Sequence Of Variables Research Articles

Six-month-old infants' perception of structural regularities in speech

In order to acquire grammar, infants need to extract regularities from the linguistic input. From birth, infants can detect regularities in speech based on identity relations, and show strong neural activation to syllable sequences containing adjacent repetitions of identical syllables (e.g. ABB: mubaba). Meanwhile, newborns' neural responses to sequences of different syllables (e.g. ABC: mubage, i.e. diversity-based relations) do not differ from baseline. However, this latter ability needs to emerge during development, as most linguistic units, such as words, are composed of highly variable sequences. As infants begin to learn their first word forms at 6 months, we hypothesize that the ability to represent sequences of different syllables might become important for them at this age. Using near-infrared spectroscopy (NIRS), we measured 6-month-old infants' brain responses to repetition- and diversity-based sequences in the bilateral temporal, parietal and frontal areas. We found that 6-month-olds discriminated the repetition- and diversity-based structures in frontal and parietal regions, and exhibited equally strong activation to both grammars as compared to baseline. These results show that by 6 months of age, infants encode sequences with diversity-based structures. They thus provide the earliest evidence that prelexical infants represent difference in speech stimuli, which behavioral studies first attest at 11 months of age.

DNA selection by the master transcription factor PU.1

The master transcriptional regulator PU.1/Spi-1 engages DNA sites with affinities spanning multiple orders of magnitude. To elucidate this remarkable plasticity, we have characterized 22 high-resolution co-crystallographic PU.1/DNA complexes across the addressable affinity range in myeloid gene transactivation. Over a purine-rich core (such as 5'-GGAA-3') flanked by variable sequences, affinity is negotiated by direct readout on the 5' flank via a critical glutamine (Q226) sidechain and by indirect readout on the 3' flank by sequence-dependent helical flexibility. Direct readout by Q226 dynamically specifies PU.1's characteristic preference for purines and explains the pathogenic mutation Q226E in Waldenström macroglobulinemia. The structures also reveal how disruption of Q226 mediates strand-specific inhibition by DNA methylation and the recognition of non-canonical sites, including the authentic binding sequence at the CD11b promoter. A re-synthesis of phylogenetic and structural data on the ETS family, considering the centrality of Q226 in PU.1, unifies the model of DNA selection by ETS proteins.

On some non parametric estimators of the quantile density function for a stationary associated process

In this article, we consider smooth estimators for the quantile density function (qdf) for a sequence { X n , n ≥ 1 } of stationary non negative associated random variables with a common marginal distribution function. The qdf is given by q ( u ) = Q ′ ( u ) , u ∈ ( 0 , 1 ) , Q ( u ) representing the corresponding quantile function. The smooth estimators of q ( u ) considered here are adapted from those of Q ( u ) considered in Chaubey, Dewan, and Li (2021). A few asymptotic properties of these estimators are established parallel to those in the i.i.d. case. A numerical study comparing the mean squared errors of various estimators indicates the advantages and a few limitations of various estimators. The smoothing parameter is selected based on the BCV and RLCV (a variation of likelihood cross-validation) criteria. It is concluded, based on the numerical studies, that the RLCV criterion may produce over-smoothing, hence BCV criterion may be preferable. The numerical studies also suggest that, overall, the estimator proposed by Soni, Dewan, and Jain (2012) seems to have some advantage over the other estimators considered in this article.

Learning State Transition Rules from High-Dimensional Time Series Data with Recurrent Temporal Gaussian-Bernoulli Restricted Boltzmann Machines

Understanding the dynamics of a system is crucial in various scientific and engineering domains. Machine learning techniques have been employed to learn state transition rules from observed time-series data. However, these data often contain sequences of noisy and ambiguous continuous variables, while we typically seek simplified dynamics rules that capture essential variables. In this work, we propose a method to extract a small number of essential hidden variables from high-dimensional time-series data and learn state transition rules between hidden variables. Our approach is based on the Restricted Boltzmann Machine (RBM), which models observable data in the visible layer and latent features in the hidden layer. However, real-world data, such as video and audio, consist of both discrete and continuous variables with temporal relationships. To address this, we introduce the Recurrent Temporal Gaussian-Bernoulli Restricted Boltzmann Machine (RTGB-RBM), which combines the Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM) to handle continuous visible variables and the Recurrent Temporal Restricted Boltzmann Machine (RT-RBM) to capture time dependencies among discrete hidden variables. Additionally, we propose a rule-based method to extract essential information as hidden variables and represent state transition rules in an interpretable form. We evaluate our proposed method on the Bouncing Ball, Moving MNIST, and dSprite datasets. Experimental results demonstrate that our approach effectively learns the dynamics of these physical systems by extracting state transition rules between hidden variables. Moreover, our method can predict unobserved future states based on observed state transitions.

Structural and mechanistic basis of neutralization by a pan-hantavirus protective antibody.

Emerging rodent-borne hantaviruses cause severe diseases in humans with no approved vaccines or therapeutics. We recently isolated a monoclonal broadly neutralizing antibody (nAb) from a Puumala virus-experienced human donor. Here, we report its structure bound to its target, the Gn/Gc glycoprotein heterodimer comprising the viral fusion complex. The structure explains the broad activity of the nAb: It recognizes conserved Gc fusion loop sequences and the main chain of variable Gn sequences, thereby straddling the Gn/Gc heterodimer and locking it in its prefusion conformation. We show that the nAb's accelerated dissociation from the divergent Andes virus Gn/Gc at endosomal acidic pH limits its potency against this highly lethal virus and correct this liability by engineering an optimized variant that sets a benchmark as a candidate pan-hantavirus therapeutic.

Diversity, composition, and networking of saliva microbiota distinguish the severity of COVID-19 episodes as revealed by an analysis of 16S rRNA variable V1-V3 region sequences.

Studies on the role of the oral microbiome in SARS-CoV-2 infection and severity of the disease are limited. We aimed to characterize the bacterial communities present in the saliva of patients with varied COVID-19 severity to learn if there are differences in the characteristics of the microbiome among the clinical groups. We included 31 asymptomatic subjects with no previous COVID-19 infection or vaccination; 176 patients with mild respiratory symptoms, positive or negative for SARS-CoV-2 infection; 57 patients that required hospitalization because of severe COVID-19 with oxygen saturation below 92%, and 18 fatal cases of COVID-19. Saliva samples collected before any treatment were tested for SARS-CoV-2 by PCR. Oral microbiota in saliva was studied by amplification and sequencing of the V1-V3 variable regions of 16S gene using an Illumina MiSeq platform. We found significant changes in diversity, composition, and networking in saliva microbiota of patients with COVID-19, as well as patterns associated with severity of disease. The presence or abundance of several commensal species and opportunistic pathogens were associated with each clinical stage. Patterns of networking were also found associated with severity of disease: a highly regulated bacterial community (normonetting) was found in healthy people whereas poorly regulated populations (disnetting) were characteristic of severe cases. Characterization of microbiota in saliva may offer important clues in the pathogenesis of COVID-19 and may also identify potential markers for prognosis in the severity of the disease. IMPORTANCE SARS-CoV-2 infection is the most severe pandemic of humankind in the last hundred years. The outcome of the infection ranges from asymptomatic or mild to severe and even fatal cases, but reasons for this remain unknown. Microbes normally colonizing the respiratory tract form communities that may mitigate the transmission, symptoms, and severity of viral infections, but very little is known on the role of these microbial communities in the severity of COVID-19. We aimed to characterize the bacterial communities in saliva of patients with different severity of COVID-19 disease, from mild to fatal cases. Our results revealed clear differences in the composition and in the nature of interactions (networking) of the bacterial species present in the different clinical groups and show community-patterns associated with disease severity. Characterization of the microbial communities in saliva may offer important clues to learn ways COVID-19 patients may suffer from different disease severities.

Percolation Problems on N-Ary Trees

Percolation theory is a subject that has been flourishing in recent decades. Because of its simple expression and rich connotation, it is widely used in chemistry, ecology, physics, materials science, infectious diseases, and complex networks. Consider an infinite-rooted N-ary tree where each vertex is assigned an i.i.d. random variable. When the random variable follows a Bernoulli distribution, a path is called head run if all the random variables that are assigned on the path are 1. We obtain the weak law of large numbers for the length of the longest head run. In addition, when the random variable follows a continuous distribution, a path is called an increasing path if the sequence of random variables on the path is increasing. By Stein’s method and other probabilistic methods, we prove that the length of the longest increasing path with a probability of one focuses on three points. We also consider limiting behaviours for the longest increasing path in a special tree.

Method EWMA (Exponentially Weighted Moving Average) as a Filter to Fine and Remove Noise on Time Series Data

The Moving Average method is the most common filter in Digital Signal Processing, the Moving Average method is optimal for reducing unstable value fluctuations due to interference by maintaining accurate readings. Therefore it is often used for signal coding in the context of time (Smith, 1999). The kind of moving average method is the Exponentially Weighted Moving Average (EWMA) which is often applied to a time sequence of random variables, by calculating the weighted average of the sequence by applying a weight that decreases geometrically with the length of observation (Perry, 2011). this method can be applied to tools/instruments that use data continuously in a certain time so that it will require a filter so that the fluctuations in the data are not extreme which will result in changes in the output response being softer so as not to damage the device on the output. This study uses the EWMA method to eliminate noise in the processing of sensor readings. The results of the study, the system is able to monitor realtime ph and temperature data. In fish farming, measuring and monitoring water temperature and ph is important, if it is not regularly measured and monitored it can cause various problems such as pH and temperature that are not optimal for fish growth and health. The research method to be carried out is R&D, EWMA will be applied to a multi-sensor circuit then processed by a data logger device to then be displayed on serial data on its output.

Effects of dietary soybean meal inclusion on calcium-binding protein expression and inflammatory gene markers in liver and intestine of Atlantic Salmon (Salmo salar L.)

Atlantic Salmon (Salmo salar L.) are sensitive to the inclusion of soybean meal in the diet which can result in inflammation in the distal intestine. Expression of S100 genes have been shown to be upregulated as part of the inflammatory response to antinutritional factors in soybean meal. These genes are also involved in calcium homeostasis in marine teleosts. We examined expression of S100I2 and S100V2 genes coding for calcium-binding proteins in the distal intestine and liver between groups of Atlantic Salmon fed a fishmeal (FM) or soybean-meal (PM) diet for 12 weeks. These two genes have been previously shown to differ in binding motifs and share variable sequence identity. Juvenile Atlantic Salmon (71.25 ± 0.76 g) were cultured in 140 L tanks using flow-through freshwater at 15 oC and randomly assigned to FM or PM diet groups in triplicate. Fish were fed daily to satiation and subsampled from each tank every four weeks. Expression of S100I2 in the distal intestine was elevated by 8 weeks but expression did not differ between diets. Expression of S100V2 was significantly elevated in the distal intestine of fish fed the PM diet compared to fish fed the FM diet. Increased expression of S100V2 but not S100I2 at 8 and 12 weeks in fish fed the PM diet differs from observations in previous studies using Rainbow Trout and implies Atlantic Salmon raised in freshwater may regulate expression of S100V2 and S100I2 in their distal intestine differently than Rainbow Trout. Expression of S100I2 and S100V2 in liver was not different between dietary treatments. Semi-quantitative histological scores confirmed mild but significant distal enteritis in the PM diet group but not in the FM diet group. Overall, these results provide further understanding in the expression of S100 genes and inflammatory processes associated with soy-induced distal enteritis in salmonids.

Antibody signatures in hospitalized hand, foot and mouth disease patients with acute enterovirus A71 infection.

Enterovirus A71 (EV-A71) infection is a major cause of severe hand, foot and mouth disease (HFMD) in young children. The characteristics of EV-A71 neutralizing antibodies in HFMD patients are not well understood. In this study, we identified and cloned EV-A71-neutralizing antibodies by single cell RNA and B cell receptor sequencing of peripheral blood mononuclear cells. From 145 plasmablasts, we identified two IgG1 monoclonal antibodies (mAbs) and six IgM mAbs that neutralized EV-A71. Four of the IgM mAbs harbor germline variable sequences and neutralize EV-A71 potently. Two genetically similar IgM antibodies from two patients have recurrent heavy chain variable domain gene usage and similar complementarity-determining region 3 sequences. We mapped the residues of EV-A71 critical for neutralization through selection of virus variants resistant to antibody neutralization in the presence of neutralizing mAbs. The residues critical for neutralization are conserved among EV-A71 genotypes. Epitopes for the two genetically similar antibodies overlap with the SCARB2 binding site of EV-A71. We used escape variants to measure the epitope-specific antibody response in acute phase serum samples from EV-A71 infected HFMD patients. We found that these epitopes are immunogenic and contributed to the neutralizing antibody response against the virus. Our findings advance understanding of antibody response to EV-A71 infection in young children and have translational potential: the IgM mAbs could potentially be used for prevention or treatment of EV-A71 infections.

Deciphering Azadirachta indica (Neem) Gum Microbiome using Metagenomic Approaches

Indian lilac or neem (Azadirachta indica) is found in tropical and subtropical regions of the Indian subcontinent. Each part of the tree is a source of various phytochemicals. Neem gum is an exudate from mature parts of the plant stem. Biochemically, it has an acidic pH range (5–6) and is composed of monosaccharides, saponins, phenols, and tannins. This study aimed to elucidate the diversity of neem gum-associated microflora through high throughput metagenomics approach using 16S rRNA variable region sequencing. The bacterial community of neem gum was dominated by Firmicutes (~82%), Proteobacteria (~18%), and Actinobacteria (~0.02%). Among the genera, Lactococcus was found to be the most dominant bacterium. The predominance of Lactococcus in neem gum is probably due to its acidic nature, which provides a suitable microenvironment for its proliferation. In addition, Lactococcus and beneficial microorganisms such as Pseudomonas, Burkholderia, Pantoea, Klebsiella, and Methylobacterium were also present in the gum. This study highlights the fact that neem gum can be exploited as a unique source of microorganisms for biotechnological and agricultural applications.

Real-time anomaly attack detection based on an improved variable length model

This paper uses a real-time anomaly attack detection based on improved variable length sequences and data mining. The method is mainly used for host-based intrusion detection systems on Linux or Unix platforms which use shell commands. The algorithm first generates a stream of command sequences with different lengths and subsumes them into a generic sequence library, de-duplicats and sortes shell command sequences. The shell command sequences are then stratified according to their weighted frequency of occurrence to define the state. Next, the behavioural patterns of normal users are mined to output the state stream and a Markov chain is constructed. Then, the state sequences are calculated based on a primary probability distribution and a transfer probability matrix. The System will check decision values of the short sequence stream. Finally, the decision values of the behavioural sequences are analysed to determine whether the current session user is behaving abnormally. The improved algorithm introduces the concept of multi-order frequencies and proposes a new separation mechanism. The extension module is integrated into the variable length model. By comparing the performance of the old and new separation mechanisms on the SEA dataset and the self-made dataset (SD), it is found that the improved model greatly improves the performance of the model and shortens the running time.

Variable Region Sequences Influence 16S rRNA Performance.

16S rRNA gene sequences are commonly analyzed for taxonomic and phylogenetic studies because they contain variable regions that can help distinguish different genera. However, intra-genus distinction using variable region homology is often impossible due to the high overall sequence identities among closely related species, even though some residues may be conserved within respective species. Using a computational method that included the allelic diversity within individual genomes, we discovered that certain Escherichia and Shigella species can be distinguished by a multi-allelic 16S rRNA variable region single nucleotide polymorphism (SNP). To evaluate the performance of 16S rRNAs with altered variable regions, we developed an in vivo system that measures the acceptance and distribution of variant 16S rRNAs into a large pool of natural versions supporting normal translation and growth. We found that 16S rRNAs containing evolutionarily disparate variable regions were underpopulated both in ribosomes and in active translation pools, even for an SNP. Overall, this study revealed that variable region sequences can substantially influence the performance of 16S rRNAs and that this biological constraint can be leveraged to justify refining taxonomic assignments of variable region sequence data. IMPORTANCE This study reevaluates the notion that 16S rRNA gene variable region sequences are uninformative for intra-genus classification and that single nucleotide variations within them have no consequence to strains that bear them. We demonstrated that the performance of 16S rRNAs in Escherichia coli can be negatively impacted by sequence changes in variable regions, even for single nucleotide changes that are native to closely related Escherichia and Shigella species; thus, biological performance is likely constraining the evolution of variable regions in bacteria. Further, the native nucleotide variations we tested occur in all strains of their respective species and across their multiple 16S rRNA gene copies, suggesting that these species evolved beyond what would be discerned from a consensus sequence comparison. Therefore, this work also reveals that the multiple 16S rRNA gene alleles found in most bacteria can provide more informative phylogenetic and taxonomic detail than a single reference allele.

Diagnostic performance of deep learning–based reconstruction algorithm in 3D MR neurography

ObjectiveThe study aims to evaluate the diagnostic performance of deep learning–based reconstruction method (DLRecon) in 3D MR neurography for assessment of the brachial and lumbosacral plexus.Materials and methodsThirty-five exams (18 brachial and 17 lumbosacral plexus) of 34 patients undergoing routine clinical MR neurography at 1.5 T were retrospectively included (mean age: 49 ± 12 years, 15 female). Coronal 3D T2-weighted short tau inversion recovery fast spin echo with variable flip angle sequences covering plexial nerves on both sides were obtained as part of the standard protocol. In addition to standard-of-care (SOC) reconstruction, k-space was reconstructed with a 3D DLRecon algorithm.Two blinded readers evaluated images for image quality and diagnostic confidence in assessing nerves, muscles, and pathology using a 4-point scale. Additionally, signal-to-noise ratio (SNR) and contrast-to-noise ratios (CNR) between nerve, muscle, and fat were measured.For comparison of visual scoring result non-parametric paired sample Wilcoxon signed-rank testing and for quantitative analysis paired sample Student’s t-testing was performed.ResultsDLRecon scored significantly higher than SOC in all categories of image quality (p < 0.05) and diagnostic confidence (p < 0.05), including conspicuity of nerve branches and pathology. With regard to artifacts there was no significant difference between the reconstruction methods.Quantitatively, DLRecon achieved significantly higher CNR and SNR than SOC (p < 0.05).ConclusionDLRecon enhanced overall image quality, leading to improved conspicuity of nerve branches and pathology, and allowing for increased diagnostic confidence in evaluation of the brachial and lumbosacral plexus.

High-Precision PCB Topologies Formation by Selective Laser Micromachining

The paper considers possibility of using laser radiation to form and correct printed circuit topology boards through micromachining. Processes arising from the interaction of ultrashort laser pulses with processed thin-film coatings are considered. The dependence of the effect of a variable sequence of laser pulses on the quality of the formed holes in coating is presented. Experimental data on the selection of optimal exposure modes of femtosecond radiation on a multilayer coating for selective ablation are presented. Femtosecond laser micromachining allows the precision formation of topological contours, regardless of treated coating type, its thermal and electric conductive properties.

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.

A Generalization of Pisier Homogeneous Banach Algebra

In 1979, Pisier proved remarkably that a sequence of independent and identically distributed standard Gaussian random variables determines, via random Fourier series, a homogeneous Banach algebra P strictly contained in C(T), the class of continuous functions on the unit circle T and strictly containing the classical Wiener algebra A(T), that is, A(T)⫋P⫋C(T). This improved some previous results obtained by Zafran in solving a long-standing problem raised by Katznelson. In this paper, we extend Pisier’s result by showing that any probability measure on the unit circle defines a homogeneous Banach algebra contained in C(T). Thus Pisier algebra is not an isolated object but rather an element in a large class of Pisier-type algebras. We consider the case of spectral measures of stationary sequences of Gaussian random variables and obtain a sufficient condition for the boundedness of the random Fourier series ∑ n∈Zfˆ(n)ξnexp(2πint) in the general setting of dependent random variables (ξn).

Double Encryption Algorithm for Massive Personal Biometric Authentication Images Based on Chaotic Mapping for Future Smart Cities

Abstract To address the problems of low encryption security, slow encryption speed, and small key space of personal biometric authentication image, a double encryption algorithm of image based on chaotic mapping is proposed. The global block-matching strategy of sparse clustering is used to remove the image noise, and the encrypted key information in the biometric authentication image is extracted by mathematical morphology. The logistic index is used to disrupt the image information distribution, bit scrambling is used to double scramble the image, and the Secure Hash Algorithm (SHA)-256 hash function is used to generate the initial value of the chaotic system to obtain the chaotic map and the random variable sequence of hyperchaotic Chen system, and the chaotic sequence is transformed into the ciphertext image matrix after exclusive OR (XOR) operation in order to realize the double encryption of personal biometric authentication image. The results show that the proposed method has large key space, high encryption accuracy, fast efficiency, and better security performance.

Third Ventricle Width is a Reliable and Clinically-Feasible Marker of Brain Atrophy in the Multiple Sclerosis Clinical Care Setting (P10-3.003)

<h3>Objective:</h3> Examine reliability and clinical utility of third ventricle width (TVW) on standard-of-care MRIs as a marker of cerebral atrophy in real-world multiple sclerosis (MS) clinical care settings. <h3>Background:</h3> Cerebral atrophy is emerging as an important marker of MS disease progression, but clinically-feasible volumetric measurements remain elusive in clinical care settings. Research supports TVW as a reliable, valid, fast, and easy manual two-dimensional MRI estimate of cerebral atrophy that correlates with clinical disability and tracks with volumetric changes. Importantly, most TVW research has utilized standardized research MRIs; it is unclear how findings translate to standard-of-care (SoC) MRIs (i.e., variable field strengths and sequence parameters). We evaluated real-world clinical utility by examining reliability and clinical relevance of TVW measured from SoC (i.e., clinical) MRIs. <h3>Design/Methods:</h3> We measured TVW from SoC brain MRIs in 563 consecutive patients aged 18–65 years. Three trained raters blind to other patient data measured TVW in millimeters from midpoints of left and right boundaries of the third ventricle in axial T1-weighted images. TVWs were adjusted for demographic characteristics. Disability was assessed with the gold-standard MS Functional Composite (MSFC) evaluating cognition (SDMT), upper extremity coordination (NHPT), and gait (T25FW). GLM assessed TVW differences across level of level of impairment (number of MSFC components &lt;5^th percentile). <h3>Results:</h3> Interrater reliability of TVW measurement indicated excellent absolute-agreement (two-way mixed effects ICCs: single-rating 0.982; 95%CI: 0.958, 0.994; mean-rating <i>k</i>=3, 0.994; 0.986, 0.998). Medium-to-large difference in TVW across MSFC impairment (<i>p</i> for trend &lt;0.001; η_p²=0.089): TVW (mean [95%CI]) increased across patients impaired on no (n=245; 3.34 [3.14, 3.54]), one (n=155; 3.74 [3.45, 3.99]), two (n=106; 4.30 [4.00, 4.60]), and three (n=57; 4.81 [4.40, 5.22]) MSFC tasks. <h3>Conclusions:</h3> Measurement of TVW from standard-of-care MRIs is a highly-reliable and clinically-relevant marker of brain atrophy in MS that can be easily implemented into routine clinical care. <b>Disclosure:</b> Dr. Levy has nothing to disclose. Dr. Patel has nothing to disclose. Mr. Sumowski has nothing to disclose.

Evolution of immunogenetic components encoding ultralong CDR H3.

The genomes of most vertebrates contain many V, D, and J gene segments within their Ig loci to construct highly variable CDR3 sequences through combinatorial diversity. This nucleotide variability translates into an antibody population containing extensive paratope diversity. Cattle have relatively few functional VDJ gene segments, requiring innovative approaches for generating diversity like the use of ultralong-encoding IGHV and IGHD gene segments that yield dramatically elongated CDR H3. Unique knob and stalk microdomains create protracted paratopes, where the antigen-binding knob sits atop a long stalk, allowing the antibody to bind both surface and recessed antigen epitopes. We examined genomes of twelve species of Bovidae to determine when ultralong-encoding IGHV and IGHD gene segments evolved. We located the 8-bp duplication encoding the unique TTVHQ motif in ultralong IGHV segments in six Bovid species (cattle, zebu, wild yak, domestic yak, American bison, and domestic gayal), but we did not find evidence of the duplication in species beyond the Bos and Bison genera. Additionally, we analyzed mRNA from bison spleen and identified a rich repertoire of expressed ultralong CDR H3 antibody mRNA, suggesting that bison use ultralong IGHV transcripts in their host defense. We found ultralong-encoding IGHD gene segments in all the same species except domestic yak, but again not beyond the Bos and Bison clade. Thus, the duplication event leading to this ultralong-encoding IGHV gene segment and the emergence of the ultralong-encoding IGHD gene segment appears to have evolved in a common ancestor of the Bos and Bison genera 5-10 million years ago.