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The chapter discusses how to process data from irregular discrete domains, an emerging area called graph signal processing (GSP). Basically, the type of graph we deal with consists of a network with distributed vertices and weighted edges defining the neighborhood and the connections among the nodes. As such, the graph signals are collected in a vector whose entries represent the values of the signal nodes at a given time. A common issue related to GSP is the sampling problem, given the irregular structure of the data, where some sort of interpolation is possible whenever the graph signals are bandlimited or nearly bandlimited. These interpolations can be performed through the extension of the conventional adaptive filtering to signals distributed on graphs where there is no traditional data structure. The chapter presents the LMS, NLMS, and RLS algorithms for GSP along with their analyses and application to estimate bandlimited signals defined on graphs. In addition, the chapter presents a general framework for data-selective adaptive algorithms for GSP.
The chapter briefly introduces the main concepts of array signal processing, emphasizing those related to adaptive beamforming, and discusses how to impose linear constraints to adaptive filtering algorithms to achieve the beamforming effect. Adaptive beamforming, emphasizing the incoming signal impinging from a known direction by means of an adaptive filter, is the primary objective of the array signal processing addressed in this chapter. We start this study with the narrowband beamformer. The constrained LMS, RLS, conjugate gradient, and SMAP algorithms are introduced along with the generalized sidelobe canceller, and the Householder constrained structures; sparse promoting adaptive beamforming algorithms are also addressed in this chapter. In the following, it introduces the concepts of frequency-domain and time-domain broadband adaptive beamforming and shows their equivalence. The chapter wraps up with brief discussions and reference suggestions on essential topics related to adaptive beamforming, including the numerical robustness of adaptive beamforming algorithms.
This chapter explains the basic concepts of kernel-based methods, a widely used tool in machine learning. The idea is to present online parameter estimation of nonlinear models using kernel-based tools. The chapters aim is to introduce the kernel version of classical algorithms such as least mean square (LMS), recursive least squares (RLS), affine projection (AP), and set membership affine projection (SM-AP). In particular, we will discuss how to keep the dictionary of the kernel finite through a series of model reduction strategies. This way, all discussed kernel algorithms are tailored for online implementation.
It provides a brief description of the classical adaptive filtering algorithms, starting with defining the actual objective function each algorithm minimizes. It also includes a summary of the expected performance according to available results from the literature.
The chapter shows how the classical adaptive filtering algorithms can be adapted to distributed learning. In distributed learning, there is a set of adaptive filtering placed at nodes utilizing a local input and desired signals. These distributed networks of sensor nodes are located at distinct positions, which might improve the reliability and robustness of the parameter estimation in comparison to stand-alone adaptive filters. In distributed adaptive networks, parameter estimation might be obtained in a centralized form or a decentralized form. The centralized case processes the signals from all nodes of the network in a single fusion center, whereas in the decentralized case, processing is performed locally followed by a proper combination of partial estimates to result in a consensus parameter estimate. The main drawbacks of the centralized configuration are its data communication and computational costs, particularly in networks with a large number of nodes. On the other hand, the decentralized estimators require fewer data to feed the estimators and improve on robustness. The provides a discussion on equilibrium and consensus using arguments drawn from the pari-mutuel betting system. The expert opinion pool is the concept to induce improved estimation and data modeling, utilizing De-Groot’s algorithm and Markov chains as tools to probate equilibrium at consensus. It also introduces the distributed versions of the LMS and RLS adaptive filtering algorithms with emphasis on the decentralized parameter estimation case. This chapter also addresses how data broadcasting can be confined to a subset of nodes so that the overall network reduces the power consumption and bandwidth usage. Then, the chapter discusses a strategy to incorporate a data selection based on the SM adaptive filtering.
Chapter 2 presents several strategies to exploit sparsity in the parameters being estimated in order to obtain better estimates and accelerate convergence, two advantages of paramount importance when dealing with real problems requiring the estimation of many parameters. In these cases, the classical adaptive filtering algorithms exhibit a slow and often unacceptable convergence rate. In this chapter, many algorithms capable of exploiting sparse models are presented. Also, the two most widely used approaches to exploit sparsity are presented, and their pros and cons are discussed. The first approach explicitly models sparsity by relying on sparsity-promoting regularization functions. The second approach utilizes updates proportional to the magnitude of the coefficient being updated, thus accelerating the convergence of large magnitude coefficients. After reading this chapter, the reader will not only obtain a deeper understanding of the subject but also be able to adapt or develop algorithms based on his own needs.
Learn to solve the unprecedented challenges facing Online Learning and Adaptive Signal Processing in this concise, intuitive text. The ever-increasing amount of data generated every day requires new strategies to tackle issues such as: combining data from a large number of sensors; improving spectral usage, utilizing multiple-antennas with adaptive capabilities; or learning from signals placed on graphs, generating unstructured data. Solutions to all of these and more are described in a condensed and unified way, enabling you to expose valuable information from data and signals in a fast and economical way. The up-to-date techniques explained here can be implemented in simple electronic hardware, or as part of multi-purpose systems. Also featuring alternative explanations for online learning, including newly developed methods and data selection, and several easily implemented algorithms, this one-of-a-kind book is an ideal resource for graduate students, researchers, and professionals in online learning and adaptive filtering.
Paediatricians play an integral role in the lifelong care of children with CHD, many of whom will undergo cardiac surgery. There is a paucity of literature for the paediatrician regarding the post-operative care of such patients.
The aim of this manuscript is to summarise essential principles and pertinent lesion-specific context for the care of patients who have undergone surgery or intervention resulting in a biventricular circulation.
Conclusions and relevance:
Familiarity with common issues following cardiac surgery or intervention, as well as key details regarding specific lesions and surgeries, will aid the paediatrician in providing optimal care for these patients.
Single ventricle CHD affects about 5 out of 100,000 newborns, resulting in complex anatomy often requiring multiple, staged palliative surgeries. Paediatricians are an essential part of the team that cares for children with single ventricle CHD. These patients often encounter their paediatrician first when a complication arises, so it is critical to ensure the paediatrician is knowledgeable of these issues to provide optimal care.
We reviewed the subtypes of single ventricle heart disease and the various palliative surgeries these patients undergo. We then searched the literature to detail the general paediatrician’s approach to single ventricle patients at different stages of surgical palliation.
Conclusions and relevance
Single ventricle patients undergo staged palliation that drastically changes physiology after each intervention. Coordinated care between their paediatrician and cardiologist is requisite to provide excellent care. This review highlights what to expect when these patients are seen by their paediatrician for either well child visits or additional visits for parental or patient concern.
Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.
To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.
This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.
The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.
Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
In 2015, plants of Sumatran fleabane [Conyza sumatrensis (Retz.) E. Walker] were identified in a crop field with an unusual rapid necrosis herbicide symptom after application of 2,4-D. An initial study identified that the symptoms began about 2 h after herbicide application, the resistance factor was high (resistance factor = 19), and the resistance decreased at low light. The mechanism of resistance is not yet known, but the symptomatology suggests it may be related to reduced translocation, ATP-binding cassette (ABC) class B transporters, changes on auxin perception genes, or induction of genes involved in response to pathogens and abiotic stresses. The objective of this study was to use inhibitors of enzymes involved in detoxification and carriers to investigate the mechanisms involved in the resistance to 2,4-D caused by rapid necrosis. Neither the inhibitors of ABC and auxin transporters, triiodobenzoic acid (TIBA), 1-N-naphythylphthalamic acid (NPA), verapamil, and orthovanadate, nor the inhibitors of detoxifying enzymes, such as malathion, 4-chloro-7-nitrobenzofurazan (NBD-Cl), and imidazole, reduced the frequency of the rapid necrosis phenotype. However, orthovanadate and sodium azide (possibly related to auxin transport) were able to partially reduce oxidative stress in leaf disks. The expression of ABCM10 (an ABCD transporter gene), TIR1_1 (an auxin receptor gene), and CAT4 (an amino acid transporter gene) was quickly reduced after 2,4-D application in the resistant accession. Contrary to our hypothesis, LESION SIMULATING DISEASE RESISTANCE 1_3 (LSD1_3) expression increased in response to 2,4-D. LSD1_3 is important for the response to pathogen and abiotic stresses. The rapid necrosis mechanism is not related to 2,4-D detoxification but might be related to changes in the TIR receptor or auxin transport. Mutations in other transporters or in proteins involved in abiotic and pathogen stresses cannot be ruled out.
Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.
To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.
Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.
Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.
AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.
Clearfield™ (CL) rice (Oryza sativa L.) is a weedy rice (Oryza spp.; synonym = red rice) control tool that has been used in Brazil since 2003. This system includes the use of an imidazolinone (IMI)-tolerant cultivar and the application of IMI herbicides. In this review article, Brazilian weed scientists evaluate the challenges and lessons learned over 18 yr of CL use. CL system benefits include selective weedy rice control, better crop establishment during the most advantageous period of the year, and more efficient fertilizer use. In Rio Grande do Sul state, the CL system, in conjunction with other improvements, has contributed to rice grain yield gains from 5,500 kg ha−1 before 2002 to around 8,400 kg ha−1 currently. In contrast, the main problem that has arisen over this period is the rapid evolution of IMI-resistant weedy rice, caused by gene flow from CL rice cultivars. The off-label use (rate and continuous use) of IMI herbicides has contributed to the evolution of resistance in Echinochloa spp. and other weeds. IMI herbicide carryover has also affected susceptible crops grown after CL rice. Crop rotation with soybean [Glycine max (L.) Merr.] is increasing, ensuring system sustainability. The importance of minimum tillage has also become apparent. Such cultivation includes applying nonselective herbicides before sowing or just before crop emergence (at the spiking stage to eliminate as much weedy rice as possible and other weeds at an early growth stage). It also includes the use of certified seeds free of weedy rice, following label instructions for IMI herbicides, applying the herbicide PRE followed by POST, and complementary weedy rice management practices, such as roguing of surviving weedy rice plants.
How are concepts related to intergenerational equity reflected in the European constitutional order? This chapter will offer answers to this challenging and constantly emerging question and shed light on the interrelationship between intergenerational solidarity and sustainable development within the European constitutional system.
Beryl from Xuebaoding, Sichuan Province, western China is known for its unusual tabular habit and W–Sn–Be paragenesis in a greisen-type deposit. The crystals are typically colourless transparent to pale blue, often with screw dislocations of hexagonal symmetry on the (0001) crystal faces. Combining electron microprobe analyses and laser ablation inductively coupled plasma mass spectrometry with single-crystal X-ray diffraction (XRD), correlated with Raman and micro-infrared (IR) spectroscopy and imaging, the crystal chemical characteristics are determined. The contents of Na+ (0.24–0.38 atoms per formula unit (apfu)) and Li+ up to 0.38 apfu are at the high end compared to beryl from other localities worldwide. Li+ substitution for Be2+ on the tetrahedral (T2) site is predominantly charge balanced by Na+ on the smaller channel (C2) site, with Na+ ranging from 91.5% to 99.7% (apfu) of the sum of all other alkali elements. Cs+ and minor Rb+ and K+ primarily charge balance the minor M2+ substitution for Al3+ at the A site; all iron at the A site is suggested to be trivalent. The a axis ranges from 9.2161(2) to 9.2171(4) Å, with unit-cell volume from 678.03(3) to 678.48(7) Å3. The c/a ratio of 1.0002–1.0005 is characteristic for T2-type beryl with unit-cell parameters controlled primarily by Be2+ substitution. Transmission micro-IR vibrational spectroscopy and imaging identifies coordination of one or two water molecules to Na+ (type IIs and type IId, respectively) as well as alkali free water (type I). Based on IR absorption cross section and XRD a C1 site water content of 0.4–0.5 apfu is derived, i.e. close to 50% site occupancy. Secondary crystal phases with a decrease in Fe and Mg, yet increase in Na, suggest early crystallisation of aquamarine, with goshenite being late. With similar crystal chemistry to beryl of columnar habit from other localities worldwide, the tabular habit of Xuebaoding beryl seems to be unrelated to chemical composition and alkali content.