Academic Digital Library for institutions, students, and solo learners

Discovery

Content search and filters

This is the first search surface for the seven content types. Next we will connect full-text search and metadata-specific filters.

Results 1,892

Periodicals

MIRD Pamphlet No. 23: Quantitative SPECT for Patient-Specific 3-Dimensional Dosimetry in Internal Radionuclide Therapy

In internal radionuclide therapy, a growing interest in voxel-level estimates of tissue-absorbed dose has been driven by the desire to report radiobiologic quantities that account for the biologic consequences of both spatial and temporal nonuniformities in these dose estimates. This report presents an overview of 3-dimensional SPECT methods and requirements for internal dosimetry at both regional and voxel levels. Combined SPECT/CT image-based methods are emphasized, because the CT-derived anatomic information allows one to address multiple technical factors that affect SPECT quantification while facilitating the patient-specific voxel-level dosimetry calculation itself. SPECT imaging and reconstruction techniques for quantification in radionuclide therapy are not necessarily the same as those designed to optimize diagnostic imaging quality. The current overview is intended as an introduction to an upcoming series of MIRD pamphlets with detailed radionuclide-specific recommendations intended to provide best-practice SPECT quantification-based guidance for radionuclide dosimetry.

Architecture & Planning• 2012• Society of Nuclear Medicine and Molecular Imaging
Access Full TextView Details
Periodicals

AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?

With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods.

Architecture & Planning• 2021• IEEE Computer Society
Access Full TextView Details
Periodicals

Recent Advances in Electrochemical Sensors for Detecting Toxic Gases: NO2, SO2 and H2S

, H₂S and other S-containing gases, cause numerous harmful effects on human health even at very low gas concentrations. Reliable detection of various gases in low concentration is mandatory in the fields such as industrial plants, environmental monitoring, air quality assurance, automotive technologies and so on. In this paper, the recent advances in electrochemical sensors for toxic gas detections were reviewed and summarized with a focus on NO₂, SO₂ and H₂S gas sensors. The recent progress of the detection of each of these toxic gases was categorized by the highly explored sensing materials over the past few decades. The important sensing performance parameters like sensitivity/response, response and recovery times at certain gas concentration and operating temperature for different sensor materials and structures have been summarized and tabulated to provide a thorough performance comparison. A novel metric, sensitivity per ppm/response time ratio has been calculated for each sensor in order to compare the overall sensing performance on the same reference. It is found that hybrid materials-based sensors exhibit the highest average ratio for NO₂ gas sensing, whereas GaN and metal-oxide based sensors possess the highest ratio for SO₂ and H₂S gas sensing, respectively. Recently, significant research efforts have been made exploring new sensor materials, such as graphene and its derivatives, transition metal dichalcogenides (TMDs), GaN, metal-metal oxide nanostructures, solid electrolytes and organic materials to detect the above-mentioned toxic gases. In addition, the contemporary progress in SO₂ gas sensors based on zeolite and paper and H₂S gas sensors based on colorimetric and metal-organic framework (MOF) structures have also been reviewed. Finally, this work reviewed the recent first principle studies on the interaction between gas molecules and novel promising materials like arsenene, borophene, blue phosphorene, GeSe monolayer and germanene. The goal is to understand the surface interaction mechanism.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review

Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up with new ideas for the future of AM explorations.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

The Ethics of Realism in Virtual and Augmented Reality

In this paper we consider ethical issues that could emerge with the widespread adoption ofvirtual and augmented reality (XR) as a consumer product. Our major focus is on a likelyfuture where, with technological advances, virtual and augmented reality may becomeindistinguishable from reality thanks to the improvements in sensory feedback, in the abilityto interact with virtual features, and in other elements that enhance the realism of the virtualexperience—what we refer to as superrealism. In the introductory section we first considermore general ethical issues related to XR use, and in later sections we consider issues specificto superrealism in XR. Finally, we propose approaches towards remedies and outline a seriesof scientific questions targeted at the research community. This work is the result of a groupmeeting under the auspices of Digital Catapult in London, and reports findings of theirdiscussions. The intention of this paper is to highlight important areas for consideration bythe wider XR community, and provoke the discussion and further research needed toinvestigate and address these important topics.

Architecture & Planning• 2020• Frontiers Media
Access Full TextView Details
Periodicals

Self-assembling dipeptide antibacterial nanostructures with membrane disrupting activity

Peptide-based supramolecular assemblies are a promising class of nanomaterials with important biomedical applications, specifically in drug delivery and tissue regeneration. However, the intrinsic antibacterial capabilities of these assemblies have been largely overlooked. The recent identification of common characteristics shared by antibacterial and self-assembling peptides provides a paradigm shift towards development of antibacterial agents. Here we present the antibacterial activity of self-assembled diphenylalanine, which emerges as the minimal model for antibacterial supramolecular polymers. The diphenylalanine nano-assemblies completely inhibit bacterial growth, trigger upregulation of stress-response regulons, induce substantial disruption to bacterial morphology, and cause membrane permeation and depolarization. We demonstrate the specificity of these membrane interactions and the development of antibacterial materials by integration of the peptide assemblies into tissue scaffolds. This study provides important insights into the significance of the interplay between self-assembly and antimicrobial activity and establishes innovative design principles toward the development of antimicrobial agents and materials.

Architecture & Planning• 2017• Nature Portfolio
Access Full TextView Details
Periodicals

Reconfigurable manufacturing systems: the state of the art

In this paper, general requirements of next generation manufacturing systems are discussed, and the strategies to meet these requirements are considered. The production paradigms which apply these strategies are also classified. Particular emphasis is put on the paradigm of Reconfigurable Manufacturing System (RMS). Some key issues of the RMS design are discussed, and a critical review is presented concerning the developments of RMSs. Finally, suggestions of the RMS research are made and future research directions are identified.

Architecture & Planning• 2006• Taylor & Francis
Access Full TextView Details
Periodicals

Skilful nowcasting of extreme precipitation with NowcastNet

Abstract Extreme precipitation is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through skilful nowcasting that has high resolution, long lead times and local details 1–3 . Current methods are subject to blur, dissipation, intensity or location errors, with physics-based numerical methods struggling to capture pivotal chaotic dynamics such as convective initiation 4 and data-driven learning methods failing to obey intrinsic physical laws such as advective conservation 5 . We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods into a neural-network framework with end-to-end forecast error optimization. On the basis of radar observations from the USA and China, our model produces physically plausible precipitation nowcasts with sharp multiscale patterns over regions of 2,048 km × 2,048 km and with lead times of up to 3 h. In a systematic evaluation by 62 professional meteorologists from across China, our model ranks first in 71% of cases against the leading methods. NowcastNet provides skilful forecasts at light-to-heavy rain rates, particularly for extreme-precipitation events accompanied by advective or convective processes that were previously considered intractable.

Architecture & Planning• 2023• Nature Portfolio
Access Full TextView Details
Periodicals

Interaction Between Macro‐ and Micro-Nutrients in Plants

Nitrogen (N), phosphorus (P), sulfur (S), zinc (Zn), and iron (Fe) are some of the vital nutrients required for optimum growth, development, and productivity of plants. The deficiency of any of these nutrients may lead to defects in plant growth and decreased productivity. Plant responses to the deficiency of N, P, S, Fe, or Zn have been studied mainly as a separate event, and only a few reports discuss the molecular basis of biological interaction among the nutrients. Macro-nutrients like N, P, and/or S not only show the interacting pathways for each other but also affect micro-nutrient pathways. Limited reports are available on the investigation of two-by-two or multi-level nutrient interactions in plants. Such studies on the nutrient interaction pathways suggest that an MYB-like transcription factor, phosphate starvation response 1 (PHR1), acts as a master regulator of N, P, S, Fe, and Zn homeostasis. Similarly, light-responsive transcription factors were identified to be involved in modulating nutrient responses in Arabidopsis . This review focuses on the recent advances in our understanding of how plants coordinate the acquisition, transport, signaling, and interacting pathways for N, P, S, Fe, and Zn nutrition at the molecular level. Identification of the important candidate genes for interactions between N, P, S, Fe, and/or Zn metabolic pathways might be useful for the breeders to improve nutrient use efficiency and yield/quality of crop plants. Integrated studies on pathways interactions/cross-talks between macro‐ and micro-nutrients in the agronomically important crop plants would be essential for sustainable agriculture around the globe, particularly under the changing climatic conditions.

Architecture & Planning• 2021• Frontiers Media
Access Full TextView Details
Periodicals

Security Issues in Healthcare Applications Using Wireless Medical Sensor Networks: A Survey

Healthcare applications are considered as promising fields for wireless sensor networks, where patients can be monitored using wireless medical sensor networks (WMSNs). Current WMSN healthcare research trends focus on patient reliable communication, patient mobility, and energy-efficient routing, as a few examples. However, deploying new technologies in healthcare applications without considering security makes patient privacy vulnerable. Moreover, the physiological data of an individual are highly sensitive. Therefore, security is a paramount requirement of healthcare applications, especially in the case of patient privacy, if the patient has an embarrassing disease. This paper discusses the security and privacy issues in healthcare application using WMSNs. We highlight some popular healthcare projects using wireless medical sensor networks, and discuss their security. Our aim is to instigate discussion on these critical issues since the success of healthcare application depends directly on patient security and privacy, for ethic as well as legal reasons. In addition, we discuss the issues with existing security mechanisms, and sketch out the important security requirements for such applications. In addition, the paper reviews existing schemes that have been recently proposed to provide security solutions in wireless healthcare scenarios. Finally, the paper ends up with a summary of open security research issues that need to be explored for future healthcare applications using WMSNs.

Architecture & Planning• 2011• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

Blockchain: case studies in food supply chain visibility

Purpose This paper aims to investigate how blockchain has moved beyond cryptocurrencies and is being deployed to enhance visibility and trust in supply chains, their limitations and potential impact. Design/methodology/approach Qualitative analysis are undertaken via case studies drawn from food companies using semi-structured interviews. Findings Blockchain is demonstrated as an enabler of visibility in supply chains. Applications at scale are most likely for products where the end consumer is prepared to pay the premium currently required to fund the technology, e.g. baby food. Challenges remain in four areas: trust of the technology, human error and fraud at the boundaries, governance, consumer data access and willingness to pay. Research limitations/implications The paper shows that blockchain can be utilised as part of a system generating visibility and trust in supply chains. Research directs academic attention to issues that remain to be addressed. The challenges pertaining to the technology itself we believe to be generalisable; those specific to the food industry may not hold elsewhere. Practical implications From live case studies, we provide empirical evidence that blockchain provides visibility of exchanges and reliable data in fully digitised supply chains. This provides provenance and guards against counterfeit goods. However, firms will need to work to gain consumer buy-in for the technology following repeated past claims of trustworthiness. Originality/value This paper provides primary evidence from blockchain use cases “in the wild”. The exploratory case studies examine application of blockchain for supply chain visibility.

Architecture & Planning• 2020• Emerald Publishing Limited
Access Full TextView Details
Periodicals

Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.

Architecture & Planning• 2011• Public Library of Science
Access Full TextView Details
Periodicals

Distinct Pathogenesis and Host Responses during Infection of C. elegans by P. aeruginosa and S. aureus

The genetically tractable model host Caenorhabditis elegans provides a valuable tool to dissect host-microbe interactions in vivo. Pseudomonas aeruginosa and Staphylococcus aureus utilize virulence factors involved in human disease to infect and kill C. elegans. Despite much progress, virtually nothing is known regarding the cytopathology of infection and the proximate causes of nematode death. Using light and electron microscopy, we found that P. aeruginosa infection entails intestinal distention, accumulation of an unidentified extracellular matrix and P. aeruginosa-synthesized outer membrane vesicles in the gut lumen and on the apical surface of intestinal cells, the appearance of abnormal autophagosomes inside intestinal cells, and P. aeruginosa intracellular invasion of C. elegans. Importantly, heat-killed P. aeruginosa fails to elicit a significant host response, suggesting that the C. elegans response to P. aeruginosa is activated either by heat-labile signals or pathogen-induced damage. In contrast, S. aureus infection causes enterocyte effacement, intestinal epithelium destruction, and complete degradation of internal organs. S. aureus activates a strong transcriptional response in C. elegans intestinal epithelial cells, which aids host survival during infection and shares elements with human innate responses. The C. elegans genes induced in response to S. aureus are mostly distinct from those induced by P. aeruginosa. In contrast to P. aeruginosa, heat-killed S. aureus activates a similar response as live S. aureus, which appears to be independent of the single C. elegans Toll-Like Receptor (TLR) protein. These data suggest that the host response to S. aureus is possibly mediated by pathogen-associated molecular patterns (PAMPs). Because our data suggest that neither the P. aeruginosa nor the S. aureus-triggered response requires canonical TLR signaling, they imply the existence of unidentified mechanisms for pathogen detection in C. elegans, with potentially conserved roles also in mammals.

Architecture & Planning• 2010• Public Library of Science
Access Full TextView Details
Periodicals

Review of Wearable Devices and Data Collection Considerations for Connected Health

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer's physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient's functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices.

Architecture & Planning• 2021• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI's applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI's transformative impact on the pharmaceutical industry and its broader implications for healthcare.

Architecture & Planning• 2024• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

The constant advances in agricultural robotics aim to overcome the challenges imposed by population growth, accelerated urbanization, high competitiveness of high-quality products, environmental preservation and a lack of qualified labor. In this sense, this review paper surveys the main existing applications of agricultural robotic systems for the execution of land preparation before planting, sowing, planting, plant treatment, harvesting, yield estimation and phenotyping. In general, all robots were evaluated according to the following criteria: its locomotion system, what is the final application, if it has sensors, robotic arm and/or computer vision algorithm, what is its development stage and which country and continent they belong. After evaluating all similar characteristics, to expose the research trends, common pitfalls and the characteristics that hinder commercial development, and discover which countries are investing into Research and Development (R&D) in these technologies for the future, four major areas that need future research work for enhancing the state of the art in smart agriculture were highlighted: locomotion systems, sensors, computer vision algorithms and communication technologies. The results of this research suggest that the investment in agricultural robotic systems allows to achieve short—harvest monitoring—and long-term objectives—yield estimation.

Architecture & Planning• 2021• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

A theoretical investigation of reference frames for the planning of speech movements.

Does the speech motor control system use invariant vocal tract shape targets when producing vowels and semivowels? A 4-part theoretical treatment favoring models whose only invariant targets are regions in auditory perceptual space over models that posit invariant constriction targets is presented. Auditory target regions are hypothesized to arise during development as an emergent property of neural map formation in the auditory system. Furthermore, speech movements are planned as trajectories in auditory perceptual space. These trajectories are then mapped into articulator movements through a neural mapping that allows motor equivalent variability in constriction locations and degrees when needed. These hypotheses are illustrated using computer simulations of the DIVA model of speech acquisition and production. Finally, several difficult challenges to proponents of constriction theories based on this theoretical treatment are posed.

Architecture & Planning• 1998• American Psychological Association
Access Full TextView Details
Periodicals

Bone morphogenetic protein 4 regulates the budding site and elongation of the mouse ureter

In the normal mouse embryo, Bmp4 is expressed in mesenchymal cells surrounding the Wolffian duct (WD) and ureter stalk, whereas bone morphogenetic protein (BMP) type I receptor genes are transcribed either ubiquitously (Alk3) or exclusively in the WD and ureter epithelium (Alk6). Bmp4 heterozygous null mutant mice display, with high penetrance, abnormalities that mimic human congenital anomalies of the kidney and urinary tract (CAKUT), including hypo/dysplastic kidneys, hydroureter, ectopic ureterovesical (UV) junction, and double collecting system. Analysis of mutant embryos suggests that the kidney hypo/dysplasia results from reduced branching of the ureter, whereas the ectopic UV junction and double collecting system are due to ectopic ureteral budding from the WD and accessory budding from the main ureter, respectively. In the cultured metanephros deprived of sulfated glycosaminoglycans (S-GAGs), BMP4-loaded beads partially rescue growth and elongation of the ureter. By contrast, when S-GAGs synthesis is not inhibited, BMP4 beads inhibit ureter branching and expression of Wnt 11, a target of glial cell-derived neurotrophic factor signaling. Thus, Bmp4 has 2 functions in the early morphogenesis of the kidney and urinary tract. One is to inhibit ectopic budding from the WD or the ureter stalk by antagonizing inductive signals from the metanephric mesenchyme to the illegitimate sites on the WD. The other is to promote the elongation of the branching ureter within the metanephros, thereby promoting kidney morphogenesis.

Architecture & Planning• 2000• American Society for Clinical Investigation
Access Full TextView Details
Periodicals

The Cognitive Atlas: Toward a Knowledge Foundation for Cognitive Neuroscience

Cognitive neuroscience aims to map mental processes onto brain function, which begs the question of what "mental processes" exist and how they relate to the tasks that are used to manipulate and measure them. This topic has been addressed informally in prior work, but we propose that cumulative progress in cognitive neuroscience requires a more systematic approach to representing the mental entities that are being mapped to brain function and the tasks used to manipulate and measure mental processes. We describe a new open collaborative project that aims to provide a knowledge base for cognitive neuroscience, called the Cognitive Atlas (accessible online at http://www.cognitiveatlas.org), and outline how this project has the potential to drive novel discoveries about both mind and brain.

Architecture & Planning• 2011• Frontiers Media
Access Full TextView Details
Previous1...2627282930...79Next