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General General

Machine learning to reveal hidden risk combinations for the trajectory of posttraumatic stress disorder symptoms.

In Scientific reports ; h5-index 158.0

The nature of the recovery process of posttraumatic stress disorder (PTSD) symptoms is multifactorial. The Massive Parallel Limitless-Arity Multiple-testing Procedure (MP-LAMP), which was developed to detect significant combinational risk factors comprehensively, was utilized to reveal hidden combinational risk factors to explain the long-term trajectory of the PTSD symptoms. In 624 population-based subjects severely affected by the Great East Japan Earthquake, 61 potential risk factors encompassing sociodemographics, lifestyle, and traumatic experiences were analyzed by MP-LAMP regarding combinational associations with the trajectory of PTSD symptoms, as evaluated by the Impact of Event Scale-Revised score after eight years adjusted by the baseline score. The comprehensive combinational analysis detected 56 significant combinational risk factors, including 15 independent variables, although the conventional bivariate analysis between single risk factors and the trajectory detected no significant risk factors. The strongest association was observed with the combination of short resting time, short walking time, unemployment, and evacuation without preparation (adjusted P value = 2.2 × 10-4, and raw P value = 3.1 × 10-9). Although short resting time had no association with the poor trajectory, it had a significant interaction with short walking time (P value = 1.2 × 10-3), which was further strengthened by the other two components (P value = 9.7 × 10-5). Likewise, components that were not associated with a poor trajectory in bivariate analysis were included in every observed significant risk combination due to their interactions with other components. Comprehensive combination detection by MP-LAMP is essential for explaining multifactorial psychiatric symptoms by revealing the hidden combinations of risk factors.

Takahashi Yuta, Yoshizoe Kazuki, Ueki Masao, Tamiya Gen, Zhiqian Yu, Utsumi Yusuke, Sakuma Atsushi, Tsuda Koji, Hozawa Atsushi, Tsuji Ichiro, Tomita Hiroaki


General General

PsychoAge and SubjAge: development of deep markers of psychological and subjective age using artificial intelligence.

In Aging ; h5-index 49.0

Aging clocks that accurately predict human age based on various biodata types are among the most important recent advances in biogerontology. Since 2016 multiple deep learning solutions have been created to interpret facial photos, omics data, and clinical blood parameters in the context of aging. Some of them have been patented to be used in commercial settings. However, psychological changes occurring throughout the human lifespan have been overlooked in the field of "deep aging clocks". In this paper, we present two deep learning predictors trained on social and behavioral data from Midlife in the United States (MIDUS) study: (a) PsychoAge, which predicts chronological age, and (b) SubjAge, which describes personal aging rate perception. Using 50 distinct features from the MIDUS dataset these models have achieved a mean absolute error of 6.7 years for chronological age and 7.3 years for subjective age. We also show that both PsychoAge and SubjAge are predictive of all-cause mortality risk, with SubjAge being a more significant risk factor. Both clocks contain actionable features that can be modified using social and behavioral interventions, which enables a variety of aging-related psychology experiment designs. The features used in these clocks are interpretable by human experts and may prove to be useful in shifting personal perception of aging towards a mindset that promotes productive and healthy behaviors.

Zhavoronkov Alex, Kochetov Kirill, Diamandis Peter, Mitina Maria


aging clock, artificial intelligence, deep learning, psychology of aging, subjective age

General General

Transforming task representations to perform novel tasks.

In Proceedings of the National Academy of Sciences of the United States of America

An important aspect of intelligence is the ability to adapt to a novel task without any direct experience (zero shot), based on its relationship to previous tasks. Humans can exhibit this cognitive flexibility. By contrast, models that achieve superhuman performance in specific tasks often fail to adapt to even slight task alterations. To address this, we propose a general computational framework for adapting to novel tasks based on their relationship to prior tasks. We begin by learning vector representations of tasks. To adapt to new tasks, we propose metamappings, higher-order tasks that transform basic task representations. We demonstrate the effectiveness of this framework across a wide variety of tasks and computational paradigms, ranging from regression to image classification and reinforcement learning. We compare to both human adaptability and language-based approaches to zero-shot learning. Across these domains, metamapping is successful, often achieving 80 to 90% performance, without any data, on a novel task, even when the new task directly contradicts prior experience. We further show that metamapping can not only generalize to new tasks via learned relationships, but can also generalize using novel relationships unseen during training. Finally, using metamapping as a starting point can dramatically accelerate later learning on a new task and reduce learning time and cumulative error substantially. Our results provide insight into a possible computational basis of intelligent adaptability and offer a possible framework for modeling cognitive flexibility and building more flexible artificial intelligence systems.

Lampinen Andrew K, McClelland James L


artificial intelligence, cognitive science, transfer, zero-shot

oncology Oncology

Investigating Serum and Tissue Expression Identified a Cytokine/Chemokine Signature as a Highly Effective Melanoma Marker.

In Cancers

The identification of reliable and quantitative melanoma biomarkers may help an early diagnosis and may directly affect melanoma mortality and morbidity. The aim of the present study was to identify effective biomarkers by investigating the expression of 27 cytokines/chemokines in melanoma compared to healthy controls, both in serum and in tissue samples. Serum samples were from 232 patients recruited at the IDI-IRCCS hospital. Expression was quantified by xMAP technology, on 27 cytokines/chemokines, compared to the control sera. RNA expression data of the same 27 molecules were obtained from 511 melanoma- and healthy-tissue samples, from the GENT2 database. Statistical analysis involved a 3-step approach: analysis of the single-molecules by Mann-Whitney analysis; analysis of paired-molecules by Pearson correlation; and profile analysis by the machine learning algorithm Support Vector Machine (SVM). Single-molecule analysis of serum expression identified IL-1b, IL-6, IP-10, PDGF-BB, and RANTES differently expressed in melanoma (p < 0.05). Expression of IL-8, GM-CSF, MCP-1, and TNF-α was found to be significantly correlated with Breslow thickness. Eotaxin and MCP-1 were found differentially expressed in male vs. female patients. Tissue expression analysis identified very effective marker/predictor genes, namely, IL-1Ra, IL-7, MIP-1a, and MIP-1b, with individual AUC values of 0.88, 0.86, 0.93, 0.87, respectively. SVM analysis of the tissue expression data identified the combination of these four molecules as the most effective signature to discriminate melanoma patients (AUC = 0.98). Validation, using the GEPIA2 database on an additional 1019 independent samples, fully confirmed these observations. The present study demonstrates, for the first time, that the IL-1Ra, IL-7, MIP-1a, and MIP-1b gene signature discriminates melanoma from control tissues with extremely high efficacy. We therefore propose this 4-molecule combination as an effective melanoma marker.

Cesati Marco, Scatozza Francesca, D’Arcangelo Daniela, Antonini-Cappellini Gian Carlo, Rossi Stefania, Tabolacci Claudio, Nudo Maurizio, Palese Enzo, Lembo Luigi, Di Lella Giovanni, Facchiano Francesco, Facchiano Antonio


Support Vector Machine, cytokines, machine learning, melanoma markers, principal component analysis

Public Health Public Health

Access denied: the shortage of digitized fitness resources for people with disabilities.

In Disability and rehabilitation ; h5-index 45.0

PURPOSE : The COVID-19 pandemic has drastically impacted every aspect of life, including how people exercise and access fitness resources. Prior to COVID-19, the global burden of disease attributable to sedentary behavior disproportionately affected the health of people with disabilities (PWD). This pre-existing gap has only widened during COVID-19 due to limited disability-friendly digital exercise resources. The purpose of this work is to examine this gap in accessibility to digital fitness resources, and re-frame the notion of accessibility to suit the contemporary context.

MATERIALS AND METHODS : Using machine learning, video titles/descriptions about home exercise ordered by relevance populated on YouTube between 1 January 2020 and 30 June 2020 were examined.

RESULTS : Using the search terms, "home exercise," "home-based exercise," "exercise no equipment," "workout no equipment," "exercise at home," or "at-home exercise," 700 videos ordered by relevance included 28 (4%) that were inclusive of participants with disabilities. Unfortunately, most digital fitness resources are therefore inaccessible to PWD. The global pause the pandemic has induced may be the right moment to construct a comprehensive, indexed digital library of home-based fitness video content for the disabled. There is a further need for more nuanced understandings of accessibility as technological advancements continue. Implications for Rehabilitation Physical activity is incredibly important to the quality of life and health of all people. Physical activity levels, however, remain lower among persons with disabilities. Access to disability-friendly resources remains a challenge and worsened by the circumstances of COVID-19 due to an apparent lack of digital fitness resources for persons with disabilities. A broader and comprehensive definition of accessibility must recognize digital advances and access to physical activity for persons with disabilities must feature digital resources.

Stratton Catherine, Kadakia Shevali, Balikuddembe Joseph K, Peterson Mark, Hajjioui Abderrazak, Cooper Rory, Hong Bo-Young, Pandiyan Uma, Muñoz-Velasco Laura Paulina, Joseph James, Krassioukov Andrei, Tripathi Deo Rishi, Tuakli-Wosornu Yetsa A


Accessibility, digital resources, home exercise, inclusive, people with disabilities

Pathology Pathology

Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis.

In The European respiratory journal

BACKGROUND : LAM is a rare multisystem disease with variable clinical manifestations and differing rates of progression that make management decisions and giving prognostic advice difficult. We used machine learning to identify clusters of associated features which could be used to stratify patients and predict outcomes in individuals.

PATIENTS AND METHODS : Using unsupervised machine learning we generated patient clusters using data from 173 women with LAM from the UK and 186 replication subjects from the NHLBI LAM registry. Prospective outcomes were associated with cluster results.

RESULTS : Two and three-cluster models were developed. A three-cluster model separated a large group of subjects presenting with dyspnoea or pneumothorax from a second cluster with a high prevalence of angiomyolipoma symptoms (p=0.0001) and TSC (p=0.041). The third cluster were older, never presented with dyspnoea or pneumothorax (p=0.0001) and had better lung function. Similar clusters were reproduced in the NHLBI cohort. Assigning patients to clusters predicted prospective outcomes: in a two-cluster model future risk of pneumothorax was 3.3 fold (95% C.I. 1.7-5.6) greater in cluster one than two (p=0.0002). Using the three-cluster model, the need for intervention for angiomyolipoma was lower in clusters two and three than cluster one (p<0.00001). In the NHLBI cohort, the incidence of death or lung transplant was much lower in clusters two and three (p=0.0045).

CONCLUSIONS : Machine learning has identified clinically relevant clusters associated with complications and outcome. Assigning individuals to clusters could improve decision making and prognostic information for patients.

Chernbumroong Saisakul, Johnson Janice, Gupta Nishant, Miller Suzanne, McCormack Francis X, Garibaldi Jonathan M, Johnson Simon R