Specific Architecture Research Articles

The Effect of Processing Techniques on the Classification Accuracy of Brain-Computer Interface Systems

Background/Objectives: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess the effects of different processing steps on classification accuracy in EEG-based BCI systems. Methods: This study explores the impact of various processing techniques and stages, including the FASTER algorithm for artifact rejection (AR), frequency filtering, transfer learning, and cropped training. The Physionet dataset, consisting of four motor imagery classes, was used as input due to its relatively large number of subjects. The raw EEG was tested with EEGNet and Shallow ConvNet. To examine the impact of adding a spatial dimension to the input data, we also used the Multi-branch Conv3D Net and developed two new models, Conv2D Net and Conv3D Net. Results: Our analysis showed that classification accuracy can be affected by many factors at every stage. Applying the AR method, for instance, can either enhance or degrade classification performance, depending on the subject and the specific network architecture. Transfer learning was effective in improving the performance of all networks for both raw and artifact-rejected data. However, the improvement in classification accuracy for artifact-rejected data was less pronounced compared to unfiltered data, resulting in reduced precision. For instance, the best classifier achieved 46.1% accuracy on unfiltered data, which increased to 63.5% with transfer learning. In the filtered case, accuracy rose from 45.5% to only 55.9% when transfer learning was applied. An unexpected outcome regarding frequency filtering was observed: networks demonstrated better classification performance when focusing on lower-frequency components. Higher frequency ranges were more discriminative for EEGNet and Shallow ConvNet, but only when cropped training was applied. Conclusions: The findings of this study highlight the complex interaction between processing techniques and neural network performance, emphasizing the necessity for customized processing approaches tailored to specific subjects and network architectures.

Gene mobility elements mediate cell type specific genome organization and radial gene movement in vivo.

Understanding the level of genome organization that governs gene regulation remains a challenge despite advancements in chromatin profiling techniques. Cell type specific chromatin architectures may be obscured by averaging heterogeneous cell populations. Here we took a reductionist perspective, starting with the relocation of the hunchback gene to the nuclear lamina in Drosophila neuroblasts. We previously found that this event terminates competence to produce early-born neurons and is mediated by an intronic 250 base-pair element, which we term gene mobility element (GME). Here we found over 800 putative GMEs globally that are chromatin accessible and are Polycomb (PcG) target sites. GMEs appear to be distinct from PcG response elements, however, which are largely chromatin inaccessible in neuroblasts. Performing in situ Hi-C of purified neuroblasts, we found that GMEs form megabase-scale chromatin interactions, spanning multiple topologically associated domain borders, preferentially contacting other GMEs. These interactions are cell type and stage-specific. Notably, GMEs undergo developmentally- timed mobilization to/from the neuroblast nuclear lamina, and domain swapping a GFP reporter transgene intron with a GME relocates the transgene to the nuclear lamina in embryos. We propose that GMEs constitute a genome organizational framework and mediate gene-to-lamina mobilization during progenitor competence state transitions in vivo .

An implemented predictive coding model of lexico-semantic processing explains the dynamics of univariate and multivariate activity within the left ventromedial temporal lobe during reading comprehension.

During language comprehension, the larger neural response to unexpected versus expected inputs is often taken as evidence for predictive coding-a specific computational architecture and optimization algorithm proposed to approximate probabilistic inference in the brain. However, other predictive processing frameworks can also account for this effect, leaving the unique claims of predictive coding untested. In this study, we used MEG to examine both univariate and multivariate neural activity in response to expected and unexpected inputs during word-by-word reading comprehension. We further simulated this activity using an implemented predictive coding model that infers the meaning of words from their orthographic form. Consistent with previous findings, the univariate analysis showed that, between 300-500ms, unexpected words produced a larger evoked response than expected words within a left ventromedial temporal region that supports the mapping of orthographic word-forms onto lexical and conceptual representations. Our model explained this larger evoked response as the enhanced lexico-semantic prediction error produced when prior top-down predictions failed to suppress activity within lexical and semantic "error units". Critically, our simulations showed that, despite producing minimal prediction error, expected inputs nonetheless reinstated top-down predictions within the model's lexical and semantic "state" units. Two types of multivariate analyses provided evidence for this functional distinction between state and error units within the ventromedial temporal region. First, within each trial, individual voxels within this region produced unique temporal patterns between 300-500ms that that resembled the temporal patterns produced within a pre-activation time-window before the expected input became available. First, within each trial, the same left ventromedial voxels that produced a larger response between 300-500ms to unexpected inputs, reinstated unique pre-activated item-specific temporal patterns in response to expected inputs within this same time window. Second, across trials, and again within the same 300-500ms time window and left ventromedial temporal region, pairs of expected words produced spatial patterns that were more similar to one another than the spatial patterns produced by pairs of expected and unexpected words, regardless of specific item. Together, these findings provide compelling evidence that the left ventromedial temporal lobe employs predictive coding to infer the meaning of incoming words from their orthographic form during reading comprehension.

Bilinear Models of Parts and Appearances in Generative Adversarial Networks.

Recent advances in the understanding of Generative Adversarial Networks (GANs) have led to remarkable progress in visual editing and synthesis tasks, capitalizing on the rich semantics that are embedded in the latent spaces of pre-trained GANs. However, existing methods are often tailored to specific GAN architectures and are limited to either discovering global semantic directions that do not facilitate localized control, or require some form of supervision through manually provided regions or segmentation masks. In this light, we present an architecture-agnostic approach that jointly discovers factors representing spatial parts and their appearances in an entirely unsupervised fashion. These factors are obtained by applying a semi-nonnegative tensor factorization on the feature maps, which in turn enables context-aware local image editing with pixel-level control. In addition, we show that the discovered appearance factors correspond to saliency maps that localize concepts of interest, without using any labels. Experiments on a wide range of GAN architectures and datasets show that, in comparison to the state of the art, our method is far more efficient in terms of training time and, most importantly, provides much more accurate localized control.

Low‐frequency noise and deep‐level transient spectroscopy in LWIR Auger‐suppressed Hg1-xCdxTe heterostructure detector

Low-frequency noise spectroscopy (LFNS) along with deep-level transient spectroscopy (DLTS) are complementary and effective tools to study and characterize the carrier traps in semiconductors. These traps caused, e.g., by contamination by foreign atoms or various types of dislocations, can significantly affect quantum efficiency, dark current, responsivity, and noise generated by devices especially when operating under bias. Since DLTS is difficult to apply in high leakage current devices, LFNS can be used to overcome this limitation, so the use of both methods gives very effective and reliable results during research on various devices. In this paper, we reported a study of defects activation energies in HgCdTe Auger-suppressed long-wavelength infrared (LWIR) heterostructure-based detector using these two experimental methods. By proper structure design, the examined detector was optimized for high operating temperature (HOT) conditions ≥ 200 K. The results obtained showed that in such detectors, grown by the metal organic chemical vapor deposition (MOCVD) technique, a few traps can be extracted. The found trap levels and activation energies were located below and above the absorber bandgap, so they can be identified in both absorber and other heterostructure layers. Due to specific multilayer architecture, a precise interpretation of the results is difficult. Nevertheless, the most probable trap locations based on the current state of knowledge were discussed and proposed.

The Antecedents of Transformer Models

Transformer models of language represent a step change in our ability to account for cognitive phenomena. Although the specific architecture that has garnered recent interest is quite young, many of its components have antecedents in the cognitive science literature. In this article, we start by providing an introduction to large language models aimed at a general psychological audience. We then highlight some of the antecedents, including the importance of scale, instance-based memory models, paradigmatic association and systematicity, positional encodings of serial order, and the learning of control processes. This article offers an exploration of the relationship between transformer models and their precursors, showing how they can be understood as a next phase in our understanding of cognitive processes.

Facile Synthesis of Polypyrrole-Decorated RGO-CuS Nanocomposite for Efficient Nickel Removal from Wastewater.

Efficient wastewater treatment, particularly the removal of heavy metal ions, remains a challenging priority in environmental remediation. This study introduces a novel sandwich-structured nanocomposite, RGO-CuS-PPy, composed of reduced graphene oxide (RGO), copper sulfide (CuS), and polypyrrole (PPy), synthesized via a straightforward hydrothermal method. The unique combination of RGO, CuS, and PPy offers enhanced adsorption capacity for Ni(II) ions due to RGO's high surface area and CuS's active binding sites, supported by PPy's structural stability contributions. This study is among the first to explore this specific nanocomposite architecture for Ni(II) removal, achieving an adsorption capacity of 166.67 mg/g and a high removal efficiency of 94.9% within 210 min for 55 mg/L of Ni(II) concentration at pH 6 and adsorbent dose of 3 mg/15 mL. The kinetic analysis shows the best fitted time-dependent experimental data with the pseudo-second-order model, indicating chemisorption. Isotherm studies confirmed the Langmuir model as the best fit, yielding a high monolayer adsorption capacity of 166.67 mg/g. Thermodynamic analysis shows the adsorption process was endothermic (ΔH° = 80.23 kJ/mol) and spontaneous (ΔG° ranging from -6.985 to -14.399 kJ/mol). Additionally, reusability tests using 0.1 M HCl for desorption demonstrated good reusability, emphasizing the RGO-CuS-PPy nanocomposite's potential as a sustainable adsorbent for Ni(II) removal in wastewater treatment applications.

Distinguishing between children referred for assessment of excessive daytime sleepiness using polysomnographic measures

BackgroundCurrently, diagnosing narcolepsy and idiopathic hypersomnia (IH) in subjectively sleepy children requires an overnight polysomnographic sleep study followed by a daytime multiple sleep latency test (MSLT). We aimed to compare sleep macro-architecture to identify differences between these groups. MethodsAll children referred for a MSLT between May 2010 to December 2023 whose parent consented for their data be used in research were eligible. Each child was age- and sex-matched to a control. Sleep stability was defined as the maintenance of a particular sleep stage before waking or transitioning to another sleep state. As a measure of sleep disturbance, the number and duration of bouts of each sleep stage was recorded. Results28 children with Narcolepsy, 11 with IH and 26 with subjective sleepiness were included. Children with narcolepsy exhibited higher numbers of transitions to wake after sleep onset compared to their controls and to the subjectively sleepy group (p < 0.001 for both). The number of REM bouts was greater in the narcolepsy group compared to their control group (p < 0.001), the IH group (p < 0.05) and the subjectively sleepy group (p < 0.05), while the average duration of REM bouts was shorter in the narcolepsy group compared to both the IH group (p < 0.05) and subjectively sleepy group (p < 0.05). Mean sleep latency on the MSLT was correlated with a number of polysomnographic variables. ConclusionsOur study suggests that specific sleep architecture patterns could potentially serve as diagnostic biomarkers for distinguishing paediatric narcolepsy from those with IH and those with a non-diagnostic MSLT.

Approximating families of sharp solutions to Fisher's equation with physics-informed neural networks

This paper employs physics-informed neural networks (PINNs) to solve Fisher's equation, a fundamental reaction-diffusion system with both simplicity and significance. The focus is on investigating Fisher's equation under conditions of large reaction rate coefficients, where solutions exhibit steep traveling waves that often present challenges for traditional numerical methods. To address these challenges, a residual weighting scheme is introduced in the network training to mitigate the difficulties associated with standard PINN approaches. Additionally, a specialized network architecture designed to capture traveling wave solutions is explored. The paper also assesses the ability of PINNs to approximate a family of solutions by generalizing across multiple reaction rate coefficients. The proposed method demonstrates high effectiveness in solving Fisher's equation with large reaction rate coefficients and shows promise for meshfree solutions of generalized reaction-diffusion systems.

Nanoarchitecture via Synchronic Stacking of Metallic and Nonmetallic Two-Dimensional Nanosheets for Optimal Light-Driven Ion Transport.

The exceptional selectivity and responsive ion transport in biological channels inspire technology breakthrough in energy, environmental, and resource sectors. However, existing nanofluidic systems with a high photothermal conversion efficiency often exhibit excessive thermal conductivity, which impedes the creation of effective temperature gradients and results in a low ion transport efficiency. In this study, a strategy based on the synchronic stacking of metallic and nonmetallic two-dimensional (2D) nanosheets was presented to construct heterogeneous nanofluidic channels. This specific nanoconfined architecture sustained high temperatures in the illuminated area while maintaining low temperatures in the nonilluminated area, thus obtaining a robust driving force from sunlight for directional ion transport. As a result, our light-responsive ion transport system demonstrated significant potential in solar energy conversion and osmotic energy harvesting, surpassing those of all previously reported nanofluidic systems. Additionally, although it is still at the proof-of-concept stage, it shows great promise in light signal monitoring. This work provides an effective strategy for developing advanced light-responsive ion transport systems and their important applications.

Misalignment analysis of WPT level 3/Z2-class of CirPT with DDPR and CirPR for EVs stationary charging.

Future inductive charging ports must possess the capability to charge any electric vehicle (EV), irrespective of the specific coil architecture it is equipped with. This study examines the misalignment scenarios of the global circular pad at transmitter side (CirPT) with circular receiver pad (CirPR) and a double-D receiver pad (DDPR). The CirPT, CirPR, and DDPR configurations for WPT3 (11.1kW) with ground clearance meeting the Z2-class specifications and above ground surface installation are built by utilizing circuit analysis and 3D-finite element simulations, as outlined by the Society of Automotive Engineering (SAE) J2954 standard. The simulated designs are employed to determine the frequency (f) and the compensating network components (CNCs) required to achieve optimal power transfer efficiency while maintaining nominal power levels. The analysis of misalignment scenarios involves examining various performance factors, including coupling coefficient (k), transmission power (Po), efficiency (η), and leakage electromagnetic fields (EMFs). These factors are assessed under conditions of ideal alignment, as well as various linear and angular misalignments within the inductive charging system. The results demonstrate that both the CirPR and DDPR configurations can successfully interface with the CirPT to provide the required Po to the EV battery with commendable efficiency. In perfect alignment, the efficiencies are 95.10% for the CirPT-CirPR model and 91.60% for the CirPT-DDPR model. In maximum misalignment, the efficiencies are 87.10% for the CirPT-CirPR model and 89.50% for the CirPT-DDPR model, all exceeding the acceptable threshold of 80%.

Measurement and Modeling of Self-Directed Channel (SDC) Memristors: An Extensive Study

This study systematically addresses the challenge of accurately modeling memristors, focusing on four distinct types doped with tungsten, tin, chromium, and carbon, fabricated by Knowm Inc. A comprehensive characterization is performed by subjecting the devices to sinusoidal excitations with varying frequencies and amplitudes, followed by data averaging and high-frequency filtering. The resulting measurements are fitted using three prominent memristor models: VTEAM, MMS, and Yakopcic. Additional bespoke modifications are assessed. These models, typically formulated as coupled algebraic differential equations integrating electrical quantities (voltage and current) with internal state variables governing device dynamics, are optimized using two robust approaches: (1) interior-point optimization with gradient-based search, and (2) Nelder–Mead gradient-free optimization, both with box constraints applied. A thorough comparison and discussion of the optimized model parameters ensue, accompanied by an examination of the sensitivity to diverse frequency and amplitude ranges. The findings inform conclusions and provide a foundation for future refinements, underscoring the importance of multi-model evaluation and advanced optimization strategies in precise memristor modeling. The presented methodology offers a valuable framework for elucidating optimal modeling paradigms tailored to specific memristor architectures and operating regimes, ultimately enhancing their integration in emerging neuromorphic and computational applications.

Exploring nanoparticle dynamics in binary chemical reactions within magnetized porous media: a computational analysis

Artificial Neural Networks are incredibly efficient at handling complicated and nonlinear mathematical problems, making them very useful for tackling these challenges. Artificial neural networks offer a special computational architecture that is extremely valuable in disciplines like biotechnology, biological computing, and computational fluid dynamics. The present work investigates the applicability of back-propagation artificial neural networks in conjunction with the Levenberg-Marquardt algorithm for evaluating heat transmission in hybrid nanofluids. This work focuses on the computational analysis of a MgO + GO/EG hybrid nanofluid’s steady mixed convection flow over an exponentially stretched sheet, considering multiple slip boundary conditions, thermal conductivity, heat generation, and thermal radiation. A nonlinear system of ordinary differential equations is produced from the basic associated partial differential system by performing the proper exponential similarities modifications. For generating benchmark datasets, the resulting ordinary differential equations are processed employing the bvp4c method. Considering benchmark datasets set aside for training (70%), testing (15%), and validation (15%), the Levenberg-Marquardt algorithm, which employs back-propagation in artificial neural networks, is implemented. The accuracy of the suggested strategy for handling nonlinear problems is verified utilizing mean squared error, error histograms, and regression analysis, which are all used to evaluate the methodology. Outstanding agreement is seen when ANN outputs are compared to numerical results. The flow properties, including temperature, velocity, and concentration profiles, are shown graphically and numerically. For practical purposes, it is therefore essential to analyze the flow and heat transfer in hybrid nanofluids over exponentially extending and shrinking surfaces under mixed convection and heat source scenarios. Hybrid nanofluid problems have a wide range of practical and industrial applications, such as medication delivery, manufacturing, microelectronics, nuclear plant cooling, and marine engineering.

Sulfur and metal mobilization during the life cycle of an oceanic core complex: Implications for seafloor massive sulfide deposits formation at slow and ultra-slow spreading ridges

Seafloor massive sulfide (SMS) deposits at slow and ultra-slow spreading ridges are often spatially related to, or hosted in oceanic core complexes (OCCs). The specific oceanic crust architecture, magmatism, hydrothermal fluid circulation and lithologies at OCCs, however, imply different S and metal (e.g. Cu, Zn, Co, Ni) fluxes relative to well-structured oceanic crust at-fast spreading ridges and which are not yet fully constrained. The study of S and metal distribution in the ODP Hole 735B deep drill core from the Atlantis bank allows to understand these fluxes along detachment faults and to better constrain the source zone of S and metals for OCC-related SMS deposits. Significant depletion of S, Cu, Zn and Ni are observed within the upper 250 m of the drill core where intense deformation and hydrothermal fluid circulation occurred. During the complex tectono-magmatic-hydrothermal evolution of the Atlantis Bank, four important stages are recognized for S and metal mobilization: 1) magmatic stratification leading to a higher proportion of sulfide-rich and S, Cu, Zn and Co fertile oxide gabbros in the root zone of the Atlantis Bank detachment, 2) high temperature ductile deformation leading to magmatic sulfide reworking and onset of sulfide leaching with limited metal mobilization, 3) extensive sulfide leaching and metal mobilization during amphibolite to greenschist facies metasomatism and, 4) late stage secondary sulfide precipitation and S enrichment during low temperature fluid circulation. Mass balance calculations from the source zones of the Atlantis Bank detachment highlights that metal mobilization during hydrothermal alteration of gabbroic rocks along detachment faults can fully account for the formation of OCC-related SMS deposits at slow and ultraslow spreading ridges. The Atlantis Bank detachment system, however, is gabbroic-dominated and represent the magmatic end-member of OCCs and further work is necessary for understanding metal fluxes in ultramafic-dominated detachment systems.

Hawk: An industrial-strength multi-label document classifier

There are a plethora of methods for solving the classical multi-label document classification problem. However, when it comes to deployment and usage in an industry setting, most if not all the contemporary approaches fail to address some of the vital aspects or requirements of an ideal solution: i) ability to operate on variable-length texts or rambling documents, ii) catastrophic forgetting problem, and iii) ability to visualize the model’s predictions. The paper describes the significance of these problems in detail and adopts the hydranet architecture to address these problems. The proposed architecture views documents as a sequence of sentences and leverages sentence-level embeddings for input representation, turning the problem into a sequence classification task. Furthermore, two specific architectures are explored as the architectures for the heads, Bi-LSTM and transformer heads. The proposed architecture is benchmarked on some of the popular benchmarking datasets such as Web of Science - 5763, Web of Science - 11967, BBC Sports, and BBC News datasets. The experimental results reveal that the proposed model performs at least as best as previous SOTA architectures and even outperforms prior SOTA in a few cases, along with the added advantages of the practicality issues discussed. The ablation study includes comparisons of the impact of the attention mechanism and the application of weighted loss functions to train the task-specific heads in the hydranet. The claims regarding catastrophic forgetfulness are further corroborated by empirical evaluations under incremental learning scenarios. The results reveal the robustness of the proposed architecture compared to other benchmarks.

Development and Characterization of a Three-Dimensional Organotypic In Vitro Oral Cancer Model with Four Co-Cultured Cell Types, Including Patient-Derived Cancer-Associated Fibroblasts.

Background/Objectives: Cancer organoids have emerged as a valuable tool of three-dimensional (3D) cell cultures to investigate tumor heterogeneity and predict tumor behavior and treatment response. We developed a 3D organotypic culture model of oral squamous cell carcinoma (OSCC) to recapitulate the tumor-stromal interface by co-culturing four cell types, including patient-derived cancer-associated fibroblasts (PD-CAFs). Methods: A stainless-steel ring was used twice to create the horizontal positioning of the cancer stroma (adjoining normal oral mucosa connective tissue) and the OSCC layer (surrounding normal oral mucosa epithelial layer). Combined with a structured bi-layered model of the epithelial component and the underlying stroma, this protocol enabled us to construct four distinct portions mimicking the oral cancer tissue arising in the oral mucosa. Results: In this model, α-smooth muscle actin-positive PD-CAFs were localized in close proximity to the OSCC layer, suggesting a crosstalk between them. Furthermore, a linear laminin-γ2 expression was lacking at the interface between the OSCC layer and the underlying stromal layer, indicating the loss of the basement membrane-like structure. Conclusions: Since the specific 3D architecture and polarity mimicking oral cancer in vivo provides a more accurate milieu of the tumor microenvironment (TME), it could be crucial in elucidating oral cancer TME.

Spatially Resolved Fibre-Optic Probe for Cervical Precancer Detection Using Fluorescence Spectroscopy and PCA-ANN-Based Classification Algorithm: An In Vitro Study.

Cervical cancer can be detected at an early stage through the changes occurring in biochemical and morphological properties of epithelium layer. Fluorescence spectroscopy has the ability to identify these subtle changes non-invasively and in real time with good accuracy in comparison with conventional techniques. In this paper, we report the usage of a custom designed spatially resolved fibre-optic probe (SRFOP), which consists of 77 fibres in two concentric rings, for the detection of cervical cancer using fluorescence spectroscopy technique. The aim of this study is to classify different grades of cervical precancer on the basis of their fluorescence spectra followed by a robust classification algorithm. Fluorescence spectra of 28 cervical tissue samples of different categories have been recorded using six detector fibres of FOP at different spatial locations with the source fibre (SF). A 405 nm laser diode source has been utilised to excite the samples and a USB 4000 Ocean Optics spectrometer to collect the output spectra in the wavelength range 400-700 nm. Principal component analysis (PCA) was applied to the collected spectra to reduce the dimensionality of the data while preserving the most significant features for classification. The first 10 principal components, which captured the majority of the variance in the spectra, were selected as input features for the classification model. Classification was then performed using an artificial neural network (ANN) with a specific architecture, including an input layer, hidden layers, and a softmax activation function in the output layer. Experimental and classification results both demonstrate that proximal fibres (PFs) perform better than distal fibres (DFs) in capturing the discriminatory features present in the epithelium layer of cervical tissue samples as PF collect most of the signal from the epithelium layer. The combined approach of spatially resolved fluorescence spectroscopy and PCA-ANN classification techniques is able to discriminate different grades of cervical precancer and normal with an average sensitivity, specificity and accuracy of 93.33%, 96.67% and 95.57%, respectively.

Substrate preference, RNA binding and active site versatility of Stenotrophomonas maltophilia nuclease SmNuc1, explained by a structural study.

Nucleases of the S1/P1 family have important applications in biotechnology and molecular biology. We have performed structural analyses of SmNuc1 nuclease from Stenotrophomonas maltophilia, including RNA cleavage product binding and mutagenesis in a newly discovered flexible Arg74-motif, involved in substrate binding and product release and likely contributing to the high catalytic rate. The Arg74Gln mutation shifts substrate preference towards RNA. Purine nucleotide binding differs compared to pyrimidines, confirming the plasticity of the active site. The enzyme-product interactions indicate a gradual, stepwise product release. The activity of SmNuc1 towards c-di-GMP in crystal resulted in a distinguished complex with the emerging product 5'-GMP. This enzyme from an opportunistic pathogen relies on specific architecture enabling high performance under broad conditions, attractive for biotechnologies.

Neurobiological correlates of comorbidity in disorders across the affective disorders-psychosis spectrum

Disorders across the affective disorders-psychosis spectrum such as major depressive disorder (MDD), bipolar disorder (BD), schizoaffective disorder (SCA), and schizophrenia (SCZ), have overlapping symptomatology and high comorbidity rates with other mental disorders. So far, however, it is largely unclear why some of the patients develop comorbidities. In particular, the specific genetic architecture of comorbidity and its relationship with brain structure remain poorly understood. Therefore, we performed systematic analyses of clinical, genetics and brain structural measures to gain further insights into the neurobiological correlates of mental disorder’s comorbidity. We investigated a sub-sample of the Marburg/Münster Cohort Study (MACS), comprising DSM-IV-TR diagnosed patients with a single disorder in the affective disorders-psychosis spectrum (SD, n=470, MDD; BD; SCA; SCZ), with additional mental disorder’s comorbidities (COM, n=310), and healthy controls (HC, n=649). We investigated group differences regarding a) the global severity index (based on SCL90-R), b) a cross-disorder polygenic risk score (PRS) calculated with PRS-continuous shrinkage (PRS-CS) using the summary statistics of a large genome-wide association study across mental disorders, and c) whole brain grey matter volume (GMV). The SCL90-R score significantly differed between groups (COM>SD>HC). While SD and COM did not differ in cross-disorder PRS and GMV, SD and COM versus HC displayed increased cross-disorder PRS and decreased GMV in the bilateral insula, the left middle temporal, the left inferior parietal, and several frontal gyri. Our results thus suggest that disorders in the affective disorders-psychosis spectrum with or without additional comorbidities differ in self-reported clinical data, but not on genetic or brain structural levels.

Center-concave nanosheets of core-shell WO3@Prussian blue based handheld microchip-devices for ultrasensitive lysine determination

Lysine is one of the essential amino acids for human body, and its imbalance is a major cause to anemia, aging process, leukemia cell proliferation and tumor growth. Therefore, its monitoring is dominative to the prevention the disease progress and guidance to the clinical treatment. However, traditional in-hospital detection methods, such as colorimetry and fluorometric, often suffer the disadvantages of high cost and long time-consuming. These drawbacks show a difficulty in the home-in and dairy monitoring for the lysine regulation in body. In this study, we have proposed an ultrasensitive microchip-based portable device to achieve the onsite and precise determination of lysine within only 10 s. This microchip was functionalized through constructing a center-concave nanosheet of core-shell WO3@Prussian blue (WO3@PB) to remarkably strengthen the generation and transfer of the detection signal. In this special architecture, the core WO3 nanosheet can be exposed at the center region of this nanocomposite to effectively promote the enzymatic oxidation, while the PB shell enables to strongly reduce the H2O2 produced by the enzymatic reaction. Under above synergetic effects, a handheld device was designed to support the plug-and-play microchip, which performed an outstanding accuracy for the lysine detection in blood.