Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual Occlusions Result in Phase Synchrony Within Multiple Brain Regions Involved in Sensory Processing and Balance Control

There is a need to develop appropriate balance training interventions to minimize the risk of falls. Recently, we found that intermittent visual occlusions can substantially improve the effectiveness and retention of balance beam walking practice (Symeonidou and Ferris 2022). We sought to determine how the intermittent visual occlusions affect electrocortical activity during beam walking. We hypothesized that areas involved in sensorimotor processing and balance control would demonstrate spectral power changes and inter-trial coherence modulations after loss and restoration of vision. Ten healthy young adults practiced walking on a treadmill-mounted balance beam while wearing high-density EEG and experiencing reoccurring visual occlusions. Results revealed spectral power fluctuations and inter-trial coherence changes in the visual, occipital, temporal, and sensorimotor cortex as well as the posterior parietal cortex and the anterior cingulate. We observed a prolonged alpha increase in the occipital, temporal, sensorimotor, and posterior parietal cortex after the occlusion onset. In contrast, the anterior cingulate showed a strong alpha and theta increase after the occlusion offset. We observed transient phase synchrony in the alpha, theta, and beta bands within the sensory, posterior parietal, and anterior cingulate cortices immediately after occlusion onset and offset. Our results provide support for cross-modal phase resetting and enhanced processing in areas involved in sensory processing and balance control as an explanation for increased long-term balance improvement when training with intermittent visual occlusions. Our training intervention could be implemented in senior and rehabilitation centers, improving the quality of life of elderly and neurologically impaired individuals.
Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer

Glucose and glutamine are major carbon and energy sources that promote the fast proliferation of cancer cells. Metabolic shifs observed on cell line or mouse models may not reflect the general metabolic shifts in real human cancer tissue. In this study, we conducted a computational characterization of the flux distribution and variations of the central energy metabolism and key branches in a pan-cancer analysis, including glycolytic pathway, production of lactate, TCA cycle, nucleic acids synthesis, glutaminolysis, glutaminate, glutamine and glutathione metabolism, amino acids synthesis, in 11 cancer subtypes and 9 matched adjacent normal tissue types, by using TCGA tissue transcriptomics data. Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of TCA cycle, i.e., Warbug effect in the analyzed cancer types. However, consistently increased lactate production and second half of TCA cycle were only seen in certain cancer types. More interestingly, we did not see cancer tissues have highly shifted glutaminolysis compared to their adjacent normal controls. A systems biology model of metabolic shifts in through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes, (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls, and (3) the different shifts happened to tissue specific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression. This study strongly suggests the possibility to have a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors.
Concurrent maintenance of both veridical and transformed working memory representations

In the dynamic environment we live in, the already limited information that human working memory can maintain needs to be constantly updated to optimally guide behaviour. Indeed, previous studies showed that leading up to a response, representations maintained in working memory representations are transformed continuously. This goes hand-in-hand with the removal of task-irrelevant items. However, does such removal also include the representations of stimuli as they were originally, prior to transformation? Here we assessed the neural representation of task-relevant transformed representations, and the no-longer-relevant veridical representations they originated from. We applied multivariate pattern analysis to electroencephalographic data during maintenance of orientation gratings with and without mental rotation. During maintenance, we perturbed the representational network by means of a visual impulse stimulus, and were thus able to successfully decode veridical as well as imaginary, transformed orientation gratings from impulse-driven activity. The impulse response reflected only task-relevant (cued), but not task-irrelevant (uncued) items, suggesting that the latter were quickly discarded from working memory. By contrast, even though the original cued orientation gratings were also no longer task-relevant after mental rotation, these items continued to be represented next to the rotated ones, in different representational formats. This seemingly inefficient use of scarce working memory capacity was associated with reduced probe response times and may thus serve to increase precision and flexibility in guiding behaviour in dynamic environments.
T Residues Preceded by Runs of G are Hotspots of T→G Mutation in Bacteria

The rate of mutation varies among positions in a genome. Local sequence context can affect the rate, and has different effects on different types of mutation. Here I report an effect of local context that operates to some extent in all bacteria examined: the rate of T→G mutation is greatly increased by preceding runs of three or more G residues. The strength of the effect increases with the length of the run. In Salmonella, in which the effect is strongest, a G run of length three increases the rate by a factor of ~26, a run of length four increases it by almost a factor of 100, and runs of length five or more increase it by a factor of more than 400 on average. The effect is much stronger when the T is on the leading rather than the lagging strand of DNA replication. Several observations eliminate the possibility that this effect is an artifact of sequencing error.
Temporary knockdown of p53 during focal limb irradiation increases the development of sarcomas

Background Approximately half of all cancer patients receive radiotherapy and, as cancer survivorship rates increase with more effective therapies, the very low rate of radiation-associated sarcomas is rising. Radiation-associated sarcomas are life-threatening cancers, and radiation exposure is a primary risk factor for sarcoma development. During radiotherapy or other genotoxic cancer therapy for p53 mutant cancers, pharmacological inhibition of p53 has been proposed to ameliorate acute injury of normal tissues. However, enhancing the survival of normal cells that sustain DNA damage by temporarily inhibiting p53 has the potential to increase the risk of cancer development. Here, we use in vivo shRNA technology to examine the consequences of temporarily reducing p53 expression on radiation-induced sarcoma development. Methods We utilized a mouse model of radiation-induced sarcoma where mice express a doxycycline (dox)-inducible p53 shRNA to temporarily and reversibly reduce p53 expression. Mice were placed on a dox diet 10 days prior to receiving 30 or 40 Gy hind limb irradiation in a single fraction and then returned to normal chow. Mice were examined weekly for sarcoma development and scored for radiation-induced normal tissue injuries. Radiation-induced sarcomas were harvested and subjected to RNA sequencing. Results Following single high-dose irradiation, 21% of temporary p53 knockdown animals developed a sarcoma in the radiation field compared to 2% of control animals. Mice with more severe acute injuries in the first 3 months after irradiation had a significantly increased risk of developing late persistent wounds in the soft tissue and bone. Chronic radiation-induced wounds were associated with sarcomagenesis. Examination of muscle stem cells by flow cytometry following hind limb irradiation indicated p53 knockdown preserves muscle stem cells in the irradiated limb, supporting the notion that temporary p53 knockdown at the time of irradiation reduces death of cells with DNA damage which may then persist to develop into a sarcoma. We performed RNA sequencing on 16 radiation-induced sarcomas compared to normal muscle controls. Gene set enrichment analysis revealed upregulation in the sarcomas of genes related to translation, epithelial mesenchymal transition (EMT), inflammation, and the cell cycle versus downregulation of genes related to myogenesis and tumor metabolism. Furthermore, genes with increased copy number such as Met and Cdk4 were overexpressed in tumors. Conclusions Temporary reduction of p53 during high-dose irradiation increases late effects including tissue injuries and sarcoma development.
Shallow environmental gradients can cause range margins to form

One hypothesis invoked to explain limits to species’ ranges is a mismatch in environmental conditions between the central and marginal areas of species’ ranges. Low population size at the margins causes genetic drift to outplay selection locally, and limits the accumulation of genetic variance, so that adaptation is hindered locally. Earlier theoretical work shows that, for a population expanding over a spatially heterogeneous environment without any geographical barriers, adaptation will fail abruptly and sharp range margins will establish only when the underlying environmental conditions change more and more severely across space, whereas an environment changing constantly will result either in infinite expansion or rapid global extinction. Here, we extend this “steepening-gradient hypothesis” to encompass situations when multiple (up to three) environmental factors impose selection on separate adaptive traits. We show that multiple selection gradients steepen each other and that it is sufficient that just one of the gradients steepen in space for sharp range margins to form. This is true even if this gradient is shallow throughout the realised range. Thus, despite its detrimental role in forming range margins, it could be overlooked in field studies. Finally, by decomposing an environmental gradient to selection on two (or three) adaptive traits, we show that a population can withstand harsher environmental conditions than when selection acts on one adaptive trait alone. This finding argues for the evolution of novel traits in harsh environments.
The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

Norepinephrine (NE) neurons in the locus coeruleus (LC) make long-range projections throughout the central nervous system, playing critical roles in arousal and mood, as well as various components of cognition including attention, learning, and memory. The LC-NE system is also implicated in multiple neurological and neuropsychiatric disorders. Importantly, LC-NE neurons are highly sensitive to degeneration in both Alzheimer’s and Parkinson’s disease. Despite the clinical importance of the brain region and the prominent role of LC-NE neurons in a variety of brain and behavioral functions, a detailed molecular characterization of the LC is lacking. Here, we used a combination of spatially-resolved transcriptomics and single-nucleus RNA-sequencing to characterize the molecular landscape of the LC region and the transcriptomic profile of LC-NE neurons in the human brain. We provide a freely accessible resource of these data in web-accessible and downloadable formats.
BrainPy: a flexible, integrative, efficient, and extensible framework towards general-purpose brain dynamics programming

The neural mechanisms underlying brain functions are extremely complicated. Brain dynamics modeling is an indispensable tool for elucidating these mechanisms by modeling the dynamics of the neural circuits that execute brain functions. To ease and facilitate brain dynamics modeling, a general-purpose programming framework is needed to enable users to freely define neural models across multiple scales; efficiently simulate, train, and analyze model dynamics; and conveniently extend new modeling approaches. By utilizing the advanced just-in-time (JIT) compilation, we developed BrainPy. BrainPy provides a rich infrastructure tailored for brain dynamics programming, which supports an integrated platform for brain dynamics model building, simulation, training, and analysis. Models in BrainPy can be JIT compiled into binary instructions for multiple devices (including CPU, GPU, and TPU) to achieve a high running performance comparable to native C or CUDA. Moreover, BrainPy features an extensible architecture allowing easy expansion of new infrastructure, utilities, and machine learning approaches.
A Common Resequencing-Based Genetic Marker Dataset for Global Maize Diversity

Maize (Zea mays ssp. mays) populations exhibit vast amounts of genetic and phenotypic diversity. As sequencing costs have declined, an increasing number of projects have sought to measure genetic differences between and within maize populations using whole genome resequencing strategies, identifying millions of segregating single-nucleotide polymorphisms (SNPs) and insertions/deletions (InDels). Unlike older genotyping strategies like microarrays and genotyping by sequencing, resequencing should, in principle, frequently identify and score common genetic variants. However, in practice, different projects frequently employ different analytical pipelines, often employ different reference genome assemblies, and consistently filter for minor allele frequency within the study population. This constrains the potential to reuse and remix data on genetic diversity generated from different projects to address new biological questions in new ways. Here we employ resequencing data from 1,276 previously published maize samples and 239 newly resequenced maize samples to generate a single unified marker set of ∼366 million segregating variants and ∼46 million high confidence variants scored across crop wild relatives, landraces as well as tropical and temperate lines from different breeding eras. We demonstrate that the new variant set provides increased power to identify known causal flowering time genes using previously published trait datasets, as well as the potential to track changes in the frequency of functionally distinct alleles across the global distribution of modern maize.
Reliable population signal of subjective economic value from unreliable neurons in primate orbitofrontal cortex

Behavior-related neuronal signals often vary between neurons. Despite the unreliability of individual neurons, brains are able to accurately represent and drive behavior. The notion may also apply to economic (‘value-based’) choices and the underlying reward signals. Reward value is subjective and can be defined by nonlinear weighting of magnitude (utility) and probability. Using a wide variety of reward magnitude and probability, we assessed subjective reward value at choice indifference between safe and risky rewards as prescribed by the continuity axiom that provides stringent criteria for meaningful choice. We found that individual neurons in the orbitofrontal cortex (OFC) of monkeys carry unreliable and heterogeneous neuronal signals for subjective value that largely fails to match the animal’s choice. However, the averaged neuronal signals matched well the animals’ choices, suggesting reliable subjective economic value encoding by the observed population of unreliable neurons. Highlights Different from widely held views, reliable neuronal information processing may not require reliable processors. Neurons in monkey orbitofrontal cortex (OFC) process reward magnitude and probability heterogeneously and unreliably. Despite unreliable neuronal processing, OFC population activity codes choices reliably. Reliability systems performance from unreliable elements seems to be a broad feature of neuronal reward coding. In brief Using stringent concepts of behavioral choice, Ferrari-Toniolo and Schultz describe unreliable individual reward neurons in monkey orbitofrontal cortex whose activity combines to a reliable population code for economic choice.
The underlying mechanisms of alignment in error backpropagation through arbitrary weights

Understanding the mechanisms by which plasticity in millions of synapses in the brain is orchestrated to achieve behavioral and cognitive goals is a fundamental quest in neuroscience. In this regard, insights from learning methods in artificial neural networks (ANNs) and in particular supervised learning using backpropagation (BP) seem inspiring. However, the implementation of BP requires exact matching between forward and backward weights, which is unrealistic given the known connectivity pattern in the brain (known as “weight transport problem”). Notably, it has been shown that under certain conditions, error BackPropagation Through Arbitrary Weights (BP-TAW) can lead to a partial alignment between forward and backward weights (weight alignment or WA). This learning algorithm, which is also known as feedback alignment (FA), can result in surprisingly good degrees of accuracy in simple classification tasks. However, the underlying mechanisms and mathematical basis of WA are not thoroughly understood. In this work, we show that the occurrence of WA is governed by statistical properties of the output and error signals of neurons, such as autocorrelation and cross-correlation, and can happen even in the absence of learning or reduction of the loss function. Moreover, we show that WA can be improved significantly by limiting the norm of input weights to neurons and that such a weight normalization (WN) method can improve the classification accuracy of BP-TAW. The findings presented can be used to further improve the performance of BP-TAW and open new ways for exploring possible learning mechanisms in biological neural networks without exact matching between forward and backward weights.
Quantum phase transitions driven by rhombic-type single-ion anisotropy in the S = 1 Haldane chain

The spin-1 Haldane chain is an example of the symmetry-protected-topological (SPT) phase in one dimension. Experimental realization of the spin chain materials usually involves both the uniaxial-type, $D(S^z)^2$, and the rhombic-type, $E[(S^x)^2-(S^y)^2]$, single-ion anisotropies. Here, we provide a precise ground-state phase diagram for spin-1 Haldane chain with these single-ion anisotropies. Using quantum numbers, we find that the $\mathbb{Z}_2$ symmetry breaking phase can be characterized by double degeneracy in the entanglement spectrum. Topological quantum phase transitions take place on particular paths in the phase diagram, from the Haldane phase to the Large-$E_x$, Large-$E_y$, or Large-$D$ phases. The topological critical points are determined by the level spectroscopy method with a newly developed parity technique in the density matrix renormalization group [Phys. Rev. B 86, 024403 (2012)], and the Haldane-Large-$D$ critical point is obtained with an unprecedentedly precision, $(D/J)_c=0.9684713(1)$. Close to this critical point, a small rhombic single-ion anisotropy $|E|/J\ll1$ can destroy the Haldane phase and bring the system into a $y$-N\'eel phase. We propose that the compound [Ni(HF$_2$)(3-Clpy)$_4$]BF$_4$ is a candidate system to search for the $y$-N\'eel phase.
N2H+ and C18O Depletion in a Cold Dark Cloud

We present sensitive, high angular resolution molecular line observations of C18O and N2H+ toward the dark globule B68. We directly compare these data with the near-infrared extinction measurements of Alves, Lada, & Lada to derive the first evidence for the depletion of N2H+, and by inference N2, in a prestellar dark cloud. We also find widespread C18O depletion throughout the centrally condensed core of the B68 cloud. Specifically, we find the N2H+ emission to peak in a shell partially surrounding the peak of dust extinction. Moreover, N2H+ peaks inside the much larger C18O depletion hole and has a smaller depletion zone, confirming theoretical predictions. These data are analyzed through a direct coupling of time-dependent chemical models to a radiation transfer code. This analysis highlights the importance of photodissociation at cloud edges and suggests that the CO abundance declines by 2 orders of magnitude from edge to center. In contrast, N2H+ declines in abundance, at minimum, by at least a factor of 2. Indeed, it is entirely possible that both N2H+ and N2 are completely absent from the central regions of the B68 core. The depletion of N2H+, and its parent molecule N2, opens up the possibility that the centers of dense cores, prior to the formation of a star, may evade detection by conventional methods of probing cores using molecular emission. Under these conditions, H2D+ may be the sole viable molecular probe of the innermost regions of star-forming cores.
Asymptotic states of accelerated detectors and universality of the Unruh effect

We treat an Unruh-DeWitt detector as an open quantum system and evaluate the response of a uniformly accelerated detector: (i) interacting locally with the derivatives of a massless scalar field and (ii) linearly coupled to an electromagnetic field. We find that the early-time transition rate of the detector strongly depends on the type of the interaction between the detector and the quantum field, and may not follow a Planck distribution. In contrast, the late time asymptotic state is always thermal at the Unruh temperature and thus provides a more fundamental and persistent characterization of the acceleration temperature: A uniformly accelerated detector experiences the field vacuum as a genuine thermal bath at the Unruh temperature and eventually settles at a thermal state, regardless of their intermediate dynamics or the type of interaction.