In the area of computer vision, deep learning techniques have recently been used to predict whether urban scenes are likely to be considered beautiful: it turns out that these techniques are able to ...make accurate predictions. Yet they fall short when it comes to generating actionable insights for urban design. To support urban interventions, one needs to go beyond
beauty, and tackle the challenge of
beauty. Unfortunately, deep learning techniques have not been designed with that challenge in mind. Given their 'black-box nature', these models cannot be directly used to explain why a particular urban scene is deemed to be beautiful. To partly fix that, we propose a deep learning framework (which we name FaceLift) that is able to both
existing urban scenes (Google Street Views) and
which urban elements make those transformed scenes beautiful. To quantitatively evaluate our framework, we cannot resort to any existing metric (as the research problem at hand has never been tackled before) and need to formulate new ones. These new metrics should ideally capture the presence (or absence) of elements that make urban spaces great. Upon a review of the urban planning literature, we identify
main metrics: walkability, green spaces, openness, landmarks and visual complexity. We find that, across all the five metrics, the beautified scenes meet the expectations set by the literature on what great spaces tend to be made of. This result is further confirmed by a 20-participant expert survey in which FaceLift has been found to be effective in promoting citizen participation. All this suggests that, in the future, as our framework's components are further researched and become better and more sophisticated, it is not hard to imagine technologies that will be able to accurately and efficiently support architects and planners in the design of the spaces we intuitively love.
This article uses John Berger’s idea (1972) that images are connected to ‘ways of seeing’ to reflect on the creation of interactive visualizations of peace agreement and peace process data. We ...reflect on three visualizations created during a three-year long collaboration. We first describe our data, the peacebuilding ambitions for its use, and why we produced interactive forms of visualization. Second, we describe how the process of producing these visualizations created an interdisciplinary conversation and collaboration, which also connected different epistemic and geographic communities involved in peace processes. We term this ‘visualization-as-scoping’. Third, we reflect on both ‘what we saw’, through the process of visualization, how it affected policy, and the lessons we learned regarding visualization in the peacebuilding field. In the article, we argue that our experience of ‘visualization-as-scoping’ inverts traditional assumptions about the connection of data visualization to policy influence. In place of the notion of visualization-as-communication, focused on transmitting clear policy ‘messages’, we point to visualization-as-scoping as a practice of interchange, critique and re-iteration. Using John Berger as inspiration, we suggest that the ‘ways of seeing’ that result can usefully disrupt the idea of a data producing singular policy prescriptions, and rather enable people to grapple better with the complex political processes they are involved in.
Over more than 20 years, isoelectric focusing (IEF) in a polyacrylamide gel (PAGIF) has been the only official method in Germany to verify the animal species in dairy products, including cheese. The ...method remains valid until now, using the analytical standards and the detection and quantification limits of that time. With the introduction of faster, cheaper and more sensitive methods, the PAGIF is in danger to lose importance in food control. Therefore, based on the § 64 method(s) of the German Food and Feed Code (LFGB), the pH gradient has been optimized, on the one hand, to sharpen the protein bands and thus to improve the detection limit (cow’s milk: 0.1%, previously 1%) and on the other hand, to make it possible to analyze simultaneously several animal species, such as cow, sheep, goat and buffalo in one single gel. By condensing the workflow and improving the original performance data, the revised PAGIF will continue to be the official method in food control alongside new analytical methods.
Zusammenfassung
Seit mehr als 20 Jahren ist die isoelektrische Fokussierung (IEF) in einem Polyacrylamidgel (PAGIF) amtliche Methode zur Untersuchung der Tierart in Milchprodukten einschließlich ...Käse. Sie ist mit den damals geltenden Analysenstandards und den zu dieser Zeit ermittelten Nachweis- und Bestimmungsgrenzen bis heute unverändert weiterhin in Kraft. Nachdem zwischenzeitlich schnellere, günstigere und empfindlichere Methoden zur Verfügung stehen, läuft die PAGIF in Gefahr, an Bedeutung zu verlieren. Aufbauend auf die Methoden nach § 64 wurde daher durch die Optimierung des pH-Gradienten einerseits die Bandenschärfe der Proteine und damit auch die Nachweisgrenze (Kuhmilch: 0,1%, zuvor 1%) der Methode verbessert und andererseits die zeitgleiche Bestimmung der Tierarten Kuh, Schaf, Ziege und Büffel in einem einzigen Gel ermöglicht. Durch die Straffung des Arbeitsablaufes und die Steigerung der ursprünglichen Leistungsdaten kann die verbesserte PAGIF auch in Zukunft als amtliche Methode in der Lebensmittelüberwachung neben den neuen Analysenmethoden bestehen.
This research investigates how people engage with data visualizations when commenting on the social platform Reddit. There has been considerable research on collaborative sensemaking with ...visualizations and the personal relation of people with data. Yet, little is known about how public audiences without specific expertise and shared incentives openly express their thoughts, feelings, and insights in response to data visualizations. Motivated by the extensive social exchange around visualizations in online communities, this research examines characteristics and motivations of people’s reactions to posts featuring visualizations. Following a Grounded Theory approach, we study 475 reactions from the /r/dataisbeautiful community, identify ten distinguishable reaction types, and consider their contribution to the discourse. A follow-up survey with 168 Reddit users clarified their intentions to react. Our results help understand the role of personal perspectives on data and inform future interfaces that integrate audience reactions into visualizations to foster a public discourse about data.
This research investigates how people engage with data visualizations when commenting on the social platform Reddit. There has been considerable research on collaborative sensemaking with ...visualizations and the personal relation of people with data. Yet, little is known about how public audiences without specific expertise and shared incentives openly express their thoughts, feelings, and insights in response to data visualizations. Motivated by the extensive social exchange around visualizations in online communities, this research examines characteristics and motivations of people's reactions to posts featuring visualizations. Following a Grounded Theory approach, we study 475 reactions from the /r/dataisbeautiful community, identify ten distinguishable reaction types, and consider their contribution to the discourse. A follow-up survey with 168 Reddit users clarified their intentions to react. Our results help understand the role of personal perspectives on data and inform future interfaces that integrate audience reactions into visualizations to foster a public discourse about data.
In the area of computer vision, deep learning techniques have recently been used to predict whether urban scenes are likely to be considered beautiful: it turns out that these techniques are able to ...make accurate predictions. Yet they fall short when it comes to generating actionable insights for urban design. To support urban interventions, one needs to go beyond predicting beauty, and tackle the challenge of recreating beauty. Unfortunately, deep learning techniques have not been designed with that challenge in mind. Given their "black-box nature", these models cannot be directly used to explain why a particular urban scene is deemed to be beautiful. To partly fix that, we propose a deep learning framework called Facelift, that is able to both beautify existing urban scenes (Google Street views) and explain which urban elements make those transformed scenes beautiful. To quantitatively evaluate our framework, we cannot resort to any existing metric (as the research problem at hand has never been tackled before) and need to formulate new ones. These new metrics should ideally capture the presence/absence of elements that make urban spaces great. Upon a review of the urban planning literature, we identify five main metrics: walkability, green spaces, openness, landmarks and visual complexity. We find that, across all the five metrics, the beautified scenes meet the expectations set by the literature on what great spaces tend to be made of. This result is further confirmed by a 20-participant expert survey in which FaceLift have been found to be effective in promoting citizen participation. All this suggests that, in the future, as our framework's components are further researched and become better and more sophisticated, it is not hard to imagine technologies that will be able to accurately and efficiently support architects and planners in the design of spaces we intuitively love.
Information visualization has great potential to make sense of the increasing amount of data generated by complex machine-learning algorithms. We design a set of visualizations for a new ...deep-learning algorithm called FaceLift (goodcitylife.org/facelift). This algorithm is able to generate a beautified version of a given urban image (such as from Google Street View), and our visualizations compare pairs of original and beautified images. With those visualizations, we aim at helping practitioners understand what happened during the algorithmic beautification without requiring them to be machine-learning experts. We evaluate the effectiveness of our visualizations to do just that with a survey among practitioners. From the survey results, we derive general design guidelines on how information visualization makes complex machine-learning algorithms more understandable to a general audience.
Tumor-derived lactic acid inhibits T and natural killer (NK) cell function and, thereby, tumor immunosurveillance. Here, we report that melanoma patients with high expression of glycolysis-related ...genes show a worse progression free survival upon anti-PD1 treatment. The non-steroidal anti-inflammatory drug (NSAID) diclofenac lowers lactate secretion of tumor cells and improves anti-PD1-induced T cell killing in vitro. Surprisingly, diclofenac, but not other NSAIDs, turns out to be a potent inhibitor of the lactate transporters monocarboxylate transporter 1 and 4 and diminishes lactate efflux. Notably, T cell activation, viability, and effector functions are preserved under diclofenac treatment and in a low glucose environment in vitro. Diclofenac, but not aspirin, delays tumor growth and improves the efficacy of checkpoint therapy in vivo. Moreover, genetic suppression of glycolysis in tumor cells strongly improves checkpoint therapy. These findings support the rationale for targeting glycolysis in patients with high glycolytic tumors together with checkpoint inhibitors in clinical trials.
Display omitted
•Glycolytic index in melanoma negatively correlates with response to anti-PD1 therapy•Blocking lactate transport or knock out of glycolytic genes improves checkpoint therapy•Diclofenac blocks the lactate transporters MCT1 and MCT4 in a COX-independent manner•Inhibition of glycolysis by MCT blockade does not impede T cell function
Renner et al. demonstrate a negative correlation between glycolytic activity in tumors and response to checkpoint therapy. Genetic blockade of glycolysis or pharmacological inhibition of the main lactate transporters MCT1 and MCT4 preserves T cell function, reverses tumor acidification, and augments response to checkpoint therapy.
B cell acute lymphoblastic leukemia (B-ALL) is characterized by an accumulation of malignant precursor cells. Treatment consists of multiagent chemotherapy followed by allogeneic stem cell ...transplantation in high-risk patients. In addition, patients bearing the BCR-ABL1 fusion gene receive concomitant tyrosine kinase inhibitor (TKI) therapy. On the other hand, monoclonal antibody therapy is increasingly used in both clinical trials and real-world settings. The introduction of rituximab has improved the outcomes in CD20 positive cases. Other monoclonal antibodies, such as tafasitamab (anti-CD19), obinutuzumab (anti-CD20) and epratuzumab (anti-CD22) have been tested in trials (NCT05366218, NCT04920968, NCT00098839). The efficacy of monoclonal antibodies is based, at least in part, on their ability to induce antibody-dependent cellular cytotoxicity (ADCC). Combination treatments, e.g., chemotherapy and TKI, should therefore be screened for potential interference with ADCC. Here, we report on in vitro data using BCR-ABL1 positive and negative B-ALL cell lines treated with rituximab and TKI. NK cell activation, proliferation, degranulation, cytokine release and tumor cell lysis were analyzed. In contrast to ATP site inhibitors such as dasatinib and ponatinib, the novel first-in-class selective allosteric ABL myristoyl pocket (STAMP) inhibitor asciminib did not significantly impact ADCC in our settings. Our results suggest that asciminib should be considered in clinical trials.
PDF
