this drug delivery approach. Topical eye drops are the most commonly used formulation in ocular drug delivery. Despite the good tolerance for patients, this topical administration is only focus on the anterior ocular diseases and had a high precorneal loss of drugs due to the tears production and ocular barriers. Antibiotics are popularly used in solution or in ointment for the ophthalmic route. However, their local bioavailability needs to be improved in order to decrease the frequency of administrations and the side effects and to increase their therapeutic efficiency. For this purpose, sustained release forms for ophthalmic delivery of antibiotics were developed. This review briefly describes the ocular administration with the ocular barriers and the currently topical forms. It focuses on experimental results to bypass the limitations of ocular antibiotic delivery with new ocular technology as colloidal and in situ gelling systems or with the improvement of existing forms as implants and contact lenses. Nanotechnology is presently a promising drug delivery way to provide protection of antibiotics and improve pathway through ocular barriers and deliver drugs to specific target sites.
SRR loaded CB-CPW fed hexagonal patch antenna for EEG monitoring applications

The new SRR loaded circular ring surrounded hexagonal patch shape conductor backed coplanar wave guide fed with configured wearable antenna is outstandingly necessary part for wearable device module for electroencephalogram (EEG) and healthcare monitoring. This antenna designed to fine-tune at resonating frequency 2.45GHz and 3.6GHz are one of the on-body narrow band communication and WLAN frequency band. This is SRR loaded antenna; hence it has high gain, bandwidth, flexible frequency tune, low-power consumption. The proposed model using a biocompatible material (Teflon) and SRR loaded structure, which is located in between radiation element and skin layer, therefore the designed antenna has enabled to have high gain and more isolation from skin layer. The simulated results for the magnitude of reflection coefficients are −16.5dB at 2.48GHz and −23.1dB at 3.6GHz for SRR loaded proposed antenna, −26.7dB at 2.47GHz and −13.39dB at 3.54GHz for non-SRR loaded proposed antenna respectively. These values are more suitable for standard ISM and WLAN communication.
The feasibility study of using multiple partial volumetric‐modulated arcs therapy in early stage left‐sided breast cancer patients

The purpose of this study was to assess the feasibility of using a multiple partial volumetric‐modulated arcs therapy (MP‐VMAT) technique on the left breast irradiation and to evaluate the dosimetry and treatment efficiency. Ten patients with left‐sided breast cancer who had been treated by whole breast irradiation were selected for the treatment plan evaluation by using six partial volumetric modulated arcs. Each arc consisted of a 50° gantry rotation. The planning target volumes and the normal organs, including the right breast, the bilateral lungs, left ventricle, heart, and unspecified tissue, were contoured on the CT images. Dose‐volume histograms were generated and the delivery time for each arc was recorded. The PTV received greater than 95% of the V95 for all cases, and the maximum dose was within ±1% of 110% of the prescription dose. The mean homogeneity index (HI) was 10.61±0.99, and mean conformity index (CI) was 1.21±0.03. The mean dose, V5, V10, V25, and V30 of the heart were 7.61±1.38 Gy, 59.73% ±15.87%, 24.39% ±6.82%, 2.52% ±1.11%, and 1.57% ±0.71%, respectively. The volume of the left ventricle receiving 25 Gy was 5.15% ±2.23%. The total lung mean dose was 5.57±0.36 Gy, with V5 of 25.39% ±3.88% and V20 of 5.66% ±0.89%. The right breast received a mean dose of 2.13±0.22 Gy, with V5 of 1.83% ±1.22% and V10 of 0.04% ±0.12%. The mean dose of unspecified tissue was 5.34±0.37 Gy and V5 was 22.23% ±1.57%. The volume of the unspecified tissue receiving 50 Gy was 0.50% ±0.14%. The mean delivery time for each arc was 13.9 seconds. The average MU among ten patients was 511 MU (range 443 to 594 MUs). The MP‐VMAT technique for the left‐sided breast cancer patients achieved adequate target dose coverage while maintaining low doses to organs‐at‐risk, and therefore reduced the potential for induction of second malignancy and side effects. The highly efficient treatment delivery would be beneficial for improving patient throughput, providing patient comfort, and achieving precise treatment with the breathing control system. PACS number: 87.55. ‐x, 87.55. D‐, 87.55. dk
Hypervisor-Induced Negative Interference in Virtualized Multi-core Platforms: The P4080 Case

Modern real-time (RT) applications heavily depend on multi-core processors to meet the increasing computing demands within strict time-constraints. To reduce the cost of overall hardware, most of these applications run along with general purpose workloads on the same hardware platform which may create data contention in shared resources and incur delay for the time critical RT applications. Virtualization of the hardware platform under the supervision of a Virtual Machine Monitor (VMM) or hypervisor allows the isolation of shared resources among concurrent applications from different domains. However, the actual partitioning of hardware resources among the hosted operating systems as well as the hypervisor play a significant role in the performance of the RT applications. In this paper we report results of an experimental investigation for this purpose on a particular multi-core platform, the P4080 developed by Freescale Semiconductor Inc. Using the actual (not simulated) P4080 hardware platform and the Topaz hypervisor, we examined the effect that the partition of the last-level cache (LLC) among a Real-Time Operating System (RTOS), a General Purpose Operating System (GPOS) and the hypervisor has on the performance of the RT tasks. We have identified inverted bell-shaped curves that seem to govern the optimal allocation of resources for P4080.
A suboptimal lossy data compression based on approximate pattern matching

A practical suboptimal (variable source coding) algorithm for lossy data compression is presented. This scheme is based on approximate string matching, and it naturally extends the lossless Lempel-Ziv (1977) data compression scheme. Among others we consider the typical length of an approximately repeated pattern within the first n positions of a stationary mixing sequence where D percent of mismatches is allowed. We prove that there exists a constant r/sub 0/(D) such that the length of such an approximately repeated pattern converges in probability to 1/r/sub 0/(D) log n (pr.) but it almost surely oscillates between 1/r/sub -/spl infin//(D) log n and 2/r/sub 1/(D) log n, where r/sub -/spl infin//(D)>r/sub 0/(D)>r/sub 1/(D)/2 are some constants. These constants are natural generalizations of Renyi entropies to the lossy environment. More importantly, we show that the compression ratio of a lossy data compression scheme based on such an approximate pattern matching is asymptotically equal to r/sub 0/(D). We also establish the asymptotic behavior of the so-called approximate waiting time N/sub l/ which is defined as the time until a pattern of length C repeats approximately for the first time. We prove that log N/sub l//l/spl rarr/r/sub 0/(D) (pr.) as l/spl rarr//spl infin/. In general, r/sub 0/(D)>R(D) where R(D) is the rate distortion function. Thus for stationary mixing sequences we settle in the negative the problem investigated by Steinberg and Gutman by showing that a lossy extension of the Wyner-Ziv (1989) scheme cannot be optimal.
Building-up the subject classification system from the collective intelligence

Systematized subject classification is essential for funding and assessing scientific projects. Conventionally, classification schemes are founded on the empirical knowledge of the group of experts; thus the expert's perspectives have influenced the current systems of the scientific classification. Those systems archived current state-of-art in practice, yet acceleration of scientific turnover over time reported so that the global effect of accelerating make it impossible to respond to update of the classifications system by experts. To overcome the aforementioned limitations, we propose an unbiased classification scheme taking advantage of collective knowledge, Wikipedia, an Internet encyclopedia edited by millions of users set the prompt classification in collective fashions. We construct the Wikipedia network for scientific disciplines and extract the backbone of the network. This structure displays the landscape of science and technology based on a collective intelligence that is more unbiased and adaptable than conventional classifications.
Active Learning Environments with Robotic Tangibles: Children's Physical and Virtual Spatial Programming Experiences

As computational thinking becomes increasingly important for children to learn, we must develop interfaces that leverage the ways that young children learn to provide opportunities for them to develop these skills. Active Learning Environments with Robotic Tangibles (ALERT) and Robopad, an analogous on-screen virtual spatial programming environment for educational Human Robot Interaction (HRI), have been developed. Evaluations of these in the context of free play and open-ended learning activities show that both systems afford opportunities for young children to engage in spatial programming, creating improvisational and sequential programs that mediate interactions between the environment, robots, and humans in responsive and creative ways. These systems demonstrate innovative opportunities for advancing mixed reality spatial programming activities as a form of HRI that fosters engaging seamless cyberlearning experiences, across formal and informal environments.
Routing structure over discrete event system specification: A DEVS adaptation to develop smart routing in simulation models

The Discrete Event System Specification (DEVS) formalism has become an engine for advances in modeling and simulation technology. Many extensions of the DEVS formalism have been developed across the years in order to solve different types of situations. However, when the acceptance of input events and the generation of output events are related to model capabilities, the current formalisms come up with complex modeling solutions. This paper presents a new simulation formalism called Routed DEVS (RDEVS) in which routing information is used to manage directed events. The behavior supported by the new formalism is useful to create simulation models of web application architectures. However, it could also be applied to other contexts. The RDEVS formalism is based on DEVS and is closure under coupling (i.e. models can be built hierarchically). The formal specification of RDEVS formalism and a briefly description of its framework implementation are presented in this work.
Extractable Bacterial Surface Proteins in Probiotic–Host Interaction

Some Gram-positive bacteria, including probiotic ones, are covered with an external proteinaceous layer called a surface-layer. Described as a paracrystalline layer and formed by the self-assembly of a surface-layer-protein (Slp), this optional structure is peculiar. The surface layer per se is conserved and encountered in many prokaryotes. However, the sequence of the corresponding Slp protein is highly variable among bacterial species, or even among strains of the same species. Other proteins, including surface layer associated proteins (SLAPs), and other non-covalently surface-bound proteins may also be extracted with this surface structure. They can be involved a various functions. In probiotic Gram-positives, they were shown by different authors and experimental approaches to play a role in key interactions with the host. Depending on the species, and sometime on the strain, they can be involved in stress tolerance, in survival within the host digestive tract, in adhesion to host cells or mucus, or in the modulation of intestinal inflammation. Future trends include the valorization of their properties in the formation of nanoparticles, coating and encapsulation, and in the development of new vaccines.
Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion

In recent years, with the deepening of China’s electricity sales side reform and electricity market opening up gradually, the forecasting of electricity consumption (FoEC) becomes an extremely important technique for the electricity market. At present, how to forecast the electricity accurately and make an evaluation of results scientifically are still key research topics. In this paper, we propose a novel prediction scheme based on the least-square support vector machine (LSSVM) model with a maximum correntropy criterion (MCC) to forecast the electricity consumption (EC). Firstly, the electricity characteristics of various industries are analyzed to determine the factors that mainly affect the changes in electricity, such as the gross domestic product (GDP), temperature, and so on. Secondly, according to the statistics of the status quo of the small sample data, the LSSVM model is employed as the prediction model. In order to optimize the parameters of the LSSVM model, we further use the local similarity function MCC as the evaluation criterion. Thirdly, we employ the K-fold cross-validation and grid searching methods to improve the learning ability. In the experiments, we have used the EC data of Shaanxi Province in China to evaluate the proposed prediction scheme, and the results show that the proposed prediction scheme outperforms the method based on the traditional LSSVM model.
Real Time Sentiment Change Detection of Twitter Data Streams

In the past few years, there has been a huge growth in Twitter sentiment analysis having already provided a fair amount of research on sentiment detection of public opinion among Twitter users. Given the fact that Twitter messages are generated constantly with dizzying rates, a huge volume of streaming data is created, thus there is an imperative need for accurate methods for knowledge discovery and mining of this information. Although there exists a plethora of twitter sentiment analysis methods in the recent literature, the researchers have shifted to real-time sentiment identification on twitter streaming data, as expected. A major challenge is to deal with the Big Data challenges arising in Twitter streaming applications concerning both Volume and Velocity. Under this perspective, in this paper, a methodological approach based on open source tools is provided for real-time detection of changes in sentiment that is ultra efficient with respect to both memory consumption and computational cost. This is achieved by iteratively collecting tweets in real time and discarding them immediately after their process. For this purpose, we employ the Lexicon approach for sentiment characterizations, while change detection is achieved through appropriate control charts that do not require historical information. We believe that the proposed methodology provides the trigger for a potential large-scale monitoring of threads in an attempt to discover fake news spread or propaganda efforts in their early stages. Our experimental real-time analysis based on a recent hashtag provides evidence that the proposed approach can detect meaningful sentiment changes across a hashtags lifetime.
Ranking Error-Correcting Output Codes for Class Retrieval

Error-Correcting Output Codes (ECOC) is a general framework for combining binary classification in order to address the multi-class categorization problem. In this paper, we include contextual and semantic information in the decoding process of the ECOC framework, defining an ECOC-rank methodology. Altering the ECOC output values by means of the adjacency of classes based on features and class relations based on ontology, we defined a new methodology for class retrieval problems. Results over public data show performance improvement when using the new ECOC-rank in the retrieval process.
Depression and anxiety mediate perceived social support to predict health-related quality of life in pregnant women living with HIV

ABSTRACT Pregnant women living with HIV represent one of the most high-priority groups for HIV treatment and health assessment. Although social support has been shown to be a protective factor for improved health-related quality of life (HRQoL), and depression and anxiety have been identified as two major causes of psychological distress among people living with HIV, it is still unclear how social support, anxiety, and depression interact to influence HRQoL. The objective of our study was to demonstrate the nature of predictors, direct effects and mediator effects among social support, anxiety, depression symptoms and HRQoL in pregnant women living with HIV. We investigated a total of 101 pregnant women living with HIV in Yunnan province in China from April 2016 to June 2016. All participants completed the Social Support Rating Scale (SSRS), the Chinese version of the Hospital Anxiety and Depression Scales (HADS) and Quality of Life instruments (EuroQoL Five Dimensions Questionnaire, EQ-5D). The relationships between the variables were examined by Pearson's or Spearman's correlation analysis. Predictor effects were tested using separate multiple regressions, controlling for demographic variables and HIV diagnosis variables. Direct and mediation effects of social support on HRQoL were tested using a structural equation model (SEM). Anxiety and depression symptoms were negatively correlated with subjective social support, support utilization, social support and HRQoL. Social support significantly predicted better HRQoL, and anxiety and depression symptoms significantly predicted poorer HRQoL. Anxiety and depression symptoms partially mediated the associations between social support and HRQoL. Anxiety and depression symptoms completely mediated the associations of objective support and support utilization with HRQoL. Interventions to improve HRQoL in pregnant women living with HIV must consider the mediation effect of anxiety and depression symptoms on the association between social support and HRQoL. Social support interventions are valid only when anxiety and depression symptoms are managed effectively.
A superposition-based analog data compression scheme for massively-parallel neural recordings

An analog data compression scheme for massively-parallel observation of neural activity is presented, that simultaneously reduces the total number of physical transmission lines. The compression is realized by superimposing signals of different recording sites on one common transmission line, a reconstruction of spatial information is achievable by correlation, for which three simple mathematical operations are presented. The generalized system-theoretical description allows to apply the concept to any multidimensional analog sensing network, and grants to extract the relation between compression and SNR reduction. Simulation-based examples of neural recording arrays revealed possible transmission line reductions of 92% in a 25 × 25 array with 11.5dB higher noise floor, or 70.8% in a 6×8 array with an SNR reduction equivalent to only 1 bit of resolution.
Design of an LPWAN communication module based on secure element for smart parking application

LPWAN is expected to be used in various IoT applications such as smart parking, environment monitoring, etc. This paper introduces a method of processing LoRaWAN protocol which is one of LPWAN using Secure Element. If important information such as keys are stored in a General purpose MCU which is used frequently by IoT device, keys can be easily leaked by hacking. Therefore, this paper proposes a method to safely store keys and to process LoRaWAN protocol more securely based on Secure Element. In this paper, security functions such as data encryption and message signing are handled by the Secure Element. Therefore, the keys required to perform these functions are also managed within the Secure Element. And, this paper also proposes efficient integrity verification method of the executable of the main MCU based on Secure Element.1
Shelah-Stupp's Iteration and Muchnik's Iteration

In the early seventies, Shelah proposed a model-theoretic construction, nowadays called "iteration". This construction is an infinite replication in a tree-like manner where every vertex possesses its own copy of the original structure. Stupp proved that the decidability of the monadic second-order (MSO) theory is transferred from the original structure onto the iterated one. In its extended version discovered by Muchnik and introduced by Semenov, the iteration became popular in computer science logic thanks to a paper by Walukiewicz. Compared to the basic iteration, Muchnik’s iteration has an additional unary predicate which, in every copy, marks the vertex that is the clone of the possessor of the copy. A widely spread belief that this extension is crucial is formally confirmed in the paper. Two hierarchies of relational structures generated from finite structures by MSO interpretations and either Shelah-Stupp’s iteration or Muchnik’s iteration are compared. It turns out that the two hierarchies coincide at level 1. Every level of the latter hierarchy is closed under Shelah-Stupp’s interation. In particular, the former hierarchy collapses at level 1.
The Polycistronic miR166k-166h Positively Regulates Rice Immunity via Post-transcriptional Control of EIN2

MicroRNAs (miRNAs) are small RNAs acting as regulators of gene expression at the post-transcriptional level. In plants, most miRNAs are generated from independent transcriptional units, and only a few polycistronic miRNAs have been described. miR166 is a conserved miRNA in plants targeting the HD-ZIP III transcription factor genes. Here, we show that a polycistronic miRNA comprising two miR166 family members, miR166k and miR166h, functions as a positive regulator of rice immunity. Rice plants with activated MIR166k-166h expression showed enhanced resistance to infection by the fungal pathogens Magnaporthe oryzae and Fusarium fujikuroi, the causal agents of the rice blast and bakanae disease, respectively. Disease resistance in rice plants with activated MIR166k-166h expression was associated with a stronger expression of defense responses during pathogen infection. Stronger induction of MIR166k-166h expression occurred in resistant but not susceptible rice cultivars. Notably, the ethylene-insensitive 2 (EIN2) gene was identified as a novel target gene for miR166k. The regulatory role of the miR166h-166k polycistron on the newly identified target gene results from the activity of the miR166k-5p specie generated from the miR166k-166h precursor. Collectively, our findings support a role for miR166k-5p in rice immunity by controlling EIN2 expression. Because rice blast is one of the most destructive diseases of cultivated rice worldwide, unraveling miR166k-166h-mediated mechanisms underlying blast resistance could ultimately help in designing appropriate strategies for rice protection.