Classical Sequence Research Articles

Spectral properties of generalized Cesàro operators in sequence spaces

The generalized Cesàro operators C_t, for tin [0,1], were first investigated in the 1980s. They act continuously in many classical Banach sequence spaces contained in {{mathbb {C}}}^{{{mathbb {N}}}_0}, such as ell ^p, c_0, c, bv_0, bv and, as recently shown in Curbera et al. (J Math Anal Appl 507:31, 2022) [26], also in the discrete Cesàro spaces ces(p) and their (isomorphic) dual spaces d_p. In most cases C_t (tnot =1) is compact and its spectra and point spectrum, together with the corresponding eigenspaces, are known. We study these properties of C_t, as well as their linear dynamics and mean ergodicity, when they act in certain non-normable sequence spaces contained in {{mathbb {C}}}^{{{mathbb {N}}}_0}. Besides {{mathbb {C}}}^{{{mathbb {N}}}_0} itself, the Fréchet spaces considered are ell (p+), ces(p+) and d(p+), for 1le p<infty , as well as the (LB)-spaces ell (p-), ces(p-) and d(p-), for 1<ple infty .

Mistle: bringing spectral library predictions to metaproteomics with an efficient search index.

Deep learning has moved to the forefront of tandem mass spectrometry-driven proteomics and authentic prediction for peptide fragmentation is more feasible than ever. Still, at this point spectral prediction is mainly used to validate database search results or for confined search spaces. Fully predicted spectral libraries have not yet been efficiently adapted to large search space problems that often occur in metaproteomics or proteogenomics. In this study, we showcase a workflow that uses Prosit for spectral library predictions on two common metaproteomes and implement an indexing and search algorithm, Mistle, to efficiently identify experimental mass spectra within the library. Hence, the workflow emulates a classic protein sequence database search with protein digestion but builds a searchable index from spectral predictions as an in-between step. We compare Mistle to popular search engines, both on a spectral and database search level, and provide evidence that this approach is more accurate than a database search using MSFragger. Mistle outperforms other spectral library search engines in terms of run time and proves to be extremely memory efficient with a 4- to 22-fold decrease in RAM usage. This makes Mistle universally applicable to large search spaces, e.g. covering comprehensive sequence databases of diverse microbiomes. Mistle is freely available on GitHub at https://github.com/BAMeScience/Mistle.

RWD141 AI-Powered Pre-Competitive Disease and Care Modeling: Building a Reference Disease-Centric Framework for Value-Based Decision Making

Drugs evaluation has been undergoing a transformational shift from the classical sequence of randomized control trials (RCT) and Real-World (RW) studies towards a more agile process capitalizing better and faster from retrospective data. The purpose of this work is to propose a regulatory-grade, data-integrative, real-world-value-based and disease-centric approach to accelerate and de-risk drugs development and launch. This approach relies on disease and care modeling on the pre-competitive space and federated data sourcing.

Boron Difluoride β-Diketonates: Structure and Phosphorescence

Phosphorescence data on boron difluoride β-diketonates of various structure have been systematized. Nonplanar boron difluoride molecules are characterized by the inversion of the S1 and T2 levels, which promotes efficient population of triplet levels and intense phosphorescence or delayed fluorescence of crystals. Planar molecules are characterized by a classical sequence of singlet and triplet levels and a coplanar arrangement of antiparallel molecules, which contributes to excimer delayed fluorescence.

Joint demosaicking and denoising benefits from a two-stage training strategy

Image demosaicking and denoising are the first two key steps of the color image production pipeline. The classical processing sequence has for a long time consisted of applying denoising first, and then demosaicking. Applying the operations in this order leads to oversmoothing and checkerboard effects. Yet, it was difficult to change this order, because once the image is demosaicked, the statistical properties of the noise are dramatically changed and hard to handle by traditional denoising models. In this paper, we address this problem by a hybrid machine learning method. We invert the traditional color filter array (CFA) processing pipeline by first demosaicking and then denoising. Our demosaicking algorithm, trained on noiseless images, combines a traditional method and a residual convolutional neural network (CNN). This first stage retains all known information, which is the key point to obtain faithful final results. The noisy demosaicked image is then passed through a second CNN restoring a noiseless full-color image. This pipeline order completely avoids checkerboard effects and restores fine image detail. Although CNNs can be trained to solve jointly demosaicking–denoising end-to-end, we find that this two-stage training performs better and is less prone to failure. It is shown experimentally to improve on the state of the art, both quantitatively and in terms of visual quality.

Reciprocal Symmetry via Inverse Series Pairs

Reciprocal series are employed to systematically review convolution sums, orthogonality relations, recurrence relations and reciprocal formulae for several classical number sequences, such as binomial coefficients, Stirling numbers, Bernoulli numbers, and Euler numbers.

Seismic Sedimentologic Study on Sequence-Sediment Evolution in Fault Basin Regions

With potential hydrocarbon source rocks, the Mesozoic Lower Cretaceous interval in the Aitegle Sag of the Yingen-Ejinaqi Basin in China has a bright petroleum exploration prospect. Considering the scarcity of wells in Aitegle Sag, we have systematically carried out the research on the sequence structure and sedimentary system evolution of the Lower Cretaceous based on seismic, core, and logging data, guided by the Vail classic sequence stratigraphy, and innovatively introduced the self-organizing mapping neural network (SOM) learning algorithm to assist in seismic geological analysis. The results are as follows: (1) after RMSAmp, LyapIndex, FrctDim, and InfoEntr clustering, five zones are divided, which are corresponding to five seismic facies (showing medium-strong amplitude and parallel-subparallel reflection; medium-strong amplitude and wedge-shaped divergent reflection; medium-strong amplitude and continuous-relatively continuous, parallel-subparallel reflection; strong amplitude and continuous sheet-like or blank reflection; and weak amplitude and mound-shaped chaotic reflection, respectively), and interpreted as five subfacies including delta plain front, prodelta-shallow lake, semideep lake, deep lake, and sublacustrine fan. (2) The Lower Cretaceous formation includes one second-order sequence, three third-order sequences (SQ2–SQ4), and six system tracts. In the SQ2–SQ4 circle, the delta system formed by sediments from southeast varies remarkably in the progradational zones in different system tracts, and tectonic movements and sediment supply control the sequence stratigraphy in the study area as major factors. (3) It is feasible to apply SOM learning algorithm to seismic attribute clustering analysis. This method effectively realizes the combination of geophysical methods based on big data analysis and geological problem analysis, which not only strengthens the use of seismic data but also ensures the effectiveness and reliability of clustering results. These findings offer basic data and scientific supports for the exploration and development of unconventional reservoirs (e.g., shale and tight oil/gas reservoirs) in the Aitegle Sag and also provide new ideas for studying the sequence-sediment in the target zones in the fault basin regions with scarce wells.

Soil filtration-sedimentation improves shelled protist recovery in eukaryotic eDNA surveys.

A large part of the soil protist diversity is missed in metabarcoding studies based on 0.25 g of soil environmental DNA (eDNA) and universal primers due to ca. 80% co-amplification of non-target plants, animals and fungi. To overcome this problem, enrichment of the substrate used for eDNA extraction is an easily implemented option but its effect has not yet been tested. In this study, we evaluated the effect of a 150 μm mesh size filtration and sedimentation method to improve the recovery of protist eDNA, while reducing the co-extraction of plant, animal and fungal eDNA, using a set of contrasted forest and alpine soils from La Réunion, Japan, Spain and Switzerland. Total eukaryotic diversity was estimated by V4 18S rRNA metabarcoding and classical amplicon sequence variant calling. A 2- to 3-fold enrichment in shelled protists (Euglyphida, Arcellinida and Chrysophyceae) was observed at the sample level with the proposed method, with, at the same time, a 2-fold depletion of Fungi and a 3-fold depletion of Embryophyceae. Protist alpha diversity was slightly lower in filtered samples due to reduced coverage in Variosea and Sarcomonadea, but significant differences were observed in only one region. Beta diversity varied mostly between regions and habitats, which explained the same proportion of variance in bulk soil and filtered samples. The increased resolution in soil protist diversity estimates provided by the filtration-sedimentation method is a strong argument in favour of including it in the standard protocol for soil protist eDNA metabarcoding studies.

Inhibition of Importin- α -Mediated Nuclear Localization of Dendrin Attenuates Podocyte Loss and Glomerulosclerosis.

Nuclear translocation of dendrin is observed in injured podocytes, but the mechanism and its consequence are unknown. In nephropathy mouse models, dendrin ablation attenuates proteinuria, podocyte loss, and glomerulosclerosis. The nuclear translocation of dendrin promotes c-Jun N -terminal kinase phosphorylation in podocytes, altering focal adhesion and enhancing cell detachment-induced apoptosis. We identified mediation of dendrin nuclear translocation by nuclear localization signal 1 (NLS1) sequence and adaptor protein importin- α . Inhibition of importin- α prevents nuclear translocation of dendrin, decreases podocyte loss, and attenuates glomerulosclerosis in nephropathy models. Thus, inhibiting importin- α -mediated nuclear translocation of dendrin is a potential strategy to halt podocyte loss and glomerulosclerosis. Nuclear translocation of dendrin is observed in the glomeruli in numerous human renal diseases, but the mechanism remains unknown. This study investigated that mechanism and its consequence in podocytes. The effect of dendrin deficiency was studied in adriamycin (ADR) nephropathy model and membrane-associated guanylate kinase inverted 2 ( MAGI2 ) podocyte-specific knockout ( MAGI2 podKO) mice. The mechanism and the effect of nuclear translocation of dendrin were studied in podocytes overexpressing full-length dendrin and nuclear localization signal 1-deleted dendrin. Ivermectin was used to inhibit importin- α . Dendrin ablation reduced albuminuria, podocyte loss, and glomerulosclerosis in ADR-induced nephropathy and MAGI2 podKO mice. Dendrin deficiency also prolonged the lifespan of MAGI2 podKO mice. Nuclear dendrin promoted c-Jun N -terminal kinase phosphorylation that subsequently altered focal adhesion, reducing cell attachment and enhancing apoptosis in cultured podocytes. Classical bipartite nuclear localization signal sequence and importin- α mediate nuclear translocation of dendrin. The inhibition of importin- α / β reduced dendrin nuclear translocation and apoptosis in vitro as well as albuminuria, podocyte loss, and glomerulosclerosis in ADR-induced nephropathy and MAGI2 podKO mice. Importin- α 3 colocalized with nuclear dendrin in the glomeruli of FSGS and IgA nephropathy patients. Nuclear translocation of dendrin promotes cell detachment-induced apoptosis in podocytes. Therefore, inhibiting importin- α -mediated dendrin nuclear translocation is a potential strategy to prevent podocyte loss and glomerulosclerosis.

Kernel based Dirichlet sequences

Let X=(X1,X2,…) be a sequence of random variables with values in a standard space (S,B). Suppose X1∼νandP(Xn+1∈⋅∣X1,…,Xn)=θν(⋅)+ ∑i=1nK(Xi)(⋅) n+θa.s. where θ>0 is a constant, ν a probability measure on B, and K a random probability measure on B. Then, X is exchangeable whenever K is a regular conditional distribution for ν given any sub-σ-field of B. Under this assumption, X enjoys all the main properties of classical Dirichlet sequences, including Sethuraman’s representation, conjugacy property, and convergence in total variation of predictive distributions. If μ is the weak limit of the empirical measures, conditions for μ to be a.s. discrete, or a.s. non-atomic, or μ≪ν a.s., are provided. Two CLT’s are proved as well. The first deals with stable convergence while the second concerns total variation distance.

Manipulation of plant immunity via an mRNA decapping pathogen effector.

This article is a Commentary on McCombe et al. (2023), 239: 222–239.

LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction.

Long time exposure to indoor air pollution environments can increase the risk of cardiovascular and respiratory system damage. Most previous studies focus on outdoor air quality, while few studies on indoor air quality. Current neural network-based methods for indoor air quality prediction ignore the optimization of input variables, process input features serially, and still suffer from loss of information during model training, which may lead to the problems of memory-intensive, time-consuming and low-precision. We present a novel concurrent indoor PM prediction model based on the fusion model of Least Absolute Shrinkage and Selection Operator (LASSO) and an Attention Temporal Convolutional Network (ATCN), together called LATCN. First, a LASSO regression algorithm is used to select features from PM1, PM2.5, PM10 and PM (>10) datasets and environmental factors to optimize the inputs for indoor PM prediction model. Then an Attention Mechanism (AM) is applied to reduce the redundant temporal information to extract key features in inputs. Finally, a TCN is used to forecast indoor particulate concentration in parallel with inputting the extracted features, and it reduces information loss by residual connections. The results show that the main environmental factors affecting indoor PM concentration are the indoor heat index, indoor wind chill, wet bulb temperature and relative humidity. Comparing with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) approaches, LATCN systematically reduced the prediction error rate (19.7% ~ 28.1% for the NAE, and 16.4% ~ 21.5% for the RMSE) and improved the model running speed (30.4% ~ 81.2%) over these classical sequence prediction models. Our study can inform the active prevention of indoor air pollution, and provides a theoretical basis for indoor environmental standards, while laying the foundations for developing novel air pollution prevention equipment in the future.

Evolution of Coastal Subarctic Lakes in the Context of Climatic and Geological Changes and Human Occupation (North-Central Labrador, Canada)

Climate fluctuations and landscape evolution, with their associated impacts on northern coastal ecosystems, likely influenced human populations of Nunatsiavut who have inhabited the region for nearly 7000 years. As part of an interdisciplinary research initiative within the Nain Archipelago on the subarctic coast of Labrador, this project sought to reconstruct the postglacial palaeoclimatic and palaeoenvironmental variability of Dog Island and document its impacts on the evolution of lakes located in the vicinity of significant archaeological sites. To address these questions, we analysed physical, geochemical, and biological indicators preserved in sediment cores of two lakes. Results from Oakes Bay West Lake revealed gradual acidification since ca. 4900 cal. yr BP, coherent with terrestrial vegetation development and/or neoglacial cooling, interrupted by periods of milder climatic conditions (ca. 4900–3640 cal. yr BP and ca. 1520 cal. yr BP—present) that favoured large sediment inputs. Evilik Lake revealed the classic sequence of isolation of the basin in three major phases in response to glacio-isostatic rebound. These complementary results allowed for the development of a local palaeoenvironmental framework that contributes to a better understanding of how landscape evolution and climate have influenced human societies through site availability and proximity to marine resources, and how, in turn, they impacted their immediate environment through activities, such as wood harvesting and its associated effects on nutrients and lake sediment inputs.

LSPpred Suite: Tools for Leaderless Secretory Protein Prediction in Plants.

Plant proteins that are secreted without a classical signal peptide leader sequence are termed leaderless secretory proteins (LSPs) and are implicated in both plant development and (a)biotic stress responses. In plant proteomics experimental workflows, identification of LSPs is hindered by the possibility of contamination from other subcellar compartments upon purification of the secretome. Applying machine learning algorithms to predict LSPs in plants is also challenging due to the rarity of experimentally validated examples for training purposes. This work attempts to address this issue by establishing criteria for identifying potential plant LSPs based on experimental observations and training random forest classifiers on the putative datasets. The resultant plant protein database LSPDB and bioinformatic prediction tools LSPpred and SPLpred are available at lsppred.lspdb.org. The LSPpred and SPLpred modules are internally validated on the training dataset, with false positives controlled at 5%, and are also able to classify the limited number of established plant LSPs (SPLpred (3/4, LSPpred 4/4). Until such time as a larger set of bona fide (independently experimentally validated) LSPs is established using imaging technologies (light/fluorescence/electron microscopy) to confirm sub-cellular location, these tools represent a bridging method for predicting and identifying plant putative LSPs for subsequent experimental validation.

Pushing Adenosine and ATP SELEX for DNA Aptamers with Nanomolar Affinity.

The classical DNA aptamer for adenosine and ATP has been the most used small molecule binding aptamer for biosensing, imaging, and DNA nanotechnology. This sequence has recurred multiple times in previous aptamer selections, and all previous selections used a high concentration of ATP as the target. Herein, two separate selections were performed using adenosine and ATP as targets. By pushing the target concentrations down to the low micromolar range, two new aptamers with Kd as low as 230 nM were obtained, showing around 30-fold higher affinity compared to the classical aptamer. The classical aptamer sequence still dominated the library in the early rounds of the selections, but it was suppressed in the later rounds. The new aptamers bind to one target molecule instead of two. Mutation studies confirmed their secondary structures and specific binding. Using the deep sequencing data from the selections, long-standing questions such as the existence of one-site aptamers and mutation distribution in the classical aptamer were addressed. Comparisons were made with previously reported DNA aptamers for ATP. Finally, a strand-displacement biosensor was tested showing selectivity for adenosine and its nucleotides.

Multidimensional encoding of restricted and anisotropic diffusion by double rotation of the qvector.

Diffusion NMR and MRI methods building on the classic pulsed gradient spin-echo sequence are sensitive to many aspects of translational motion, including time and frequency dependence ("restriction"), anisotropy, and flow, leading to ambiguities when interpreting experimental data from complex heterogeneous materials such as living biological tissues. While the oscillating gradient technique specifically targets frequency dependence and permits control of the sensitivity to flow, tensor-valued encoding enables investigations of anisotropy in orientationally disordered materials. Here, we propose a simple scheme derived from the "double-rotation" technique in solid-state NMR to generate a family of modulated gradient waveforms allowing for comprehensive exploration of the 2D frequency-anisotropy space and convenient investigation of both restricted and anisotropic diffusion with a single multidimensional acquisition protocol, thereby combining the desirable characteristics of the oscillating gradient and tensor-valued encoding techniques. The method is demonstrated by measuring multicomponent isotropic Gaussian diffusion in simple liquids, anisotropic Gaussian diffusion in a polydomain lyotropic liquid crystal, and restricted diffusion in a yeast cell sediment.

Mass and Color Dependence of the Hubble Spiral Sequence

In the classic Hubble spiral sequence, arm windiness correlates with bulge size; Sa type spiral galaxies with larger bulges also have the most tightly wound spirals. Exceptions to this have long been known, and in recent work using Galaxy Zoo morphologies no strong correlation was seen in a volume limited sample. In this Research Note, we explore the impact of galaxy mass and integrated color upon this correlation in the Galaxy Zoo sample, finding that bluer and lower mass spirals show the “expected” correlation; however, it becomes slightly negative for redder and/or more massive spiral galaxies.

A deep Gaussian process model for seismicity background rates

SUMMARYThe spatio-temporal properties of seismicity give us incisive insight into the stress state evolution and fault structures of the crust. Empirical models based on self-exciting point processes continue to provide an important tool for analysing seismicity, given the epistemic uncertainty associated with physical models. In particular, the epidemic-type aftershock sequence (ETAS) model acts as a reference model for studying seismicity catalogues. The traditional ETAS model uses simple parametric definitions for the background rate of triggering-independent seismicity. This reduces the effectiveness of the basic ETAS model in modelling the temporally complex seismicity patterns seen in seismic swarms that are dominated by aseismic tectonic processes such as fluid injection rather than aftershock triggering. In order to robustly capture time-varying seismicity rates, we introduce a deep Gaussian process (GP) formulation for the background rate as an extension to ETAS. GPs are a robust non-parametric model for function spaces with covariance structure. By conditioning the length-scale structure of a GP with another GP, we have a deep-GP: a probabilistic, hierarchical model that automatically tunes its structure to match data constraints. We show how the deep-GP-ETAS model can be efficiently sampled by making use of a Metropolis-within-Gibbs scheme, taking advantage of the branching process formulation of ETAS and a stochastic partial differential equation (SPDE) approximation for Matérn GPs. We illustrate our method using synthetic examples, and show that the deep-GP-ETAS model successfully captures multiscale temporal behaviour in the background forcing rate of seismicity. We then apply the results to two real-data catalogues: the Ridgecrest, CA 2019 July 5 Mw 7.1 event catalogue, showing that deep-GP-ETAS can successfully characterize a classical aftershock sequence; and the 2016–2019 Cahuilla, CA earthquake swarm, which shows two distinct phases of aseismic forcing concordant with a fluid injection-driven initial sequence, arrest of the fluid along a physical barrier and release following the largest Mw 4.4 event of the sequence.

RKIP localizes to the nucleus through a bipartite nuclear localization signal and interaction with importin α to regulate mitotic progression

Raf kinase inhibitor protein (RKIP) is a multifunctional modulator of intracellular signal transduction. Although most of its functions have been considered cytosolic, we show here that the localization of RKIP is primarily nuclear in both growing and quiescent Madin-Darby canine kidney epithelial cells and in Cal-51 and BT-20 human breast cancer cells. We have identified a putative bipartite nuclear localization signal (NLS) in RKIP that maps to the surface of the protein surrounding a known regulatory region. Like classical NLS sequences, the putative NLS of RKIP is rich in arginine and lysine residues. Deletion of and point mutations in the putative NLS lead to decreased nuclear localization. Point mutation of all the basic residues in the putative NLS of RKIP particularly strongly reduces nuclear localization. We found consistent results in reexpression experiments with wildtype or mutant RKIP in RKIP-silenced cells. A fusion construct of the putative NLS of RKIP alone to a heterologous reporter protein leads to nuclear localization of the fusion protein, demonstrating that this sequence alone is sufficient for import into the nucleus. We found that RKIP interacts with the nuclear transport factor importin α in BT-20 and MDA-MB-231 human breast cancer cells, suggesting importin-mediated active nuclear translocation. Evaluating the biological function of nuclear localization of RKIP, we found that the presence of the putative NLS is important for the role of RKIP in mitotic checkpoint regulation in MCF-7 human breast cancer cells. Taken together, these findings suggest that a bipartite NLS in RKIP interacts with importin α for active transport of RKIP into the nucleus and that this process may be involved in the regulation of mitotic progression.

Principal Solutions of Recurrence Relations and Irrationality Questions in Number Theory

We apply the theory of disconjugate linear recurrence relations to the study of irrational quantities in number theory. In particular, for an irrational number associated with solutions of linear three-term recurrence relations, we show that there exists a four-term linear recurrence relation whose solutions show that the number has an irrational square if and only if the four-term recurrence relation has a principal solution of a certain type. The result is extended to higher-order recurrence relations, and a transcendence criterion can also be formulated in terms of these principal solutions. The method generates new series expansions of positive integer powers of ζ(3) and ζ(2) in terms of Apéry’s now classic sequences.