Miracle-dz/UniFAR-FTUs huggingface dataset:
Dip slide culture of intraoperative peritoneal irrigation fluid for prediction of septic complication in elective colorectal surgery

A controlled prospective study of a simplified technique, the dip slide culture method, for assessment of bacterial concentration in peritoneal irrigation fluid at the end of an elective colorectal operation is presented. The prediction of postoperative surgical infection based on intraoperative culture was compared between this method and a standard streak-plate technique in 190 patients. One gram of metronidazole was given intravenously as prophylaxis on induction of anesthesia and 12 h postoperatively. Intraoperative growth of members of the family Enterobacteriaceae or Staphylococcus aureus was strongly correlated to infection (P less than 0.001). By using this finding as a single criterion for the prediction of sepsis, sensitivity and specificity for the dip slide method were 70.8 and 94.3%, respectively, compared with 79.2 and 94.6%, respectively, for the streak-plate method. Of the 24 infections, 20 (83.3%) were correctly predicted when a combination of the two methods was used. An increasing number of Enterobacteriaceae or S. aureus in the dip slide culture resulted in a steady rise in the rate of infections from 5.7% at 0 CFU to 57% at greater than or equal to 80 CFU compared with 4.1% at 0 CFU/ml and 45.4% at greater than or equal to 800 CFU/ml with the streak-plate method. The differences were statistically significant at the greater than or equal to 5 CFU level with regard to wound infection (P less than 0.001) and deep surgical infection (P less than 0.01). It is concluded that the dip slide is a simple, rapid, and reliable method for the routine assessment of bacterial contamination in colorectal operations.
Global trends and biases in new mammal species discoveries.

Contrary to common perception, the number of living mammal species and the relationship of those species with one another are incompletely understood. Taxonomic revisions within mammals are frequent and are often motivated by the discovery of new species. In fact, an analysis of patterns of discovery suggests that complete alpha-taxonomic characterization of living mammals remains a far-off goal. Examination of chronological, geographical, and taxonomic trends in new species discoveries reveals interesting trends, telling biases, and priorities for further study. An average of 223 new valid species have been described per decade since the birth of modern taxonomic nomenclature in 1758, and this rate is increasing. Over 300 new mammal species are expected to be described this decade and some estimates suggest that 7,000+ living species of mammals will eventually be recognized. An analysis of 341 recently described species indicates that the great majority of them are restricted to threatened areas of high endemism—reiterating the biotic richness of these regions, but also indicating that most new species and the regions in which they occur require urgent conservation attention. That the global mammal fauna remains so incompletely characterized reflects the woeful state of knowledge of global biodiversity.
Identification and Characterization of Novel Sir3/MeCP2-Chromatin Interactions

ii ACKNOWLEDGMENTS . iv LIST OF FIGURES . v LIST OF TABLES . vi LIST OF ABBREVIATIONS . vii TABLE OF CONTENTS . x CHAPTER ONE: BACKGROUND AND SIGNIFIGANCE .
A Motion Deblur Method Based on Multi-Scale High Frequency Residual Image Learning

Non-uniform blind deblurring of dynamic scenes has always been a challenging problem in image processing because of the diverse of blurring sources. Traditional methods based on energy minimization cannot make accurate kernel estimation. It leads to that some high frequency details cannot be fully recovered. Recently, many methods based on convolution neural networks (CNNs) have been proposed to improve the overall performance. Followed by this trend, we first propose a two-stage deblurring module to recover the blur images of dynamic scenes based on high frequency residual image learning. The first stage performs initial deburring with the blur kernel estimated by the salient structure. The second stage calculates the difference of input image and initially deblurred image, referred to as residual image, and adopt an encoder-decoder network to refine the residual image. Finally, we can combine the refined residual image with the input blurred image to obtain the latent image. To increase deblurring performance, we further propose a coarse-to-fine framework based on the deblurring module. It performs the deblurring module many times in a multi-scale manner which can gradually restore the sharp edge details of different scales. Experiments conducted on three benchmark datasets demonstrate the proposed method achieves competitive performance of state-of-art methods.
Novel Triple-Band Antenna for Wireless Communication

This manuscript presents a compact triple frequency band antenna for wireless applications such as WiMAX and WLAN application. The planned antenna has a rectangular microstrip patch with rectangular shape ring slot and a small patch at the back-side of the upper conducting patch and a 9mm x 25mm ground plane. The dimension of the ground plane patch is optimized to obtain 2.4 Giga-Hertz, 3.6 Giga-Hertz and 5 Giga-Hertz frequencies with a sharp cut off at 2.4 GHz and 905 MHz bandwidth at 3.6 GHz and 481MHz bandwidth at 5 GHz resonant frequencies. Simple structure, wider bandwidth, compact size and flat gain make this antenna useful for wireless applications.
Performance Evaluation of Phase and Weather-Based Models in Atmospheric Correction With Sentinel-1Data: Corvara Landslide in the Alps

Phase delay caused by atmospheric effects due to spatial and temporal variations of pressure, temperature, and water vapor content is one of the major error sources in estimation of ground deformation by interferometric synthetic aperture radar (InSAR>). Therefore, accuracy of ground deformation measurement is highly contingent on the robustness of the atmospheric correction techniques. These techniques rely either on auxiliary data such as numerical weather models (NWMs) or on the analysis of the interferometric phase itself. The accuracy in phase delays estimation of mixing effects of turbulent delay in atmosphere and stratified delay in lower troposphere is a key factor in determination of performance of each technique. Hence, the performance evaluation of the techniques is required in order to assess their potentials, robustness, and limitations. This article analyzes and evaluates the performance of four NWMs (i.e., ERA-Interim, ERA5, MERRA2, and WRF) and two phase-based techniques (i.e., linear and power law) to estimate phase delay using Sentinel-1A/B data over the Corvara landslide located in the Alps. The GPS data and GACOS product were used to validate the results. We generally found that ERA5 outperformed among other weather models with a phase standard deviation reduction of 77.7% (with respect to the InSAR phase), a correlation coefficient of 0.86 (between InSAR phase and estimated tropospheric delay) and a less significant error in the velocity estimation of the landslide.
The Implementation of a Discharge Planning to Improve Quality of Life in Breast Cancer Patients: A Quasi-Experimental Study

Background: Breast surgery for women newly diagnosed with breast cancer is associated with poor postoperative quality of life (QOL). The aim of this study was to determine the effectiveness of a programmed discharge planning in improving overall QOL and its physical, emotional, social, and spiritual domains. Method: In this quasi-experimental study, we evaluated the newly diagnosed breast cancer women undergoing breast-conserving therapy in two groups, an experimental group (n = 35) and a control group (n = 34). The experimental group received programmed discharge planning at the time of hospital admission until six weeks after discharge. The control group received routine hospital care. Participants completed the QOL questionnaires before and after the intervention. The data were analyzed using the independent-samples t test, Fisher exact test, and Mann-Whitney U. Results: Before the intervention, there were no significant differences between the control and experimental groups in overall QOL or its physical, emotional, social, and spiritual domains. The data analysis after intervention showed significant improvement in QOL in the experimental group as compared with the control group. The changes in the scores of various domains of QOL were statistically significant (P < 0.01). Conclusion: This study emphasizes that programmed discharge planning is useful for improving QOL after breast-conserving surgery. Our finding could be applied to breast cancer patients with radiation therapy or chemotherapy.
Optimal Collaborative Spectrum Sharing for a Cognitive Multi-Antenna Relay With Full-Duplex Capability and Stability Constraint

Collaborative spectrum sharing (CSS) between primary and secondary users (PU and SU) is an effective way of utilizing limited radio spectrum. In CSS, cognitive SU acts as a relay for PU, which facilitates the PU to send its packet with a lower error probability by the help of the SU, and consequently, the SU has more chances to find a vacant spectrum. When SU is equipped with multiple antennas, it can further efficiently utilize the radio spectrum by adopting a growing self-interference cancelation technique for full-duplex (FD) transmission. In this paper, both an aggressive and a passive secondary usage of the spectrum are proposed, the operational principles of which are defined using spectrum-sharing probability <inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula> in FD CSS environments. For the spectrum-sharing probability <inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula>, SU for each of the methods sends its own signal and the PU’s (relayed) signal together by using superposition transmission. For the remaining probability <inline-formula> <tex-math notation="LaTeX">$1-\phi $ </tex-math></inline-formula>, the two methods work differently: SU sends its own signal only in the aggressive mode while it sends only the relaying signal for PU in the passive mode. We formulate an FD CSS problem with various physical-layer parameters and the proposed operating modes as a function of the spectrum-sharing probability. Our goal is to maximize the secondary stable throughput while keeping a primary traffic constraint. Closed-form optimal solutions on <inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula> are provided in the paper, the value of which heavily depends on the primary traffic volume, the operating modes, the relative locations of the collaborative nodes and the transmit power budget at SU. The analytical results in the paper are verified with numerical investigation, and the performance enhancement by the proposed methods is evaluated in comparison with benchmark systems. The results show that the aggressive mode is promising if SU has a relatively small transmit power and the primary traffic load is low, while the passive mode is suitable when the primary traffic load is high.
Nonrigid 3D Shape Retrieval with HAPPS: A Novel Hybrid Augmented Point Pair Signature

A robust, yet computationally efficient signature for describing 3D shape remains a challenge for 3D computer vision and related applications. Having a signature that is generalizable across a wider range of datasets becomes another important research issue. This paper proposes a novel Hybrid signature, the Augmented Point Pair Signature (HAPPS), that is robust, highly discriminating, efficient, and capable of effectively representing 3D point cloud and polygon mesh surfaces. We tested the overall performances of HAPPS on three standardized benchmark datasets for 3D shape retrieval: The Shape Retrieval Contest 2018 (SHREC'18) protein shapes benchmark, with 2,267 protein conformers, SHREC'17 Point cloud Retrieval of Nonrigid Toys (PRoNTo), with 100 3D point clouds, and SHREC'10 Nonrigid shape retrieval having 200 triangular meshes. Using 6 standard retrieval performance metrics to evaluate our results, we demonstrated the superiority of our HAPPS retrieval method over several other state-of-the-art methods for the SHREC'18 protein dataset, while also competing side-by-side with the best 2 performing methods for the other benchmark datasets.
A Drug-Induced Liver Injury Prediction Model using Transcriptional Response Data with Graph Neural Network

Drug-Induced Liver Injury (DILI) is a major cause of failed drug candidates in clinical trials and withdrawal of approved drugs from the market. Therefore, machine learning-based DILI prediction can be key in increasing the success rate of drug discovery because drug candidates that are predicted to potentially induce liver injury can be rejected before clinical trials. However, existing DILI prediction models mainly focus on the chemical structures of drugs. Since we cannot determine whether a drug will cause liver injury based solely on its structure, DILI prediction based on the transcriptional effect of a drug on a cell is necessary. In this paper, we propose GLIT which is a model that uses transcriptional response data and chemical structures and can be used for drug-induced liver injury prediction. GLIT learns the embedding vectors of drug structures and drug-induced gene expression profiles using graph attention networks in a biological knowledge graph for predicting DILI. GLIT outperformed a baseline model that uses only drug structure information by 7% and 19.2% in terms of correct classification rate (CCR) and Matthews correlation coefficient (MCC), respectively. In addition, we conducted a literature survey to confirm whether the class labels of drugs, in the unknown DILI class, predicted by GLIT are correct.
Adversarial Auto-encoders for Image Generation from standard EEG features

New deep-learning-based classification and generative models have recently found applications in EEG and fMRI-based brain studies. In this manuscript, we propose a simple solution of finding feature representations of EEG signals using statistical analysis which can be used for classification of stimuli in real time. Also, we use a deep generative model called adversarial auto-encoder (AAE) and attempt to reconstruct the stimuli back from these features. The EEG data was recorded from visual stimuli of subjects who were shown images from the ImageNet dataset and the proposed model aims to reconstruct images directly from the EEG feature manifold. Classification accuracy of 90% was achieved for the classification of images based on EEG features. Qualitative evaluation of the reconstructed images demonstrates the efficacy of our experimental model. The models can be trained on a PC with classification in real time
Aloha-like Neighbor Discovery in a WSN Star Topology using a Sink-side Beam Antenna

In data collection area, wireless sensor networks are usually used for their ease of deployment and auto-configuration capabilities. In this paper, we consider a star topology, where the central node is equipped with a switched-beam antenna, while other nodes are using omnidirectional antennas. This configuration enables enhancing the network performance without increasing the cost and complexity of the deployment, and may be suitable for some emerging applications like smart home, smart parking, etc. When directional antennas are used, some common methods like MAC (Medium Access Control) protocols, routing protocols and neighbor discovery protocols need to redesigned. In this work, we propose a neighbor discovery method based on Aloha approach that helps the sink node to discover its neighborhood. Our discovery method uses idle slots reduction mechanism and exploits collaboration between nodes and the sink in order to speed up the discovery process. Collaboration occurs during discovery process when a node shares with the sink its local knowledge of its own neighborhood. We implemented our protocol on contiki OS, and performed several simulations on Cooja. We compared the proposed protocol with a state of the art protocol adapted to work with our network configuration. The results show that our protocol improves the discovery delay by about 25% compared with the PND protocol chosen as reference.
Assessment of Nurses' Knowledge and Attitude toward Infection Standards Precautions in Primary Health Care Settings

Background: Standards precautions are designed to protect staff from risks resulting from exposure to blood and body fluids and to protect patients from potential cross infection. Knowledge of clinical infection control practices is continually growing and changing. Objectives: the present study was conducted to assess nurses' Knowledge about Infection control standards precautions in primary health care settings. Methods: This study was cross sectional study that was conducted in six primary health care settings (PHC) in Manzala District affiliated to the Ministry of Health and Population. PHC settings were selected by stratified random sample method. Seventy nine nurses were included in the study. Data were collected by using open ended questions sheet, which was self-administered. Results: The nurses showed a poor level of knowledge regarding most categories of standard precautions. Conclusion and Recommendations: Mandatory regular base educational program must be planned to overcome the weak level of knowledge and improve and update the nursing performance regarding standard precautions. Key wards: Standard precautions; Knowledge; Attitude; Nurses
The Contribution of Neuroscience to Biodynamic Lighting System

Biodynamic lighting refers to lighting that provides different lighting scenarios, in specific combinations of illuminance and CCT, plays an important role in supporting health, well-being and relaxation of the people. The paper deals with biodynamic luminaires with adjustable correlated color temperature (CCT) and their influence on the perception of light scenes and on biological functions of human body. The first part indicates the impact of lighting on human health and performance. Due to the impact of lighting on human health and performance, the biodynamic lighting luminaire has been produced. The second part demonstrates the analysis of photometric parameters of the sample of biodynamic lighting luminaire with variable CCT.
Three-class Motor Imagery Classification Based on FBCSP Combined with Voting Mechanism

Common Spatial Pattern (CSP) is an effective algorithm in constructing optimal spatial filters, which is widely used to discriminate two classes of electroencephalogram (EEG) signal in Motor Imagery (MI) based Brain Computer Interface (BCI). To extend CSP algorithm to three-class motor imagery of left-hand, right-hand, both-feet, in this paper a three-class classification strategy based on Filter Bank Common Spatial Pattern (FBCSP) and voting mechanism is proposed. The strategy reduces a three-class problem to two binary-class problems. Two binary-class classifiers are constructed for the three-class classification, both-hands vs both-feet and left-hand vs right-hand. The result shows an average three-class classification accuracy of 68.6% with BCI competition IV Datasets 2a, which is an encouraging result in motor imagery pattern recognition. And demonstrated that both-hands can be considered as one class in MI based BCI system.
Miniaturization of Dielectric Resonator Antenna by Using Artificial Magnetic Conductor Surface

In this work, a dielectric resonator antenna (DRA) is miniaturized by using Artificial Magnetic conductor (AMC) surface without disturbing other important parameters. The design has three main features: (i) simple micro-strip line feeding, (ii) use of parasitic metallic strip to achieve impedance matching, and (iii) use of AMC surface for design miniaturization. The miniaturization is performed for dielectric resonator antenna at 3.5 GHz. Without changing the basic characteristic of the antenna such as gain, resonance frequency and efficiency, the size of the antenna was reduced by 85%. The AMC surface and DRA both are positioned on the FR4 substrate. The AMC surface consists of small patches of copper. DRA is mounted on the AMC surface, which significantly reduces the overall DRA volume. Nine AMC patches were introduced with a small gap between them. AMC patches were shorted with the ground metal with the help of small metallic vias. For an overall performance analysis, the design was fabricated, and measured results were taken. The fabricated design covers a bandwidth of 180 MHz for −10 dB reference value of the reflection coefficient, which is mainly used for 5G wireless application. The design has 14.2% impedance bandwidth. Based on the analysis made for the proposed design, it is found that this simple technique highly reduces the DR volume (85%) and ground surface (15.5%) while maintaining the overall performance of the square DRA.
Multi-objective Eco-routing in a Distributed Routing Framework

A multi-objective eco-routing is developed and implemented in a real-time dynamic routing system based on a distributed network of intelligent intersections and connected & autonomous vehicles (CAVs). The performance of the proposed eco-routing system and its impact on travel time (TT), vehicle kilometres travelled (VKT), greenhouse gas (GHG), and Nitrogen Oxide (NOx) emissions are analyzed on Downtown Toronto network in an agent-based traffic simulation platform. A comparison between estimation approaches of TT based on two levels of spatial and temporal resolution is applied. The results showed that routing to optimize TT while adopting higher level of spatial and temporal resolution is the best among the single objective routing scenarios. Multi-objective routing strategies further reduced TT, GHG, and NOx when comparing to routing solely based on TT. Estimation approach based on a higher level of disaggregation produced substantially better results due to its ability to capture traffic characteristics more efficiently.
Deep Learning for Fading Channel Prediction

Channel state information (CSI), which enables wireless systems to adapt their transmission parameters to instantaneous channel conditions and consequently achieve great performance boost, plays an increasingly vital role in mobile communications. However, getting accurate CSI is challenging due mainly to rapid channel variation caused by multi-path fading. The inaccuracy of CSI imposes a severe impact on the performance of a wide range of adaptive wireless systems, highlighting the significance of channel prediction that can combat outdated CSI effectively. The aim of this article is to shed light on the state of the art in this field and then go beyond by proposing a novel predictor that leverages the strong time-series prediction capability of deep recurrent neural networks incorporating long short-term memory or gated recurrent unit. In addition to an analytical comparison of computational complexity, performance evaluation in terms of prediction accuracy is carried out upon multi-antenna fading channels. Numerical results reveal that deep learning brings a notable performance gain compared with the conventional predictors built on shallow recurrent neural networks.