Multiple Patterns Research Articles

Analysis of Crude, Diverse, and Multiple Advanced Glycation End-Product Patterns May Be Important and Beneficial.

Lifestyle-related diseases (LSRDs), such as diabetes mellitus, cardiovascular disease, and nonalcoholic steatohepatitis, are a global crisis. Advanced glycation end-products (AGEs) have been extensively researched because they trigger or promote LSRDs. Recently, techniques such as fluorimetry, immunostaining, Western blotting, slot blotting, enzyme-linked immunosorbent assay, gas chromatography-mass spectrometry, matrix-assisted laser desorption-mass spectrometry (MALDI-MS), and electrospray ionization-mass spectrometry (ESI-MS) have helped prove the existence of intra/extracellular AGEs and revealed novel AGE structures and their modifications against peptide sequences. Therefore, we propose modifications to the existing categorization of AGEs, which was based on the original compounds identified by researchers in the 20th century. In this investigation, we introduce the (i) crude, (ii) diverse, and (iii) multiple AGE patterns. The crude AGE pattern is based on the fact that one type of saccharide or its metabolites or derivatives can generate various AGEs. Diverse and multiple AGE patterns were introduced based on the possibility of combining various AGE structures and proteins and were proven through mass analysis technologies such as MALDI-MS and ESI-MS. Kampo medicines are typically used to treat LSRDs. Because various compounds are contained in Kampo medicines and metabolized to exert effects on various organs or tissues, they may be suitable against various AGEs.

Multiple patterning via layout decomposition method for directed self-assembly lithography

Multiple patterning via layout decomposition method for directed self-assembly lithography

Scalable layout decomposition implemented by a distribution evolutionary algorithm

As the feature size of semiconductor technology shrinks to 7 nm and beyond, the multiple patterning lithography (MPL) attracts more attention from the industry. In this paper, we model the layout decomposition of MPL as a generalized graph coloring problem, which is addressed by a distribution evolutionary algorithm based on a population of probabilistic model (DEA-PPM). DEA-PPM can strike a balance between decomposition quality and running time, being scalable for varied settings of mask number and lithography resolution. Thanks to the robustness of the DEA-PPM, we get an alternative technique for multiple patterning layout decomposition in next-generation technology nodes.

Degradation of pharmaceuticals in UV/H2O2 process: EEM-based kinetic model, transformation products and corresponding toxicity evaluation

Advanced oxidation process (AOP) by UV/H2O2 presented excellent performance on removing trace pharmaceuticals in wastewater. Monitoring the removal efficiency of pharmaceuticals rapidly can evaluate the effectiveness of UV/H2O2 process. And evaluating the potential risks of this technology is essential to facilitate its engineering application. In this study, diclofenac, ibuprofen, naproxen, carbamazepine and citalopram, which have potential biological effect to ecological environment, were degraded by UV/H2O2 in wastewater. Results suggested that UV/H2O2 could degrade 5 pharmaceuticals effectively. An EEM-based degradation kinetic model was constructed by combining pseudo first-order kinetic patterns of pharmaceuticals degradation and multiple exponential decay pattern of fluorescence degradation. This model could estimate the removal of pharmaceuticals rapidly and exhibited excellent predictive capability (R2 values > 0.90). Based on suspect and untargeted screening, 40 transformation products (TPs) were identified, with hydroxylation, (de)methylation and decarboxylation being the dominant pathways. Among them, 20 TPs were newly discovered. Furthermore, the predicted toxicity results from Ecological Structure-Activity Relationship (ECOSAR) model suggested that 37.5%− 42.5% of TPs exhibited higher toxicity than parent compounds. The increased toxicity of TPs of DCF and IBP could be attributed to decarboxylation. This study provides a promising and rapid method for monitoring the degradation of pharmaceuticals and estimating the toxicity of TPs. These findings facilitate further evaluation of the practicality and risks associated with the UV/H2O2 process for pharmaceutical removal in wastewater.

Robust and fast stochastic 4D Flow Vector-field signature technique for quantifying composite flow dynamics from 4D Flow MRI: Application to left atrial flow in atrial fibrillation

4D flow MRI is an emerging imaging modality that maps voxel-wise blood flow information as velocity vector fields that is acquired in 7-dimensional image volumes (3 spatial dimensions + 3 velocity directions + time). Blood flow in the cardiovascular system is often complex and composite involving multiple flow dynamics and patterns (e.g., vortex flow, jets, stagnating flow) that occur and interact simultaneously. The spectrum of such complex flow dynamics is embedded in the velocity vector field dynamics derived from 4D Flow MRI. However, current flow metrics cannot fully measure high-dimensional vector-field data and embedded complex composite flow data. Instead, these methods need to break down the vector-field data into secondary scalar fields of individual flow components using fluid dynamics operators. These methods are gradient-based and sensitive to data uncertainties, and only focus on individual flow components of the overall composite flow, therefore potentially underestimating the severity of overall flow changes associated with cardiovascular diseases. To address these limitations, in MICCAI 2021, we introduced a novel comprehensive stochastic 4D Flow vector-field signature technique that works directly on the entire spatiotemporal velocity vector field. This technique uses efficient stochastic gradient-free interrogation of multi-million flow vector-pairs per patient to derive the patient's unique flow profile of the complex composite flow alterations and in real-time processing. The signature technique's probabilistic gradient-free formulation should allow for highly robust quantification despite inherent errors in 4D flow MRI acquisitions. Here, we extend the application of the 4D flow vector-field signature technique to the left atrium to analyze complex composite flow changes in patients with atrial fibrillation. In 128 subjects, we performed extensive sensitivity testing and determined that the vector-field signature technique is highly robust to typical sources of data uncertainties in 4D flow MRI: degradation in spatiotemporal resolution, added gaussian noise, and segmentation errors. We demonstrate the excellent generalizability of the stochastic convergence from the aorta to the left atrium and between different 4D Flow MRI acquisition protocols. We compare the robustness of our technique to existing advanced flow quantification metrics of kinetic energy, vorticity, and energy loss demonstrating a superior performance of up-to 14-fold. Our results show the potential diagnostic and clinical utility of our signature technique in identifying distinctly altered composite flow signatures in atrial fibrillation patients independent of existing flow metrics.

Multiple HPV integration mode in the cell lines based on long-reads sequencing.

The integration of human papillomavirus (HPV) is closely related to the occurrence of cervical cancer. However, little is known about the complete state of HPV integration into the host genome. In this study, three HPV-positive cell lines, HeLa, SiHa, and CaSki, were subjected to NANOPORE long-read sequencing to detect HPV integration. Analysis of viral integration patterns using independently developed software (HPV-TSD) yielded multiple complete integration patterns for the three HPV cell lines. We found distinct differences between the integration patterns of HPV18 and HPV16. Furthermore, the integration characteristics of the viruses were significantly different, even though they all belonged to HPV16 integration. The HPV integration in the CaSki cells was relatively complex. The HPV18 integration status in HeLa cells was the dominant, whereas the percentage of integrated HPV 16 in SiHa and CaSki cells was significantly lower. In addition, the virus sequences in the HeLa cells were incomplete and existed in an integrated state. We also identified a large number of tandem repeats in HPV16 and HPV18 integration. Our study not only clarified the feasibility of high-throughput long-read sequencing in the study of HPV integration, but also explored a variety of HPV integration models, and confirmed that viral integration is an important form of HPV in cell lines. Elucidating HPV integration patterns will provide critical guidance for developing a detection algorithm for HPV integration, as well as the application of virus integration in clinical practice and drug research and development.

Self-Expansion Based Multi-Patterning for 2D Materials Fabrication beyond the Lithographical Limit.

Two-dimensional (2D) materials are promising successors for silicon transistor channels in ultimately scaled devices, necessitating significant research efforts to study their behavior at nanoscopic length scales. Unfortunately, current research has limited itself to direct patterning approaches, which limit the achievable resolution to the diffraction limit and introduce unwanted defects into the 2D material. The potential of multi-patterning to fabricate 2D materials features with unprecedented precision and low complexity at large scale is demonstrated here. By combining lithographic patterning of a mandrel and bottom-up self-expansion, this approach enables pattern resolution one order of magnitude below the lithographical resolution. In-depth characterization of the self-expansion double patterning (SEDP) process reveals the ability to manipulate the critical dimension with nanometer precision through a self-limiting and temperature-controlled oxidation process. These results indicate that the SEDP process can regain the quality and morphology of the 2D material, as shown by high-resolution microscopy and optical spectroscopy. This approach is shown to open up new avenues for research into high-performance, ultra-scaled 2D materials devices for future electronics.

Towards an accurate autism spectrum disorder diagnosis: multiple connectome views from fMRI data.

Functional connectome has revealed remarkable potential in the diagnosis of neurological disorders, e.g. autism spectrum disorder. However, existing studies have primarily focused on a single connectivity pattern, such as full correlation, partial correlation, or causality. Such an approach fails in discovering the potential complementary topology information of FCNs at different connection patterns, resulting in lower diagnostic performance. Consequently, toward an accurate autism spectrum disorder diagnosis, a straightforward ambition is to combine the multiple connectivity patterns for the diagnosis of neurological disorders. To this end, we conduct functional magnetic resonance imaging data to construct multiple brain networks with different connectivity patterns and employ kernel combination techniques to fuse information from different brain connectivity patterns for autism diagnosis. To verify the effectiveness of our approach, we assess the performance of the proposed method on the Autism Brain Imaging Data Exchange dataset for diagnosing autism spectrum disorder. The experimental findings demonstrate that our method achieves precise autism spectrum disorder diagnosis with exceptional accuracy (91.30%), sensitivity (91.48%), and specificity (91.11%).

Spatial game with multiple interaction patterns in constrained interaction environment: A computational method based on opponent’s ability

In previous studies of spatial game, it is usually assumed that all individuals in a population play the game in the same interaction pattern. However, in real life, a variety of interaction patterns often emerge spontaneously, especially in a constrained interaction environment. Furthermore, some scenarios reflect the fact that individuals are generally more inclined to choose the capable partners for interactions. For these reasons, we propose a computational method on evolutionary game with multiple interaction patterns based on opponent’s ability and investigate the cooperative dynamics of spatial prisoner’s dilemma. For simplicity, in this work, we just consider the case of two interaction patterns, which are conditional interaction and stochastic interaction. For the individual who adopt the conditional interaction pattern, if the opponent is strong in ability, it will be perfectly willing to interact with opponent, while if the opponent is weak in ability, it will not do that. For the individual who adopt the stochastic interaction pattern, the willingness of interaction with other party varies with the opponent’s abilities, the more powerful the opponent, the stronger the willingness of interaction. Notably, for paired two individuals, regardless of the combined configuration of interaction patterns, the final interaction intensity between them depends on the result of information fusion of their unilateral interaction willingness. Experimental results show that the mechanism with multiple interaction patterns can effectively propagate cooperative behavior in networked population, and the moderate estimation level can lead to the flourish of cooperation. Besides, we find that the more the straightforward individuals (who adopt the conditional interaction pattern) in the population, the more prosperous the propagation of cooperative behavior, while the more the euphemistic individuals (who adopt the stochastic interaction pattern), the easier the emergence of selfish behavior. Considering such a real environment with multiple interaction patterns can provide a brand new perspective for further study on the dynamics of evolutionary game in structured population.

In situ nanofibers patterned deposition based on electrostatic attraction–repulsion induction

The in situ nanofibers patterned deposition technique, enabling polymer nanofibers to be deposited into pre-designed patterns with high accuracy, have an extensive application prospects in many fields. Currently, the solutions of realizing the highly accurate patterning deposition all have shortcomings, such as simple pattern structure, inaccurate nanofibers deposition, fewer available material types, complex and expensive manufacturing processes, chemical pollution, etc. Here, in order to enable polymer nanofibers to be deposited into pre-designed patterns with comprehensive advantages including highly accurate deposition of multiple pattern structures, more available materials, economically efficient and environmentally friendly, an in-situ nanofibers patterned deposition technique(in-situ NFs-PDT) is proposed, based on the induction of attractive and repulsive forces of static electricity. This in-situ NFs-PDT is achieved by improving the collection terminal of electrospinning, which includes a pre-designed target pattern electrode, an insulating layer and a repulsive positive bottom electrode. By controlling the repulsion voltage and thickness of target pattern electrode, the amount of nanofibers deposited in the unpatterned template region can reach 0. In addition, in order to validate and demonstrate high-accuracy in-situ NFs-PDT, a sensor array that can sense the tensile deformation of the epidermal layer of human skin is successfully designed and fabricated.

Multi-exposure diffraction pattern fusion applied to enable wider-angle transmission Kikuchi diffraction with direct electron detectors

Diffraction pattern analysis can be used to reveal the crystalline structure of materials, and this information is used to nano- and micro-structure of advanced engineering materials that enable modern life. For nano-structured materials typically diffraction pattern analysis is performed in the transmission electron microscope (TEM) and TEM diffraction patterns typically have a limited angular range (less than a few degrees) due to the long camera length, and this requires analysis of multiple patterns to probe a unit cell. As a different approach, wide angle Kikuchi patterns can be captured using an on-axis detector in the scanning electron microscope (SEM) with a shorter camera length. These ‘transmission Kikuchi diffraction’ (TKD) patterns present a direct projection of the unit cell and can be routinely analysed using EBSD-based methods and dynamical diffraction theory. In the present work, we enhance this analysis significantly and present a multi-exposure diffraction pattern fusion method that increases the dynamic range of the detected patterns captured with a Timepix3-based direct electron detector (DED). This method uses an easy-to-apply exposure fusion routine to collect data and extend the dynamic range, as well as normalise the intensity distribution within these very wide (>95°) angle patterns. The potential of this method is demonstrated with full diffraction sphere reprojection and highlight potential of the approach to rapidly probe the structure of nano-structured materials in the scanning electron microscope.

Identifying critical landscape patterns for simultaneous provision of multiple ecosystem services – A case study in the central district of Wuhu City, China

Landscape patterns have a global impact on ecosystem services (ESs). Managing land to enhance the provision of multiple ESs is vital for ecological resilience and sustainable development. Identifying critical landscape patterns that simultaneously influence multiple ESs provision based on understanding ES trade-offs is needed. Our study aims to develop an approach to find the relationship between multiple ESs provision and landscape patterns, identify the critical landscape patterns that simultaneously promote multiple ESs provision, and give suggestions on land management based on an understanding of the relationships of ESs. Taking the central district of Wuhu City as an example, production possibility frontier (PPF) and Pareto ranks are used to identify relationships between ESs and extract the landscape pattern characteristics of samples that effectively enhance multiple ESs. Our analysis of trade-offs between ESs highlights the complexity of ES relationships and emphasizes the necessity for multi-dimensional investigative approaches. Landscape metrics of Mean Perimeter Area Ratio (PARA_MN), Coefficient of Variation of Perimeter (PERI_CV), Area-weighted Mean Fractal Dimension (FRAC_AM), and Relative Mutual Information (RELMUTINF) emerged as critical determinants of multiple ESs supply. Our findings suggest enhancing patch shape complexity when conducting land use planning to avoid homogenization and keeping differences between the diversity of land use categories and the diversity of adjacencies small. Also, we recommend caution in using high contagion as the only criterion for land use planning, as its effects may vary on different ES targets.

Record-high Antarctic Peninsula temperatures and surface melt in February 2022: a compound event with an intense atmospheric river

The Antarctic Peninsula (AP) experienced a new extreme warm event and record-high surface melt in February 2022, rivaling the recent temperature records from 2015 and 2020, and contributing to the alarming series of extreme warm events over this region showing stronger warming compared to the rest of Antarctica. Here, the drivers and impacts of the event are analyzed in detail using a range of observational and modeling data. The northern/northwestern AP was directly impacted by an intense atmospheric river (AR) attaining category 3 on the AR scale, which brought anomalous heat and rainfall, while the AR-enhanced foehn effect further warmed its northeastern side. The event was triggered by multiple large-scale atmospheric circulation patterns linking the AR formation to tropical convection anomalies and stationary Rossby waves, with an anomalous Amundsen Sea Low and a record-breaking high-pressure system east of the AP. This multivariate and spatial compound event culminated in widespread and intense surface melt across the AP. Circulation analog analysis shows that global warming played a role in the amplification and increased probability of the event. Increasing frequency of such events can undermine the stability of the AP ice shelves, with multiple local to global impacts, including acceleration of the AP ice mass loss and changes in sensitive ecosystems.

Deep Learning-Based Metasurface Design for Smart Cooling of Spacecraft

A reconfigurable metasurface constitutes an important block of future adaptive and smart nanophotonic applications, such as adaptive cooling in spacecraft. In this paper, we introduce a new modeling approach for the fast design of tunable and reconfigurable metasurface structures using a convolutional deep learning network. The metasurface structure is modeled as a multilayer image tensor to model material properties as image maps. We avoid the dimensionality mismatch problem using the operating wavelength as an input to the network. As a case study, we model the response of a reconfigurable absorber that employs the phase transition of vanadium dioxide in the mid-infrared spectrum. The feed-forward model is used as a surrogate model and is subsequently employed within a pattern search optimization process to design a passive adaptive cooling surface leveraging the phase transition of vanadium dioxide. The results indicate that our model delivers an accurate prediction of the metasurface response using a relatively small training dataset. The proposed patterned vanadium dioxide metasurface achieved a 28% saving in coating thickness compared to the literature while maintaining reasonable emissivity contrast at 0.43. Moreover, our design approach was able to overcome the non-uniqueness problem by generating multiple patterns that satisfy the design objectives. The proposed adaptive metasurface can potentially serve as a core block for passive spacecraft cooling applications. We also believe that our design approach can be extended to cover a wider range of applications.

2D chemical evolution models

Context. According to observations and numerical simulations, the Milky Way could exhibit several spiral-arm modes of various pattern speeds, with the slower patterns located at larger galactocentric distances. Aims. Our aim is to quantify the effects of the spiral arms on the azimuthal variations in the chemical abundances of oxygen and iron and for the first time of neutron-capture elements (europium and barium) in the Galactic disc. We assume a model based on multiple spiral-arm modes with different pattern speeds. The resulting model is an updated version of previous 2D chemical evolution models. Methods. We apply new analytical prescriptions for the spiral arms in a 2D Galactic disc chemical evolution model, exploring the possibility that the spiral structure is formed by the overlap of chunks with different pattern speeds and spatial extent. Results. The predicted azimuthal variations in abundance gradients are dependent on the considered chemical element. Elements synthesised on short timescales (i.e. oxygen and europium in this study) exhibit larger abundance fluctuations. Moreover, for progenitors with short lifetimes, the chemical elements returned to the ISM perfectly trace the star formation perturbed by the passage of the spiral arms. The map of the star formation rate (SFR) predicted by our chemical evolution model with multiple patterns of spiral arms presents arcs and arms compatible with those revealed by multiple tracers (young upper-main sequence stars, Cepheids, and the distribution of stars with low radial actions). Finally, our model predictions are in good agreement with the azimuthal variations that emerged from the analysis of Gaia DR3 GSP-Spec [M/H] abundance ratios, if at most recent times the pattern speeds match the Galactic rotational curve at all radii. Conclusions. We provide an updated version of a 2D chemical evolution model capable of tracing the azimuthal density variations created by the presence of multiple spiral patterns. We show that elements synthesised on short timescales exhibit larger abundance fluctuations.

Investigating the influence of 3D building models on pedestrian wind comfort through wind computational fluid dynamics analysis

Wind flow plays a crucial role in determining pedestrian comfortability in the surrounding environment. However, the shape and detailing of buildings, as well as the angle of the wind, can manipulate the wind flow patterns and velocity. Different urbanized areas have different building shapes and detailing, which can lead to diverse wind flow patterns and velocities, affecting pedestrian wind comfort (PWC). This study utilized wind Computational Fluid Dynamics (CFD) technique to investigate the influence of building models on wind velocity and its impact on PWC. The CFD simulation integrated with a 3D urbanized building model provided wind velocity values throughout the study area, which were used to analyze wind comfort at 1.75m elevation based on Beaufort wind force criteria. The study found that diverse effects due to different building detailing were observed, leading to multiple flow patterns and changes in wind velocity. The downstream building recorded a lower velocity value than the upstream building mainly because of wind flow separation from the building frontal. Wind comfort levels were represented using the Beaufort wind force scale (BWS), and all values fell in level 2 and 3, indicating good wind comfort level because of the BWS ≤ 3. This study highlights the significance of building models in determining wind flow patterns and velocity, which ultimately impacts PWC. Despite the study area involving multiple buildings with different shapes and detailing, the wind comfort levels were found to be suitable for pedestrians.

An algorithm of unordered wavefront propagation in terahertz phase retrieval with dense multiplane data acquisition

Iterative phase retrieval algorithms from multiple diffraction patterns in the terahertz (THz) frequency range are a promising tool of computational imaging capable of providing high spatial resolution of reconstructed phase images. One of the commonly used algorithms is SBMIR, which employs multiple intensity distributions of the diffraction object wavefield as input data. Compared with single-frame methods, the multi-plane approach allows for a faster convergence, but requires time-consuming data acquisition from a receiver positioned at a variety of distances from the object. Previously, we proposed a method for THz data acquisition in a single scan mode, which allows one to quickly obtain an exhaustive set of diffraction distributions. In this paper we evaluate an up-to-date phase retrieval algorithm based on the SBMIR/R-SBMIR method (which utilizes stochastic wavefront propagation) on the experimental data captured by a single-scan technique. Unlike a number of conventional phase retrieval algorithms, which may require a series of numerical experiments for determining optimal intensity distributions from a large dataset, disordered propagation of the estimation wavefront guarantees the high-contrast and high-resolution image reconstruction without pre-setting the parameters. It is shown that the package use of the single-scan technique with the subsequent data processing using the R-SBMIR algorithm has application potential for automation of the multi-plane phase retrieval in the THz range.

Abstract A002: Alcohol use patterns and patient-provider discussions about alcohol use among cancer survivors: Differences by race and ethnicity in the National Survey of Drug Use and Health

Abstract Background: Alcohol is a leading modifiable risk factor for cancer risk and recurrence. The American Cancer Society recommends abstaining from alcohol for cancer prevention and advises that those who choose to drink should limit their intake. Healthcare providers are uniquely positioned to counsel cancer survivors about alcohol’s harms. Prior research has revealed stark differences in heavy drinking among cancer survivors by race and ethnicity. We build on this work by examining the prevalence of multiple alcohol use patterns, alcohol screening, and patient-provider discussions about alcohol by race and ethnicity among a cancer survivor population. Methods: We conducted a cross-sectional study using data from the National Survey on Drug Use and Health (NSDUH) 2015-2019. Eligible respondents included adults (18+ years) who reported a prior cancer diagnosis. Outcomes included alcohol use patterns (lifetime abstention, alcohol use and binge drinking in the past 30 days), alcohol screening in the past year, and report of an in-person alcohol discussion with a healthcare provider in the past year. We estimated the adjusted prevalence of these outcomes by race and ethnicity from marginal effects of logistic regression models that accounted for sociodemographic characteristics (i.e., age, gender, educational attainment, household income) and health insurance type. Models included cluster, strata, and weight variables to account for the complex survey design and nonresponse bias. Results: The unweighted analytic sample size included 6,240 cancer survivors – 431 identified as Black, Non-Hispanic; 461 identified as Hispanic, and 5,348 identified as White, Non-Hispanic. Though not statistically significant, adjusted binge drinking prevalence was highest among Black, Non-Hispanic survivors (17.3% [95% CI: 11.6 – 23.0]) followed by White survivors (13.4% [95% CI: 12.2 – 14.6]) and Hispanic survivors (13.1% [95% CI: 9.8 – 16.4]). Significant differences in adjusted binge drinking prevalence by race and ethnicity were found for binge drinking among current drinkers – Black (41.4% [95% CI: 30.7 – 52.1]), Hispanic (31.7% [95% CI: 22.6 – 40.8]), White (26.7% [95% CI: 24.6 – 28.8]). No significant differences were found for alcohol screening prevalence by race and ethnicity. Black (44.7% [955 CI: 36.4 – 53.0]) and Hispanic (48.8% [95% CI: 40.1 – 57.6%]) cancer survivors were less likely than White survivors (56.9% [95% CI: 54.9 – 59.0]) to report an in-person provider discussion about alcohol. Conclusions: There is a significant racial disparity in problematic drinking between Black, Hispanic, and White cancer survivors, with higher proportions of Black and Hispanic survivors reporting binge drinking than their White peers. Yet, Black and Hispanic cancer survivors are less likely to have an in-person discussion with a provider about alcohol use compared with White cancer survivors. Black and Hispanic cancer survivors may not be receiving important messages about alcohol’s harms from their providers, who could be important sources of this information. Citation Format: Naomi K. Greene, David Dean Jr. Alcohol use patterns and patient-provider discussions about alcohol use among cancer survivors: Differences by race and ethnicity in the National Survey of Drug Use and Health [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A002.

Source and drain formation in FinFETs

This paper talks about the background and motivation of fin field-effect transistors (FinFETs) to address the short-channel effect and improve the performance of transistors. Also, this paper focuses on the manufacturing formation of source and drain in FinFETs, which is Self-Aligned Double Patterning (SADP) and discusses the challenges faced in fabrication and design, including self-heating effect and body punch-through in FinFET devices. Furthermore, this paper mentions the prospects of vision and design technology. The insight gained from this paper can enhance understanding of some parts of the manufacturing process in FinFETs and help improve fabrication technology.

MRI deep learning demonstrates featured neuropathological deteriorations in the AD brain

AbstractBackgroundDeep learning (DL) of non‐invasive brain structural magnetic resonance imaging (sMRI) has shown superb performance in differentiating Alzheimer’s disease (AD) from cognitively healthy participants (HC). Attention is needed to tackle AD pathological progression on sMRI using interpretable DL methods. Here, we studied the feasibility of DL in identifying neurodegenerative progression patterns in AD.MethodData applied to the study were from the multi‐centre Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) with 2,369 T1‐weighted brain images of 1,005 subjects. An interpretable DL technique named the ensemble 3‐dimensional convolutional neural network (Ensemble 3DCNN) was used to detect the longitudinal trajectory of sMRI changes during AD progression. The model used multiple 3D convolutional neural networks and a meta‐classifier to assess the degree of brain changes through generating an index score, named P‐score. The score was derived as the outcome of training and verifying the Ensemble 3DCNN model for difference in the whole‐brain sMRI between AD and HC. The predictive P‐scores values were used to analyze the sMRI images for mining neurodegenerative brain regions. In addition, dementia stages, and the temporal and spatial connectivity patterns of neurodegeneration progression were also analyzed.ResultBrain regions showing high P‐scores (> 0.73/1.00) included the amygdala, nucleus accumbens, agranular insular cortex, and the hippocampus, matching the isocortex, basal magnocellular complex, and transentorhinal regions identified in Braak staging. The P‐score increased over time in 82% of the degenerative brain regions. The impaired areas were often spatially connected, consistently across multiple time points in the progression of AD. The trajectory of regional brain changes displayed multiple patterns with complex individual variability.ConclusionThe interpretable 3D DL model demonstrated featured deteriorations on the AD sMRI images. The finding confirmed the neuropathological degeneration of AD and captured additional whole‐brain changes, especially, relating to the heterogeneity of AD expression.