Holistic Information Research Articles

Face cells encode object parts more than facial configuration of illusory faces.

Humans perceive illusory faces in everyday objects with a face-like configuration, an illusion known as face pareidolia. Face-selective regions in humans and monkeys, believed to underlie face perception, have been shown to respond to face pareidolia images. Here, we investigated whether pareidolia selectivity in macaque inferotemporal cortex is explained by the face-like configuration that drives the human perception of illusory faces. We found that face cells responded selectively to pareidolia images. This selectivity did not correlate with human faceness ratings and did not require the face-like configuration. Instead, it was driven primarily by the "eye" parts of the illusory face, which are simply object parts when viewed in isolation. In contrast, human perceptual pareidolia relied primarily on the global configuration and could not be explained by "eye" parts. Our results indicate that face-cells encode local, generic features of illusory faces, in misalignment with human visual perception, which requires holistic information.

MULTI-AGENCY DEVELOPMENT OF A NEW INFORMATION RESOURCE FOR PATIENTS WITH BRAIN METASTASES

Abstract AIMS Based in a large neuro-oncology centre which provides specialist care for hundreds of patients with BM per year, the authors identified a need for relevant, non-disease specific, technically accurate and holistically focussed patient information material. This project aimed to develop a comprehensive and patient-focussed information resource for patients with BM. METHOD A scoping exercise of twenty UK-based cancer charity websites was carried out. Generic cancer charity sites and sites of charities supporting the four main primary cancer groups which metastasize to the brain were targeted. Information pertaining to BM was collated and reviewed by a multidisciplinary group to assess for scope and quality of information provided. Particular attention was paid to identifying information which was technically accurate and specifically addressed the holistic needs of this patient group. RESULTS Information found was primary disease-specific, technically inaccurate and did not acknowledge the holistic needs of the patient. This exercise highlighted the need for comprehensive and relevant patient-focussed information material. A working group consisting of health professionals and BrainsTrust charity staff was formed to create an effective patient information resource. Existing BrainsTrust information templates were used to structure the new resource. Evidence-based clinical information was provided by the healthcare professionals involved and holistic information was created collaboratively with charity input. A patient reviewer was enlisted to provide feedback on draft versions. CONCLUSION This project resulted in production of a patient-focussed and comprehensive information resource on BM. Patient and healthcare professional feedback to date has been positive.

The optimization of water tourism management in Sumber Maron, Malang and Umbul Ponggok, Klaten

Abstract Water tourism is an eminent sector potential to build Indonesians’ welfare. Water tourist destination that can attract tourists today is Umbul Ponggok, located in Klaten, Central Java and Sumber Maron in Malang, East Java. Both tourist destinations have water-based tourism concept by utilizing surrounding natural environment and empowering local people in their management. Therefore, this research aims to compare the optimization of water tourism management based on natural environment and the community empowerment. This research used a qualitative research method with phenomenological approach to see holistic information on water tourism management developed in both tourist objects. The result shows that in its management, Umbul Ponggok keeps attempting to maintain natural conservation and pays much attention to natural conservation and surrounding environment. It is similar to that in Sumber Maron prioritizing natural environment aspect and likewise surrounding community empowerment. Sumber Maron always attempts to review all tourism activities. However, the management existing in Sumber Maron is still far below standard existing in Umbul Ponggok. Therefore, the future tourism management should pass through some stages including trial, update, and public and village government participation to support the advancement of water tourism.

A generalist vision-language foundation model for diverse biomedical tasks.

Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize holistic information. Generalist AI holds the potential to address these limitations due to its versatility in interpreting different data types and generating tailored outputs for diverse needs. However, existing biomedical generalist AI solutions are typically heavyweight and closed source to researchers, practitioners and patients. Here, we describe BiomedGPT, the first open-source and lightweight vision-language foundation model, designed as a generalist capable of performing various biomedical tasks. BiomedGPT achieved state-of-the-art results in 16 out of 25 experiments while maintaining a computing-friendly model scale. We also conducted human evaluations to assess the capabilities of BiomedGPT in radiology visual question answering, report generation and summarization. BiomedGPT exhibits robust prediction ability with a low error rate of 3.8% in question answering, satisfactory performance with an error rate of 8.3% in writing complex radiology reports, and competitive summarization ability with a nearly equivalent preference score to human experts. Our method demonstrates that effective training with diverse data can lead to more practical biomedical AI for improving diagnosis and workflow efficiency.

Showing authentic examples of academic and career trajectories to influence college students' career exploration

College students increasingly use digital information resources to help them make academic and career decisions, but the effects of digital information tools on students' career exploration outcomes are not well understood. We investigate the impact of an online tool that shows the full sequence of course enrollments and the first career destination of recent graduates with matched interests. We conducted a randomized controlled experiment with 234 undergraduate students at a U.S. university to examine the tool's impact on students' career exploration outcomes, specifically their self-efficacy in academic planning for career exploration and behavioral intentions for career exploration. We did not find clear evidence that the tool increased students' self-efficacy and behavioral intentions. However, our exploratory analysis shows that the tool benefited students who were at a later stage in their career decision-making process. We also found that students' awareness of official information sources from archival student records and awareness of holistic information that combines academic and career decisions are important predictors of self-efficacy and behavioral intentions. This study presents a nuanced view of the potential of digital tools to enhance career exploration outcomes through information support. It has important implications for institutional administrators to integrate existing digital resources with traditional career services, creating a more comprehensive support system for students.

I[formula omitted]U-Net: A dual-path U-Net with rich information interaction for medical image segmentation

Although the U-shape networks have achieved remarkable performances in many medical image segmentation tasks, they rarely model the sequential relationship of hierarchical layers. This weakness makes it difficult for the current layer to effectively utilize the historical information of the previous layer, leading to unsatisfactory segmentation results for lesions with blurred boundaries and irregular shapes. To solve this problem, we propose a novel dual-path U-Net, dubbed I2U-Net. The newly proposed network encourages historical information re-usage and re-exploration through rich information interaction among the dual paths, allowing deep layers to learn more comprehensive features that contain both low-level detail description and high-level semantic abstraction. Specifically, we introduce a multi-functional information interaction module (MFII), which can model cross-path, cross-layer, and cross-path-and-layer information interactions via a unified design, making the proposed I2U-Net behave similarly to an unfolded RNN and enjoying its advantage of modeling time sequence information. Besides, to further selectively and sensitively integrate the information extracted by the encoder of the dual paths, we propose a holistic information fusion and augmentation module (HIFA), which can efficiently bridge the encoder and the decoder. Extensive experiments on four challenging tasks, including skin lesion, polyp, brain tumor, and abdominal multi-organ segmentation, consistently show that the proposed I2U-Net has superior performance and generalization ability over other state-of-the-art methods. The code is available at https://github.com/duweidai/I2U-Net.

On performance evaluation of high‐power, high‐bandwidth current measurement technologies for SiC switching devices

AbstractSilicon carbide (SiC) power metal‐oxide‐semiconductor field‐effect transistors (MOSFETs) switch at an unprecedented speed, even at high currents. For accurate dynamic characterization, current sensors must measure high currents at a high bandwidth. Moreover, at high switching speeds, parasitic impedances in the commutation loop become critical. To ensure high‐accuracy measurements, the current sensor insertion impedance must be minimal. Here, a two‐step current sensor evaluation method is proposed. This method serves the characterization and suitability assessment of high‐power, high‐bandwidth current sensors for fast‐switching applications using SiC power MOSFETs. Conducting a small‐ and a large‐signal transmission behaviour analysis separately results in holistic information about the current sensor behaviour in both time and frequency domain. The proposed method is validated using four commercially available current sensors that are widely used for SiC power MOSFET characterization. The work concludes transferring the knowledge derived in the conducted experiments to a practical, application‐oriented sensor selection guide.

Complete genome characterization of chilli veinal mottle virus associated with mosaic and mottling disease of tomato and development of LAMP assay for quick detection.

Chilli veinal mottle virus (ChiVMV) is a potyvirus known to cause havoc in many solanaceous crops. Samples from tomato plants exhibiting typical mosaic and mottling symptoms in two locations from farmers' fields were collected and tested using DAC ELISA for the presence of ChiVMV and other viruses known to infect tomato. ChiVMV Gauribidanur isolate from infected tomato was mechanically inoculated to Datura metel, Nicotiana tabacum, Nicotiana benthamiana, Nicotiana glutinosa, chilli, and tomato plants which exhibited systemic mosaic and mottling symptoms 10days post-inoculation. This results were further confirmed by RT-PCR and DAC ELISA using CP gene-specific primers and ChiVMV antisera, respectively. Transmission electron microscopy revealed the presence of long filamentous particles (800×11nm) resembling viruses in the Potyviridae family. The complete genome of ChiVMV comprised 9716 nucleotides except for poly A tail, with a predicted open reading frame spanning 9270 nucleotides encoding polyproteins of 3089 amino acids. Comparative analysis revealed that ChiVMV-tomato isolates reported across the world shared maximum nucleotide identity (93-96.7%) with chilli isolates from India and Pakistan. These results were well supported by sequence demarcation analysis. Further, the Neibhor-Net network analysis of the complete genome of ChiVMV-tomato, along with other host isolates, formed a reticular network phylogenetic tree suggesting recombination events. Subsequently, RDP5 detected intra-specific recombination breakpoints at the positions 1656-5666 nucleotides with major parent ChiVMV (MN508960) Uravakonda and minor parent ChiVMV (MN508956) with a significant average p value of 1.905×10-22. The LAMP assay using ChiVMV-specific primers resulted in ladder-like amplified products on electrophoresed gel and a distinct red colour pattern with hydroxy naphthalene blue, indicating a positive reaction for the presence of ChiVMV in infected tomato samples. To validate LAMP-designed primers, RNA extracted from ChiVMV-infected tomato, chilli, datura, and tobacco samples were subjected to LAMP assay and it accurately detected the presence of ChiVMV in infected plant samples. Overall, this study provides holistic information of ChiVMV infecting tomato, spanning diagnosis, transmission, genetic characterization, and detection of recombination events, which collectively contribute to effective disease management, crop protection, and informed decision-making in agricultural practices.

Study protocol for two stepped-wedge interventional trials evaluating the effects of holistic information technology-based patient-oriented management in older multimorbid patients with cancer: The GERONTE trials

IntroductionCurrent hospital-based care pathways are generally single-disease centred. As a result, coexisting morbidities are often suboptimally evaluated and managed, a deficiency becoming increasingly apparent among older patients who exhibit heterogeneity in health status, functional abilities, frailty, and other geriatric impairments. To address this issue, our study aims to assess a newly developed patient-centred care pathway for older patients with multimorbidity and cancer.The new care pathway was based on currently available evidence and co-designed by end-users including health care professionals, patients, and informal caregivers.Within this care pathway, all healthcare professionals involved in the care of older patients with multimorbidity and cancer will form a Health Professional Consortium (HPC). The role of the HPC will be to centralise oncologic and non-oncologic treatment recommendations in accordance with the patient's priorities. Moreover, an Advanced Practice Nurse will act as case-manager by being the primary point of contact for the patient, thus improving coordination between specialists, and by organising and leading the consortium. Patient monitoring and the HPC collaboration will be facilitated by digital communication tools designed specifically for this purpose, with the added benefit of being customisable for each patient. Materials and MethodsThe GERONTE study is a prospective international, multicentric study consisting of two stepped-wedge trials performed at 16 clinical sites across three European countries. Each trial will include 720 patients aged 70 years and over with a new or progressive cancer (breast, lung, colorectal, prostate) and at least one moderate or severe multimorbidity. The patients in the intervention group will receive the new care pathway whereas patients in the control group will receive usual oncologic care. DiscussionGERONTE will evaluate whether this kind of holistic, patient-oriented healthcare management can improve quality of life (primary outcome) and other valuable endpoints in older patients with multimorbidity and cancer. An ancillary study will assess in depth the socio-economic impact of the intervention and deliver concrete implementation guidelines for the GERONTE intervention care pathway.Trial registration: FRONE: NCT05720910TWOBE: NCT05423808

Unsupervised Group Re-identification via Adaptive Clustering-Driven Progressive Learning

Group re-identification (G-ReID) aims to correctly associate groups with the same members captured by different cameras. However, supervised approaches for this task often suffer from the high cost of cross-camera sample labeling. Unsupervised methods based on clustering can avoid sample labeling, but the problem of member variations often makes clustering unstable, leading to incorrect pseudo-labels. To address these challenges, we propose an adaptive clustering-driven progressive learning approach (ACPL), which consists of a group adaptive clustering (GAC) module and a global dynamic prototype update (GDPU) module. Specifically, GAC designs the quasi-distance between groups, thus fully capitalizing on both individual-level and holistic information within groups. In the case of great uncertainty in intra-group members, GAC effectively minimizes the impact of non-discriminative features and reduces the noise in the model's pseudo-labels. Additionally, our GDPU devises a dynamic weight to update the prototypes and effectively mine the hard samples with complex member variations, which improves the model's robustness. Extensive experiments conducted on four popular G-ReID datasets demonstrate that our method not only achieves state-of-the-art performance on unsupervised G-ReID but also performs comparably to several fully supervised approaches.

Holism in Dealing with Global Problems

To the extent that we humans believe that we can solve our species-level problems with technology, we remain on the path to self-destruction. But there is an alternative; we can see other species like ours as empirical examples of how to be a species—how to participate in ultimate reality. This article exemplifies how holistic information provided systemically by other species can be used. Such information reveals the magnitude of humanity’s challenges and what is needed to address the myriad interrelated global problems, including climate change. Such systemic thinking involves a shift from conceptually extracting things from their context to seeing everything embedded in its context.Seeing other species as role models for how to participate in ecosystems requires a paradigm different from that prevailing in today’s world. Words often attributed to Albert Einstein put it succinctly: “We can’t solve problems by using the same kind of thinking we used when we created them.” Following the leads of Gregory Bateson, Thomas Berry, Fritjof Capra, and Albert Einstein, this article implements systemic thinking by using other species as role models. Replacing conventional thinking with systemic thinking leads to holistic approaches to global problems. The solutions to many of these problems, however, may very well have to be accomplished by the forces of nature; such solutions would be both natural and normal.

AgriFireInfo v1.0: An Open-Source Platform for the Monitoring and Management of Open-Field Crop Residue Burning

Open-field crop residue burning (OCRB) is a widespread agricultural practice with significant impacts on regional environments and public health. The effective management of OCRB remains a challenging task that requires timely access to various forms of monitored and forecasted information. Addressing this worldwide need, an open-source platform named AgriFireInfo v1.0, which is specifically tailored to monitoring and regulating regional OCRB activities, was developed. This technical note thoroughly illustrates the platform’s architecture, major modules, and visualization processes. Through AgriFireInfo v1.0, government agencies can access timely information about the spatial distribution of fire spots and emissions as well as meteorological conditions and air quality status. AgriFireInfo v1.0 also introduces an innovative Prevention Alarming Index, designed to identify regions prone to OCRB and promote comprehensive crop residue utilization. Furthermore, it offers the burning window and crop residue yields for controlled OCRB activities and can be used to analyze shifts in farmers’ burning behaviors and intensities. Future enhancements will focus on supplying holistic information on the burning windows and burning amounts of crop residues to further facilitate refined controlled burning activities and optimize decision-making processes. The flexibility and scalability of this platform can potentially allow users to easily customize and apply it to other regions or countries.

The Development of Composites Materials: From Conventional to Innovative Uses

This paper explores the evolution, development, and application of composite substances from conventional methodologies to their innovative uses throughout numerous sectors. Composite materials, known for their strength, versatility, and resilience, have seen substantial advances, especially with the incorporation of nanotechnologies and hybrid fiber reinforcements. By means of analyzing exclusive matrix substances, reinforcement sorts, and fabrication techniques, this study highlights the transition from traditional composites to advanced metallic matrix nanocomposites (MMNCS) and fiber-reinforced polymers (FRPS). Special interest is given to the demanding situations associated with manufacturing, together with uniform dispersion of nanoparticles and damage-free machining of fiber composites. Moreover, the paper discusses the environmental impact that specialize in sustainable options like natural fiber reinforcements. Through comprehensive critiques and case studies, this research objectives to offer a holistic information of the cutting-edge state and future potential of composite materials in improving industrial purposes even as addressing environmental concerns.

Developing an Instrument to Evaluate the Quality of Dementia Websites.

In today's digital era, health information, especially for conditions like dementia, is crucial. This study aims to develop an instrument, demenTia wEbsite measSurement insTrument (TEST), through four steps: identifying existing instruments, determining criteria, selecting and revising measurement statements, and validating the instrument from March to August 2020. Five health informatics experts used the content validity ratio (CVR) test for validation. Thirteen evaluators compared Fleiss Kappa and intraclass correlation coefficient (ICC) values across four dementia websites using TEST and another tool, DISCERN. TEST consists of seven criteria and 25 measurement statements focusing on content quality (relevance, credibility, currency) and user experience (accessibility, interactivity, attractiveness, privacy). CVR = 1 confirmed all statements as essential. The TEST demonstrated stronger consistency and assessor agreement compared to DISCERN, measured by Fleiss Kappa and ICC. Overall, it is a robust tool for reliable and user-friendly dementia resources, ensuring health holistic information accessibility.

Holistic bursting cells store long-term memory in auditory cortex

The sensory neocortex has been suggested to be a substrate for long-term memory storage, yet which exact single cells could be specific candidates underlying such long-term memory storage remained neither known nor visible for over a century. Here, using a combination of day-by-day two-photon Ca2+ imaging and targeted single-cell loose-patch recording in an auditory associative learning paradigm with composite sounds in male mice, we reveal sparsely distributed neurons in layer 2/3 of auditory cortex emerged step-wise from quiescence into bursting mode, which then invariably expressed holistic information of the learned composite sounds, referred to as holistic bursting (HB) cells. Notably, it was not shuffled populations but the same sparse HB cells that embodied the behavioral relevance of the learned composite sounds, pinpointing HB cells as physiologically-defined single-cell candidates of an engram underlying long-term memory storage in auditory cortex.

Diag-IoU Loss for Object Detection

Existing IoU-based loss functions have achieved promising performance for bounding box regression in object detection. However, they cannot fully reflect the relation between the predicted and target boxes in the case of box inclusions, and might thus deteriorate detection accuracy and efficiency. In this paper, we design a novel similarity measurement based on the box diagonal called Diag-IoU to well represent the divergence between the predicted and target boxes even in the case of box inclusions, and thus achieve superior localization accuracy and fast convergence. In particular, we equivalently represent a rectangular box with its box diagonal, which contains exclusive and informative geometrical factors, and define the Diag-IoU based on the similarities of a set of sampled point pairs from the predicted and target box diagonals. Based on the Diag-IoU, we design a general Diag-IoU loss, which can provide holistic information in measuring two boxes and thus differentiate the two boxes in the case of box inclusions. To validate the effectiveness of the proposed method, we apply the Diag-IoU loss to several representative object detectors, including YOLO v5s, Faster R-CNN, and FCOS. Extensive experiments on the synthetic data and two challenging object detection benchmark datasets, i.e., MS COCO and PASCAL VOC, demonstrate the superior performance of the proposed Diag-IoU loss compared to previous IoU-based losses as well as other metrics.

The nature of the psyche and consciousness, quantum computers and information models in psychoinformatics (socionics)

Modeling the psyche, consciousness and artificial intelligence have encountered conceptual difficulties in understanding real mental processes. Numerous modern studies and experiments presented in the review indicate the probable quantum nature of the psyche and consciousness. The analysis shows that a number of quantum processes are associated with known classical processes and structures, from a single cell to the brain. Therefore, adequate modeling of mental processes, including consciousness and intelligence, is possible only on multiprocessor quantum computers with a special architecture corresponding to the real structure of the psyche. This possible architecture is proposed by the theory of information metabolism - socionics (psychoinformatics), in which detailed holistic information models of the psyche have been developed, which have been used in practice for more than 30 years and have proven their effectiveness. These models represent a system of mental functions of information metabolism, which can be simulated by special quantum processors interacting with each other according to certain laws

Lungs X-Ray Image Segmentation and Classification of Lung Disease using Convolutional Neural Network Architectures

Lung disease is one of the biggest causes of death in the world. The SARS-CoV-2 virus causes diseases like COVID-19, and the bacteria Streptococcus sp., which causes pneumonia, are two sample causes of lung disease. X-ray images are used to detect the lung disease. This study aimed to combine the stages of segmentation and classification of lung disease. This study in segmentation aims to separate the features contained in the lung images. The classification aimed to provide holistic information on lung disease. This research method used the Deep Residual U-Net (DrU-Net) segmentation architecture and the Deep Residual Neural Network (DResNet) classification architecture. DrU-Net is a modified U-Net architecture with dropout added in its convolutional layers. DResNet is a modified Residual Network (ResNet) architecture with dropout added in its convolutional block layers. The result of this study was segmentation using the DrU-Net architecture obtained 99% for accuracy, 98% for precision, 98% for recalls, 98% for F1-Score, and 96.1% for IoU. The classification results of the segmented images using the DResNet architecture obtained 91% for accuracy, 86% for precision, 85% for recalls, and 84% for F1-Score. The performance results of DrU-Net architecture were excellent and robust in image segmentation. Unfortunately, the average performance of DResNet in classification was still below 90%. These results indicate that Dres-Net performs well in classifying lung disorders in 3 labels, namely Covid, Normal, and Pneumonia, but still needs improvement.

"No One Asked Me If I'm Alright": A Mixed-Methods Study Exploring Information/Support Needs and Challenges Engaging Men Diagnosed With Male-Factor Infertility.

There is limited research exploring men's experiences of infertility, and fewer previous studies have examined what information and support men desire after being diagnosed specifically with male-factor infertility. We conducted a mixed-methods study utilizing a combined sequential, concurrent design (online survey/semi-structured interviews). Survey outcomes (N =12) were analyzed using quantitative data analysis, while qualitative survey data (N = 5) was analyzed by reflexive thematic analysis. Heterosexual men (>18 years), fluent in English, diagnosed solely with male-factor infertility/sub-fertility, who required assisted reproductive treatment within Australia in the past 5 years were recruited online and through fertility clinics Australia-wide. Most men reported that their information and support needs were only somewhat, slightly or not at all met. Preferred information sources on male infertility were a dedicated online resource, app, or fertility doctor/specialist, while support was preferred from fertility specialists and partners. Three themes were identified from the qualitative analysis about men's experiences and support needs when diagnosed with male infertility (a) Ultimate threat to masculinity; (b) Holistic care, and (c) the power of words. The information-rich data collected provided valuable insights into men's experiences of male-factor infertility and important considerations to improve recruitment for future research. A diagnosis of male-factor infertility has the potential to be deeply impactful and difficult to navigate for men. Adequate and holistic information, recognition of emotional impacts, proactive offers of support and sensitive language are needed to improve men's experiences when undergoing assisted reproductive technology.

Perspectives for the green hydrogen energy-based economy

Hydrogen as an energy vector offers enormous potential compared to other renewable energy routes in the deep decarbonization of industrial sectors. However, globally, developments across the entire hydrogen value chain are still emerging. The specific facets of hydrogen energy, like production, storage, transportation, applications, and policies, are individually evaluated. Nevertheless, a comprehensive viewpoint on the prospects and challenges across the entire hydrogen value chain is essential, and such holistic information is rare. This study addresses the knowledge gap by mapping the potential of the hydrogen economy from an all-rounded perspective. In addition to the advancements in materials, wastewater recycling and the use of microbes or enzymes in electrolysis, offer potential alternative routes to support a hydrogen-based economy. Significant technological advancements are warranted for safe hydrogen transportation and storage. A vast network of refueling stations is essential for hydrogen fuel penetration in the transportation sector, and conducive governing policies are needed to decarbonize heavy industries. Lastly, government incentives are crucial to promote the socioeconomic acceptance of the transition from fossil fuels to green hydrogen.