Unknown Background Research Articles

Explicit-memory multiresolution adaptive framework for speech and music separation

The human auditory system employs a number of principles to facilitate the selection of perceptually separated streams from a complex sound mixture. The brain leverages multi-scale redundant representations of the input and uses memory (or priors) to guide the selection of a target sound from the input mixture. Moreover, feedback mechanisms refine the memory constructs resulting in further improvement of selectivity of a particular sound object amidst dynamic backgrounds. The present study proposes a unified end-to-end computational framework that mimics these principles for sound source separation applied to both speech and music mixtures. While the problems of speech enhancement and music separation have often been tackled separately due to constraints and specificities of each signal domain, the current work posits that common principles for sound source separation are domain-agnostic. In the proposed scheme, parallel and hierarchical convolutional paths map input mixtures onto redundant but distributed higher-dimensional subspaces and utilize the concept of temporal coherence to gate the selection of embeddings belonging to a target stream abstracted in memory. These explicit memories are further refined through self-feedback from incoming observations in order to improve the system’s selectivity when faced with unknown backgrounds. The model yields stable outcomes of source separation for both speech and music mixtures and demonstrates benefits of explicit memory as a powerful representation of priors that guide information selection from complex inputs.

Penetrative convection: heat transport with marginal stability assumption

This paper investigates heat transport in penetrative convection with a marginally stable temporal-horizontal-averaged field or background field. Assuming that the background field is steady and is stabilised by the nonlinear perturbation terms, we obtain an eigenvalue problem with an unknown background temperature $\tau$ by truncating the nonlinear terms. Using a piecewise profile for $\tau$ , we derived an analytical scaling law for heat transport in penetrative convection as $Ra\rightarrow \infty$ : $Nu=(1/8)(1-T_M)^{5/3}Ra^{1/3}$ ( $Nu$ is the Nusselt number; $Ra$ is the Rayleigh number and $T_M$ corresponds to the temperature at which the density is maximal). A conditional lower bound on $Nu$ , under the marginal stability assumption, is then derived from a variational problem. All the solutions to the full system should deliver a higher heat flux than the lower bound if they satisfy the marginal stability assumption. However, data from the present direct numerical simulations and previous optimal steady solutions by Ding & Wu (J. Fluid Mech., vol. 920, 2021, A48) exhibit smaller $Nu$ than the lower bound at large $Ra$ , indicating that these averaged fields are over-stabilised by the nonlinear terms. To incorporate a more physically plausible constraint to bound heat transport, an alternative approach, i.e. the quasilinear approach is invoked which delivers the highest heat transport and agrees well with Veronis's assumption, i.e. $Nu\sim Ra^{1/3}$ (Astrophys. J., vol. 137, 1963, p. 641). Interestingly, the background temperature $\tau$ yielded by the quasilinear approach can be non-unique when instability is subcritical.

Cerebral abscesses with odontogenic origin: a population-based cohort study

ObjectivesRecent studies have indicated that cerebral abscess (CA) patients with odontogenic origin are on the rise. However, CA patients are often poorly characterized and with an unknown etiologic background. The purpose of this study is to identify and characterize CA patients that may have an odontogenic origin based on microbiologic, radiographic, and/or clinical findings.Materials and methodsThis is a population-based cohort study analyzing retrospective and prospective data from CA patients. Radiographic examinations of panoramic radiographs (PRs) or computed tomography (CT) scans were conducted. CA patients characterized with odontogenic origin required the fulfilment of the following criteria on admission: (1) Oral pathologic conditions were the only bacterial infections present, (2) oral microorganisms were isolated in the purulent exudate from the brain, and (3) radiographically and/or clinical recordings of oral pathologic conditions.ResultsA total of 44 patients could be included in this study of which 25 (57%) were characterized as having CA with a likely odontogenic origin. Type two diabetes (T2D) (p = 0.014) and microorganisms of the Streptococcus anginosus group (SAG) (p < 0.01) were overrepresented in patients with CAs of odontogenic origin.ConclusionsOdontogenic infections may cause CAs to a greater extent than previously assumed. T2D was overrepresented among patients with odontogenic CA. When microorganisms of the SAG were isolated from the brain pus, CA patients had a predisposing odontogenic or sinus infection.Clinical relevanceThe identification of patients with a likely odontogenic CA will contribute to understanding the etiology of the infectious disease and highlighting the importance of preserving oral health.

Query semantic reconstruction for background in few-shot segmentation

Few-shot segmentation (FSS) aims to segment unseen classes using a few annotated samples. Typically, a prototype representing the foreground class is extracted from annotated support image(s) and is matched to features representing each pixel in the query image. However, models learnt in this way are insufficiently discriminatory, and often produce false positives: misclassifying background pixels as foreground. Some FSS methods try to address this issue by using the background in the support image(s) to help identify the background in the query image. However, the backgrounds of these images are often quite distinct, and hence, the support image background information is uninformative. This article proposes a method, QSR, that extracts the background from the query image itself, and as a result is better able to discriminate between foreground and background features in the query image. This is achieved by modifying the training process to associate prototypes with class labels including known classes from the training data and latent classes representing unknown background objects. This class information is then used to extract a background prototype from the query image. To successfully associate prototypes with class labels and extract a background prototype that is capable of predicting a mask for the background regions of the image, the machinery for extracting and using foreground prototypes is induced to become more discriminative between different classes. Experiments achieves state-of-the-art results for both 1-shot and 5-shot FSS on the PASCAL-5i\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5^{i}$$\\end{document} and COCO-20i\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$20^{i}$$\\end{document} dataset. As QSR operates only during training, results are produced with no extra computational complexity during testing.

Genetics of nonlesional focal epilepsy in adults and surgical implications

AbstractNonlesional focal epilepsies (nlFE) represent a heterogenous group of syndromes. They encompass self-limited focal epilepsies of childhood and youth, rare focal, familial epilepsies, epilepsies associated with brain somatic variants, and to a large extent nonfamilial epilepsies that have a complex genetic or unknown background. Genetic testing should be performed in cases of a family history suggestive of monogenic inheritance and in cases that show additional symptoms, such as intellectual impairment, autism, or dysmorphic features. Whole-exome or whole-genome sequencing is the method of choice. Growing evidence suggests including genetic testing also in the presurgical workup of individuals with drug-resistant epilepsy. While individuals that harbor variants in genes of the mammalian target of rapamycin (mTOR) pathway tend to achieve better seizure control following epilepsy surgery, the postsurgical outcome of genetic epilepsies associated with channel function or synaptic transmission appears to be poor. The aim of this article is to review the genetic background of focal epilepsies that occur or persist in adults, provide guidance for genetic testing, and discuss potential implications for presurgical evaluation.

Genome and Transcriptome Identification of a Rice Germplasm with High Cadmium Uptake and Translocation.

The safe production of food on Cd-polluted land is an urgent problem to be solved in South China. Phytoremediation or cultivation of rice varieties with low Cd are the main strategies to solve this problem. Therefore, it is very important to clarify the regulatory mechanism of Cd accumulation in rice. Here, we identified a rice variety with an unknown genetic background, YSD, with high Cd accumulation in its roots and shoots. The Cd content in the grains and stalks were 4.1 and 2.8 times that of a commonly used japonica rice variety, ZH11, respectively. The Cd accumulation in the shoots and roots of YSD at the seedling stage was higher than that of ZH11, depending on sampling time, and the long-distance transport of Cd in the xylem sap was high. Subcellular component analysis showed that the shoots, the cell wall, organelles, and soluble fractions of YSD, showed higher Cd accumulation than ZH11, while in the roots, only the cell wall pectin showed higher Cd accumulation. Genome-wide resequencing revealed mutations in 22 genes involved in cell wall modification, synthesis, and metabolic pathways. Transcriptome analysis in Cd-treated plants showed that the expression of pectin methylesterase genes was up-regulated and the expression of pectin methylesterase inhibitor genes was down-regulated in YSD roots, but there were no significant changes in the genes related to Cd uptake, translocation, or vacuole sequestration. The yield and tiller number per plant did not differ significantly between YSD and ZH11, but the dry weight and plant height of YSD were significantly higher than that of ZH11. YSD provides an excellent germplasm for the exploration of Cd accumulation genes, and the cell wall modification genes with sequence- and expression-level variations provide potential targets for phytoremediation.

Open-Set Object Detection Using Classification-Free Object Proposal and Instance-Level Contrastive Learning

Detecting both known and unknown objects is a fundamental skill for robot manipulation in unstructured environments. Open-set object detection (OSOD) is a promising direction to handle the problem consisting of two subtasks: objects and background separation, and open-set object classification. In this letter, we present Openset RCNN to address the challenging OSOD. To disambiguate unknown objects and background in the first subtask, we propose to use classification-free region proposal network (CF-RPN) which estimates the objectness score of each region purely using cues from object's location and shape preventing overfitting to the training categories. To identify unknown objects in the second subtask, we propose to represent them using the complementary region of known categories in a latent space which is accomplished by a prototype learning network (PLN). PLN performs instance-level contrastive learning to encode proposals to a latent space and builds a compact region centering with a prototype for each known category. Further, we note that the detection performance of unknown objects can not be unbiasedly evaluated on the situation that commonly used object detection datasets are not fully annotated. Thus, a new benchmark is introduced by reorganizing GraspNet-1billion, a robotic grasp pose detection dataset with complete annotation. Extensive experiments demonstrate the merits of our method. We finally show that our Openset RCNN can endow the robot with an open-set perception ability to support robotic rearrangement tasks in cluttered environments.

Enhancement of Underwater Images with Retinex Transmission Map and Adaptive Color Correction

Underwater images often suffer from low contrast, low visibility, and color deviation. In this work, we propose a hybrid underwater enhancement method consisting of addressing an inverse problem with novel Retinex transmission map estimation and adaptive color correction. Retinex transmission map estimation does not rely on channel priors and aims to decouple from the unknown background light, thus avoiding error accumulation problem. To this end, global white balance is performed before estimating the transmission map using multi-scale Retinex. To further improve the enhancement performance, we design the adaptive color correction which cleverly chooses between two color correction procedures and prevents channel stretching imbalance. Quantitative and qualitative comparisons of our method and state-of-the-art underwater image enhancement methods demonstrate superiority of the proposed method. It achieves the best performance in terms of full-reference image quality assessment. In addition, it also achieves superior performance in the non-reference evaluation.

SI2FM: SID Isolation Double Forest Model for Hyperspectral Anomaly Detection

Hyperspectral image (HSI) anomaly detection (HSI-AD) has become a hot issue in hyperspectral information processing as a method for detecting undesired targets without a priori information against unknown background and target information, which can be better adapted to the needs of practical applications. However, the demanding detection environment with no prior and small targets, as well as the large data and high redundancy of HSI itself, make the study of HSI-AD very challenging. First, we propose an HSI-AD method based on the nonsubsampled shearlet transform (NSST) domain spectral information divergence isolation double forest (SI2FM) in this paper. Further, the method excavates the intrinsic deep correlation properties between NSST subband coefficients of HSI in two ways to provide synergistic constraints and guidance on the prediction of abnormal target coefficients. On the one hand, with the “difference band” as a guide, the global isolation forest and local isolation forest models are constructed based on the spectral information divergence (SID) attribute values of the difference band and the low-frequency and high-frequency subbands, and the anomaly scores are determined by evaluating the path lengths of the isolation binary tree nodes in the forest model to obtain a progressively optimized anomaly detection map. On the other hand, based on the relationship of NSST high-frequency subband coefficients of spatial-spectral dimensions, the three-dimensional forest structure is constructed to realize the co-optimization of multiple anomaly detection maps obtained from the isolation forest. Finally, the guidance of the difference band suppresses the background noise and anomaly interference to a certain extent, enhancing the separability of target and background. The two-branch collaborative optimization based on the NSST subband coefficient correlation mining of HSI enables the prediction of anomaly sample coefficients to be gradually improved from multiple perspectives, which effectively improves the accuracy of anomaly detection. The effectiveness of the algorithm is verified by comparing real hyperspectral datasets captured in four different scenes with eleven typical anomaly detection algorithms currently available.

Mapping the Minimum Detectable Activities of Gamma-Ray Sources in a 3-D Scene

The ability to formulate maps of minimum detectable activities (MDAs) that describe the sensitivity of an ad hoc measurement that used one or more freely moving radiation detector systems would be significantly beneficial for the conduct and understanding of many radiological search activities. In a real-time scenario with a free-moving detector system, an MDA map can provide useful feedback to the operator about which areas have not been searched as thoroughly as others, thereby allowing the operator to prioritize future actions. Similarly, such a calculation could be used to inform subsequent navigation decisions of autonomous platforms. Here we describe a near real-time MDA mapping approach that can be applied when searching for point sources using detected events in a spectral region of interest (ROI) while assuming a constant, unknown background rate. We show the application of this MDA mapping method to a real scenario, a survey of the interior of a small building using a handheld detector system. Repeated measurements with no sources and with 137Cs sources of different strengths yield results consistent with the estimated thresholds and MDA values; namely, that for background-only measurements no sources are seen above threshold anywhere in the scene, while when sources are present they are detected above the thresholds calculated for their locations.

Searching for cosmic string induced stochastic gravitational wave background with the Parkes Pulsar Timing Array

We search for stochastic gravitational wave background emitted from cosmic strings using the Parkes Pulsar Timing Array data over 15 years. While we find that the common power-law excess revealed by several pulsar timing array experiments might be accounted for by the gravitational wave background from cosmic strings, the lack of the characteristic Hellings-Downs correlation cannot establish its physical origin yet. The constraints on the cosmic string model parameters are thus derived with conservative assumption that the common power-law excess is due to unknown background. Two representative cosmic string models with different loop distribution functions are considered. We obtain constraints on the dimensionless string tension parameter $G\mu<10^{-11}\sim10^{-10}$, which is more stringent by two orders of magnitude than that obtained by the high-frequency LIGO-Virgo experiment for one model, and less stringent for the other. The results provide the chance to test the Grand unified theories, with the spontaneous symmetry breaking scale of $U(1)$ being two-to-three orders of magnitude below $10^{16}$ GeV. The pulsar timing array experiments are thus quite complementary to the LIGO-Virgo experiment in probing the cosmic strings and the underlying beyond standard model physics in the early Universe.

Radar-Based Gesture Recognition Using a Variational Autoencoder With Deep Statistical Metric Learning

Radar-based gesture recognition systems are a promising concept for new human–machine interfaces. However, the systems are sensitive to user-dependent gesture patterns, sensor noise characteristics, background environments, and unknown gestures or background motions. To overcome such challenges, we propose a variational autoencoder architecture-based deep metric learning, which is optimized using a novel loss function combining a statistical distance triplet loss and the center loss. By learning the statistical distance between distributions, the proposed solution, compared with triplet loss, is able to better learn the nonlinear characteristics in the data, is less sensitivity to training strategy, and is capable of creating close knit embedding clusters. Moreover, it is experimentally demonstrated that the proposed gesture recognition system using radar spectrograms has improved classification accuracy and random gesture rejection accuracy compared with the state-of-the-art deep metric learning approaches. Furthermore, we proof the real-world capability of the system by implementing a real-time demonstrator, which allows playing “Tetris” with hand gestures.

Hyperspectral imaging for chemicals identification: a human-inspired machine learning approach

Data analysis has increasingly relied on machine learning in recent years. Since machines implement mathematical algorithms without knowing the physical nature of the problem, they may be accurate but lack the flexibility to move across different domains. This manuscript presents a machine-educating approach where a machine is equipped with a physical model, universal building blocks, and an unlabeled dataset from which it derives its decision criteria. Here, the concept of machine education is deployed to identify thin layers of organic materials using hyperspectral imaging (HSI). The measured spectra formed a nonlinear mixture of the unknown background materials and the target material spectra. The machine was educated to resolve this nonlinear mixing and identify the spectral signature of the target materials. The inputs for educating and testing the machine were a nonlinear mixing model, the spectra of the pure target materials (which are problem invariant), and the unlabeled HSI data. The educated machine is accurate, and its generalization capabilities outperform classical machines. When using the educated machine, the number of falsely identified samples is ~ 100 times lower than the classical machine. The probability for detection with the educated machine is 96% compared to 90% with the classical machine.

A review of perceptions, challenges and prospects of child adoption in nigeria

Child adoption in Nigeria is still an evolving process .The enactment of the Child's Right Act in Nigeria has legalized child adoption. The Objective of this study include to access the perception of child adoption in Nigeria; to determine the challenges of child adoption in Nigeria, and to investigate the prospects of child adoption in Nigeria. The method used was to explore secondary data, policy documents, existing studies and reviews from scholars, local and international organizations. Perception of child adoption slightly vary across the country, with most Nigerian having fair knowledge of the term “child adoption” which does not translate into its practice .It was also shown that infertility is the main factor that has influenced the rate of willingness and acceptability of child adoption in Nigeria. However, the ideology that biological ties are fundamental to family relations is associated with several stigmatizing beliefs about adoption, which may cause both the adoptee and the adoptive parents to feel socially marginalized and devalued. Just like other part of the world, child adoption in Nigeria is faced with several challenges ranging from stigmatization, unknown parental background, possible inherited or genetic diseases, psychological and religious beliefs, poverty, establishment of illegal adoption and orphanage homes, future claim by the biological parents, disloyalty or abandonment of adoptive parents the child when the child learns that he or she was adopted. The future prospects of child adoption lie in Nigerians taking seriously the principles and standards enunciated in the international adoption law of Nigeria that the best interest of children (which include their survival, development, protection and participation) should be the guiding principle in the whole adoption process. In conclusion , the perception, challenges and prospects of child adoption reviewed in this study shows child adoption in Nigeria is still evolving and requires the government, churches, other religious bodies, NGOs, individuals and the public at large to join hands in welcoming and implementing the right practices of child adoption, as well as ‘Child's Right Laws' .

Characterizing “no treatment” decisions in patients with advanced-stage cancers.

76 Background: Cancer (CA) is one of the leading causes of death worldwide. In 2020 CA was the second leading cause of mortality in the US. Novel treatments for various malignancies are discovered each passing year. Despite these advancements, some patients refuse to receive treatment. Our study focused on characterizing the group of patients with advanced malignancies who refused therapy. Methods: We performed a retrospective chart review of patients diagnosed with advanced malignancies, aged 18-75 years, between 01/01/ 2010 and 12/31/2015, who refused any therapy. We used Geisinger Health System’s data. Results: Our search provided 644 patients who met our inclusion criteria. The mean age of patients was 63.2 years (standard deviation (σ) 8.3). Female patients made up 40.4% (260/644), while 59.6% (384/644) were male. Most patients were Caucasian (96.9%, 624/644), 1.9% (12/644) were Black; 0.6% (4/644) were Asian, and 0.6% (4/644) were of unknown racial background. Marital status was evenly distributed with 50.0% (322/644) being married and 50.0% (322/644) being non-married. Most of the study population (52.3%, 337/644) had government-funded insurance at the time of CA diagnosis; 46.6% (300/644) had private insurance, and 1.1% (7/644) had no insurance. Stage 3 disease, according to American Joint Committee on Cancer staging was noted in 21.1% (136/644), while 78.9% (508/644) had stage 4 disease. The mean BMI was 28.2 (σ 8.3). Most patients were either overweight or obese (60.6%, 350/577; 67 patients did not have data on BMI available). A history of cigarette smoking was present in 73.0% (438/600). Adenocarcinomas (323/639, 50.5%) were the most prominent histological subtype. The most common site of malignancy was the respiratory system (220/644, 34.2%). Only 9.6% (62/644) of patients had a history of previous CA, while a family history of CA was present in 29.5% (190/644). Most patients (62.1%, 400/644) had a Charleston Comorbidity Index (CCI) &gt; 5; the mean CCI was 6.4 (σ 3.7). In 17.1% (110/644) a palliative medicine referral was made. Substance use disorders were present in 23.8% (153/644); 14.4% (93/644) had a history of depression, and anxiety 10.1% (65/644). Conclusions: Male patients made up a larger proportion of the group of advanced-stage patients who declined therapy. The racial distribution in this group of patients appeared to reflect the local population. Most patients who refused therapy had stage 4 disease, and they appeared to have severe underlying comorbidities, with an average CCI &gt;5. Despite treatment refusal, a surprisingly low percentage of patients were referred to palliative medicine.[Table: see text][Table: see text]

Rookognise: Acoustic detection and identification of individual rooks in field recordings using multi-task neural networks

Individual-level monitoring is essential in many behavioural and bioacoustics studies. Collecting and annotating those data is costly in terms of human effort, but necessary prior to conducting analysis. In particular, many studies on bird vocalisations also involve manipulating the animals or human presence during observations, which may bias vocal production. Autonomous recording units can be used to collect large amounts of data without human supervision, largely removing those sources of bias. Deep learning can further facilitate the annotation of large amounts of data, for instance to detect vocalisations, identify the species, or recognise the vocalisation types in recordings. Acoustic individual identification, however, has so far largely remained limited to a single vocalisation type for a given species. This has limited the use of those techniques for automated data collection on raw recordings, where many individuals can produce vocalisations of varying complexity, potentially overlapping one another, with the additional presence of unknown and varying background noise. This paper aims at bridging this gap by developing a system to identify individual animals in those difficult conditions. Our system leverages a combination of multi-scale information integration, multi-channel audio and multi-task learning. The multi-task learning paradigm is based the overall task into four sub-tasks, three of which are auxiliary tasks: the detection and segmentation of vocalisations against other noises, the classification of individuals vocalising at any point during a sample, and the sexing of detected vocalisations. The fourth task is the overall identification of individuals. To test our approach, we recorded a captive group of rooks, a Eurasian social corvid with a diverse vocal repertoire. We used a multi-microphone array and collected a large scale dataset of time-stamped and identified vocalisations recorded, and found the system to work reliably for the defined tasks. To our knowledge, the system is the first to acoustically identify individuals regardless of the vocalisation produced. Our system can readily assist data collection and individual monitoring of groups of animals in both outdoor and indoor settings, even across long periods of time, and regardless of a species’ vocal complexity. All data and code used in this article is available online.

Robust new physics searches with unknown backgrounds

Searches for new physics often face unknown backgrounds, causing false detections or weakened upper limits. This paper introduces the ``deficit hawk'' technique, which mitigates unknown backgrounds by testing multiple options for data cuts, such as fiducial volumes or energy thresholds. Combining the power of likelihood ratios with the robustness of the interval-searching techniques, deficit hawks could improve mean upper limits on new physics by a factor 2 for experiments with partial or speculative background knowledge. Deficit hawks are well suited to analyses that use machine learning or other multidimensional discrimination techniques and can be extended to permit discoveries in regions without unknown background.

Identifying distinct plastics in hyperspectral experimental lab-, aircraft-, and satellite data using machine/deep learning methods trained with synthetically mixed spectral data

The growing production and use of plastics are becoming a serious progressive issue and people pay increasing attention to the effects of plastics on ecosystems and human health. The availability of hyperspectral data from space sensors inspired us to study the feasibility to detect and identify different types of plastics in aircraft -, Goafen-5 (GF-5) and PRISMA satellite data by means of deep -, and machine learning models trained with spectral signatures. In this context, various inhouse and public spectral libraries are used to create a comprehensive database with mixed pixels of different plastic and non-plastic materials. The endmembers of plastic types involved in this study are polyethylene (PE), polypropylene (PP), polyvinyl chloride (PVC), polyethylene terephthalate (PET) and polystyrene (PS), covering 95% of the global production. Additionally, some important varieties of industrial plastics types such as acrylonitrile butadiene styrene (ABS), ethylene vinyl acetate (EVA), polyamide (PA), polycarbonate (PC), and polymethyl methacrylate (PMMA) were included in the investigations. Different samples with varying optical properties (color, brightness, transmissivity) have been selected for each plastic type. As non-plastic materials we have chosen spectra of vegetation, rocks, soils and minerals contained in the public US libraries (ECOSTRESS and USGS). The number of spectra for the training of the deep learning and machine learning models was enlarged by a random linear mixing method and the resulting database was separated into a training and a test group for subsequent multi-label classification. Algorithms selected are a convolutional neural network (CNN), random forest (RF) and support vector machine (SVM). To investigate the transferability to any hyperspectral image data obtained by air-, and spacecraft sensors, we opted for a unification of the spectral response functions (SRF) and the spectral sampling intervals of all data. Validation is accomplished based on the test group of the spectral database, and tested by controlled laboratory and aircraft experiments recorded over surfaces with varying background materials. Results are further analyzed for the influence of different noise quantities and abundance levels. The performance of the three models is roughly balanced for the validation of the spectral data with an overall accuracy of 97%, 96%, and 95% for the CNN, RF, and SVM, models respectively. In the controlled lab experiments, various accuracy indicators, such as the recall rates and the comprehensive metrics F1-score, OA, and Kappa suggest the RF classifier as the most robust one, followed by the SVM and CNN models. As for the evaluation of the aircraft data from controlled experiments, the RF further outperforms the other two models, behaving most robustly and reliably against conditions with unknown plastics and unknown background surfaces. Thus, the RF was used to classify the ten types of plastics mentioned above in one GF-5 and two PRISMA satellite recordings of the same area. In comparison of both sensor systems, the RF produced high quality and transferable results for detecting plastic mainly related to greenhouses, sport fields, photovoltaic constructions and industrial sites that are discussed in detail in this paper.

NLRP3 gene variants and serum NLRP3 levels in periodic fever, aphthous stomatitis, pharyngitis, and adenitis (PFAPA) syndrome.

Although most of the autoinfammatory disorders have a confirmed genetic cause, periodic fever, aphthous stomatitis, pharyngitis, and cervical adenitis (PFAPA) syndrome still has an unknown genetic background. However, familial cases of PFAPA syndrome have been reported suggesting a genetic its basis. PFAPA syndrome may also be considered an infammasome disorder as variants in infammasome-associated genes such as CARD8, NLRP3, and MEFV have been reported to contribute to the disease. Polymerase chain reaction (PCR)/Sanger sequencing analysis was performed for the detection of the variations in 71 PFAPA patients and 71 healthy controls. NLRP3 concentrations in serum were measured in 71 PFAPA patients and 71 healthy controls. No statistically significant differences were observed in the allele or genotype frequencies of the NLRP3 polymorphisms between the controls and patients (P > 0.05). We found no significant differences for NLRP3 serum levels between PFAPA patients and controls (p > 0.05). Mutations in the MEFV gene were detected in 32.5% of our patients (13/40). It seems that the synergistic effect of different genes plays a role in the formation of PFAPA syndrome. For this reason, it may be useful to examine the presence of mutations in genes such as NLRP3, MEFV, and CARD8 together while investigating the genetics of PFAPA syndrome. Key points • Familial cases of PFAPA syndrome have been reported suggesting a genetic basis for this syndrome. • Elevated serum or plasma levels of IL-1β, IL-6, and IL-18 have been demonstrated during PFAPA flares in several studies. • It seems that the synergistic effect of different genes plays a role in the formation of PFAPA syndrome.

Observation and analysis of a large banana orbit in the diocotron mode of a coaxial Malmberg–Penning trap

The diocotron mode of an off-axis electron column is studied in a coaxial version of the Malmberg–Penning trap. Measurements of the diocotron frequency as a function of the bias on the central conductor agree well with a derived theory including finite-length corrections and confinement potential contributions. When the experimental parameters are adjusted to give a very low diocotron frequency, the column motion abruptly changes from an axis-encircling orbit to a large banana-shaped orbit in the r−θ plane with extent Δθ≈270° and Δr/R≈0.25, where R is the wall radius. This banana motion is apparently in response to a previously unknown background construction asymmetry. The size of the asymmetric potential is deduced from orbit data and found to be 45–100 mV. Theoretical modeling shows this to be consistent with a small radial offset δ in the center wire position of δ/R=0.034. Implications and applications of these findings are discussed and a note on obtaining the line density of an electron column in a coaxial trap is given.