Modern internet search analytics and thyroidectomy: What are patients asking?

Objectives: Thyroidectomy is among the most commonly performed head and neck surgeries, however, limited existing information is available on topics of interest and concern to patients. Study Design: Observational. Setting: Online. Methods: A search engine optimization tool was utilized to extract metadata on Google-suggested questions that "People Also Ask" (PAA) pertaining to "thyroidectomy" and "thyroid surgery." These questions were categorized by Rothwell criteria and topics of interest. The Journal of the American Medical Association (JAMA) benchmark criteria enabled quality assessment. Results: A total of 250 PAA questions were analyzed. Future-oriented PAA questions describing what to expect during and after the surgery on topics such as postoperative management, risks or complications of surgery, and technical details were significantly less popular among the "thyroid surgery" group (P < 0.001, P = 0.005, and P < 0.001, respectively). PAA questions about scarring and hypocalcemia were nearly threefold more popular than those related to pain (335 and 319 vs. 113 combined search engine response page count, respectively). The overall JAMA quality score remained low (2.50 ± 1.07), despite an increasing number of patients searching for "thyroidectomy" (r(77) = 0.30, P = 0.007). Conclusions: Patients searching for the nonspecific term "thyroid surgery" received a curated collection of PAA questions that were significantly less likely to educate them on what to expect during and after surgery, as compared to patients with higher health literacy who search with the term "thyroidectomy." This suggests that the content of PAA questions differs based on the presumed health literacy of the internet user.

Unpleasant emotion inhibits attentional focus toward a peripheral target in a visual search: an ERP study.

Previous studies have found that emotional states affect the extent of attention, and the effect has been explained by adaptive views. If the adaptive explanations are true, emotion should modulate attentional focus toward a peripheral stimulus. The present study investigated if emotion affects the focus of attention toward a peripheral target in a visual search paradigm with event-related brain potential (ERP) measurement. In each trial of the experiment, participants performed a visual search task after an emotion (unpleasant, neutral, or pleasant) was induced by presenting an international affective picture system (IAPS) image. We measured N2pc, which is an ERP index reflecting attentional focus toward a peripheral target in a visual search, and compared the amplitudes among the emotion conditions. According to the adaptive view of emotional effects on cognition, this study hypothesized that unpleasant emotion would enhance the focus of attention, and pleasant emotion would inhibit it. These hypotheses predicted that N2pc amplitude would increase with unpleasant emotion and decrease with pleasant emotion. However, this study obtained inconsistent results; N2pc amplitude decreased in the unpleasant condition, and there was no significant effect of pleasant emotion on the ERP. The results suggest that unpleasant emotion inhibited the attentional focusing process. This is the first report to examine how emotion modulates the focus of attention toward a peripheral target in a visual search by using ERP. The findings contribute to understanding the relationship between emotion and cognition.

A dataset for analyzing the climate change response of grain quality of 48 Japanese rice cultivars with contrasting levels of heat tolerance.

Climate change has a significant impact on rice grain appearance quality; in particular, high temperatures during the grain filling period increase the rate of chalky immature grains, reducing the marketability of rice. Heat-tolerant cultivars have been bred and released to reduce the rate of chalky grain and improve rice quality under high temperatures, but the ability of these cultivars to actually reduce chalky grain content has never been demonstrated due to the lack of integrated datasets. Here, we present a dataset collected through a systematic literature search from publicly available data sources, for the quantitative analysis of the impact of meteorological factors on grain appearance quality of various rice cultivars with contrasted heat tolerance levels. The dataset contains 1302 field observations of chalky grain rates (%) - a critical trait affecting grain appearance sensitive to temperature shocks - for 48 cultivars covering five different heat-tolerant ranks (HTRs) collected at 44 sites across Japan. The dataset also includes the values of key meteorological variables during the grain filling period, such as the cumulative mean air temperature above the threshold temperature (TaHD), mean solar radiation, and mean relative humidity over 20 days after heading, obtained from a gridded daily meteorological dataset with a 1-km resolution developed by the National Agriculture and Food Research Organization. The dataset covers major commercial rice cultivars cultivated in Japan in different environmental conditions. It is a useful resource for analyzing the climate change impact on crop quality and assess the effectiveness of genetic improvements in heat tolerance. Its value has been illustrated in the research article entitled "Effectiveness of heat tolerance rice cultivars in preserving grain appearance quality under high temperatures - A meta-analysis", where the dataset was used to develop a statistical model quantifying the effects of high temperature on grain quality as a function of cultivar heat tolerance.

Quantifying task-related gaze.

Competing theories attempt to explain what guides eye movements when exploring natural scenes: bottom-up image salience and top-down semantic salience. In one study, we apply language-based analyses to quantify the well-known observation that task influences gaze in natural scenes. Subjects viewed ten scenes as if they were performing one of two tasks. We found that the semantic similarity between the task and the labels of objects in the scenes captured the task-dependence of gaze (t(39) = 13.083; p < 0.001). In another study, we examined whether image salience or semantic salience better predicts gaze during a search task, and if viewing strategies are affected by searching for targets of high or low semantic relevance to the scene. Subjects searched 100 scenes for a high- or low-relevance object. We found that image salience becomes a worse predictor of gaze across successive fixations, while semantic salience remains a consistent predictor (X2(1, N=40) = 75.148, p < .001). Furthermore, we found that semantic salience decreased as object relevance decreased (t(39) = 2.304; p = .027). These results suggest that semantic salience is a useful predictor of gaze during task-related scene viewing, and that even in target-absent trials, gaze is modulated by the relevance of a search target to the scene in which it might be located.

Impaired Ability in Visual-Spatial Attention in Chinese Children With Developmental Dyslexia.

A growing body of evidence suggests that children with dyslexia in alphabetic languages exhibit visual-spatial attention deficits that can obstruct reading acquisition by impairing their phonological decoding skills. However, it remains an open question whether these visual-spatial attention deficits are present in children with dyslexia in non-alphabetic languages. Chinese, with its logographic writing system, offers a unique opportunity to explore this question. The presence of visual-spatial attention deficits in Chinese children with dyslexia remains insufficiently investigated. Therefore, this study aimed to explore whether such deficits exist, employing a visual search paradigm. Three visual search tasks were conducted, encompassing two singleton feature search tasks and a serial conjunction search task. The results indicated that Chinese children with dyslexia performed as well as chronological age-matched control children in color search tasks but less effectively in orientation search, suggesting a difficulty in the rapid visual processing of orientation: a deficit potentially specific to Chinese dyslexia. Crucially, Chinese children with dyslexia also exhibited lower accuracy, longer reaction times, and steeper slopes in the reaction times by set size function in the conjunction search task compared to control children, which is indicative of a visual-spatial attention deficit.

[Prediction Spatial Distribution of Soil Organic Matter Based on Improved BP Neural Network with Optimized Sparrow Search Algorithm].

Soil organic matter is an important indicator of soil fertility, and it is necessary to improve the accuracy of regional organic matter spatial distribution prediction. In this study, we analyzed the organic matter content of 1 690 soil surface layers (0-20 cm) and collected data on the natural environment and human activities in the Weining Plain of the Yellow River Basin. The SOM spatial distribution prediction model was established with 1 348 points using classical statistics, deterministic interpolation, geostatistical interpolation, and machine learning, respectively, and 342 sample points data were used as the test set to test and analyze the prediction accuracy of different models. The results showed that the average SOM content of the surface soil of the Weining Plain was 14.34 g·kg-1, and the average soil organic matter variation across 1 690 sampling points was 34.81%, indicating a medium degree of variability. The results also revealed a spatial distribution trend, with low soil organic matter content in the northeast and southwest, high soil organic matter on the left and right banks of the Yellow River in the middle, and relatively high soil organic matter in the sloping terrain of the Weining Plain. The four types of methods in order of high to low prediction accuracy were the machine learning method, geostatistical interpolation method, deterministic interpolation method, and classical statistical method. Through comparison, the BP neural network that was improved based on the optimized sparrow search algorithm had the best prediction accuracy, and the optimized sparrow search algorithm had better convergence accuracy, avoided falling into local optimization, prevented data overfitting, and had better prediction ability. This optimization algorithm can improve the accuracy of SOM prediction and has good application prospects in soil attribute prediction.

Inserting Pre-analytical Chromatographic Priming Runs Significantly Improves Targeted Pathway Proteomics with Sample Multiplexing.

GoDig, a platform for targeted pathway proteomics without the need for manual assay scheduling or synthetic standards, is a powerful, flexible, and easy-to-use method that uses tandem mass tags to increase sample throughput up to 18-fold relative to label-free methods. Though the protein-level success rates of GoDig are high, the peptide-level success rates are more limited, hampering assays of harder-to-quantify proteins and site-specific phenomena. To guide the optimization of GoDig assays as well as improvements to the GoDig platform, we created GoDigViewer, a new stand-alone software that provides detailed visualizations of GoDig runs. GoDigViewer guided the implementation of "priming runs," an acquisition mode with significantly higher success rates. In this mode, two or more chromatographic priming runs are automatically performed to improve the accuracy and precision of target elution orders, followed by analytical runs which quantify targets. Using priming runs, success rates exceeded 97% for a list of 400 peptide targets and 95% for a list of 200 targets that are usually not quantified using untargeted mass spectrometry. We used priming runs to establish a quantitative assay of 125 macroautophagy proteins that had a >95% success rate and revealed differences in macroautophagy expression profiles across four human cell lines.

Efficient parameter extraction of photovoltaic models with a novel enhanced prairie dog optimization algorithm.

The growing demand for solar energy conversion underscores the need for precise parameter extraction methods in photovoltaic (PV) plants. This study focuses on enhancing accuracy in PV system parameter extraction, essential for optimizing PV models under diverse environmental conditions. Utilizing primary PV models (single diode, double diode, and three diode) and PV module models, the research emphasizes the importance of accurate parameter identification. In response to the limitations of existing metaheuristic algorithms, the study introduces the enhanced prairie dog optimizer (En-PDO). This novel algorithm integrates the strengths of the prairie dog optimizer (PDO) with random learning and logarithmic spiral search mechanisms. Evaluation against the PDO, and a comprehensive comparison with eighteen recent algorithms, spanning diverse optimization techniques, highlight En-PDO's exceptional performance across different solar cell models and CEC2020 functions. Application of En-PDO to single diode, double diode, three diode, and PV module models, using experimental datasets (R.T.C. France silicon and Photowatt-PWP201 solar cells) and CEC2020 test functions, demonstrates its consistent superiority. En-PDO achieves competitive or superior root mean square error values, showcasing its efficacy in accurately modeling the behavior of diverse solar cells and performing optimally on CEC2020 test functions. These findings position En-PDO as a robust and reliable approach for precise parameter estimation in solar cell models, emphasizing its potential and advancements compared to existing algorithms.

AMPFLDAP: Adaptive Message Passing and Feature Fusion on Heterogeneous Network for LncRNA-Disease Associations Prediction.

Exploration of the intricate connections between long noncoding RNA (lncRNA) and diseases, referred to as lncRNA-disease associations (LDAs), plays a pivotal and indispensable role in unraveling the underlying molecular mechanisms of diseases and devising practical treatment approaches. It is imperative to employ computational methods for predicting lncRNA-disease associations to circumvent the need for superfluous experimental endeavors. Graph-based learning models have gained substantial popularity in predicting these associations, primarily because of their capacity to leverage node attributes and relationships within the network. Nevertheless, there remains much room for enhancing the performance of these techniques by incorporating and harmonizing the node attributes more effectively. In this context, we introduce a novel model, i.e., Adaptive Message Passing and Feature Fusion (AMPFLDAP), for forecasting lncRNA-disease associations within a heterogeneous network. Firstly, we constructed a heterogeneous network involving lncRNA, microRNA (miRNA), and diseases based on established associations and employing Gaussian interaction profile kernel similarity as a measure. Then, an adaptive topological message passing mechanism is suggested to address the information aggregation for heterogeneous networks. The topological features of nodes in the heterogeneous network were extracted based on the adaptive topological message passing mechanism. Moreover, an attention mechanism is applied to integrate both topological and semantic information to achieve the multimodal features of biomolecules, which are further used to predict potential LDAs. The experimental results demonstrated that the performance of the proposed AMPFLDAP is superior to seven state-of-the-art methods. Furthermore, to validate its efficacy in practical scenarios, we conducted detailed case studies involving three distinct diseases, which conclusively demonstrated AMPFLDAP's effectiveness in the prediction of LDAs.

Analysis of Tandem Mass Spectrometry Data with CONGA: Combining Open and Narrow Searches with Group-Wise Analysis.

Searching for tandem mass spectrometry proteomics data against a database is a well-established method for assigning peptide sequences to observed spectra but typically cannot identify peptides harboring unexpected post-translational modifications (PTMs). Open modification searching aims to address this problem by allowing a spectrum to match a peptide even if the spectrum's precursor mass differs from the peptide mass. However, expanding the search space in this way can lead to a loss of statistical power to detect peptides. We therefore developed a method, called CONGA (combining open and narrow searches with group-wise analysis), that takes into account results from both types of searches─a traditional "narrow window" search and an open modification search─while carrying out rigorous false discovery rate control. The result is an algorithm that provides the best of both worlds: the ability to detect unexpected PTMs without a concomitant loss of power to detect unmodified peptides.

Mobile Apps for Patients with Peritoneal Dialysis: Systematic App Search and Evaluation.

BACKGROUND: Peritoneal dialysis (PD) is one type of renal replacement therapy. If patients have problems during the dialysis process, healthcare providers may not be able assist the patients immediately. mHealth can provide patients with information and help them to solve problems in real-time, potentially increasing their willingness to choose PD. OBJECTIVE: The objectives of this study were to conduct a comprehensive review of free mobile applications for patients with PD on the Internet and to recommend suitable mobile applications to facilitate patient self-management and health. METHODS: We conducted a systematic search for PD mobile applications on Google Play and the Apple iTunes Store from 3 to 16 June 2023. RESULTS: A total of 828 identifiable mobile applications were initially identified, and ultimately, 21 met the inclusion criteria. The Mobile App Rating Scale (MARS) assessment of the applications revealed the highest score in the functionality domain, followed by the aesthetics, information, app-specific, subjective quality, and engagement domains, respectively. In the comprehensive self-management of PD, the highest percentage was related to disease-related information. CONCLUSION: The findings of this study suggest that some applications, with the highest quality, can be recommended to patients for use in English or traditional Chinese.

Developmental and Housekeeping Genes: Two Types of Genetic Organization in the Drosophila Genome.

We developed a procedure for locating genes on Drosophila melanogaster polytene chromosomes and described three types of chromosome structures (gray bands, black bands, and interbands), which differed markedly in morphological and genetic properties. This was reached through the use of our original methods of molecular and genetic analysis, electron microscopy, and bioinformatics data processing. Analysis of the genome-wide distribution of these properties led us to a bioinformatics model of the Drosophila genome organization, in which the genome was divided into two groups of genes. One was constituted by 65, in which the genome was divided into two groups, 62 genes that are expressed in most cell types during life cycle and perform basic cellular functions (the so-called "housekeeping genes"). The other one was made up of 3162 genes that are expressed only at particular stages of development ("developmental genes"). These two groups of genes are so different that we may state that the genome has two types of genetic organization. Different are the timings of their expression, chromatin packaging levels, the composition of activating and deactivating proteins, the sizes of these genes, the lengths of their introns, the organization of the promoter regions of the genes, the locations of origin recognition complexes (ORCs), and DNA replication timings.

Deep transfer learning with gravitational search algorithm for enhanced plant disease classification.

Plant diseases annually cause damage and loss of much of the crop, if not its complete destruction, and this constitutes a significant challenge for farm owners, governments, and consumers alike. Therefore, identifying and classifying diseases at an early stage is very important in order to sustain local and global food security. In this research, we designed a new method to identify plant diseases by combining transfer learning and Gravitational Search Algorithm (GSA). Two state-of-the-art pretrained models have been adopted for extracting features in this study, which are MobileNetV2 and ResNe50V2. Multilayer feature extraction is applied in this study to ensure representations of plant leaves from different levels of abstraction for precise classification. These features are then concatenated and passed to GSA for optimizing them. Finally, optimized features are passed to Multinomial Logistic Regression (MLR) for final classification. This integration is essential for categorizing 18 different types of infected and healthy leaf samples. The performance of our approach is strengthened by a comparative analysis that incorporates features optimized by the Genetic Algorithm (GA). Additionally, the MLR algorithm is contrasted with K-Nearest Neighbors (KNN). The empirical findings indicate that our model, which has been refined using GSA, achieves very high levels of precision. Specifically, the average precision for MLR is 99.2%, while for KNN it is 98.6%. The resulting results significantly exceed those achieved with GA-optimized features, thereby highlighting the superiority of our suggested strategy. One important result of our study is that we were able to decrease the number of features by more than 50%. This reduction greatly reduces the processing requirements without sacrificing the quality of the diagnosis. This work presents a robust and efficient approach to the early detection of plant diseases. The work demonstrates the utilization of sophisticated computational methods in agriculture, enabling the development of novel data-driven strategies for plant health management, therefore enhancing worldwide food security.

Application of machine learning in the study of cobalt-based oxide catalysts for antibiotic degradation: An innovative reverse synthesis strategy.

This study addresses antibiotic pollution in global water bodies by integrating machine learning and optimization algorithms to develop a novel reverse synthesis strategy for inorganic catalysts. We meticulously analyzed data from 96 studies, ensuring quality through preprocessing steps. Employing the AdaBoost model, we achieved 90.57% accuracy in classification and an R²value of 0.93 in regression, showcasing strong predictive power. A key innovation is the Sparrow Search Algorithm (SSA), which optimizes catalyst selection and experimental setup tailored to specific antibiotics. Empirical experiments validated SSA's efficacy, with degradation rates of 94% for Levofloxacin and 97% for Norfloxacin, aligning closely with predictions within a 2% margin of error. This research advances theoretical understanding and offers practical applications in material science and environmental engineering, significantly enhancing catalyst design efficiency and accuracy through the fusion of advanced machine learning techniques and optimization algorithms.

Smart active particles learn and transcend bacterial foraging strategies.

Throughout evolution, bacteria and other microorganisms have learned efficient foraging strategies that exploit characteristic properties of their unknown environment. While much research has been devoted to the exploration of statistical models describing the dynamics of foraging bacteria and other (micro-) organisms, little is known, regarding the question of how good the learned strategies actually are. This knowledge gap is largely caused by the absence of methods allowing to systematically develop alternative foraging strategies to compare with. In the present work, we use deep reinforcement learning to show that a smart run-and-tumble agent, which strives to find nutrients for its survival, learns motion patterns that are remarkably similar to the trajectories of chemotactic bacteria. Strikingly, despite this similarity, we also find interesting differences between the learned tumble rate distribution and the one that is commonly assumed for the run and tumble model. We find that these differences equip the agent with significant advantages regarding its foraging and survival capabilities. Our results uncover a generic route to use deep reinforcement learning for discovering search and collection strategies that exploit characteristic but initially unknown features of the environment. These results can be used, e.g., to program future microswimmers, nanorobots, and smart active particles for tasks like searching for cancer cells, micro-waste collection, or environmental remediation.

Validity of Major Osteoporotic Fracture Diagnoses in the Danish National Patient Registry.

Objective: To evaluate the validity of diagnosis codes for Major Osteoporotic Fracture (MOF) in the Danish National Patient Registry (NPR) and secondly to evaluate whether the fracture was incident/acute using register-based definitions including date criteria and procedural codes. Methods: We identified a random sample of 2400 records with a diagnosis code for a MOF in the NPR with dates in the year of 2018. Diagnoses were coded with the 10th revision of the International Classification of Diseases (ICD-10). The sample included 2375 unique fracture patients from the Region of Southern Denmark. Medical records were retrieved for the study population and reviewed by an algorithmic search function and medical doctors to verify the MOF diagnoses. Register-based definitions of incident/acute MOF was evaluated in NPR data by applying date criteria and procedural codes. Results: The PPV for MOF diagnoses overall was 0.99 (95% CI: 0.98;0.99) and PPV=0.99 for the four individual fracture sites, respectively. Further, analyses of incident/acute fractures applying date criteria, procedural codes and using patients' first contact in the NPR resulted in PPV=0.88 (95% CI: 0.84;0.91) for hip fractures, PPV=0.78 (95% CI: 0.74;0.83) for humerus fractures, PPV=0.78 (95% CI: 0.73;0.83) for clinical vertebral fractures and PPV=0.87 (95% CI: 0.83;0.90) for wrist fractures. Conclusion: ICD-10 coded MOF diagnoses are valid in the NPR. Furthermore, a set of register-based criteria can be applied to qualify if the MOF fracture was incident/acute. Thus, the NPR is a valuable and reliable data source for epidemiological research on osteoporotic fractures.

Pistol shooting performance under pressure: Longitudinal changes in gaze behavior of male and female Army cadets.

We found evidence that Army cadets improved their gaze behavior and performance across time under high and low pressure in a shooting task. The purpose of the study was to determine if male and female cadets developed an optimal quiet eye (QE) onset, a longer QE duration, and decreased pupil diameter variability (PDV) over time under low (LP) and high pressure (HP) conditions. The study was carried out over four sessions, with intervals of 4.5 months. During each session, 16 men and 12 women, first-year cadets of The Brazilian Army Academy, performed ten pistol shots under counterbalanced LP and HP conditions. The cadets shot in the upright position and wore an eye-tracker. Shooting accuracy improved and did not differ for men and women in the LP condition, however during HP the women performed more poorly than the men in session 1 but improved to a level similar to the men in session 4. QE duration Pre (aiming) did not differ during LP, while during HP QE Post (execution) increased across the session for men and women. QE onset 2 (execution) occurred earlier for the men than women during LP, while during HP the women improved to a level similar to the men in sessions 3 and 4. PDV declined across sessions for men and women with the lowest values in sessions 3 and 4. The findings are discussed within social facilitation theory, which states the context of training affects the rate at which improvements in motor skills occur. The results show that women cadets can improve their shooting performance, quiet eye duration, quiet eye onset and pupil diameter variability to a level similar to men if three to four LP and HP training sessions are scheduled across approximately 12-18 months.

Search engine optimization and its association with readability and accessibility of diabetic retinopathy websites.

PURPOSE: This study investigated whether websites regarding diabetic retinopathy are readable for patients, and adequately designed to be found by search engines. METHODS: The term "diabetic retinopathy" was queried in the Google search engine. Patient-oriented websites from the first 10 pages were categorized by search result page number and website organization type. Metrics of search engine optimization (SEO) and readability were then calculated. RESULTS: Among the 71 sites meeting inclusion criteria, informational and organizational sites were best optimized for search engines, and informational sites were the most visited. Better optimization as measured by authority score was correlated with lower Flesch Kincaid Grade Level (r = 0.267, P = 0.024). There was a significant increase in Flesch Kincaid Grade Level with successive search result pages (r = 0.275, P = 0.020). Only 2 sites met the 6th grade reading level AMA recommendation by Flesch Kincaid Grade Level; the average reading level was 10.5. There was no significant difference in readability between website categories. CONCLUSION: While the readability of diabetic retinopathy patient information was poor, better readability was correlated to better SEO metrics. While we cannot assess causality, we recommend websites improve their readability, which may increase uptake of their resources.

A new method for tracking the preparatory activation of target templates for visual search with high temporal precision.

Efficiently selecting task-relevant objects during visual search depends on foreknowledge of their defining characteristics, which are represented within attentional templates. These templates bias attentional processing toward template-matching sensory signals and are assumed to become anticipatorily activated prior to search display onset. However, a direct neural signal for such preparatory template activation processes has so far remained elusive. Here, we introduce a new high-definition rapid serial probe presentation paradigm (RSPP-HD), which facilitates high temporal resolution tracking of target template activation processes in real time via monitoring of the N2pc component. In the RSPP-HD procedure, task-irrelevant probe displays are presented in rapid succession throughout the period between task-relevant search displays. The probe and search displays are homologously formed by lateralized "clouds" of colored dots, yielding probes that occur at task-relevant locations without confounding template-guided and salience-driven attentional shifts. Target color probes appearing at times when a corresponding target template is active should attract attention, thereby eliciting an N2pc. In a condition where new probe displays appeared every 50 ms, probe N2pcs were reliably elicited during the final 800 ms prior to search display onset, increasing in amplitude toward the end of this preparation period. Analogous temporal profiles were also observed with longer intervals between probes. These findings show that search template activation processes are transient and that their temporal profile can be reliably monitored at high-sampling frequencies with the RSPP-HD paradigm. This procedure offers a new route to approach various questions regarding the content and temporal dynamics of attentional control processes.

A memristive all-inclusive hypernetwork for parallel analog deployment of full search space architectures.

In recent years, there has been a significant advancement in memristor-based neural networks, positioning them as a pivotal processing-in-memory deployment architecture for a wide array of deep learning applications. Within this realm of progress, the emerging parallel analog memristive platforms are prominent for their ability to generate multiple feature maps in a single processing cycle. However, a notable limitation is that they are specifically tailored for neural networks with fixed structures. As an orthogonal direction, recent research reveals that neural architecture should be specialized for tasks and deployment platforms. Building upon this, the neural architecture search (NAS) methods effectively explore promising architectures in a large design space. However, these NAS-based architectures are generally heterogeneous and diversified, making it challenging for deployment on current single-prototype, customized, parallel analog memristive hardware circuits. Therefore, investigating memristive analog deployment that overrides the full search space is a promising and challenging problem. Inspired by this, and beginning with the DARTS search space, we study the memristive hardware design of primitive operations and propose the memristive all-inclusive hypernetwork that covers 2×1025 network architectures. Our computational simulation results on 3 representative architectures (DARTS-V1, DARTS-V2, PDARTS) show that our memristive all-inclusive hypernetwork achieves promising results on the CIFAR10 dataset (89.2% of PDARTS with 8-bit quantization precision), and is compatible with all architectures in the DARTS full-space. The hardware performance simulation indicates that the memristive all-inclusive hypernetwork costs slightly more resource consumption (nearly the same in power, 22%∼25% increase in Latency, 1.5× in Area) relative to the individual deployment, which is reasonable and may reach a tolerable trade-off deployment scheme for industrial scenarios.