Interplay between β-propeller subunits WDR26 and muskelin regulates the CTLH E3 ligase supramolecular complex.

The Pro/N-degron recognizing C-terminal to LisH (CTLH) complex is an E3 ligase of emerging interest in the developmental biology field and for targeted protein degradation (TPD) modalities. The human CTLH complex forms distinct supramolecular ring-shaped structures dependent on the multimerization of WDR26 or muskelin β-propeller proteins. Here, we find that, in HeLa cells, CTLH complex E3 ligase activity is dictated by an interplay between WDR26 and muskelin in tandem with muskelin autoregulation.

Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization.

Neurons in the brain have rich and adaptive input-output properties. Features such as heterogeneous f-I curves and spike frequency adaptation are known to place single neurons in optimal coding regimes when facing changing stimuli. Yet, it is still unclear how brain circuits exploit single-neuron flexibility, and how network-level requirements may have shaped such cellular function.

Targeted proteomics of plasma extracellular vesicles uncovers MUC1 as combinatorial biomarker for the early detection of high-grade serous ovarian cancer.

Background: The five-year prognosis for patients with late-stage high-grade serous carcinoma (HGSC) remains dismal, underscoring the critical need for identifying early-stage biomarkers. This study explores the potential of extracellular vesicles (EVs) circulating in blood, which are believed to harbor proteomic cargo reflective of the HGSC microenvironment, as a source for biomarker discovery.

Results: We conducted a comprehensive proteomic profiling of EVs isolated from blood plasma, ascites, and cell lines of patients, employing both data-dependent (DDA) and data-independent acquisition (DIA) methods to construct a spectral library tailored for targeted proteomics.

Expressivity of Neural Networks with Random Weights and Learned Biases.

Landmark universal function approximation results for neural networks with trained weights and biases provided impetus for the ubiquitous use of neural networks as learning models in Artificial Intelligence (AI) and neuroscience. Recent work has pushed the bounds of universal approximation by showing that arbitrary functions can similarly be learned by tuning smaller subsets of parameters, for example the output weights, within randomly initialized networks. Motivated by the fact that biases can be interpreted as biologically plausible mechanisms for adjusting unit outputs in neural networks, such as tonic inputs or activation thresholds, we investigate the expressivity of neural networks with random weights where only biases are optimized.

Using neural biomarkers to personalize dosing of vagus nerve stimulation.

Background: Vagus nerve stimulation (VNS) is an established therapy for treating a variety of chronic diseases, such as epilepsy, depression, obesity, and for stroke rehabilitation. However, lack of precision and side-effects have hindered its efficacy and extension to new conditions. Achieving a better understanding of the relationship between VNS parameters and neural and physiological responses is therefore necessary to enable the design of personalized dosing procedures and improve precision and efficacy of VNS therapies.

Host specificity of plant-associated bacteria is negatively associated with genome size and host abundance along a latitudinal gradient.

Host specialization plays a critical role in the ecology and evolution of plant-microbe symbiosis. Theory predicts that host specialization is associated with microbial genome streamlining and is influenced by the abundance of host species, both of which can vary across latitudes, leading to a latitudinal gradient in host specificity. Here, we quantified the host specificity and composition of plant-bacteria symbioses on leaves across 329 tree species spanning a latitudinal gradient.

How tree traits modulate tree methane fluxes: A review.

Trees can play different roles in the regulation of fluxes of methane (CH), a greenhouse gas with a warming potential 83 times greater than that of carbon dioxide. Forest soils have the greatest potential for methane uptake compared to other land uses. In addition to their influence on soil CH fluxes, trees can act directly as a source or sink of CH, by transporting CH produced in the soil and harbouring the key microorganisms involved in CH production and consumption (methanogens and methanotrophs).

Assistive sensory-motor perturbations influence learned neural representations.

Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using Brain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days.

Sources of richness and ineffability for phenomenally conscious states.

Conscious states-state that there is something it is like to be in-seem both rich or full of detail and ineffable or hard to fully describe or recall. The problem of ineffability, in particular, is a longstanding issue in philosophy that partly motivates the explanatory gap: the belief that consciousness cannot be reduced to underlying physical processes. Here, we provide an information theoretic dynamical systems perspective on the richness and ineffability of consciousness.

Online Bayesian optimization of vagus nerve stimulation.

In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs, modifying their function. Stimulation devices capable of triggering exogenous neural signals using electrical waveforms require a complex and multi-dimensional parameter space to control such waveforms. Determining the best combination of parameters (waveform optimization or dosing) for treating a particular patient's illness is therefore challenging.

Gaussian-process-based Bayesian optimization for neurostimulation interventions in rats.

Effective neural stimulation requires adequate parametrization. Gaussian-process (GP)-based Bayesian optimization (BO) offers a framework to discover optimal stimulation parameters in real time. Here, we first provide a general protocol to deploy this framework in neurostimulation interventions and follow by exemplifying its use in detail.

Connectome-based reservoir computing with the conn2res toolbox.

The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how connection patterns shape computational capacity in biological brain networks.

Personalized inference for neurostimulation with meta-learning: a case study of vagus nerve stimulation.

. Neurostimulation is emerging as treatment for several diseases of the brain and peripheral organs. Due to variability arising from placement of stimulation devices, underlying neuroanatomy and physiological responses to stimulation, it is essential that neurostimulation protocols are personalized to maximize efficacy and safety.

A Unified, Scalable Framework for Neural Population Decoding.

Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both model size and datasets. However, the integration of many neural recordings into one unified model is challenging, as each recording contains the activity of different neurons from different individual animals. In this paper, we introduce a training framework and architecture designed to model the population dynamics of neural activity across diverse, large-scale neural recordings.

How connectivity structure shapes rich and lazy learning in neural circuits.

In theoretical neuroscience, recent work leverages deep learning tools to explore how some network attributes critically influence its learning dynamics. Notably, initial weight distributions with small (resp. large) variance may yield a rich (resp.

Multi-view manifold learning of human brain-state trajectories.

The complexity of the human brain gives the illusion that brain activity is intrinsically high-dimensional. Nonlinear dimensionality-reduction methods such as uniform manifold approximation and t-distributed stochastic neighbor embedding have been used for high-throughput biomedical data. However, they have not been used extensively for brain activity data such as those from functional magnetic resonance imaging (fMRI), primarily due to their inability to maintain dynamic structure.

Data-driven identification of major axes of functional variation in bacteria.

The discovery of major axes of correlated functional variation among species and habitats has revealed the fundamental trade-offs structuring both functional and taxonomic diversity in eukaryotes such as plants. Whether such functional axes exist in the bacterial realm and whether they could explain bacterial taxonomic turnover among ecosystems remains unknown. Here, we use a data-driven approach to leverage global genomic and metagenomic datasets to reveal the existence of major axes of functional variation explaining both evolutionary differentiation within Bacteria and their ecological sorting across diverse habitats.

Specific quinone reductase 2 inhibitors reduce metabolic burden and reverse Alzheimer's disease phenotype in mice.

Biological aging can be described as accumulative, prolonged metabolic stress and is the major risk factor for cognitive decline and Alzheimer's disease (AD). Recently, we identified and described a quinone reductase 2 (QR2) pathway in the brain, in which QR2 acts as a removable memory constraint and metabolic buffer within neurons. QR2 becomes overexpressed with age, and it is possibly a novel contributing factor to age-related metabolic stress and cognitive deficit.

Noncanonical functions of Ku may underlie essentiality in human cells.

The Ku70/80 heterodimer is a key player in non-homologous end-joining DNA repair but is involved in other cellular functions like telomere regulation and maintenance, in which Ku's role is not fully characterized. It was previously reported that knockout of Ku80 in a human cell line results in lethality, but the underlying cause of Ku essentiality in human cells has yet to be fully explored. Here, we established conditional Ku70 knockout cells using CRISPR/Cas9 editing to study the essentiality of Ku70 function.

Bacterial endophytes of sugar maple leaves vary more idiosyncratically than epiphytes across a large geographic area.

Bacteria from the leaf surface and the leaf tissue have been attributed with several beneficial properties for their plant host. Though physically connected, the microbial ecology of these compartments has mostly been studied separately such that we lack an integrated understanding of the processes shaping their assembly. We sampled leaf epiphytes and endophytes from the same individuals of sugar maple across the northern portion of its range to evaluate if their community composition was driven by similar processes within and across populations differing in plant traits and overall abiotic environment.

Investigation of ,,-Trimethyl-L-alanyl-L-proline Betaine (TMAP) as a Biomarker of Kidney Function.

Glomerular filtration rate (GFR) is the most widely used tool for the measurement of kidney function, but endogenous biomarkers such as cystatin C and creatinine have limitations. A previous metabolomic study revealed ,,-trimethyl-L-alanyl-L-proline betaine (TMAP) to be reflective of kidney function. In this study, we developed a quantitative LCMS assay for the measurement of TMAP and evaluated TMAP as a biomarker of GFR.

Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations.

Invasive brain-computer interfaces hold promise to alleviate disabilities in individuals with neurologic injury, with fully implantable brain-computer interface systems expected to reach the clinic in the upcoming decade. Children with severe neurologic disabilities, like quadriplegic cerebral palsy or cervical spine trauma, could benefit from this technology. However, they have been excluded from clinical trials of intracortical brain-computer interface to date.

RNA cytosine methyltransferase NSUN5 promotes protein synthesis and tumorigenic phenotypes in glioblastoma.

Glioblastoma (GBM) is the most common and aggressive malignant primary brain tumor in adults. The standard treatment achieves a median overall survival for GBM patients of only 15 months. Hence, novel therapies based on an increased understanding of the mechanistic underpinnings of GBM are desperately needed.