Raman spectroscopy in head and neck cancer

In recent years there has been much interest in the use of optical diagnostics in cancer detection. Early diagnosis of cancer affords early intervention and greatest chance of cure. Raman spectroscopy is based on the interaction of photons with the target material producing a highly detailed biochemical 'fingerprint' of the sample. It can be appreciated that such a sensitive biochemical detection system could confer diagnostic benefit in a clinical setting. Raman has been used successfully in key health areas such as cardiovascular diseases, and dental care but there is a paucity of literature on Raman spectroscopy in Head and Neck cancer. Following the introduction of health care targets for cancer, and with an ever-aging population the need for rapid cancer detection has never been greater. Raman spectroscopy could confer great patient benefit with early, rapid and accurate diagnosis. This technique is almost labour free without the need for sample preparation. It could reduce the need for whole pathological specimen examination, in theatre it could help to determine margin status, and finally peripheral blood diagnosis may be an achievable target

Non-Pharmaceutical Interventions for Self-Regulatory Failures in Adolescents Suffering from Externalizing Symptoms: A Scoping Review.

Deficits of self-regulation (SR) are a hallmark of externalizing (EXT: offending or aggressive behaviors) symptoms in adolescence. This scoping review aims (1) to map non-pharmaceutical interventions targeting SR processes to reduce EXT symptoms in adolescents and (2) to identify research gaps, both of which will provide recommendations for future studies. Systematic searches were carried out in eight bibliographic databases up to March 2021, combining the following concepts: self-regulation, externalizing symptoms, adolescents, and non-pharmaceutical interventions. We identified 239 studies, including 24,180 youths, mainly from North America, which described a plethora of non-pharmaceutical interventions targeting SR to alleviate EXT symptoms in adolescents (10-18 years of age). The majority of studies (about 70%, k = 162) represent samples with interventions exposed to "selective" or "indicated" prevention. Curriculum-based (i.e., multiple approaches targeting several domains such as emotion, cognition, and social) interventions (31.4%) were the most common type of intervention. Moreover, studies on cognitive-based interventions, mind-based interventions, and emotional-based interventions have increased over the last decades. Network analyses allowed us to identify several hubs between curriculum-based interventions, cognitive SR processes, as well as aggressiveness, conduct problems, and irritability/anger dysregulation. In addition, we identified gaps of studies concerning the physiological SR processes and on some types of interventions (i.e., body-based interventions and externally mediated interventions) or, more specifically, on promising tools, such as biofeedback, neurofeedback, as well as programs targeting neuropsychological processes (e.g., cognitive remediation). This scoping review stresses the plethora of interventions, identified hubs, and emerging fields, as well as some gaps in the literature, which together may orient future studies

Recursive Regularization for Large-scale Classification with Hierarchical and Graphical Dependencies

The two key challenges in hierarchical classification are to leverage the hierarchical dependencies between the class-labels for improving performance, and, at the same time maintaining scalability across large hierarchies. In this paper we propose a regularization framework for large-scale hierarchical classification that addresses both the problems. Specifically, we incorporate the hierarchical dependencies between the class-labels into the regularization structure of the parameters thereby encouraging classes nearby in the hierarchy to share similar model parameters. Furthermore, we extend our approach to scenarios where the dependencies between the class-labels are encoded in the form of a graph rather than a hierarchy. To enable large-scale training, we develop a parallel-iterative optimization scheme that can handle datasets with hundreds of thousands of classes and millions of instances and learning terabytes of parameters. Our experiments showed a consistent improvement over other competing approaches and achieved state-of-the-art results on benchmark datasets. Categories and Subject Descriptor