Vertical finger displacement is reduced in index finger tapping during repeated bout rate enhancement

The present study analyzed (a) whether a recently reported phenomenon of repeated bout rate enhancement in finger tapping (i.e., a cumulating increase in freely chosen finger tapping frequency following submaximal muscle activation in the form of externally unloaded voluntary tapping) could be replicated and (b) the hypotheses that the faster tapping was accompanied by changed vertical displacement of the fingertip and changed peak force during tapping. Right-handed, healthy, and recreationally active individuals (n = 24) performed two 3-min index finger tapping bouts at freely chosen tapping frequency, separated by 10-min rest. The recently reported phenomenon of repeated bout rate enhancement was replicated. The faster tapping (8.8 ± 18.7 taps/min, corresponding to 6.0 ± 11.0%, p = .033) was accompanied by reduced vertical displacement (1.6 ± 2.9 mm, corresponding to 6.3 ± 14.9%, p = .012) of the fingertip. Concurrently, peak force was unchanged. The present study points at separate control mechanisms governing kinematics and kinetics during finger tapping.</jats:p

The role of stride frequency for walk-to-run transition in humans

AbstractIt remains unclear why humans spontaneously shift from walking to running at a certain point during locomotion at gradually increasing velocity. We show that a calculated walk-to-run transition stride frequency (70.6 ± 3.2 strides min−1) agrees with a transition stride frequency (70.8 ± 3.1 strides min−1) predicted from the two stride frequencies applied during treadmill walking and running at freely chosen velocities and freely chosen stride frequencies. The agreement is based on Bland and Altman’s statistics. We found no essential mean relative difference between the two transition frequencies, i.e. −0.5% ± 4.2%, as well as limits of agreement of −8.7% and 7.7%. The particular two freely chosen stride frequencies used for prediction are considered behavioural attractors. Gait is predicted to be shifted from walking to running when the stride frequency starts getting closer to the running attractor than to the walking attractor. In particular, previous research has focussed on transition velocity and optimisation theories based on minimisation of, e.g., energy turnover or biomechanical loadings of the legs. Conversely, our data support that the central phenomenon of walk-to-run transition during human locomotion could be influenced by behavioural attractors in the form of stride frequencies spontaneously occurring during behaviourally unrestricted gait conditions of walking and running.</jats:p

Initial Development toward Non-Invasive Drug Monitoring via Untargeted Mass Spectrometric Analysis of Human Skin.

Drug monitoring is crucial for providing accurate and effective care; however, current methods (e.g., blood draws) are inconvenient and unpleasant. We aim to develop a non-invasive method for the detection and monitoring of drugs via human skin. The initial development toward this aim required information about which drugs, taken orally, can be detected via the skin. Untargeted liquid chromatography-mass spectrometry (LC-MS) was used as it was unclear if drugs, known drug metabolites, or other transformation products were detectable. In accomplishing our aim, we analyzed samples obtained by swabbing the skin of 15 kidney transplant recipients in five locations (forehead, nasolabial area, axillary, backhand, and palm), bilaterally, on two different clinical visits. Untargeted LC-MS data were processed using molecular networking via the Global Natural Products Social Molecular Networking platform. Herein, we report the qualitative detection and location of drugs and drug metabolites. For example, escitalopram/citalopram and diphenhydramine, taken orally, were detected in forehead, nasolabial, and hand samples, whereas N-acetyl-sulfamethoxazole, a drug metabolite, was detected in axillary samples. In addition, chemicals associated with environmental exposure were also detected from the skin, which provides insight into the multifaceted chemical influences on our health. The proof-of-concept results presented support the finding that the LC-MS and data analysis methodology is currently capable of the qualitative assessment of the presence of drugs directly via human skin

Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra

Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely used and sensitive technique to obtain structural information on complex mixtures. However, just knowing the molecular masses of the mixture’s constituents is almost always insufficient for confident assignment of the associated chemical structures. Structural information can be augmented through MS fragmentation experiments whereby detected metabolites are fragmented giving rise to MS/MS spectra. However, how can we maximize the structural information we gain from fragmentation spectra? We recently proposed a substructure-based strategy to enhance metabolite annotation for complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows us to discover - unsupervised - groups of mass fragments and/or neutral losses termed Mass2Motifs that often correspond to substructures. After manual annotation, these Mass2Motifs can be used in subsequent MS2LDA analyses of new datasets, thereby providing structural annotations for many molecules that are not present in spectral databases. Here, we describe how additional strategies, taking advantage of i) combinatorial in-silico matching of experimental mass features to substructures of candidate molecules, and ii) automated machine learning classification of molecules, can facilitate semi-automated annotation of substructures. We show how our approach accelerates the Mass2Motif annotation process and therefore broadens the chemical space spanned by characterized motifs. Our machine learning model used to classify fragmentation spectra learns the relationships between fragment spectra and chemical features. Classification prediction on these features can be aggregated for all molecules that contribute to a particular Mass2Motif and guide Mass2Motif annotations. To make annotated Mass2Motifs available to the community, we also present motifDB: an open database of Mass2Motifs that can be browsed and accessed programmatically through an Application Programming Interface (API). MotifDB is integrated within ms2lda.org, allowing users to efficiently search for characterized motifs in their own experiments. We expect that with an increasing number of Mass2Motif annotations available through a growing database we can more quickly gain insight in the constituents of complex mixtures. That will allow prioritization towards novel or unexpected chemistries and faster recognition of known biochemical building blocks

Evolutionary prediction of medicinal properties in the genus <i>Euphorbia </i>L.

The current decrease of new drugs brought to the market has fostered renewed interest in plant-based drug discovery. Given the alarming rate of biodiversity loss, systematic methodologies in finding new plant-derived drugs are urgently needed. Medicinal uses of plants were proposed as proxy for bioactivity, and phylogenetic patterns in medicinal plant uses have suggested that phylogeny can be used as predictive tool. However, the common practice of grouping medicinal plant uses into standardised categories may restrict the relevance of phylogenetic predictions. Standardised categories are mostly associated to systems of the human body and only poorly reflect biological responses to the treatment. Here we show that medicinal plant uses interpreted from a perspective of a biological response can reveal different phylogenetic patterns of presumed underlying bioactivity compared to standardised methods of medicinal plant use classification. In the cosmopolitan and pharmaceutically highly relevant genus Euphorbia L., identifying plant uses modulating the inflammatory response highlighted a greater phylogenetic diversity and number of potentially promising species than standardised categories. Our interpretation of medicinal plant uses may therefore allow for a more targeted approach for future phylogeny-guided drug discovery at an early screening stage, which will likely result in higher discovery rates of novel chemistry with functional biological activity

Sparse Training Theory for Scalable and Efficient Agents

A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven to cope perfectly with all learning paradigms, i.e. supervised, unsupervised, and reinforcement learning. Nevertheless, traditional deep learning approaches make use of cloud computing facilities and do not scale well to autonomous agents with low computational resources. Even in the cloud, they suffer from computational and memory limitations, and they cannot be used to model adequately large physical worlds for agents which assume networks with billions of neurons. These issues are addressed in the last few years by the emerging topic of sparse training, which trains sparse networks from scratch. This paper discusses sparse training state-of-the-art, its challenges and limitations while introducing a couple of new theoretical research directions which has the potential of alleviating sparse training limitations to push deep learning scalability well beyond its current boundaries. Nevertheless, the theoretical advancements impact in complex multi-agents settings is discussed from a real-world perspective, using the smart grid case study

Sparse Training Theory for Scalable and Efficient Agents:Blue Sky Ideas Track

A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven to cope perfectly with all learning paradigms, i.e. supervised, unsupervised, and reinforcement learning. Nevertheless, traditional deep learning approaches make use of cloud computing facilities and do not scale well to autonomous agents with low computational resources. Even in the cloud, they suffer from computational and memory limitations, and they cannot be used to model adequately large physical worlds for agents which assume networks with billions of neurons. These issues are addressed in the last few years by the emerging topic of sparse training, which trains sparse networks from scratch. This paper discusses sparse training state-of-the-art, its challenges and limitations while introducing a couple of new theoretical research directions which has the potential of alleviating sparse training limitations to push deep learning scalability well beyond its current boundaries. Nevertheless, the theoretical advancements impact in complex multi-agents settings is discussed from a real-world perspective, using the smart grid case study