Social coevolution and Sine chaotic opposition learning Chimp Optimization Algorithm for feature selection.

Feature selection is a hot problem in machine learning. Swarm intelligence algorithms play an essential role in feature selection due to their excellent optimisation ability. The Chimp Optimisation Algorithm (CHoA) is a new type of swarm intelligence algorithm. It has quickly won widespread attention in the academic community due to its fast convergence speed and easy implementation. However, CHoA has specific challenges in balancing local and global search, limiting its optimisation accuracy and leading to premature convergence, thus affecting the algorithm's performance on feature selection tasks. This study proposes Social coevolution and Sine chaotic opposition learning Chimp Optimization Algorithm (SOSCHoA). SOSCHoA enhances inter-population interaction through social coevolution, improving local search. Additionally, it introduces sine chaotic opposition learning to increase population diversity and prevent local optima. Extensive experiments on 12 high-dimensional classification datasets demonstrate that SOSCHoA outperforms existing algorithms in classification accuracy, convergence, and stability. Although SOSCHoA shows advantages in handling high-dimensional datasets, there is room for future research and optimization, particularly concerning feature dimensionality reduction.

Towards greener futures: SVR-based CO2 prediction model boosted by SCMSSA algorithm.

This research presents the utilization of an enhanced Sine cosine perturbation with Chaotic perturbation and Mirror imaging strategy-based Salp Swarm Algorithm (SCMSSA), which incorporates three improvement mechanisms, to enhance the convergence accuracy and speed of the optimization algorithm. The study assesses the SCMSSA algorithm's performance against other optimization algorithms using six test functions to show the efficacy of the enhancement strategies. Furthermore, its efficacy in improving Support Vector Regression (SVR) models for CO2 prediction is assessed. The results reveal that the SVR-SCMSSA hybrid model surpasses other hybrid models and standard SVR in terms of training and prediction accuracy by obtaining 95 % accuracy. Its swift convergence, precision, and resistance to local optima position make it an excellent choice for addressing complex problems such as CO2 prediction, with critical implications for sustainability efforts. Moreover, feature importance analysis by SVR-SCMSSA offers valuable insights into the key contributors to CO2 prediction in the dataset, emphasizing the significance and impact of factors such as fossil fuel, Biomass, and Wood as major contributors to CO2 emission. The research suggests the adoption of the SVR-SCMSSA hybrid model for more accurate and reliable CO2 prediction to researchers and policymakers, which is essential for environmental sustainability and climate change mitigation.

Germline PTCH1: c.361_362insAlu alteration identified by comprehensive exome and RNA sequencing in a patient with Gorlin syndrome.

Gorlin syndrome can be caused by pathogenic/likely pathogenic (P/LP) variants in the tumor suppressor gene PTCH1 (9q22.1-q31), which encodes the receptor for the sonic hedgehog (SHH) ligand. We present a 12-month-old boy clinically diagnosed with Gorlin syndrome who was found to have significantly delayed development, palmar pitting, palmar and plantar keratosis, short hands, frontal bossing, coarse face, hypertelorism, a bifid rib, misaligned and missing teeth, and SHH-activated medulloblastoma. Genetic testing, including a pediatric cancer panel and genome sequencing with peripheral blood, failed to identify any P/LP variants in PTCH1. Paired tumor/normal exome sequencing was performed, which identified a germline NM_000264.5 (PTCH1): c.361_362ins? alteration through manual review of sequencing reads. Clinical RNA sequencing further demonstrated an Alu insertion at this region (PTCH1: c.361_362insAlu), providing molecular confirmation of Gorlin syndrome. This finding exemplifies a unique mechanism for PTCH1 disruption in the germline and highlights the importance of comprehensive analysis, including manual review of DNA sequencing reads and the utility of RNA analysis to detect variant types which may not be identified by routine genetic screening techniques.

A Case of Aortic Strain due to Spring Back Force by Stent Graft 3 Years after Thoracic Endovascular Aortic Repair.

A 79-year-old man underwent 1-debranched thoracic endovascular aortic repair (TEVAR) for a saccular aneurysm of the distal arch of the aorta. Computed tomography performed 3 years after surgery revealed a significant displacement of the distal side of the stent graft and severe deformity due to displacement of the aorta. There were no obvious findings after aortic dissection. Endovascular treatment was selected, and surgery was performed semiemergency. Additional TEVAR was performed to restore the aortic shape and displacement to its normal position.

Study of the Bias of the Initial Phase Estimation of a Sinewave of Known Frequency in the Presence of Phase Noise.

The estimation of the parameters of a sinusoidal signal is of paramount importance in various applications in the fields of sensors, signal processing, parameter estimation, and device characterization, among others. The presence, in the measurement system, of non-ideal phenomena such as additive noise in the signals, phase noise in the stimulus generation, jitter in the sampling system, frequency error in the experimental setup, among others, leads to increased uncertainty and bias in the estimated quantities obtained by least squares methods and those derived from them. Therefore, from a metrological point of view, it is important to be able to theoretically predict and quantify those uncertainties in order to properly design the measurement system and its parameters, such as the number of samples to acquire or the stimulus signal amplitude to use to minimize the uncertainty in the estimated values. Previous works have shown that the presence of these non-ideal phenomena leads to increased uncertainty and bias in the estimation of the sinewave amplitude. The present work complements this knowledge by focusing specifically on the effect of phase noise and sampling jitter in the bias of the initial phase estimation of a sinusoidal signal of known frequency (three­parameter sine fitting procedure). A theoretical derivation of the bias of initial phase estimation that takes into consideration the presence of phase noise in the sinewave is presented. Since a Taylor series approximation was used where only the first term was retained, it was necessary to validate the analytical derivations with numerical simulations using a Monte Carlo type of procedure. This process was applied to different conditions regarding the phase noise standard deviation, initial phase value, and number of samples. It is concluded that, in most scenarios, initial phase estimation using sine fitting is unbiased in the presence of phase noise or jitter. It is shown, however, that in cases of extremely high phase noise standard deviation and a very low number of samples, a bias occurs.

High-resolution methylome analysis uncovers stress-responsive genomic hotspots and drought-sensitive TE superfamilies in the clonal Lombardy poplar.

DNA methylation is environment-sensitive and can mediate stress responses. In long-lived trees, changing environments might cumulatively shape the methylome landscape over their lifetime. However, because high-resolution methylome studies usually focus on single environmental cues, it remains unclear to what extent the methylation responses are generic or stress-specific, and how this relates to their long-term stability. Here, we studied the methylome plasticity of a Populus nigra cv. 'Italica' clone that is widespread across Europe. Adult trees from a variety of geographic locations were clonally propagated in a common garden experiment, and the ramets were exposed to cold, heat, drought, herbivory, rust infection, and salicylic acid treatments. Through comprehensive whole-genome bisulfite sequencing, we analyzed stress-induced and naturally occurring DNA methylation variants. Stress-induced methylation changes predominantly targeted transposable elements. When occurring in CG/CHG contexts, the same regions were often affected by multiple stresses, suggesting a generic response of the methylome. Drought stress caused a distinct CHH hypermethylation response in transposable elements, affecting entire TE superfamilies near drought-responsive genes. Methylation differences in CG/CHG contexts that were induced by stress treatments showed striking overlap with methylation differences observed between trees from distinct geographical locations. Thus, we revealed genomic hotspots of methylation change that are not stress-specific and that contribute to natural DNA methylation variation, and we identified specific transposable element superfamilies that respond to a specific stress with possible functional consequences. Our results underscore the importance of studying the effects of multiple stressors in a single experiment for recognizing general versus stress-specific methylome responses.

Array-optimized artificial olfactory sensor enabling cost-effective and non-destructive detection of mycotoxin-contaminated maize.

The MobileNetV3-based improved sine-cosine algorithm (ISCA-MobileNetV3) was combined with an artificial olfactory sensor (AOS) to address the redundancy in olfactory arrays, thereby achieving low-cost and high-precision detection of mycotoxin-contaminated maize. Specifically, volatile organic compounds of maize interacted with unoptimized AOS containing eight porphyrins and eight dye-attached nanocomposites to obtain the scent fingerprints for constructing the initial data set. The optimal decision model was MobileNetV3, with more than 98.5% classification accuracy, and its output training loss would be input into the optimizer ISCA. Remarkably, the number of olfactory arrays was reduced from 16 to 6 by ISCA-MobileNetV3 with about a 1% decrease in classification accuracy. Additionally, the developed system showed that each online evaluation was less than one second on average, demonstrating outstanding real-time performance for ensuring food safety. Therefore, AOS combined with ISCA-MobileNetV3 will encourage the development of an affordable and on-site platform for maize quality detection.

A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application.

The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened optimization ability in the process of finding the optimal solution. At the same time, it is easy to fall into local extremes and other limitations. In this paper, a novel golden jackal optimization algorithm (SCMGJO) combining sine-cosine and Cauchy mutation is proposed. On one hand, tent mapping reverse learning is introduced in population initialization, and sine and cosine strategies are introduced in the update of prey positions, which enhances the global exploration ability of the algorithm. On the other hand, the introduction of Cauchy mutation for perturbation and update of the optimal solution effectively improves the algorithm's ability to obtain the optimal solution. Through the optimization experiment of 23 benchmark test functions, the results show that the SCMGJO algorithm performs well in convergence speed and accuracy. In addition, the stretching/compression spring design problem, three-bar truss design problem, and unmanned aerial vehicle path planning problem are introduced for verification. The experimental results prove that the SCMGJO algorithm has superior performance compared with other intelligent optimization algorithms and verify its application ability in engineering applications.

Utilizing sine trigonometric q-spherical fuzzy rough aggregation operators for group decision-making and their role in digital transformation.

q-spherical fuzzy rough set (q-SFRS) is also one of the fundamental concepts for addressing more uncertainties in decision problems than the existing structures of fuzzy sets, and thus its implementation was more substantial. The well-known sine trigonometric function maintains the periodicity and symmetry of the origin in nature and thus satisfies the expectations of the experts over the multi-parameters. Taking this feature and the significance of the q-SFRSs into consideration, the main objective of the article is to describe some reliable sine trigonometric laws for SFSs. Associated with these laws, we develop new average and geometric aggregation operators to aggregate the q-spherical fuzzy rough numbers. Then, we presented a group decision-making strategy to address the multi-attribute group decision-making problem using the developed aggregation operators. To verify the value of the defined operators, a MAGDM strategy is provided along with applications for selecting a Cloud Service Provider and a Digital Transformation Vendor for digital transformation. Moreover, a comparative study is also performed to present the effectiveness of the developed approach.

Magnetic levitation system control based on a novel tracking differentiator.

This study proposes a novel tracking differentiator and applies it to the sliding-mode control (SMC) algorithm to address the unsatisfactory disturbance suppression and low tracking accuracy of magnetic levitation (maglev) systems. First, to assess performance in terms of filtering, tracking, and differentiation, an inverse hyperbolic sine function and a two-phase power function are introduced to improve the tracking differentiator. This can accelerate the global convergence speed, ensure smooth convergence at the equilibrium point, reduce system jitter, and enhance the noise-suppression ability of the system. The differentiator parameter-adjustment rules are derived from a system sweep. A comparison of the simulation results show that the proposed differentiator effectively suppresses noise and performs signal tracking and differentiation. Finally, the new differentiator is applied to the SMC of a maglev system. Simulation and experimental results show that the response speed of the maglev system under the SMC based on the new tracking differentiator is high, the jitter is effectively reduced, and the noise-suppression ability is improved.

A new family of distributions using a trigonometric function: Properties and applications in the healthcare sector.

Probability distributions play a pivotal and significant role in modeling real-life data in every field. For this activity, a series of probability distributions have been introduced and exercised in applied sectors. This paper also contributes a new method for modeling continuous data sets. The proposed family is called the exponent power sine-G family of distributions. Based on the exponent power sine-G method, a new model, namely, the exponent power sine-Weibull model is studied. Several mathematical properties such as quantile function, identifiability property, and rth moment are derived. For the exponent power sine-G method, the maximum likelihood estimators are obtained. Simulation studies are also presented. Finally, the optimality of the exponent power sine-Weibull model is shown by taking two applications from the healthcare sector. Based on seven evaluating criteria, it is demonstrated that the proposed model is the best competing distribution for analyzing healthcare phenomena.

Imatinib-induced dermatomyositis sine dermatitis - a rare case report.

Idiopathic Inflammatory Myopathies are rare conditions with several heterogeneous disease subtypes. They can range from limited muscle or skin involvement to severe, systemic, life-threatening disease. Although the etiology is unknown, some evidence suggests a role for external agents, particularly drugs. Herein, we present a case of a 71-year-old woman with chronic myeloid leukemia who developed imatinib-induced dermatomyositis sine dermatitis. The presentation was predominantly muscular, characterized by proximal muscle weakness and myalgia of the lower limbs, with positive anti-Mi2a antibodies. Spontaneous recovery was observed after drug discontinuation, without the need for immunosuppressive therapy. This is the first confirmed description of an imatinib-induced dermatomyositis sine dermatitis. It reflects the importance of a high awareness from rheumatologists and hematologists to accurately anticipate and identify similar situations.

Novel Alu insertion in the ZEB2 gene causing Mowat-Wilson syndrome.

Alu elements are short, interspersed elements located throughout the genome, playing a role in human diversity, and occasionally causing genetic diseases. Here, we report a novel Alu insertion causing Mowat-Wilson syndrome, a rare neurodevelopmental disorder, in an 8-year-old boy displaying the typical clinical features for Mowat-Wilson syndrome. The variant was not initially detected in genome sequencing data, but through deep phenotyping, which pointed to only one plausible candidate gene, manual inspection of genome sequencing alignment data enabled us to identify a de novo heterozygous Alu insertion in exon 8 of the ZEB2 gene. Nanopore long-read sequencing confirmed the Alu insertion, leading to the formation of a premature stop codon and likely haploinsufficiency of ZEB2. This underscores the importance of deep phenotyping and mobile element insertion analysis in uncovering genetic causes of monogenic disorders as these elements might be overlooked in standard next-generation sequencing protocols.

Regulatory SVA retrotransposons and classical HLA genotyped-transcripts associated with Parkinson's disease.

Introduction: Parkinson's disease (PD) is a neurodegenerative and polygenic disorder characterised by the progressive loss of neural dopamine and onset of movement disorders. We previously described eight SINE-VNTR-Alu (SVA) retrotransposon-insertion-polymorphisms (RIPs) located and expressed within the Human Leucocyte Antigen (HLA) genomic region of chromosome 6 that modulate the differential co-expression of 71 different genes including the HLA classical class I and class II genes in a Parkinson's Progression Markers Initiative (PPMI) cohort. Aims and methods: In the present study, we (1) reanalysed the PPMI genomic and transcriptomic sequencing data obtained from whole blood of 1521 individuals (867 cases and 654 controls) to infer the genotypes of the transcripts expressed by eight classical HLA class I and class II genes as well as DRA and the DRB3/4/5 haplotypes, and (2) examined the statistical differences between three different PD subgroups (cases) and healthy controls (HC) for the HLA and SVA transcribed genotypes and inferred haplotypes. Results: Significant differences for 57 expressed HLA alleles (21 HLA class I and 36 HLA class II alleles) up to the three-field resolution and four of eight expressed SVA were detected at p<0.05 by the Fisher's exact test within one or other of three different PD subgroups (750 individuals with PD, 57 prodromes, 60 individuals who had scans without evidence of dopamine deficits [SWEDD]), when compared against a group of 654 HCs within the PPMI cohort and when not corrected by the Bonferroni test for multiple comparisons. Fourteen of 20 significant alleles were unique to the PD-HC comparison, whereas 31 of the 57 alleles overlapped between two or more different subgroup comparisons. Only the expressed HLA-DRA*01:01:01 and -DQA1*03:01:01 protective alleles (PD v HC), the -DQA1*03:03:01 risk (HC v Prodrome) or protective allele (PD v Prodrome), the -DRA*01:01:02 and -DRB4*01:03:02 risk alleles (SWEDD v HC), and the NR_SVA_381 present genotype (PD v HC) at a 5% homozygous insertion frequency near HLA-DPA1, were significant (Pc<0.1) after Bonferroni corrections. The homologous NR_SVA_381 insertion significantly decreased the transcription levels of HLA-DPA1 and HLA-DPB1 in the PPMI cohort and its presence as a homozygous genotype is a risk factor (Pc=0.012) for PD. The most frequent NR_SVA_381 insertion haplotype in the PPMI cohort was NR_SVA_381/DPA1*02/DPB1*01 (3.7%). Although HLA C*07/B*07/DRB5*01/DRB1*15/DQB1*06 was the most frequent HLA 5-loci phased-haplotype (n, 76) in the PPMI cohort, the NR_SVA_381 insertion was present in only six of them (8%). Conclusions: These data suggest that expressed SVA and HLA gene alleles in circulating white blood cells are coordinated differentially in the regulation of immune responses and the long-term onset and progression of PD, the mechanisms of which have yet to be elucidated.

A Novel Approach for Glioblastoma Treatment by Combining Apoptosis Inducers (TMZ, MTX, and Cytarabine) with E.V.A. (Eltanexor, Venetoclax, and A1210477) Inhibiting XPO1, Bcl-2, and Mcl-1.

Adjuvant treatment for Glioblastoma Grade 4 with Temozolomide (TMZ) inevitably fails due to therapeutic resistance, necessitating new approaches. Apoptosis induction in GB cells is inefficient, due to an excess of anti-apoptotic XPO1/Bcl-2-family proteins. We assessed TMZ, Methotrexate (MTX), and Cytarabine (Ara-C) (apoptosis inducers) combined with XPO1/Bcl-2/Mcl-1-inhibitors (apoptosis rescue) in GB cell lines and primary GB stem-like cells (GSCs). Using CellTiter-Glo® and Caspase-3 activity assays, we generated dose-response curves and analyzed the gene and protein regulation of anti-apoptotic proteins via PCR and Western blots. Optimal drug combinations were examined for their impact on the cell cycle and apoptosis induction via FACS analysis, paralleled by the assessment of potential toxicity in healthy mouse brain slices. Ara-C and MTX proved to be 150- to 10,000-fold more potent in inducing apoptosis than TMZ. In response to inhibitors Eltanexor (XPO1; E), Venetoclax (Bcl-2; V), and A1210477 (Mcl-1; A), genes encoding for the corresponding proteins were upregulated in a compensatory manner. TMZ, MTX, and Ara-C combined with E, V, and A evidenced highly lethal effects when combined. As no significant cell death induction in mouse brain slices was observed, we conclude that this drug combination is effective in vitro and expected to have low side effects in vivo.

Rational Sine-Gordon expansion method to analyze the dynamical behavior of the time-fractional phi-four and (2 + 1) dimensional CBS equations.

This study uses the rational Sine-Gordon expansion (RSGE) method to investigate the dynamical behavior of traveling wave solutions of the water wave phenomena for the time-fractional phi-four equation and the (2 + 1) dimensional Calogero-Bogoyavlanskil schilf (CBS) equation based on the conformable derivative. The technique uses the sine-Gordon equation as an auxiliary equation to generalize the well-known sine-Gordon expansion. It adopts a more broad strategy, a rational function rather than a polynomial one, of the solutions of the auxiliary equation, in contrast to the traditional sine-Gordon expansion technique. Several explanations for hyperbolic functions may be produced using the previously stated approach. The approach mentioned above is employed to provide diverse solutions of the time-fractional phi-four equation and the (2 + 1) dimensional CBS equations involving hyperbolic functions, such as soliton, single soliton, multiple-soliton, kink, cusp, lump-kink, kink double-soliton, and others. The RSGE approach enhances our comprehension of nonlinear processes, offers precise solutions to nonlinear equations, facilitates the investigation of solitons, propels the development of mathematical tools, and is applicable in many scientific and technical fields. The solutions are graphically shown in three-dimensional (3D) surface and contour plots using MATLAB software. All screens display the absolute wave configurations in the resolutions of the equation with the proper parameters. Furthermore, it can be deduced that the physical properties of the found solutions and their characteristics may help us comprehend how shallow water waves move in nonlinear dynamics.

Efficacy and safety of selinexor for patients with relapsed and refractory multiple myeloma: A meta-analysis.

PURPOSE: Selinexor is a first-in-class, oral selective-inhibitor-of-nuclear-export, granted accelerated approval by FDA (2019) for relapsed and refractory multiple myeloma (RRMM). We sought to quantitatively summarize the selinexor efficacy and safety in RRMM. METHODS: We searched PubMed, EMBASE, CENTRAL, clinicaltrial.gov, and google scholar, until May 2023, studies about selinexor use in RRMM. The outcome measures of interest were primarily efficacy outcomes, in addition to safety outcomes. Random-effect model analyses were performed, at statistical significance of P<0.05, using the RevMan software. RESULTS: Meta-analyses of eleven included clinical trials yielded a significant 56.21% overall clinical benefit, 46.91% overall response, 4.89% complete response, 23.41% very good partial response, 24.68% partial response, and 28.06% stable disease rates with selinexor. Due to safety reasons, selinexor caused significant increase in discontinuation rate, 16.80%. Subgroup analyses demonstrated higher efficacy with selinexor plus dexamethasone and proteasome inhibitor combinations than with selinexor alone. The multiple myeloma type, high cytogenetic risk, refractory state, and advanced disease state did not affect performance. Risk of selection, performance, and detection biases were unclear in the included trials. CONCLUSION: Selinexor led to significant positive responses with an acceptable safety profile in RRMM patients, despite higher rates of safety-related discontinuations. Selinexor-based combinations further enhanced response.

Regulation and function of transposable elements in cancer genomes.

Over half of human genomic DNA is composed of repetitive sequences generated throughout evolution by prolific mobile genetic parasites called transposable elements (TEs). Long disregarded as "junk" or "selfish" DNA, TEs are increasingly recognized as formative elements in genome evolution, wired intimately into the structure and function of the human genome. Advances in sequencing technologies and computational methods have ushered in an era of unprecedented insight into how TE activity impacts human biology in health and disease. Here we discuss the current views on how TEs have shaped the regulatory landscape of the human genome, how TE activity is implicated in human cancers, and how recent findings motivate novel strategies to leverage TE activity for improved cancer therapy. Given the crucial role of methodological advances in TE biology, we pair our conceptual discussions with an in-depth review of the inherent technical challenges in studying repeats, specifically related to structural variation, expression analyses, and chromatin regulation. Lastly, we provide a catalog of existing and emerging assays and bioinformatic software that altogether are enabling the most sophisticated and comprehensive investigations yet into the regulation and function of interspersed repeats in cancer genomes.

Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications.

To address the issues of lacking ability, loss of population diversity, and tendency to fall into the local extreme value in the later stage of optimization searching, resulting in slow convergence and lack of exploration ability of the artificial gorilla troops optimizer algorithm (AGTO), this paper proposes a gorilla search algorithm that integrates the positive cosine and Cauchy's variance (SCAGTO). Firstly, the population is initialized using the refractive reverse learning mechanism to increase species diversity. A positive cosine strategy and nonlinearly decreasing search and weight factors are introduced into the finder position update to coordinate the global and local optimization ability of the algorithm. The follower position is updated by introducing Cauchy variation to perturb the optimal solution, thereby improving the algorithm's ability to obtain the global optimal solution. The SCAGTO algorithm is evaluated using 30 classical test functions of Test Functions 2018 in terms of convergence speed, convergence accuracy, average absolute error, and other indexes, and two engineering design optimization problems, namely, the pressure vessel optimization design problem and the welded beam design problem, are introduced for verification. The experimental results demonstrate that the improved gorilla search algorithm significantly enhances convergence speed and optimization accuracy, and exhibits good robustness. The SCAGTO algorithm demonstrates certain solution advantages in optimizing the pressure vessel design problem and welded beam design problem, verifying the superior optimization ability and engineering practicality of the SCAGTO algorithm.

Exploring SVA Insertion Polymorphisms in Shaping Differential Gene Expressions in the Central Nervous System.

Transposable elements (TEs) are repetitive elements which make up around 45% of the human genome. A class of TEs, known as SINE-VNTR-Alu (SVA), demonstrate the capacity to mobilise throughout the genome, resulting in SVA polymorphisms for their presence or absence within the population. Although studies have previously highlighted the involvement of TEs within neurodegenerative diseases, such as Parkinson's disease and amyotrophic lateral sclerosis (ALS), the exact mechanism has yet to be identified. In this study, we used whole-genome sequencing and RNA sequencing data of ALS patients and healthy controls from the New York Genome Centre ALS Consortium to elucidate the influence of reference SVA elements on gene expressions genome-wide within central nervous system (CNS) tissues. To investigate this, we applied a matrix expression quantitative trait loci analysis and demonstrate that reference SVA insertion polymorphisms can significantly modulate the expression of numerous genes, preferentially in the trans position and in a tissue-specific manner. We also highlight that SVAs significantly regulate mitochondrial genes as well as genes within the HLA and MAPT loci, previously associated within neurodegenerative diseases. In conclusion, this study continues to bring to light the effects of polymorphic SVAs on gene regulation and further highlights the importance of TEs within disease pathology.