Inledning: Med tiden har folkhälsa och vad som ses som folksjukdomar förändrats. Idag har vi folksjukdomar som inte var vanliga för årtionden sedan, sjukdomar som nu blivit ett globalt folkhälsoproblem. En av dessa sjukdomar är cancer. Bröstcancer är den vanligaste cancerform för kvinnor. Brösten är en stor del för att en kvinna ska känna sig kvinnlig. Syfte: Syftet med denna litteraturstudie är att beskriva kvinnans psykiska hälsa efter en bröstcancerdiagnos med fokus på ångest, depression och stress. Metod: Vald metod är en systematisk litteraturöversikt. Artiklar söktes i databasen PubMed som analyserades systematiskt. Under sökprocessen valdes 15 vetenskapliga artiklar ut med relevant information för att svara på syftet till studien. Tre teman valdes ut under analysen av artiklarna. Temaorden: Rädsla för återfall av bröstcancer, kvinnlighet, psykologiska hälsoeffekter. Resultat: Resultatet visar att kvinnor känner sig mindre kvinnlig och attraktiv när ett bröst opererats bort. Kvinnor med en bröstcancerhistorik är rädd för att få ett återfall vilket påverkar deras liv genom oro, ångest och stress. Yngre kvinnor är mer rädd för döden än äldre vilket kan bero på, yngre kvinnor har små barn och är rädd för att inte se barnen växa upp. Diskussion: En bröstcancerdiagnos kan vända upp och ner på tillvaron för en kvinna. Efter att hon har fått en diagnos behöver hon ändra om sin planering inför framtiden Detta kan skapa ångest och oro inför hur det kommer att bli, hur hon kommer att må samt oro för vilken utgång sjukdomen har. Det finns ett behov för mer träning för läkaren och sjuksköterskor för att bättre hjälpa bröstcancerpatienter med psykologiska konsekvenser. / Introduction: Over time, disease patterns affecting the population have changes. Today, many people are diagnosed with diseases that were not common decades ago, which have now become a global public health problem. One of these diseases is cancer. Breast cancer is the most common cancer form for women, and stressful in part because of the association of breasts with femininity.   Purpose: The purpose of this literature study is to describe the mental health of the woman after a breast cancer diagnosis focusing on anxiety, depression and stress. Method: The chosen method is a systematic literature review. Articles have been systematically searched in the PubMed database. During the search process, 15 scientific articles were selected with relevant information to respond to the purpose of the study. Three themes emerged during the analysis of the articles: Fear of breast cancer recurrence, femininity, and psychological health effects. Result: The results show that women feel less feminine and attractive when a breast has been removed. Women with breast cancer history are afraid of recurrence which affects their lives through anxiety, anxiety and stress. Younger women are more afraid of death than older women, which may be because younger women have young children and are fearful of not seeing them grow up. Discussion: A breast cancer diagnosis can strongly affect a woman. After a woman's diagnosis, she needs to change her planning for the future. This can create anxiety and anxiety about recovery and how she will feel and lead to worry about the outcome of the disease. There is a need for more training for doctors and nurses to better help breast cancer patients who experience stress, anxiety and depression.

Breast cancer is the most common cancer in women. It is a highly heterogeneous disease in terms of histology, therapeutic response and patient outcomes. Early and accurate detection of breast cancer is crucial as the patient prognosis varies greatly depending on the diagnosis of the disease. Nonetheless current breast cancer classification methods fail to precisely sub-classify the disease, resulting in potential inadequate therapeutic management of patients and subsequent poor clinical outcomes. Substantial effort is therefore put in cancer research to develop methods and find new biomarkers efficiently identifying and characterizing breast tumor cells. Moreover it is now well-recognized that the intensive cross-talk between cancer cells and their microenvironment (including non-tumor cells) highly influences cancer progression. Recently, a growing body of clinical evidence reported the prognostic and predictive value associated with the presence of tumor infiltrating lymphocytes (TILs) in the microenvironment of breast tumors. Although the evaluation of TILs would be of great value for the management of patients and the development of new immunotherapies, it is currently not assessed in routine practice. Furthermore Fourier transform infrared (FTIR) imaging has shown its usefulness to study a panel of human cancers. Infrared (IR) spectroscopy coupled to microscopy provides images composed of multiple spectra reflecting the biochemical composition and subtle modifications within biological samples. IR imaging therefore provides useful information to improve breast cancer identification and characterization. The ultimate aim of this thesis is to improve breast cancer diagnosis using FTIR imaging to better identify and characterize cancer cells and the tumor microenvironment of breast cancers. In a first step we carried out a feasibility study aiming at evaluating the impact of the sample fixation process on IR spectra. While spectra were undeniably influenced by this biochemical alteration, our results indicated that closely-related cell types were influenced similarly and could still be discriminated on the basis of their spectral features. We then demonstrated the capability of IR imaging to discriminate a tumor from a normal tissue environment based on the spectral features of tumor cells and the surrounding extracellular matrix. A particular focus was placed on the identification of lymphocyte spectral signatures of cells isolated from blood or present within secondary lymphoid organs such as tonsils. Our results revealed that IR imaging was sensitive enough to discriminate lymphocyte subpopulations and to identify a particular spectral signature that we assigned to lymphocyte activation. Finally we highlighted the potential value of IR imaging as complementary tool to identify and characterize TILs in breast tumor samples. Altogether, our results suggest that IR imaging provides interesting and reliable information to improve breast cancer characterization and to assess the immune microenvironment of breast tumors.<p>/<p>Le cancer du sein est le carcinome le plus fréquent chez la femme. C’est une maladie très hétérogène du point de vue histologique, de la réponse thérapeutique et de l’évolution clinique. Une détection rapide et précise de la maladie est cruciale, un diagnostic du cancer du sein dès les premiers stades de la maladie permet une meilleure prise en charge du patient et est directement associé à un meilleur pronostic. Néanmoins la classification actuelle des cancers du sein ne permet souvent pas de caractériser la maladie de manière précise, ce qui donne lieu à la mise en place de traitements moins ciblés et une évolution clinique peu favorable. Pour remédier à cela, des efforts conséquents sont réalisés en recherche, dans le but de mettre au point des méthodes capables d’identifier et de caractériser les cellules tumorales. De plus il est actuellement reconnu que le micro-environnement tumoral (composé des cellules non-tumorales) influence fortement la progression du cancer. Récemment de nombreuses études ont montré que la présence de lymphocytes au niveau des tumeurs mammaires (TILs) était corrélée à un meilleur facteur pronostic et prédictif. Bien que l’évaluation des TILs soit de grande importance dans le cadre des immunothérapies, cet élément n’est actuellement pas pris en compte dans les analyses de routine. Par ailleurs, l’imagerie infrarouge par transformée de Fourier (FTIR) a démontré son utilité dans l’étude de plusieurs cancers humains. La spectroscopie infrarouge (IR) couplée à la microscopie fourni des images composées de multiples spectres qui reflètent la composition biochimique et les modifications dans les échantillons biologiques. De ce fait l’imagerie infrarouge procure des informations utiles pour améliorer l’identification et la caractérisation du cancer du sein. L’objectif général de cette thèse est d’améliorer le diagnostic du cancer du sein par imagerie FTIR pour mieux identifier et caractériser les cellules cancéreuses et le micro-environnement tumoral des tumeurs mammaires. Dans un premier temps nous avons effectué une étude de faisabilité afin d’évaluer l’impact du protocole de fixation des tissus sur les spectres IR. Bien que les spectres soient indéniablement influencés par cette altération biochimique, nos résultats indiquent que des types cellulaires proches sont influencés de manière similaire et peuvent donc être discriminés sur base de leurs caractéristiques spectrales. Nous avons ensuite démontré la capacité de l’imagerie IR de distinguer un environnement tumoral d’un environnement normal sur base des particularités spectrales des cellules tumorales et de la matrice extracellulaire. Une attention particulière a ensuite été portée afin d’identifier des signatures spectrales de cellules immunitaires du sang et au sein d’organes lymphoïdes secondaires, tels que les amygdales. Nos résultats ont révélé que l’imagerie IR permet d'identifier une signature spectrale particulière, que nous avons associée à une stimulation lymphocytaire. Finalement nous avons mis en évidence l’utilité de l’imagerie IR en tant qu’outil complémentaire pour identifier et caractériser les TILs dans les échantillons tumoraux mammaires. De manière générale, nos résultats suggèrent que l’imagerie IR fournit des informations intéressantes et fiables pour améliorer la caractérisation et l’évaluation du micro-environnement immunitaire dans les tumeurs mammaires. / Doctorat en Sciences agronomiques et ingénierie biologique / info:eu-repo/semantics/nonPublished

Le cancer est l'une des principales causes de décès dans le monde et reste une maladie difficile à traiter du fait des difficultés de pronostic, du développement rapide de métastases et de la résistance aux médicaments. Il en résulte une forte demande en méthodologies d'imagerie innovantes pour le diagnostic précoce et précis ainsi qu’en nouveaux agents anticancéreux possédant de nouveaux mécanismes pour surmonter la résistance aux médicaments. Le but de mon projet de recherche de doctorat était donc de contribuer à cet objectif.La première partie de ma thèse de doctorat a porté sur la création de systèmes sensibles et précis d'imagerie pour la détection de tumeurs cancéreuses en utilisant une nanotechnologie novatrice permettant la délivrance des agents d'imagerie spécifiquement dans les lésions tumorales. Nous avons conçu de nouveaux dendrimères amphiphiles pour assurer le transport de différents agents d'imagerie pour les imageries PET/SPECT, par résonance magnétique et par fluorescence optique. Ces systèmes d'imagerie ont été préparés soit par encapsulation de petites sondes d'imagerie à l'intérieur de nanomicelles dendritiques ou par fonctionnalisation de la surface hydrophile ou de la queue hydrophobe du dendrimère. La deuxième partie a eu pour objectif de développer de nouveaux agents anticancéreux possédant nouveaux mécanismes d’action et une meilleure activité antitumorale. A cet effet, nous avons conçu une série de nucléosides arylvinyltriazoles par réaction oxydante de Heck, ce qui nous a permis d'obtenir les composés désirés pourtant difficiles à synthétiser avec un très large éventail de substrats et une stéréosélectivité unique. / Cancer is one of the leading causes of death in the world, and remains a difficult disease to treat because of poor prognosis, rapid tumor metastasis and drug resistance. Therefore, innovative imaging modalities for early and precise diagnosis as well as new anticancer drug candidates with novel mechanisms to overcome drug resistance are in high demand. The aim of my PhD research project was to contribute to this goal.The first part of my PhD thesis was focused on establishing sensitive and precise imaging systems for cancer detection using innovative nanotechnology to deliver imaging agents specifically into tumor lesions. We designed and constructed novel amphiphilic dendrimers to carry different imaging agents for PET/SPECT imaging, magnetic resonance imaging and optical fluorescence imaging. These innovative imaging systems were prepared by either encapsulation of small imaging probes within the dendrimer nanomicelles, or functionalization of the dendrimer hydrophilic surface or hydrophobic tail. The second part of my PhD program aimed to develop new anticancer drug candidates with novel mechanisms for better anticancer activity. Therefore, we designed and synthesized a series of challenging arylvinyltriazole nucleosides via the oxidative Heck reaction, which allowed us to obtain the desired compounds with excellent substrate scope and unique stereoselectivity.

This thesis is part of the project undertaken to develop a diffusive acoustic confocal imaging system (DACI) that aims to differentiate between healthy and the diseased tissues in the prostate. Speed of sound is chosen as the tool to quantify the alterations in the tissues’ mechanical properties at different pathological states. The current work presents a scanning configuration that features three components: an acoustic emitter, a focusing mirror and a point receiver. The focusing mirror brings the collimated acoustic beam from the emitter into a focused probe position, which needs to be located within the bladder or at the near surface of the prostate. This position is introduced as the virtual source, where the acoustic intensity diffusively scatters into all directions and propagates through the specimen. The system design was simulated using ZEMAX and COMSOL to validate the concept of the virtual source. Lesions in a phantom prostate were found in the simulated amplitude and phase images. The speed of sound variation was estimated from the 1D unwrapped phase distribution indicating where the phase discontinuities existed. The measurements were conducted in a water aquarium using the tissue-mimicking prostate phantom. Two-dimensional projected images of the amplitude and the phase distributions of the investigating acoustic beam were measured. A USRP device was set up as the signal generation and acquisition device for the experiment. Two different signal extractions methods were developed to extract the amplitude and the phase information. The experimental results were found to generally agree with the simulation results. The proof-of-concept design was successful in measuring both the phase and the amplitude information of the acoustic signal passing through the prostate phantom. In future, the 2D/3D speed of sound variation needs to be estimated by an appropriate image reconstruction method. / Graduate / 2018-12-06

In cancer diagnosis there is a goal of having the treatment being tailored to each patient. This in order to increase efficiency and reduce side effects. Using more data on each patient can help in achieving this. One such data source is histological images on tissues, such as lymph nodes. This report sets out to find a method in which such images on lymph nodes can be automatically segmented. This so that they can later be analysed and maybe tell in what stage a cancer is in. Such work is today done by hand, and this makes it a subjective process, that might differ between doctors and institutions. If there was a method done by a computer, the process would be replicable and objective. Also, a lot of time would be saved. The results show that such a method is reachable in this early stage of development. It is also quite efficient when segmenting the lymph node itself. The segmentation of smaller areas of the lymph nodes is not as efficient, but with further work in the area it might improve enough to be useful. Some issues are still had since the method relies in part on a person to decide a parameter in order to get a clean segmentation. The final conclusion is that one model is to prefer compared to the others and that further work on this might make it a useful tool in analysing histological images.

This thesis discusses the research, experimental methods, and data gathered for the investigation of a novel method for the diagnosis of melanoma skin cancer. First, a background about human skin tissue is presented. Then, a detailed description of melanoma along with current diagnosis techniques and treatment options are presented. In the experimental methods, the electrical properties of several types of tissue were analyzed, the purpose of which was to discover if a tissue type can be distinguished by its electrical properties alone. This would allow for the diagnosis of melanoma to be done by examining the electrical properties of the suspected tumor and comparing the results to known values of healthy and cancerous skin. After analyzing the data, it was concluded that tissue types can be identified by their electrical properties and it may be possible to diagnose melanoma through this method. Finally, the possibility of using a similar technology and radiofrequency tissue ablation to treat melanoma is presented.

Background. While the prevalence of a pathogenic variant in the BRCA1 and BRCA2 genes occurs in about 1:400 (0.25%) in the general population, the prevalence is as high as 1:40 (2.5%) among the Ashkenazi Jewish population. Despite cost-effective preventive measures for mutation carriers, Orthodox Jews constitute a cultural and religious group that presents challenges to BRCA1 and BRCA2 genetic testing. This study analyzed a dialogue of key stakeholders and community members to explore factors that influence decision-making about BRCA1 and BRCA2 genetic testing in the New York Orthodox Jewish community. Methods. Qualitative research methods, based in Grounded Theory and Narrative Research, were utilized to analyze the narratives of key stakeholders and community members in an analysis of qualitative data collected from 49 stakeholders. A content analysis was conducted to identify themes; inter-rater reliability was 71%. Results. Facilitators to genetic testing were prevention and education, while barriers to genetic testing included negative emotions, impact on family/romantic relationships, cost, and stigma. The role of religious figures and healthcare professionals in medical decision-making were viewed as controversial. Education, health, and community were discussed as influential factors. There were issues around disclosure, implementation, and information needs. Conclusion. This study revealed the voices of the Orthodox Jewish women (decision-makers) and key stakeholders (influencers) who play a critical role in the medical decision-making process. The findings have broad implications for engaging community stakeholders within faith-based or culturally distinct groups to ensure better utilization of healthcare services for cancer screening and prevention designed to improve population health.

<p>Mass spectrometry (MS) can provide rapid, sensitive, and specific analysis, making it a valuable tool to characterize biomolecules, especially their dynamic changes when involved in significant processes.  Compared to other analytical techniques, which mostly focus on solution-phase or solid-phase characterization, MS enjoys a more general and efficient detection of gas-phase analytes since it ultimately measures abundances of bare ions in vacuum. This unique detection capability of MS has been demonstrated, in this dissertation, by characterizing the neutral serine octamer, a gas-phase amino acid cluster that has been detected by MS only so far. Besides its existence, the progress of chiral enrichment has also been monitored and quantified by MS during octamer formation. The acquired MS data is crucial to interpreting the mechanism of chiral enrichment achieved by serine octamer and might suggest its involvement in the prebiotic world to eventually achieve biohomochirality. The work also showcases the capability of detecting neutral compounds by MS, which breaks the stereotype that MS is exclusively an ion-based technique. </p> <p><br></p> <p>Besides process monitoring in the open air, MS also monitors the highly complicated metabolism processes inside biosamples, primarily benefiting from its excellent sensitivity, specificity, and throughput of ion detection. Since altered cellular metabolism is being recognized as a hallmark of cancer, MS is suitable for cancer diagnostics, whose performance of diagnosing glioma, a common brain cancer, has been tested.  Desorption electrospray ionization(DESI) has been used as it avoids sample preparation and allows direct characterization of raw tissue, therefore well suited for on-site analysis such as in the operating room. In short, we have applied intraoperative DESI-MS analysis on raw brain biopsies to provide glioma diagnostics within 5 min. Specifically, the molecular features revealed by MS are translated into pathological information of analyzed tissue, like genetic mutations and tumor concentrations, which is highly desired during surgeries to guide tumor resection and improve patient management. </p> <p><br></p> <p>Knowledge of diagnostic biomarkers is essential to the translation from MS data to pathology, which can be obtained by metabolic profiling using MS. Despite the tradeoff between comprehensive characterization and analysis time, we have extensively explored endogenous metabolites by using tandem MS and expedited analysis by avoiding the use of chromatography. After fast profiling, statistical analysis of all MS features has been applied to discover diagnostic markers to distinguish healthy brain tissue from cancerous tissue. DESI-MS methods have been developed to facilitate a simple and rapid characterization of these biomarkers in tissue for a smooth clinical transition. </p> <p><br></p> <p>However, the complete characterization of endogenous metabolites in a complicated biomixture, like tissue, is challenging, especially without the orthogonal separation provided by chromatography. This unmet demand calls for the development of novel MS scans to improve the metabolite coverage. For lipidomics by direct infusion MS, the MS scans used for lipid profiling have not been greatly expanded since its introduction. These conventionalMS scans only target one structural moiety of lipids and leave the rest unresolved, which limits the structure elucidation and biological interpretation of diagnostic lipids. We have introduced additional lipid scans that target both the lipid headgroup and one fatty acyl chain, leaving the other fatty acyl chain flexible. These scans with higher specificity can further alleviate the matrix effect by uncovering fewer ions in each scan and provide more structural information to support lipid identification. As a proof-of-concept, we have used them to profile both common phospholipids and the rarer ether lipids that display significant variations between healthy mice tissue and those with metabolic syndrome. The additional structural information provided by these scans ensures a clear message expressed by the disease metabolism and potentially indicates invention points and therapeutic candidates.</p>

In breast cancer assessment, tissue removed during biopsy or surgery is sent to a pathology lab for analysis. To achieve high sensitivity for detecting disease, the diagnostic gold standard requires submission of a substantial portion of the resected specimen, which results in a labor and time-intensive process to obtain a diagnosis. There is an unmet need to identify regions of diagnostic interest in breast tissue samples to increase the efficiency of the clinical pathology workflow. Optical coherence tomography (OCT), a noninvasive imaging modality capable of depth-resolved, high-resolution, and in vivo imaging of tissue at large fields of view, enables effective assessment of this tissue. However, there is a two-fold problem: the large size of resected tissue to be imaged within clinical time constraints, and the high density of multi-dimensional OCT image data. An approach that enables comprehensive imaging by reducing both imaging time and data density is compressed sensing (CS), a theory that enables undersampling far below the Nyquist sampling rate and guarantees high accuracy image recovery. Therefore, the objective of this work is to demonstrate that compressed sensing techniques can be applied to OCT imaging to revise current optical hardware and improve the efficiency of image acquisition. CS-OCT has high potential for significantly altering the presently established workflow for breast cancer assessment. In this work, we prove that current OCT systems require further reduction of data sampling rate, to enable effective integration of the systems into the clinical pathology workflow. In addition, we identify challenges associated with the matching of OCT and histologic data that may be important to consider in the context of in vivo imaging. Further, we demonstrate the application of a novel and improved compressed sensing algorithm capable of reconstructing OCT volumes from highly undersampled imaging data. We show that these reconstructions preserve high spatial resolution and key image features, and we illustrate its improved performance over traditional reconstruction methods. Lastly, we integrate our compressed sensing techniques to physical OCT hardware. We demonstrate a pilot OCT system that integrates efficient undersampling schemes with subsequently successful 3-D image reconstructions. We evaluate acquisition patterns that take advantage of the typical forward and backward scan cycle of OCT systems to accomplish native subsampling of target data to varying degrees of compression. Using our CS-OCT algorithm, we successfully reconstruct OCT image volumes and demonstrate qualitative and quantitative preservation of image quality down to compression levels of 5% of total data.

Chronic obstructive pulmonary disease (COPD) and emphysema together are one of the leading causes of death in the United States and worldwide; meanwhile, breast cancer has the highest incidence and second-highest mortality burden of all cancers in women. Imaging markers relevant to each of these conditions are readily identifiable on chest computed tomography (CT): (1) visually-appreciable variants in airway tree structure exist which are associated with increased odds for development of COPD; (2) CT emphysema subtypes (CTES), based on lung texture and spatial features, have been identified by unsupervised clustering and correlate with functional measures and clinical outcomes; (3) dysanapsis, or the ratio of airway caliber to lung volume, is the strongest known predictor of COPD risk, and (4) breast density (i.e., the extent of fibroglandular tissue within the breast) is strongly associated with breast cancer risk. Machine- and deep-learning frameworks present an opportunity to address unmet needs in each of these directions, leveraging the data from large CT cohorts. Application of unsupervised learning approaches serves to discover new, image-based phenotypes. While topologic and geometric variation in the structure of the CT-resolved airway tree are well-described, tree- structural subtypes are not fully characterized. Similarly, while the clinical correlates of CTES have been described in large cohort studies, the association of CTES with structural and functional measures of the lung parenchyma are only partially described, and the time-dependent evolution of emphysematous lung texture has not been studied. Supervised approaches are required to automate CT image assessment, or to estimate CT- based measures from incomplete input data. While dysanapsis can be directly quantified on full- lung CT, the lungs are often only partially imaged in large CT datasets; total lung volume must then be regressed from the observed partial image. Breast density grades, meanwhile, are generally visually assessed, which is laborious to perform at scale. Moreover, current automated methods rely on segmentation followed by intensity thresholding, excluding higher-order features which may contribute to the radiologist assessment. In this thesis, we present a series of machine-learning methods which address each of these gaps in the field, using CT scans from the Multi-Ethnic Study of Atherosclerosis (MESA), the SubPopulations and InteRmediate Outcome Measures in COPD (SPIROMICS) Study, and an institutional chest CT dataset acquired at Columbia University Irving Medical Center. First, we design a novel graph-based clustering framework for identifying tree-structure subtypes in Billera-Holmes-Vogtmann (BHV) tree-space, using the airway trees segmented from the full-lung CT scans of MESA Lung Exam 5. We characterize the behavior of our clustering algorithm on a synthetic dataset, describe the geometric and topological variation across tree-structure clusters, and demonstrate the algorithm’s robustness to perturbation of the input dataset and graph tuning parameter. Second, in MESA Lung Exam 5 CT scans, we quantify the loss of small-diameter airway and pulmonary vessel branches within CTES-labeled lung tissue, demonstrating that depletion of these structures is concentrated within CTES regions, and that the magnitude of this effect is CTES-specific. In a sample of 278 SPIROMICS Visit 1 participants, we find that CTES demonstrate distinct patterns of gas trapping and functional small airways disease (fSAD) on expiratory CT imaging. In the CT scans of SPIROMICS participants imaged at Visit 1 and Visit 5, we update the CTES clustering pipeline to identify longitudinal emphysema patterns (LEPs), which refine CTES by defining subphenotypes informative of time-dependent texture change. Third, we develop a multi-view convolutional neural network (CNN) model to estimate total lung volume (TLV) from cardiac CT scans and lung masks in MESA Lung Exam 5. We demonstrate that our model outperforms regression on imaged lung volume, and is robust to same- day repeated imaging and longitudinal follow-up within MESA. Our model is directly applicable to multiple large-scale cohorts containing cardiac CT and totaling over ten thousand participants. Finally, we design a 3-D CNN model for end-to-end automated breast density assessment on chest CT, trained and evaluated on an institutional chest CT dataset of patients imaged at Columbia University Irving Medical Center. We incorporate ordinal regression frameworks for density grade prediction which outperform binary or multi-class classification objectives, and we demonstrate that model performance on identifying high breast density is comparable to the inter-rater reliability of expert radiologists on this task.