Mapping Function Research Articles

Exploring chaos and ergodic behavior of an inductorless circuit driven by stochastic parameters

There exist extensive studies on periodic and random perturbations of various smooth maps investigating their dynamics. Unlike smooth maps, non-smooth maps are yet to be studied extensively under a stochastic regime. This paper presents a stochastic piecewise-smooth map derived from a simple inductorless switching circuit. The stochasticity is introduced in parameter values. The distribution of the parameter values is bounded and randomly selected from uniform and triangular distributions and ranges between high and low bifurcation parameter values of the deterministic map. Due to this inherent stochasticity in parameter values, the time evolution of the state variable cannot be predicted at a specific time instant. We observe that the state variable exhibits completely ergodic behavior when the minimum value of the parameter is the same as the minimum bifurcation parameter of the deterministic system. However, the ensemble average of the state variable converges to a fixed value. The system demonstrates nonchaotic behavior for a particular range of parameter values but the deterministic map in that bifurcation range shows interplay between chaos and periodic orbits. The values of Lyapunov exponents decrease monotonically with increased asymmetry of the distribution from which the bifurcation parameter values are chosen. We determine the probability density function of the stochastic map and verify its invariance under initial conditions. The most noteworthy result is the disappearance of chaotic behavior when the lower range of the distribution is varied while maintaining a fixed upper threshold for a particular distribution, even though the deterministic map exhibits an array of periodic and chaotic behaviors within the range. As the period-incrementing cascade with chaotic inclusion only occurs in nonsmooth maps, this paper numerically shows the stochasticity of a piecewise-smooth map obtained from a practical system for the first time where randomness is introduced in the parameter space.

From a Logical Connective to a Discourse Marker: The Functions of “So” Dependent on its Location

Traditional studies have been referring to Halliday’s meta-function of language to classify the uses of “so” as a discourse marker. However, there are some overlaps in the theories that were accepted earlier. Thus, this study first seeks to explore the discourse roles of “so” in terms of where it appears (utterance-initial, -middle, and -final) in dialogues and monologues of informal English language based on the TV Corpus and AntConc tool. Second, this paper tries to explain the extended use and internal functional mapping of “so” from a logical connective to a discourse marker in spoken English by building a connection between the two uses and providing an account for its functional borrowing. The ultimate goal is to render a new perspective of presenting both the logical and the discourse functions of “so” related to the syntactical structure and pragmatic context it is in.

A novel turbidity compensation method for water measurements by UV/Vis and fluorescence spectroscopy

The study presented here addresses the influence of turbidity on UV/Vis spectroscopy measurements of water bodies, a factor that affects the accurate identification and quantification of substances. The novelty of the developed turbidity compensation method is its applicability over the entire UV/Vis spectrum (250 – 900 nm), enabling the accurate detection of substances which appear in the UV/Vis spectrum at higher wavelengths (e.g. chlorophylls), and at high turbidity amounts. Commonly used approaches for turbidity compensation bases on the relationship before and after its correction through mapping functions or subtracting the turbidity-related absorbance from the original spectrum. However, these methods usually consider only certain wavelength ranges, such as 200 – 400 nm, and often reach limitations when applied to real water samples due to increase of turbidity. A significant improvement in our method was achieved by simultaneous determination of turbidity in the sample using scattered light measurement at 850 nm. This information was then applied to generate a turbidity-compensation curve, which was then used to correct the absorption spectrum according to Lambert-Beer’s law. The experimental results show that this novel method reduces the root mean square error (RMSE) from 0.5935 mg L–1 to 0.0218 mg L–1 of the rhodamine B predictions. For better comparability, COD measurements were also carried out and an RMSE of 0.3096 mg L–1 and an R2 of 0.979 were obtained. In summary, it corrects the absorbance deviation caused by turbidity, improves the accuracy of substance prediction and works even at low substance concentrations in turbid solutions or for realistic measurements.

Enhanced multi-constraint dung beetle optimization-kernel extreme learning machine for lithium-ion battery state of health estimation with adaptive enhancement ability

Accurately estimating the state of health (SOH) of lithium batteries is a critical and challenging task in battery management systems. Data-driven models are widely used for SOH estimation but still suffer from the difficulty of balancing speed, accuracy, and adaptability. Therefore, this study constructs the dung beetle optimization algorithm to optimize the kernel extreme learning machine model. This paper addresses the issues of long iteration time and mismatches in kernel function mapping in data-driven models. To improve the model's generality, an adaptive learning kernel function is designed to complement the polynomial kernel function and form a joint function. This joint function is then introduced into a single implicit-layer extreme learning machine, which achieves fast speed and strong adaptive capability. To enhance the algorithmic parameter search capability, the optimal Latin hypercube idea, and the Osprey algorithm's global exploration strategy are introduced, which effectively improves the algorithm's global search capability. Additionally, it successfully regulated the positional update through the design of the logarithmic weighting factor, which improved the local search and convergence capabilities of the algorithm. The experiment validates the effectiveness and rationality of the proposed model for advancing battery management system applications.

Assessment of selected ionospheric mapping functions using SF-PPP on different solar activities

In single-frequency precise point positioning (SF-PPP), the ionospheric delays provided by global ionosphere maps (GIMs) are in the vertical direction. Therefore, an ionospheric mapping function is applied to convert the vertical direction to the slant one. However, the performance of mapping functions (MF) applied in SF-PPP under different solar activities is unknown. Meanwhile, understanding their performance can help us better improve the accuracy of the ionospheric mapping function. For this purpose, three traditional ionospheric mapping functions, such as the standard single-layer model mapping function (SLM MF), the modified single-layer model mapping function (MSLM MF), and the Klobuchar MF, are evaluated. Additionally, the mapping function named SGG MF, which considers the effect of ionospheric gradients, is also assessed. The positioning results indicate that the SGG MF has an improvement of (50.3 %, 37.3 %), (31.7 %, 23.4 %), and (16.8 %, 13.3 %) compared with Klobuchar MF, SLM MF, and MSLM MF during the year (2014, 2021), respectively. The mean positioning errors of SLM MF, MSLM MF, and SGG MF are about (0.20 m, 0.05 m), (0.25 m, 0.10 m), and (0.30 m, 0.10 m) smaller than that of Klobuchar MF over high-/mid- latitude during the year (2014, 2021), while the values are (0.25 m, 0.20 m), (0.40 m, 0.35 m), and (0.55 m, 0.50 m) over low-latitude region. Furthermore, the correlation coefficients between positioning results and solar activities are (0.114, 0.354), (0.058, 0.324), (0.098, 0.295), and (0.235, 0.271) for Klobucahr MF, SLM MF, MSLM MF, and SGG MF during corresponding year.

What can cartographers learn from artistic paintings when stylizing maps? A preliminary synthesis from the perspective of visual neuroscience

ABSTRACT Creating a map style from scratch is challenging. Cartographers may find inspirations from artistic paintings and transfer their style to maps. However, the reference painting and the extracted style elements may not fit well with the content and function of maps, resulting in undesirable or even frustrating map stylization. The question of how to choose appropriate paintings as inspirations and utilize their essential visual elements to stylize maps remains unanswered. As neuroscience has developed, an increasing number of neural mechanisms underlying visual perception have been discovered, deepening our understanding of artistic styles and their adaptation to maps. In this paper, we draw connections between visual neurons and style elements of both artistic paintings and maps by examining seven neural mechanisms (i.e. central-peripheral vision, color-to-contour adhesion, double-opponent effects, visual filling-in, orientation selectivity, latency variation, and what-where separation) that are typical of artistic paintings. We also demonstrate that these mechanisms are transferable to maps. We then create a cartographic style syntactics to generally indicate how and to what degree these mechanisms can be reflected in four cartographic stylistic criteria (i.e. identification, expressiveness, aesthetic appeal and emotional impact). Our work contributes to the integration of principles from neuroscience with cartographic design to deepen our understanding of map perception and to facilitate map style transfer.

AB004. Transsylvian approach for radical resection of insular gliomas: emphasizing the importance of the transsylvian approach for elderly patients with Zone II and III insular gliomas.

Insular gliomas present significant challenges due to their deep-seated location and proximity to critical structures, including sylvian veins, middle cerebral arteries (MCAs), lenticulostriate arteries, long insular arteries, and functional cortices and white matter tracts. The Berger-Sanai classification categorizes them into four zones (I-IV), providing a framework for understanding insular gliomas. The key factors for successful insular glioma removal are achieving the greatest insular exposure and surgical freedom. There are two main types of approach methods, such as transsylvian approach with meticulous wider dissection of the sylvian fissure and transcorticosubcortical approach with intraoperative functional brain mapping under awake surgery to remove the functionally silent cortices and white matter tracts. Because splitting the distal sylvian fissure is more challenging, a transcortical approach through the parietorolandic operculum in awake patients has been reported to be more effective access to the posterior insular gliomas (Zone II and III) in the dominant hemisphere. The object of this study emphasize the importance of the transsylvian approach for radical resection of insular gliomas. We retrospectively analyzed our experiences with radically resected insulo-opercular gliomas. Basically, we pursue the transsylvian approach for resecting insular gliomas without removal of any normal brain. Motor pathways running beneath the parietorolandic operculum can be damaged by ischemia caused by sacrificing the medullary arteries (MAs) arising from the pial arteries of the M3 and M4 portions of the MCA. Motor deficit after resection of this area was significantly found in the elderly patients. This phenomenon might be described by the age-associated decreasing the vascular reserve capacity. Autopsy brains showed that the sclerotic rate of the MAs increased with age and hypertension. Even with the intraoperative functional brain mapping, we cannot avoid the ischemic complication caused by sacrificing the MAs during stepwise removal of the functionally silent cortices and white matter tracts. We make a suggestion not to remove the parietorolandic operculum in elderly patients with insular gliomas located at Zone II and III. Distal transsylvian approach should be applied.

Genome-wide association analysis of hypertension and epigenetic aging reveals shared genetic architecture and identifies novel risk loci

Hypertension is a disease associated with epigenetic aging. However, the pathogenic mechanism underlying this relationship remains unclear. We aimed to characterize the shared genetic architecture of hypertension and epigenetic aging, and identify novel risk loci. Leveraging genome-wide association studies (GWAS) summary statistics of hypertension (129,909 cases and 354,689 controls) and four epigenetic clocks (N = 34,710), we investigated genetic architectures and genetic overlap using bivariate casual mixture model and conditional/conjunctional false discovery rate methods. Functional gene-sets pathway analyses were performed by functional mapping and gene annotation (FUMA) protocol. Hypertension was polygenic with 2.8 K trait-influencing genetic variants. We observed cross-trait genetic enrichment and genetic overlap between hypertension and all four measures of epigenetic aging. Further, we identified 32 distinct genomic loci jointly associated with hypertension and epigenetic aging. Notably, rs1849209 was shared between hypertension and three epigenetic clocks (HannumAge, IEAA, and PhenoAge). The shared loci exhibited a combination of concordant and discordant allelic effects. Functional gene-set analyses revealed significant enrichment in biological pathways related to sensory perception of smell and nervous system processes. We observed genetic overlaps with mixed effect directions between hypertension and all four epigenetic aging measures, and identified 32 shared distinct loci with mixed effect directions, 25 of which were novel for hypertension. Shared genes enriched in biological pathways related to olfaction.

Functional mapping of movement and speech using task-based electrophysiological changes in stereoelectroencephalography.

Stereoelectroencephalography (SEEG) has become the predominant method for intracranial seizure localization. When imaging, semiology, and scalp EEG findings are not in full agreement or definitively localizing, implanted SEEG recordings are used to test candidate seizure onset zones (SOZs). Discovered SOZs may then be targeted for resection, laser ablation, or neurostimulation. If an SOZ is eloquent, resection and ablation are both contraindicated, so identifying functional representation is crucial for therapeutic decision-making. The authors present a novel functional brain mapping technique that utilizes task-based electrophysiological changes in SEEG during behavioral tasks and test this in pediatric and adult patients. SEEG was recorded in 20 patients with epilepsy who ranged in age from 6 to 39 years (12 female, 18 of 20 patients < 21 years of age) and underwent implanted monitoring to identify seizure onset. Each performed 1) visually cued simple repetitive movements of the hand, foot, or tongue while electromyography was recorded; and 2) simple picture-naming or verb-generation speech tasks while audio was recorded. Broadband changes in the power spectrum of the SEEG recording were compared between behavior and rest. Electrophysiological functional mapping of movement and/or speech areas was completed in all 20 patients. Eloquent representation was identified in both cortex and white matter and generally corresponded to classically described functional anatomical organization as well as other clinical mapping results. Robust maps of brain activity were identified in healthy brain, regions of developmental or acquired structural abnormality, and SOZs. Task-based electrophysiological mapping using broadband changes in the SEEG signal reliably identifies movement and speech representation in pediatric and adult epilepsy patients.

Novel Susceptibility Genes and Biomarkers for Obstructive Sleep Apnea: Insights from Genetic and Inflammatory Proteins.

Numerous observational studies link obstructive sleep apnea (OSA) to inflammatory proteins, yet the directionality of these associations remains ambiguous. Therefore, we aimed to clarify the potential associations of gene-predicted inflammatory proteins with OSA. Based on genome-wide association study data, we applied Mendelian randomization (MR) to explore potential connections between circulating inflammatory proteins and OSA, primarily using the inverse variance weighting method for robustness. Cochran's Q test, MR‒Egger intercept test, MR-PRESSO, and leave-one-out method were used to perform sensitivity tests for pleiotropy and heterogeneity. Replication analyses and meta-analyses were performed using other independent data. Steiger tests and multivariate MR assessed the independent effects of exposure factors, and the functional mapping and annotation (FUMA) platform was used to identify key genes to enhance the understanding of genetics. Our investigation revealed 21 circulating inflammatory proteins significantly associated with OSA-related phenotypes. Notably, IL-10RA, IL-18R1, TNFSF14, CCL23, ADA, and SLAMF1 had significant effects on multiple phenotypes. After FDR correction, IL-18R1, SLAMF1, IL-10RA, and IL-17C were identified as important candidates for OSA, and multivariate MR analysis strengthened the independent heritability of 20 inflammatory factors. The FUMA platform revealed seven overlapping genes: ROBO1, PRIM1, NACA, SHBG, HSD17B6, RBMS2, and WWOX. All reverse MR analyses and sensitivity analyses confirmed the robustness of these associations. Our results underscore crucial associations between inflammatory proteins and OSA pathogenesis, revealing new correlates and susceptibility genes. These findings advance biomarker identification for OSA risk and highlight the importance of genetic and inflammatory profiles in OSA management.

Solar wind data analysis aided by synthetic modeling: A better understanding of plasma frame variations from temporal data

Context. In situ measurements of the solar wind, a turbulent and anisotropic plasma flow originating at the Sun, are mostly carried out by single spacecraft, resulting in one-dimensional time series. Aims. The conversion of these measurements to the spatial frame of the plasma is a great challenge, but it is required for direct comparison of the measurements with magnetohydrodynamic turbulence theories. Methods. We present a tool kit based on the synthetic modeling of solar wind fluctuations as two-dimensional noise maps with adjustable spectral and power anisotropy that can help with the temporal-spatial conversion of real data. Specifically, by following the spacecraft trajectory through a noise map (relative velocity and angle relative to some mean magnetic field) with properties tuned to mimic those of the solar wind, the likelihood that the temporal data fluctuations represent parallel or perpendicular fluctuations in the plasma frame can be quantified by correlating structure functions of the noise map. Synthetic temporal data can also be generated, which can provide a testing ground for analysis applied to the solar wind data. Results. We demonstrate this tool by investigating Parker Solar Probe’s seventh encounter trajectory and data, and we showcase several possible ways in which it can be used. We find that whether temporal variations in the spacecraft frame come from parallel or perpendicular variations in the plasma frame strongly depends on the spectral and power anisotropy of the measured wind. Conclusions. Data analysis assisted by such underlying synthetic models as presented here could open up new ways to interpret measurements in the future, specifically in the more reliable determination of plasma frame quantities from temporal measurements.

Medial to lateral frontal functional connectivity mapping reveals the organization of cingulate cortex.

The functional organization of the frontal lobe is a source of debate, focusing on broad functional subdivisions, large-scale networks, or local refined specificities. Multiple neurocognitive models have tried to explain how functional interactions between cingulate and lateral frontal regions contribute to decision making and cognitive control, but their neuroanatomical bases remain unclear. We provide a detailed description of the functional connectivity between cingulate and lateral frontal regions using resting-state functional MRI in rhesus macaques. The analysis focuses on the functional connectivity of the rostral part of the cingulate sulcus with the lateral frontal cortex. Data-driven and seed-based analysis revealed three clusters within the cingulate sulcus organized along the rostro-caudal axis: the anterior, mid, and posterior clusters display increased functional connectivity with, respectively, the anterior lateral prefrontal regions, face-eye lateral frontal motor cortical areas, and hand lateral frontal motor cortex. The location of these clusters can be predicted in individual subjects based on morphological landmarks. These results suggest that the anterior cluster corresponds to the anterior cingulate cortex, whereas the posterior clusters correspond to the face-eye and hand cingulate motor areas within the anterior midcingulate cortex. These data provide a comprehensive framework to identify cingulate subregions based on functional connectivity and local organization.

Brain Network Localization of Gray Matter Atrophy and Neurocognitive and Social Cognitive Dysfunction in Schizophrenia

BackgroundNumerous studies have established the presence of gray matter atrophy and brain activation abnormalities during neurocognitive and social cognitive tasks in schizophrenia. Despite a growing consensus that diseases localize better to distributed brain networks than individual anatomical regions, relatively few studies have examined brain network localization of gray matter atrophy and neurocognitive and social cognitive dysfunction in schizophrenia. MethodsTo address this gap, we initially identified brain locations of structural and functional abnormalities in schizophrenia from 301 published neuroimaging studies with 8712 individuals with schizophrenia and 9275 healthy control participants. By applying novel functional connectivity network mapping to large-scale resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 3 brain abnormality networks of schizophrenia. ResultsThe gray matter atrophy network of schizophrenia comprised a broadly distributed set of brain areas predominantly implicating the ventral attention, somatomotor, and default networks. The neurocognitive dysfunction network was also composed of widespread brain areas primarily involving the frontoparietal and default networks. By contrast, the social cognitive dysfunction network consisted of circumscribed brain regions mainly implicating the default, subcortical, and visual networks. ConclusionsOur findings suggest shared and unique brain network substrates of gray matter atrophy and neurocognitive and social cognitive dysfunction in schizophrenia, which may not only refine the understanding of disease neuropathology from a network perspective but may also contribute to more targeted and effective treatments for impairments in different cognitive domains in schizophrenia.

ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting.

Short-term load forecasting (STLF) is challenging due to complex time series (TS) which express three seasonal patterns and a nonlinear trend. This article proposes a novel hybrid hierarchical deep-learning (DL) model that deals with multiple seasonality and produces both point forecasts and predictive intervals (PIs). It combines exponential smoothing (ES) and a recurrent neural network (RNN). ES extracts dynamically the main components of each individual TS and enables on-the-fly deseasonalization, which is particularly useful when operating on a relatively small dataset. A multilayer RNN is equipped with a new type of dilated recurrent cell designed to efficiently model both short and long-term dependencies in TS. To improve the internal TS representation and thus the model's performance, RNN learns simultaneously both the ES parameters and the main mapping function transforming inputs into forecasts. We compare our approach against several baseline methods, including classical statistical methods and machine learning (ML) approaches, on STLF problems for 35 European countries. The empirical study clearly shows that the proposed model has high expressive power to solve nonlinear stochastic forecasting problems with TS including multiple seasonality and significant random fluctuations. In fact, it outperforms both statistical and state-of-the-art ML models in terms of accuracy.

HPRN: Holistic Prior-Embedded Relation Network for Spectral Super-Resolution.

Spectral super-resolution (SSR) refers to the hyperspectral image (HSI) recovery from an RGB counterpart. Due to the one-to-many nature of the SSR problem, a single RGB image can be reprojected to many HSIs. The key to tackle this ill-posed problem is to plug into multisource prior information such as the natural spatial context prior of RGB images, deep feature prior, or inherent statistical prior of HSIs so as to effectively alleviate the degree of ill-posedness. However, most current approaches only consider the general and limited priors in their customized convolutional neural networks (CNNs), which leads to the inability to guarantee the confidence and fidelity of reconstructed spectra. In this article, we propose a novel holistic prior-embedded relation network (HPRN) to integrate comprehensive priors to regularize and optimize the solution space of SSR. Basically, the core framework is delicately assembled by several multiresidual relation blocks (MRBs) that fully facilitate the transmission and utilization of the low-frequency content prior of RGBs. Innovatively, the semantic prior of RGB inputs is introduced to mark category attributes, and a semantic-driven spatial relation module (SSRM) is invented to perform the feature aggregation of clustered similar ranges for refining recovered characteristics. In addition, we develop a transformer-based channel relation module (TCRM), which breaks the habit of employing scalars as the descriptors of channelwise relations in the previous deep feature prior and replaces them with certain vectors to make the mapping function more robust and smoother. In order to maintain the mathematical correlation and spectral consistency between hyperspectral bands, the second-order prior constraints (SOPCs) are incorporated into the loss function to guide the HSI reconstruction. Finally, extensive experimental results on four benchmarks demonstrate that our HPRN can reach the state-of-the-art performance for SSR quantitatively and qualitatively. Furthermore, the effectiveness and usefulness of the reconstructed spectra are verified by the classification results on the remote sensing dataset. Codes are available at https://github.com/Deep-imagelab/HPRN.

AB073. Electrocorticography high-gamma dynamics during intraoperative hand movement mapping.

Intraoperative functional mapping for glioma resection often necessitates awake craniotomies, requiring active patient participation. This procedure presents challenges for both the surgical team and the patient. Thus, minimizing mapping time becomes crucial. Passive mapping utilizing electrocorticography (ECoG) presents a promising approach to reduce intraoperative mapping efforts via direct electrical stimulation. This study aims to identify an efficient mapping protocol for hand movement by optimizing mapping duration and localization accuracy. Three glioma patients (two males, one female) underwent awake craniotomy for tumor resection at Asahikawa Medical University Hospital and Kindai University in Osaka. Patients were maintained at a bispectral index (BIS) level above 90 to ensure wakefulness during mapping. Data were collected using a DC-coupled g.HIamp biosignal amplifier, digitized with 24-bit resolution at a minimum sampling rate of 1,200 Hz. Each session comprised ten runs, each lasting 250 seconds, consisting of a 12-second rest phase (baseline) followed by a 12-second grasping period containing ten grasping movements. High-gamma activity (HGA, 60-170 Hz) was recorded from ECoG locations on the pre- and postcentral gyrus. Locations exhibiting significant grasping-related HGA, with stronger responses during early trials within a run, were classified as "attenuated". Among 37 electrodes on the sensorimotor cortex, 16 exhibited significant HGA during grasping. Three locations demonstrated significant attenuation after three runs, with one location showing attenuation after the first three trials within a run. The observed attenuation effect of short-term repeated movements during intraoperative monitoring is relatively modest initially. However, as the number of repeated grasping blocks increases, the number of attenuated locations also rises. Consequently, minimizing overall mapping time, rather than reducing the number of tasks per block, is paramount. For statistical analysis, a minimum of 20 grasping trials (two runs of ten movements) or 48 seconds of motor mapping is recommended. Alternatively, a mapping protocol involving a third run or 30 grasping trials (72 seconds) may enhance data robustness. These preliminary findings, though based on a limited patient cohort, warrant confirmation and further investigation, particularly in epilepsy patients.

THE BIBLIOMETRIC NETWORK TO IDENTIFY RESEARCH TRENDS IN MULTI-INPUT TRANSFER FUNCTION

Bibliometrics has become one of the most widely used approaches to examining academic publications. Bibliometric analysis helps researchers obtain references from other researchers, strengthening recent research. In a bibliometric analysis, VOSviewer made the analysis process easier. VOSviewer is used to build and visualize bibliometric networks. This study aims to present and visualize an overview of the development of multi-input transfer function research trends and collaboration networks. The research method used is a literature review from Scopus using VOSviewer in 2007-2023. This study shows a growing trend in multi-input transfer function research, with China leading in publications. The development map of the multi-input transfer function is based on nine co-words and ten co-author clusters. The study's results are dominated by applying the model to real data. However, the estimation of multi-input transfer function parameters has yet to be carried out using a numerical approach.

The promise of precision functional mapping for neuroimaging in psychiatry.

Precision functional mapping (PFM) is a neuroimaging approach to reliably estimate metrics of brain function from individual people via the collection of large amounts of fMRI data (hours per person). This method has revealed much about the inter-individual variation of functional brain networks. While standard group-level studies, in which we average brain measures across groups of people, are important in understanding the generalizable neural underpinnings of neuropsychiatric disorders, many disorders are heterogeneous in nature. This heterogeneity often complicates clinical care, leading to patient uncertainty when considering prognosis or treatment options. We posit that PFM methods may help streamline clinical care in the future, fast-tracking the choice of personalized treatment that is most compatible with the individual. In this review, we provide a history of PFM studies, foundational results highlighting the benefits of PFM methods in the pursuit of an advanced understanding of individual differences in functional network organization, and possible avenues where PFM can contribute to clinical translation of neuroimaging research results in the way of personalized treatment in psychiatry.

Mechanism Analysis and Response of Digital Financial Inclusion to Labor Economy based on ANN and Contribution Analysis

Abstract Given the inclusiveness of digital inclusive finance (DFI) and its complex impact mechanism on the labor economy, this study uses the characteristics of adaptive and self-learning ability of artificial neural network (ANN) to simulate the process of delivering stimuli to nerve cells in the human brain through linear weighted summarization and functional mapping, and implement the optimization learning algorithm to adjust the weights in the network structure, thus completing the hierarchical analysis of index weight. At the same time, the neural network structure is used to approach the greatest extent and Garson algorithm is used for sensitivity analysis. We use data on the labor economy and digital financial inclusion in Heilongjiang, Jilin, and Liaoning provinces in China from 2011 to 2021 as a training dataset. The study found that (1) the indexes of DFI have different importance to the indexes of labor economy, among which the most important are the number and amount of insurance per capita and the proportion of the number and amount paid by digital technology, which have a normalized importance of 100 and 99.3%, further, R-square coverage is above 0.95, respectively, for labor economy indicators; (2) For different subdivided indicators, the indexes of DFI determine different significance. This study employs tools and policies related to DFI to address labor economy challenges, so as to promote the overall economic construction. This study studies the response and transmission mechanism of the concept of DFI to the labor economy, and explores the labor economy problems such as improving labor productivity and labor mismatch in the economy under its “inclusive” principle. Compared with the traditional weight analysis, it is closer to the real situation and has a stronger ability to fit the reality. In the future, the model could be rebased and measured against absolute indicators and a wider dataset could be adopted for extension to more areas.

Mapping the Influence of Light Intensity on the Transgenerational Genetic Architecture of Arabidopsis thaliana.

Light is a crucial environmental factor that influences the phenotypic development of plants. Despite extensive studies on the physiological, biochemical, and molecular mechanisms of the impact of light on phenotypes, genetic investigations regarding light-induced transgenerational plasticity in Arabidopsis thaliana remain incomplete. In this study, we used thaliana as the material, then gathered phenotypic data regarding leaf number and plant height under high- and low-light conditions from two generations. In addition to the developed genotype data, a functional mapping model was used to locate a series of significant single-nucleotide polymorphisms (SNPs). Under low-light conditions, a noticeable adaptive change in the phenotype of leaf number in the second generation suggests the presence of transgenerational genetic effects in thaliana under environmental stress. Under different lighting treatments, 33 and 13 significant genes associated with transgenerational inheritance were identified, respectively. These genes are largely involved in signal transduction, technical hormone pathways, light responses, and the regulation of organ development. Notably, genes identified under high-light conditions more significantly influence plant development, whereas those identified under low-light conditions focus more on responding to external environmental stimuli.