Genome-scale mutational signature analysis in fixed archived tissues.

Mutation spectra and mutational signatures in cancerous and non-cancerous tissues can be identified by various established techniques of massively parallel sequencing (or next-generation sequencing) including whole-exome or whole-genome sequencing, and more recently by error-corrected/duplex sequencing. One rather underexplored area has been the genome-scale analysis of mutational signatures as markers of mutagenic exposures, and their impact on cancer driver events applied to formalin-fixed or alcohol-fixed paraffin embedded archived biospecimens. This review showcases successful applications of the next-generation sequencing methodologies in archived fixed tissues, including the delineation of the specific tissue fixation-related DNA damage manifesting as artifactual signatures, distinguishable from the true signatures that arise from biological mutagenic processes. Overall, we discuss and demonstrate how next-generation sequencing techniques applied to archived fixed biospecimens can enhance our understanding of cancer causes including mutagenic effects of extrinsic cancer risk agents, and the implications for prevention efforts aimed at reducing avoidable cancer-causing exposures.

Sensorizing a Beehive: A Study on Potential Embedded Solutions for Internal Contactless Monitoring of Bees Activity.

Winter is the season of main concern for beekeepers since the temperature, humidity, and potential infection from mites and other diseases may lead the colony to death. As a consequence, beekeepers perform invasive checks on the colonies, exposing them to further harm. This paper proposes a novel design of an instrumented beehive involving color cameras placed inside the beehive and at the bottom of it, paving the way for new frontiers in beehive monitoring. The overall acquisition system is described focusing on design choices towards an effective solution for internal, contactless, and stress-free beehive monitoring. To validate our approach, we conducted an experimental campaign in 2023 and analyzed the collected images with YOLOv8 to understand if the proposed solution can be useful for beekeepers and what kind of information can be derived from this kind of monitoring, including the presence of Varroa destructor mites inside the beehive. We experimentally found that the observation point inside the beehive is the most challenging due to the frequent movements of the bees and the difficulties related to obtaining in-focus images. However, from these images, it is possible to find Varroa destructor mites. On the other hand, the observation point at the bottom of the beehive showed great potential for understanding the overall activity of the colony.

Smart Spare Parts (SSP) in the Context of Industry 4.0: A Systematic Review.

The implementation of Industry 4.0 has integrated manufacturing, electronics, and engineering materials, leading to the creation of smart parts (SPs) that provide information on production system conditions. However, SP development faces challenges due to limitations in manufacturing processes and integrating electronic components. This systematic review synthesizes scientific articles on SP fabrication using additive manufacturing (AM), identifying the advantages and disadvantages of AM techniques in SP production and distinguishing between SPs and smart spare parts (SSPs). The methodology involves establishing a reference framework, formulating SP-related questions, and applying inclusion criteria and keywords, initially resulting in 1603 articles. After applying exclusion criteria, 70 articles remained. The results show that while SP development is advancing, widespread application of AM-manufactured SP is recent. SPs can anticipate production system failures, minimize design artifacts, and reduce manufacturing costs. Furthermore, the review highlights that SSPs, a subcategory of SPs, primarily differs by replacing conventional critical parts in the industry, offering enhanced functionality and reliability in industrial applications. The study concludes that continued research and development in this field is essential for further advancements and broader adoption of these technologies.

From Near-Sensor to In-Sensor: A State-of-the-Art Review of Embedded AI Vision Systems.

In modern cyber-physical systems, the integration of AI into vision pipelines is now a standard practice for applications ranging from autonomous vehicles to mobile devices. Traditional AI integration often relies on cloud-based processing, which faces challenges such as data access bottlenecks, increased latency, and high power consumption. This article reviews embedded AI vision systems, examining the diverse landscape of near-sensor and in-sensor processing architectures that incorporate convolutional neural networks. We begin with a comprehensive analysis of the critical characteristics and metrics that define the performance of AI-integrated vision systems. These include sensor resolution, frame rate, data bandwidth, computational throughput, latency, power efficiency, and overall system scalability. Understanding these metrics provides a foundation for evaluating how different embedded processing architectures impact the entire vision pipeline, from image capture to AI inference. Our analysis delves into near-sensor systems that leverage dedicated hardware accelerators and commercially available components to efficiently process data close to their source, minimizing data transfer overhead and latency. These systems offer a balance between flexibility and performance, allowing for real-time processing in constrained environments. In addition, we explore in-sensor processing solutions that integrate computational capabilities directly into the sensor. This approach addresses the rigorous demand constraints of embedded applications by significantly reducing data movement and power consumption while also enabling in-sensor feature extraction, pre-processing, and CNN inference. By comparing these approaches, we identify trade-offs related to flexibility, power consumption, and computational performance. Ultimately, this article provides insights into the evolving landscape of embedded AI vision systems and suggests new research directions for the development of next-generation machine vision systems.

Beehive Smart Detector Device for the Detection of Critical Conditions That Utilize Edge Device Computations and Deep Learning Inferences.

This paper presents a new edge detection process implemented in an embedded IoT device called Bee Smart Detection node to detect catastrophic apiary events. Such events include swarming, queen loss, and the detection of Colony Collapse Disorder (CCD) conditions. Two deep learning sub-processes are used for this purpose. The first uses a fuzzy multi-layered neural network of variable depths called fuzzy-stranded-NN to detect CCD conditions based on temperature and humidity measurements inside the beehive. The second utilizes a deep learning CNN model to detect swarming and queen loss cases based on sound recordings. The proposed processes have been implemented into autonomous Bee Smart Detection IoT devices that transmit their measurements and the detection results to the cloud over Wi-Fi. The BeeSD devices have been tested for easy-to-use functionality, autonomous operation, deep learning model inference accuracy, and inference execution speeds. The author presents the experimental results of the fuzzy-stranded-NN model for detecting critical conditions and deep learning CNN models for detecting swarming and queen loss. From the presented experimental results, the stranded-NN achieved accuracy results up to 95%, while the ResNet-50 model presented accuracy results up to 99% for detecting swarming or queen loss events. The ResNet-18 model is also the fastest inference speed replacement of the ResNet-50 model, achieving up to 93% accuracy results. Finally, cross-comparison of the deep learning models with machine learning ones shows that deep learning models can provide at least 3-5% better accuracy results.

Active inference goes to school: the importance of active learning in the age of large language models.

Human learning essentially involves embodied interactions with the material world. But our worlds now include increasing numbers of powerful and (apparently) disembodied generative artificial intelligence (AI). In what follows we ask how best to understand these new (somewhat 'alien', because of their disembodied nature) resources and how to incorporate them in our educational practices. We focus on methodologies that encourage exploration and embodied interactions with 'prepared' material environments, such as the carefully organized settings of Montessori education. Using the active inference framework, we approach our questions by thinking about human learning as epistemic foraging and prediction error minimization. We end by arguing that generative AI should figure naturally as new elements in prepared learning environments by facilitating sequences of precise prediction error enabling trajectories of self-correction. In these ways, we anticipate new synergies between (apparently) disembodied and (essentially) embodied forms of intelligence. This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.

Dandelion inspired microparticles with highly efficient drug delivery to deep lung.

Active pharmaceutical ingredient (API) embedded dry powder for inhalation (AeDPI) shows higher drug loading and delivery dose for directly treating various lung infections. Inspired by the dandelion, we propose a novel kind of AeDPI microparticle structure fabricated by spray freeze drying technology, which would potentially enhance the alveoli deposition efficiency. When inhaling, such microparticles are expected to be easily broken-up into fragments containing API that acts as 'seed' and could be delivered to alveoli aided by the low density 'pappus' composed of excipient. Herein, itraconazole (ITZ), a first-line drug for treating pulmonary aspergillosis, was selected as model API. TPGS, an amphiphilic surfactant, was used to achieve stable primary ITZ nanocrystal (INc) suspensions for spray freeze drying. A series of microparticles were prepared, and the dandelion-like structure was successfully achieved. The effects of feed liquid compositions and freezing parameters on the microparticle size, morphology, surface energy, crystal properties and in vitro aerosol performance were systematically investigated. The optimal sample (SF(-50)D-INc7Leu3-2) in one-way experiment showed the highest fine particle fraction of ∼ 68.96 % and extra fine particle fraction of ∼ 36.87 %, equivalently ∼ 4.60 mg and ∼ 2.46 mg could reach the lung and alveoli, respectively, when inhaling 10 mg dry powders. The response surface methodology (RSM) analysis provided the optimized design space for fabricating microparticles with higher deep lung deposition performance. This study demonstrates the advantages of AeDPI microparticle with dandelion-like structure on promoting the delivery efficiency of high-dose drug to the deep lung.

Obstructive Hydrocephalus and Cardiomyopathy Secondary to Disseminated Protothecosis in a Boxer Dog.

Canine protothecosis is a rare disease caused by saprophytic unicellular achlorophyllous aerobic algae that are ubiquitous in the environment. We report a novel case of neurological and cardiological manifestations associated with disseminated protothecosis. An adult spayed female Boxer dog was presented with a 1-week history of anorexia, progressive central vestibular signs, and a Grade III/VI systolic heart murmur. Magnetic resonance (MR) imaging revealed obstructive hydrocephalus at the level of the mesencephalic aqueduct, while echocardiography and elevated troponin levels suggested an infiltrative cardiomyopathy. No obvious cause was identified. Cerebrospinal fluid (CSF) collection was not performed due to associated procedural risks. Despite receiving symptomatic treatment and maintaining stability for 3 weeks, the dog eventually suffered cardiorespiratory arrest. Postmortem examination revealed disseminated protothecosis, predominantly affecting the heart and brain. We recommend that in cases where the cause of obstructive hydrocephalus is unclear, especially when CSF collection is not feasible, a comprehensive diagnostic method should be implemented. This includes meticulous investigations to identify infected tissues, followed by sampling and performing cytology/histology and culture tests to confirm the presence of the algal organism. Early diagnosis may allow early treatment, although long-term prognosis remains largely unfavorable due to the absence of effective treatments.

Omnidirectional anisotropic embedded 3D bioprinting.

Anisotropic microstructures resulting from a well-ordered arrangement of filamentous extracellular matrix (ECM) components or cells can be found throughout the human body, including skeletal muscle, corneal stroma, and meniscus, which play a crucial role in carrying out specialized physiological functions. At present, due to the isotropic characteristics of conventional hydrogels, the construction of freeform cell-laden anisotropic structures with high-bioactive hydrogels is still a great challenge. Here, we proposed a method for direct embedded 3D cell-printing of freeform anisotropic structure with shear-oriented bioink (GelMA/PEO). This study focuses on the establishment of an anisotropic embedded 3D bioprinting system, which effectively utilizes the shear stress generated during the extrusion process to create cells encapsulating tissues with distinct anisotropy. In conjunction with the water-solubility of PEO and the in-situ encapsulation effect provided by the carrageenan support bath, high-precise cell-laden bioprinting of intricate anisotropic and porous bionic artificial tissues can be effectively implemented in one-step. Additionally, anisotropic permeable blood vessel has been taken as a representation to validate the effectiveness of the shear-oriented bioink system in fabricating intricate structures with distinct directional characteristics. Lastly, the successful preparation of muscle patches with anisotropic properties and their guiding role for cell cytoskeleton extension have provided a significant research foundation for the application of the anisotropic embedded 3D bioprinting system in the ex-vivo production and in-vivo application of anisotropic artificial tissues.

People, places, things and communities: expanding behaviour settings theory in the twenty-first century.

Trends and developments in recent behavioural and cognitive sciences demonstrate the need for a well-developed theoretical and empirical framework for examining the ecology of human behaviour. The increasing recognition of the role of the environment and interaction with the environment in the organization of behaviour within the cognitive sciences has not been met with an equally disciplined and systematic account of that environment (Heft 2018 Ecol. Psychol. 30, 99-123 (doi:10.1080/10407413.2018.1410045); McGann 2014 Synth. Philos. 29, 217-233). Several bodies of work in behavioural ecology, anthropology and ecological psychology provide some frameworks for such an account. At present, however, the most systematic and theoretically disciplined account of the human behavioural ecosystem is that of behaviour settings, as developed by the researchers of the Midwest Psychological Field Station (see Barker 1968 Ecological psychology: concepts and methods for studying the environment of human behavior). The articles in this theme issue provide a critical examination of these theoretical and methodological resources. The collection addresses their theoretical value in connecting with contemporary issues in cognitive science and research practice in psychology, as well as the importance of the methodological specifics of behaviour settings research. Additionally, articles diagnose limitations and identify points of potential extension of both theory and methods, particularly with regard to changes owing to the advance of technology, and the complex relationship between the individual and the collective in behaviour settings work. This article is part of the theme issue 'People, places, things, and communities: expanding behaviour settings theory in the twenty-first century'.

Flow cytometric analysis for Ki67 assessment in formalin-fixed paraffin-embedded breast cancer tissue.

BACKGROUND: Pathologists commonly employ the Ki67 immunohistochemistry labelling index (LI) when deciding appropriate therapeutic strategies for patients with breast cancer. However, despite several attempts at standardizing the Ki67 LI, inter-observer and inter-laboratory bias remain problematic. We developed a flow cytometric assay that employed tissue dissociation, enzymatic treatment and a gating process to analyse Ki67 in formalin-fixed paraffin-embedded (FFPE) breast cancer tissue. RESULTS: We demonstrated that mechanical homogenizations combined with thrombin treatment can be used to recover efficiently intact single-cell nuclei from FFPE breast cancer tissue. Ki67 in the recovered cell nuclei retained reactivity against the MIB-1 antibody, which has been widely used in clinical settings. Additionally, since the method did not alter the nucleoskeletal structure of tissues, the nuclei of cancer cells can be enriched in data analysis based on differences in size and complexity of nuclei of lymphocytes and normal mammary cells. In a clinical study using the developed protocol, Ki67 positivity was correlated with the Ki67 LI obtained by hot spot analysis by a pathologist in Japan (rho = 0.756, P < 0.0001). The number of cancer cell nuclei subjected to the analysis in our assay was more than twice the number routinely checked by pathologists in clinical settings. CONCLUSIONS: The findings of this study showed the application of this new flow cytometry method could potentially be used to standardize Ki67 assessments in breast cancer.

Machine learning for environmental justice: Dissecting an algorithmic approach to predict drinking water quality in California.

The potential for machine learning to answer questions of environmental science, monitoring, and regulatory enforcement is evident, but there is cause for concern regarding potential embedded bias: algorithms can codify discrimination and exacerbate systematic gaps. This paper, organized into two halves, underscores the importance of vetting algorithms for bias when used for questions of environmental science and justice. In the first half, we present a case study of using machine learning for environmental justice-motivated research: prediction of drinking water quality. While performance varied across models and contaminants, some performed well. Multiple models had overall accuracy rates at or above 90 % and F2 scores above 0.60 on their respective test sets. In the second half, we dissect this algorithmic approach to examine how modeling decisions affect modeling outcomes - and not only how these decisions change whether the model is correct or incorrect, but for whom. We find that multiple decision points in the modeling process can lead to different predictive outcomes. More importantly, we find that these choices can result in significant differences in demographic characteristics of false negatives. We conclude by proposing a set of practices for researchers and policy makers to follow (and improve upon) when applying machine learning to questions of environmental science, management, and justice.

Adaptive Detection in Real-Time Gait Analysis through the Dynamic Gait Event Identifier.

The Dynamic Gait Event Identifier (DGEI) introduces a pioneering approach for real-time gait event detection that seamlessly aligns with the needs of embedded system design and optimization. DGEI creates a new standard for gait analysis by combining software and hardware co-design with real-time data analysis, using a combination of first-order difference functions and sliding window techniques. The method is specifically designed to accurately separate and analyze key gait events such as heel strike (HS), toe-off (TO), walking start (WS), and walking pause (WP) from a continuous stream of inertial measurement unit (IMU) signals. The core innovation of DGEI is the application of its dynamic feature extraction strategies, including first-order differential integration with positive/negative windows, weighted sleep time analysis, and adaptive thresholding, which together improve its accuracy in gait segmentation. The experimental results show that the accuracy rate of HS event detection is 97.82%, and the accuracy rate of TO event detection is 99.03%, which is suitable for embedded systems. Validation on a comprehensive dataset of 1550 gait instances shows that DGEI achieves near-perfect alignment with human annotations, with a difference of less than one frame in pulse onset times in 99.2% of the cases.

Rapid, sensitive, and non-destructive on-site quantitative detection of nanoplastics in aquatic environments using laser-backscattered fiber-embedded optofluidic chip.

A definitive link between the micro- and nano-plastics (NPLs) and human health has been firmly established, emphasizing the higher risks posed by NPLs. The urgent need for a rapid, non-destructive, and reliable method to quantify NPLs remains unmet with current detection techniques. To address this gap, a novel laser-backscattered fiber-embedded optofluidic chip (LFOC) was constructed for the rapid, sensitive, and non-destructive on-site quantitation of NPLs based on 180º laser-backscattered mechanism. Our theoretical and experimental findings reveal that the 180º laser-backscattered intensities of NPLs were directly proportional to their mass and particle number concentration. Using the LFOC, we have successfully detected polystyrene (PS) NPLSs of varying sizes, with a minimum detection limit of 0.23 µg/mL (equivalent to 5.23 ×107 particles/mL). Moreover, PS NPLs of different sizes can be readily differentiated through a simple membrane-filtering method. The LFOC also demonstrates high sensitivity in detecting other NPLs, such as polyethylene, polyethylene terephthalate, polypropylene, and polymethylmethacrylate. To validate its practical application, the LFOC was used to detect PS NPLs in various aquatic environments, exhibiting excellent accuracy, reproducibility, and reliability. The LFOC provides a simple, versatile, and efficient tool for direct, on-site, quantitative detection of NPLs in aquatic environments.

A Miniaturized and Highly Stable Frequency-Selective Rasorber Incorporating an Embedded Transmission Window.

In this article, a miniaturized and highly stable frequency-selective rasorber (FSR) incorporating an embedded transmission window is designed. This FSR consists of a lossy layer loaded with resistors, an air layer, and a bandpass layer. The lossy layer is provided with a rectangular, square ring structure loaded with four 180 Ω resistors and four quadrilateral metal plates. The four metal plates are connected to the four corners of the inner ring around the square ring and are radially distributed along the diagonal. The bandpass layer is a square metal patch that a cross-ring slot structure is loaded inside of, and the cross points lie in the direction along the diagonal of the unit. The inner boundary of the cross-ring is composed of two mutually perpendicular and long rectangular elements. This FSR shows an embedded transmission window from 3.63 GHz to 3.80 GHz and has a transmission rate of 93% at 3.72 GHz. Moreover, both sides of the transmission band, namely, 1.86-3.35 GHz and 3.99-8.28 GHz, have an absorption rate of more than 80% and bilateral relative bandwidth of more than 50%. In addition, this structure exhibits excellent miniaturization performance, polarization insensitivity, and angular stability. Finally, a prototype of the designed FSR is processed and measured. The measured results are basically consistent with the simulation results.

A High-Performance FPGA PRNG Based on Multiple Deep-Dynamic Transformations.

Pseudo-random number generators (PRNGs) are important cornerstones of many fields, such as statistical analysis and cryptography, and the need for PRNGs for information security (in fields such as blockchain, big data, and artificial intelligence) is becoming increasingly prominent, resulting in a steadily growing demand for high-speed, high-quality random number generators. To meet this demand, the multiple deep-dynamic transformation (MDDT) algorithm is innovatively developed. This algorithm is incorporated into the skewed tent map, endowing it with more complex dynamical properties. The improved one-dimensional discrete chaotic mapping method is effectively realized on a field-programmable gate array (FPGA), specifically the Xilinx xc7k325tffg900-2 model. The proposed pseudo-random number generator (PRNG) successfully passes all evaluations of the National Institute of Standards and Technology (NIST) SP800-22, diehard, and TestU01 test suites. Additional experimental results show that the PRNG, possessing high novelty performance, operates efficiently at a clock frequency of 150 MHz, achieving a maximum throughput of 14.4 Gbps. This performance not only surpasses that of most related studies but also makes it exceptionally suitable for embedded applications.

Printable Spin-Multiplexed Metasurfaces for Ultraviolet Holographic Displays.

Multiplexed ultraviolet (UV) metaholograms, which are capable of displaying multiple holographic images from a single-layer device, are promising for enhancing tamper resistance and functioning as optical encryption devices. Despite considerable interest in optical security, the commercialization of UV metaholograms encounters obstacles, such as high-resolution patterning and material choices. Here, we realize spin-multiplexed UV metaholograms using a high-throughput printable platform that incorporates a zirconium dioxide (ZrO2) particle-embedded resin (PER). Utilizing ZrO2 PER, which is transparent and exhibits a refractive index of approximately 1.8 at 320 nm, we fabricated a single device capable of encoding dual holographic information depending on polarization states is fabricated. We demonstrate UV metaholograms achieving efficiencies of 56.23% with left circularly polarized incident beams and 57.28% with right circularly polarized incident beams. These multiplexed UV metaholograms fabricated using a one-step platform enable real-world applications in anticounterfeiting and encryption.

Molecular and ultrastructural morphological analyses of highly metamorphosed Aspergillus fumigatus on human formalin-fixed paraffin-embedded tissue.

Invasive fungal infections including invasive pulmonary aspergillosis (IPA) generally have a poor prognosis, because the fungi spread throughout various organs. Therefore, it is important to accurately identify the fungal species for treatment. In this article, we present the results of pathological and molecular morphological analyses that were performed to elucidate the cause of respiratory failure in a patient who died despite suspicion of IPA and treatment with micafungin (MCFG). Pathological analysis revealed the existence of cystic and linear fungi in lung tissue. The fungi were identified as Aspergillus fumigatus (A. fumigatus) by partial sequencing of genomic DNA. Correlative light microscopy and electron microscopy (CLEM) analysis confirmed that fungi observed with light microscopy can also be observed with scanning electron microscopy (SEM) using formalin-fixed paraffin-embedded tissue sections. SEM revealed an atypical ultrastructure of the fungi including inhomogeneous widths, rough surfaces, and numerous cyst-like structures of various sizes. The fungi showed several morphological changes of cultured A. fumigatus treated with MCFG that were previously reported. Our results indicate that integrated analysis of ultrastructural observation by SEM and DNA sequencing may be an effective tool for analyzing fungi that are difficult to identify by conventional pathological analysis.

Embedded finite element modeling of the mechanics of brain axonal fiber tracts under head impact conditions.

Investigating and understanding the biomechanical kinematics and kinetics of human brain axonal fibers during head impact process is crucial to study the mechanisms of Traumatic Axonal Injury (TAI). Such a study may require the explicit incorporation of brain fiber tracts into the host brain in order to distinguish the mechanical states of axonal fibers and brain tissue. Herein we extend our previously developed human head model by using an embedded element method to include fiber tracts reconstructed from diffusion tensor images in a host brain with the purpose of numerically tracking the deformation state of axonal fiber tracts during a head impact simulation. The updated model is validated by comparing its prediction of intracranial pressures with experimental data, followed by a thorough study of the effects of element types used for fiber tracts and the stiffness ratios of fiber to host brain. The validated model is also used to predict and visualize the damaged region of fiber tracts during the head impact process based on different injury criteria. The model is promising in tracking the state of fiber tracts and can add more objective functions such as axonal fiber deformation if used in the future design optimization of head protective equipment such as a football helmet.

Systematic review and feasibility study on pre-analytical factors and genomic analyses on archival formalin-fixed paraffin-embedded breast cancer tissue.

Formalin-fixed paraffin-embedded (FFPE) tissue represents a valuable source for translational cancer research. However, the widespread application of various downstream methods remains challenging. Here, we aimed to assess the feasibility of a genomic and gene expression analysis workflow using FFPE breast cancer (BC) tissue. We conducted a systematic literature review for the assessment of concordance between FFPE and fresh-frozen matched tissue samples derived from patients with BC for DNA and RNA downstream applications. The analytical performance of three different nucleic acid extraction kits on FFPE BC clinical samples was compared. We also applied a newly developed targeted DNA Next-Generation Sequencing (NGS) 370-gene panel and the nCounter BC360® platform on simultaneously extracted DNA and RNA, respectively, using FFPE tissue from a phase II clinical trial. Of the 3701 initial search results, 40 articles were included in the systematic review. High degree of concordance was observed in various downstream application platforms. Moreover, the performance of simultaneous DNA/RNA extraction kit was demonstrated with targeted DNA NGS and gene expression profiling. Exclusion of variants below 5% variant allele frequency was essential to overcome FFPE-induced artefacts. Targeted genomic analyses were feasible in simultaneously extracted DNA/RNA from FFPE material, providing insights for their implementation in clinical trials/cohorts.