Miracle-dz/UniFAR-FTUs huggingface dataset:
Phosphoethanolamine cellulose enhances curli-mediated adhesion of uropathogenic Escherichia coli to bladder epithelial cells

Significance Escherichia coli secrete polysaccharides, protein adhesins, and fibers to promote adhesion and biofilm formation. Curli are functional amyloid fibers produced by many human and environmental E. coli isolates. Furthermore, we recently discovered that E. coli produces a chemically modified form of cellulose, phosphoethanolamine cellulose. Here, we use a custom-built live-cell monolayer rheometer, housed within a microscope, to examine curli-based adhesion and reveal the molecular role of the modified cellulose in influencing adhesion of uropathogenic E. coli to bladder epithelial cells. In a high-shear environment, phosphoethanolamine cellulose confers a mortar-like function in maintaining cellular association of curli. Curli are readily dissociated from the cell in the absence of cellulose. This approach is applicable to other cellular and host-pathogen interfaces. Uropathogenic Escherichia coli (UPEC) are the major causative agents of urinary tract infections, employing numerous molecular strategies to contribute to adhesion, colonization, and persistence in the bladder niche. Identifying strategies to prevent adhesion and colonization is a promising approach to inhibit bacterial pathogenesis and to help preserve the efficacy of available antibiotics. This approach requires an improved understanding of the molecular determinants of adhesion to the bladder urothelium. We designed experiments using a custom-built live cell monolayer rheometer (LCMR) to quantitatively measure individual and combined contributions of bacterial cell surface structures [type 1 pili, curli, and phosphoethanolamine (pEtN) cellulose] to bladder cell adhesion. Using the UPEC strain UTI89, isogenic mutants, and controlled conditions for the differential production of cell surface structures, we discovered that curli can promote stronger adhesive interactions with bladder cells than type 1 pili. Moreover, the coproduction of curli and pEtN cellulose enhanced adhesion. The LCMR enables the evaluation of adhesion under high-shear conditions to reveal this role for pEtN cellulose which escaped detection using conventional tissue culture adhesion assays. Together with complementary biochemical experiments, the results support a model wherein cellulose serves a mortar-like function to promote curli association with and around the bacterial cell surface, resulting in increased bacterial adhesion strength at the bladder cell surface.
We need to consider collateral damage to resistomes when we decide how frequently to screen for chlamydia/gonorrhoea in preexposure prophylaxis cohorts

&NA; Numerous preexposure prophylaxis (PrEP) guidelines recommend three-monthly screening for gonorrhoea and chlamydia. To quantify the risk of antimicrobial resistance this may induce, we conducted a literature review of the incidence of gonorrhoea and chlamydia in PrEP studies that conducted 3-site, 3-monthly screening. We found that screening could result in macrolide consumption rates that exceed those in high macrolide consumption populations where consumption has been strongly associated with macrolide resistance.
Ionic Conductive Polyurethane-Graphene Nanocomposite for Performance Enhancement of Optical Fiber Bragg Grating Temperature Sensor

Polyurethane-graphene (PU-graphene) nanocomposite was utilized as the sensing material for a fiber Bragg grating (FBG) temperature sensor. The nanocomposite was characterized using a Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and electrochemical impedance spectroscopy (EIS) to study the morphology and physical properties of the materials for FBG temperature sensing application. The physical, chemical, and conductivity of PU-graphene improve after graphene was introduced in pristine PU. The FTIR shows that the strong intermolecular interaction between–O–C = O (ester) and hydrogen in graphene in the PU-graphene was indicated by the shift to lower wavenumber of ether (C–O–C) peak at ~1220 cm<sup>−1</sup> to ~1218 cm<sup>−1</sup>. TGA shows the thermal stability of PU increases to 217 °C due to the strong intermolecular interaction with the presence of graphene flakes. EIS shows a good electrical conductivity of <inline-formula> <tex-math notation="LaTeX">$1.39\times 10^{-9}$ </tex-math></inline-formula> Scm<sup>−1</sup> in the PU-graphene due to the electron transfer provided by the graphene. The SEM shows a rough and uneven texture on the surface of FBG coated by PU-graphene nanocomposite which shows that the graphene flakes are completely coated by polyurethane polymer. The PU-graphene was then dip-coated on the optical fiber-based Bragg grating, and the sensor performance for a temperature sensor was evaluated, where a good linearity with the sensitivity of 6 pm/°C was achieved.
The uncertain first-time voter: Effects of political media exposure on young citizens’ formation of vote choice in a digital media environment

The digital media environment changes the way citizens receive political information, also during an election campaign. Particularly first-time voters increasingly use social media platforms as news sources. Yet, it is less clear how accessing political information in such a unique social setting affects these cohorts’ decision-making processes during an election campaign, compared to experienced voters. We compare effects of these two groups’ political information exposure on their vote choice certainty during the 2015 Danish national election. We furthermore test how the relation between exposure and certainty can be mediated by active campaign participation. An 11-wave national panel study was conducted, using a smartphone-based assessment of citizens’ (n = 1108) media exposure and vote choice certainty across the campaign period. Results suggest that first-time voters’ social media exposure is responsible for their increase in certainty as the campaign progresses, while this effect is absent for experienced voters.
Noninvasive Atrophic Acne Scar Treatment in Asians With a 755-nm Picosecond Laser Using A Diffractive Optic Lens—A Retrospective Photographic Review

BACKGROUND The diffractive lens of the picosecond laser is relatively new, and there are few reports on its efficacy in treating atrophic acne scars, especially in Asian populations. OBJECTIVE Evaluating the efficacy of diffractive lens 755-nm picosecond laser for atrophic acne scar treatment in Asians. PATIENTS AND METHODS Forty-two patients who were treated for facial atrophic acne scars at a private dermatological clinic were enrolled in this retrospective analysis. Mean session count was 4.28. Before and after photographs were assessed by 2 blinded dermatologists, who rated the amount of overall skin quality improvement on a 5-point scale. RESULTS All patients experienced improvements in scar texture and overall skin quality after 2 to 6 sessions, with scores of +1.4, 1.45, 1.7, 1.33, 2.3, and 1.66 points after 2, 3, 4, 5, 6, and >6 treatments, respectively. There were no obvious adverse effects after treatment. The postinflammatory hyperpigmentation (PIH) risk was 4.7% (2 of 42, both spontaneously resolved). CONCLUSION The 755-nm diffractive lens picosecond laser showed good efficacy and low PIH rates when treating atrophic acne scars in darker skin–type patients. In addition to treatment results, additional improvements in overall skin quality and pigmentation make the picosecond laser an effective and desirable treatment option for Asians.
Securing Internet-of-Things Systems Through Implicit and Explicit Reputation Models

Internet-of-Things (IoT) systems are usually composed of thousands of different components among hardware devices and different software modules. In order to address the design of these complex systems, different abstraction layers are usually defined. However, as these layers are isolated, high-level components always have uncertainty about the nature of the low-level components they relate with. In particular, as low-level component identities are not known by user applications, and current IoT systems are vulnerable to the injection of new components and to the modification of the behavior of existing ones (adequate security solutions at the network level for these problems have not been found yet), the reliability of the received data is generally compromised. In this context, new mechanisms are required to avoid the interactions or directly remove the malicious components relying on high-level information. This paper describes a statistical framework to discover IoT components with malicious behaviors, using a hybrid reputation model. On the one hand, an implicit reputation definition is employed, based on the observations made by a certain IoT component and other modules it relies on. On the other hand, an explicit reputation model considers a scheme of recommendations and negative grades. The proposed solution is evaluated in a simulation scenario by using the NS3 simulator, in order to perform an experimental validation.
Enhanced Network Anomaly Detection Based on Deep Neural Networks

Due to the monumental growth of Internet applications in the last decade, the need for security of information network has increased manifolds. As a primary defense of network infrastructure, an intrusion detection system is expected to adapt to dynamically changing threat landscape. Many supervised and unsupervised techniques have been devised by researchers from the discipline of machine learning and data mining to achieve reliable detection of anomalies. Deep learning is an area of machine learning which applies neuron-like structure for learning tasks. Deep learning has profoundly changed the way we approach learning tasks by delivering monumental progress in different disciplines like speech processing, computer vision, and natural language processing to name a few. It is only relevant that this new technology must be investigated for information security applications. The aim of this paper is to investigate the suitability of deep learning approaches for anomaly-based intrusion detection system. For this research, we developed anomaly detection models based on different deep neural network structures, including convolutional neural networks, autoencoders, and recurrent neural networks. These deep models were trained on NSLKDD training data set and evaluated on both test data sets provided by NSLKDD, namely NSLKDDTest+ and NSLKDDTest21. All experiments in this paper are performed by authors on a GPU-based test bed. Conventional machine learning-based intrusion detection models were implemented using well-known classification techniques, including extreme learning machine, nearest neighbor, decision-tree, random-forest, support vector machine, naive-bays, and quadratic discriminant analysis. Both deep and conventional machine learning models were evaluated using well-known classification metrics, including receiver operating characteristics, area under curve, precision-recall curve, mean average precision and accuracy of classification. Experimental results of deep IDS models showed promising results for real-world application in anomaly detection systems.
An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units

To improve the performance of network intrusion detection systems (IDS), we applied deep learning theory to intrusion detection and developed a deep network model with automatic feature extraction. In this paper, we consider the characteristics of the time-related intrusion and propose a novel IDS that consists of a recurrent neural network with gated recurrent units (GRU), multilayer perceptron (MLP), and softmax module. Experiments on the well-known KDD 99 and NSL-KDD data sets show that the system has leading performance. The overall detection rate was 99.42% using KDD 99 and 99.31% using NSL-KDD with false positive rates as low as 0.05% and 0.84%, respectively. In particular, for detecting the denial of service attacks, the system achieved detection rates of 99.98% and 99.55%, respectively. Comparative experiments showed that the GRU is more suitable as a memory unit for IDS than LSTM, and proved that it is an effective simplification and improvement of LSTM. Moreover, the bidirectional GRU can reach the best performance compared with the recently published methods.
A Multilevel Cooperative Multi-Population Cultural Algorithm

A new architecture for Multi-Population Cultural Algorithm is proposed which incorporates a new Multilevel Selection framework (ML-MPCA). The approach used in this paper is based on biological group selection theory which aims to improve the capability of MPCA to tackle evolution of cooperation. A two-level selection process is introduced namely within-group selection and between-group selection. Individuals interact with the other members of the group in an evolutionary game that determines their fitness. If the group reaches a certain size, it splits into two daughter groups. We test our algorithm on CEC 2015 expensive benchmark functions to evaluate its performance. We show that our proposed algorithm improves solution accuracy and consistency. The model can be extended to more than two levels of selection and can also include migration.
Factors Responsible for Redisplacement of Pediatric Forearm Fractures Treated by Closed Reduction and Cast: Role of casting indices and three point index

Background Pediatric forearm fractures are still considered an enigma in view of their propensity to redisplace in cast. The redisplacement may be a potential cause for malalignment. We prospectively analyzed the role of risk factors and above casting indices in predicting significant redisplacement of pediatric forearm fractures treated by closed reduction and cast. Materials and Methods 113 patients of age range 2–13 years with displaced forearm fractures, treated by closed reduction and cast were included in this prospective study. Prereduction and postreduction angulation, translation, and shortening were noted. In addition, for distal metaphyseal fractures, obliquity angle was noted. In postreduction X-ray, apart from fracture variables, casting indices were also noted (cast index [CI] for all patients with three-point index [TPI] and second metacarpal radius angle in addition for distal metaphyseal fractures). In 2^nd week, X-rays were again obtained to check for significant redisplacement. These patients were managed with remanipulation and casting or were operated if remanipulation failed. Comparison of various risk factors was made between patients with significant redisplacement and those which were acceptably reduced. A subgroup analysis of patients with distal metaphyseal fractures was done. Results Thirteen (11.5%) patients had significant redisplacement; all of them required remanipulation. No association with respect to age, sex, level of fracture, side of injury, surgeon’s experience, number of bones fractured, and injury to definitive cast interval was seen. The presence of complete displacement in any of the plane in either of the bones was seen to be highly significant predictor of redisplacement (P < 0.001). Postreduction angulation more than 10° in any plane in either of the bone and fracture obliquity angle in distal metaphyseal fracture also had a highly significant association with redisplacement. There was a significant difference in the mean values of all three casting indices assessed. TPI was the most sensitive casting index (87.5%). Conclusions Conservative management with aim of anatomical reduction, especially in patients with complete displacement, should be the approach of choice in closed pediatric forearm fractures. Casting indices are good markers of quality of cast.
A Simplified approach to Visual Odometry using Graph Cut RANSAC

In the presence of excessive data, the rejection of outliers in data interpretation has gained a lot of importance. In this context, the original Random Sample Consensus (RANSAC) algorithm and lately, its variants have been a hot topic of interest for researchers in recent years. These techniques have been successfully employed in various fields including Visual Odometry, ORB-SLAM, Point Set Registration and 3D Reconstruction etc. Visual Odometry specifically relies heavily on outlier detection and rejection, as false correspondences in stereo images might lead to wrong 3D world point perception. In this paper, a simplified framework for Visual Odometery is put forth, by using the recently proposed Graph Cut RANSAC on the standard benchmark KITTI dataset. GC RANSAC exploits standard RANSACs local optimization step and uses Graph Cut technique to approach a better estimate in less iterations. Moreover, this approach produces appreciable results with less computational cost and limited complexity, as compared to some of the other recent methods, which require the use of Extended Kalman Filter framework to perform Inertial Measurement Unit data fusion for achieving better results.
Gadolinium Deposition in Brain: Current Scientific Evidence and Future Perspectives

In the past 4 years, many publications described a concentration-dependent deposition of gadolinium in the brain both in adults and children, seen as high signal intensities in the globus pallidus and dentate nucleus on unenhanced T1-weighted images. Postmortem human or animal studies have validated gadolinium deposition in these T1-hyperintensity areas, raising new concerns on the safety of gadolinium-based contrast agents (GBCAs). Residual gadolinium is deposited not only in brain, but also in extracranial tissues such as liver, skin, and bone. This review summarizes the current evidence on gadolinium deposition in the human and animal bodies, evaluates the effects of different types of GBCAs on the gadolinium deposition, introduces the possible entrance or clearance mechanism of the gadolinium and potential side effects that may be related to the gadolinium deposition on human or animals, and puts forward some suggestions for further research.
Utilization of PMU Measurements for Three-Phase Line Parameter Estimation in Power Systems

A new method is developed for synchrophasor-based estimation of three-phase transmission line (TL) impedance parameters, applicable for both fully transposed and untransposed TLs. The method is based on accurate time-synchronized measurements of the voltage and current phasors at both ends of a TL. The method estimates the line impedance values using a robust estimator with the property that the influence of outliers on the final results is minimized. A robust estimator is chosen as an alternative to well-known ordinary least-squares estimators which are highly sensitive to outliers. The performance of the method has been evaluated using a laboratory setup simulating real power system conditions. The necessary conditions for algorithm convergence as well as the requirements for measurement accuracy have been studied. In addition, this paper describes validation results of the proposed method in real high-voltage power system conditions by measuring voltage and current phasors with two phasor measurement units installed on both terminals of a 400-kV fully transposed overhead TL.
Residual Super-Resolution Single Shot Network for Low-Resolution Object Detection

For object detection in computer vision, detection models trained by high-resolution images often fail to recognize or localize objects on low-resolution images. To tackle this problem, we propose a fully convolutional network named residual super-resolution single shot network (RSRSSN). RSRSSN consists of two sub-networks, super-resolution sub-network and detection sub-network. The super-resolution sub-network in RSRSSN is achieved by stacking of identity residual blocks while the detection sub-network adopts the single shot multibox detector (SSD). Based on multi-task learning, we design a novel objective function called feature maps multibox loss to enforce low-resolution images to produce similar feature maps with their corresponding high-resolution ones. This information sharing mechanism is proved to be critical for solving the resolution mismatch problem in the experiments. A two-step training scheme is also proposed to train the RSRSSN in an end-to-end manner. Without any data augmentation, RSRSSN outperforms the SSD on both down-sampled PASCAL VOC and MS COCO in real-time object detection.
Effects of cyclic AMP on the differentiation and bioenergetics of rat C6 glioma cells

Abstract Introduction: Elevation in the level of intracellular cAMP is known to induce astrocytic differentiation of C6 glioma cells by unknown mechanisms. Methods: Therefore, cytoskeletal protein genes (phalloidin) fluorescents to investigate morphological changes, cell proliferation assay, MTT assay, flow cytometry, western blotting, in-cell western, immune-cytochemical (protein expression and localization), and oxygen electrodes (oxygen consumption rate) after a treatment with 0.25 mM dbcAMP were conducted. Results: Undifferentiated cells (media without dbcAMP) showed a flat polygonal appearance, whereas those cultured in the presence of 0.25 mM dbcAMP exhibited a more differentiated astrocytic morphology. They had more numerous neurite-like thin processes. The cell proliferation of differentiated c6 glioma reduced at day 2 and then started to increase at day 3 till day 5 compared to undifferentiated c6 glioma cells. In terms of flow-cytometry data, dbcAMP had no apoptotic effect on the C6 glioma cells. There was an increase in the protein expression GFAP (specific marker for astrocytes). There was no significant effect between undifferentiated and 5-day differentiation regarding their response to glucose 10 mM. In addition, there were no significant effects of glucose on the basal of 5-day differentiation of C6 glioma cells. However, there was a significant correlation between the concentration of glucose and inhibition of the basal oxygen consumption. Finally, glucose 10 mM did not stimulate NAD (P)H levels of C6 glioma cells. Conclusion: The above results showed that cAMP induce C6 glioma cells differentiation without affecting its bioenergetics. Therefore cAMP is considered to be the best differentiating agent.
Mobility Anisotropy in Black Phosphorus MOSFETs With HfO2 Gate Dielectrics

Precise measurements of the mobility anisotropy along high-symmetry crystal axes in black phosphorus (BP) MOSFETs are reported. Locally back-gated BP MOSFETs with 13-nm HfO<sub>2</sub> dielectric and channel length ranging from 0.3 to 0.7 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> are fabricated. A single BP flake of a uniform thickness is exfoliated and etched along armchair (AC) and zigzag (ZZ) crystal axes, and the orientations are confirmed using optical and transmission electron microscopy analyses. The hole and electron mobilities along each direction are extracted using the transfer length method. The AC-to-ZZ hole mobility ratio is found to increase from 1.4 (1.5) to 2.0 (2.9) as the sheet concentration increased from <inline-formula> <tex-math notation="LaTeX">$5.1\times 10^{\textsf {11}}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$1.9\times 10^{\textsf {12}}$ </tex-math></inline-formula> cm<sup>−2</sup> at room temperature (77 K). The room-temperature electron mobility anisotropy is found to be similar to that for holes with an AC-to-ZZ mobility ratio increasing from 1.4 to 2.1 from <inline-formula> <tex-math notation="LaTeX">$5.1\times 10^{\textsf {11}}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$1.9\times 10^{\textsf {12}}$ </tex-math></inline-formula> cm<sup>−2</sup> though electrons showed only a very weak temperature dependence. A Boltzmann transport model is used to explain the concentration- and temperature-dependent mobility anisotropies which can be well described using a charge center scattering model.