Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of age on genetic risk for common diseases

Inherited genetic variation contributes to individual risk for many complex diseases and is increasingly being used for predictive patient stratification. Previous work has shown that genetic factors are not equally relevant to human traits across age and other contexts, though the reasons for such variation are not clear. Here, we introduce methods to infer the form of the longitudinal relationship between genetic relative risk for disease and age and to test whether all genetic risk factors behave similarly. We use a proportional hazards model within an interval-based censoring methodology to estimate age-varying individual variant contributions to genetic relative risk for 24 common diseases within the British ancestry subset of UK Biobank, applying a Bayesian clustering approach to group variants by their relative risk profile over age and permutation tests for age dependency and multiplicity of profiles. We find evidence for age-varying relative risk profiles in nine diseases, including hypertension, skin cancer, atherosclerotic heart disease, hypothyroidism and calculus of gallbladder, several of which show evidence, albeit weak, for multiple distinct profiles of genetic relative risk. The predominant pattern shows genetic risk factors having the greatest relative impact on risk of early disease, with a monotonic decrease over time, at least for the majority of variants, although the magnitude and form of the decrease varies among diseases. As a consequence, for diseases where genetic relative risk decreases over age, genetic risk factors have stronger explanatory power among younger populations, compared to older ones. We show that these patterns cannot be explained by a simple model involving the presence of unobserved covariates such as environmental factors. We discuss possible models that can explain our observations and the implications for genetic risk prediction.
Visual Recognition Is Heralded by Shifts in Local Field Potential Oscillations and Inhibitory Networks in Primary Visual Cortex

Learning to recognize and filter familiar, irrelevant sensory stimuli eases the computational burden on the cerebral cortex. Inhibition is a candidate mechanism in this filtration process, and oscillations in the cortical local field potential (LFP) serve as markers of the engagement of different inhibitory neurons. We show here that LFP oscillatory activity in visual cortex is profoundly altered as male and female mice learn to recognize an oriented grating stimulus—low-frequency (∼15 Hz peak) power sharply increases, whereas high-frequency (∼65 Hz peak) power decreases. These changes report recognition of the familiar pattern as they disappear when the stimulus is rotated to a novel orientation. Two-photon imaging of neuronal activity reveals that parvalbumin-expressing inhibitory neurons disengage with familiar stimuli and reactivate to novelty, whereas somatostatin-expressing inhibitory neurons show opposing activity patterns. We propose a model in which the balance of two interacting interneuron circuits shifts as novel stimuli become familiar. SIGNIFICANCE STATEMENT Habituation, familiarity, and novelty detection are fundamental cognitive processes that enable organisms to adaptively filter meaningless stimuli and focus attention on potentially important elements of their environment. We have shown that this process can be studied fruitfully in the mouse primary visual cortex by using simple grating stimuli for which novelty and familiarity are defined by orientation and by measuring stimulus-evoked and continuous local field potentials. Altered event-related and spontaneous potentials, and deficient habituation, are well-documented features of several neurodevelopmental psychiatric disorders. The paradigm described here will be valuable to interrogate the origins of these signals and the meaning of their disruption more deeply.
Sensory modulation of gait characteristics in human locomotion: A neuromusculoskeletal modeling study

The central nervous system of humans and animals is able to modulate the activity in the spinal cord to achieve several locomotion behaviors. Previous neuromechanical models investigated the modulation of human gait changing selected parameters belonging to the CPGs (Central Pattern Generators) feedforward oscillatory structures or to the feedback reflex circuits. CPG-based models could replicate slow and fast walking by changing only the oscillation’s properties. On the other hand, reflex-based models could achieve different behaviors mainly through optimizations of a large dimensional parameter space, but could not identify effectively individual key reflex parameters responsible for the modulation of gait characteristics. This study, investigates which reflex parameters modulate the gait characteristics through neuromechanical simulations. A recently developed reflex-based model is used to perform optimizations with different target behaviors on speed, step length and step duration in order to analyse the correlation between reflex parameters and their influence on these gait characteristics. We identified 9 key parameters that influence the target speed ranging from slow to fast walking (0.48 and 1.71 m/s) as well as a large range of step lengths (0.43 and 0.88 m) and step duration (0.51, 0.98 s). The findings show that specific reflexes during stance have a major effect on step length regulation mainly given by the contribution of positive force feedback on the ankle plantarflexors’ group. On the other hand, stretch reflexes active during swing of iliopsoas and gluteus maximus regulate all the gait characteristics under analysis. Additionally, the results show that the stretch reflex of the hamstring’s group during landing phase is responsible for modulating the step length and step duration. Additional validation studies in simulations demonstrated that the identified reflexes are sufficient to modulate gait in human locomotion. Thus, this study provides an overview of the possible reflexes to control the gait characteristics. Author summary
Free recall scaling laws and short-term memory effects in a latching attractor network

Significance The ability to store and retrieve information is a crucial function of memory in the brain. One well-established paradigm in which this is tested is free recall, in which a number of robust features and biases characterize the statistics of recall. Although these laws are consistently reproduced, the neuronal basis of how they emerge is not clear. In this manuscript, we propose a neural network model with a plausible learning rule that expresses these laws that may provide a mechanistic insight into memory function. In particular, our model reproduces power laws governing the recall capacity, as well as the biases that human subjects use to guide recall, such as the use of temporal and semantic cues. Despite the complexity of human memory, paradigms like free recall have revealed robust qualitative and quantitative characteristics, such as power laws governing recall capacity. Although abstract random matrix models could explain such laws, the possibility of their implementation in large networks of interacting neurons has so far remained underexplored. We study an attractor network model of long-term memory endowed with firing rate adaptation and global inhibition. Under appropriate conditions, the transitioning behavior of the network from memory to memory is constrained by limit cycles that prevent the network from recalling all memories, with scaling similar to what has been found in experiments. When the model is supplemented with a heteroassociative learning rule, complementing the standard autoassociative learning rule, as well as short-term synaptic facilitation, our model reproduces other key findings in the free recall literature, namely, serial position effects, contiguity and forward asymmetry effects, and the semantic effects found to guide memory recall. The model is consistent with a broad series of manipulations aimed at gaining a better understanding of the variables that affect recall, such as the role of rehearsal, presentation rates, and continuous and/or end-of-list distractor conditions. We predict that recall capacity may be increased with the addition of small amounts of noise, for example, in the form of weak random stimuli during recall. Finally, we predict that, although the statistics of the encoded memories has a strong effect on the recall capacity, the power laws governing recall capacity may still be expected to hold.
The transcription factor FoxO1 is required for the establishment of the human definitive endoderm

The transcription factor FoxO1 has been shown to dynamically regulate cell fate across diverse cell types. Here, we employ a human induced pluripotent stem cell (hiPSC)-to-hepatocyte differentiation system that recapitulates the process of hepatocyte specification and differentiation in the human embryo to investigate FoxO1 as a participant in the molecular events required to execute the initial stages of liver development. We demonstrate that FoxO1 is expressed in hiPSC and at all stages of hepatocyte differentiation: definitive endoderm, specified hepatocytes, immature hepatoblasts, and mature hepatocyte-like cells. Disruption of FoxO1 activity by addition of the small molecule inhibitor AS1842856 at the beginning of the differentiation protocol abolishes the formation of definitive endoderm, as indicated by the loss of endoderm gene expression and the gain in expression of multiple mesoderm genes. Moreover, we show that FoxO1 binds to the promoters of two genes with important roles in endoderm differentiation whose expression is significantly downregulated in AS1842856 treated versus untreated cells. These findings reveal a new role for FoxO1 as an essential transcriptional regulator for the establishment of definitive endoderm in humans.
ISSRseq: an extensible method for reduced representation sequencing

The capability to generate densely sampled single nucleotide polymorphism (SNP) data is essential in diverse subdisciplines of biology, including crop breeding, pathology, forensics, forestry, ecology, evolution, and conservation. However, the wet-lab expertise and bioinformatics training required to conduct genome-scale variant discovery remain limiting factors for investigators with limited resources. Here we present ISSRseq, a PCR-based method for reduced representation of genomic variation using simple sequence repeats as priming sites to sequence inter simple sequence repeat (ISSR) regions. Briefly, ISSR regions are amplified with single primers, pooled, used to construct sequencing libraries with a commercially-available kit, and sequenced on the Illumina platform. We also present a flexible bioinformatic pipeline that assembles ISSR loci, calls and hard filters variants, outputs data matrices in common formats, and conducts population analyses using R. Using three angiosperm species as case studies, we demonstrate that ISSRseq is highly repeatable, necessitates only simple wet-lab skills and commonplace instrumentation, is flexible in terms of the number of single primers used, and can generate genomic-scale variant discovery on par with existing RRS methods which require more complex wet lab procedures. ISSRseq represents a straightforward approach to SNP genotyping in any organism, and we predict that this method will be particularly useful for those studying population genomics and phylogeography of non-model organisms. Furthermore, the ease of ISSRseq relative to other RRS methods should prove useful to those lacking advanced expertise in wet lab methods or bioinformatics.
To See, Not to See, or to See Poorly: Perceptual Quality and Guess Rate as a Function of Electroencephalography (EEG) Brain Activity in an Orientation Perception Task

Detection of visual stimuli fluctuates over time, and these fluctuations have been shown to correlate with time-domain evoked activity and frequency-domain periodic activity. However, it is unclear if these fluctuations are related to a change in guess rate, perceptual quality, or both. Here we determined whether the quality of perception randomly varies across trials or is fixed so that the variability is the same. Then we estimated how perceptual quality and guess rate on an orientation perception task relates to EEG activity. Response errors were fitted to variable precision models and the standard mixture model to determine whether perceptual quality is from a varying or fixed distribution. Overall, the best fit was the standard mixture model that assumes response variability can be defined by a fixed distribution. The power and phase of 2-7 Hz post-target activities were found to vary along with task performance in that more accurate trials had greater power, and the preferred phase differed significantly between accurate and guess trials. Guess rate and σ were significantly lower on trials with high 2-3 Hz power than low and the difference started around 250 ms post-target. These effects coincide with changes in the P3 ERP: there was a more positive deflection in the accurate trials vs guesses. These results suggest that the spread of errors (perceptual quality) can be characterized by a fixed range of values. Where the errors fall within that range is modulated by the post-target power in the lower frequency bands and their analogous ERPs.
Information Rate in Humans during Visuomotor Tracking

While previous studies of human information rate focused primarily on discrete forced-choice tasks, we extend the scope of the investigation to the framework of sensorimotor tracking of continuous signals. We show how considering information transfer in this context sheds new light on the problem; crucially, such an analysis requires one to consider and carefully disentangle the effects due to real-time information processing of surprising inputs (feedback component) from the contribution to performance due to prediction (feedforward component). We argue that only the former constitutes a faithful representation of the true information processing rate. We provide information-theoretic measures which separately quantify these components and show that they correspond to a decomposition of the total information shared between target and tracking signals. We employ a linear quadratic regulator model to provide evidence for the validity of the measures, as well as of the estimator of visual-motor delay (VMD) from experimental data, instrumental to compute them in practice. On experimental tracking data, we show that the contribution of prediction as computed by the feedforward measure increases with the predictability of the signal, confirming previous findings. Importantly, we further find the feedback component to be modulated by task difficulty, with higher information transmission rates observed with noisier signals. Such opposite trends between feedback and feedforward point to a tradeoff of cognitive resources/effort and performance gain. Author summary Previous investigations concluded that the human brain’s information processing rate remains fundamentally constant, irrespective of task demands. However, their conclusion rested in analyses of simple discrete-choice tasks. The present contribution recasts the question of human information rate within the context of visuomotor tasks, which provides a more ecologically relevant arena, albeit a more complex one. We argue that, while predictable aspects of inputs can be encoded virtually free of charge, real-time information transfer should be identified with the processing of surprises. We formalise this intuition by deriving from first principles a decomposition of the total information shared by inputs and outputs into a feedforward, predictive component and a feedback, error-correcting component. We find that the information measured by the feedback component, a proxy for the brain’s information processing rate, scales with the difficulty of the task at hand, in agreement with cost-benefit models of cognitive effort.
Systematic analysis of goal-related movement sequences during maternal behavior in a female mouse model for Rett syndrome

Parenting is an ethologically relevant social behavior consisting of stereotypic components involving the care and nourishment of young. First-time rodent dams seek and gather wandering/scattered pups back to the nest (pup retrieval), an essential aspect of maternal care. Over the decades, qualitative observations of the behaving animal have been presented in quantitative discrete units. However, systematic analysis of the dynamic sequences of goal-related movements that comprise the entire behavioral sequence, which would be ultimately essential for understanding the underlying neurobiology, is usually not analyzed. Here, we present systematic analysis of pup retrieval behavior across three days in alloparental female mice (Surrogates or Sur) of two genotypes; Mecp2Heterozygotes (Het), a female mouse model for a neuropsychiatric disorder called Rett syndrome and their wild type (WT) siblings. Additionally, we analyzed CBA/CaJ and C57BL/6J WT surrogates for within-strain comparisons. Frame-by-frame analysis over different phases was performed manually using DataVyu software. We previously showed that Het are inefficient, by measuring latency and errors, at pup retrieval. Here, we show that the sequence of searching, pup-approach and good retrieval crystallizes over time for WT; this sequence does not crystallize in Het. We found that goal-related movements of Het in different phases were similar to WT, suggesting context-driven atypical dynamic patterns in Het. We also identified pup approach and pup grooming as atypical tactile interactions between pups and Het, which contribute to inefficient pup retrieval. Day-by-day analysis showed dynamic changes in goal-related movements in individual animals across genotypes and strains in response to the growing pups. Overall, our approach 1) embraces natural variation in individual mice on different days of pup retrieval behavior, 2) establishes a “gold-standard” manually curated dataset to next build behavioral repertoires using machine learning approaches, and 3) identifies distinct atypical tactile sensory processing in a female mouse model for Rett syndrome.
Identification and classification of innexin gene transcripts in the central nervous system of the terrestrial slug Limax valentianus

In invertebrates, innexin is involved in the formation of single-cell membrane channels and intercellular gap junction channels. Generally, there are multiple isoforms of innexin family proteins in various animal species, which enable the precise regulation of channel function. In molluscan species, the sequence information of innexins is still limited and the sequences have not been classified. This study examined the innexin transcripts expressed in the central nervous system of the terrestrial slug Limax valentianus and identified 16 transcripts of 12 innexin isoforms, including the splicing variants. To examine the function of molluscan innexin isoforms, phylogenetic analysis was performed using the innexin sequences of molluscan species. Next, the phosphorylation, N-glycosylation, and S-nitrosylation sites in the isoforms were predicted to characterize the innexin isoforms. Further, 16 circular RNA sequences of nine innexin isoforms were identified in the central nervous system of Limax. The identification and classification of the gene transcripts of molluscan innexins provided novel insights for understanding the regulatory mechanism of innexins in the central nervous system.
Temporal Microbial Community Dynamics Within a Unique Acid Saline Lake

Lake Magic is one of the acidic hypersaline lakes (ca. 1 km in diameter) present within the Yilgarn Craton in WA. This unique lake exhibits extremely low pH (<1.6) coupled to very high salinity (32% TDS) with the highest concentration of aluminium (1774 mg/L) and silica (510 mg/L) in the world. Previous studies on Lake Magic diversity has revealed that the lake hosts acidophilic, acidotolerant, halophilic and halotolerant bacterial species. These studies provide indicators of the population residing within the lake. However, they do not emphasize the survival mechanisms adopted by the resident microorganisms and how the diversity of microbial populations residing within the lake changes during the dynamic stages of flooding, evapo-concentration and desiccation. We have studied the bacterial and fungal diversity in Lake Magic via amplicon sequencing and functional analysis through different stages of the lake in a span of one year, in the salt and sediment layer. Our results highlight that the diversity in Lake Magic is strongly driven by the pH and salt concentrations at different stages of the lake. The microbial community becomes more specialised in specific functions during more extreme stages. This also suggests that microbial interactions are involved in stabilising the ecosystem and is responsible for the resistance and resilience of these communities as the interactions of these microbes create a safe haven for other microbes to survive during more extreme stages.
AINTEGUMENTA and AINTEGUMENTA-LIKE6 directly regulate floral homeotic and growth genes in young flowers

Arabidopsis flower primordia give rise to floral organ primordia in stereotypical positions within four concentric whorls. Floral organ primordia in each whorl undergo distinct developmental programs to become one of four organ types (sepals, petals, stamens, and carpels). The Arabidopsis transcription factors AINTEGUMENTA (ANT) and AINTEGUMENTA-LIKE6 (AIL6) play critical and partially overlapping roles during floral organogenesis. They are required for correct positioning of floral organ initiation, contribute to the specification of floral organ identity, and regulate the growth and morphogenesis of developing floral organs. To gain insight into the molecular means by which ANT and AIL6 contribute to floral organogenesis, we identified the genome-wide binding sites of both ANT and AIL6 in stage 3 flower primordia, the developmental stage at which sepal primordia become visible and class B and C floral homeotic genes are first expressed. AIL6 binds to a subset of ANT sites, suggesting that AIL6 regulates some but not all of the same target genes as ANT. ANT and AIL6 binding sites are associated with genes involved in many biological processes related to meristem and flower organ development. Comparison of genes associated with both ANT and AIL6 ChIP-Seq peaks and those differentially expressed after perturbation of ANT or AIL6 activity identified likely direct targets of ANT and AIL6 regulation. These include the floral homeotic genes APETALA3 (AP3) and AGAMOUS (AG) and four growth regulatory genes: BIG BROTHER (BB), ROTUNDIFOLIA3 (ROT3), ANGUSTIFOLIA3/GRF INTERACTING FACTOR (AN3/GIF1), and XYLOGLUCAN ENDOTRANSGLUCOLSYLASE/HYDROLASE9 (XTH9). One Sentence Summary The transcription factors ANT and AIL6 directly regulate genes involved in different aspects of flower development including genes that specify floral organ identity and those that regulate growth.