Processing Platform Research Articles

An Experimental Study of Radar-Centric Transmission for Integrated Sensing and Communications

This study proposes a dual-function radar and communication (DFRC) system that utilizes radar transmission parameters as modulation indexes to transmit data to the users while performing radar sensing as its primary function. The proposed technique exploits index modulation (IM) using the center frequency of radar chirps, their bandwidths, and polarization states as indexes to modulate the communication data within each radar chirp. By utilizing the combination of these indexes, the proposed DFRC system can reach up to 17 Mb/s throughput, while observing a robust radar performance. Through our experimental study, we also reveal the trade-off between the radar sensing performance and communication data rate, depending on the radar waveform parameters selected in the DFRC system. This study also demonstrates the implementation of the proposed DFRC system and presents its real-time over-the-air experimental measurements. Consequently, the simulation results are verified by real-time over-the-air experiments, where ARESTOR, a high-speed signal processing and experimental radar platform, has been employed.

Application Status and Prospects of Precision Nursing Under the Concept of Enhanced Recovery After Surgery

With the development of enhanced recovery after surgery (ERAS), major breakthroughs have been made in this field of study. However, the research fields still need to be continuously expanded to meet the needs of patients. The concept of precision therapy is widely applied in the field of nursing. Under the concept of ERAS, practical studies of applying precision nursing for the perioperative period have already been conducted, exploring such issues as precision nursing assessment, precision nursing intervention design, precision risk prediction model, and information technology to assist precision nursing practice. Research findings have preliminarily validated the safety and effectiveness of applying precision nursing for ERAS in the perioperative period. Herein, we reviewed the reported findings of relevant research published in recent years and identified the following problems in the implementation of precision nursing under the ERAS concept, a lack of implementation standards, challenges concerning the the role of nurses, a lack of high-quality research evidence in the existing literature, and a relevant big data processing platform that China does not have and therefore cannot carry out data sharing, integration, mining, and utilization. We also made suggestions for effective improvement and discussed research prospects. In the future, multidisciplinary collaboration, translational medical research, and the development of various innovative tools are to be strengthened to help improve the quality and effectiveness of nursing care. We hope to provide reference for improving the scientific and targeted implementation of precision nursing for ERAS in the perioperative period.

Visual Communication Design Method Based on Multimedia Information Processing Technology and Its Application

Abstract When the information society has become the mainstream environment, various applications of big data and computers have been popularized in various industries. Multimedia technology has always been a special part of the computer field. In audio, video, image processing, and transmission, multimedia information processing has become the main means to solve the above problems. The performance of this information processing technology is extremely powerful. It can not only process dynamic data but also complete the transmission task in the big data environment. Visual communication design is a way of transmitting information to the masses by using various effects, and it has its advantages in the selection of images, video, text, and other processing technologies. This paper mainly studies the concept, method, and application effect of visual communication design under multimedia information processing. Firstly, the application of multimedia information processing technology in various fields and countries is investigated. Based on the literature analysis and statistics, the advantage tendency of this technology is judged. Starting from the visual functions involved in visual communication, the application effect of the multimedia 3D image processing and rendering communication platform is used. The gray function is used to build the multimedia information processing platform and form a complete and effective evaluation system. Finally, the visual communication design optimized by multimedia processing technology is further analyzed to improve the defects of dynamic images in visual applications and further improve the impact of visual communication design on the masses. The results show that multimedia information processing technology can improve the efficiency of image data processing in visual communication design, and has a good impact on visual communication of dynamic information.

Enhancement on the mechanical properties of 3D printing PEI composites via high thermal processing and fiber reinforcing

AbstractPolyetherimide (PEI) is a high‐performance material that maintains good properties at high temperature. Combining PEI with 3D printing is helpful to broaden the application of PEI, but the printed PEI parts have poor mechanical properties due to its interlayer porous structure. Herein, high thermal processing (cavity and platform temperature) is utilized to improve the bonding of PEI filaments during 3D printing. After optimizing, the mechanical properties of printed PEI parts can reach to the performance of injected PEI. Moreover, carbon fiber (CF) is used to further enhance the thermal and mechanical properties of PEI. The results show that the mechanical enhancement of short length CF (0.15 mm) is less effective than the longer CF (3 mm‐CF). After the CF oxidation treatment, the tensile strength of printed PEI is increased by nearly 6.07% compared to the untreated CF/PEI. The work provides a novel approach for developing 3D printed CF/PEI composites for the further application.

OPENHERITAGE3D: BUILDING AN OPEN VISUAL ARCHIVE FOR SITE SCALE GIGA-RESOLUTION LIDAR AND PHOTOGRAMMETRY DATA

Abstract. LiDAR and photogrammetry are common mechanisms for the documentation of cultural heritage sites. Their outputs provide foundational primary sources for research and important engineering decisions contributing to the on-going conservation of sites and structures. Unfortunately, due to the complex nature of these data, they are rarely shared with stakeholders in their full forms. Raw data is routinely transferred to portable hard drives and forgotten at a project’s end. Digital heritage documentation, often acquired with great effort and cost, is at risk of loss (UNESCO 2003). When these data are shared, they are rarely presented or formatted in ways which enable their widespread re-use. As processing tools improve at a rapid pace, and offer new pipelines for improved reconstruction and fusion with additional data sources, it is important to preserve the data in a way which maintains all functionality and interoperability between processing platforms. It's not enough to share data or to simply make them "available." Data must be technologically and intellectually intelligible (and useful). A new system framework is required to ensure their integrity and wide-spread utility, and to democratize these data for re-use by the diverse communities of stakeholders with whose purposes fall outside the narrow scopes of a 3D documentation project’s original goals. In this paper we present OpenHeritage3D.org as a platform and framework for an open visual archive seeking to provide authoritative and democratized access to site-scale lidar and photogrammetry through data curation, file-sharing, and web-visualization systems with granular segmentation and data conversion capabilities.

Adversarial autoencoder concurrent projection to latent structure and its application

AbstractMultivariate monitoring plays an important role in process monitoring. Among the multivariate monitoring methods, the projection to latent structure method (PLS) has been most widely used in the fields of quality control and fault diagnosis. To improve the monitoring capability of traditional PLS methods in nonlinear and non‐Gaussian multivariate systems, this paper proposes an innovative multivariate monitoring strategy, which combines adversarial autoencoder (AAE) and the concurrent projection to latent structure method (CPLS). In the proposed strategy, the original data are mapped to the high‐dimensional space using the AAE method with the Gaussian prior distribution to realize data transformation. The mapped data are linearly divisible and approach the Gaussian distribution. Then, the projection with orthogonality is realized using the CPLS method. In addition, the reconstruction error and distribution properties are used as evaluation indexes of the high‐dimensional mapping performance. The corresponding monitoring strategy is established using the traditional statistical method based on the Gaussian distribution. Finally, the simulations are performed on the Tennessee‐Eastman process platform, and the results show that the proposed method could efficiently extract the principal components, especially in quality‐relevant fault monitoring.

Металлургический «завод» раннего железного века в Уральских горах

The authors describe the westernmost metallurgical center of the 5th—2nd centuries BC left by the aboriginal Itkul’ culture of the forest Trans-Urals in 8th/7th—3rd/2 nd centuries BC. It was founded in the upper reaches of the Ufa river, in Serny Klyuch tract, on top of a high (20—25 m) limestone rock with three steep edges, on the ruins of sites and settlements of the Eneolithic and Bronze Age. After the construction of the first copper-smelting complexes, the site of the settlement from the floor side was fenced with a log defensive wall with an external moat 40—42 m long. The ancient settlement is miniature, with an area of about 1,000 sq. m. Two excavations (502 sq. m) explored half of the site and 26 m of the fortification line. The remains of 19 adobe blast furnaces, three adobe platforms for metal processing, several pits and fireplaces for melting metal, three industrial and residential buildings of a frame-and-pillar structure were uncovered. In addition to the clan of metallurgists of the aboriginal Itkul’ culture, the population of the settlement included small groups of newcomers: initially, the descendants of the taiga communities of the Gamayun culture, who migrated from the north, from the upper reaches of the Iset’ river, later — the bearers of the Gorokhovo culture, who fled from the Tobol region and the lower reaches of the Iset’ river under the pressure of the warlike Sargat tribes, the ancient ancestors of the Hungarians who came from the east, from the Ishim-Irtysh forest-steppe. Traces of full cycle metallurgical production were revealed on the site — from copper smelting to casting and forging of finished products. The main part of non-ferrous metal produced by metallurgists of the mountain-forestry Trans-Urals was intended for export to neighboring and more distant territories. A blank of an iron knife-dagger found in one of the furnaces, and a series of Itkul’ iron products found in other places, confirm the hypothesis of the emergence of its own ferrous metallurgy in the forests of the Middle Urals around the 5 th—4 th centuries BC.

Quantifying Uncertainties in OC-SMART Ocean Color Retrievals: A Bayesian Inversion Algorithm

The Ocean Color—Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) is a robust data processing platform utilizing scientific machine learning (SciML) in conjunction with comprehensive radiative transfer computations to provide accurate remote sensing reflectances (Rrs estimates), aerosol optical depths, and inherent optical properties. This paper expands the capability of OC-SMART by quantifying uncertainties in ocean color retrievals. Bayesian inversion is used to relate measured top of atmosphere radiances and a priori data to estimate posterior probability density functions and associated uncertainties. A framework of the methodology and implementation strategy is presented and uncertainty estimates for Rrs retrievals are provided to demonstrate the approach by applying it to MODIS, OLCI Sentinel-3, and VIIRS sensor data.

Implementation of Real-Time Space Target Detection and Tracking Algorithm for Space-Based Surveillance

Space-based target surveillance is important for aerospace safety. However, with the increasing complexity of the space environment, the stellar target and strong noise interference pose difficulties for space target detection. Simultaneously, it is hard to balance real-time processing with computational performance for the onboard processing platform owing to resource limitations. The heterogeneous multi-core architecture has corresponding processing capabilities, providing a hardware implementation platform with real-time and computational performance for space-based applications. This paper first developed a multi-stage joint detection and tracking model (MJDTM) for space targets in optical image sequences. This model combined an improved local contrast method and the Kalman filter to detect and track the potential targets and use differences in movement status to suppress the stellar targets. Then, a heterogeneous multi-core processing system based on a field-programmable gate array (FPGA) and digital signal processor (DSP) was established as the space-based image processing system. Finally, MJDTM was optimized and implemented on the above image processing system. The experiments conducted with simulated and actual image sequences examine the accuracy and efficiency of the MJDTM, which has a 95% detection probability while the false alarm rate is 10−4. According to the experimental results, the algorithm hardware implementation can detect targets in an image with 1024 × 1024 pixels in just 22.064 ms, which satisfies the real-time requirements of space-based surveillance.

Long Duration Persistent Photocurrent in 3 nm Thin Doped Indium Oxide for Integrated Light Sensing and In‐Sensor Neuromorphic Computation

AbstractMiniaturization and energy consumption by computational systems remain major challenges to address. Optoelectronics based synaptic and light sensing provide an exciting platform for neuromorphic processing and vision applications offering several advantages. It is highly desirable to achieve single‐element image sensors that allow reception of information and execution of in‐memory computing processes while maintaining memory for much longer durations without the need for frequent electrical or optical rehearsals. In this work, ultra‐thin (<3 nm) doped indium oxide (In2O3) layers are engineered to demonstrate a monolithic two‐terminal ultraviolet (UV) sensing and processing system with long optical state retention operating at 50 mV. This endows features of several conductance states within the persistent photocurrent window that are harnessed to show learning capabilities and significantly reduce the number of rehearsals. The atomically thin sheets are implemented as a focal plane array (FPA) for UV spectrum based proof‐of‐concept vision system capable of pattern recognition and memorization required for imaging and detection applications. This integrated light sensing and memory system is deployed to illustrate capabilities for real‐time, in‐sensor memorization, and recognition tasks. This study provides an important template to engineer miniaturized and low operating voltage neuromorphic platforms across the light spectrum based on application demand.

QaaD (Query-as-a-Data): Scalable Execution of Massive Number of Small Queries in Spark

Spark big data processing platform is heavily used in today's IT services for various critical applications such as machine learning tasks for service recommendations or massive volumes of raw sales data analysis. Spark is designed to deliver high performance by enabling a high degree of parallelism while processing various heavy-weight queries that require homogeneous operations on large data. However, it has been observed that workloads made of small and short-running queries coming from various sources are becoming dominant in practice. Unfortunately, the current Spark architecture is unfit to process workloads made of a large number of small queries optimally due to excessive I/Os with small computations. We present a technique, called QaaD, that addresses this problem fundamentally by applying i) transparent conversion of workloads made of small queries into one with large queries and ii) dynamic partition size adjustment for runtime overhead minimization. For this, we introduce a new abstraction, microRDD, to support our design of query merging, the embedding of queries as part of data, and an opportunistic sharing of common input data among queries. Comprehensive evaluation using real-world data shows that QaaD is able to deliver 10.6x to 36.6x speed-up against standard Spark executions for small query workloads.

Experimental Comparison of a New 1.2 kV 4H-SiC Split-Gate MOSFET with Conventional SiC MOSFETs in Terms of Reliability Robustness

In this paper, we compare a new 1.2 kV rated 4H-SiC split-gate (SG) MOSFET with the conventional planar-gate (PG) MOSFETs. Both structures were fabricated with the same design rules and process platform. Therefore, the structures have similar electrical parameters, such as ON-state drain-source resistance (RON), breakdown voltage (BV), threshold voltage (Vth), and body diode forward voltage (VSD). It is shown that the Ciss/Coss/Crss capacitances of the SG-MOSFET can be reduced by 7%/8%/17%, respectively, compared with PG-MOSFET. It is also shown that the SG-MOSFET has the potential to reduce switching losses without compromising the static performance. Moreover, it maintains the robustness of the device, and an optimized layout design with spaced holes in the gate poly is adopted. Therefore, there is no obvious degradation between the SG-MOSFET and the PG-MOSFET in terms of avalanche and short-circuit endurance capabilities.

Underwater Small Target Detection Based on YOLOX Combined with MobileViT and Double Coordinate Attention

The underwater imaging environment is complex, and the application of conventional target detection algorithms to the underwater environment has yet to provide satisfactory results. Therefore, underwater optical image target detection remains one of the most challenging tasks involved with neighborhood-based techniques in the field of computer vision. Small underwater targets, dispersion, and sources of distortion (such as sediment and particles) often render neighborhood-based techniques insufficient, as existing target detection algorithms primarily focus on improving detection accuracy and enhancing algorithm complexity and computing power. However, excessive extraction of deep-level features leads to the loss of small targets and decrease in detection accuracy. Moreover, most underwater optical image target detection is performed by underwater unmanned platforms, which have a high demand of algorithm lightweight requirements due to the limited computing power of the underwater unmanned platform with the mobile vision processing platform. In order to meet the lightweight requirements of the underwater unmanned platform without affecting the detection accuracy of the target, we propose an underwater target detection model based on mobile vision transformer (MobileViT) and YOLOX, and we design a new coordinate attention (CA) mechanism named a double CA (DCA) mechanism. This model utilizes MobileViT as the algorithm backbone network, improving the global feature extraction ability of the algorithm and reducing the amount of algorithm parameters. The double CA (DCA) mechanism can improve the extraction of shallow features as well as the detection accuracy, even for difficult targets, using a minimum of parameters. Research validated in the Underwater Robot Professional Contest 2020 (URPC2020) dataset revealed that this method has an average accuracy rate of 72.00%. In addition, YOLOX’s ability to compress the model parameters by 49.6% efficiently achieves a balance between underwater optical image detection accuracy and parameter quantity. Compared with the existing algorithm, the proposed algorithm can carry on the underwater unmanned platform better.

Do Traffic Countermeasures Improve the Safety of Vulnerable Road Users at Signalized Intersections? A Combination of Case-Control and Cross-Sectional Studies Using Video-Based Traffic Conflicts

Driven by the vision of eliminating road fatalities, Vision Zero initiatives have been widely adopted by many cities around the world, with significant investment of resources in various safety countermeasures. However, there is still a lack of reliable quantitative evidence on the effectiveness of those countermeasures on the traffic conflict frequency at intersections. This research attempts to address this challenge with a combination of case-control and cross-sectional studies, aiming at quantifying the safety effects of three commonly applied Vision Zero countermeasures, namely, leading pedestrian interval, no right turn on red, and installation of a dedicated bicycle lane. A case study was conducted using video trajectory data from 10 signalized intersections in the City of Toronto, Canada. The traffic interactions between vehicles and vulnerable road users were extracted using a video data processing platform, and two surrogate measures of safety, including post-encroachment time and conflict speed, were obtained, and then used to classify the conflict severity into different levels. A comparative analysis using mixed-effects negative binomial regression was conducted to quantify the impacts of different treatments on the frequency of traffic conflicts under specific road weather and traffic conditions. The results show that these three types of traffic countermeasures can effectively reduce the frequency of high-risk and moderate-risk traffic conflicts, moderated by various traffic exposure and weather and environmental conditions and accessible pedestrian signals. These findings could help road safety engineers and decision makers make better informed decisions on their road safety initiatives and projects.

An Event-Driven Architecture for Genomics-Based Diagnostic Data Processing

Genomics-based diagnostic data (GBDD) are becoming increasingly important for laboratory diagnostics. Due to the large quantity of data and their heterogeneity, GBDD poses a big data challenge. Current analysis tools for GBDD are primarily designed for research and do not meet the requirements of laboratory diagnostics for automation, reliability, transparency, reproducibility, robustness, and accessibility. This makes it difficult for laboratories to use these tools in tests that need to be validated according to regulatory frameworks and to execute tests in a time- and cost-efficient manner. In order to better address these requirements, we propose an event-driven workflow-based architecture as the basis for a processing platform that is highly scalable using container technologies and microservices. A prototype implementation of this approach, called GenomicInsights, has been developed and evaluated to demonstrate its feasibility and suitability for laboratory diagnostics.

Ontological integration of digital sub-platforms of plant and animal husbandry

The purpose of the work is to develop digital mechanisms for the integration of information systems of plant and animal husbandry with the analysis of their implementation conditions. The relevance of research is determined by the need for such a procedure due to a significantly increased list of tasks to be solved and data volumes due to the improvement of information and communication technologies, which led to the emergence of technologies for precision animal husbandry and agriculture, Earth’s remote sensing, requiring a combination of a large number of overlapping structured data and technologies for their processing, as well as solving problems of intersectoral relationships reflecting their interaction through the feed base, raw materials for processing, as well as the demands of the population and state bodies that are gaining momentum about the possibility of checking information on the legality, quality, and safety of products online. Digital standards obtained by mathematical modeling of them are proposed as the main research method. As a result of the research, digital mechanisms were obtained for solving integration problems not only of crop and livestock information systems, but also their integration with digital logistics and processing platforms. Conclusions: the obtained digital mechanisms provide fundamentally new opportunities for the digital transformation of industries with a multiple reduction in its cost.

Нечіткі дані у семантичних wiki-ресурсах: моделі, джерела та методи обробки

We analyze main types of dirty data processed by intelligente information systems, criteria of data classification and means of detection non-classical properties of data. Results of this analysis are represented by ontological model that contains taxonomy of classical and nonclassical data and knowledge-oriented methods of their transformation. Special attention is paid to semantically incorrect data that corresponds to vague knowledge. This ontological model intended to provide more effectively methods for transforming raw data into smart data suitable for automatic analysis, knowledge acquisition and reuse in other information systems. The ontological approach provides integration of the proposed model with other external ontologies that formalize characteristics of various methods and software tools that can be used fo data analysis (data mining, inductive inference, semantic queries, and instrimental tools for testing various aspects of the ontology quality, etc.). The work uses the experience of knowledge base developing of the portal version of the Great Ukrainian Encyclopedia e-VUE. This information resource is based on the semantic Wiki technology, it has a large volume, a complex structure and contains a large number of various heterogeneous information objects. Wiki resources are interesting from the point of view of collaborative processing the fuzzy data that describe heterogeneous information objects and knowledge structures. Due to the fact that the creation of this information resource involves a large number of specialists of various scientific fields, who have different areas of expertise and qualifications in use of knowledge-oriented technologies, there are many differences in the understanding of the rules for presenting and structuring data, and therefore a significant part of the Encyclopedia content needs additional verification of its correctness. Therefore, we need in formalized and scalable solutions for detection and processing various types of inconsistence, incompleteness and semantic incorrectness of data. The proposed approach can be useful for the creation of other large-scale resources based on both the semantic Wiki technology and other technological platforms for collaborative processing of distributed data and knowledge.

Understanding the impact of social stigma on the patient journey in Alzheimer’s disease through social media narratives

AbstractBackgroundPatients living with Alzheimer’s disease (AD) and their caregivers/health care providers (HCP) recognize the importance of social stigma; however, traditional surveys do not capture nuances of experienced stigma. We sought to systematically study stigma in AD using social media narratives.MethodOnline narratives posted by patients with AD, caregivers, and HCPs (ie, stakeholders) were collected from 84 public social media sites from 1998‐2021. Using RLS Reveal’s Natural Language Processing (NLP) platform, we identified stakeholder narratives containing ‘stigma’‐related terms. NLP algorithms and manual curation, alongside subject matter expert review, were used to analyze the narratives. Data were examined by stages of the patient journey: pre‐diagnosis, diagnosis, and post‐diagnosis.ResultA total of 112,464 online narratives from AD stakeholders were screened; 4,392 (4%) that specifically mentioned stigma‐related terms were identified and are the focus of this analysis. Most narratives were by caregivers (n = 1917), followed by patients (n = 342) and HCP (n = 306). The majority of narratives (n = 3968; 90.3%) were in the post‐diagnosis phase of the patient journey, followed by diagnosis (n = 304; 6.9%), and pre‐diagnosis (n = 120; 2.7%). In the pre‐diagnosis phase, 78% of the narratives were posted by caregivers, 11% by HCPs, and 11% by patients. Lack of support was the most common theme identified in 73/120 (60.8%) of pre‐diagnosis narratives – mostly by caregivers (38/73; 52.1%). During the diagnosis phase, 47% of the narratives were posted by caregivers, 28% by HCPs, and 25% by patients. A feeling that support/advocacy is lacking was a common theme, and identified in 128/304 (42.1%) of the diagnosis phase narratives. In the post‐diagnosis phase, 78% of the narratives were posted by caregivers, 10% by HCPs, and 12% by patients; the fear/worry caused by stigma was the most common theme and identified in 2467/3968 (62.2%) of these narratives. In addition, stakeholders discussed the feeling of shame related to the AD diagnosis – a theme identified in 1,017/3,968 (25.6%) of post‐diagnosis narratives.ConclusionWe found that social media narratives by various AD stakeholders identified a negative impact of stigma throughout the patient journey.

Batch effect correction methods for NASA GeneLab transcriptomic datasets

Introduction:RNA sequencing (RNA-seq) data from space biology experiments promise to yield invaluable insights into the effects of spaceflight on terrestrial biology. However, sample numbers from each study are low due to limited crew availability, hardware, and space. To increase statistical power, spaceflight RNA-seq datasets from different missions are often aggregated together. However, this can introduce technical variation or “batch effects”, often due to differences in sample handling, sample processing, and sequencing platforms. Several computational methods have been developed to correct for technical batch effects, thereby reducing their impact on true biological signals.Methods:In this study, we combined 7 mouse liver RNA-seq datasets from NASA GeneLab (part of the NASA Open Science Data Repository) to evaluate several common batch effect correction methods (ComBat and ComBat-seq from the sva R package, and Median Polish, Empirical Bayes, and ANOVA from the MBatch R package). Principal component analysis (PCA) was used to identify library preparation method and mission as the primary sources of batch effect among the technical variables in the combined dataset. We next quantitatively evaluated the ability of each of the indicated methods to correct for each identified technical batch variable using the following criteria: BatchQC, PCA, dispersion separability criterion, log fold change correlation, and differential gene expression analysis. Each batch variable/correction method combination was then assessed using a custom scoring approach to identify the optimal correction method for the combined dataset, by geometrically probing the space of all allowable scoring functions to yield an aggregate volume-based scoring measure.Results and Discussion:Using the method described for the combined dataset in this study, the library preparation variable/ComBat correction method pair out ranked the other candidate pairs, suggesting that this combined dataset should be corrected for library preparation using the ComBat correction method prior to downstream analysis. We describe the GeneLab multi-study analysis and visualization portal which will allow users to access the publicly available space biology ‘omics data, select multiple studies to combine for analysis, and examine the presence or absence of batch effects using multiple metrics. If the user chooses to perform batch effect correction, the scoring approach described here can be implemented to identify the optimal correction method to use for their specific combined dataset prior to analysis.

Characterization of intratumoral tertiary lymphoid structures in pancreatic ductal adenocarcinoma: cellular properties and prognostic significance

BackgroundTumor-associated tertiary lymphoid structures (TLSs) are functional immune-responsive niches that are not fully understood in pancreatic ductal adenocarcinoma (PDAC).MethodsFluorescent multiplex immunohistochemistry was performed on sequential sections of surgically resected tumor...