Structural Variations Research Articles

Structural variants linked to Alzheimer's Disease and other common age-related clinical and neuropathologic traits.

Advances have led to a greater understanding of the genetics of Alzheimer's Disease (AD). However, the gap between the predicted and observed genetic heritability estimates when using single nucleotide polymorphisms (SNPs) and small indel data remains. Large genomic rearrangements, known as structural variants (SVs), have the potential to account for this missing genetic heritability. By leveraging data from two ongoing cohort studies of aging and dementia, the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP), we performed genome-wide association analysis testing around 20,000 common SVs from 1,088 participants with whole genome sequencing (WGS) data. A range of Alzheimer's Disease and Related Disorders (AD/ADRD) clinical and pathologic traits were examined. Given the limited sample size, no genome-wide significant association was found, but we mapped SVs across 81 AD risk loci and discovered 22 SVs in linkage disequilibrium (LD) with GWAS lead variants and directly associated with AD/ADRD phenotypes (nominal P < 0.05). The strongest association was a deletion of an Alu element in the 3'UTR of the TMEM106B gene. This SV was in high LD with the respective AD GWAS locus and was associated with multiple AD/ADRD phenotypes, including tangle density, TDP-43, and cognitive resilience. The deletion of this element was also linked to lower TMEM106B protein abundance. We also found a 22 kb deletion associated with depression in ROSMAP and bearing similar association patterns as AD GWAS SNPs at the IQCK locus. In addition, genome-wide scans allowed the identification of 7 SVs, with no LD with SNPs and nominally associated with AD/ADRD traits. This result suggests potentially new ADRD risk loci not discoverable using SNP data. Among these findings, we highlight a 5.6 kb duplication of coding regions of the gene C1orf186 at chromosome 1 associated with indices of cognitive impairment, decline, and resilience. While further replication in independent datasets is needed to validate these findings, our results support the potential roles of common structural variations in the pathogenesis of AD/ADRD.

Seaweeds and Their Secondary Metabolites: A Promising Drug Candidate With Novel Mechanisms Against Cancers and Tumor Angiogenesis.

Cancer continually remains a severe threat to public health and requires constant demand for novel therapeutic drug candidates. Due to their multi-target orientation, lesser toxicity, and easy availability, natural compounds attract more attention from current scientific research interest than synthetic drug molecules. The plants and microorganisms produce a huge variety of secondary metabolites because of their physiological diversification, and the seaweeds occupy a prominent position as effective drug resources. Seaweeds comprise microscopic or macroscopic photosynthetic, multicellular, eukaryotic marine algae that commonly inhabit the coastal regions. Several molecules (such as polysaccharides, lipids, proteinaceous fractions, phenolic compounds, and alkaloids) are derived from seaweeds, and those small molecules are well attractive and more effective in cancer research programs. Their structural variation, derivative diversity, and quantity vary with seaweed species and geographical origin. Their smaller molecular weight, unique derivatives, hydrophobicity, and degree of sulfation are reported to be causes of their crucial role against different cancer cells in vitro. Several reports showed that those compounds selectively discriminate between normal and cancer cells based on receptor variations, enzyme deficiency, and structural properties. The present review aimed to give a concise explanation regarding their structural diversity, extractability, and mechanism of action related to their anti-cancer activities based on recently published data.

No-Reference Image Quality Assessment Combining Swin-Transformer and Natural Scene Statistics.

No-reference image quality assessment aims to evaluate image quality based on human subjective perceptions. Current methods face challenges with insufficient ability to focus on global and local information simultaneously and information loss due to image resizing. To address these issues, we propose a model that combines Swin-Transformer and natural scene statistics. The model utilizes Swin-Transformer to extract multi-scale features and incorporates a feature enhancement module and deformable convolution to improve feature representation, adapting better to structural variations in images, apply dual-branch attention to focus on key areas, and align the assessment more closely with human visual perception. The Natural Scene Statistics compensates information loss caused by image resizing. Additionally, we use a normalized loss function to accelerate model convergence and enhance stability. We evaluate our model on six standard image quality assessment datasets (both synthetic and authentic), and show that our model achieves advanced results across multiple datasets. Compared to the advanced DACNN method, our model achieved Spearman rank correlation coefficients of 0.922 and 0.923 on the KADID and KonIQ datasets, respectively, representing improvements of 1.9% and 2.4% over this method. It demonstrated outstanding performance in handling both synthetic and authentic scenes.

Pore-Scale Formation Characteristics of Impermeable Frozen Walls for Shallow Groundwater Contamination Remediation

Impermeability and water blocking are crucial for remediating shallow groundwater contamination. Traditional methods often employ curtain-grouting technology to create impermeable layers. However, cement slurry curing is irreversible, leading to permanent closure of underground aquifers and secondary pollution. This study employs an innovative approach by fabricating cylindrical models that simulate actual strata and utilizing a high-temperature and high-pressure displacement device. It systematically analyzes the variations in soil pore structure, distribution, porosity, and permeability under different temperatures, pressures, and freezing durations. The microscopic characteristics of the freezing process in water-bearing soils were studied. Results demonstrate that longer freezing time improves the effectiveness of soil freezing, reaching complete freezing at temperatures as low as −4 °C for samples with low water content. For water-saturated samples, freezing below −6 °C results in nearly zero porosity. Increased pressure at a certain freezing temperature significantly reduces permeability. When freezing temperature falls below −4 °C, water permeability in saturated samples after freezing reaches near-zero levels, while unsaturated samples experience complete freezing. These findings provide a theoretical foundation for constructing freezing curtains in remediating shallow groundwater pollution.

Spatial-temporal distributions of phytoplankton shifting, chlorophyll-a, and their influencing factors in shallow lakes using remote sensing

Understanding phytoplankton dynamics is crucial for assessing water ecosystem health. However, traditional in-situ investigations often fall short of capturing the spatial-temporal variations of phytoplankton community structure and biomass on large scales. This study introduces a novel approach that harnesses the power of machine learning models coupled with Sentinel-2 satellite data to predict phytoplankton shifting and chlorophyll-a (Chla) in three large shallow lakes in central China from 2016 to 2021. We employed an array of machine learning algorithms, including the extreme gradient boosting method (XGBoost), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) to retrieve Chla and phytoplankton groups clustered by hierarchical cluster analysis based on the Bray-Curtis similarity index (HCA). The influences of environmental factors, including meteorology and nutrients, on phytoplankton dynamics were then explored. Our results identified four distinct phytoplankton groups, each exhibiting unique structural characteristics according to the HCA. The RF and XGBoost, utilizing top-of-atmosphere reflectance, provided the most accurate estimations of phytoplankton shifting and Chla, respectively. The red edge-red ratio emerged as a key variable in both models, underlining the significance of red edge bands in phytoplankton monitoring. We mapped spatial-temporal patterns of phytoplankton group occurrence and average Chla concentration across the three lakes. Our findings indicated that eutrophication extended periods of cyanobacteria domination and algal blooms, particularly in bays and nearshore areas. Redundancy analysis and classification and regression tree model highlighted temperature as a significant driver of phytoplankton shifting, while nutrient levels exhibited stronger influences on Chla. Our study underscores the potential of integrating machine learning models with Sentinel-2 data to enhance the monitoring and prediction of phytoplankton dynamics in shallow lakes. This approach offers valuable insights for the early detection of algal blooms and informed management strategies, contributing to the preservation and sustainable management of aquatic ecosystems.

BrainAgeNeXt: Advancing Brain Age Modeling for Individuals with Multiple Sclerosis.

Aging is associated with structural brain changes, cognitive decline, and neurodegenerative diseases. Brain age, an imaging biomarker sensitive to deviations from healthy aging, offers insights into structural aging variations and is a potential prognostic biomarker in neurodegenerative conditions. This study introduces BrainAgeNeXt, a novel convolutional neural network inspired by the MedNeXt framework, designed to predict brain age from T1-weighted magnetic resonance imaging (MRI) scans. BrainAgeNeXt was trained and validated on 11,574 MRI scans from 33 private and publicly available datasets of healthy volunteers, aged 5 to 95 years, imaged with 3T and 7T MRI. Performance was compared against three state-of-the-art brain age prediction methods. BrainAgeNeXt achieved a mean absolute error (MAE) of 2.78 ± 3.64 years, lower than the compared methods (MAE = 3.55, 3.59, and 4.16 years, respectively). We tested all methods also across different levels of image quality, and BrainAgeNeXt performed well even with motion artifacts and less common 7T MRI data. In three longitudinal multiple sclerosis (MS) cohorts (273 individuals), brain age was, on average, 4.21 ± 6.51 years greater than chronological age. Longitudinal analysis indicated that brain age increased by 1.15 years per chronological year in individuals with MS (95% CI = [1.05, 1.26]). Moreover, in early MS, individuals with worsening disability had a higher annual increase in brain age compared to those with stable clinical assessments (1.24 vs. 0.75, p < 0.01). These findings suggest that brain age is a promising prognostic biomarker for MS progression and potentially a valuable endpoint for clinical trials.

Study of oblique granular impingement flow through CFD-DEM simulations and energy consumption statistics

This paper investigates an experimental granular impingement flow system through simulations and energy consumption statistics. With the increase of solid concentration, flow patterns undergo cross flow (dense edge and dilute center), dispersed flow, and mixed-jet flow (dilute edge and dense center) in turn. The influence of gas phase on the flow pattern was investigated. According to the analysis of force and time-averaged velocity, it is revealed that gas-solid interaction will cause energy dissipation of granular flow, which cannot be ignored in specific cases. Then, a flow map is proposed according to both the Archimedes number and solid concentration. In addition, the flow pattern transition and local structure variations can be reflected by energy consumption statistics. Based on the simulations and methods developed in this work, it is promising to deepen the understanding and study of granular impingement flow from the viewpoints of energy consumption and new-type flow map.

Thermal structure variations and influence factors in a subtropical reservoir, China: explanations from multiple research methods

ABSTRACT Revealing the subtle variations in thermal structure in a reservoir is crucial for accurately understanding the interaction between water and the carbon cycle. This study provides a detailed description of the thermal structure of Dalongdong reservoir, a representative subtropical stratified reservoir in Guangxi, China. It unveils the three stable state changes in water – mixing, microstratification, and stratification – along with their influencing factors. The study emphasizes air temperature (AT) as the primary factor influencing water temperature (WT) stability, followed by water level. Cross-correlation analysis shows that adjacent WTs experience a prolonged response hysteresis during the stratification period, while the mixing period exhibits a shorter hysteresis. Wavelet analysis revealed that AT and surface-layer WT exhibited a dominant oscillation period of 16–32 h. The periodicity of other water layers is not obvious. This framework can help to enhance our understanding of material cycles and protection of the water environment.

Evaluation of deep learning for predicting rice traits using structural and single-nucleotide genomic variants

BackgroundStructural genomic variants (SVs) are prevalent in plant genomes and have played an important role in evolution and domestication, as they constitute a significant source of genomic and phenotypic variability. Nevertheless, most methods in quantitative genetics focusing on crop improvement, such as genomic prediction, consider only Single Nucleotide Polymorphisms (SNPs). Deep Learning (DL) is a promising strategy for genomic prediction, but its performance using SVs and SNPs as genetic markers remains unknown.ResultsWe used rice to investigate whether combining SVs and SNPs can result in better trait prediction over SNPs alone and examine the potential advantage of Deep Learning (DL) networks over Bayesian Linear models. Specifically, the performances of BayesC (considering additive effects) and a Bayesian Reproducible Kernel Hilbert space (RKHS) regression (considering both additive and non-additive effects) were compared to those of two different DL architectures, the Multilayer Perceptron, and the Convolution Neural Network, to explore their prediction ability by using various marker input strategies. We found that exploiting structural and nucleotide variation slightly improved prediction ability on complex traits in 87% of the cases. DL models outperformed Bayesian models in 75% of the studied cases, considering the four traits and the two validation strategies used. Finally, DL systematically improved prediction ability of binary traits against the Bayesian models.ConclusionsOur study reveals that the use of structural genomic variants can improve trait prediction in rice, independently of the methodology used. Also, our results suggest that Deep Learning (DL) networks can perform better than Bayesian models in the prediction of binary traits, and in quantitative traits when the training and target sets are not closely related. This highlights the potential of DL to enhance crop improvement in specific scenarios and the importance to consider SVs in addition to SNPs in genomic selection.

Human rod photoreceptor outer segments are supported by accessory inner segment structures.

The first steps in vision take place in photoreceptor cells, which are highly compartmentalized neurons exhibiting significant structural variation across species. The light-sensitive ciliary compartment, called the outer segment, is located atop of the cell soma, called the inner segment. In this study, we present an ultrastructural analysis of human photoreceptors, which reveals that, in contrast to this classic arrangement, the inner segment of human rods extends alongside the outer segment to form a structure hereby termed the "accessory inner segment". While reminiscent of the actin-based microvilli known as "calyceal processes" observed in other species, the accessory inner segment is a unique structure: (1) it contains an extensive microtubule-based cytoskeleton, (2) it extends far alongside the outer segment, (3) its diameter is comparable to that of the outer segment, (4) it contains numerous mitochondria, and (5) it forms electron-dense structures that likely mediate adhesion to the outer segment. Given that the spacing of extrafoveal human photoreceptors is more sparse than in non-primate species, with vast amounts of interphotoreceptor matrix present between cells, the closely apposed accessory inner segment likely provides structural support to the outer segment. This discovery expands our understanding of the human retina and directs future studies of human photoreceptor function in health and disease.

The molecular architecture of the nuclear basket

The nuclear pore complex (NPC) is the sole mediator of nucleocytoplasmic transport. Despite great advances in understanding its conserved core architecture, the peripheral regions can exhibit considerable variation within and between species. One such structure is the cage-like nuclear basket. Despite its crucial roles in mRNA surveillance and chromatin organization, an architectural understanding has remained elusive. Using in-cell cryo-electron tomography and subtomogram analysis, we explored the NPC's structural variations and the nuclear basket across fungi (yeast; S.cerevisiae), mammals (mouse; M.musculus), and protozoa (T.gondii). Using integrative structural modeling, we computed a model of the basket in yeast and mammals that revealed how a hub of nucleoporins (Nups) in the nuclear ring binds to basket-forming Mlp/Tpr proteins: the coiled-coil domains of Mlp/Tpr form the struts of the basket, while their unstructured termini constitute the basket distal densities, which potentially serve as a docking site for mRNA preprocessing before nucleocytoplasmic transport.

Microstructural Analysis of Lithium-Ion Battery Cathode Carbon-Binder Domain across Processing Conditions

Improvement of lithium-ion battery (LIB) energy density and rate capability is necessary to increase adoption of electric vehicles (EVs) and curb transportation emissions. The cathode structure must support facile ion and electron transport, enabling rapid charging while maintaining near-theoretical capacity. Much investigation focuses on active material design for fast-charging cathodes, [1] but less is understood about the effect of electrode processing on carbon-binder domain (CBD) structure and its impact on rate capability. As conductive carbon facilitates electron transfer, slow Li+ diffusion through cathode micropores is rate-limiting and prevents full active material utilization at high charge rates. [2,3] However, efforts to improve Li+ diffusion, such as increasing porosity, introduce charge-transfer limitations. [4] Thus, cathode processing conditions and resulting microstructure must be carefully designed to optimize for both electronic and ionic conductivity. For example, an increase in coating shear rate and decrease in slurry solid content improves long-range electronic pathways [5,6] and calendaring strengthens active material-carbon contacts to reduce charge-transfer resistance. [2] However, the impact of processing variations on ionic transport pathways, which can be described by CBD porosity and tortuosity, is not well understood. Further, because the CBD is traditionally characterized as a single phase, the distinct influences of insulating binder and conductive carbon distributions on structure and performance are poorly defined.In this talk, I will discuss how electrode coating conditions impact pathways available to Li+ and electrons within the CBD with the goal of identifying optimal microstructures for efficient transport. We fabricate cathodes with varying solid content, coating shear rate, and degree of calendaring to explore disparate CBD microstructures. We use electrochemical impedance spectroscopy (EIS) with a symmetric cell & blocking electrolyte to determine cathode resistivity and tortuosity and plasma focused ion beam scanning electron microscopy (PFIB-SEM) to examine CBD morphology over a representative cathode volume (~80 x 80 x 50 µm3). We then use small-angle neutron scattering (SANS) with solvent contrast variation to distinguish between binder and carbon distributions and determine the surface area of each component within the CBD. Relationships between processing conditions, structural variations, and rate capability will be discussed, informing the design of the CBD for facile transport and improved fast charging performance. Further, reducing ionic transport limitations through intentional design of LIB cathodes will enable thicker electrodes to increase cell energy density.

Algorithm for estimating cultivar-specific parameters in crop models for newer crop cultivars

Model parameters in mechanistic process-based crop models are broadly classified into general parameters (environmental, management, and parameters related to soil processes), species-specific parameters, and cultivar-dependent parameters. While general and species-specific parameters typically remain unchanged, identifying cultivar-dependent parameters is crucial during calibration. With the continuous development of new cultivars, updating cultivar-specific parameters in crop models is necessary. However, gathering data on the behavior of these newer cultivars and calibrating the model to accommodate them is challenging. Currently, there is no standard approach for determining cultivar-specific parameters in crop models due to the difficulty of identifying variations in the growth and development process in newer cultivars, as well as the variations in the model structure, correlations between sub-model components and between different parameters in the crop models. The present study develops a standard methodology for determining cultivar-dependent parameters based on experiments and incorporating them into process-based crop growth models. The developed methodology is demonstrated using the cotton crop and the GOSSYM, a mechanistic process-based cotton crop simulation model. Experiments are conducted on 40 major cotton cultivars grown in the USA to quantify their growth and development under the same environmental and management conditions. Cultivar-dependent parameters are derived and integrated into the GOSSYM model based on the experimental data. By establishing a standardized methodology and demonstrating its application in the context of cotton and the GOSSYM model, this study provides practical guidance for incorporating cultivar-dependent parameters into crop simulation models. Researchers and agronomists can effectively utilize this methodology to integrate new cultivars into their crop models.

(Invited) Wearable Pocket-Sized Fully Non-Contact Cardiorespiratory Sensor

Cardiovascular and respiratory diseases have emerged as the leading cause of global mortality over the past decade, encompassing conditions such as atrial fibrillation and chronic obstructive pulmonary disease. Consequently, continuous monitoring plays a crucial role in managing these conditions, as it enables early detection, improves patient survival rates, and alleviates the burden on emergency medical resources. Our research group has developed a wearable pocket-sized fully non-contact cardiorespiratory sensor that utilizes electromagnetic induction technology to measure dynamic changes in electrical conductivity resulting from cardiac contractions and pulmonary volume variations. This enables the continuous acquisition of eddy current damping response signals to achieve cardiorespiratory monitoring. In comparison to commercially available ECGs, PPG smartwatches, and respiratory sensors, our proposed pocket-sized sensor offers three major advantages: exceptional concealment, convenience, and comfort for health monitoring due to its pocket-sized and micro-structured design; simultaneous acquisition of two-dimensional cardiac and respiratory signals, facilitating comprehensive cardiorespiratory health monitoring; and the ability to adjust sensing depth through frequency modulation of the electromagnetic field, thus eliminating variations in physiological structures among subjects and achieving optimized sensing and personalized medical benefits.

First-Principle Insight of Synergistic Modification Mechanism in P2-Type Layered Oxide Cathode Material with Anionic Redox Activity for Sodium-Ion Batteries

Layered oxides exhibiting anionic redox activity have gained attention as a promising material in both Na-ion batteries owing to their additional capacity provided through oxygen redox chemistry. However, unfavorable side effects such as irreversible phase transformation, hysteresis, and gas evolution, usually arise during the stabilization process of the unstable electron holes generated by the redox reaction of oxygen. To address these challenges, we co-dope Cu and Ca in the pristine Nax[Mg0.28Mn0.72]O2 (NMMO) and propose the P2-type NaxCa0.03Mg0.22Cu0.11Mn0.67O2 (NCaMCMO) Na-deficient cathode material which demonstrates high specific capacity with minimal phase change. In this work, we employed the density functional theory (DFT) calculations to investigate the underlying mechanisms of the synergistic modification strategy in both transition metal and alkali metal layers. The substitution of electrochemically active Cu induced variations in the electronic structure near the Fermi level, indicating an inherent change in redox chemistry. The computational results of the local structure provide a deeper understanding of the oxygen-stabilizing effect of Cu. Furthermore, we analyze the nature of the Ca insertion as a pillar in an alkali-metal layer, which mitigates phase transformation to the kinetically unfavorable O2 phase through phase energy comparison. The calculations manifest that Ca doping effectively extends the P2 phase into a deeper charging state. This work elucidates fundamental mechanisms for enhancing cathode materials in future battery design.

Hybrid Molecular Layer Deposition of Molybdenum-Aminates as Hydrogen Evolution Reaction Electrocatalysts

The development of high-performance hydrogen evolution reaction (HER) electrocatalysts from noble metal-free materials is critical to achieving cost-effective water splitting and ultimately realizing affordable hydrogen based on renewable energy. Compounds based on earth-abundant transition metals have been the subject of great research interest for this purpose, with metal nitrides gaining recent attention due to their high conductivity and corrosion resistance. Molybdenum nitride-based electrocatalysts have shown particular promise, with many of the best reported activities occurring in composite systems wherein crystalline MoNx domains are embedded in an amorphous N-doped carbon matrix. The present work demonstrates the formation of such uniquely active morphologies in films deposited by organic-inorganic hybrid molecular layer deposition (MLD).Hybrid MLD is a hybridization of other well-established layer-by-layer deposition techniques – namely atomic layer deposition (used to deposit inorganic films) and purely organic MLD. This family of techniques involves the cyclical exposure of a growth surface to alternating vapor-phase chemical precursors which react in a self-limiting fashion to saturate available surface sites, offering atomic- and molecular-level control over the resulting films. This presentation will introduce the successful development of hybrid MLD deposition procedures for new Mo-aminate materials using Mo(CO)6 as a Mo precursor and various amine precursors, including ethylenediamine, p-phenylenediamine, and tris(2-aminoethyl)amine.We will describe how the molecular level of control offered by this technique enables tunable variations in film composition and structure, lending unique insights into how the local bonding environment of Mo active sites affects their catalytic properties towards HER. Increased nitrogen loading in the MLD films was shown to decrease the overpotentials required to drive HER in acidic media (0.5 M H2SO4) and improve film stability. Catalysts with N/Mo ratios above 2 demonstrated the best performance, with overpotentials lower than 400 mV at -10 mA/cm2. By incorporating organic components in the MLD films, it was possible to induce desirable morphological changes in the catalytic material – notably including the development of increased active site density – simply through initial exposure to HER testing conditions. The choice of organic precursor affected the rate and extent of these changes through structural effects like crosslinking and crystallinity, which led to more stable catalysts, slowed the rate of morphological changes, and impacted final film structure.

Strategies and Perspectives on Enhancing Energy Density in Lithium-Ion Batteries

The rapidly growing demand for clean energy transportation and storage systems emphasizes the need for the development of large-scale lithium-ion batteries. Achieving this goal requires a comprehensive approach that considers not only technical aspects but also social, environmental, and economic perspectives. Specifically, the next-generation batteries should exhibit high energy and power densities, long-term cycle stability, and the efficient utilization of low-cost, abundant raw materials. The cathode material is the key determinant of these requirements. This presentation will introduce progress and strategies for enhancing cathode materials and their corresponding cell performances. These strategies include goals to enable reversible redox reaction processes through elemental and compositional choices, as well as crystal structural variations ranging from order to disorder. Furthermore, we combine these with advanced characterization techniques to understand the correlation between material properties and electrochemical behavior at multiple length and depth scales. The discussion also delves into a deeper understanding of the fundamental mechanisms of performance, coupled with advancements in new materials. Overall, the presentation aims to provide valuable insights into the strategy for developing the next generation of lithium-ion batteries.

Grating Bio-Microelectromechanical Platform Architecture for Multiple Biomarker Detection.

A label-free biosensor based on a tunable MEMS metamaterial structure is proposed in this paper. The adopted structure is a one-dimensional array of metamaterial gratings with movable and fixed fingers. The moving unit of the optical detection system is a component of the MEMS structure, driven by the surface stress effect. Thus, these suspended optical nanoribbons can be moved and change the grating pattern by the biological bonds that happened on the modified cantilever surface. Such structural variations lead to significant changes in the optical response of the metamaterial system under illuminating angled light and subsequently shift its resonance wavelength spectrum. As a result, the proposed biosensor shows appropriate analytical characteristics, including the mechanical sensitivity of Sm = 11.55 μm/Nm-1, the optical sensitivity of So = Δλ/Δd = 0.7 translated to So = Δλ/Δσ = 8.08 μm/Nm-1, and the quality factor of Q = 102.7. Also, considering the importance of multi-biomarker detection, a specific design of the proposed topology has been introduced as an array for identifying different biomolecules. Based on the conducted modeling and analyses, the presented device poses the capability of detecting multiple biomarkers of disease at very low concentrations with proper precision in fluidic environments, offering a suitable bio-platform for lab-on-chip structures.

Exploring unsolved cases of lissencephaly spectrum: integrating exome and genome sequencing for higher diagnostic yield.

Lissencephaly is a rare brain malformation characterized by abnormal neuronal migration during cortical development. In this study, we performed a comprehensive genetic analysis using next-generation sequencing in 12 unsolved Japanese lissencephaly patients, in whom PAFAH1B1, DCX, TUBA1A, and ARX variants were excluded using the Sanger method. Exome sequencing (ES) was conducted on these 12 patients, identifying pathogenic variants in CEP85L, DYNC1H1, LAMC3, and DCX in four patients. Next, we performed genome sequencing (GS) on eight unsolved patients, and structural variants in PAFAH1B1, including an inversion and microdeletions involving several exons, were detected in three patients. Notably, these microdeletions in PAFAH1B1 could not to be detected by copy number variation (CNV) detection tools based on the depth of coverage methods using ES data. The density of repeat sequences, including Alu sequences or segmental duplications, which increase the susceptibility to structural variations, is very high in some lissencephaly spectrum genes (PAFAH1B1, TUBA1A, DYNC1H1). These missing CNVs were due to the limitations of detecting repeat sequences in ES-based CNV detection tools. Our study suggests that a combined approach integrating ES with GS can contribute to a higher diagnostic yield and a better understanding of the genetic landscape of the lissencephaly spectrum.

(Invited) Bioinspired Molecular Electrets: “Misbehavior” of Dipoles, Solvation, and Interfaces

Charge transfer (CT) and charge transport are of fundamental importance for sustaining life on Earth and for making our modern ways of living possible (Phys. Chem. Chem. Phys. 2020, 22, 21583-21629). Concurrently, the ubiquitous nature of electric dipoles warrants deep understanding of how they affect CT. Possessing order electric dipoles, electrets are the electrostatic analogues of magnets, and they present outstanding paradigms for exploring dipole effects on CT (Can. J. Chem. 2018, 96, 843-858). Polypeptide α-helices are some of the best-known molecular electrets. These natural structures, however, do not efficiently mediate CT at distances exceeding 2 nm, which limits their utility. Therefore, we undertake the task for developing bioinspired molecular electrets based on synthetic amino acids that provide sites for charge-hopping and allow CT beyond the 2-nm limit. Even a single electret amino-acid residue can rectify CT (Angew. Chem. Int. Ed. 2018, 57, 12365-12369; J. Am. Chem. Soc. 2014, 136, 12966-12973). The molecular macrodipoles and the CT properties of these bioinspired molecular electrets strongly depend on their structure and conformational flexibility. Sinergy between molecular-dynamics (MD) simulations and quantum-mechanical (QM) calculations provides insights into the structural properties of these macromolecules. The structures of the molecular electrets do not fluctuate much. Nevertheless, the macrodipoles of these conjugates show picosecond fluctuations with amplitudes amounting to a factor of two or three from the average values. The structural variations of the electret oligomers cannot account for the dipole fluctuations. The analysis, however, reveals that the solvent dynamics, inducing fast-oscillating Onsager reaction fields in the solvated cavities, is principally responsible for the huge dipole fluctuations. The impact of this discovery extends way beyond the realm of electrets and our explorations show that solvent-induced picosecond dipole fluctuations are a norm, rather than an exception, for solvated species and interfaces. Averaging these dipole fluctuations over 10-ps or 20-ps intervals drastically decreases their amplitudes. It indicates that they would have a minimum effect on nanosecond and sub-nanosecond CT. Nevertheless, these dipole fluctuations may have decisive effects on picosecond and sub-picosecond processes, such as ultrafast CT. These revelations about the dipole dynamics present unexplored paradigms for condensed-phase interfaces and electronic materials.