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Borrelia burgdorferi, the agent of Lyme disease, is estimated to cause >400,000 annual infections in the United States. Serology is the primary laboratory method to support the diagnosis of Lyme disease, but current methods have intrinsic limitations that require alternative approaches or targets. We used a high-density peptide array that contains >90,000 short overlapping peptides to catalog immunoreactive linear epitopes from >60 primary antigens of B. burgdorferi. We then pursued a machine learning approach to identify immunoreactive peptide panels that provide optimal Lyme disease serodiagnosis and can differentiate antibody responses at various stages of disease. We examined 226 serum samples from the Lyme Biobank and the National Institutes of Health, which included sera from 110 individuals diagnosed with Lyme disease, 31 probable cases from symptomatic individuals, and 85 healthy controls. Cases were grouped based on disease stage and presentation and included individuals with early localized, early disseminated, and late Lyme disease. We identified a peptide panel originating from 14 different epitopes that differentiated cases versus controls, whereas another peptide panel built from 12 unique epitopes differentiated subjects with various disease manifestations. Our method demonstrated an improvement in B. burgdorferi antibody detection over the current two-tiered testing approach and confirmed the key diagnostic role of VlsE and FlaB antigens at all stages of Lyme disease. We also uncovered epitopes that triggered a temporal antibody response that was useful for differentiation of early and late disease. Our findings can be used to streamline serologic targets and improve antibody-based diagnosis of Lyme disease. IMPORTANCE: Serology is the primary method of Lyme disease diagnosis, but this approach has limitations, particularly early in disease. Currently employed antibody detection assays can be improved by the identification of alternative immunodominant epitopes and the selection of optimal diagnostic targets. We employed high-density peptide arrays that enabled precise epitope mapping for a wide range of B. burgdorferi antigens. In combination with machine learning, this approach facilitated the selection of serologic targets early in disease and the identification of serological indicators associated with different manifestations of Lyme disease. This study provides insights into differential antibody responses during infection and outlines a new approach for improved serologic diagnosis of Lyme disease.

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Recently airborne standing-wave acoustic levitation has seen great advances, and its applicability has been broadened due to the development of cavities constructed with arrays of compact ultrasonic sources. Yet, the numerical methods employed to study and predict the pressure distributions inside these cavities do not consider the effect of multiple reflections on the boundaries, hiding their resonant effects. This work presents an analytical, numerical, and experimental study of the effect of multiple reflections inside ultrasonic cavities based on arrays of transducers exhibiting their influence on the pressure amplitudes of focused standing waves. Our numerical results come from a modified version of the Matrix Method to numerically compute the multiple wave reflections of cavities constructed by two opposite arrays of multiple compact sources as boundaries. The correlation between numerical and experimental results reveals that intra-cavity reflections are relevant in focused axisymmetric cavities based on two arrays of multiple ultrasonic sources having a considerable impact on the amplitude of the standing waves and consequently, on the acoustic levitation performance. Thus, intra-cavity reflections must be considered for optimal cavity designs.

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In this work, we combined plasmon-enhanced fluorescence and electrochemical (PEF-EC) transduction mechanisms to realize a highly sensitive dual-transducer aptasensor. To implement two traducers in one biosensor, a novel large-scale nanoimprint lithography process was introduced to fabricate gold nanopit arrays (AuNpA) with unique fringe structures. Light transmitting through the AuNpA samples exhibited a surface plasmon polariton peak overlapping with the excitation peak of the C7 aptamer-associated fluorophore methylene blue (MB). We observed a five and seven-times higher average fluorescence intensity over the AuNpA and fringe structure, respectively, in comparison to a plane Au film. Furthermore, the MB fluorophore was simultaneously utilized as a redox probe for electrochemical investigations and is described here as a dual transduction label for the first time. The novel dual transducer system was deployed for the detection of SARS-CoV-2 Spike protein via a C7 aptamer in combination with a strand displacement protocol. The PEF transducer exhibited a detection range from 1 fg/mL to 10 ng/mL with a detection limit of 0.07 fg/mL, while the EC traducer showed an extended dynamic range from 1 fg/mL to 100 ng/mL with a detection limit of 0.15 fg/mL. This work provides insights into an easy-to-perform, large-scale fabrication process for nanostructures enabling plasmon-enhanced fluorescence, and the development of an advanced but universal aptasensor platform.

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Microelectrode arrays (MEAs) enable simultaneous measurement of spike trains from numerous neurons, owing to advancements in microfabrication technology. These probes are highly valuable for comprehending the intricate dynamics of neuronal networks. Spike sorting is a pivotal step in comprehensively analyzing the activity of neuronal networks from extracellular recordings. However, the accuracy of spike sorting is relatively low due to the dense sampling of spikes in MEAs. Here, we propose an unsupervised pipeline named UMAP-COM method, which utilizes combined features to address this problem. These combined features comprise dominant spike shape features extracted by the uniform manifold approximation and projection (UMAP), as well as spike locations estimated by the center of mass (COM). We validate the UMAP-COM method on publicly available datasets from different kinds of probes, demonstrating that it is more accurate than other spike sorting methods. Furthermore, we conduct separate evaluations of spike shape feature extraction methods and spike localization methods. In this comparison, UMAP emerges as the superior feature extraction method, demonstrating its effectiveness in accurately representing spike shapes. Additionally, we find that the COM method outperforms other spike localization methods, highlighting its ability to enhance the accuracy of spike sorting.

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Background and purpose: In sliding-window intensity-modulated radiotherapy, increased plan modulation often leads to increased plan complexities and dose uncertainties. Dose calculation and/or measurement checks are usually adopted for pre-treatment verification. This study aims to evaluate the relationship among plan complexities, calculated doses and measured doses. Materials and methods: A total of 53 plan complexity metrics (PCMs) were selected, emphasizing small field characteristics and leaf speed/acceleration. Doses were retrieved from two beam-matched treatment devices. The intended dose was computed employing the Anisotropic Analytical Algorithm and validated through Monte Carlo (MC) and Collapsed Cone Convolution (CCC) algorithms. To measure the delivered dose, 3D diode arrays of various geometries, encompassing helical, cross, and oblique cross shapes, were utilized. Their interrelation was assessed via Spearman correlation analysis and principal component linear regression (PCR). Results: The correlation coefficients between calculation-based (CQA) and measurement-based verification quality assurance (MQA) were below 0.53. Most PCMs showed higher correlation rpcm-QA with CQA (max: 0.84) than MQA (max: 0.65). The proportion of rpcm-QA  ≥ 0.5 was the largest in the pelvis compared to head-and-neck and chest-and-abdomen, and the highest rpcm-QA occurred at 1 %/1mm. Some modulation indices for the MLC speed and acceleration were significantly correlated with CQA and MQA. PCR's determination coefficients (R2 ) indicated PCMs had higher accuracy in predicting CQA (max: 0.75) than MQA (max: 0.42). Conclusions: CQA and MQA demonstrated a weak correlation. Compared to MQA, CQA exhibited a stronger correlation with PCMs. Certain PCMs related to MLC movement effectively indicated variations in both quality assurances.

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Large-scale multimodal neural recordings on high-density biosensing microelectrode arrays (HD-MEAs) offer unprecedented insights into the dynamic interactions and connectivity across various brain networks. However, the fidelity of these recordings is frequently compromised by pervasive noise, which obscures meaningful neural information and complicates data analysis. To address this challenge, we introduce DENOISING, a versatile data-derived computational engine engineered to adjust thresholds adaptively based on large-scale extracellular signal characteristics and noise levels. This facilitates the separation of signal and noise components without reliance on specific data transformations. Uniquely capable of handling a diverse array of noise types (electrical, mechanical, and environmental) and multidimensional neural signals, including stationary and non-stationary oscillatory local field potential (LFP) and spiking activity, DENOISING presents an adaptable solution applicable across different recording modalities and brain networks. Applying DENOISING to large-scale neural recordings from mice hippocampal and olfactory bulb networks yielded enhanced signal-to-noise ratio (SNR) of LFP and spike firing patterns compared to those computed from raw data. Comparative analysis with existing state-of-the-art denoising methods, employing SNR and root mean square noise (RMS), underscores DENOISING's performance in improving data quality and reliability. Through experimental and computational approaches, we validate that DENOISING improves signal clarity and data interpretation by effectively mitigating independent noise in spatiotemporally structured multimodal datasets, thus unlocking new dimensions in understanding neural connectivity and functional dynamics.

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The rapid, precise, and high-throughput identification of multiple heavy metals ions holds immense importance in ensuring food safety and promoting public health. This study presents a novel smartphone-assisted colorimetric sensor array for the rapid and precise detection of multiple heavy metals ions. The sensor array is based on three signal recognition elements (AuPt@Fe-N-C, AuPt@N-C, and Fe-N-C) and the presence of different heavy metal ions affects the nanozymes-chromogenic substrate (TMB) catalytic color production, enabling the differentiation and quantification of various heavy metal ions. Combined with a smartphone-based RGB mode, the colorimetric sensor array can successfully identify five different heavy metal ions (Hg2+, Pb2+, Co2+, Cr6+, and Fe3+) as low as 0.5 µM and different ratios of binary and ternary mixed heavy metal ions in just 5 min. The sensor array successfully tested seawater and salmon samples with a total heavy metal content of 10 µM in the South China Sea (Haikou and Wenchang). Overall, this study highlights the potential of smartphone-assisted colorimetric sensor arrays for the rapid and precise detection of multiple heavy metal ions, which could significantly contribute to food safety and public health monitoring.

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Chemoresponsive dyes offer the potential to selectively detect volatile organic compounds (VOCs) unique to certain disease states. Among different VOC sensing techniques, colorimetric sensing offers the advantage of facile recognition. However, it is often challenging to discern the color changes by the naked eye. Here, highly sensitive colorimetric VOC sensor arrays from dye-incorporated colloidal photonic crystals (dye-cPhCs) are reported. cPhCs are scalably fabricated on a 4-inch wafer by spin-coating of silica nanoparticles (NPs) dispersed in a photo-cross-linkable monomer, where the gradient shear flow along the film thickness creates densely-packed square arrays of NPs in the top layers, whereas the bulk is quasi-amorphous with larger periodicities. The broadened reflection peak allows for augmented dye absorption originating from the overlap between the photonic bandgap edge of the cPhC and the dye absorption peak, leading to a more noticeable color change upon exposure to VOCs. The sensor array generates distinct color difference maps for acetaldehyde, acetone, and acetic acid, respectively, without any data amplification. The limit of detection for acetaldehyde, acetone, and acetic acid is 1, 0.1, and 0.02 ppm, respectively. Moreover, VOC can be diagonalized by visually intuitive pattern recognition, and principal component analysis at reduced dimensionality is demonstrated.

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To solve the problem that the hydrophone arrays are disturbed by ocean noise when collecting signals in shallow seas, resulting in reduced accuracy and resolution of target orientation estimation, a direction-of-arrival (DOA) estimation algorithm based on iterative EMD interval thresholding (EMD-IIT) and off-grid sparse Bayesian learning is proposed. Firstly, the noisy signal acquired by the hydrophone array is denoised by the EMD-IIT algorithm. Secondly, the singular value decomposition is performed on the denoised signal, and then an off-grid sparse reconstruction model is established. Finally, the maximum a posteriori probability of the target signal is obtained by the Bayesian learning algorithm, and the DOA estimate of the target is derived to achieve the orientation estimation of the target. Simulation analysis and sea trial data results show that the algorithm achieves a resolution probability of 100% at an azimuthal separation of 8° between adjacent signal sources. At a low signal-to-noise ratio of -9 dB, the resolution probability reaches 100%. Compared with the conventional MUSIC-like and OGSBI-SVD algorithms, this algorithm can effectively eliminate noise interference and provides better performance in terms of localization accuracy, algorithm runtime, and algorithm robustness.

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Hydrogen fuel holds promise for clean energy solutions, particularly in onboard applications such as fuel cell vehicles. However, the development of efficient hydrogen storage systems remains a critical challenge. This study addresses this challenge by exploring the potential of high-strength novel materials, including glass, to maximize onboard hydrogen storage capacity. A mathematical approach was employed to evaluate the feasibility and efficacy of various high-strength materials for hydrogen storage. This study focused on capillary arrays as a promising storage medium and utilized mathematical modeling techniques to estimate the storage capacity enhancement achievable with different materials. The analysis revealed significant variations in storage capacity enhancements in different high-strength novel materials, with glass having promising results. Glass-based materials demonstrated the potential to meet or exceed US Department of Energy (DOE) targets for both gravimetric and volumetric hydrogen storage capacities in capillary arrays. By leveraging a mathematical approach, this study identified high-strength novel materials, including glass and polymers, capable of substantially improving onboard hydrogen storage capacity: 29 wt.% with 40 g/L for quartz glass and 25 wt.% with 38 g/L for Kevlar compared to 5.2 wt.% with 26.3 g/L from a conventional type IV tank. These findings underscore the importance of material selection in optimizing hydrogen storage systems and provide valuable insights for the design and development of next-generation hydrogen storage technologies for onboard applications.

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Epilepsy is a common neurological disorder, affecting over 65 million people worldwide. Unfortunately, despite resective surgery, over 30 % of patients with drug-resistant epilepsy continue to experience seizures. Retrospective studies considering connectivity using intracranial electrocorticography (ECoG) obtained during neuromonitoring have shown that treatment failure is likely driven by failure to consider critical components of the seizure network, an idea first formally introduced in 2002. However, current studies only capture snapshots in time, precluding the ability to consider seizure network development. Over the past few years, multiwell microelectrode arrays have been increasingly used to study neuronal networks in vitro. As such, we sought to develop a novel in vitro MEA seizure model to allow for study of seizure networks. Specifically, we used 4-aminopyridine (4-AP) to capture hyperexcitable activity, and then show increased network changes after 2 days of chronic treatment. We characterize network changes using functional connectivity measures and a novel technique using dimensionality reduction. We find that 4-AP successfully captures persistently elevated mean firing rate and significant changes in underlying connectivity patterns. We believe this affords a robust in vitro seizure model from which longitudinal network changes can be studied, laying groundwork for future studies exploring seizure network development.

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Systemic bacterial infections represent a significant clinical challenge due to the increasing resistance rate towards antimicrobials. An essential key to controlling antimicrobial resistance spread is to administer targeted therapy after a precise minimum inhibitory concentration reporting. Among the available fast technologies for antimicrobial susceptibility testing (AST), the VITEKⓇ REVEAL™ (Biomerieux, Florence, Italy) proposes volatile organic compounds (VOC) colourimetric arrays to discriminate between susceptible and resistant Gram-negative isolates directly from positive blood cultures. We evaluated this methodology during a four-month laboratory experience on 40 positive blood culture samples, reporting a comparison to standard culture-based methods. The protocol revealed an essential agreement of 100 % between the conventional and the experimental procedures, while the categorical agreement resulted in 97.5 % due to one very major error (VME) for meropenem/vaborbactam in K. pneumoniae. Although further studies will be necessary to investigate its performance on rare microorganisms, the VITEKⓇ REVEAL™ demonstrated an optimal sensitivity in defining MIC values for multi-drug resistant (MDR) microorganisms. These results encourage the application of the method in all high-risk epidemiological areas, confirming the effectiveness of VOC detection in monitoring bacterial susceptibility profiles.

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There is evidence that Murciano Granadina (MG), the most important caprine dairy breed in Spain, has been introgressed by African goats, but the precise geographic origin of such introgression has not been identified yet. Moreover, an accurate estimate of the magnitude of this African introgression is lacking, since current estimates are based on small numbers of sampled individuals. The aim of our work was to tackle these two issues by genotyping 500 MG goats with the Goat SNP50 BeadChip and comparing their genotypes with those of reference populations from Spain (Bermeya), France (Saanen), Morocco (Barcha, Draa, Ghazalia, Noire de Atlas, Nord, Moroccan), Egypt (Barki, Oasis, Saidi), Algeria (Arabia, Makatia, M'Zabite, Kabyle), Tunisia (Tunisian native breeds) and Sudan (Desert, Nilotic, Taggar). The population of 500 MG goats was subdivided into 10 datasets of 50 individuals to ensure that sample sizes of the target (MG) and reference populations are balanced. Performance of an unsupervised ADMIXTURE analysis demonstrated that MG goats have a North African ancestry, with an average proportion of 4.4 ± 2.3%. Next, we did a supervised ADMIXTURE analysis that revealed that the Moroccan genetic component reaches a proportion of 4.01 ± 3.9% in MG goats, while the Algerian (0.001 ± 0.001%), Egyptian (0.2 ± 0.1%), Sudanese (0.1 ± 0.1%) and Tunisian (0.3 ± 0.4%) components are present in extremely small proportions. The historical circumstances of this introgression event are currently unknown, but several plausible scenarios are outlined. Moreover, our results show considerable inter-individual heterogeneity regarding the magnitude of the Moroccan introgression of MG goats (0%- 12% depending on the MG data set under analysis). This result implies that reliable estimates about the introgression of autochthonous livestock by exotic breeds can only be obtained by extensively sampling target populations.

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Arthropod-borne viruses, such as dengue virus (DENV), pose significant global health threats, with DENV alone infecting around 400 million people annually and causing outbreaks beyond endemic regions. This study aimed to enhance serological diagnosis and discover new drugs by identifying immunogenic protein regions of DENV. Utilizing a comprehensive approach, the study focused on peptides capable of distinguishing DENV from other flavivirus infections through serological analyses. Over 200 patients with confirmed arbovirus infection were profiled using high-density pan flavivirus peptide arrays comprising 6253 peptides and the computational method matrix of local coupling energy (MLCE). Twenty-four peptides from nonstructural and structural viral proteins were identified as specifically recognized by individuals with DENV infection. Six peptides were confirmed to distinguish DENV from Zika virus (ZIKV), West Nile virus (WNV), Yellow Fever virus (YFV), Usutu virus (USUV), and Chikungunya virus (CHIKV) infections, as well as healthy controls. Moreover, the combination of two immunogenic peptides emerged as a potential serum biomarker for DENV infection. These peptides, mapping to highly accessible regions on protein structures, show promise for diagnostic and prophylactic strategies against flavivirus infections. The described methodology holds broader applicability in the serodiagnosis of infectious diseases.

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The utilization of perovskite materials in flexible optoelectronics is experiencing distinct diversification including X-ray detection applications. Here, we report the oriented alignment of cesium lead bromide (CsPbBr3) single-crystal arrays on flexible polydimethylsiloxane (PDMS) substrates. By precisely confining the crystallization process within spatially delimited precursor droplets, we achieve a well-oriented crystal alignment through the spontaneous rotation of the CsPbBr3 microcuboids. This approach allows for precise control over the microcuboid morphologies by varying the growth temperature. We design flexible X-ray detector arrays by seamlessly integrating CsPbBr3 microcuboids with electrode arrays. The flexible X-ray detector can output a high sensitivity of 1.97 × 105 µC·Gyair-1·cm-2 and a low detection limit of 89 nGyair·s-1 after the surface passivation process. The excellent mechanical properties, outstanding X-ray detection capabilities, and high pixel uniformity are also demonstrated in conformal X-ray imaging of curved surfaces.

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Selective CO2-to-CO photoreduction is under intensive research and requires photocatalysts with tuned microstructures to accelerate the reaction kinetics. Here, we report CuInS2 nanosheet arrays with sulfur vacancies (VS) grown on the two-dimensional (2D) support of Ti3C2Tx MXene for CO2-to-CO photoreduction. Our results reveal that the use of Ti3C2Tx induces strong support effect, which causes the hierarchical nanosheet arrays growth of CuInS2 and simultaneously leads to charge transfer from CuInS2 to Ti3C2Tx support, resulting in VS formed in CuInS2. The strong support effect based on Ti3C2Tx is proven to be applicable to prepare a series of different metal indium sulfide arrays with VS. CuInS2 nanosheet arrays with VS supported on Ti3C2Tx benefit the photocatalytic selective reduction of CO2 to CO, manifesting a remarkable over 14.8-fold activity enhancement compared with pure CuInS2. The experimental and computational investigations pinpoint that VS of CuInS2 resulting from the support effect of Ti3C2Tx lowers the barrier of the rate-limiting step of *COOH → *OH + *CO, which is the key to the photoactivity enhancement. This work demonstrates MXene support effects and offers an effective approach to regulate the atomic microstructure of metal sulfides toward enhancing photocatalytic performance.

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Small-angle X-ray and neutron scattering (SAXS and SANS) patterns from certain semicrystalline polymers and liquid crystals contain discrete reflections from ordered assemblies and central diffuse scattering (CDS) from uncorrelated structures. Systems with imperfectly ordered lamellar structures aligned by stretching or by a magnetic field produce four distinct SAXS patterns: two-point 'banana', four-point pattern, four-point 'eyebrow' and four-point 'butterfly'. The peak intensities of the reflections lie not on a layer line, or the arc of a circle, but on an elliptical trajectory. Modeling shows that randomly placed lamellar stacks modified by chain slip and stack rotation or interlamellar shear can create these forms. On deformation, the isotropic CDS becomes an equatorial streak with an oval, diamond or two-bladed propeller shape, which can be analyzed by separation into isotropic and oriented components. The streak has elliptical intensity contours, a natural consequence of the imperfect alignment of the elongated scattering objects. Both equatorial streaks and two- and four-point reflections can be fitted in elliptical coordinates with relatively few parameters. Equatorial streaks can be analyzed to obtain the size and orientation of voids, fibrils or surfaces. Analyses of the lamellar reflection yield lamellar spacing, stack orientation (interlamellar shear) angle α and chain slip angle ϕ, as well as the size distribution of the lamellar stacks. Currently available computational tools allow these microstructural parameters to be rapidly refined.

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The development of core-shelled heterostructures with the unique morphology can improve the electrochemical properties of hybrid supercapacitors (HSC). Here, CuCo2S4 nanowire arrays (NWAs) are vertically grown on nickel foam (NF) utilizing hydrothermal synthesis. Then, CoMo-LDH nanosheets are uniformly deposited on the CuCo2S4 NWAs by electrodeposition to obtain the CoMo-LDH@CuCo2S4 NWAs/NF electrode. Due to the superior conductivity of CuCo2S4 (core) and good redox activity of CoMo-LDH (shell), the electrode shows excellent electrochemical properties. The electrode's specific capacity is 1271.4 C g-1 at 1 A g-1, and after 10, 000 cycles, its capacity retention ratio is 92.2 % at 10 A g-1. At a power density of 983.9 W kg-1, the CoMo-LDH@CuCo2S4 NWAs/NF//AC/NF device has an energy density of 52.2 Wh kg-1. This indicates that CoMo-LDH@CuCo2S4/NF has a great potential for supercapacitors.

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Overgrowth syndromes (OGS) comprise a heterogeneous group of disorders whose main characteristic is that the weight, height or the head circumference are above the 97th centile or 2-3 standard deviations above the mean for age, gender, and ethnic group. Several copy-number variants (CNVs) have been associated with the development of OGS, such as the 5q35 microdeletion or the duplication of the 15q26.1-qter, among many others. In this study, we have applied 850K SNP-arrays to 112 patients and relatives with OGS from the Spanish OverGrowth Registry Initiative. We have identified CNVs associated with the disorder in nine individuals (8%). Subsequently, whole genome sequencing (WGS) analysis was performed in these nine samples in order to better understand these genomic imbalances. All the CNVs were detected by both techniques, settling that WGS is a useful tool for CNV detection. We have found six patients with genomic abnormalities associated with previously well-established disorders and three patients with CNVs of unknown significance, which may be related to OGS, based on scientific literature. In this report, we describe these findings and comment on genes associated with OGS that are located within the CNV regions.

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2D materials with atomically thin nature are promising to develop scaled transistors and enable the extreme miniaturization of electronic components. However, batch manufacturing of top-gate 2D transistors remains a challenge since gate dielectrics or gate electrodes transferred from 2D material easily peel away as gate pitch decreases to the nanometer scale during lift-off processes. In this study, an oxidation-assisted etching technique is developed for batch manufacturing of nanopatterned high-κ/metal gate (HKMG) stacks on 2D materials. This strategy produces nano-pitch self-oxidized Al2O3/Al patterns with a resolution of 150 nm on 2D channel material, including graphene, MoS2, and WS2 without introducing any additional damage. Through a gate-first technology in which the Al2O3/Al gate stacks are used as a mask for the formation of source and drain, a short-channel HKMG MoS2 transistor with a nearly ideal subthreshold swing (SS) of 61 mV dec-1, and HKMG graphene transistor with a cut-off frequency of 150 GHz are achieved. Moreover, both graphene and MoS2 HKMG transistor arrays exhibit high uniformity. The study may bring the potential for the massive production of large-scale integrated circuits using 2D materials.