Commercial Platforms Research Articles

NAP-XPS study of surface chemistry of CO and ethanol sensing with WO3 nanowires-based gas sensor

The nanowires prepared on a commercial Al2O3-based sensor platform were studied by near ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) upon detecting CO and ethanol. Our findings indicated that the ex-situ prepared WO3 NWs-based gas sensor might not be suitable for NAP-XPS studies, as the surfaces tend to accumulate significant carbon contamination, which remains on the surface even after long-term annealing in mbar range oxygen atmosphere at 593 K. To address this challenge, we developed a methodology involving exposure of the sensor to NOx, which effectively cleans the surface and enables the detection of subtle chemical changes upon exposure to various carbon-containing analytes. Using NAP-XPS analysis, we investigated the gas sensing mechanisms of WO3 NWs for CO and ethanol at different temperatures, revealing that it might combine several mechanisms commonly observed in metal-oxide chemiresistors. It is shown that the response to CO rather arises from the quick interaction of CO molecules with the ionosorbed oxygen. In the case of ethanol sensing, the main driving force for the sensor macroscopic electrical response at low temperatures is the adsorption of ethanol molecules. In contrast, at high temperatures, it combines the adsorption of ethoxy groups with the strong reducing effect of EtOH on the WO3 surface.

Pulse parameter optimizer: an efficient tool for achieving prescribed dose and dose rate with electron FLASH platforms

Purpose. Commercial electron FLASH platforms deliver ultra-high dose rate doses at discrete combinations of pulse parameters including pulse width (PW), pulse repetition frequency (PRF) and number of pulses (N), which dictate unique combinations of dose and dose rates. Additionally, collimation, source to surface distance, and airgaps also vary the dose per pulse (DPP). Currently, obtaining pulse parameters for the desired dose and dose rate is a cumbersome manual process involving creating, updating, and looking up values in large spreadsheets for every treatment configuration. This work presents a pulse parameter optimizer application to match intended dose and dose rate precisely and efficiently. Methods. Dose and dose rate calculation methods have been described for a commercial electron FLASH platform. A constrained optimization for the dose and dose rate cost function was modelled as a mixed integer problem in MATLAB (The MathWorks Inc., Version9.13.0 R2022b, Natick, Massachusetts). The beam and machine data required for the application were acquired using GafChromic film and alternating current current transformers (ACCTs). Variables for optimization included DPP for every collimator, PW and PRF measured using ACCT and airgap factors. Results. Using PW, PRF, N and airgap factors as parameters, a software was created to optimize dose and dose rate, reaching the closest match if exact dose and dose rates are not achievable. Optimization took 20 s or less to converge to results. This software was validated for accuracy of dose calculation and precision in matching prescribed dose and dose rate. Conclusion. A pulse parameter optimization application was built for a commercial electron FLASH platform to increase efficiency in dose, dose rate, and pulse parameter prescription process. Automating this process reduces safety concerns associated with manual look up and calculation of these parameters, especially when many subjects at different doses and dose rates are to be safely managed.

Emulating underdogs: Tactical drones in the Russia-Ukraine war

ABSTRACT Early studies on state drone proliferation argued that it would be temperate, constrained by high financial, technical, and infrastructural requisites and fielded according to the logic of scarce, exquisite airpower. While this rationale has held for limited conflicts, the high attrition and massive demand of a total war compelled strong standing armies to follow a different model of adoption: emulating weaker violent nonstate actors leveraging low-cost commercial platforms. The Russia-Ukraine war has captured this trend. Despite earlier expectations of armies maintaining advanced airpower for strategic ends, underdog Ukraine, followed by Russia have developed heavy reliance on commercial drone technologies for tactical aims. Framing this in military and battlefield innovation literature and drawing on studies on commercial drone use among violent nonstate actors, we argue that this constitutes a new trajectory involving mixed military arsenals enhanced with dual-use commercial platforms.

OPEN leaf: an open-source cloud-based phenotyping system for tracking dynamic changes at leaf-specific resolution in Arabidopsis.

The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation of OPEN leaf, an open-source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf-specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify and characterize previously unidentified phenotypes in a leaf-specific time-dependent manner. Moreover, the modular and open-source design of OPEN leaf allows seamless integration of additional sensors based on users and experimental needs.

Using ChatGPT and Persuasive Technology for Personalized Recommendation Messages in Hotel Upselling

Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity, and personalization, recommender systems can effectively influence user decision making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present pilot experiments with a case study involving a hotel recommender system. Our inhouse commercial hotel marketing platform, eXclusivi, was extended with a new software module working with ChatGPT prompts and persuasive ads created for its recommendations. In particular, we developed an intelligent advertisement (ad) copy generation tool for the hotel marketing platform. The proposed approach allows for the hotel team to target all guests in their language, leveraging the integration with the hotel’s reservation system. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between ChatGPT and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue.

Glitchy, Caring, Tactical

This paper directs attention to the parameters of creative resistance to large-scale commercial digital platforms. It does so by enhancing the understanding of minor tech through the analysis and case study of the artwork, VPN. While minor tech might sound unfamiliar to the many, examples of its existence are at the same time incredibly familiar through examples of digital commoning, sharing of skills, and organisational systems. In the case of VPN, the work existed as a growing emancipatory multimedia archive, executed as a transparent server architecture revealing its technical workings to its users. This format exemplified tactics of intentional glitches through an artful inclusion of persons, space, and objects. By identifying the elements of tactics and care within the VPN, the paper draws parallels of overlapping tendencies within the movement of minor tech. Drawing on Olga Gorionova's research on 'Shadow librarians' and including former digital examples of knowledge sharing furthermore assists in sketching a web development towards the nature of minor tech and VPN. By analysing the significance of these initiatives, the paper raises the questions: What are the drives across creative resistance practices? And (how) do such creative contributions help to critically nuance various existing understandings of large-scale digital platforms?

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease.

Cerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms. We review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice. The incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers. The implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.

Synthetic Aperture Anomaly Imaging for Through-Foliage Target Detection

The presence of foliage is a serious problem for target detection with drones in application fields such as search and rescue, surveillance, early wildfire detection, or wildlife observation. Visual as well as automatic computational methods, such as classification and anomaly detection, fail in the presence of strong occlusion. Previous research has shown that both benefit from integrating multi-perspective images recorded over a wide synthetic aperture to suppress occlusion. In particular, commonly applied anomaly detection methods can be improved by the more uniform background statistics of integral images. In this article, we demonstrate that integrating the results of anomaly detection applied to single aerial images instead of applying anomaly detection to integral images is significantly more effective and increases target visibility as well as precision by an additional 20% on average in our experiments. This results in enhanced occlusion removal and outlier suppression, and consequently, in higher chances of detecting targets that remain otherwise occluded. We present results from simulations and field experiments, as well as a real-time application that makes our findings available to blue-light organizations and others using commercial drone platforms. Furthermore, we outline that our method is applicable for 2D images as well as for 3D volumes.

Performatively Archiving the Early Web: One Terabyte of Kilobyte Age

The abrupt closing of the web hosting service GeoCities – one of the most popular websites inhabited by users in the 90s – is a well-known example of the importance of web archiving for saving digital cultural heritage. GeoCities’ shutdown is a warning for today’s social media user-generated content that might suffer the same fate. Various web archiving organisations archived and nowadays present GeoCities pages. Olia Lialina and Dragan Espenschied engage with GeoCities’ legacy in their project One Terabyte of Kilobyte Age (2010–ongoing). This paper expands on the archival strategies of the artists and places their practice in the context of web archiving organisations – specifically Archive Team, the Internet Archive, and Restorativland – to understand how an artistic position may open up other ways of engaging with digital cultural heritage. This paper considers the archival practices of the organisations, artists, and users as a network of care, enabling different forms of remembrance. Whereas the archiving organisations preserve and present GeoCities statically, Lialina and Espenschied take a performative archival approach, in which they re-perform the dataset with old and new users. Through circulation and narration, One Terabyte of Kilobyte Age builds on the memories of the artists and users and aims to give the GeoCities heritage back to users. The project invites users to interpret and make meaning of GeoCities and embraces the fluidity of digital cultur e, whilst embodying the future archival insecurity of commercial platforms.

Correlation of a commercial platform's results with post-vaccination SARS-CoV-2 neutralizing antibody response and clinical host factors.

The objective of this study was to describe the correlation between the commercially available assay for anti-S1/RBD IgG and protective serum neutralizing antibodies (nAb) against SARS-CoV-2 in an adult population after SARS-CoV-2 vaccination, and determine if clinical variables impact this correlation. We measured IgG anti-S1/RBD using the IgG-II CMIA assay and nAb IC50 values against SARS-CoV-2 WA-1 in sera serially collected post-mRNA vaccination in veterans and healthcare workers of the Veterans Affairs Connecticut Healthcare System (VACHS) between December 2020 and January 2022. The correlation between IgG and IC50 was measured using Pearson correlation. Clinical variables (age, sex, race, ethnicity, prior COVID infection defined by RT-PCR, history of malignancy, estimated glomerular filtration rate (GFR calculated using CKD-EPI equation) were collected by manual chart review. The impact of these clinical variables on the IgG-nAb correlation was analyzed first with univariable regression. Variables with a significance of p < 0.15 were analyzed with forward stepwise regression analysis. From 127 sera samples in 100 unique subjects (age 20-93 years; mean 63.83; SD 15.63; 29% female; 67% White), we found a robust correlation between IgG anti-S1/RBD and nAb IC50 (R2 = 0.83, R2adj = 0.70, p < 0.0001). Race, ethnicity, and a history of malignancy were not significant on univariable analysis. GFR (p < 0.05) and prior COVID infection (p < 0.001) had a significant impact on the correlation between IgG anti-S1/RBD and nAb IC50. Age (p = 0.06) and sex (p = 0.07) trended towards significance on univariable analysis, but were not significant on multivariable regression. There was a strong correlation between IgG anti-S1/RBD and nAb IC50 after SARS-CoV-2 vaccination. Clinical comorbidities, such as prior COVID infection and renal function, impacted this correlation. These results may assist the prediction of post-vaccination immune protection in clinical settings using cost-effective commercial platforms.

Veterinary telemedicine: Tips for expanding your practice virtually

Telemedicine services are a means to provide valuable informa­tion in an efficient and economical manner. They can be used to supplement traditional veterinary medicine services for clients with an established VCPR. A variety of commercial platforms are available; however, in most cases the technologies provided by a smart phone are more than adequate. Telemedicine may be implemented into daily practice in the form of triage, diag­nosis, assessing welfare, herd or case monitoring, and virtual consultations. Establishing a standardized fee structure to compensate you for your time and expertise is critical in order to ensure success.

Thermomechanical shakedown analysis considering temperature-dependent yield stress based on the primal-dual eigenstress-driven method

Many industrial structural components are loaded repeatedly in high-temperature environments. The shakedown limit under variable thermomechanical loads is a critical factor in thermomechanical elastoplastic structure design assessment. The shakedown limit is weakened due to material strength reduction at high-temperature conditions.This paper develops an efficient shakedown analysis Primal-dual Eigenstress-driven Method(PEM) algorithm for temperature-dependent yield condition, which overcomes the obstacle of the original PEM method [1] which can only deal with constant material properties. The developed temperature-dependent PEM algorithm proposes a two-level iteration algorithm to determine the shakedown limit and the time-independent residual stress field. At the inner level the theoretical framework tailored to the temperature dependence problem is derived to solve the Karush-Kuhn-Tucker (KKT) conditions of the extended temperature-dependent shakedown theorem and establish an increment algorithm for forming residual stress fields. Besides, the lower bound safe multipliers are computed to verify the residual stress field obtained by inner-level algorithm. At the outer level an efficient fixed-point algorithm is applied to search the shakedown multiplier, where the corresponding updating strategy for the yield stress with temperature is nested. For practicability, the proposed algorithm is seamlessly integrated into commercial platform ANSYS, which is applicable to industrial structures. Four numerical examples from multiple perspectives show the effectiveness of developed PEM method.

Easy and Accessible Workflow for Label-Free Single-Cell Proteomics.

Single-cell proteomics (SCP) can provide information that is unattainable through either bulk-scale protein measurements or single-cell profiling of other omes. Maximizing proteome coverage often requires custom instrumentation, consumables, and reagents for sample processing and separations, which has limited the accessibility of SCP to a small number of specialized laboratories. Commercial platforms have become available for SCP cell isolation and sample preparation, but the high cost of these platforms and the technical expertise required for their operation place them out of reach of many interested laboratories. Here, we assessed the new HP D100 Single Cell Dispenser for label-free SCP. The low-cost instrument proved highly accurate and reproducible for dispensing reagents in the range from 200 nL to 2 μL. We used the HP D100 to isolate and prepare single cells for SCP within 384-well PCR plates. When the well plates were immediately centrifuged following cell dispensing and again after reagent dispensing, we found that ∼97% of wells that were identified in the instrument software as containing a single cell indeed provided the proteome coverage expected of a single cell. This commercial dispenser combined with one-step sample processing provides a very rapid and easy-to-use workflow for SCP with no reduction in proteome coverage relative to a nanowell-based workflow, and the commercial well plates also facilitate autosampling with unmodified instrumentation. Single-cell samples were analyzed using home-packed 30 μm i.d. nanoLC columns as well as commercially available 50 μm i.d. columns. The commercial columns resulted in ∼35% fewer identified proteins. However, combined with the well plate-based preparation platform, the presented workflow provides a fully commercial and relatively low-cost alternative for SCP sample preparation and separation, which should greatly broaden the accessibility of SCP to other laboratories.

Abstract P1104: Mantarray 3D Engineered Muscle Tissue Platform Demonstrates Clinically-relevant Disease Stratification Of An In Vitro Human Duchenne Muscular Dystrophy Model

Accurately modeling disease in vitro is vital for the development of new treatment strategies and therapeutics. For cardiac diseases, direct assessment of contractile output is the “ultimate phenotype” and a reliable metric to study overall tissue function, as other ‘proxy’ measurements or biomarkers are not good predictors of muscle strength. Human 3D engineered heart tissues (EHTs) from induced pluripotent stem cells hold great potential for modeling contractile function. However, the bioengineering strategies required to generate these predictive models present limitations for many investigators. Here, we have developed an instrument platform that utilizes 3D EHTs in conjunction with a label-free, magnetic sensing array (Mantarray). The platform enables facile and reproducible fabrication of 3D EHTs using virtually any cell source and is coupled with parallel direct measurement of contractility. This approach enables clinically relevant functional measurements of muscle, stratification of healthy vs. diseased muscle phenotypes, and facilitates therapeutic modality-agnostic compound safety and efficacy screening. We present a 3D model of Duchenne muscular dystrophy (DMD) that utilizes EHTs formed from an isogenic pair of healthy and diseased cells. DMD tissues display functional deficits across numerous metrics of contractility, including force and relaxation kinetics, though beat rate remains unchanged. EHTs remain functional over months in culture and provide a large experimental window to not only study therapeutic effect, but also disease phenotypes that may present at later stages of development and maturity. We show both acute and chronic effects of compounds in EHTs, including a drug (BMS-986094) that failed clinical trials due to unanticipated cardiotoxicity. These data demonstrate a first-and-only commercial platform that integrates individual, well-based control of electrical stimulation across a 24-well plate of tissues to pace muscle constructs, model force-frequency, and enable exercise regimens or damage protocols. Stimulation is coupled with automated assessment of muscle contraction, providing an inclusive, medium-throughput platform for disease modeling and therapeutic discovery.

Digital Tomosynthesis: Review of Current Literature and Its Impact on Diagnostic Bronchoscopy.

Bronchoscopy has garnered increased popularity in the biopsy of peripheral lung lesions. The development of navigational guided bronchoscopy systems along with radial endobronchial ultrasound (REBUS) allows clinicians to access and sample peripheral lesions. The development of robotic bronchoscopy improved localization of targets and diagnostic accuracy. Despite such technological advancements, published diagnostic yield remains lower compared to computer tomography (CT)-guided biopsy. The discordance between the real-time location of peripheral lesions and anticipated location from preplanned navigation software is often cited as the main variable impacting accurate biopsies. The utilization of cone beam CT (CBCT) with navigation-based bronchoscopy has been shown to assist with localizing targets in real-time and improving biopsy success. The resources, costs, and radiation associated with CBCT remains a hindrance in its wider adoption. Recently, digital tomosynthesis (DT) platforms have been developed as an alternative for real-time imaging guidance in peripheral lung lesions. In North America, there are several commercial platforms with distinct features and adaptation of DT. Early studies show the potential improvement in peripheral lesion sampling with DT. Despite the results of early observational studies, the true impact of DT-based imaging devices for peripheral lesion sampling cannot be determined without further prospective randomized trials and meta-analyses.

Geospatial Tool Development for the Management of Historical Hiking Trails—The Case of the Holy Site of Meteora

This paper presents a holistic guiding methodology for the development of a geospatial tool to be used for the documentation, planning, smart management and dissemination of a country’s network of historic hiking trails. To deal with the challenges and to ensure the sustainability of a historic site, geospatial documentation merging authoritative and crowdsourced data and a WebGIS-based spatial analysis is necessary. Geospatial data collection should include professional field surveys, professional and crowdsourced photographic documentation and video recording of the existing historic walking/hiking trails. A geodatabase, structured using relational model technology, including vector spatial entities (feature classes), mosaics (raster) and tabulated data (geodatabase tables), should be developed on a commercial or open platform; in this case, the ArcGIS Pro is used. Entities with embedded descriptive information and metadata for the technical, legal, historical, and administrative context may then be created. An object-oriented data model is needed to connect spatial and descriptive information. Spatial and descriptive queries or correlations between attribute fields of spatial entities must be enabled for specialized information retrieval by either experts or users. Next, a web GIS application to present the developed geodatabase in a visually appealing and informative way is created. It should integrate 2D maps with built-in tools and should support advanced functionalities, such as: (i) pop-ups that display brief information and images about specific spots along the trails; (ii) dynamic visualization of the vertical profile of each trail; (iii) multimedia information about landmarks, natural features and scenic viewpoints. Finally, the tool includes a feedback service and continuous efficiency monitoring and assessment, and enables adjustments, if and where needed. The tool is tested and used for 10 historical walking/hiking trails of the archaeological and Holy Site of Meteora, Central Greece. This is a UNESCO World Heritage site. The network, with a total length of 35 km, leads to six monasteries, still active since the 12th century, passing by gigantic rocks and beautiful natural landscapes. The site is famous globally and the greater area is continuously overcrowded with visitors. The tool is anticipated to be used for the documentation and management of the whole walking/hiking historic trail network of Greece in the future.

Would the choice of multiplex platform impact the management of the allergic patient? A first approach focusing on LTPs.

In the Mediterranean area, patients with LTP syndrome who are sensitized to multiple allergens are often tested for sIgE using multiplex platforms. The results obtained from different commercial platforms are not interchangeable, so it is important to compare and validate the platform selected for use. The objective of this study is to compare and validate the performance of the ImmunoCAP ISAC E112i and the macroarray ALEX2 in our daily practice. From August 2021 to March 2022, we tested 20 random serum samples from polysensitized patients using the ALEX2 test (MADx) and ImmunoCAP tIgE and ISAC E112i (Thermo Fisher Scientific). We compared the total IgE (tIgE) and sIgE levels for shared allergens. The heatmap generally showed more intense results for ISAC. The overall correlation was good, but some exceptions were noted. The main discrepancies were found for Ole e 7, which was positive for 11 patients in ISAC but negative for all patients in ALEX2, and for nut LTPs, for which ISAC showed a threefold higher detection rate for Ara h 9 and a fivefold higher detection rate for Cor a 8 and Jug r 3 compared to ALEX2. The regression model showed no interchangeability of tIgE results. Despite our small sample size and the complexity of comparing a quantitative and a semi-quantitative platform, our results suggest that patient diagnosis and management can be influenced by the platform used. Therefore, our findings must be taken into consideration when choosing a platform to use for some profiles of LTP-polysensitized patients, even though more data is needed.

Updates on Recent Clinical Assessment of Commercial Chronic Wound Care Products.

Impaired wound healing after trauma,disorders, and surgeries impact millions of people globally every year. Dysregulation inorchestrated healing mechanisms and underlying medical complications make chronic wound management extremely challenging. Besidesstandard-of-care treatments including broad spectrum antibioticsand wound-debridement,novel adjuvant therapies are clinically tested and commercialized. These include topical agents, skin substitutes, growth factor delivery, and stem cell therapies. With a goal to overcome factors playing pivotal role in delayed wound healing, researchers are exploring novel approaches to elicit desirable healing outcomes in chronic wounds. Although recent innovations in wound care products, therapies, and devices are extensively reviewed in past, a comprehensive review summarizing their clinical outcomes is surprisingly lacking. Herein, this work reviews the commercially available wound care products and their performance in clinical trials to provide a statistically comprehensive understanding of their safety and efficacy. The performance and suitability of various commercial wound care platforms, including xenogeneic and allogenic products, wound care devices, and novel biomaterials, are discussed forchronic wounds. The current clinical evaluation will provide a comprehensive understanding of the benefits and drawbacks of the most-recent approaches and will enable researchers and healthcare providers to develop next-generation technologies for chronic wound management.

Responsive Janus droplets as modular sensory layers for the optical detection of bacteria

The field of biosensor development is fueled by innovations in new functional transduction materials and technologies. Material innovations promise to extend current sensor hardware limitations, reduce analysis costs, and ensure broad application of sensor methods. Optical sensors are particularly attractive because they enable sensitive and noninvasive analyte detection in near real-time. Optical transducers convert physical, chemical, or biological events into detectable changes in fluorescence, refractive index, or spectroscopic shifts. Thus, in addition to sophisticated biochemical selector designs, smart transducers can improve signal transmission and amplification, thereby greatly facilitating the practical applicability of biosensors, which, to date, is often hampered by complications such as difficult replication of reproducible selector-analyte interactions within a uniform and consistent sensing area. In this context, stimuli-responsive and optically active Janus emulsions, which are dispersions of kinetically stabilized biphasic fluid droplets, have emerged as a novel triggerable material platform that provides as a versatile and cost-effective alternative for the generation of reproducible, highly sensitive, and modular optical sensing layers. The intrinsic and unprecedented chemical-morphological-optical coupling inside Janus droplets has facilitated optical signal transduction and amplification in various chemo- and biosensor paradigms, which include examples for the rapid and cost-effective detection of major foodborne pathogens. These initial demonstrations resulted in detection limits that rival the capabilities of current commercial platforms. This trend article aims to present a conceptual summary of these initial efforts and to provide a concise and comprehensive overview of the pivotal kinetic and thermodynamic principles that govern the ability of Janus droplets to sensitively and selectively respond to and interact with bacteria.Graphical abstract

Hierarchical Bayesian propulsion power models — A simplified example with cruise ships

Mathematical models for ships’ consumption are in a central role in assessing the CO2 emissions of marine traffic. Moreover, such models are needed when optimizing the ways the vessels are operated (e.g. routing). Nowadays, many ships are equipped with data collection systems, enabling data-based calibration of the models. Typically this calibration is done independently for each ship. In this paper, we demonstrate a hierarchical Bayesian approach, where we fit a single model over many vessels, with the assumption that the parameters of vessels of similar characteristics are likely close to each other. The benefits of such an approach are two-fold; (1) we can borrow information about parameters that are not well informed by the vessel-specific data using data from similar ships, and (2) we can use the hierarchical model to predict the behavior of a vessel from which we have no data, based only on its characteristics. In this paper, we discuss the basic concept and present a simple version of the model using cruise vessels. We apply the Stan modeling tool for the fitting and use real data from 64 ships collected via the commercial Eniram platform. The prediction accuracy of the model is compared to an existing data-free method. We demonstrate that the accuracy of such an approach can improve upon the classical resistance calculation-based methods.