Pfp-fm: an accelerated FM-index

FM-indexes are crucial data structures in DNA alignment, but searching with them usually takes at least one random access per character in the query pattern. Ferragina and Fischer [1] observed in 2007 that word-based indexes often use fewer random accesses than character-based indexes, and thus support faster searches. Since DNA lacks natural word-boundaries, however, it is necessary to parse it somehow before applying word-based FM-indexing. In 2022, Deng et al. [2] proposed parsing genomic data by induced suffix sorting, and showed that the resulting word-based FM-indexes support faster counting queries than standard FM-indexes when patterns are a few thousand characters or longer. In this paper we show that using prefix-free parsing-which takes parameters that let us tune the average length of the phrases-instead of induced suffix sorting, gives a significant speedup for patterns of only a few hundred characters. We implement our method and demonstrate it is between 3 and 18 times faster than competing methods on queries to GRCh38, and is consistently faster on queries made to 25,000, 50,000 and 100,000 SARS-CoV-2 genomes. Hence, it seems our method accelerates the performance of count over all state-of-the-art methods with a moderate increase in the memory. The source code for PFP - FM is available at https://github.com/AaronHong1024/afm .

Median quartet tree search algorithms using optimal subtree prune and regraft

Gene trees can be different from the species tree due to biological processes and inference errors. One way to obtain a species tree is to find one that maximizes some measure of similarity to a set of gene trees. The number of shared quartets between a potential species tree and gene trees provides a statistically justifiable score; if maximized properly, it could result in a statistically consistent estimator of the species tree under several statistical models of discordance. However, finding the median quartet score tree, one that maximizes this score, is NP-Hard, motivating several existing heuristic algorithms. These heuristics do not follow the hill-climbing paradigm used extensively in phylogenetics. In this paper, we make theoretical contributions that enable an efficient hill-climbing approach. Specifically, we show that a subtree of size m can be placed optimally on a tree of size n in quasi-linear time with respect to n and (almost) independently of m. This result enables us to perform subtree prune and regraft (SPR) rearrangements as part of a hill-climbing search. We show that this approach can slightly improve upon the results of widely-used methods such as ASTRAL in terms of the optimization score but not necessarily accuracy.

Exploring Jahn-Teller distortions: a local vibrational mode perspective

The characterization of normal mode (CNM) procedure coupled with an adiabatic connection scheme (ACS) between local and normal vibrational modes, both being a part of the Local Vibrational Mode theory developed in our group, can identify spectral changes as structural fingerprints that monitor symmetry alterations, such as those caused by Jahn-Teller (JT) distortions. Employing the PBE0/Def2-TZVP level of theory, we investigated in this proof-of-concept study the hexaaquachromium cation case,[ Cr ( OH 2 ) 6 ] 3 + /[ Cr ( OH 2 ) 6 ] 2 + , as a commonly known example for a JT distortion, followed by the more difficult ferrous and ferric hexacyanide anion case,[ Fe ( CN ) 6 ] 4 - /[ Fe ( CN ) 6 ] 3 - . We found that in both cases CNM of the characteristic normal vibrational modes reflects delocalization consistent with high symmetry and ACS confirms symmetry breaking, as evidenced by the separation of axial and equatorial group frequencies. As underlined by the Cremer-Kraka criterion for covalent bonding, from[ Cr ( OH 2 ) 6 ] 3 + to[ Cr ( OH 2 ) 6 ] 2 + there is an increase in axial covalency whereas the equatorial bonds shift toward electrostatic character. From[ Fe ( CN ) 6 ] 4 - to[ Fe ( CN ) 6 ] 3 - we observed an increase in covalency without altering the bond nature. Distinct π back-donation disparity could be confirmed by comparison with the isolated CN- system. In summary, our study positions the CNM/ACS protocol as a robust tool for investigating less-explored JT distortions, paving the way for future applications.

Fractional order memcapacitive neuromorphic elements reproduce and predict neuronal function

There is an increasing need to implement neuromorphic systems that are both energetically and computationally efficient. There is also great interest in using electric elements with memory, memelements, that can implement complex neuronal functions intrinsically. A feature not widely incorporated in neuromorphic systems is history-dependent action potential time adaptation which is widely seen in real cells. Previous theoretical work shows that power-law history dependent spike time adaptation, seen in several brain areas and species, can be modeled with fractional order differential equations. Here, we show that fractional order spiking neurons can be implemented using super-capacitors. The super-capacitors have fractional order derivative and memcapacitive properties. We implemented two circuits, a leaky integrate and fire and a Hodgkin-Huxley. Both circuits show power-law spiking time adaptation and optimal coding properties. The spiking dynamics reproduced previously published computer simulations. However, the fractional order Hodgkin-Huxley circuit showed novel dynamics consistent with criticality. We compared the responses of this circuit to recordings from neurons in the weakly-electric fish that have previously been shown to perform fractional order differentiation of their sensory input. The criticality seen in the circuit was confirmed in spontaneous recordings in the live fish. Furthermore, the circuit also predicted long-lasting stimulation that was also corroborated experimentally. Our work shows that fractional order memcapacitors provide intrinsic memory dependence that could allow implementation of computationally efficient neuromorphic devices. Memcapacitors are static elements that consume less energy than the most widely studied memristors, thus allowing the realization of energetically efficient neuromorphic devices.

Short and long range 2D 15N-15N NMR correlations among peptide groups by novel solid state dipolar mixing schemes

A recently developed homonuclear dipolar recoupling scheme, Adiabatic Linearly FREquency Swept reCOupling (AL FRESCO), was applied to record two-dimensional (2D) 15N-15N correlations on uniformly 15N-labeled GB1 powders. A major feature exploited in these 15N-15N correlations was AL FRESCO's remarkably low RF power demands, which enabled seconds-long mixing schemes when establishing direct correlations. These 15N-15N mixing schemes proved efficient regardless of the magic-angle spinning (MAS) rate and, being nearly free from dipolar truncation effects, they enabled the detection of long-range, weak dipolar couplings, even in the presence of strong short-range dipolar couplings. This led to a connectivity information that was significantly better than that obtained with spontaneously proton-driven, 15N spin-diffusion experiments. An indirect approach producing long-range 15N-15N correlations was also tested, relying on short (ms-long) 1HN-1HN mixings schemes while applying AL FRESCO chirped pulses along the 15N channel. These indirect mixing schemes produced numerous long-distance Ni-Ni±n (n = 2 - 5) correlations, that might be useful for characterizing three-dimensional arrangements in proteins. Once again, these AL FRESCO mediated experiments proved more informative than variants based on spin-diffusion-based 1HN-1HN counterparts.

Reconstitution and resonance assignments of yeast OST subunit Ost4 and its critical mutant Ost4V23D in liposomes by solid-state NMR

N-linked glycosylation is an essential and highly conserved co- and post-translational protein modification in all domains of life. In humans, genetic defects in N-linked glycosylation pathways result in metabolic diseases collectively called Congenital Disorders of Glycosylation. In this modification reaction, a mannose rich oligosaccharide is transferred from a lipid-linked donor substrate to a specific asparagine side-chain within the -N-X-T/S- sequence (where X ≠ Proline) of the nascent protein. Oligosaccharyltransferase (OST), a multi-subunit membrane embedded enzyme catalyzes this glycosylation reaction in eukaryotes. In yeast, Ost4 is the smallest of nine subunits and bridges the interaction of the catalytic subunit, Stt3, with Ost3 (or its homolog, Ost6). Mutations of any C-terminal hydrophobic residues in Ost4 to a charged residue destabilizes the enzyme and negatively impacts its function. Specifically, the V23D mutation results in a temperature-sensitive phenotype in yeast. Here, we report the reconstitution of both purified recombinant Ost4 and Ost4V23D each in a POPC/POPE lipid bilayer and their resonance assignments using heteronuclear 2D and 3D solid-state NMR with magic-angle spinning. The chemical shifts of Ost4 changed significantly upon the V23D mutation, suggesting a dramatic change in its chemical environment.

Tropical decadal variability in nutrient supply and phytoplankton community in the Central Equatorial Pacific during the late Holocene

We have reconstructed baseline δ15N and δ13C of export production at Kingman Reef in the Central Equatorial Pacific (CEP) at sub-decadal resolution, nearly continuously over the last 2000 years. The changes in δ15N reflects the strength of the North Equatorial Counter Current (NECC) relative to the South Equatorial Current (SEC), and to a lesser extent, the North Equatorial Current (NEC). Seasonal to multi-decadal variation in the strength of these currents, through the redistribution of heat, have global climate impacts and influence marine and terrestrial ecosystems. We use modern El Niño-La Nina dynamics and the Tropical Pacific Decadal Variability (TPDV) pattern, which is defined in the CEP, as a framework for analyzing the isotopic data. The CEP δ15N and δ13C records exhibit multi-decadal (50-60 year) variability consistent with TPDV. A large multi-centennial feature in the CEP δ15N data, within age-model uncertainties, is consistent with one of the prolonged dry-pluvial sequences in the American west at the end of the Medieval Climate Anomaly, where low TPDV is correlated with drier conditions. This unique record shows that the strength of the NECC, as reflected in baseline δ15N and δ13C, has at quasi-predictable intervals throughout the late Holocene, toggled the phytoplankton community between prokaryotes and picoplankton versus eukaryotes.

A scalable platform for efficient CRISPR-Cas9 chemical-genetic screens of DNA damage-inducing compounds

Current approaches to define chemical-genetic interactions (CGIs) in human cell lines are resource-intensive. We designed a scalable chemical-genetic screening platform by generating a DNA damage response (DDR)-focused custom sgRNA library targeting 1011 genes with 3033 sgRNAs. We performed five proof-of-principle compound screens and found that the compounds' known modes-of-action (MoA) were enriched among the compounds' CGIs. These scalable screens recapitulated expected CGIs at a comparable signal-to-noise ratio (SNR) relative to genome-wide screens. Furthermore, time-resolved CGIs, captured by sequencing screens at various time points, suggested an unexpected, late interstrand-crosslinking (ICL) repair pathway response to camptothecin-induced DNA damage. Our approach can facilitate screening compounds at scale with 20-fold fewer resources than commonly used genome-wide libraries and produce biologically informative CGI profiles.

Avoidant/restrictive food intake disorder differs from anorexia nervosa in delay discounting

BACKGROUND

Avoidant/restrictive food intake disorder (ARFID) and anorexia nervosa (AN) are the two primary restrictive eating disorders; however, they are driven by differing motives for inadequate dietary intake. Despite overlap in restrictive eating behaviors and subsequent malnutrition, it remains unknown if ARFID and AN also share commonalities in their cognitive profiles, with cognitive alterations being a key identifier of AN. Discounting the present value of future outcomes with increasing delay to their expected receipt represents a core cognitive process guiding human decision-making. A hallmark cognitive characteristic of individuals with AN (vs. healthy controls [HC]) is reduced discounting of future outcomes, resulting in reduced impulsivity and higher likelihood of favoring delayed gratification. Whether individuals with ARFID display a similar reduction in delay discounting as those with AN (vs. an opposing bias towards increased delay discounting or no bias) is important in informing transdiagnostic versus disorder-specific cognitive characteristics and optimizing future intervention strategies.

METHOD

To address this research question, 104 participants (ARFID: n = 57, AN: n = 28, HC: n = 19) completed a computerized Delay Discounting Task. Groups were compared by their delay discounting parameter (ln)k.

RESULTS

Individuals with ARFID displayed a larger delay discounting parameter than those with AN, indicating steeper delay discounting (M ± SD = -6.10 ± 2.00 vs. -7.26 ± 1.73, p = 0.026 [age-adjusted], Hedges' g = 0.59), with no difference from HC (p = 0.514, Hedges' g = -0.35).

CONCLUSION

Our findings provide a first indication of distinct cognitive profiles among the two primary restrictive eating disorders. The present results, together with future research spanning additional cognitive domains and including larger and more diverse samples of individuals with ARFID (vs. AN), will contribute to identifying maintenance mechanisms that are unique to each disorder as well as contribute to the optimization and tailoring of treatment strategies across the spectrum of restrictive eating disorders.

Evaluating density-weighted connectivity of black bears (Ursus americanus) in Glacier National Park with spatial capture-recapture models

BACKGROUND

Improved understanding of wildlife population connectivity among protected area networks can support effective planning for the persistence of wildlife populations in the face of land use and climate change. Common approaches to estimating connectivity often rely on small samples of individuals without considering the spatial structure of populations, leading to limited understanding of how individual movement links to demography and population connectivity. Recently developed spatial capture-recapture (SCR) models provide a framework to formally connect inference about individual movement, connectivity, and population density, but few studies have applied this approach to empirical data to support connectivity planning.

METHODS

We used mark-recapture data collected from 924 genetic detections of 598 American black bears (Ursus americanus) in 2004 with SCR ecological distance models to simultaneously estimate density, landscape resistance to movement, and population connectivity in Glacier National Park northwest Montana, USA. We estimated density and movement parameters separately for males and females and used model estimates to calculate predicted density-weighted connectivity surfaces.

RESULTS

Model results indicated that landscape structure influences black bear density and space use in Glacier. The mean density estimate was 16.08 bears/100 km2 (95% CI 12.52-20.6) for females and 9.27 bears/100 km2 (95% CI 7.70-11.14) for males. Density increased with forest cover for both sexes. For male black bears, density decreased at higher grizzly bear (Ursus arctos) densities. Drainages, valley bottoms, and riparian vegetation decreased estimates of landscape resistance to movement for male and female bears. For males, forest cover also decreased estimated resistance to movement, but a transportation corridor bisecting the study area strongly increased resistance to movement presenting a barrier to connectivity.

CONCLUSIONS

Density-weighed connectivity surfaces highlighted areas important for population connectivity that were distinct from areas with high potential connectivity. For black bears in Glacier and surrounding landscapes, consideration of both vegetation and valley topography could inform the placement of underpasses along the transportation corridor in areas characterized by both high population density and potential connectivity. Our study demonstrates that the SCR ecological distance model can provide biologically realistic, spatially explicit predictions to support movement connectivity planning across large landscapes.

Prop3D: A flexible, Python-based platform for machine learning with protein structural properties and biophysical data

BACKGROUND

Machine learning (ML) has a rich history in structural bioinformatics, and modern approaches, such as deep learning, are revolutionizing our knowledge of the subtle relationships between biomolecular sequence, structure, function, dynamics and evolution. As with any advance that rests upon statistical learning approaches, the recent progress in biomolecular sciences is enabled by the availability of vast volumes of sufficiently-variable data. To be useful, such data must be well-structured, machine-readable, intelligible and manipulable. These and related requirements pose challenges that become especially acute at the computational scales typical in ML. Furthermore, in structural bioinformatics such data generally relate to protein three-dimensional (3D) structures, which are inherently more complex than sequence-based data. A significant and recurring challenge concerns the creation of large, high-quality, openly-accessible datasets that can be used for specific training and benchmarking tasks in ML pipelines for predictive modeling projects, along with reproducible splits for training and testing.

RESULTS

Here, we report 'Prop3D', a platform that allows for the creation, sharing and extensible reuse of libraries of protein domains, featurized with biophysical and evolutionary properties that can range from detailed, atomically-resolved physicochemical quantities (e.g., electrostatics) to coarser, residue-level features (e.g., phylogenetic conservation). As a community resource, we also supply a 'Prop3D-20sf' protein dataset, obtained by applying our approach to CATH . We have developed and deployed the Prop3D framework, both in the cloud and on local HPC resources, to systematically and reproducibly create comprehensive datasets via the Highly Scalable Data Service ( HSDS ). Our datasets are freely accessible via a public HSDS instance, or they can be used with accompanying Python wrappers for popular ML frameworks.

CONCLUSION

Prop3D and its associated Prop3D-20sf dataset can be of broad utility in at least three ways. Firstly, the Prop3D workflow code can be customized and deployed on various cloud-based compute platforms, with scalability achieved largely by saving the results to distributed HDF5 files via HSDS . Secondly, the linked Prop3D-20sf dataset provides a hand-crafted, already-featurized dataset of protein domains for 20 highly-populated CATH families; importantly, provision of this pre-computed resource can aid the more efficient development (and reproducible deployment) of ML pipelines. Thirdly, Prop3D-20sf's construction explicitly takes into account (in creating datasets and data-splits) the enigma of 'data leakage', stemming from the evolutionary relationships between proteins.

Preserving Derivative Information while Transforming Neuronal Curves

The international neuroscience community is building the first comprehensive atlases of brain cell types to understand how the brain functions from a higher resolution, and more integrated perspective than ever before. In order to build these atlases, subsets of neurons (e.g. serotonergic neurons, prefrontal cortical neurons etc.) are traced in individual brain samples by placing points along dendrites and axons. Then, the traces are mapped to common coordinate systems by transforming the positions of their points, which neglects how the transformation bends the line segments in between. In this work, we apply the theory of jets to describe how to preserve derivatives of neuron traces up to any order. We provide a framework to compute possible error introduced by standard mapping methods, which involves the Jacobian of the mapping transformation. We show how our first order method improves mapping accuracy in both simulated and real neuron traces under random diffeomorphisms. Our method is freely available in our open-source Python package brainlit.

Flagellar dynamics reveal fluctuations and kinetic limit in the Escherichia coli chemotaxis network

The Escherichia coli chemotaxis network, by which bacteria modulate their random run/tumble swimming pattern to navigate their environment, must cope with unavoidable number fluctuations ("noise") in its molecular constituents like other signaling networks. The probability of clockwise (CW) flagellar rotation, or CW bias, is a measure of the chemotaxis network's output, and its temporal fluctuations provide a proxy for network noise. Here we quantify fluctuations in the chemotaxis signaling network from the switching statistics of flagella, observed using time-resolved fluorescence microscopy of individual optically trapped E. coli cells. This approach allows noise to be quantified across the dynamic range of the network. Large CW bias fluctuations are revealed at steady state, which may play a critical role in driving flagellar switching and cell tumbling. When the network is stimulated chemically to higher activity, fluctuations dramatically decrease. A stochastic theoretical model, inspired by work on gene expression noise, points to CheY activation occurring in bursts, driving CW bias fluctuations. This model also shows that an intrinsic kinetic ceiling on network activity places an upper limit on activated CheY and CW bias, which when encountered suppresses network fluctuations. This limit may also prevent cells from tumbling unproductively in steep gradients.

Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package

Analyzing different omics data types independently is often too restrictive to allow for detection of subtle, but consistent, variations that are coherently supported based upon different assays. Integrating multi-omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package ( https://bioconductor.org/packages/iNETgrate/ ) that efficiently integrates transcriptome and DNA methylation data in a single gene network. Applying iNETgrate on five independent datasets improved prognostication compared to common clinical gold standards and a patient similarity network approach.

Behavioural relevance of redundant and synergistic stimulus information between functionally connected neurons in mouse auditory cortex

Measures of functional connectivity have played a central role in advancing our understanding of how information is transmitted and processed within the brain. Traditionally, these studies have focused on identifying redundant functional connectivity, which involves determining when activity is similar across different sites or neurons. However, recent research has highlighted the importance of also identifying synergistic connectivity-that is, connectivity that gives rise to information not contained in either site or neuron alone. Here, we measured redundant and synergistic functional connectivity between neurons in the mouse primary auditory cortex during a sound discrimination task. Specifically, we measured directed functional connectivity between neurons simultaneously recorded with calcium imaging. We used Granger Causality as a functional connectivity measure. We then used Partial Information Decomposition to quantify the amount of redundant and synergistic information about the presented sound that is carried by functionally connected or functionally unconnected pairs of neurons. We found that functionally connected pairs present proportionally more redundant information and proportionally less synergistic information about sound than unconnected pairs, suggesting that their functional connectivity is primarily redundant. Further, synergy and redundancy coexisted both when mice made correct or incorrect perceptual discriminations. However, redundancy was much higher (both in absolute terms and in proportion to the total information available in neuron pairs) in correct behavioural choices compared to incorrect ones, whereas synergy was higher in absolute terms but lower in relative terms in correct than in incorrect behavioural choices. Moreover, the proportion of redundancy reliably predicted perceptual discriminations, with the proportion of synergy adding no extra predictive power. These results suggest a crucial contribution of redundancy to correct perceptual discriminations, possibly due to the advantage it offers for information propagation, and also suggest a role of synergy in enhancing information level during correct discriminations.

A computational spatial whole-Cell model for hepatitis B viral infection and drug interactions

Despite a vaccine, hepatitis B virus (HBV) remains a world-wide source of infections and deaths. We develop a whole-cell computational platform combining spatial and kinetic models describing the infection cycle of HBV in a hepatocyte host. We simulate key parts of the infection cycle with this whole-cell platform for 10 min of biological time, to predict infection progression, map out virus-host and virus-drug interactions. We find that starting from an established infection, decreasing the copy number of the viral envelope proteins shifts the dominant infection pathway from capsid secretion to re-importing the capsids into the nucleus, resulting in more nuclear-localized viral covalently closed circular DNA (cccDNA) and boosting transcription. This scenario can mimic the consequence of drugs designed to manipulate viral gene expression. Mutating capsid proteins facilitates capsid destabilization and disassembly at nuclear pore complexes, resulting in an increase in cccDNA copy number. However, excessive destabilization leads to premature cytoplasmic disassembly and does not increase the cccDNA counts. Finally, our simulations can predict the best drug dosage and its administration timing to reduce the cccDNA counts. Our adaptable computational platform can be parameterized to study other viruses and identify the most central viral pathways that can be targeted by drugs.

Backbone and side chain NMR assignments and secondary structure calculation of the pheromone binding protein3 of Ostrinia nubilalis, an agricultural pest

Ostrinia nubilalis, also known as European Corn Borer (ECB), is a serious pest in Europe and North America, as well as in Central Asia and Northern Africa. It damages a variety of agricultural crops such as corn, oats, buckwheat, millet, and soybeans. causing annually at least one billion dollars in loss. The Ostrinia nubilalis pheromone-binding protein3 (OnubPBP3), preferentially expressed in the male moth antenna, has been implicated in the detection of the female-secreted pheromone blend during the mating process. Understanding the structure of and function of OnubPBP3, including the mechanism of pheromone binding and its release at the dendritic olfactory neuron (ORN), is essential if integrated pest management through sensory inhibition is to be achieved. We report here the backbone and side-chain resonance assignments of OnubPBP3 at pH 6.5 using various triple resonance NMR experiments on a 13C, 15N-labeled protein sample. The secondary structure of OnubPBP3 consists of six α-helices and an unstructured C-terminus based on backbone chemical shifts.

The role of Culex territans mosquitoes in the transmission of Batrachochytrium dendrobatidis to amphibian hosts

BACKGROUND

Mosquitoes are the deadliest organisms in the world, killing an estimated 750,000 people per year due to the pathogens they can transmit. Mosquitoes also pose a major threat to other vertebrate animals. Culex territans is a mosquito species found in temperate zones worldwide that feeds almost exclusively on amphibians and can transmit parasites; however, little is known about its ability to transmit other pathogens, including fungi. Batrachochytrium dendrobatidis (Bd) is a topical pathogenic fungus that spreads through contact. With amphibian populations around the world experiencing mass die-offs and extinctions due to this pathogen, it is critical to study all potential modes of transmission. Because Cx. territans mosquitoes are in contact with their hosts for long periods of time while blood-feeding, we hypothesize that they can transmit and pick up Bd.

METHODS

In this study, we first assessed Cx. territans ability to transfer the fungus from an infected surface to a clean one under laboratory conditions. We also conducted a surveillance study of Bd infections in frogs and mosquitoes in the field (Mountain Lake Biological station, VA, USA). In parallel, we determined Cx. territans host preference via blood meal analysis of field caught mosquitoes.

RESULTS

We found that this mosquito species can carry the fungus to an uninfected surface, implying that they may have the ability to transmit Bd to their amphibian hosts. We also found that Cx. territans feed primarily on green frogs (Rana clamitans) and bullfrogs (Rana catesbeiana) and that the prevalence of Bd within the frog population at our field site varied between years.

CONCLUSIONS

This study provides critical insights into understanding the role of amphibian-biting mosquitoes in transmitting pathogens, which can be applied to disease ecology of susceptible amphibian populations worldwide.

Neuroanatomical asymmetry in the canine brain

The brains of humans and non-human primates exhibit left/right asymmetries in grey matter morphology, white matter connections, and functional responses. These asymmetries have been implicated in specialized behavioral adaptations such as language, tool use, and handedness. Left/right asymmetries are also observed in behavioral tendencies across the animal kingdom, suggesting a deep evolutionary origin for the neural mechanisms underlying lateralized behavior. However, it is still unclear to what extent brain asymmetries supporting lateralized behaviors are present in other large-brained animals outside the primate order. Canids and other carnivorans evolved large, complex brains independently and convergently with primates, and exhibit lateralized behaviors. Therefore, domestic dogs offer an opportunity to address this question. We examined T2-weighted MRI images of 62 dogs from 33 breeds, opportunistically collected from a veterinary MRI scanner from dogs who were referred for neurological examination but were not found to show any neuropathology. Volumetrically asymmetric regions of gray matter included portions of the temporal and frontal cortex, in addition to portions of the cerebellum, brainstem, and other subcortical regions. These results are consistent with the perspective that asymmetry may be a common feature underlying the evolution of complex brains and behavior across clades, and provide neuro-organizational information that is likely relevant to the growing field of canine behavioral neuroscience.

Circadian, Reward, and Emotion Systems in Teens prospective longitudinal study: protocol overview of an integrative reward-circadian rhythm model of first onset of bipolar spectrum disorder in adolescence

BACKGROUND

Bipolar spectrum disorders (BSDs) are associated with a heightened sensitivity to rewards and elevated reward-related brain function in cortico-striatal circuitry. A separate literature documents social and circadian rhythm disruption in BSDs. Recently, integrated reward-circadian models of BSDs have been proposed. These models draw on work indicating that the two systems influence each other and interact to affect mood functioning. When dysregulated, reward and circadian system signaling may combine to form a positive feedback loop, whereby dysregulation in one system exacerbates dysregulation in the other. Project CREST (Circadian, Reward, and Emotion Systems in Teens) provides a first systematic test of reward-circadian dysregulation as a synergistic and dynamic vulnerability for first onset of BSD and increases in bipolar symptoms during adolescence.

METHODS

This NIMH-funded R01 study is a 3-year prospective, longitudinal investigation of approximately 320 community adolescents from the broader Philadelphia area, United States of America. Eligible participants must be 13-16 years old, fluent in English, and without a prior BSD or hypomanic episode. They are being selected along the entire dimension of self-reported reward responsiveness, with oversampling at the high tail of the dimension in order to increase the likelihood of BSD onsets. At Times 1-6, every 6 months, participants will complete assessments of reward-relevant and social rhythm disruption life events and self-report and diagnostic assessments of bipolar symptoms and episodes. Yearly, at Times 1, 3, and 5, participants also will complete self-report measures of circadian chronotype (morningness-eveningness) and social rhythm regularity, a salivary dim light melatonin onset (DLMO) procedure to assess circadian phase, self-report, behavioral, and neural (fMRI) assessments of monetary and social reward responsiveness, and a 7-day ecological momentary assessment (EMA) period. During each EMA period, participants will complete continuous measures of sleep/wake and activity (actigraphy), a daily sleep diary, and three within-day (morning, afternoon, evening) measures of life events coded for reward-relevance and social rhythm disruption, monetary and social reward responsiveness, positive and negative affect, and hypo/manic and depressive symptoms. The fMRI scan will occur on the day before and the DLMO procedure will occur on the first evening of the 7-day EMA period.

DISCUSSION

This study is an innovative integration of research on multi-organ systems involved in reward and circadian signaling in understanding first onset of BSD in adolescence. It has the potential to facilitate novel pharmacological, neural, and behavioral interventions to treat, and ideally prevent, bipolar conditions.

Latent sub-structural resilience mechanisms in temporal human mobility networks during urban flooding

In studying resilience in temporal human networks, relying solely on global network measures would be inadequate; latent sub-structural network mechanisms need to be examined to determine the extent of impact and recovery of these networks during perturbations, such as urban flooding. In this study, we utilize high-resolution aggregated location-based data to construct temporal human mobility networks in Houston in the context of the 2017 Hurricane Harvey. We examine motif distribution, motif persistence, temporal stability, and motif attributes to reveal latent sub-structural mechanisms related to the resilience of human mobility networks during disaster-induced perturbations. The results show that urban flood impacts persist in human mobility networks at the sub-structure level for several weeks. The impact extent and recovery duration are heterogeneous across different network types. Also, while perturbation impacts persist at the sub-structure level, global topological network properties indicate that the network has recovered. The findings highlight the importance of examining the microstructures and their dynamic processes and attributes in understanding the resilience of temporal human mobility networks (and other temporal networks). The findings can also provide disaster managers, public officials, and transportation planners with insights to better evaluate impacts and monitor recovery in affected communities.

Matching medical staff to long term care facilities to respond to COVID-19 outbreak

BACKGROUND

Staff shortage is a long-standing issue in long term care facilities (LTCFs) that worsened with the COVID-19 outbreak. Different states in the US have employed various tools to alleviate this issue in LTCFs. We describe the actions taken by the Commonwealth of Massachusetts to assist LTCFs in addressing the staff shortage issue and their outcomes. Therefore, the main question of this study is how to create a central mechanism to allocate severely limited medical staff to healthcare centers during emergencies.

METHODS

For the Commonwealth of Massachusetts, we developed a mathematical programming model to match severely limited available staff with LTCF demand requests submitted through a designed portal. To find feasible matches and prioritize facility needs, we incorporated restrictions and preferences for both sides. For staff, we considered maximum mileage they are willing to travel, available by date, and short- or long-term work preferences. For LTCFs, we considered their demand quantities for different positions and the level of urgency for their demand. As a secondary goal of this study, by using the feedback entries data received from the LTCFs on their matches, we developed statistical models to determine the most salient features that induced the LTCFs to submit feedback.

RESULTS

We used the developed portal to complete about 150 matching sessions in 14 months to match staff to LTCFs in Massachusetts. LTCFs provided feedback for 2,542 matches including 2,064 intentions to hire the matched staff during this time. Further analysis indicated that nursing homes and facilities that entered higher levels of demand to the portal were more likely to provide feedback on the matches and facilities that were prioritized in the matching process due to whole facility testing or low staffing levels were less likely to do so. On the staffing side, matches that involved more experienced staff and staff who can work afternoons, evenings, and overnight were more likely to generate feedback from the facility that they were matched to.

CONCLUSION

Developing a central matching framework to match medical staff to LTCFs at the time of a public health emergency could be an efficient tool for responding to staffing shortages. Such central approaches that help allocate a severely limited resource efficiently during a public emergency can be developed and used for different resource types, as well as provide crucial demand and supply information in different regions and/or demographics.

1D Electronic Flat Bands in Untwisted Moiré Superlattices

After the preparation of 2D electronic flat band (EFB) in van der Waals (vdW) superlattices, recent measurements suggest the existence of 1D electronic flat bands (1D-EFBs) in twisted vdW bilayers. However, the realization of 1D-EFBs is experimentally elusive in untwisted 2D layers, which is desired considering their fabrication and scalability. Herein, the discovery of 1D-EFBs is reported in an untwisted in situ-grown two atomic-layer Bi(110) superlattice self-aligned on an SnSe(001) substrate using scanning probe microscopy measurements and density functional theory calculations. While the Bi-Bi dimers of Bi zigzag (ZZ) chains are buckled, the epitaxial lattice mismatch between the Bi and SnSe layers induces two 1D buckling reversal regions (BRRs) extending along the ZZ direction in each Bi(110)-11 × 11 supercell. A series of 1D-EFBs arises spatially following BRRs that isolate electronic states along the armchair (AC) direction and localize electrons in 1D extended states along ZZ due to quantum interference at a topological node. This work provides a generalized strategy for engineering 1D-EFBs in utilizing lattice mismatch between untwisted rectangular vdW layers.

Simultaneous selection of multiple important single nucleotide polymorphisms in familial genome wide association studies data

We propose a resampling-based fast variable selection technique for detecting relevant single nucleotide polymorphisms (SNP) in a multi-marker mixed effect model. Due to computational complexity, current practice primarily involves testing the effect of one SNP at a time, commonly termed as 'single SNP association analysis'. Joint modeling of genetic variants within a gene or pathway may have better power to detect associated genetic variants, especially the ones with weak effects. In this paper, we propose a computationally efficient model selection approach-based on the e-values framework-for single SNP detection in families while utilizing information on multiple SNPs simultaneously. To overcome computational bottleneck of traditional model selection methods, our method trains one single model, and utilizes a fast and scalable bootstrap procedure. We illustrate through numerical studies that our proposed method is more effective in detecting SNPs associated with a trait than either single-marker analysis using family data or model selection methods that ignore the familial dependency structure. Further, we perform gene-level analysis in Minnesota Center for Twin and Family Research (MCTFR) dataset using our method to detect several SNPs using this that have been implicated to be associated with alcohol consumption.

Can a powered knee-ankle prosthesis improve weight-bearing symmetry during stand-to-sit transitions in individuals with above-knee amputations?

BACKGROUND

After above-knee amputation, the missing biological knee and ankle are replaced with passive prosthetic devices. Passive prostheses are able to dissipate limited amounts of energy using resistive damper systems during "negative energy" tasks like sit-down. However, passive prosthetic knees are not able to provide high levels of resistance at the end of the sit-down movement when the knee is flexed, and users need the most support. Consequently, users are forced to over-compensate with their upper body, residual hip, and intact leg, and/or sit down with a ballistic and uncontrolled movement. Powered prostheses have the potential to solve this problem. Powered prosthetic joints are controlled by motors, which can produce higher levels of resistance at a larger range of joint positions than passive damper systems. Therefore, powered prostheses have the potential to make sitting down more controlled and less difficult for above-knee amputees, improving their functional mobility.

METHODS

Ten individuals with above-knee amputations sat down using their prescribed passive prosthesis and a research powered knee-ankle prosthesis. Subjects performed three sit-downs with each prosthesis while we recorded joint angles, forces, and muscle activity from the intact quadricep muscle. Our main outcome measures were weight-bearing symmetry and muscle effort of the intact quadricep muscle. We performed paired t-tests on these outcome measures to test for significant differences between passive and powered prostheses.

RESULTS

We found that the average weight-bearing symmetry improved by 42.1% when subjects sat down with the powered prosthesis compared to their passive prostheses. This difference was significant (p = 0.0012), and every subject's weight-bearing symmetry improved when using the powered prosthesis. Although the intact quadricep muscle contraction differed in shape, neither the integral nor the peak of the signal was significantly different between conditions (integral p > 0.01, peak p > 0.01).

CONCLUSIONS

In this study, we found that a powered knee-ankle prosthesis significantly improved weight-bearing symmetry during sit-down compared to passive prostheses. However, we did not observe a corresponding decrease in intact-limb muscle effort. These results indicate that powered prosthetic devices have the potential to improve balance during sit-down for individuals with above-knee amputation and provide insight for future development of powered prosthetics.

Candidate variants in DNA replication and repair genes in early-onset renal cell carcinoma patients referred for germline testing

BACKGROUND

Early-onset renal cell carcinoma (eoRCC) is typically associated with pathogenic germline variants (PGVs) in RCC familial syndrome genes. However, most eoRCC patients lack PGVs in familial RCC genes and their genetic risk remains undefined.

METHODS

Here, we analyzed biospecimens from 22 eoRCC patients that were seen at our institution for genetic counseling and tested negative for PGVs in RCC familial syndrome genes.

RESULTS

Analysis of whole-exome sequencing (WES) data found enrichment of candidate pathogenic germline variants in DNA repair and replication genes, including multiple DNA polymerases. Induction of DNA damage in peripheral blood monocytes (PBMCs) significantly elevated numbers of [Formula: see text]H2AX foci, a marker of double-stranded breaks, in PBMCs from eoRCC patients versus PBMCs from matched cancer-free controls. Knockdown of candidate variant genes in Caki RCC cells increased [Formula: see text]H2AX foci. Immortalized patient-derived B cell lines bearing the candidate variants in DNA polymerase genes (POLD1, POLH, POLE, POLK) had DNA replication defects compared to control cells. Renal tumors carrying these DNA polymerase variants were microsatellite stable but had a high mutational burden. Direct biochemical analysis of the variant Pol δ and Pol η polymerases revealed defective enzymatic activities.

CONCLUSIONS

Together, these results suggest that constitutional defects in DNA repair underlie a subset of eoRCC cases. Screening patient lymphocytes to identify these defects may provide insight into mechanisms of carcinogenesis in a subset of genetically undefined eoRCCs. Evaluation of DNA repair defects may also provide insight into the cancer initiation mechanisms for subsets of eoRCCs and lay the foundation for targeting DNA repair vulnerabilities in eoRCC.

Gene loss during a transition to multicellularity

Multicellular evolution is a major transition associated with momentous diversification of multiple lineages and increased developmental complexity. The volvocine algae comprise a valuable system for the study of this transition, as they span from unicellular to undifferentiated and differentiated multicellular morphologies despite their genomes being similar, suggesting multicellular evolution requires few genetic changes to undergo dramatic shifts in developmental complexity. Here, the evolutionary dynamics of six volvocine genomes were examined, where a gradual loss of genes was observed in parallel to the co-option of a few key genes. Protein complexes in the six species exhibited novel interactions, suggesting that gene loss could play a role in evolutionary novelty. This finding was supported by gene network modeling, where gene loss outpaces gene gain in generating novel stable network states. These results suggest gene loss, in addition to gene gain and co-option, may be important for the evolution developmental complexity.

Corn360: a method for quantification of corn kernels

BACKGROUND

The rapidly advancing corn breeding field calls for high-throughput methods to phenotype corn kernel traits to estimate yield and to study their genetic inheritance. Most of the existing methods are reliant on sophisticated setup, expertise in statistical models and programming skills for image capturing and analysis.

RESULTS

We demonstrated a portable, easily accessible, affordable, panoramic imaging capturing system called Corn360, followed by image analysis using freely available software, to characterize total kernel count and different patterned kernel counts of a corn ear. The software we used did not require programming skills and utilized Artificial Intelligence to train a model and to segment the images of mixed patterned corn ears. For homogeneously patterned corn ears, our results showed accuracies of 93.7% of total kernel count compared to manual counting. Our method allowed to save an average of 3 min 40 s per image. For mixed patterned corn ears, our results showed accuracies of 84.8% or 61.8% of segmented kernel counts. Our method has the potential to greatly decrease counting time per image as the number of images increases. We also demonstrated a case of using Corn360 to count different categories of kernels on a mixed patterned corn ear resulting from a cross of sweet corn and sticky corn and showed that starch:sweet:sticky segregated in a 9:4:3 ratio in its F2 population.

CONCLUSIONS

The panoramic Corn360 approach enables for a portable low-cost high-throughput kernel quantification. This includes total kernel quantification and quantification of different patterned kernels. This can allow for quick estimate of yield component and for categorization of different patterned kernels to study the inheritance of genes controlling color and texture. We demonstrated that using the samples resulting from a sweet × sticky cross, the starchiness, sweetness and stickiness in this case were controlled by two genes with epistatic effects. Our achieved results indicate Corn360 can be used to effectively quantify corn kernels in a portable and cost-efficient way that is easily accessible with or without programming skills.

Uracil-DNA glycosylase efficiency is modulated by substrate rigidity

Uracil DNA-glycosylase (UNG) is a DNA repair enzyme that removes the highly mutagenic uracil lesion from DNA using a base flipping mechanism. Although this enzyme has evolved to remove uracil from diverse sequence contexts, UNG excision efficiency depends on DNA sequence. To provide the molecular basis for rationalizing UNG substrate preferences, we used time-resolved fluorescence spectroscopy, NMR imino proton exchange measurements, and molecular dynamics simulations to measure UNG specificity constants (kcat/KM) and DNA flexibilities for DNA substrates containing central AUT, TUA, AUA, and TUT motifs. Our study shows that UNG efficiency is dictated by the intrinsic deformability around the lesion, establishes a direct relationship between substrate flexibility modes and UNG efficiency, and shows that bases immediately adjacent to the uracil are allosterically coupled and have the greatest impact on substrate flexibility and UNG activity. The finding that substrate flexibility controls UNG efficiency is likely significant for other repair enzymes and has major implications for the understanding of mutation hotspot genesis, molecular evolution, and base editing.

The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Background

The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide.

Results

The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https://zenodo.org/communities/norman-sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA's CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101).

Conclusions

The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the "one substance, one assessment" approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12302-022-00680-6.

Activity-driven network modeling and control of the spread of two concurrent epidemic strains

The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be readily adapted to the study of multiple, coexisting strains. In this context, particularly lacking are models that could elucidate re-infection with the same strain or a different strain-phenomena that we are seeing experiencing more and more with COVID-19. Here, we establish a novel mathematical model to study the simultaneous spreading of two strains over a class of temporal networks. We build on the classical susceptible-exposed-infectious-removed model, by incorporating additional states that account for infections and re-infections with multiple strains. The temporal network is based on the activity-driven network paradigm, which has emerged as a model of choice to study dynamic processes that unfold at a time scale comparable to the network evolution. We draw analytical insight from the dynamics of the stochastic network systems through a mean-field approach, which allows for characterizing the onset of different behavioral phenotypes (non-epidemic, epidemic, and endemic). To demonstrate the practical use of the model, we examine an intermittent stay-at-home containment strategy, in which a fraction of the population is randomly required to isolate for a fixed period of time.

Deep learning data augmentation for Raman spectroscopy cancer tissue classification

Recently, Raman Spectroscopy (RS) was demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS measurements in revealing molecular biochemical changes between cancerous vs. normal tissues and cells. In order to design computational approaches for cancer detection, the quality and quantity of tissue samples for RS are important for accurate prediction. In reality, however, obtaining skin cancer samples is difficult and expensive due to privacy and other constraints. With a small number of samples, the training of the classifier is difficult, and often results in overfitting. Therefore, it is important to have more samples to better train classifiers for accurate cancer tissue classification. To overcome these limitations, this paper presents a novel generative adversarial network based skin cancer tissue classification framework. Specifically, we design a data augmentation module that employs a Generative Adversarial Network (GAN) to generate synthetic RS data resembling the training data classes. The original tissue samples and the generated data are concatenated to train classification modules. Experiments on real-world RS data demonstrate that (1) data augmentation can help improve skin cancer tissue classification accuracy, and (2) generative adversarial network can be used to generate reliable synthetic Raman spectroscopic data.

Education shapes the structure of semantic memory and impacts creative thinking

Education is central to the acquisition of knowledge, such as when children learn new concepts. It is unknown, however, whether educational differences impact not only what concepts children learn, but how those concepts come to be represented in semantic memory-a system that supports higher cognitive functions, such as creative thinking. Here we leverage computational network science tools to study hidden knowledge structures of 67 Swiss schoolchildren from two distinct educational backgrounds-Montessori and traditional, matched on socioeconomic factors and nonverbal intelligence-to examine how educational experience shape semantic memory and creative thinking. We find that children experiencing Montessori education show a more flexible semantic network structure (high connectivity/short paths between concepts, less modularity) alongside higher scores on creative thinking tests. The findings indicate that education impacts how children represent concepts in semantic memory and suggest that different educational experiences can affect higher cognitive functions, including creative thinking.

Children do not distinguish efficient from inefficient actions during observation

Observation is a powerful way to learn efficient actions from others. However, the role of observers' motor skill in assessing efficiency of others is unknown. Preschoolers are notoriously poor at performing multi-step actions like grasping the handle of a tool. Preschoolers (N = 22) and adults (N = 22) watched video-recorded actors perform efficient and inefficient tool use. Eye tracking showed that preschoolers and adults looked equally long at the videos, but adults looked longer than children at how actors grasped the tool. Deep learning analyses of participants' eye gaze distinguished efficient from inefficient grasps for adults, but not for children. Moreover, only adults showed differential action-related pupil dilation and neural activity (suppressed oscillation power in the mu frequency) while observing efficient vs. inefficient grasps. Thus, children observe multi-step actions without "seeing" whether the initial step is efficient. Findings suggest that observer's own motor efficiency determines whether they can perceive action efficiency in others.

Predicting species distributions and community composition using satellite remote sensing predictors

Biodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using satellite remote sensing products as covariates, we constructed stacked species distribution models (S-SDMs) under a Bayesian framework to build next-generation biodiversity models. Model performance of these models was assessed using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON). This study represents an attempt to evaluate the integrated predictions of biodiversity models-including assemblage diversity and composition-obtained by stacking next-generation SDMs. We found that applying constraints to assemblage predictions, such as using the probability ranking rule, does not improve biodiversity prediction models. Furthermore, we found that independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), these kinds of next-generation biodiversity models do not accurately recover the observed species composition at the plot level or ecological-community scales (NEON plots are 400 m2). However, these models do return reasonable predictions at macroecological scales, i.e., moderately to highly correct assignments of species identities at the scale of NEON sites (mean area ~ 27 km2). Our results provide insights for advancing the accuracy of prediction of assemblage diversity and composition at different spatial scales globally. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models that accurately predict and monitor ecological assemblages through time and space.

Improved methods for RNAseq-based alternative splicing analysis

The robust detection of disease-associated splice events from RNAseq data is challenging due to the potential confounding effect of gene expression levels and the often limited number of patients with relevant RNAseq data. Here we present a novel statistical approach to splicing outlier detection and differential splicing analysis. Our approach tests for differences in the percentages of sequence reads representing local splice events. We describe a software package called Bisbee which can predict the protein-level effect of splice alterations, a key feature lacking in many other splicing analysis resources. We leverage Bisbee's prediction of protein level effects as a benchmark of its capabilities using matched sets of RNAseq and mass spectrometry data from normal tissues. Bisbee exhibits improved sensitivity and specificity over existing approaches and can be used to identify tissue-specific splice variants whose protein-level expression can be confirmed by mass spectrometry. We also applied Bisbee to assess evidence for a pathogenic splicing variant contributing to a rare disease and to identify tumor-specific splice isoforms associated with an oncogenic mutation. Bisbee was able to rediscover previously validated results in both of these cases and also identify common tumor-associated splice isoforms replicated in two independent melanoma datasets.

OptoGap is an optogenetics-enabled assay for quantification of cell-cell coupling in multicellular cardiac tissue

Intercellular electrical coupling is an essential means of communication between cells. It is important to obtain quantitative knowledge of such coupling between cardiomyocytes and non-excitable cells when, for example, pathological electrical coupling between myofibroblasts and cardiomyocytes yields increased arrhythmia risk or during the integration of donor (e.g., cardiac progenitor) cells with native cardiomyocytes in cell-therapy approaches. Currently, there is no direct method for assessing heterocellular coupling within multicellular tissue. Here we demonstrate experimentally and computationally a new contactless assay for electrical coupling, OptoGap, based on selective illumination of inexcitable cells that express optogenetic actuators and optical sensing of the response of coupled excitable cells (e.g., cardiomyocytes) that are light-insensitive. Cell-cell coupling is quantified by the energy required to elicit an action potential via junctional current from the light-stimulated cell(s). The proposed technique is experimentally validated against the standard indirect approach, GapFRAP, using light-sensitive cardiac fibroblasts and non-transformed cardiomyocytes in a two-dimensional setting. Its potential applicability to the complex three-dimensional setting of the native heart is corroborated by computational modelling and proper calibration. Lastly, the sensitivity of OptoGap to intrinsic cell-scale excitability is robustly characterized via computational analysis.

Joint speed feedback improves myoelectric prosthesis adaptation after perturbed reaches in non amputees

Accurate control of human limbs involves both feedforward and feedback signals. For prosthetic arms, feedforward control is commonly accomplished by recording myoelectric signals from the residual limb to predict the user's intent, but augmented feedback signals are not explicitly provided in commercial devices. Previous studies have demonstrated inconsistent results when artificial feedback was provided in the presence of vision; some studies showed benefits, while others did not. We hypothesized that negligible benefits in past studies may have been due to artificial feedback with low precision compared to vision, which results in heavy reliance on vision during reaching tasks. Furthermore, we anticipated more reliable benefits from artificial feedback when providing information that vision estimates with high uncertainty (e.g. joint speed). In this study, we test an artificial sensory feedback system providing joint speed information and how it impacts performance and adaptation during a hybrid positional-and-myoelectric ballistic reaching task. We found that overall reaching errors were reduced after perturbed control, but did not significantly improve steady-state reaches. Furthermore, we found that feedback about the joint speed of the myoelectric prosthesis control improved the adaptation rate of biological limb movements, which may have resulted from high prosthesis control noise and strategic overreaching with the positional control and underreaching with the myoelectric control. These results provide insights into the relevant factors influencing the improvements conferred by artificial sensory feedback.

Reconstructing phase-resolved hysteresis loops from first-order reversal curves

The first order reversal curve (FORC) method is a magnetometry based technique used to capture nanoscale magnetic phase separation and interactions with macroscopic measurements using minor hysteresis loop analysis. This makes the FORC technique a powerful tool in the analysis of complex systems which cannot be effectively probed using localized techniques. However, recovering quantitative details about the identified phases which can be compared to traditionally measured metrics remains an enigmatic challenge. We demonstrate a technique to reconstruct phase-resolved magnetic hysteresis loops by selectively integrating the measured FORC distribution. From these minor loops, the traditional metrics-including the coercivity and saturation field, and the remanent and saturation magnetization-can be determined. In order to perform this analysis, special consideration must be paid to the accurate quantitative management of the so-called reversible features. This technique is demonstrated on three representative materials systems, high anisotropy FeCuPt thin-films, Fe nanodots, and SmCo/Fe exchange spring magnet films, and shows excellent agreement with the direct measured major loop, as well as the phase separated loops.

Molar occlusion and jaw roll in early crown mammals

Triconodontidae are considered the first carnivorous crown mammals. A virtual reconstruction of the masticatory cycle in the Late Jurassic Priacodon showed that triconodontid dental function is characterized by precise cutting on elongated crests. The combination of traits linked to both carnivorous diets (e.g. fore-aft cutting edges) and insectivorous diets (transverse crests and lobes) suggests a varied faunivorous diet appropriate to the small body size of most triconodontids. Total length of molar shear decreased with wear, suggesting a dietary shift during ontogeny. Embrasure occlusion is confirmed for P. fruitaensis as indicated by premolar positioning, facet orientation, and collision areas. Embrasure occlusion is considered a general feature of all Eutriconodonta, whereas the previously assumed Morganucodon-like pattern is limited to few early mammaliaforms. Unlike modern carnivores, significant roll of around 10° of the active hemimandible occurred during the power stroke. Roll was likely passive in Triconodontidae in contrast to active roll described for extant therians. The triconodontid molar series was highly uniform and adapted to a precise fit, with self-sharpening lower molar cusps. Whereas the uniformity ensured good cutting capabilities, it likely put the dentition under greater constraints, conserving the highly stereotyped nature of triconodontid molars for 60-85 Ma.

Annotation-free learning of plankton for classification and anomaly detection

The acquisition of increasingly large plankton digital image datasets requires automatic methods of recognition and classification. As data size and collection speed increases, manual annotation and database representation are often bottlenecks for utilization of machine learning algorithms for taxonomic classification of plankton species in field studies. In this paper we present a novel set of algorithms to perform accurate detection and classification of plankton species with minimal supervision. Our algorithms approach the performance of existing supervised machine learning algorithms when tested on a plankton dataset generated from a custom-built lensless digital device. Similar results are obtained on a larger image dataset obtained from the Woods Hole Oceanographic Institution. Additionally, we introduce a new algorithm to perform anomaly detection on unclassified samples. Here an anomaly is defined as a significant deviation from the established classification. Our algorithms are designed to provide a new way to monitor the environment with a class of rapid online intelligent detectors.

Taxonomic Revision of Neotropical Downeshelea Wirth and Grogan Predaceous Midges (Diptera: Ceratopogonidae)

The genus Downeshelea was described by Wirth and Grogan based on the diagnostic characters of the Monohelea multilineata species group. The first descriptions of species were based on body coloration, which resulted in confusion and misunderstanding of their identification. The aim of this study was to provide an updated diagnosis and description of Downeshelea, describe 18 new species, and redescribe 10 previously poorly described species. New records of species, a key for identification of all New World species, and a table with important morphometric data to distinguish both males and females of the various species are provided along with distribution maps of the 46 known New World species.

Epidermal biopolysaccharides from plant seeds enable biodegradable turbulent drag reduction

The high cost of synthetic polymers has been a key impediment limiting the widespread adoption of polymer drag reduction techniques in large-scale engineering applications, such as marine drag reduction. To address consumable cost constraints, we investigate the use of high molar mass biopolysaccharides, present in the mucilaginous epidermis of plant seeds, as inexpensive drag reducers in large Reynolds number turbulent flows. Specifically, we study the aqueous mucilage extracted from flax seeds (Linum usitatissimum) and compare its drag reduction efficacy to that of poly(ethylene oxide) or PEO, a common synthetic polymer widely used as a drag reducing agent in aqueous flows. Macromolecular and rheological characterisation confirm the presence of high molar mass (≥2 MDa) polysaccharides in the extracted mucilage, with an acidic fraction comprising negatively charged chains. Frictional drag measurements, performed inside a bespoke Taylor-Couette apparatus, show that the as-extracted mucilage has comparable drag reduction performance under turbulent flow conditions as aqueous PEO solutions, while concurrently offering advantages in terms of raw material cost, availability, and bio-compatibility. Our results indicate that plant-sourced mucilage can potentially serve as a cost-effective and eco-friendly substitute for synthetic drag reducing polymers in large scale turbulent flow applications.