Relevant Filters Research Articles

Causal role of a neural system for separating and selecting multidimensional social cognitive information

People are multi-faceted, typically good at some things but bad at others, and a critical aspect of social judgement is the ability to focus on those traits relevant for the task at hand. However, it remains unknown how the brain supports such context-dependent social judgement. Here, we examine how people represent multidimensional individuals, and how the brain extracts relevant information and filters out irrelevant information when comparing individuals within a specific dimension. Using human fMRI, we identify distinct neural representations in dorsomedial prefrontal cortex (dmPFC) and anterior insula (AI) supporting separation and selection of information for context-dependent social judgement. Causal evaluation using non-invasive brain stimulation shows that AI disruption alters the impact of relevant information on social comparison, whereas dmPFC disruption only affects the impact of irrelevant information. This neural circuit is distinct from the one supporting integration across, as opposed to separation of, different features of a multidimensional cognitive space.

Assessment of Understandability and Actionability of YouTube Videos on Hemolytic Disease of the Newborn.

Introduction With revolutions in Information Technology, information and misinformation are easier to be found online. YouTube is the largest and most commonly searched video content website in the world. It is assumed that, due to the coronavirus pandemic, most patients try to know about diseases through the internet and reduce the number of hospital exposures unless otherwise. In order to assess the understandability and actionability of such YouTube videos available freely online about the disease, Hemolytic disease of the newborn(HDN), this study was planned. Methods This is a cross-sectional study conducted with the first 160 videos available on May 14, 2021, with the search keyword "HDN" with a relevance filter and a duration of 4 to 20 minutes. The videos were further screened regarding the information content and language. These videos were assessed by three independent assessors using the patient educational materials assessment tool for audio-visual content. Results Out of the first 160 videos selected for screening, 58 videos were excluded due to a lack of content about the searched disease "HDN". Another 63 videos were excluded due to the language of instruction not being in English. Finally, 39 videos were assessed by three assessors. The understandability and actionability responses were checked for reliability and a Cronbach's alpha of 93.6% was found, indicating good data reliability. To reduce subjectivity, average scores of understandability and actionability were taken based on the scores of these three assessors. There were eight and 34 videos with average understandability and actionability scores of <70% respectively. The median average understandability and actionability scores were 84.4% and 50% respectively. There was a statistically significant difference between understandability and actionability scores withconsiderably lower actionability scores of YouTube videos on the disease, HDN (p<0.001). Conclusion There is a great need to include actionable information by content developers in videos. Most information available has adequate understandable content making it easier for the general public to know about the diseases. YouTube and similar social sites thus possibly are helping in the dissemination of information promoting awareness among the public in general and patients in particular.

Fun Selfie Filters in Face Recognition: Impact Assessment and Removal

This work investigates the impact of fun selfie filters, which are frequently used to modify selfies, on face recognition systems. Based on a qualitative assessment and classification of freely available mobile applications, ten relevant fun selfie filters are selected to create a database. To this end, the selected filters are automatically applied to face images of public face image databases. Different state-of-the-art methods are used to evaluate the influence of fun selfie filters on the performance of face detection using dlib, RetinaFace, and a COTS method, sample quality estimated by FaceQNet and MagFace, and recognition accuracy employing ArcFace and a COTS algorithm. The obtained results indicate that selfie filters negatively affect face recognition modules, especially if fun selfie filters cover a large region of the face, where the mouth, nose, and eyes are covered. To mitigate such unwanted effects, a GAN-based selfie filter removal algorithm is proposed which consists of a segmentation module, a perceptual network, and a generation module. In a cross-database experiment the application of the presented selfie filter removal technique has shown to significantly improve the biometric performance of the underlying face recognition systems.

Factors Influencing Aspects of Quality of Life (QoL) in Young Carers: A Translational Review of Literature Giving Specific Consideration to Young Cancer Caregivers and Education

Research Background: Cancer caregivers and other carers are a niche community, particularly within education. Research has identified that quality of life may be negatively affected in carers, including mental health problems, as well as educational and employment issues, all of which are the focus of this review. Methods: Literature was identified for inclusion in the review by utilising relevant search engines, Boolean searches, and filters. Results: Findings indicate that there is a prevalence of mental health issues in carers to include cancer caregivers and that educational attainment is reduced when compared to non-caring peers. Carers also experience employment difficulties which may be attributed, in part, to reduced educational attainment. Conclusion: Initiatives suggested to address this and change the trajectory of carers to include cancer caregivers are those at an educational level, such as adapting pedagogical approaches, providing funding options for carers and opportunities to disclose they are a carer at initial application. Charities could be more involved with educational institutions to offer advice and guidance on the support needs of carers and the government could introduce policy specifically aimed at carers within education to provide consistency in the support and opportunities available to carers across the United Kingdom.

Novel method based on Teager Energy Operator for online tracking of power quality disturbances

• A TEO -based PQ disturbance tracking algorithm is proposed for the voltage fluctuations with sag disturbance. • The inverter-based DG, nonlinear load and fault event influence on the voltage flicker and sag levels. • The new approach can be used to track multiple PQ disturbance with a complex structure. • This method with proper accuracy can track the voltage amplitude variations in the presence of notch and harmonics. This paper presents an optimal algorithm for simultaneous tracking of power quality (PQ) events in distribution systems in the presence of the inverter-based distributed generation and nonlinear load. The algorithm is based on Teager Energy Operator (TEO). The main specification of the Teager Energy Operator, compared with the commonly used methods in the literature, is the simplicity of its mathematical. Using a proposed method, a suitable combination of the TEO and a relevant filter set is defined as a criterion to reduce envelope ripples and improve a suitable tracking for the power quality disturbances. This approach is able to accurately detect the distortions and sudden changes of voltage under different conditions. The paper considers the voltage fluctuations and voltage sag at the point of common coupling (PCC) and nearby points of the PCC as these voltage disturbances. Extensive simulations for the several power quality disturbance signals and the distribution network based on nonlinear elements using the new approach are performed and performance evaluation is clearly distinguished from the results. Simulation results for single and multiple power quality disturbances present that the proposed method is precise and suitable in detecting voltage disturbances.

Systematic literature review on surgical site preparation in paediatric surgery

BackgroundSurgical site infections (SSIs) in children represent a common and serious postoperative complication. Surgical skin preparation is an essential preventive measure in every surgical procedure. The most commonly used antiseptic agents for surgical skin preparation are chlorhexidine gluconate and iodophors in alcohol-based solutions. In adult patients the use of chlorhexidine-containing antiseptic solutions for preoperative skin preparation has been advocated to reduce SSI rates. Our objective was to conduct a systematic literature review on use of antiseptic agents for surgical skin preparation in children less than 16 years of age.MethodsA systematic review of MEDLINE, EMBASE, CINAHL and CENTRAL was performed using both MeSH and free text terms and using the relevant Cochrane filter to identify full text randomized trials (RCTs) and comparative observational studies. Interventions of interest were the choice of main agent in antiseptic solutions (chlorhexidine/povidone-iodine/alcohol) compared with each other or with other antiseptic agents. Primary outcome was the reported rate of surgical site infections.ResultsIn total 8 studies were included in the review; 2 RCTs and 6 observational studies. Observational studies generally did not primarily investigate the association of different antiseptics with subsequent SSI. The identified randomised controlled trials included only 61 children in total, and were of low quality. Consequently, we did not conduct a formal meta-analysis. Since the publication of a comprehensive systematic review of perioperative measures for the prevention of SSI in 2016, no randomized controlled trials comparing antiseptic agents for surgical skin preparation in paediatric surgery have been conducted.ConclusionRobust evidence on the optimal skin antisepsis to reduce SSIs in children is lacking. Direct extrapolation of effects from trials involving adults is not appropriate as physiologic characteristics and risk factors for SSIs differ between adults and children. It is therefore essential to conduct high quality RCT investigating interventions to identify optimal measures to reduce SSI rates in children.Trial registrationProspero registration (CRD42020166193).

A Comparative Analysis of Video-Assisted Thoracoscopic Surgery and Thoracotomy in Non-Small-Cell Lung Cancer in Terms of Their Oncological Efficacy in Resection: A Systematic Review.

Video-assisted thoracoscopic surgery (VATS) is considered the standard procedure for surgical resection in non-small-cell lung cancer (NSCLC). However, there is still lingering speculation on its adequacy of lymph node (LN) dissection or sampling and the long-term survival benefits when compared to open thoracotomy. Given the above, we conducted a systematic review comparing VATS and thoracotomy in terms of their oncological effectiveness in resection.We explored major research literature databases and search engines such as MEDLINE, PubMed, PubMed Central, Google Scholar, and ResearchGate to find pertinent articles. After the meticulous screening, quality check, and applying relevant filters according to our eligibility criteria, we identified 16 studies relevant to our research question, out of which one was a randomized controlled trial, one meta-analysis, and 14 were observational studies. The study comprised 44,673 patients with NSCLC, out of whom 15,093 patients were operated by VATS and the remaining 29,580 patients by thoracotomy. The results indicate that VATS is equivalent to thoracotomy in total LNs (N1 + N2) and LN stations dissected. However, a thoracotomy may achieve slightly better mediastinal lymph node dissection (N2) in terms of assessing a greater number of mediastinal lymph nodes and nodal stations. This may be attributed to a better visual field during mediastinal nodal clearance by an open approach. Also, nodal upstaging was consistently more common with an open approach. In terms of long-term outcomes, both overall survival and disease-free survival rates were similar between the two groups, with VATS offering a slightly better survival benefit.Irrespective of the increased rates of nodal upstaging by an open approach, we conclude that VATS should be considered a highly efficient alternative to thoracotomy in both early and locally advanced NSCLC.

Towards Secure and Efficient Equality Conjunction Search Over Outsourced Databases

Searchable symmetric encryption enables a cloud server to answer queries directly over encrypted data. Two key requirements are a strong security guarantee and a sub-linear search performance. The bucketization approach in the literature addresses these requirements at the expense of downloading false positives and requiring the local search at the client side. In this article, we propose a novel approach to meet these requirements while minimizing the clients work and communication cost. First, a relaxed notion of ciphertext indistinguishability on partitioned data is formalized, called class indistinguishability, which provides a level of ciphertext indistinguishability similar to that of bucketization but allows the server to perform search of relevant data and filter false positives. We present a construction for achieving these goals through a two-phase search algorithm. The first phase finds a candidate set through a sub-linear search. The second phase finds the exact query result using a linear search applied to the candidate set. The experiment results on large real-world data-sets show that our approach outperforms the state-of-the-art. This article focuses on the class of equality conjunction search, but it applies to the general class of Boolean queries of equalities because the latter can be reduced to several equality conjunction queries.

ESCMID guidelines on testing for SARS-CoV-2 in asymptomatic individuals to prevent transmission in the health care setting

ScopeThis guideline addresses the indications for direct testing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in asymptomatic individuals in health care facilities, with the aim to prevent SARS-CoV-2 transmissions in these settings. The benefit of testing asymptomatic individuals to create a safe environment for patients and health care workers must be weighed against potential unintended consequences, including delaying necessary treatments owing to false positive results and lower quality of care owing to strict isolation measures. MethodsA total of nine PICOs (population, intervention, comparison, outcome) on the topic of testing asymptomatic individuals was selected by the panel members. Subsequently, a literature search for existing guidelines and systematic reviews was performed on PubMed, Epistemonikos, and RecMap using relevant filters available in each database. Data on article/recommendation type, setting, target population, intervention, and quality of the evidence were extracted. Credibility of the systematic reviews was evaluated using the AMSTAR tool, and level of agreement with available recommendation was evaluated with the AGREE II score. Because the evidence available from systematic reviews was deemed insufficiently updated to formulate relevant recommendations, an additional search targeting relevant guidance documents from major public health institutions and original studies was performed. Provisional recommendations were discussed via web conferences until agreement was reached, and final recommendations were formulated according to the GRADE approach. RecommendationsRecommendations were formulated regarding systematic testing in asymptomatic individuals upon admission to a health care setting, during hospital stay, before elective procedures, and before scheduled nonsurgical procedures. Moreover, recommendations regarding testing of asymptomatic visitors, personal caregivers, and health care workers in health care facilities were presented. Recommendations also were given on contact tracing in asymptomatic patients or health care workers and the possibility of a negative screening test to shorten the quarantine period. Furthermore, if applicable, recommendations were specified to transmission rate and vaccination coverage.

A Categorical Review on First Order Universal Filters

Abstract: This paper highlights the notable individual contributions of the researchers in the field of first-order universal filters, and additionally, segregates the reviewed works amongst various scientifically chosen categories. Relevant first-order universal filter circuits reported in the last two decades are chosen for the review. The segregation categories are based on the well-recognized signal processing features, such as the mode of operation, number of active and passive elements, grounded nature of the passive components, simultaneous availability of the outputs, and the provision for resistorless realization. These works are also analyzed for various other performance enhancing attributes, like support for cascadability, transistor count, load insensitivity, matching constraints, etc. Moreover, exhaustive observations regarding existing works are also included individually in tabular form to mainly emphasize newness, uniqueness and downsides of reported ideas, which further enrich the quality of the review paper.

Human to Humanoid: An Evolving Concept; Issues and Concerns of Neuroethics.

Neuroethics is a subsection of ethics which allows us to express our concerns with advances in neurotechnology, threat to human race, our freedom of expression, autonomy, and justice. Aim of the review is to familiarize readers about the concept of neuroethics and alarming threats with newer neurotechnology and to sensitize them about our responsibilities as neuroscientists. Systematic literature search was conducted from 2010 to 2021 in PubMed to look at previously published review articles related to neuroethics. Relevant filters were added addressing autonomy and consent. Of the 426 articles, only 12 addressed the issue of autonomy and neuroethics and two on consent and neuroethics. International literature has expressed limited concern on the scope of neuroethics. Newer threats are getting added on with advancing technology, and as neuroscientists, we all need to understand and educate neuroscientists about our social responsibilities in curtailing an out-of-reach situation for the mankind.

Extension of luminance component based demosaicking algorithm to 4- and 5-band multispectral images

Multispectral imaging systems are currently expanding with a variety of multispectral demosaicking algorithms. But these algorithms have limitations due to the remarkable presence of artifacts in the reconstructed image. In this paper, we propose a powerful multispectral image demosaicking method that focuses on the G band and luminance component. We've first identified a relevant 4-and 5-band multispectral filter array (MSFA) with the dominant G band and then proposed an algorithm that consistently estimates the missing G values and other missing components using a convolution operator and a weighted bilinear interpolation algorithm based on the luminance component. Using the considered MSFA patterns, we've also demonstrated that our algorithm outperforms existing approaches both visually and quantitatively in terms of the PSNR and SSIM.

A four-dimensional analysis of partitioned approximate filters

With today's data deluge, approximate filters are particularly attractive to avoid expensive operations like remote data/disk accesses. Among the many filter variants available, it is non-trivial to find the most suitable one and its optimal configuration for a specific use-case. We provide open-source implementations for the most relevant filters (Bloom, Cuckoo, Morton, and Xor filters) and compare them in four key dimensions: the false-positive rate, space consumption, build, and lookup throughput. We improve upon existing state-of-the-art implementations with a new optimization, radix partitioning, which boosts the build and lookup throughput for large filters by up to 9x and 5x. Our in-depth evaluation first studies the impact of all available optimizations separately before combining them to determine the optimal filter for specific use-cases. While register-blocked Bloom filters offer the highest throughput, the new Xor filters are best suited when optimizing for small filter sizes or low false-positive rates.

Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.

Whether radiomics methods are useful in prediction of therapeutic response to neoadjuvant chemoradiotherapy (nCRT) is unclear. This study aimed to investigate multiple magnetic resonance imaging (MRI) sequence-based radiomics methods in evaluating therapeutic response to nCRT in patients with locally advanced rectal cancer (LARC). This retrospective study enrolled patients with LARC (06/2014-08/2017) and divided them into nCRT-sensitive and nCRT-resistant groups according to postoperative tumor regression grading results. Radiomics features from preoperative MRI were extracted, followed by dimension reduction using the minimum redundancy maximum relevance filter. Three machine-learning classifiers and an ensemble classifier were used for therapeutic response prediction. Radiomics nomogram incorporating clinical parameters were constructed using logistic regression. The receiver operating characteristic (ROC), decision curves analysis (DCA) and calibration curves were also plotted to evaluate the prediction performance. The machine learning classifiers showed good prediction performance for therapeutic responses in LARC patients (n=189). The ROC curve showed satisfying performance (area under the curve [AUC], 0.830; specificity, 0.794; sensitivity, 0.815) in the validation group. The radiomics signature included 30 imaging features derived from axial T1-weighted imaging with contrast and sagittal T2-weighted imaging and exhibited good predictive power for nCRT. A radiomics nomogram integrating carcinoembryonic antigen levels and tumor diameter showed excellent performance with an AUC of 0.949 (95% confidence interval, 0.892-0.997; specificity, 0.909; sensitivity, 0.879) in the validation group. DCA confirmed the clinical usefulness of the nomogram model. The radiomics method using multiple MRI sequences can be used to achieve individualized prediction of nCRT in patients with LARC before treatment.

Deep reinforcement learning based ensemble model for rumor tracking

Fully automated rumor defeating is meaningful for reducing hazards of misinformation in social networks. As one of the automated approaches, content-based rumor defeating is a pipeline that could be divided into four sequential sub-tasks: detection, tracking, sentence classification, and veracity. Specifically, rumor tracking gathers relevant posts and filters unrelated posts for a potential rumor news, which is significant for rumor defeating and has not been studied extensively. However, the existing proposals only consider rumor tracking as an auxiliary task in multi-task learning without special optimization, therefore restraining the accuracy of tracking performance. To this end, we propose a deep reinforcement learning based ensemble model for rumor tracking (RL-ERT), which aggregates multiple components by a weight-tuning policy network, and utilizes specific social features to improve the performance. Finally, we conduct experiments on public datasets and the experimental results show the superiority of RL-ERT on efficiency and effectiveness.

Pruning by explaining: A novel criterion for deep neural network pruning

The success of convolutional neural networks (CNNs) in various applications is accompanied by a significant increase in computation and parameter storage costs. Recent efforts to reduce these overheads involve pruning and compressing the weights of various layers while at the same time aiming to not sacrifice performance. In this paper, we propose a novel criterion for CNN pruning inspired by neural network interpretability: The most relevant units, i.e. weights or filters, are automatically found using their relevance scores obtained from concepts of explainable AI (XAI). By exploring this idea, we connect the lines of interpretability and model compression research. We show that our proposed method can efficiently prune CNN models in transfer-learning setups in which networks pre-trained on large corpora are adapted to specialized tasks. The method is evaluated on a broad range of computer vision datasets. Notably, our novel criterion is not only competitive or better compared to state-of-the-art pruning criteria when successive retraining is performed, but clearly outperforms these previous criteria in the resource-constrained application scenario in which the data of the task to be transferred to is very scarce and one chooses to refrain from fine-tuning. Our method is able to compress the model iteratively while maintaining or even improving accuracy. At the same time, it has a computational cost in the order of gradient computation and is comparatively simple to apply without the need for tuning hyperparameters for pruning.

Monte Carlo study of a convergent X-ray beam for high resolution X-ray fluorescence imaging

The traditional X-ray tube design is built upon the impact of energetic electrons on high atomic number absorbers producing the X-ray output, consisting of photons due to Bremsstrahlung and fluorescence. Typically, electrons current hits the target within a limited area of a few millimeters square stopping the electrons, which lose their energy and produce the X rays constituting an inherently divergent beam. This geometrical property of traditional X-ray beams is responsible for several undesirable effects when trying to optimize applications requiring high incident fluence spatial concentration, like X-ray fluorescence imaging. This work presents a Monte Carlo study about a novel X-ray tube design, based on a cylindrical target that is capable of producing a convergent X-ray beam aimed at improving overall performance and spatial resolution in certain applications, like X-ray fluorescence imaging. Main design characteristics for relevant parts, like target/anode, filter, and collimator, have been carefully investigated by means of Monte Carlo simulation using two independent codes: FLUKA and PENELOPE. The obtained results suggest the feasibility of the proposed design remarking that high fluence concentration can be achieved, which can be particularly useful for further applications, like tumor targeting by X-ray fluorescence imaging by means of high atomic number nanoparticle infusion, as reported in this work.

Asynchronous l 2 – l ∞ filtering of discrete-time impulsive switched systems with admissible edge-dependent average dwell time switching signal

In this paper, the asynchronous filtering problem is directed at a kind of discrete-time impulsive switched systems, where ‘asynchronous’ implies that there is a delay when switching occurs between filter modes and system modes. A novel Lyapunov function has been constructed. The inventiveness lies in discontinuity of Lyapunov-like function at both the switching time and the instant when the system modes and filter modes are matched. An improved inequality portraying the relationship among Lyapunov-like function, impulse, asynchronous phenomena and disturbance is built to pledge the performance. On the basis of directed graph, the novel Lyapunov function, the improved inequality and admissible edge-dependent average dwell time technology, some criteria suitable for proving the globally uniformly exponential stability of filtering error system are proposed with performance index, and the relevant filter can also be designed. Finally, three numerical examples show the availability of the obtained conclusions.

Should the Black Box Warning of Brodalumab and Apremilast Deter Prescribing in Psoriasis Patients with Depression?

Introduction: Psoriasis, an immune-mediated disease that manifests cutaneously with possible arthritic complications, affects millions of people in the United States and worldwide. Depression and suicidal ideation and behavior (SIB) are two prevalent comorbidities associated with psoriasis, due to the chronic nature of the disease, lack of a cure, as well as social stigma, all of which are detrimental to quality of life. Among the options available for management of moderate-severe psoriasis, apremilast and brodalumab represent recent additions to the therapeutic armamentarium for managing psoriasis. It has been suggested that the aforementioned drugs can lead to depression and possibly increase the risk for SIB. Furthermore, a black box warning was issued for brodalumab. This review challenges opinions that the drugs are solely responsible for exacerbating depression and SIB, when in fact it could be psoriasis itself. Methods: An extensive search of available literature linking cytokines to suicidal behavior was performed. After filtering for relevance, 22 articles were reviewed in detail. Results: Brodalumab and apremilast, both molecularly and clinically, do not objectively increase the risk for depression and/or suicidal ideation and behavior. Conclusion: After careful review of the appropriate studies and relevant literature, patients with moderate-severe psoriasis, including those that experience depression resulting from their chronic condition, would likely benefit from early, rather than delayed initiation of effective medications like apremilast and brodalumab. The speed of response and high level of efficacy of brodalumab make it an ideal intervention for patients suffering depression caused by their psoriasis.

Enhanced efficiency prediction of an electrified off-highway vehicle transmission utilizing machine learning methods

The increasingly noticeable effects of climate change require action to reduce greenhouse gas emissions. In the transport sector, the increase in electromobility is an important tool for reducing CO2 emissions. In order to meet the essential requirements, a particularly detailed system knowledge, for example on energy efficiency in the powertrain, is needed for the development and improvement of electric vehicles.The present work contributes to this by mapping the efficiency of a planetary gearbox for an electric vehicle. The mapping is done by experimental modelling with machine learning methods based on a standard efficiency description. The data necessary for the neural network training is generated by efficiency experiments on the powertrain test rig. For suitable results the data was pre-processed by applying relevant low-pass filters. A comparison of the Bayesian-Regularization algorithm, the Levenberg-Marquardt algorithm and the scaled-conjugate-gradient algorithm for training exhibited the strengths and weaknesses of the individual algorithms for the efficiency mapping. By comparing each algorithms performance metrics, the one matching the requirements best is chosen for the efficiency mapping.The result is a continuous function that determines the efficiency based on speed- and torque-inputs. Based on a key performance indicator, statistical validation by evaluating the standard deviations is used to ensure the quality of the results. In this paper the suitable algorithm for the given use case was determined. It can be applied for further research due to the significant results shown.