Exploring the bio-insecticidal activity of Eucalyptus globulus essential oil/ß-cyclodextrin inclusion complexes: In vitro and in silico assessment against Ephestia kuehniella larvae.

Owing to their beneficial functional capabilities, essential oils were largely used. However, their low aqueous solubility, instability, and high volatility urged scientists to their encapsulation with cyclodextrins (CDs) to tackle their shortcomings. In this study, the co-precipitation method was used to prepare ß-CD/Eucalyptus globulus essential oil (EGEO) inclusion complexes (ICs). ß-CD/EGEO ICs were prepared at ratios (w:w) 1:2 and 1:4 with an encapsulation efficiency of 93 and 96%, respectively. The ICs characterization using the Fourier transform Infrared spectroscopy, differential scanning calorimetry, X-ray powder diffraction, Dynamic Light Scattering, and Laser Doppler Velocimetry confirmed the formation of ß-CD/EGEO ICs. The insecticidal activity of the free EGEO and ICs was explored and displayed that the complex ß-CD/EGEO 1:4 had the highest activity with the lowest LC50 against Ephestia kuehniella larvae (5.03 ± 1.16 mg/g) when compared to the free oil (8.38 ± 1.95 mg/g). Molecular docking simulations stipulated that the compound α-Bisabolene epoxide had the best docking score (ΔG = -7.4 Kcal/mol) against the selected insecticidal target α-amylase. Additionally, toxicity evaluation of the studied essential oil suggested that it could be safely used as a potent bioinsecticide as compared to chemical insecticides. This study reveals that the formation of ß-CD/EGEO ICs enhanced the oil activity and stability and could be a promising and safe tool to boost its application in food or pharmaceutical fields.

Ultrastructural differences impact cilia shape and external exposure across cell classes in the visual cortex.

A primary cilium is a membrane-bound extension from the cell surface that contains receptors for perceiving and transmitting signals that modulate cell state and activity. Primary cilia in the brain are less accessible than cilia on cultured cells or epithelial tissues because in the brain they protrude into a deep, dense network of glial and neuronal processes. Here, we investigated cilia frequency, internal structure, shape, and position in large, high-resolution transmission electron microscopy volumes of mouse primary visual cortex. Cilia extended from the cell bodies of nearly all excitatory and inhibitory neurons, astrocytes, and oligodendrocyte precursor cells (OPCs) but were absent from oligodendrocytes and microglia. Ultrastructural comparisons revealed that the base of the cilium and the microtubule organization differed between neurons and glia. Investigating cilia-proximal features revealed that many cilia were directly adjacent to synapses, suggesting that cilia are poised to encounter locally released signaling molecules. Our analysis indicated that synapse proximity is likely due to random encounters in the neuropil, with no evidence that cilia modulate synapse activity as would be expected in tetrapartite synapses. The observed cell class differences in proximity to synapses were largely due to differences in external cilia length. Many key structural features that differed between neuronal and glial cilia influenced both cilium placement and shape and, thus, exposure to processes and synapses outside the cilium. Together, the ultrastructure both within and around neuronal and glial cilia suggest differences in cilia formation and function across cell types in the brain.

Hydroxytyrosol recovery from olive pomace: a simple process using olive mill industrial equipment and membrane technology.

In this work, pilot-scale nanofiltration was used to obtain aqueous solutions rich in hydroxytyrosol and tyrosol from olive oil by-products. A large-scale simple process involving olive mill standard machinery (blender and decanter) was used for the olive pomace pre-treatment with water. The aqueous extract was then directly fed to a nanofiltration unit and concentrated by reverse osmosis. Final concentration factors ranged between 7 and 9 for hydroxytyrosol and between 4 and 7 for tyrosol. The final aqueous solution, obtained as retentate stream of reverse osmosis, was highly concentrated in hydroxytyrosol and tyrosol and their concentrations remained stable over at least 14 months.

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex.

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

Avelumab First-line Maintenance for Advanced Urothelial Carcinoma: Analysis from JAVELIN Bladder 100 by Duration of First-line Chemotherapy and Interval Before Maintenance.

BACKGROUND: In the JAVELIN Bladder 100 phase 3 trial, avelumab first-line maintenance + best supportive care (BSC) prolonged overall survival (OS) and progression-free survival (PFS) versus BSC alone in patients with advanced urothelial carcinoma (advanced UC) without progression after first-line platinum-based chemotherapy. OBJECTIVE: To report post hoc analyses of subgroups defined by the duration of first-line chemotherapy and interval before maintenance. DESIGN, SETTING, AND PARTICIPANTS: Patients with advanced UC without progression after four to six cycles of platinum-based chemotherapy and a 4-10-wk interval after chemotherapy (n = 700) were randomized to receive avelumab + BSC or BSC alone. Subgroups were defined by duration (quartile [Q]) and estimated number of cycles of chemotherapy, and interval between chemotherapy and maintenance. The median follow-up was >19 mo in both arms. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: OS (primary endpoint), PFS, and safety were assessed. RESULTS AND LIMITATIONS: Hazard ratios (95% confidence interval) for OS with avelumab + BSC versus BSC alone were as follows: by chemotherapy duration-Q3: 0.63 (0.39-1.00); by number of cycles-four cycles: 0.69 (0.48-1.00), five cycles: 0.98 (0.57-1.71), and six cycles: 0.66 (0.47-0.92); and by interval-4-<6 wk: 0.75 (0.54-1.04), 6-<8 wk: 0.67 (0.43-1.06), and 8-10 wk: 0.69 (0.47-1.02). Results were similar for PFS. Safety was similar across subgroups. All analyses were exploratory. CONCLUSIONS: Post hoc analyses of OS and PFS in subgroups defined by first-line chemotherapy duration and interval before maintenance were generally consistent with the results in the overall population, with similar safety findings. Prospective trials are warranted to confirm these findings. PATIENT SUMMARY: Avelumab maintenance treatment helped patients with advanced urothelial cancer without disease progression after at least four cycles of prior chemotherapy, and who started maintenance treatment at least 4 wk after chemotherapy, to live longer.

As funções, a formação e a extinção dos sangradores em Portugal / The functions, training, and extinction of bloodletters in Portugal / Las funciones, la formación y la extinción de los sangradores en Portugal

Resumo Enquadramento: O século XIX caracteriza-se por profunda alteração no campo assistencial. O higienismo, a bacteriologia, impulsionam a reorganização dos serviços de saúde. Os sangradores, ofício que subsistiu regulado ao longo de quase quatro séculos é oficialmente extinto em 1870. Objetivos: Compreender de que forma os sangradores estiveram presentes na sociedade e instituições assistenciais e como o seu lento desaparecimento foi acompanhado pelo desenvolvimento do processo de profissionalização dos enfermeiros em Portugal. Metodologia: Inferência histórica com base no levantamento e análise crítica de fontes primárias, como seja um manual coevo e atas dos debates parlamentares; e de fontes secundárias, nomeadamente material editado (artigos, livros, teses), que no seu conjunto levem à produção de uma síntese final interpretativa. Resultados: Enquanto perdurou a teoria dos humores, sangrar era remédio para tudo. Com o despertar da nova medicina assiste-se ao ocaso progressivo dos sangradores a partir do final do séc. XVIII. Conclusão: O desaparecimento oficial dos sangradores coincide com o acentuar da profissionalização dos enfermeiros, nomeadamente com o surgimento de formação estruturada.

Abstract Background: The 19th century was characterized by profound changes in health care. The development of hygiene standards and bacteriology reorganized health services. In Portugal, the job of bloodletters, regulated for almost four centuries, officially became extinct in 1870. Objectives: To understand bloodletters' presence in society and healthcare institutions and how their gradual disappearance accompanied the professionalization process of nursing in Portugal. Methodology: Historical inference based on the survey and critical analysis of primary sources, such as a contemporary manual and the minutes of parliamentary sessions, and secondary sources, such as articles, books, and theses. Analyzing the data collected from these sources produced a final interpretative synthesis. Results: While the humoral theory persisted, bloodletting was considered a remedy for everything. However, from the end of the 18th century, the development of modern medicine led to the progressive decline of this health-related practice. Conclusion: The official disappearance of bloodletters coincided with the intensification of nurses' professionalization process, particularly with the emergence of formal nursing training.

Resumen Marco contextual: El siglo XIX se caracteriza por un profundo cambio en el campo de la salud, con el higienismo, la bacteriología y la reorganización de los servicios de salud. Los sangradores fueron uno de los oficios que permaneció reglamentado oficialmente durante casi cuatro siglos hasta su extinción oficial en 1870. Objetivos: Comprender cómo las hemorragias estuvieron presentes en la sociedad y en las instituciones asistenciales y cómo su lenta desaparición fue concomitante con el desarrollo del proceso de profesionalización de los enfermeros en Portugal. Metodología: Inferencia histórica basada en el levantamiento y análisis crítico de fuentes primarias, tales como uno manual antiguo y debates parlamentarios, y fuentes secundarias, a saber, material editado, artículos, libros, tesis que en conjunto conducen a la producción de una síntesis interpretativa final. Resultados: mientras duró la teoría de los humores, sangrar fue una cura para todo. Con el despertar de la nueva medicina asistimos al progresivo declive de los sangradores a partir de finales del siglo XVIII. Conclusión: La desaparición oficial de los sangradores coincide con el acentuar de la profesionalización de los enfermeros, es decir, con el surgimiento de la formación estructurada.

Multi-layered maps of neuropil with segmentation-guided contrastive learning.

Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 µm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex.

Nanometer-scale views of visual cortex reveal anatomical features of primary cilia poised to detect synaptic spillover.

A primary cilium is a thin membrane-bound extension off a cell surface that contains receptors for perceiving and transmitting signals that modulate cell state and activity. While many cell types have a primary cilium, little is known about primary cilia in the brain, where they are less accessible than cilia on cultured cells or epithelial tissues and protrude from cell bodies into a deep, dense network of glial and neuronal processes. Here, we investigated cilia frequency, internal structure, shape, and position in large, high-resolution transmission electron microscopy volumes of mouse primary visual cortex. Cilia extended from the cell bodies of nearly all excitatory and inhibitory neurons, astrocytes, and oligodendrocyte precursor cells (OPCs), but were absent from oligodendrocytes and microglia. Structural comparisons revealed that the membrane structure at the base of the cilium and the microtubule organization differed between neurons and glia. OPC cilia were distinct in that they were the shortest and contained pervasive internal vesicles only occasionally observed in neuron and astrocyte cilia. Investigating cilia-proximal features revealed that many cilia were directly adjacent to synapses, suggesting cilia are well poised to encounter locally released signaling molecules. Cilia proximity to synapses was random, not enriched, in the synapse-rich neuropil. The internal anatomy, including microtubule changes and centriole location, defined key structural features including cilium placement and shape. Together, the anatomical insights both within and around neuron and glia cilia provide new insights into cilia formation and function across cell types in the brain.

A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection.

Surface defect detection with machine learning has become an important tool in industries and a large field of study for researchers or workers in recent years. It is necessary to have a simplified source of information that helps us to better focus on one type of surface. In this systematic review, we present a classification for surface defect detection based on convolutional neural networks (CNNs) focused on surface types. Findings: Out of 253 records identified, 59 primary studies were eligible. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed the structures of each study and the concepts related to defects and their types on surfaces. The presented review is mainly focused on finding a classification for the types of surfaces most used in industry (metal, building, ceramic, wood, and special). We delve into the specifics of each surface category, offering illustrative examples of their applications within both industrial and laboratory settings. Furthermore, we propose a new taxonomy of machine learning based on the obtained results and collected information. We summarized the studies and extracted the main characteristics such as type of surface, problem types, timeline, type of network, techniques, and datasets. Among the most relevant results of our analysis, we found that the metallic surface is the most used, as it is the one found in 62.71% of the studies, and the most prevalent problem type is classification, accounting for 49.15% of the total. Furthermore, we observe that transfer learning was employed in 83.05% of the studies, while data augmentation was utilized in 59.32%. Our findings also provide insights into the cameras most frequently employed, along with the strategies adopted to address illumination challenges present in certain articles and the approach to creating datasets for real-world applications. The main results presented in this review allow for a quick and efficient search of information for researchers and professionals interested in improving the results of their defect detection projects. Finally, we analyzed the trends that could open new fields of study for future research in the area of surface defect detection.

CAVE: Connectome Annotation Versioning Engine.

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets (~1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVE's data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.

Modified Atmosphere Packaging (MAP) for Seaweed Conservation: Impact on Physicochemical Characteristics and Microbiological Activity.

Conventional conservation techniques such as drying, salting or freezing do not allow for preserving the original characteristics of seaweeds. The present work aims to study the impact of minimal processing, in particular "Modified Atmosphere Packaging" (MAP), on the physicochemical characteristics and food safety of two seaweed species, "laver" (Porphyra umbilicalis) and "sea-lettuce" (Ulva lactuca), stored at 6 °C for 15 days. Different parameters were evaluated using analytical methods, namely the composition of headspace gases, color, texture, microorganisms, and volatile organic compounds (VOCs). The main findings of this study were that the MAP treatment was able to inhibit the respiration rate of minimally processed seaweeds, also preserving their color and texture. There was a remarkable reduction in the microbial load for P. umbilicalis treated under modified and vacuum atmospheres, and U. lactuca exhibited relatively steady values with no notable differences between the treatments and the control. Therefore, during the 15-day study period, both seaweeds met the requirements for food safety. GC-TOF-MS allowed to conclude that both MAP and vacuum treatments were more efficient in maintaining the odor characteristics of U. lactuca compared to P. umbilicalis with no significant differences throughout the storage days. Metabolic responses to diverse sources of abiotic stress seemed to account for most of the changes observed.

Fusion Object Detection and Action Recognition to Predict Violent Action.

In the context of Shared Autonomous Vehicles, the need to monitor the environment inside the car will be crucial. This article focuses on the application of deep learning algorithms to present a fusion monitoring solution which was three different algorithms: a violent action detection system, which recognizes violent behaviors between passengers, a violent object detection system, and a lost items detection system. Public datasets were used for object detection algorithms (COCO and TAO) to train state-of-the-art algorithms such as YOLOv5. For violent action detection, the MoLa InCar dataset was used to train on state-of-the-art algorithms such as I3D, R(2+1)D, SlowFast, TSN, and TSM. Finally, an embedded automotive solution was used to demonstrate that both methods are running in real-time.

Emerging roles of oligodendrocyte precursor cells in neural circuit development and remodeling.

Oligodendrocyte precursor cells (OPCs) are non-neuronal brain cells that give rise to oligodendrocytes, glia that myelinate the axons of neurons in the brain. Classically known for their contributions to myelination via oligodendrogenesis, OPCs are increasingly appreciated to play diverse roles in the nervous system, ranging from blood vessel formation to antigen presentation. Here, we review emerging literature suggesting that OPCs may be essential for the establishment and remodeling of neural circuits in the developing and adult brain via mechanisms that are distinct from the production of oligodendrocytes. We discuss the specialized features of OPCs that position these cells to integrate activity-dependent and molecular cues to shape brain wiring. Finally, we place OPCs within the context of a growing field focused on understanding the importance of communication between neurons and glia in the contexts of both health and disease.

Evaluating Real-World Characteristics of Patients With Advanced Urothelial Carcinoma Eligible for Avelumab Maintenance Therapy: A Multicountry Retrospective Medical Chart Review.

INTRODUCTION: Urothelial carcinoma (UC) is a malignancy of the urothelium that encompasses the renal pelvis, bladder, and urethra. Current treatment guidelines for advanced (ie, locally advanced or metastatic) UC recommend using avelumab maintenance therapy in patients with nonprogressive disease following first-line platinum-based chemotherapy. This study aimed to assess the representativeness of the patient population in the JAVELIN Bladder 100 (JB-100) trial, which examined the efficacy and safety of avelumab first-line maintenance, vs. real-world patients with advanced UC that had not progressed with first-line platinum-based chemotherapy treated between 2015 and 2018 by reviewing demographic and clinical characteristics. PATIENTS AND METHODS: A medical chart review (MCR) study collected demographics and treatment characteristics for patients with advanced UC in the United States, the United Kingdom, and France. Data were analyzed descriptively for review with data collected from patients enrolled in JB-100. RESULTS: Clinical characteristics were consistent between JB-100 and the MCR. Most patients were male, received 4 to 6 cycles of platinum-based chemotherapy, and had an Eastern Cooperative Oncology Group performance status of 0 or 1. All patients in the MCR had either stable disease or a response with platinum-based chemotherapy (∼75% achieved a complete or partial response). Fewer than half (42.5%) of all patients in the MCR received subsequent therapy. CONCLUSION: Patient demographics, clinical characteristics, and treatment patterns from a MCR of patients with advanced UC that had not progressed following first-line platinum-based chemotherapy appeared similar to data from patients enrolled in JB-100. Future studies should examine whether real-world outcomes are consistent with findings from JB-100. GOV IDENTIFIER: NCT02603432.

The Effect of Microwave Radiation on the Self-Healing Performance of Asphalt Mixtures with Steel Slag Aggregates and Steel Fibers.

Self-healing in asphalt mixtures is a property that can be enhanced by external heating, which causes a thermal expansion that increases the flow of bitumen with reduced viscosity through the cracks. Therefore, this study aims to evaluate the effects of microwave heating on the self-healing performance of three asphalt mixtures: (1) conventional, (2) with steel wool fibers (SWF), and (3) with steel slag aggregates (SSA) and SWF. After evaluating the microwave heating capacity of the three asphalt mixtures with a thermographic camera, their self-healing performance was determined with fracture or fatigue tests and microwave heating recovery cycles. The results demonstrated that the mixtures with SSA and SWF promoted higher heating temperatures and presented the best self-healing capacity during the semicircular bending test and heating cycles, with significant strength recovery after a total fracture. In contrast, the mixtures without SSA presented inferior fracture results. Both the conventional mixture and that containing SSA and SWF presented high healing indexes after the four-point bending fatigue test and heating cycles, with a fatigue life recovery of around 150% after applying two healing cycles. Therefore, the conclusion is that SSA greatly influences the self-healing performance of asphalt mixtures after microwave radiation heating.

Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies.

BACKGROUND: COVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection. METHODS: We present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination. RESULTS: Regression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions. CONCLUSIONS: This article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.

Avelumab First-line Maintenance Therapy for Advanced Urothelial Carcinoma: Comprehensive Clinical Subgroup Analyses from the JAVELIN Bladder 100 Phase 3 Trial.

BACKGROUND: In the phase 3 JAVELIN Bladder 100 trial, avelumab first-line (1L) maintenance + best supportive care (BSC) significantly prolonged overall survival (OS) and progression-free survival (PFS) versus BSC alone in patients with advanced urothelial carcinoma (aUC) who were progression-free following 1L platinum-based chemotherapy, leading to regulatory approval in various countries. OBJECTIVE: To analyze clinically relevant subgroups from JAVELIN Bladder 100. DESIGN, SETTING, AND PARTICIPANTS: Patients with unresectable locally advanced or metastatic UC without progression on 1L gemcitabine + cisplatin or carboplatin were randomized to receive avelumab + BSC (n = 350) or BSC alone (n = 350). Median follow-up was >19 mo in both arms (data cutoff October 21, 2019). This trial is registered on ClinicalTrials.gov as NCT02603432. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: OS (primary endpoint) and PFS were analyzed in protocol-specified and post hoc subgroups using the Kaplan-Meier method and Cox proportional hazards models. RESULTS AND LIMITATIONS: Hazard ratios (HRs) for OS with avelumab + BSC versus BSC alone were <1.0 across all subgroups examined, including patients treated with 1L cisplatin + gemcitabine (HR 0.69, 95% confidence interval [CI] 0.50-0.93) or carboplatin + gemcitabine (HR 0.64, 95% CI 0.46-0.90), patients with PD-L1+ tumors treated with carboplatin + gemcitabine (HR 0.67, 95% CI 0.39-1.14), and patients whose best response to chemotherapy was a complete response (HR 0.80, 95% CI 0.46-1.37), partial response (HR 0.62, 95% CI 0.46-0.84), or stable disease (HR 0.70, 95% CI 0.46-1.06). Observations were similar for PFS. Limitations include the smaller size and post hoc evaluation without multiplicity adjustment for some subgroups. CONCLUSIONS: Analyses of OS and PFS in clinically relevant subgroups were consistent with results for the overall population, further supporting avelumab 1L maintenance as standard-of-care treatment for patients with aUC who are progression-free following 1L platinum-based chemotherapy. PATIENT SUMMARY: In the JAVELIN Bladder 100 study, maintenance treatment with avelumab helped many different groups of people with advanced cancer of the urinary tract to live longer.

Sensor Fusion Approach for Multiple Human Motion Detection for Indoor Surveillance Use-Case.

Multi-human detection and tracking in indoor surveillance is a challenging task due to various factors such as occlusions, illumination changes, and complex human-human and human-object interactions. In this study, we address these challenges by exploring the benefits of a low-level sensor fusion approach that combines grayscale and neuromorphic vision sensor (NVS) data. We first generate a custom dataset using an NVS camera in an indoor environment. We then conduct a comprehensive study by experimenting with different image features and deep learning networks, followed by a multi-input fusion strategy to optimize our experiments with respect to overfitting. Our primary goal is to determine the best input feature types for multi-human motion detection using statistical analysis. We find that there is a significant difference between the input features of optimized backbones, with the best strategy depending on the amount of available data. Specifically, under a low-data regime, event-based frames seem to be the preferred input feature type, while higher data availability benefits the combined use of grayscale and optical flow features. Our results demonstrate the potential of sensor fusion and deep learning techniques for multi-human tracking in indoor surveillance, although it is acknowledged that further studies are needed to confirm our findings.

Augmented Reality-Assisted Ultrasound Breast Biopsy.

Breast cancer is the most prevalent cancer in the world and the fifth-leading cause of cancer-related death. Treatment is effective in the early stages. Thus, a need to screen considerable portions of the population is crucial. When the screening procedure uncovers a suspect lesion, a biopsy is performed to assess its potential for malignancy. This procedure is usually performed using real-time Ultrasound (US) imaging. This work proposes a visualization system for US breast biopsy. It consists of an application running on AR glasses that interact with a computer application. The AR glasses track the position of QR codes mounted on an US probe and a biopsy needle. US images are shown in the user's field of view with enhanced lesion visualization and needle trajectory. To validate the system, latency of the transmission of US images was evaluated. Usability assessment compared our proposed prototype with a traditional approach with different users. It showed that needle alignment was more precise, with 92.67 ± 2.32° in our prototype versus 89.99 ± 37.49° in a traditional system. The users also reached the lesion more accurately. Overall, the proposed solution presents promising results, and the use of AR glasses as a tracking and visualization device exhibited good performance.

A Real-Time Automated Defect Detection System for Ceramic Pieces Manufacturing Process Based on Computer Vision with Deep Learning.

Defect detection is a key element of quality control in today's industries, and the process requires the incorporation of automated methods, including image sensors, to detect any potential defects that may occur during the manufacturing process. While there are various methods that can be used for inspecting surfaces, such as those of metal and building materials, there are only a limited number of techniques that are specifically designed to analyze specialized surfaces, such as ceramics, which can potentially reveal distinctive anomalies or characteristics that require a more precise and focused approach. This article describes a study and proposes an extended solution for defect detection on ceramic pieces within an industrial environment, utilizing a computer vision system with deep learning models. The solution includes an image acquisition process and a labeling platform to create training datasets, as well as an image preprocessing technique, to feed a machine learning algorithm based on convolutional neural networks (CNNs) capable of running in real time within a manufacturing environment. The developed solution was implemented and evaluated at a leading Portuguese company that specializes in the manufacturing of tableware and fine stoneware. The collaboration between the research team and the company resulted in the development of an automated and effective system for detecting defects in ceramic pieces, achieving an accuracy of 98.00% and an F1-Score of 97.29%.