Prediction, detection, and correction of misunderstandings in interactive tasks

Technology has allowed all kinds of devices and software to come into our lives. Advances in GPS, Virtual Reality, and wearable computers with increased computing power and Internet connectivity open the doors for interactive systems that were considered science fiction less than a decade ago, and are capable of guiding us in a variety of environments. This increased accessibility comes at the cost of increasing both the scale of problems that can be realistically tackled and the capabilities that we expect from such systems. Indoor navigation is an example of such a task: although guiding a car is a solved problem, guiding humans for instance inside a museum is much more challenging. Unlike cars, pedestrians use landmarks rather than absolute distances. They must discriminate from a larger number of distractors, and expect sentences of higher complexity than those appropriate for a car driver. A car driver prefers short, simple instructions that do not distract them from traffic. A tourist inside a museum on the contrary can afford the mental effort that a detailed grounding process would require. Both car and indoor navigation are specific examples of a wider family of collaborative tasks known as “Instruction Following”. In these tasks, agents with the two clearly defined roles of Instruction Giver and Instruction Follower must cooperate to achieve a joint objective. The former has access to all required information about the environment, including (but not limited to) a detailed map of the environment, a clear list of objectives, and a profound understanding of the effect that specific actions have in the environment. The latter is tasked with following the instructions, interacting with the environment and moving the undertaking forward. It is then the Instruction Giver’s responsibility to assess a detailed plan of action, segment it into smaller subgoals, and present instructions to the Instruction Follower in a language that is clear and understandable. No matter how carefully crafted the Instruction Giver’s utterances are, it is expected that misunderstandings will take place. Although some of these misunderstandings are easy to detect and repair, others can be very difficult or even impossible to solve. It is therefore important for the Instruction Giver to generate instructions that are as clear as possible, to detect misunderstandings as early as possible, and to correct them in the most effective way. This thesis introduces several algorithms and strategies designed to tackle the aforementioned problems from end to end, presenting the individual aspects of a system that successfully predicts, detects, and corrects misunderstandings in interactive Instruction Following tasks. We focus on one particular type of instruction: those involving Referring Expressions. A Referring Expression identifies a single object out of many, such as “the red button” or “the tall plant”. Generating Referring Expressions is a key component of Inst. Following tasks, since any kind of object manipulation is likely to require a description of the object. Due to its importance and complexity, this is one of the most widely studied areas of Natural Language Generation. In this thesis we use Semantically Interpreted Grammars, an approach that integrates both Referring Expression Generation (identifying which properties are required for a unique description) and Surface realization (combining those properties into a concrete Noun Phrase). The complexity of performing, recording, and analyzing Instruction Following tasks in the real world is one of the major challenges of Instruction Following research. In order to simplify both the development of new algorithms and the access to those results by the research community, our work is evaluated in what we call a Virtual Environment—an environment that mimics the main aspects of the real world and abstracts distractions while preserving enough characteristics of the real world to be useful for research. Selecting the appropriate virtual environment for a research task ensures that results will be applicable in the real world. We have selected the Virtual Environment of the GIVE Challenge, an environment designed for an Instruction Following task in which a human Instruction Follower is paired with an automated Instruction Giver in a maze-like 3D world. Completing the task requires navigating the space, avoiding alarms, interacting with objects, generating instructions in Natural Language, and preventing mistakes that can bring the task to a premature end. Even under these simplified conditions, the task presents several computational challenges: performing these tasks in real time require fast algorithms, and ensuring the efficiency of our approaches remains a priority at every step. Our first experimental study identifies the most challenging type of mistakes that our system is expected to find. Creating an Inst. Following system that leverages previously-recorded human data and follows instructions using a simple greedy algorithm, we clearly separate those situations for which no further study is warranted from those that are of interest for our research. We test our algorithm with similarity metrics of varying complexity, ranging from overlap measures such as Jaccard and edit distances to advanced machine learning algorithms such as Support Vector Machines. The best performing algorithms achieve not only good accuracy, but we show in fact that mistakes are highly correlated with situations that are also challenging for human annotators. Going a step further, we also study the type of improvement that can be expected from our system if we give it the chance of retrying after a mistake was made. This system has no prior beliefs on which actions are more likely to be selected next, and our results make a good case for this vision to be one of its weakest points. Moving away from a paradigm where all actions are considered equally likely, and moving towards a model in which the Inst. Follower’s own action is taken into account, our subsequent step is the development of a system that explicitly models listener’s understanding. Given an instruction containing a Referring Expression, we approach the Instruction Follower’s understanding of it with a combination of two probabilistic models. The Semantic model uses features of the Referring Expression to identify which object is more likely to be selected: if the instruction mentions a red button, it is unlikely that the Inst. Follower will select a blue one. The Observational model, on the other hand, predicts which object will be selected by the Inst. Follower based on their behavior: if the user is walking straight towards a specific object, it is very likely that this object will be selected. These two log-linear, probabilistic models were trained with recorded human data from the GIVE Challenge, resulting in a model that can effectively predict that a misunderstanding is about to take place several seconds before it actually happens. Using our Combined model, we can easily detect and predict misunderstandings — if the Inst. Giver tells the Inst. Follower to “click the red button”, and the Combined model detects that the Inst. Follower will select a blue one, we know that a misunderstanding took place, we know what the misunderstood object is, and we know both facts early enough to generate a correction that will stop the Inst. Follower from making the mistake in the first place. A follow-up study extends the Observational model introducing features based on the gaze of the Inst. Follower. Gaze has been shown to correlate with human attention, and our study explores whether gaze-based features can improve the accuracy of the Observational model. Using previouslycollected data from the GIVE Environment in which gaze was recorded using eye-tracking equipment, the resulting Extended Observational model improves the accuracy of predictions in challenging scenes where the number of distractors is high. Having a reliable method for the detection of misunderstandings, we turn our attention towards corrections. A corrective Referring Expression is one designed not only for the identification of a single object out of many, but rather, for identifying a previously-wrongly-identified object. The simplest possible corrective Referring Expression is repetition: if the user misunderstood the expression “the red button” the first time, it is possible that they will understand it correctly the second time. A smarter approach, however, is to reformulate the Referring Expression in a way that makes it easier for the Inst. Follower to understand. We designed and evaluated two different strategies for the generation of corrective feedback. The first of these strategies exploits the pragmatics concept of a Context Set, according to which human attention can be segmented into objects that are being attended to (that is, those inside the Context Set) and those that are ignored. According to our theory, we could virtually ignore all objects outside the Context Set and generate Referring Expressions that would not be uniquely identifying with respect to the entire context, but would still be identifying enough for the Inst. Follower. As an example, if the user is undecided between a red button and a blue one, we could generate the Referring Expression “the red one” even if there are other red buttons on the scene that the user is not paying attention to. Using our probabilistic models as a measure for which elements to include in the Context Set, we modified our Referring Expression Generation algorithm to build sentences that explicitly account for this behavior. We performed experiments over the GIVE Challenge Virtual Environment, crowdsourcing the data collection process, with mixed results: even if our definition of a Context Set were correct (a point that our results can neither confirm nor deny), our strategy generates Referring Expressions that prevents some mistakes, but are in general harder to understand than the baseline approach. The results are presented along with an extensive error analysis of the algorithm. They imply that corrections can cause the Instruction Follower to re-evaluate the entire situation in a new light, making our previous definition of Context Set impractical. Our approach also fails at identifying previously grounded referents, compounding the number of pragmatic effects that conspire against this approach. The second strategy for corrective feedback consists on adding Contrastive focus to a second, corrective Referring Expression In a scenario in which the user receives the Referring Expression “the red button” and yet mistakenly selects a blue one, an approach with contrastive focus would generate “no, the RED button” as a correction. Such a Referring Expression makes it clear to the Inst. Follower that on the one hand their selection of an object of type “button” was correct, and that on the other hand it is the property “color” that needs re-evaluation. In our approach, we model a misunderstanding as a noisy channel corruption: the Inst. Giver generates a correct Referring Expression for a given object, but it is corrupted in transit and reaches the Inst. Follower in the form of an altered, incorrect Referring Expression We correct this misconstrual by generating a new, corrective Referring Expression: starting from the original Referring Expression and the misunderstood object, we identify the constituents of the Referring Expression that were corrupted and place contrastive focus on them. Our hypothesis states that the minimum edit sequence between the original and misunderstood Referring Expression correctly identifies the constituents requiring contrastive focus, a claim that we verify experimentally. We perform crowdsourced preference tests over several variations of this idea, evaluating Referring Expressions that either present contrast side by side (as in “no, not the BLUE button, the RED button”) or attempt to remove redundant information (as in “no, the RED one”). We evaluate our approaches using both simple scenes from the GIVE Challenge and more complicated ones showing pictures from the more challenging TUNA people corpus. Our results show that human users significantly prefer our most straightforward contrastive algorithm. In addition to detailing models and strategies for misunderstanding detection and correction, this thesis also includes practical considerations that must be taken into account when dealing with similar tasks to those discussed here. We pay special attention to Crowdsourcing, a practice in which data about tasks can be collected from participants all over the world at a lower cost than traditional alternatives. Researchers interested in using crowdsourced data must often deal both with unmotivated players and with players whose main motivation is to complete as many tasks as possible in the least amount of time. Designing a crowdsourced experiment requires a multifaceted approach: the task must be designed in such a way as to motivate honest players, discourage other players from cheating, implementing technical measures to detect bad data, and prevent undesired behavior looking at the entire pipeline with a Security mindset. We dedicate a Chapter to this issue, presenting a full example that will undoubtedly be of help for future research. We also include sections dedicated to the theory behind our implementations. Background literature includes the pragmatics of dialogue, misunderstandings, and focus, the link between gaze and visual attention, the evolution of approaches towards Referring Expression Generation, and reports on the motivations of crowdsourced workers that borrow from fields such as psychology and economics. This background contextualizes our methods and results with respect to wider fields of study, enabling us to explain not only that our methods work but also why they work. We finish our work with a brief overview of future areas of study. Research on the prediction, detection, and correction of misunderstandings for a multitude of environments is already underway. With the introduction of more advanced virtual environments, modern spoken, dialoguebased tools revolutionizing the market of home devices, and computing power and data being easily available, we expect that the results presented here will prove useful for researchers in several areas of Natural Language Processing for many years to come.Die Technologie hat alle möglichen Arten von unterstützenden Geräten und Softwares in unsere Leben geführt. Fortschritte in GPS, Virtueller Realität, und tragbaren Computern mit wachsender Rechenkraft und Internetverbindung öffnen die Türen für interaktive Systeme, die vor weniger als einem Jahrzehnt als Science Fiction galten, und die in der Lage sind, uns in einer Vielfalt von Umgebungen anzuleiten. Diese gesteigerte Zugänglichkeit kommt zulasten sowohl des Umfangs der Probleme, die realistisch gelöst werden können, als auch der Leistungsfähigkeit, die wir von solchen Systemen erwarten. Innennavigation ist ein Beispiel einer solcher Aufgaben: obwohl Autonavigation ein gelöstes Problem ist, ist das Anleiten von Meschen zum Beispiel in einem Museum eine größere Herausforderung. Anders als Autos, nutzen Fußgänger eher Orientierungspunkte als absolute Distanzen. Sie müssen von einer größeren Anzahl von Ablenkungen unterscheiden können und Sätze höherer Komplexität erwarten, als die, die für Autofahrer angebracht sind. Ein Autofahrer bevorzugt kurze, einfache Instruktionen, die ihn nicht vom Verkehr ablenken. Ein Tourist in einem Museum dagegen kann die metale Leistung erbringen, die ein detaillierter Fundierungsprozess benötigt. Sowohl Auto- als auch Innennavigation sind spezifische Beispiele einer größeren Familie von kollaborativen Aufgaben bekannt als Instruction Following. In diesen Aufgaben müssen die zwei klar definierten Akteure des Instruction Givers und des Instruction Followers zusammen arbeiten, um ein gemeinsames Ziel zu erreichen. Der erstere hat Zugang zu allen benötigten Informationen über die Umgebung, inklusive (aber nicht begrenzt auf) einer detallierten Karte der Umgebung, einer klaren Liste von Zielen und einem genauen Verständnis von Effekten, die spezifische Handlungen in dieser Umgebung haben. Der letztere ist beauftragt, den Instruktionen zu folgen, mit der Umgebung zu interagieren und die Aufgabe voranzubringen. Es ist dann die Verantwortung des Instruction Giver, einen detaillierten Handlungsplan auszuarbeiten, ihn in kleinere Unterziele zu unterteilen und die Instruktionen dem Instruction Follower in einer klaren, verständlichen Sprache darzulegen. Egal wie sorgfältig die Äußerungen des Instruction Givers erarbeitet sind, ist es zu erwarten, dass Missverständnisse stattfinden. Obwohl einige dieser Missverständnisse einfach festzustellen und zu beheben sind, können anderen sehr schwierig oder gar unmöglich zu lösen sein. Daher ist es wichtig, dass der Instruction Giver die Anweisungen so klar wie möglich formuliert, um Missverständnisse so früh wie möglich aufzudecken, und sie in der effektivstenWeise zu berichtigen. Diese Thesis führt mehrere Algorithmen und Strategien ein, die dazu entworfen wurden, die oben genannten Probleme in einem End-to-End Prozess zu lösen. Dabei werden die individuellen Aspekte eines Systems präsentiert, dass erfolgreich Missverständnisse in interaktiven Instruction Following Aufgaben vorhersagen, feststellen und korrigieren kann.Wir richten unsere Aufmerksamkeit auf eine bestimmte Art von Instruktion: die sogennanten Referring Expressions. Eine Referring Expression idenfiziert ein einzelnes Objekt aus vielen, wie zum Beispiel „der rote Knopf” oder „die große Pflanze”. Das Generieren von Referring Expressions ist eine Schlüsselkomponente von Instruction Following Aufgaben, da jegliche Art von Manipulation sehr wahrscheinlich eine Beschreibung des Objektes erfordert. Wegen derWichtigkeit und Komplexität ist dies eine der am meisten untersuchten Gebiete der Textgenerierung. In dieser Thesis verwenden wir Semantisch Interpretierte Grammatik, eine Methode, die sowohl die Generierung von Referring Expressions (Identifizierung von Eigenschaften für eine eindeutige Beschreibung) als auch Surface Realization (Kombinieren dieser Eigenschaften in eine konkrete Substantivgruppe) integriert. Die Komplexität der Durchführung, Aufzeichnung und Analyse von Instruction Following Aufgaben in der realen Welt ist eine der großen Herausforderungen der Instruction Following Forschung. Um sowohl die Entwicklung neuer Algorithmen und den Zugang zu diesen Ergebnissen durch die Wissenschaftsgemeinde zu vereinfachen, wird unsere Arbeit in einer Virtuellen Umgebung bewertet. Eine virtuelle Umgebung ahmt die Hauptaspekte der realen Welt nach und nimmt Ablenkungen weg, während genug Eigenschaften der realen Welt erhalten bleiben, um verwendbar für die Untersuchung zu sein. Die Auswahl der angebrachten virtuellen Umgebung für eine Forschungsaufgabe gewährleistet, dass die Ergebnisse auch in der realenWelt anwendbar sind. Wir haben eine virtuelle Umgebung der GIVE Challenge ausgesucht â˘A ¸S eine Umgebung, die für eine Instruction Following Aufgabe entworfen wurde, in der ein menschlicher Instruction Follower mit einem automatischen Instruction Giver in einer Labyrinth-artigen 3D Welt verbunden wird. Die Aufgabe zu beenden erfordert Navigation im Raum, Vermeidung von Alarmen, Interagieren mit Objekten, Textgenerierung und Verhindern von Fehlern, die zu einer vorzeitigen Beendung der Aufgabe führen. Sogar unter diesen vereinfachten Bedingungen stellt die Aufgabe mehrere rechentechnische Herausforderungen dar: die Aufgabe in Echtzeit durchzuführen erfordert schnelle Algorithmen, und die Effizienz unserer Methode zu gewährleisten bleibt Priorotät in jedem Schritt. Unser erstes Experiment identifiziert die herausfordernste Art von Fehlern, die unser System erwartungsgemäß finden soll. Durch den Entwurf eines Instruction Following Systems, das sich zuvor aufgezeichnete menschliche Daten zu Nutze macht und durch die Nutzung eines einfachen gierigen Algorithmus Intruktionen folgt, grenzen wir klar die Situationen ab, die keine weitere Studie rechtfertigen, von denen, die interessant für unsere Forschung sind. Wir testen unseren Algorithmus mit Ähnlichkeitsmaßen verschiedener Komplexität, die sich von Überlappungsmaßnahmen wie Jaccard und Editierdistanzen, bis zu fortgeschrittenen Algorithmen des Maschinellen Lernens erstrecken. Die am besten ausführenden Algorithmen erreichen nicht nur gute Genauigkeit sondern tatsächlich zeigen wir, dass Fehler hoch korreliert sind mit Situationen, die auch herausfordernd für menschliche Kommentatoren sind. In einem weiteren Schritt untersuchen wir die Art von Verbesserung, die von unserem System erwartet werden kann wenn wir ihm die Chance geben, es wieder zu versuchen nachdem ein Fehler gemacht wurde. Dieses System macht keine vorherigen Annahmen darüber, welche Aktionen am wahrscheinlichsten als nächstes ausgewählt werden und unsere Ergebnisse liefern gute Argumente dafür, dass dieser Ansatz einer der schwächsten Aspekte ist. Um sich von einem Paradigma wegzubewegen, in dem alle Aktionen gleich wahrscheinlich betrachtet werden, zu einem Model, in dem das Handeln des Instruction Followers in Betracht gezogen wird, ist unser folgender Schritt die Entwicklung eines Systems, dass explizit das Verständnis des Anwenders modelliert. Voraussetzend, dass die Instruktion eine Referring Expression beinhaltet, gehen wir das Verstehen des Instruction Followers mit einer Kombination aus zwei probabilistischen Modellen an. Das semantische Modell verwendet Eigenschaften der Referring Expression um zu identifizieren, welches Objekt wahrscheinlicher a

Investigaciones en iluminación natural en clima soleado: la aplicación de iluminación natural en la producción de plantas

Las crisis energéticas que se han vivido internacionalmente y a nivel regional, renovaron el interés en las energías renovables en el marco de la sustentabilidad, destacándose la promoción de la iluminación natural como una de las estrategias de diseño bioclimático. Para analizar, desarrollar y transferir estas interacciones donde la iluminación natural es protagonista, diferentes enfoques disciplinarios se interrelacionan para su óptima aplicación. Una de ellas dio lugar a implementar una aplicación de iluminación natural en construcciones de espacios para la propagación agámica de plantas.os dispositivos innovativos de iluminación natural representan actualmente una oportunidad de aportar respuestas de desarrollo regional replicable de uso de energía renovable para iluminación interior diurna. No son de aplicación limitada a edificios residenciales (viviendas y edificios públicos) y pueden resolver problemas asociados a los altos costos de energía en la producción, como en el presente caso, de plantas. Se ha diseñado un dispositivo anidólico que pretende optimizar el uso de luz natural en una aplicación que requiere niveles altos (1500 a 3000 lux). Las primeras mediciones verifican los valores esperados.Fil: Pattini, Andrea Elvira. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ambiente, Habitat y Energia.; ArgentinaFil: Villalba, Ayelén María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ambiente, Habitat y Energia.; ArgentinaFil: Ferron, Leandro Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ambiente, Habitat y Energia.; Argentin

El efecto de la temperatura de conservación sobre la viabilidad de las semillas del pijío cavanillesia platanifolia

Cavanillesia platanifolia es una especie de árbol distribuido en el ecosistema de bosque seco tropical que se encuentra amenazado por la fragmentación y pérdida de su hábitat. La reforestación es una actividad a considerar para su conservación, y para ello es clave optimizar la producción de individuos jóvenes ex situ. En el presente estudio comparamos la capacidad de reclutamiento en vivero de dos tratamientos de conservación de las semillas: conservación en frío vs. conservación a temperatura ambiente. La tasa de emergencia se incrementó y las plántulas crecieron más vigorosas en las semillas conservadas en frío. Además, mediante la conservación en frío de las semillas se redujo notablemente la mortalidad de las plántulas, lo cual creemos que es debido a una ralentización del envejecimiento de las semillas y a la disminución de la actividad patogénica. Esta fácilmente replicable y económica metodología de reproducción en vivero puede ser incorporada a los potenciales planes de reforestación de C. platanifolia, así como a otras especies vegetales amenazadas del neotrópico con semillas de similares características.Cavanillesia platanifolia es una especie de árbol distribuido en el ecosistema de bosque seco tropical que se encuentra amenazado por la fragmentación y pérdida de su hábitat. La reforestación es una actividad a considerar para su conservación, y para ello es clave optimizar la producción de individuos jóvenes ex situ. En el presente estudio comparamos la capacidad de reclutamiento en vivero de dos tratamientos de conservación de las semillas: conservación en frío vs. conservación a temperatura ambiente. La tasa de emergencia se incrementó y las plántulas crecieron más vigorosas en las semillas conservadas en frío. Además, mediante la conservación en frío de las semillas se redujo notablemente la mortalidad de las plántulas, lo cual creemos que es debido a una ralentización del envejecimiento de las semillas y a la disminución de la actividad patogénica. Esta fácilmente replicable y económica metodología de reproducción en vivero puede ser incorporada a los potenciales planes de reforestación de C. platanifolia, así como a otras especies vegetales amenazadas del neotrópico con semillas de similares características.

Development of new nano-enhanced phase change materials (NEPCM) to improve energy efficiency in buildings: Lab-scale characterization

Fatty acids are promising organic phase change materials (PCMs) for thermal energy storage (TES) in buildings because of their high storage capacity, non-toxic nature and little subcooling. Their phase change temperatures make them suitable for heating, ventilating and air conditioning (HVAC) applications in the building sector. However, one of their main drawbacks is their poor thermal conductivity which limits their application. In the present study two fatty acids within the building application temperature range, capric acid (CA) and capric-myristic acid (CA-MA) eutectic mixture, were nano-enhanced throughout silicon dioxide nanoparticles (nSiO2) addition (0.5 wt.%, 1.0 wt.% and 1.5 wt.%). Main properties of the nano-enhanced phase change materials (NEPCM) obtained were characterized by means of differential scanning calorimetry (DSC), Hot wire technique, Fourier transformed infrared (FT-IR) spectroscopy, thermogravimetric analyses (TGA), scanning electron microscopy (SEM), and rheological measurements. Furthermore, their long-term performance was evaluated after 2000 cycles by means of cycling stability tests. The NEPCM obtained showed high thermal conductivity and specific heat capacity. Additionally, both are thermally stable within their working temperature range and ensure a long-term performance

An open-source, high-resolution, automated fluorescence microscope

Fluorescence microscopy is a fundamental tool in the life sciences, but the availability of sophisticated equipment required to yield high-quality, quantitative data is a major bottleneck in data production in many laboratories worldwide. This problem has long been recognized and the abundancy of low-cost electronics and the simplification of fabrication through 3D-printing have led to the emergence of open-source scientific hardware as a research field. Cost effective fluorescence microscopes can be assembled from cheaply mass-produced components, but lag behind commercial solutions in image quality. On the other hand, blueprints of sophisticated microscopes such as light-sheet or super-resolution systems, custom-assembled from high quality parts, are available, but require a high level of expertise from the user. Here, we combine the UC2 microscopy toolbox with high-quality components and integrated electronics and software to assemble an automated high-resolution fluorescence microscope. Using this microscope, we demonstrate high resolution fluorescence imaging for fixed and live samples. When operated inside an incubator, long-term live-cell imaging over several days was possible. Our microscope reaches single molecule sensitivity, and we performed single particle tracking and SMLM super-resolution microscopy experiments in cells. Our setup costs a fraction of its commercially available counterparts but still provides a maximum of capabilities and image quality. We thus provide a proof of concept that high quality scientific data can be generated by lay users with a low-budget system and open-source software. Our system can be used for routine imaging in laboratories that do not have the means to acquire commercial systems and through its affordability can serve as teaching material to students

NK cells in peripheral blood carry trogocytosed tumor antigens from solid cancer cells

The innate immune lymphocyte lineage natural killer (NK) cell infiltrates tumor environment where it can recognize and eliminate tumor cells. NK cell tumor infiltration is linked to patient prognosis. However, it is unknown if some of these antitumor NK cells leave the tumor environment. In blood-borne cancers, NK cells that have interacted with leukemic cells are recognized by the co-expression of two CD45 isoforms (CD45RARO cells) and/or the plasma membrane presence of tumor antigens (Ag), which NK cells acquire by trogocytosis. We evaluated solid tumor Ag uptake by trogocytosis on NK cells by performing co-cultures in vitro. We analyzed NK population subsets by unsupervised dimensional reduction techniques in blood samples from breast tumor (BC) patients and healthy donors (HD). We confirmed that NK cells perform trogocytosis from solid cancer cells in vitro. The extent of trogocytosis depends on the target cell and the antigen, but not on the amount of Ag expressed by the target cell or the sensitivity to NK cell killing. We identified by FlowSOM (Self-Organizing Maps) several NK cell clusters differentially abundant between BC patients and HD, including anti-tumor NK subsets with phenotype CD45RARO+CD107a+. These analyses showed that bona-fide NK cells that have degranulated were increased in patients and, additionally, these NK cells exhibit trogocytosis of solid tumor Ag on their surface. However, the frequency of NK cells that have trogocytosed is very low and much lower than that found in hematological cancer patients, suggesting that the number of NK cells that exit the tumor environment is scarce. To our knowledge, this is the first report describing the presence of solid tumor markers on circulating NK subsets from breast tumor patients. This NK cell immune profiling could lead to generate novel strategies to complement established therapies for BC patients or to the use of peripheral blood NK cells in the theranostic of solid cancer patients after treatment

Gestión del teletrabajo y su influencia en el estrés laboral en una empresa de Servicios, Lima, 2022

El presente trabajo tuvo como objetivo el determinar como la gestión del teletrabajo influye en el estrés laboral de los teletrabajadores en una empresa que provee servicios en Lima, 2022. El estudio fue de enfoque cuantitativo, diseño no experimental, de corte transversal y causal. Se basó en la observación de los hechos, que demostraron la validez de las hipótesis formuladas, que plantearon que la gestión no adecuada del teletrabajo, elevaban los niveles de estrés laboral. Para la recolección de datos, se usó la encuesta y un cuestionario como instrumento respondido por 113 teletrabajadores. Se analizaron los datos usando la estadística descriptiva e inferencial. Mediante el análisis de regresión logística ordinal, se obtuvo como resultado valores de significancia menores a 0.05, comprobando la relación causal entre la gestión del teletrabajo y el estrés laboral; los valores R-cuadrado de Cox y Snell y Nagelkerke, mostraron que parte de la variabilidad del estrés laboral se debe a la gestión del teletrabajo. Existe una oportunidad de mejora por ejecutar en la organización, debido a que la gestión del teletrabajo no está bien calificada, elevando los niveles de estrés en los teletrabajadores; esta mejora no solo a los trabajadores, también a la organización

A Novel Functional Interaction between Vav and PKCθ Is Required for TCR-Induced T Cell Activation

AbstractVav and PKCθ play an early and important role in the TCR/CD28-induced stimulation of MAP kinases and activation of the IL-2 gene. Vav is also essential for actin cytoskeleton reorganization and TCR capping. Here, we report that PKCθ function was selectively required in a Vav signaling pathway that mediates the TCR/CD28-induced activation of JNK and the IL-2 gene and the upregulation of CD69 expression. Vav also promoted PKCθ translocation from the cytosol to the membrane and cytoskeleton and induced its enzymatic activation in a CD3/CD28-initiated pathway that was dependent on Rac and on actin cytoskeleton reorganization. These findings reveal that the Vav/Rac pathway promotes the recruitment of PKCθ to the T cell synapse and its activation, essential processes for T cell activation and IL-2 production

Two electron entanglement enhancement by an inelastic scattering process

In order to assess inelastic effects on two fermion entanglement production, we address an exactly solvable two-particle scattering problem where the target is an excitable scatterer. Useful entanglement, as measured by the two particle concurrence, is obtained from post-selection of oppositely scattered particle states. The $S$ matrix formalism is generalized in order to address non-unitary evolution in the propagating channels. We find the striking result that inelasticity can actually increase concurrence as compared to the elastic case by increasing the uncertainty of the single particle subspace. Concurrence zeros are controlled by either single particle resonance energies or total reflection conditions that ascertain precisely one of the electron states. Concurrence minima also occur and are controlled by entangled resonance situations were the electron becomes entangled with the scatterer, and thus does not give up full information of its state. In this model, exciting the scatterer can never fully destroy phase coherence due to an intrinsic limit to the probability of inelastic events.Comment: 8 pages, to appear in Phys. Rev

Field-induced pseudo-skyrmion phase in the antiferromagnetic kagome lattice

We study the effects of an in-plane Dzyaloshinskii-Moriya interaction under an external magnetic field in the highly frustrated kagome antiferromagnet. We focus on the low-temperature phase diagram, which we obtain through extensive Monte Carlo simulations. We show that, given the geometric frustration of the lattice, highly nontrivial phases emerge. At low fields, lowering the temperature from a cooperative paramagnet phase, the kagome elementary plaquettes form noncoplanar arrangements with nonzero chirality, retaining a partial degeneracy. As the field increases, there is a transition from this “locally chiral phase” to an interpenetrated spiral phase with broken Z3 symmetry. Furthermore, we identify a quasi-skyrmion phase in a large portion of the magnetic phase diagram, which we characterize with a topological order parameter, the scalar chirality by triangular sublattice. This pseudo-skyrmion phase consists of a crystal arrangement of three interpenetrated non-Bravais lattices of skyrmionlike textures, but with a non-(fully)-polarized core. The edges of these pseudo-skyrmions remain polarized with the field, as the cores are progressively canted. Results show that this pseudo-skyrmion phase is stable up to the lowest simulated temperatures and for a broad range of magnetic fields.Fil: Villalba, Martin Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; ArgentinaFil: Gómez Albarracín, Flavia Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Ciencias Básicas; ArgentinaFil: Rosales, Hector Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Ciencias Básicas; ArgentinaFil: Cabra, Daniel Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentin