Grip force variability and its effects on children's handwriting legibility, form, and strokes

A comprehensive understanding of the underlying biomechanical processes during handwriting is needed to accurately guide clinical interventions. To date, quantitative measurement of such biomechanical processes has largely excluded measurements of the forces exerted radially on the barrel of the writing utensil (grip forces) and how they vary over time during a handwriting task. An instrumented writing utensil was deployed for a direct measurement of kinematic and temporal information during a writing task, as well as forces exerted on the writing surface and on the barrel of the pen. The writing utensil was used by a cohort of 35 students (19 males), 16 in first grade and 19 in second grade, as they performed the Minnesota Handwriting Assessment (MHA) test. Quantitative grip force variability measures were computed and tested as correlates of handwriting legibility, form, and strokes. Grip force variability was shown to correlate strongly with handwriting quality, in particular for students classified by the MHA as nonproficient writers. More specifically, static grip force patterns were shown to result in poor handwriting quality and in greater variation in handwriting stroke durations. Grip force variability throughout the writing task was shown to be significantly lower for nonproficient writers (t-test, p<0.01) while the number of strokes and per-stroke durations were shown to be higher (p<0.03). The results suggest that grip force dynamics play a key role in determining handwriting quality and stroke characteristics. In particular, students with writing difficulties exhibited more static grip force patterns, lower legibility and form scores, as well as increased variation in stroke durations. These findings shed light on the underlying processes of handwriting and grip force modulation and may help to improve intervention planning.     

The structures of letters and symbols throughout human history are selected to match those found in objects in natural scenes

Are there empirical regularities in the shapes of letters and other human visual signs, and if so, what are the selection pressures underlying these regularities? To examine this, we determined a wide variety of topologically distinct contour configurations and examined the relative frequency of these configuration types across writing systems, Chinese writing, and nonlinguistic symbols. Our first result is that these three classes of human visual sign possess a similar signature in their configuration distribution, suggesting that there are underlying principles governing the shapes of human visual signs. Second, we provide evidence that the shapes of visual signs are selected to be easily seen at the expense of the motor system. Finally, we provide evidence to support an ecological hypothesis that visual signs have been culturally selected to match the kinds of conglomeration of contours found in natural scenes because that is what we have evolved to be good at visually processing.     

Universal scaling laws for hierarchical complexity in languages, organisms, behaviors and other combinatorial systems.

There are many complex systems in nature where components, or "words", are combined together to make expressions, or "sentences". Such combinatorial systems include: (1) human language, where sentences are composed of words; (2) bird vocalization, where songs are built from syllables; (3) organisms, where organism-expressions (e.g. the tonsil) are made out of cells; (4) behavioral repertoire, where mammalian behavior consists of a temporal arrangement of muscle contractions; (5) universities, where student academic degrees are comprised of departmental concentrations; and (6) electronic devices, where the device's actions are implemented via strings of button-presses. My central aim here is to discover how combinatorial systems accommodate greater numbers of expressions; that is, what changes do combinatorial systems undergo when they "say more things?" Are there general laws characterizing the properties of combinatorial systems as the number of expressions increases? If so, what are they? My main result is that, in all the kinds of combinatorial system mentioned above, there appear to be general laws describing how combinatorial systems change as they become more expressive. In particular, in each of these cases, increase in expression complexity (i.e. number of expressions the combinatorial system allows) is achieved, at least in part, by increasing the number of component types. Each kind of system follows one of two kinds of scaling law. In the first kind of scaling law, expression complexity increase is carried out exclusively by increasing the number of component types; the number of components per expression (i.e. the expression length) remains invariant. This applies to human language over history, bird vocalization, organisms in phylogeny and ontogeny, and universities. In the second kind of scaling law, expression complexity is accomplished by increasing in a law-like manner both the number of component types and the expression length. This applies to two cases of the ontogeny of language-the development of words and sentences, and the development of phonemes and morphemes-and to mammalian behavior. By treating these diverse systems as combinatorial systems we, in addition to elucidating general principles underlying such systems, gain insight into each kind of system mentioned.     

Going with the wind – Adaptive dynamics of plant secondary meristems

The developmental plasticity of organisms is a natural consequence of adaptation. Classical approaches targeting developmental processes usually focus on genetics as the essential factor underlying phenotypic differences. However, such differences are often based on the inherent plasticity of developmental programs. Due to their dependence on environmental stimuli, plants represent ideal experimental systems in which to dissect the contribution of genetic and environmental variation to phenotypic plasticity. An evident example is the vast repertoire of growth forms observed in plant shoot systems. A fundamental factor underlying the broadness of this repertoire is the activity of secondary meristems, namely the axillary meristems that give rise to side shoots, and the cambium essential for stem thickening. Differential activities of both meristem types are crucial to the tremendous variation seen in higher plant architecture. In this review, we discuss the role of secondary meristems in the adaptation of plant growth forms, and the ways in which they integrate environmental input. In particular, we explore potential approaches for dissecting the degree to which this flexibility and its consequences for plant architecture is genetically predetermined and how much it represents an adaptive value.     

Spontaneous decisions and operant conditioning in fruit flies

Already in the 1930s Skinner, Konorskiand colleagues debated the commonalities, differences and interactions among the processes underlying what was then known as “conditioned reflexes type I and II”, but which is today more well-known as classical (Pavlovian) and operant (instrumental) conditioning. Subsequent decades of research have confirmed that the interactions between the various learning systems engaged during operant conditioning are complex and difficult to disentangle. Today, modern neurobiological tools allow us to dissect the biological processes underlying operant conditioning and study their interactions. These processes include initiating spontaneous behavioral variability, world-learning and self-learning. The data suggest that behavioral variability is generated actively by the brain, rather than as a by-product of a complex, noisy input-output system. The function of this variability, in part, is to detect how the environment responds to such actions. World-learning denotes the biological process by which value is assigned to environmental stimuli. Self-learning is the biological process which assigns value to a specific action or movement. In an operant learning situation using visual stimuli for flies, world-learning inhibits self-learning via a prominent neuropil region, the mushroom-bodies. Only extended training can overcome this inhibition and lead to habit formation by engaging the self-learning mechanism. Self-learning transforms spontaneous, flexible actions into stereotyped, habitual responses.     

Evolutionary ecology of opsin gene sequence,expression and repertoire

Linking molecular evolution to biological function is a long‐standing challenge in evolutionary biology. Some of the best examples of this involve opsins, the genes that encode the molecular basis of light reception. In this issue of Molecular Ecology, three studies examine opsin gene sequence, expression and repertoire to determine how natural selection has shaped the visual system. First, Escobar‐Camacho et al. ( 2017 ) use opsin repertoire and expression in three Amazonian cichlid species to show that a shift in sensitivity towards longer wavelengths is coincident with the long‐wavelength‐dominated Amazon basin. Second, Stieb et al. ( 2017 ) explore opsin sequence and expression in reef‐dwelling damselfish and find that UV‐ and long‐wavelength vision are both important, but likely for different ecological functions. Lastly, Suvorov et al. ( 2017 ) study an expansive opsin repertoire in the insect order Odonata and find evidence that copy number expansion is consistent with the permanent heterozygote model of gene duplication. Together these studies emphasize the utility of opsin genes for studying both the local adaptation of sensory systems and, more generally, gene family evolution.     

Learning and neural plasticity in visual object recognition

The capability of the adult primate visual system for rapid and accurate recognition of targets in cluttered, natural scenes far surpasses the abilities of state-of-the-art artificial vision systems. Understanding this capability remains a fundamental challenge in visual neuroscience. Recent experimental evidence suggests that adaptive coding strategies facilitated by underlying neural plasticity enable the adult brain to learn from visual experience and shape its ability to integrate and recognize coherent visual objects.     

Simplicity, function, and legibility in an enhanced ambigraphic nucleic acid notation

We previously showed that an ambigraphic nucleic acid notation, based on symmetrical lowercase Roman characters, permits users to complement DNA by physically rotating the sequence text 180 degrees . This article describes an enhanced ambigraphic notation, which uses concept-related symbol design, rather than the arbitrary set of symbols that constitute the Roman alphabet, to logically encode the four DNA bases and 11 ambiguity characters. As ambigrams, the symbols continue to permit the rapid derivation of complementary sequences and visualization of palindromic DNA. In addition, the new AmbiScript notation uses legibility principles to support the identification of sequence polymorphism and improves writing efficiency by requiring fewer strokes per character than the International Union of Pure and Applied Chemistry (IUPAC) notation.     

A case for spiking neural network simulation based on configurable multiple-FPGA systems

Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.     

Analyzing movement trajectories using a Markov bi-clustering method

In this study we treat scribbling motion as a compositional system in which a limited set of elementary strokes are capable of concatenating amongst themselves in an endless number of combinations, thus producing an unlimited repertoire of complex constructs. We broke the continuous scribblings into small units and then calculated the Markovian transition matrix between the trajectory clusters. The Markov states are grouped in a way that minimizes the loss of mutual information between adjacent strokes. The grouping algorithm is based on a novel markov-state bi-clustering algorithm derived from the Information-Bottleneck principle. This approach hierarchically decomposes scribblings into increasingly finer elements. We illustrate the usefulness of this approach by applying it to human scribbling.     

Attention controls multisensory perception via two distinct mechanisms at different levels of the cortical hierarchy

To form a percept of the multisensory world, the brain needs to integrate signals from common sources weighted by their reliabilities and segregate those from independent sources. Previously, we have shown that anterior parietal cortices combine sensory signals into representations that take into account the signals’ causal structure (i.e., common versus independent sources) and their sensory reliabilities as predicted by Bayesian causal inference. The current study asks to what extent and how attentional mechanisms can actively control how sensory signals are combined for perceptual inference. In a pre- and postcueing paradigm, we presented observers with audiovisual signals at variable spatial disparities. Observers were precued to attend to auditory or visual modalities prior to stimulus presentation and postcued to report their perceived auditory or visual location. Combining psychophysics, functional magnetic resonance imaging (fMRI), and Bayesian modelling, we demonstrate that the brain moulds multisensory inference via two distinct mechanisms. Prestimulus attention to vision enhances the reliability and influence of visual inputs on spatial representations in visual and posterior parietal cortices. Poststimulus report determines how parietal cortices flexibly combine sensory estimates into spatial representations consistent with Bayesian causal inference. Our results show that distinct neural mechanisms control how signals are combined for perceptual inference at different levels of the cortical hierarchy.

A combination of psychophysics, computational modelling and fMRI reveals novel insights into how the brain controls the binding of information across the senses, such as the voice and lip movements of a speaker.     

Evolution and development — the nematode vulva as a case study

To understand how morphological characters change during evolution, we need insight into the evolution of developmental processes. Comparative developmental approaches that make use of our fundamental understanding of development in certain model organisms have been initiated for different animal systems and flowering plants. Nematodes provide a useful experimental system with which to investigate the genetic and molecular alterations underlying evolutionary changes of cell fate specification in development, by comparing different species to the genetic model system Caenorhabditis elegans. In this review, I will first discuss the different types of evolutionary alterations seen at the cellular level by focusing mainly on the analysis of vulva development in different species. The observed alterations involve changes in cell lineage, cell migration and cell death, as well as induction and cell competence. I then describe a genetic approach in the nematode Pristionchus pacificus that might identify those genetic and molecular processes that cause evolutionary changes of cell fate specification.     

The visual ecology of fiddler crabs

With their eyes on long vertical stalks, their panoramic visual field and their pronounced equatorial acute zone for vertical resolving power, the visual system of fiddler crabs is exquisitely tuned to the geometry of vision in the flat world of inter-tidal mudflats. The crabs live as burrow-centred grazers in dense, mixed-sex, mixed-age and mixed-species colonies, with the active space of an individual rarely exceeding 1 m². The full behavioural repertoire of fiddler crabs can thus be monitored over extended periods of time on a moment to moment basis together with the visual information they have available to guide their actions. These attributes make the crabs superb subjects for analysing visual tasks and the design of visual processing mechanisms under natural conditions, a prerequisite for understanding the evolution of visual systems. In this review we show, on the one hand, how deeply embedded fiddler crab vision is in the behavioural and the physical ecology of these animals and, on the other hand, how their behavioural options are constrained by their perceptual limitations. Studying vision in fiddler crabs reminds us that vision has a topography, that it is context-dependent and pragmatic and that there are perceptual limits to what animals can know and therefore care about. For Mike Land     

Coevolution of active vision and feature selection

We show that complex visual tasks, such as position- and size-invariant shape recognition and navigation in the environment, can be tackled with simple architectures generated by a coevolutionary process of active vision and feature selection. Behavioral machines equipped with primitive vision systems and direct pathways between visual and motor neurons are evolved while they freely interact with their environments. We describe the application of this methodology in three sets of experiments, namely, shape discrimination, car driving, and robot navigation. We show that these systems develop sensitivity to a number of oriented, retinotopic, visual-feature-oriented edges, corners, height, and a behavioral repertoire to locate, bring, and keep these features in sensitive regions of the vision system, resembling strategies observed in simple insects.     

The analysis of clonal expansions in normal and autoimmune B cell repertoires

Clones are the fundamental building blocks of immune repertoires. The number of different clones relates to the diversity of the repertoire, whereas their size and sequence diversity are linked to selective pressures. Selective pressures act both between clones and within different sequence variants of a clone. Understanding how clonal selection shapes the immune repertoire is one of the most basic questions in all of immunology. But how are individual clones defined? Here we discuss different approaches for defining clones, starting with how antibodies are diversified during different stages of B cell development. Next, we discuss how clones are defined using different experimental methods. We focus on high-throughput sequencing datasets, and the computational challenges and opportunities that these data have for mining the antibody repertoire landscape. We discuss methods that visualize sequence variants within the same clone and allow us to consider collections of shared mutations to determine which sequences share a common ancestry. Finally, we comment on features of frequently encountered expanded B cell clones that may be of particular interest in the setting of autoimmunity and other chronic conditions.     

Nucleoporins’ exclusive amino acid sequence features regulate their transient interaction with and selectivity of cargo complexes in the nuclear pore

Nucleocytoplasmic traffic of nucleic acids and proteins across the nuclear envelop via the nuclear pore complexes (NPCs) is vital for eukaryotic cells. NPCs screen transported macromolecules based on their morphology and surface chemistry. This selective nature of the NPC-mediated traffic is essential for regulating the fundamental functions of the nucleus, such as gene regulation, protein synthesis, and mechanotransduction. Despite the fundamental role of the NPC in cell and nuclear biology, the detailed mechanisms underlying how the NPC works have remained largely unknown. The critical components of NPCs enabling their selective barrier function are the natively unfolded phenylalanine- and glycine-rich proteins called “FG-nucleoporins” (FG Nups). These intrinsically disordered proteins are tethered to the inner wall of the NPC, and together form a highly dynamic polymeric meshwork whose physicochemical conformation has been the subject of intense debate. We observed that specific sequence features (called largest positive like-charge regions, or lpLCRs), characterized by extended subsequences that only possess positively charged amino acids, significantly affect the conformation of FG Nups inside the NPC. Here we investigate how the presence of lpLCRs affects the interactions between FG Nups and their interactions with the cargo complex. We combine coarse-grained molecular dynamics simulations with time-resolved force distribution analysis to disordered proteins to explore the behavior of the system. Our results suggest that the number of charged residues in the lpLCR domain directly governs the average distance between Phe residues and the intensity of interaction between them. As a result, the number of charged residues within lpLCR determines the balance between the hydrophobic interaction and the electrostatic repulsion and governs how dense and disordered the hydrophobic network formed by FG Nups is. Moreover, changing the number of charged residues in an lpLCR domain can interfere with ultrafast and transient interactions between FG Nups and the cargo complex.     

Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the “causal inference problem.” Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation). At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion). Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world.     

Comprehensive analysis of locomotion dynamics in the protochordate Ciona intestinalis reveals how neuromodulators flexibly shape its behavioral repertoire

Vertebrate nervous systems can generate a remarkable diversity of behaviors. However, our understanding of how behaviors may have evolved in the chordate lineage is limited by the lack of neuroethological studies leveraging our closest invertebrate relatives. Here, we combine high-throughput video acquisition with pharmacological perturbations of bioamine signaling to systematically reveal the global structure of the motor behavioral repertoire in the Ciona intestinalis larvae. Most of Ciona’s postural variance can be captured by 6 basic shapes, which we term “eigencionas.” Motif analysis of postural time series revealed numerous stereotyped behavioral maneuvers including “startle-like” and “beat-and-glide.” Employing computational modeling of swimming dynamics and spatiotemporal embedding of postural features revealed that behavioral differences are generated at the levels of motor modules and the transitions between, which may in part be modulated by bioamines. Finally, we show that flexible motor module usage gives rise to diverse behaviors in response to different light stimuli.

Vertebrate nervous systems can generate a remarkable diversity of behaviors, but how did these evolve in the chordate lineage? A study of the protochordate Ciona intestinalis reveals novel insights into how a simple chordate brain uses neuromodulators to control its behavioral repertoire.     

Development of continuous and discrete neural maps

Two qualitatively different kinds of neural map have been described: continuous maps exemplified by the visual retinotopic map, and discrete maps exemplified by the olfactory glomerular map. Here, we review developmental mechanisms of retinotopic and olfactory glomerular mapping and discuss underlying commonalities that have emerged from recent studies. These include the use of molecular gradients, axon-axon interactions, and the interplay between labeling molecules and neuronal activity in establishing these maps. Since visual retinotopic and olfactory glomerular maps represent two ends of a continuum that includes many other types of neural map in between, these emerging general principles may be widely applicable to map formation throughout the nervous system.     

Scaling in Transportation Networks

Subway systems span most large cities, and railway networks most countries in the world. These networks are fundamental in the development of countries and their cities, and it is therefore crucial to understand their formation and evolution. However, if the topological properties of these networks are fairly well understood, how they relate to population and socio-economical properties remains an open question. We propose here a general coarse-grained approach, based on a cost-benefit analysis that accounts for the scaling properties of the main quantities characterizing these systems (the number of stations, the total length, and the ridership) with the substrate''s population, area and wealth. More precisely, we show that the length, number of stations and ridership of subways and rail networks can be estimated knowing the area, population and wealth of the underlying region. These predictions are in good agreement with data gathered for about subway systems and more than railway networks in the world. We also show that train networks and subway systems can be described within the same framework, but with a fundamental difference: while the interstation distance seems to be constant and determined by the typical walking distance for subways, the interstation distance for railways scales with the number of stations.