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CDK1 has been known to be the sole cyclin-dependent kinase (CDK) partner of cyclin B1 to drive mitotic progression1. Here we demonstrate that CDK5 is active during mitosis and is necessary for maintaining mitotic fidelity. CDK5 is an atypical CDK owing to its high expression in post-mitotic neurons and activation by non-cyclin proteins p35 and p392. Here, using independent chemical genetic approaches, we specifically abrogated CDK5 activity during mitosis, and observed mitotic defects, nuclear atypia and substantial alterations in the mitotic phosphoproteome. Notably, cyclin B1 is a mitotic co-factor of CDK5. Computational modelling, comparison with experimentally derived structures of CDK–cyclin complexes and validation with mutational analysis indicate that CDK5–cyclin B1 can form a functional complex. Disruption of the CDK5–cyclin B1 complex phenocopies CDK5 abrogation in mitosis. Together, our results demonstrate that cyclin B1 partners with both CDK5 and CDK1, and CDK5–cyclin B1 functions as a canonical CDK–cyclin complex to ensure mitotic fidelity.Cyclin B1 is a mitotic co-factor of CDK5. [ABSTRACT FROM AUTHOR]

Immunotherapies are revolutionizing cancer care, but many patients do not achieve durable responses and immune-related adverse events are difficult to predict. Quantifying the hundreds of proteins involved in cancer immunity has the potential to provide biomarkers to monitor and predict tumor response. We previously developed robust, multiplexed quantitative assays for immunomodulatory proteins using targeted mass spectrometry, providing measurements that can be performed reproducibly and harmonized across laboratories. Here, we expand upon those efforts in presenting data from a multiplexed immuno-oncology (IO)-3 assay panel targeting 43 peptides representing 39 immune- and inflammation-related proteins. A suite of novel monoclonal antibodies was generated as assay reagents, and the fully characterized antibodies are made available as a resource to the community. The publicly available dataset contains complete characterization of the assay performance, as well as the mass spectrometer parameters and reagent information necessary for implementation of the assay. Quantification of the proteins will provide benefit to correlative studies in clinical trials, identification of new biomarkers, and improve understanding of the immune response in cancer. [ABSTRACT FROM AUTHOR]

Having a direct or indirect band gap can influence the potential applications of a semiconductor, for indirect band gap materials are usually not suitable for optoelectronic devices. Even though this is a fundamental property of semiconducting materials, discussed in textbooks, no unified theory exists to explain why a material has a direct or indirect band gap. Here we used an interpretable machine learning model, the multiVariate dAta eXplanation (VAX) method, to gather information from a dataset of materials extracted from the Materials Project. The dataset contains more than 10000 entries, and atomic properties such as the number of electrons, electronic affinity and orbital energies were used as features to build random forest models that successfully explain the directness of the band gaps. Our results indicate that symmetry is an important feature that dictates the target property, which is the reason why our analysis is made based on sub-groups with similar structures. These sub-groups include materials with zincblende, rocksalt, wurtzite, and perovskite structures. Besides the symmetry of the materials, the existence or not of d bands and the relative energy of atomic orbitals were found to be important in defining whether a material's band gap is direct or indirect. In conclusion, interpretable machine learning methods such as VAX can be useful in obtaining physical interpretation from materials databases. [ABSTRACT FROM AUTHOR]

A wealth of proteogenomic data has been generated using cancer samples to deepen our understanding of the mechanisms of cancer and how biological networks are altered in association with somatic mutation of tumor suppressor genes, such as TP53 and PTEN. To generate functional signatures of TP53 or PTEN loss, we profiled the RNA and phosphoproteomes of the MCF10A epithelial cell line, along with its congenic TP53- or PTEN-knockout derivatives, upon perturbation with the monofunctional DNA alkylating agent methyl methanesulfonate (MMS) vs. mock treatment. To enable quantitative and reproducible mass spectrometry data generation, the cell lines were SILAC-labeled (stable isotope labeling with amino acids in cell culture), and the experimental design included label swapping and biological replicates. All data are publicly available and may be used to advance our understanding of the TP53 and PTEN tumor suppressor genes and to provide functional signatures for bioinformatic analyses of proteogenomic datasets. [ABSTRACT FROM AUTHOR]

Fanconi anemia (FA) is a heritable malformation, bone marrow failure and cancer predisposition syndrome that confers an exceptionally high risk of squamous carcinomas. These carcinomas originate in epithelia lining the mouth, proximal esophagus, vulva and anus: their origins are not understood, and no effective ways have been identified to prevent or delay their appearance. Many FA‐associated carcinomas are also therapeutically challenging: they may be multi‐focal and stage‐advanced at diagnosis, and most individuals with FA cannot tolerate standard‐of‐care systemic therapies such as DNA cross‐linking drugs or ionizing radiation due to constitutional DNA damage hypersensitivity. We developed the Fanconi Anemia Cancer Cell Line Resource (FA‐CCLR) to foster new work on the origins, treatment and prevention of FA‐associated carcinomas. The FA‐CCLR consists of Fanconi‐isogenic head and neck squamous cell carcinoma (HNSCC) cell line pairs generated from five individuals with FA‐associated HNSCC, and five individuals with sporadic HNSCC. Sporadic, isogenic HNSCC cell line pairs were generated in parallel with FA patient‐derived isogenic cell line pairs to provide comparable experimental material to use to identify cell and molecular phenotypes driven by germline or somatic loss of Fanconi pathway function, and the subset of these FA‐dependent phenotypes that can be modified, complemented or suppressed. All 10 FANC‐isogenic cell line pairs are available to academic, non‐profit and industry investigators via the "Fanconi Anemia Research Materials" Resource and Repository at Oregon Health & Sciences University, Portland OR. [ABSTRACT FROM AUTHOR]

Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real‐world applications. Among a series of diverse techniques to solve this problem, sampling algorithms are regarded as an efficient solution. However, the problem is more fundamental, with many works emphasizing the importance of instance hardness. This issue refers to the significance of managing unsafe or potentially noisy instances that are more likely to be misclassified and serve as the root cause of poor classification performance. This paper introduces HardVis, a visual analytics system designed to handle instance hardness mainly in imbalanced classification scenarios. Our proposed system assists users in visually comparing different distributions of data types, selecting types of instances based on local characteristics that will later be affected by the active sampling method, and validating which suggestions from undersampling or oversampling techniques are beneficial for the ML model. Additionally, rather than uniformly undersampling/oversampling a specific class, we allow users to find and sample easy and difficult to classify training instances from all classes. Users can explore subsets of data from different perspectives to decide all those parameters, while HardVis keeps track of their steps and evaluates the model's predictive performance in a test set separately. The end result is a well‐balanced data set that boosts the predictive power of the ML model. The efficacy and effectiveness of HardVis are demonstrated with a hypothetical usage scenario and a use case. Finally, we also look at how useful our system is based on feedback we received from ML experts. [ABSTRACT FROM AUTHOR]

Background: Applying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of complex diseases. However, there remain unsolved challenges in DAG learning to jointly model binary clinical outcome variables and continuous biomarker measurements. Results: In this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models, DAGBagM allows for either continuous or binary nodes to be parent or child nodes. It employs a bootstrap aggregating strategy to reduce false positives in edge inference. At the same time, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges. Conclusions: Through extensive simulation experiments, we demonstrate that DAGBagM has superior performance compared to alternative strategies for modeling mixed types of nodes. In addition, DAGBagM is computationally more efficient than two competing methods. When applying DAGBagM to proteogenomic datasets from ovarian cancer studies, we identify potential protein biomarkers for platinum refractory/resistant response in ovarian cancer. DAGBagM is made available as a github repository at https://github.com/jie108/dagbagM. [ABSTRACT FROM AUTHOR]

Background: Applying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of complex diseases. However, there remain unsolved challenges in DAG learning to jointly model binary clinical outcome variables and continuous biomarker measurements. Results: In this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models, DAGBagM allows for either continuous or binary nodes to be parent or child nodes. It employs a bootstrap aggregating strategy to reduce false positives in edge inference. At the same time, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges. Conclusions: Through extensive simulation experiments, we demonstrate that DAGBagM has superior performance compared to alternative strategies for modeling mixed types of nodes. In addition, DAGBagM is computationally more efficient than two competing methods. When applying DAGBagM to proteogenomic datasets from ovarian cancer studies, we identify potential protein biomarkers for platinum refractory/resistant response in ovarian cancer. DAGBagM is made available as a github repository at https://github.com/jie108/dagbagM. [ABSTRACT FROM AUTHOR]

Knowing how to construct text-based Search Queries (SQs) for use in Search Engines (SEs) such as Google or Wikipedia has become a fundamental skill. Though much data are available through such SEs, most structured datasets live outside their scope. Visualization tools aid in this limitation, but no such tools come close to the sheer amount of information available through general-purpose SEs. To fill this gap, this paper presents Q4EDA, a novel framework that converts users' visual selection queries executed on top of time series visual representations, providing valid and stable SQs to be used in general-purpose SEs and suggestions of related information. The usefulness of Q4EDA is presented and validated by users through an application linking a Gapminder's line-chart replica with a SE populated with Wikipedia documents, showing how Q4EDA supports and enhances exploratory analysis of United Nations world indicators. Despite some limitations, Q4EDA is unique in its proposal and represents a real advance towards providing solutions for querying textual information based on user interactions with visual representations. [ABSTRACT FROM AUTHOR]

The striking results of deep neural networks (DNN) have motivated its wide acceptance to tackle large datasets and complex tasks such as natural language processing, facial recognition, and artificial image generation. However, DNN parameters are often empirically selected on a trial-and-error approach without detailed information on convergence behavior. While some visualization techniques have been proposed to aid the comprehension of general-purpose neural networks, only a few explore the training process, lacking the ability to adequately display how abstract representations are formed and represent the influence of training parameters during this process. This paper describes neural network training fingerprint (NNTF), a visual analytics approach to investigate the training process of any neural network performing classification. NNTF allows understanding how classification decisions change along the training process, displaying information about convergence, oscillations, and training rates. We show its usefulness through case studies and demonstrate how it can support the analysis of training parameters. [ABSTRACT FROM AUTHOR]

Amongst the data mining techniques for exploratory analysis, association rule mining is a popular strategy given its ability to find causal rules between items to express regularities in a database. With large datasets, many rules can be generated, and visualization has shown to be instrumental in such scenarios. Despite the relative success, existing visual representations are limited and suffer from analytical capability and low interactive support issues. This paper presents ARMatrix, a visual analytics framework for the analysis of association rules based on an interactive item-to-rule matrix metaphor which aims to help users to navigate sets of rules and get insights about co-occurrence patterns. The usability of the proposed framework is illustrated using two user scenarios and then confirmed from the feedback received through a user test with 20 participants. [ABSTRACT FROM AUTHOR]

Co-design, where designers work directly with end-users to create design outcomes, is a successful and popular method for generating user-centred solutions in healthcare. However, partnering directly with end-users is not always possible or appropriate. This article looks at how the author's 3-Corner Collaborative Design Model was utilized as an alternative to traditional co-design methods in the production of education materials for children and adolescents with Chronic Functional Constipation. With more designers venturing into the health and medical space, it is important that we consider how they can partner with discipline experts to work confidently, effectively and safely in this environment. This article positions the 3-Corner Collaborative Design Model as an appropriate fit for health education projects, when interaction with patients as end-users is not possible, due to its strategic positioning of designer, clinician and patient as knowledge experts and valued contributors. [ABSTRACT FROM AUTHOR]

Stepping up CAR T cell sensitivity: Chimeric antigen receptors (CARs) were designed to mimic activated endogenous T cell receptors (TCRs) and induce a cytotoxic response to tumor antigen. However, CARs require more antigen than TCRs for activation, thereby causing treatment exclusion or failure and relapse in many patients. To design CARs with a lower activation threshold, Salter et al. compared antigen-induced pathway signaling in human T cells expressing a current CAR construct to that in cells with endogenous TCRs. The analysis informed the design of new CARs that more robustly activated a T cell co-receptor and an adaptor, resulting in CAR T cells that more effectively responded to low antigen density tumor cells in culture and decreased tumor burden and mortality in mice. Chimeric antigen receptor (CAR)–modified T cell therapy is effective in treating lymphomas, leukemias, and multiple myeloma in which the tumor cells express high amounts of target antigen. However, achieving durable remission for these hematological malignancies and extending CAR T cell therapy to patients with solid tumors will require receptors that can recognize and eliminate tumor cells with a low density of target antigen. Although CARs were designed to mimic T cell receptor (TCR) signaling, TCRs are at least 100-fold more sensitive to antigen. To design a CAR with improved antigen sensitivity, we directly compared TCR and CAR signaling in primary human T cells. Global phosphoproteomic analysis revealed that key T cell signaling proteins—such as CD3δ, CD3ε, and CD3γ, which comprise a portion of the T cell co-receptor, as well as the TCR adaptor protein LAT—were either not phosphorylated or were only weakly phosphorylated by CAR stimulation. Modifying a commonplace 4-1BB/CD3ζ CAR sequence to better engage CD3ε and LAT using embedded CD3ε or GRB2 domains resulted in enhanced T cell activation in vitro in settings of a low density of antigen, and improved efficacy in in vivo models of lymphoma, leukemia, and breast cancer. These CARs represent examples of alterations in receptor design that were guided by in-depth interrogation of T cell signaling. [ABSTRACT FROM AUTHOR]

Signatures of genotoxic sensitivity: Transforming growth factor–β (TGFβ) has been linked to DNA repair, including error-prone alternative end joining (alt-EJ). Now, Liu et al. show that chemical inhibition of TGFβ signaling promoted a shift to alt-EJ across a variety of cancer cell lines, augmenting sensitivity to ionizing radiation. Gene expression signatures of TGFβ and alt-EJ inversely correlated across a variety of solid cancers, and a low TGFβ/high alt-EJ signature predicted better outcomes after genotoxic therapy in multiple patient cohorts, suggesting the translational potential of these findings. Among the pleotropic roles of transforming growth factor–β (TGFβ) signaling in cancer, its impact on genomic stability is least understood. Inhibition of TGFβ signaling increases use of alternative end joining (alt-EJ), an error-prone DNA repair process that typically functions as a "backup" pathway if double-strand break repair by homologous recombination or nonhomologous end joining is compromised. However, the consequences of this functional relationship on therapeutic vulnerability in human cancer remain unknown. Here, we show that TGFβ broadly controls the DNA damage response and suppresses alt-EJ genes that are associated with genomic instability. Mechanistically based TGFβ and alt-EJ gene expression signatures were anticorrelated in glioblastoma, squamous cell lung cancer, and serous ovarian cancer. Consistent with error-prone repair, more of the genome was altered in tumors classified as low TGFβ and high alt-EJ, and the corresponding patients had better outcomes. Pan-cancer analysis of solid neoplasms revealed that alt-EJ genes were coordinately expressed and anticorrelated with TGFβ competency in 16 of 17 cancer types tested. Moreover, regardless of cancer type, tumors classified as low TGFβ and high alt-EJ were characterized by an insertion-deletion mutation signature containing short microhomologies and were more sensitive to genotoxic therapy. Collectively, experimental studies revealed that loss or inhibition of TGFβ signaling compromises the DNA damage response, resulting in ineffective repair by alt-EJ. Translation of this mechanistic relationship into gene expression signatures identified a robust anticorrelation that predicts response to genotoxic therapies, thereby expanding the potential therapeutic scope of TGFβ biology. [ABSTRACT FROM AUTHOR]

Over the past decades, classification models have proven to be essential machine learning tools given their potential and applicability in various domains. In these years, the north of the majority of the researchers had been to improve quantitative metrics, notwithstanding the lack of information about models' decisions such metrics convey. This paradigm has recently shifted, and strategies beyond tables and numbers to assist in interpreting models' decisions are increasing in importance. Part of this trend, visualization techniques have been extensively used to support classification models' interpretability, with a significant focus on rule-based models. Despite the advances, the existing approaches present limitations in terms of visual scalability, and the visualization of large and complex models, such as the ones produced by the Random Forest (RF) technique, remains a challenge. In this paper, we propose Explainable Matrix (ExMatrix), a novel visualization method for RF interpretability that can handle models with massive quantities of rules. It employs a simple yet powerful matrix-like visual metaphor, where rows are rules, columns are features, and cells are rules predicates, enabling the analysis of entire models and auditing classification results. ExMatrix applicability is confirmed via different examples, showing how it can be used in practice to promote RF models interpretability. [ABSTRACT FROM AUTHOR]

Early detection of Primary Immunodeficiencies Disorders (PIDDs) is of paramount importance for effective treatment and disease management. Many PIDDs would be strong candidates for newborn screening (NBS) if robust screening methods could identify patients from dried blood spots (DBS) during the neonatal period. As majority of congenital PIDDs result in the reduction or absence of specific proteins, direct quantification of these target proteins represents an attractive potential screening tool. Unfortunately, detection is often limited by the extremely low protein concentrations in blood cells and limited blood volume present in DBS. We have recently developed a robust novel method for quantification of low abundance proteins in DBS for PIDDs using peptide immunoaffinity enrichment coupled to selected reaction monitoring (immuno-SRM). Here, we further generated a multiplexed Immuno-SRM panel for simultaneous screening of eight signature peptides representing five PIDD-specific and two cell-type specific proteins from DBS. In samples from 28 PIDD patients including two carriers, representing X-Linked Agammaglobulinemia (XLA), Wiskott-Aldrich Syndrome (WAS), X-Linked Chronic Granulomatous Disease (XL-CGD), DOCK8 Deficiency and ADA deficiency, peptides representing each disease are significantly reduced relative to normal controls and patient identification had excellent agreement with clinical and molecular diagnosis. Also included in the multiplex panel are cell specific markers for platelets (CD42) and Natural Killer Cells (CD56). In patients with WAS, CD42 levels were found to be significantly reduced consistent with characteristic thrombocytopenia. A patient with WAS analyzed before and after bone marrow transplant showed normalized WAS protein and platelet CD42 after treatment highlighting the ability of immuno-SRM to monitor the effects of PIDD treatment. The assay was readily reproduced in two separate laboratories with similar analytical performance and complete agreement in patient diagnosis demonstrating the effective standardized methods. A high-throughput Immuno-SRM method screens PIDD-specific peptides in a 2.5-min runtime meeting high volume NBS workflow requirements was also demonstrated in this report. This high-throughput method returned identical results to the standard Immuno-SRM PIDD panel. Immuno-SRM peptide analysis represents a robust potential clinical diagnostic for identifying and studying PIDD patients from easily collected and shipped DBS and supports a significant potential for early PIDD diagnosis through newborn screening. [ABSTRACT FROM AUTHOR]

Background: AZD0156 and AZD6738 are potent and selective inhibitors of ataxia-telangiectasia-kinase (ATM) and ataxia-telangiectasia-mutated and Rad3-related (ATR), respectively, important sensors/signallers of DNA damage.Methods: We used multiplexed targeted-mass-spectrometry to select pRAD50(Ser635) as a pharmacodynamic biomarker for AZD0156-mediated ATM inhibition from a panel of 45 peptides, then developed and tested a clinically applicable immunohistochemistry assay for pRAD50(Ser635) detection in FFPE tissue.Results: We found moderate pRAD50 baseline levels across cancer indications. pRAD50 was detectable in 100% gastric cancers (n = 23), 99% colorectal cancers (n = 102), 95% triple-negative-breast cancers (TNBC) (n = 40) and 87.5% glioblastoma-multiformes (n = 16). We demonstrated AZD0156 target inhibition in TNBC patient-derived xenograft models; where AZD0156 monotherapy or post olaparib treatment, resulted in a 34-72% reduction in pRAD50. Similar inhibition of pRAD50 (68%) was observed following ATM inhibitor treatment post irinotecan in a colorectal cancer xenograft model. ATR inhibition, using AZD6738, increased pRAD50 in the ATM-proficient models whilst in ATM-deficient models the opposite was observed, suggesting pRAD50 pharmacodynamics post ATR inhibition may be ATM-dependent and could be useful to determine ATM functionality in patients treated with ATR inhibitors.Conclusion: Together these data support clinical utilisation of pRAD50 as a biomarker of AZD0156 and AZD6738 pharmacology to elucidate clinical pharmacokinetic/pharmacodynamic relationships, thereby informing recommended Phase 2 dose/schedule. [ABSTRACT FROM AUTHOR]

Chimeric antigen receptors (CARs) link an antigen recognition domain to intracellular signaling domains to redirect T cell specificity and function. T cells expressing CARs with CD28/CD3ς or 4-1BB/CD3ς signaling domains are effective at treating refractory B cell malignancies but exhibit differences in effector function, clinical efficacy, and toxicity that are assumed to result from the activation of divergent signaling cascades. We analyzed stimulation-induced phosphorylation events in primary human CD8+ CD28/CD3ς and 4-1BB/CD3ς CAR T cells by mass spectrometry and found that both CAR constructs activated similar signaling intermediates. Stimulation of CD28/CD3ς CARs activated faster and larger-magnitude changes in protein phosphorylation, which correlated with an effector T cell-like phenotype and function. In contrast, 4-1BB/CD3ς CAR T cells preferentially expressed T cell memory-associated genes and exhibited sustained antitumor activity against established tumors in vivo. Mutagenesis of the CAR CD28 signaling domain demonstrated that the increased CD28/CD3ς CAR signal intensity was partly related to constitutive association of Lck with this domain in CAR complexes. Our data show that CAR signaling pathways cannot be predicted solely by the domains used to construct the receptor and that signal strength is a key determinant of T cell fate. Thus, tailoring CAR design based on signal strength may lead to improved clinical efficacy and reduced toxicity. [ABSTRACT FROM AUTHOR]

Copyright of Hungarian Medical Journal / Orvosi Hetilap is the property of Akademiai Kiado and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A lack of analytically robust and multiplexed assays has hampered studies of the large, branched phosphosignaling network responsive to DNA damage. To address this need, we developed and fully analytically characterized a 62-plex assay quantifying protein expression and post-translational modification (phosphorylation and ubiquitination) after induction of DNA damage. The linear range was over 3 orders of magnitude, the median inter-assay variability was 10% CV and the vast majority (∼85%) of assays were stable after extended storage. The multiplexed assay was applied in proof-of-principle studies to quantify signaling after exposure to genotoxic stress (ionizing radiation and 4-nitroquinoline 1-oxide) in immortalized cell lines and primary human cells. The effects of genomic variants and pharmacologic kinase inhibition (ATM/ATR) were profiled using the assay. This study demonstrates the utility of a quantitative multiplexed assay for studying cellular signaling dynamics, and the potential application to studies on inter-individual variation in the radiation response. [ABSTRACT FROM AUTHOR]

Designing information for pediatric health education that is easily interpreted by patients is complicated by factors including attitudes and complex power relationships. This study examines such factors in relation to three pediatric health education case studies on asthma management, chronic functional constipation and rehabilitation. They emphasize direct communication with pediatric patients with a view to increasing patient empowerment. Strategic graphic design is employed in collaboration with clinicians to create multimodal semiotic resources. This paper examines these cases within Wahlin's CHaSSMM Model (Wahlin 2015), which combines social semiotics with multimodality and critical hermeneutics. CHaSSMM's potential is making explicit the project's underlying power structures and meaning potential of multimodal, semiotic resources. It begins with an overview of the pediatric health education case studies and the CHaSSMM Model of analysis, and then explains how CHaSSMM can provide support for the planning and interpretive stages of design projects. [ABSTRACT FROM AUTHOR]

A music piece contains a large amount of information represented as a series of instructions corresponding to notes that must be played at specific times. These simple notes are combined to form complex harmonic structures that can be difficult to identify and analyze. Due to its simplicity and straightforward interpretation, music sheets and piano rolls have been the visual metaphor employed by most music visualization tools to support interpretation. Albeit it can represent all necessary elements to perform a music piece, these metaphors do not explicitly show many of the patterns and structures inherent to music arrangements, such as rhythm progression and harmonic interactions, needing users to create a mental model of them. Moreover, comparing different pieces and visualizing how a particular instrument track relates to the others is an issue not only for music sheet-based techniques, but also for most existing music visualization methods. In this paper, we present a novel visualization framework, called Music Overview, Stability, and Harmony Visualization (MoshViz), which facilitates the visualization and understanding of music renditions, focusing mainly on the visual analysis of specific musical instruments. Our approach creates a high-level model of music data and highlights structures of interest, enabling a detail+overview visualization to assist users in the task of identifying harmonic and melodic patterns. The usefulness and representativeness of MoshViz are confirmed by a set of user tests which demonstrate that the proposed visual metaphor matches, with a high degree of accuracy, the mental model of different users regarding the recognizable patterns of sounds. [ABSTRACT FROM PUBLISHER]

Copyright of Journal of Food Science (John Wiley & Sons, Inc.) is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An overview is provided of the challenges involved in building computer-aided diagnosis systems capable of precise medical diagnostics based on integration and interpretation of data from different sources and formats. The availability of massive amounts of data and computational methods associated with the Big Data paradigm has brought hope that such systems may soon be available in routine clinical practices, which is not the case today. We focus on visual and machine learning analysis of medical data acquired with varied nanotech-based techniques and on methods for Big Data infrastructure. Because diagnosis is essentially a classification task, we address the machine learning techniques with supervised and unsupervised classification, making a critical assessment of the progress already made in the medical field and the prospects for the near future. We also advocate that successful computer-aided diagnosis requires a merge of methods and concepts from nanotechnology and Big Data analysis. [ABSTRACT FROM AUTHOR]

Growing food demand has been addressed by protecting crops from insects, weeds, and other organisms by increasing the application of pesticides, thus increasing the risk of environmental contamination. Many pesticides, such as the triazines, are poorly soluble in water and require trace detection methods, which are normally achieved with high-cost sophisticated chromatography techniques. Here, we combine surface-enhanced Raman scattering (SERS) with multidimensional projection techniques to detect the toxic herbicide prometryn in ultrapure, deionized, and tap waters. The SERS spectra for prometryn were recorded with good signal-to-noise ratio down to 5 × 10 mol/L in ultrapure water, approaching single-molecule levels, and 5 × 10 mol/L in tap water. The latter is one order of magnitude below the threshold allowed for drinking water. In addition to providing a fingerprint of prometryn molecules at low concentrations, SERS is advantageous compared to other methods since it does not require pretreatment or chemical separation. The multidimensional projection methods and the detection procedure with SERS are entirely generic, and may be extended to any other pesticide or water contaminants, thus allowing environmental control to be potentially low cost if portable Raman spectrophotometers are used. [ABSTRACT FROM AUTHOR]

Kernel-based methods have experienced a substantial progress in the last years, tuning out an essential mechanism for data classification, clustering and pattern recognition. The effectiveness of kernel-based techniques, though, depends largely on the capability of the underlying kernel to properly embed data in the feature space associated to the kernel. However, visualizing how a kernel embeds the data in a feature space is not so straightforward, as the embedding map and the feature space are implicitly defined by the kernel. In this work, we present a novel technique to visualize the action of a kernel, that is, how the kernel embeds data into a high-dimensional feature space. The proposed methodology relies on a solid mathematical formulation to map kernelized data onto a visual space. Our approach is faster and more accurate than most existing methods while still allowing interactive manipulation of the projection layout, a game-changing trait that other kernel-based projection techniques do not have. [ABSTRACT FROM PUBLISHER]

For a long time, women in Georgia were involved in bringing up children and caring for families, while men were the main breadwinners and performed only a supportive role in bringing up children and managing the domestic economy. Now many women are actually breadwinners in all the meanings of this term. Such positions for contemporary Georgian women are largely due to the current socio-economic situation in the country, caused by the collapse of the Soviet Union, the wars in Abkhazia and South Ossetia, and the civil war during this period. It was women who began to earn money for their families, while many men were unable to deal with the 'post-war syndrome' and lost their jobs. The problems from the early 1990s combined with high unemployment levels in the country again mainly affected men. Given these circumstances, women work hard outside the home to support their families, but also put a lot of energy in caring for the members of the family at home. They thus place their career and taking care of other members of their family at the centre of their identity, claiming that these two spheres allow them to express themselves. This allows me to state that these women possess agency. Activities of women (at home and outside) are becoming a key point of cultural production and social reproduction that allow these women to move between the household and the public sphere. [ABSTRACT FROM AUTHOR]

Poor communication in health is a persistent problem. Transient conversations, extreme time constraints, stress, trauma, clinical factors and the restrictive environment make effective communication between health professionals and patients difficult to achieve. Children, especially, are often positioned as passive participants in the healthcare paradigm. It is hypothesized that providing children with visual health information (well-designed, accurate, age appropriate, and presented in a way that they can understand,) can empower them to take charge of their health and well-being. For visual health education materials to be effective, accurate, and engaging, they need to be designed with input from design practitioners, health experts, and the target audience. However, constraints within the health field, such as restricted access to patients, make this difficult. Furthermore, when children are involved, ethical and practical obstacles can hinder the process. The research presented in this paper navigates the complexities of the health field and presents a realistic participatory design model that responds to the specific challenges associated with designing in a healthcare environment. The efficacy of the approach is demonstrated through successful designs and positive health professional feedback. [ABSTRACT FROM AUTHOR]