Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from Pediatric Bone Age Challenge organized by RSNA 2017. The dataset for this competition is consisted of 12.6k radiological images of left hand labeled by the bone age and sex of patients. Our approach utilizes several deep learning architectures: U-Net, ResNet-50, and custom VGG-style neural networks trained end-to-end. We use images of whole hands as well as specific parts of a hand for both training and inference. This approach allows us to measure importance of specific hand bones for the automated bone age analysis. We further evaluate performance of the method in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.Comment: 14 pages, 9 figure

Cerebellar–M1 connectivity changes associated with motor learning are somatotopic specific

One of the functions of the cerebellum in motor learning is to predict and account for systematic changes to the body or environment. This form of adaptive learning is mediated by plastic changes occurring within the cerebellar cortex. The strength of cerebellar-to-cerebral pathways for a given muscle may reflect aspects of cerebellum-dependent motor adaptation. These connections with motor cortex (M1) can be estimated as cerebellar inhibition (CBI): a conditioning pulse of transcranial magnetic stimulation delivered to the cerebellum before a test pulse over motor cortex. Previously, we have demonstrated that changes in CBI for a given muscle representation correlate with learning a motor adaptation task with the involved limb. However, the specificity of these effects is unknown. Here, we investigated whether CBI changes in humans are somatotopy specific and how they relate to motor adaptation. We found that learning a visuomotor rotation task with the right hand changed CBI, not only for the involved first dorsal interosseous of the right hand, but also for an uninvolved right leg muscle, the tibialis anterior, likely related to inter-effector transfer of learning. In two follow-up experiments, we investigated whether the preparation of a simple hand or leg movement would produce a somatotopy-specific modulation of CBI. We found that CBI changes only for the effector involved in the movement. These results indicate that learning-related changes in cerebellar– M1 connectivity reflect a somatotopy-specific interaction. Modulation of this pathway is also present in the context of interlimb transfer of learning

Combining reward and M1 transcranial direct current stimulation enhances the retention of newly learnt sensorimotor mappings

Background: Reward-based feedback given during motor learning has been shown to improve the retention of the behaviour being acquired. Interestingly, applying transcranial direct current stimulation (tDCS) during learning over the primary motor cortex (M1), an area associated with motor retention, also results in enhanced retention of the newly formed motor memories. However, it remains unknown whether combining these distinct interventions result in an additive benefit of motor retention. Methods: We investigated whether combining both interventions while participants learned to account for a visuomotor transformation results in enhanced motor retention (total n = 56; each group n = 14). To determine whether these interventions share common physiological mechanisms underpinning learning, we assessed motor cortical excitability and inhibition (i.e. SICI) on a hand muscle before and after all participants learned the visuomotor rotation using their entire arm and hand. Results: We found that both the Reward-Stim (i.e. reward + tDCS) and Reward-Sham (i.e. reward-only) groups had increased retention at the beginning of the retention phase, indicating an immediate effect of reward on behaviour. However, each intervention on their own did not enhance retention when compared to sham, but rather, only the combination of both reward and tDCS demonstrated prolonged retention. We also found that only the Reward-Stim group had a significant reduction in SICI after exposure to the perturbation. Conclusions: We show that combining both interventions are additive in providing stronger retention of motor adaptation. These results indicate that the reliability and validity of using tDCS within a clinical context may depend on the type of feedback individuals receive when learning a new motor pattern

Quantitative Systems Pharmacology and Biased Agonism at Opioid Receptors: A Potential Avenue for Improved Analgesics

Chronic pain is debilitating and represents a significant burden in terms of personal and socio-economic costs. Although opioid analgesics are widely used in chronic pain treatment, many patients report inadequate pain relief or relevant adverse effects, highlighting the need to develop analgesics with improved efficacy/safety. Multiple evidence suggests that G protein-dependent signaling triggers opioid-induced antinociception, whereas arrestin-mediated pathways are credited with modulating different opioid adverse effects, thus spurring extensive research for G protein-biased opioid agonists as analgesic candidates with improved pharmacology. Despite the increasing expectations of functional selectivity, translating G protein-biased opioid agonists into improved therapeutics is far from being fully achieved, due to the complex, multidimensional pharmacology of opioid receptors. The multifaceted network of signaling events and molecular processes underlying therapeutic and adverse effects induced by opioids is more complex than the mere dichotomy between G protein and arrestin and requires more comprehensive, integrated, network-centric approaches to be fully dissected. Quantitative Systems Pharmacology (QSP) models employing multidimensional assays associated with computational tools able to analyze large datasets may provide an intriguing approach to go beyond the greater complexity of opioid receptor pharmacology and the current limitations entailing the development of biased opioid agonists as improved analgesics

SICI during changing brain states: Differences in methodology can lead to different conclusions

Background Short-latency intracortical inhibition (SICI) is extensively used to probe GABAergic inhibitory mechanisms in M1. Task-related changes in SICI are presumed to reflect changes in the central excitability of GABAergic pathways. Usually, the level of SICI is evaluated using a single intensity of conditioning stimulus so that inhibition can be compared in different brain states. Objective Here, we show that this approach may sometimes be inadequate since distinct conclusions can be drawn if a different CS intensity is used. Methods We measured SICI using a range of CS intensities at rest and during a warned simple reaction time task. Conclusions Our results show that SICI changes that occurred during the task could be either larger or smaller than at rest depending on the intensity of the CS. These findings indicate that careful interpretation of results are needed when a single intensity of CS is used to measure task-related physiological changes

Opioid activity profiles of oversimplified peptides lacking in the protonable N-terminus

Recently, we described cyclopeptide opioid agonists containing the D-Trp-Phe sequence. To expand the scope of this atypical pharmacophore, we tested the activity profiles of the linear peptides Ac-Xaa-Phe-Yaa (Xaa = L/D-Trp, D-His/Lys/Arg; Yaa = H, GlyNH2). Ac-D-Trp-PheNH2 appeared to be the minimal binding sequence, while Ac-D-Trp-Phe-GlyNH 2 emerged as the first noncationizable short peptide (partial) agonist with high \u3bc-opioid receptor affinity and selectivity. Conformational analysis suggested that 5 adopts in solution a \u3b2-turn conformation. \ua9 2012 American Chemical Society

Activity Regimes on Mt Etna inferred from Automatic Unsupervised

Mt Etna is among the best monitored basaltic volcano worldwide. High-quality, multidisciplinary data set are continuously available for around-the-clock surveillance. Seismic data sets cover decades long local recordings, obtained during different regimes of eruptive activity, from Strombolian eruptions to lava fountains and lava flows. Earthquakes swarms have often heralded effusive activity. However, volcanic tremor – the persistently radiated signal by the volcano - has proved to be a key indicator of impending eruptive activity. Changes in the volcano feeder show up in the signature of tremor, its spectral characteristics and source location. We apply a recently developed software for the analysis of volcanic tremor, combining Kohonen Maps along with Cluster and Fuzzy Analysis, in order to identify transitions from pre-eruptive to eruptive activity. Throughout the analysis of the data flow, the software provides an unsupervised classification of the spectral characteristics (i.e., amplitude and frequency content) of the signal, which is interpreted in the context of a specific state of the volcano. We present an application on the eruptive events occurred during the 2007-2009 time period, encompassing 7 episodes of lava fountaining, periodic Strombolian activity at the summit craters, and a lava emission on the upper east flank of the volcano, which started on 13 May 2008 and ended on 6 July 2009. In this time span the source of volcanic tremor was always shallow (less than 3 km), i. e., within the volcano edifice. From the analysis we conclude that the upraise of magma to the surface was fast, taking several hours to a few minutes. We discuss the possible reasons of such variability in the light of the characteristics of the overall seismicity preceding the eruptions in the study period, taking into account field observations and rheology of the ascending magma as well

Pattern classification of volcanic tremor data related to the 2007-2012 Mt. Etna (Italy) eruptive episodes

From March 2007 to April 2012 one of the main craters of Mt. Etna volcano, the South East Crater, was frequently active with spectacular, even though low dangerous, eruptions mainly in form of lava fountains. Thirty-three eruptive episodes occurred at that crater, encompassing thirty-two paroxysmal lava fountains (seven in 2007-2008 and twenty-five in 2011-2012), and a lava emission, started on 13 May 2008 and ended on 6 July 2009, along the upper eastern flank of the volcano. From the seismic point of view, the onset of all these eruptions was heralded by changes in the spectral characteristics of volcanic tremor recorded by digital broadband stations, which permanently monitor the volcanic region. On the basis of the tremor data collected between 2007 and 2009, some of us (Messina and Langer) developed a software which, combining unsupervised classification methods based on Kohonen Maps and the fuzzy cluster analysis, allows to identify transitions from pre-eruptive to eruptive activity through the classification of the tremor characteristics (i.e. amplitude and frequency content). Since 2010 an on-line version of this software is adopted at the Osservatorio Etneo as one of the automatic alerting tools to identify early stages of eruptive events. The software carries out the analysis of the continuous data stream of two key seismic stations, for which reference datasets were elaborated taking into account the tremor data recorded during the eruptive episodes from 2007 to 2009. The numerous paroxysmal eruptions occurred in 2011-2012 and the improved network density, in particular on the summit crater area, after 2009, lead us to extend the application of automatic volcanic tremor classification by using a larger number of stations at different elevation and distance from the summit craters. Datasets have been formed for the new stations, while for the previous key stations, the reference datasets were updated adding new patterns of the tremor signal. We discuss the performances of the classifier for the various stations in terms of timing of the early variations and spatial distribution of the stations

Application of a multi­station alert method for short­-term forecasting of eruptions at Etna, Italy

From 11 January to 15 November 2011, 18 paroxysmal eruptions occurred at Etna, Italy. These events belong to a long sequence of eruptive episodes, which marked the prevalent explosive style of the volcano since the early 2000s. Applying “KKAnalysis”, a software for pattern classification that combines Self­Organizing Maps and fuzzy clustering, to the background seismic radiation (so-called volcanic tremor), we were able to detect critical changes in the spectral characteristics (amplitude and frequency content) at a very early stage of the volcano unrest. The online implementation for surveillance purposes of KKAnalysis provided automatic alert of the impending eruptive events from hours to a few days in advance. In its original version, the classifier analyzed the data stream continuously recorded at a single seismic station. By using offline a modified version of KKAnalysis, here we apply the software to the seismic signal recorded at 11 broadband stations in 2011. The seismic sensors were located at various distances (from 1 to 8 km) from the active craters. The continuous records and the optimal geometry of the seismic network offer us the possibility to track the spectral variations in time and space. We show the new results of pattern classification and propose a revised, more powerful multi­station alert method that now provides short­ term forecasting also in the form of animated maps that flag the detection of changes at each station. This allows us to observe how the unrest develops in various sectors of the volcano. We discuss the performance of the method and the robustness of the eruption forecasts in the context of the complex dynamics of a volcanic system such as Etna