Learning from machine learning: prediction of age-related athletic performance decline trajectories

Factors that determine individual age-related decline rates in physical performance are poorly understood and prediction poses a challenge. Linear and quadratic regression models are usually applied, but often show high prediction errors for individual athletes. Machine learning approaches may deliver more accurate predictions and help to identify factors that determine performance decline rates. We hypothesized that it is possible to predict the performance development of a master athlete from a single measurement, that prediction by a machine learning approach is superior to prediction by the average decline curve or an individually shifted decline curve, and that athletes with a higher starting performance show a slower performance decline than those with a lower performance. The machine learning approach was implemented using a multilayer neuronal network. Results showed that performance prediction from a single measurement is possible and that the prediction by a machine learning approach was superior to the other models. The estimated performance decline rate was highest in athletes with a high starting performance and a low starting age, as well as in those with a low starting performance and high starting age, while the lowest decline rate was found for athletes with a high starting performance and a high starting age. Machine learning was superior and predicted trajectories with significantly lower prediction errors compared to conventional approaches. New insights into factors determining decline trajectories were identified by visualization of the model outputs. Machine learning models may be useful in revealing unknown factors that determine the age-related performance decline

Video-Based Hand Movement Analysis of Parkinson Patients before and after Medication Using High-Frame-Rate Videos and MediaPipe

Tremor is one of the common symptoms of Parkinson’s disease (PD). Thanks to the recent evolution of digital technologies, monitoring of PD patients’ hand movements employing contactless methods gained momentum. Objective: We aimed to quantitatively assess hand movements in patients suffering from PD using the artificial intelligence (AI)-based hand-tracking technologies of MediaPipe. Method: High-frame-rate videos and accelerometer data were recorded from 11 PD patients, two of whom showed classical Parkinsonian-type tremor. In the OFF-state and 30 Minutes after taking their standard oral medication (ON-state), video recordings were obtained. First, we investigated the frequency and amplitude relationship between the video and accelerometer data. Then, we focused on quantifying the effect of taking standard oral treatments. Results: The data extracted from the video correlated well with the accelerometer-based measurement system. Our video-based approach identified the tremor frequency with a small error rate (mean absolute error 0.229 (±0.174) Hz) and an amplitude with a high correlation. The frequency and amplitude of the hand movement before and after medication in PD patients undergoing medication differ. PD Patients experienced a decrease in the mean value for frequency from 2.012 (±1.385) Hz to 1.526 (±1.007) Hz and in the mean value for amplitude from 8.167 (±15.687) a.u. to 4.033 (±5.671) a.u. Conclusions: Our work achieved an automatic estimation of the movement frequency, including the tremor frequency with a low error rate, and to the best of our knowledge, this is the first paper that presents automated tremor analysis before/after medication in PD, in particular using high-frame-rate video data

Estrategias y coordinación en el subsistema de agronegocios de cacao orgánico en la región de San Martín : el caso ACOPAGRO

Actualmente grandes cambios en las cadenas agroalimentarias causadas por los consumidores, han generado el desarrollo de productos diferenciados, que en su mayoría son producidos por pequeños productores, tal es el caso del cacao orgánico. Durante los últimos años el Perú se ha consolidado como un importante exportador de cacao fino, aromático y orgánico, siendo San Martín una de las principales regiones productoras. Este subsistema de agronegocios, presentaba serias perturbaciones, tales como, inacceso a créditos para productores, inexistencia de alianzas estratégicas, y una baja coordinación entre los actores del sistema, así mismo la existencia de intermediarios que generaban gran oportunismo e incertidumbre por el precio del cacao que percibían los productores; todo ello ocasionaba elevados costos en las transacciones. Es en este contexto que se crea la Cooperativa Agraria Cacaotera ACOPAGRO, que se ha desempeñado de manera exitosa en los últimos años, siendo líder en la exportación de cacao orgánico. De aquí parte el objetivo de esta investigación, la cual es conocer las nuevas estrategias y formas de coordinación en agronegocios, utilizando el caso ACOPAGRO. La metodología utilizada fue la epistemología fenomenológica a través de un estudio de caso. Sustentada teóricamente en la Nueva Economía Institucional aplicada a los Negocios Agroalimentarios, fundamentalmente la economía de los costos de transacción. Los resultados obtenidos definen a ACOPAGRO como subsistema estrictamente coordinado, producto de la acción colectiva, con objeto de abastecer al mercado internacional exigente. Pues esta cooperativa se ha desarrollado eficientemente, adaptándose a las distintas perturbaciones y oportunidades de mercado; estratégicamente con una innovación en su diseño organizacional que permitió implementar tecnología, crear incentivos y poder adecuar controles. Además de convertirse en el coordinador del subsistema, ACOPAGRO optó en nuevas formas de gobernancia en sus transacciones, alineándolas de tal forma que permitan salvaguardar los activos específicos invertidos por los productores y reducir la incertidumbre, bajando así los costos de transacción

ATP synthase deficiency due to TMEM70 mutation leads to ultrastructural mitochondrial degeneration and is amenable to treatment.

TMEM70 is involved in the biogenesis of mitochondrial ATP synthase and mutations in the TMEM70 gene impair oxidative phosphorylation. Herein, we report on pathology and treatment of ATP synthase deficiency in four siblings. A consanguineous family of Roma (Gipsy) ethnic origin gave birth to 6 children of which 4 were affected presenting with dysmorphic features, failure to thrive, cardiomyopathy, metabolic crises, and 3-methylglutaconic aciduria as clinical symptoms. Genetic testing revealed a homozygous mutation (c.317-2A>G) in the TMEM70 gene. While light microscopy was unremarkable, ultrastructural investigation of muscle tissue revealed accumulation of swollen degenerated mitochondria with lipid crystalloid inclusions, cristae aggregation, and exocytosis of mitochondrial material. Biochemical analysis of mitochondrial complexes showed an almost complete ATP synthase deficiency. Despite harbouring the same mutation, the clinical outcome in the four siblings was different. Two children died within 60 h after birth; the other two had recurrent life-threatening metabolic crises but were successfully managed with supplementation of anaplerotic amino acids, lipids, and symptomatic treatment during metabolic crisis. In summary, TMEM70 mutations can cause distinct ultrastructural mitochondrial degeneration and almost complete deficiency of ATP synthase but are still amenable to treatment

Human cytomegalovirus infection in tumor cells of the nervous system is not detectable with standardized pathologico-virological diagnostics.

BACKGROUND Experimental findings have suggested that human cytomegalovirus (HCMV) infection of tumor cells may exert oncomodulatory effects that enhance tumor malignancy. However, controversial findings have been published on the presence of HCMV in malignant tumors. Here, we present the first study that systematically investigates HCMV infection in human nervous system tumors by highly sensitive immunohistochemistry in correlation with the HCMV serostatus of the patients. METHODS Immunohistochemical and quantitative PCR-based methods to detect different HCMV antigens and genomic HCMV DNA were optimized prior to the investigation of pathological samples. Moreover, the pathological results were matched with the HCMV serostatus of the patients. RESULTS HCMV immediate-early, late, and pp65 antigens could be detected in single cells from HCMV strain Hi91-infected UKF-NB-4 neuroblastoma cells after 1:1024 dilution with noninfected UKF-NB-4 cells. Genomic HCMV DNA could be detected in copy numbers as low as 430 copies/mL. However, we did not detect HCMV in tumors from a cohort of 123 glioblastoma, medulloblastoma, or neuroblastoma patients. Notably, we detected nonspecifically positive staining in tumor tissues of HCMV seropositive and seronegative glioblastoma patients. The HCMV serostatus of 67 glioblastoma patients matched the general epidemiological prevalence data for Western countries (72% of female and 57% of male glioblastoma patients were HCMV seropositive). Median survival was not significantly different in HCMV seropositive versus seronegative glioblastoma patients. CONCLUSIONS The prevalence of HCMV-infected tumor cells may be much lower than previously reported based on highly sensitive detection methods

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology

IKKα controls ATG16L1 degradation to prevent ER stress during inflammation

Inhibition of the IκB kinase complex (IKK) has been implicated in the therapy of several chronic inflammatory diseases including inflammatory bowel diseases. In this study, using mice with an inactivatable IKKα kinase (IkkαAA/AA), we show that loss of IKKα function markedly impairs epithelial regeneration in a model of acute colitis. Mechanistically, this is caused by compromised secretion of cytoprotective IL-18 from IKKα-mutant intestinal epithelial cells because of elevated caspase 12 activation during an enhanced unfolded protein response (UPR). Induction of the UPR is linked to decreased ATG16L1 stabilization in IkkαAA/AA mice. We demonstrate that both TNF-R and nucleotide-binding oligomerization domain stimulation promote ATG16L1 stabilization via IKKα-dependent phosphorylation of ATG16L1 at Ser278. Thus, we propose IKKα as a central mediator sensing both cytokine and microbial stimulation to suppress endoplasmic reticulum stress, thereby assuring antiinflammatory function during acute intestinal inflammation

Alpha-Synuclein-Specific Naturally Occurring Antibodies Inhibit Aggregation In Vitro and In Vivo

Parkinson’s disease (PD) is associated with motor and non-motor symptoms and characterized by aggregates of alpha-synuclein (αSyn). Naturally occurring antibodies (nAbs) are part of the innate immune system, produced without prior contact to their specific antigen, and polyreactive. The abundance of nAbs against αSyn is altered in patients with PD. In this work, we biophysically characterized nAbs against αSyn (nAbs-αSyn) and determined their biological effects. nAbs-αSyn were isolated from commercial intravenous immunoglobulins using column affinity purification. Biophysical properties were characterized using a battery of established in vitro assays. Biological effects were characterized in HEK293T cells transiently transfected with fluorescently tagged αSyn. Specific binding of nAbs-αSyn to monomeric αSyn was demonstrated by Dot blot, ELISA, and Surface Plasmon Resonance. nAbs-αSyn did not affect viability of HEK293T cells as reported by Cell Titer Blue and LDH Assays. nAbs-αSyn inhibited fibrillation of αSyn reported by the Thioflavin T aggregation assay. Altered fibril formation was confirmed with atomic force microscopy. In cells transfected with EGFP-tagged αSyn we observed reduced formation of aggresomes, perinuclear accumulations of αSyn aggregates. The results demonstrate that serum of healthy individuals contains nAbs that specifically bind αSyn and inhibit aggregation of αSyn in vitro. The addition of nAbs-αSyn to cultured cells affects intracellular αSyn aggregates. These findings help understanding the role of the innate immune systems for the pathogenesis of PD and suggest that systemic αSyn binding agents could potentially affect neuronal αSyn pathology

Longitudinal master track and field performance decline rates are lower and performance is better compared to athletes competing only once.

In master athletics research, cross-sectional data are easier to obtain than longitudinal data. While cross-sectional data give the age-related performance-decline for a population, longitudinal data show individual trajectories. It is not known whether athletes who repeatedly compete have 1) a better performance and 2) a slower age-related decline in performance than that obtained from cross-sectional data from athletes competing only once. To investigate this, we analyzed 33,254 results of 14,118 male athletes from 8 disciplines in the database of 'Swedish Veteran Athletics'. For each discipline and for the pooled data of all disciplines, quadratic models of the evolution of performance over time were analyzed by ANCOVA/ANOCOVA using MATLAB. The performance was higher in athletes with 2 or more data-points compared to those with only n = 1 (p < 0.001), with further increases in performance with an increasing number of data-points per athlete. The estimated performance decline was lower in people with 2 or more results (sprint, 10 km, jumps; p < 0.001). In conclusion, we showed that longitudinal data are associated with a higher performance and lower performance decline rate