Show newer

AI of Brain and Cognitive Sciences: From the Perspective of First Principles. (arXiv:2301.08382v1 [q-bio.NC]) arxiv.org/abs/2301.08382

AI of Brain and Cognitive Sciences: From the Perspective of First Principles

Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation. Despite these powerful applications, there are still many tasks in our daily life that are rather simple to humans but pose great challenges to AI. These include image and language understanding, few-shot learning, abstract concepts, and low-energy cost computing. Thus, learning from the brain is still a promising way that can shed light on the development of next-generation AI. The brain is arguably the only known intelligent machine in the universe, which is the product of evolution for animals surviving in the natural environment. At the behavior level, psychology and cognitive sciences have demonstrated that human and animal brains can execute very intelligent high-level cognitive functions. At the structure level, cognitive and computational neurosciences have unveiled that the brain has extremely complicated but elegant network forms to support its functions. Over years, people are gathering knowledge about the structure and functions of the brain, and this process is accelerating recently along with the initiation of giant brain projects worldwide. Here, we argue that the general principles of brain functions are the most valuable things to inspire the development of AI. These general principles are the standard rules of the brain extracting, representing, manipulating, and retrieving information, and here we call them the first principles of the brain. This paper collects six such first principles. They are attractor network, criticality, random network, sparse coding, relational memory, and perceptual learning. On each topic, we review its biological background, fundamental property, potential application to AI, and future development.

arxiv.org

Brain Model State Space Reconstruction Using an LSTM Neural Network. (arXiv:2301.08391v1 [cs.LG]) arxiv.org/abs/2301.08391

Brain Model State Space Reconstruction Using an LSTM Neural Network

Objective Kalman filtering has previously been applied to track neural model states and parameters, particularly at the scale relevant to EEG. However, this approach lacks a reliable method to determine the initial filter conditions and assumes that the distribution of states remains Gaussian. This study presents an alternative, data-driven method to track the states and parameters of neural mass models (NMMs) from EEG recordings using deep learning techniques, specifically an LSTM neural network. Approach An LSTM filter was trained on simulated EEG data generated by a neural mass model using a wide range of parameters. With an appropriately customised loss function, the LSTM filter can learn the behaviour of NMMs. As a result, it can output the state vector and parameters of NMMs given observation data as the input. Main Results Test results using simulated data yielded correlations with R squared of around 0.99 and verified that the method is robust to noise and can be more accurate than a nonlinear Kalman filter when the initial conditions of the Kalman filter are not accurate. As an example of real-world application, the LSTM filter was also applied to real EEG data that included epileptic seizures, and revealed changes in connectivity strength parameters at the beginnings of seizures. Significance Tracking the state vector and parameters of mathematical brain models is of great importance in the area of brain modelling, monitoring, imaging and control. This approach has no need to specify the initial state vector and parameters, which is very difficult to do in practice because many of the variables being estimated cannot be measured directly in physiological experiments. This method may be applied using any neural mass model and, therefore, provides a general, novel, efficient approach to estimate brain model variables that are often difficult to measure.

arxiv.org

SpaceTx: A Roadmap for Benchmarking Spatial Transcriptomics Exploration of the Brain. (arXiv:2301.08436v1 [q-bio.NC]) arxiv.org/abs/2301.08436

SpaceTx: A Roadmap for Benchmarking Spatial Transcriptomics Exploration of the Brain

Mapping spatial distributions of transcriptomic cell types is essential to understanding the brain, with its exceptional cellular heterogeneity and the functional significance of its spatial organization. Spatial transcriptomics techniques are hoped to accomplish these measurements, but each method uses different experimental and computational protocols, with different trade-offs and optimizations. In 2017, the SpaceTx Consortium was formed to compare these methods and determine their suitability for large-scale spatial transcriptomic atlases. SpaceTx work included progress in tissue processing, taxonomy development, gene selection, image processing and data standardization, cell segmentation, cell type assignments, and visualization. Although the landscape of experimental methods has changed dramatically since the beginning of SpaceTx, the need for quantitative and detailed benchmarking of spatial transcriptomics methods in the brain is still unmet. Here, we summarize the work of SpaceTx and highlight outstanding challenges as spatial transcriptomics grows into a mature field. We also discuss how our progress provides a roadmap for benchmarking spatial transcriptomics methods in the future. Data and analyses from this consortium, along with code and methods are publicly available at https://spacetx.github.io/.

arxiv.org

Ontology Pre-training for Poison Prediction. (arXiv:2301.08577v1 [cs.AI]) arxiv.org/abs/2301.08577

Ontology Pre-training for Poison Prediction

Integrating human knowledge into neural networks has the potential to improve their robustness and interpretability. We have developed a novel approach to integrate knowledge from ontologies into the structure of a Transformer network which we call ontology pre-training: we train the network to predict membership in ontology classes as a way to embed the structure of the ontology into the network, and subsequently fine-tune the network for the particular prediction task. We apply this approach to a case study in predicting the potential toxicity of a small molecule based on its molecular structure, a challenging task for machine learning in life sciences chemistry. Our approach improves on the state of the art, and moreover has several additional benefits. First, we are able to show that the model learns to focus attention on more meaningful chemical groups when making predictions with ontology pre-training than without, paving a path towards greater robustness and interpretability. Second, the training time is reduced after ontology pre-training, indicating that the model is better placed to learn what matters for toxicity prediction with the ontology pre-training than without. This strategy has general applicability as a neuro-symbolic approach to embed meaningful semantics into neural networks.

arxiv.org

Degradation of crude oil and pure hydrocarbon fractions by some wild bacterial and fungal species. (arXiv:2301.08715v1 [q-bio.MN]) arxiv.org/abs/2301.08715

Degradation of crude oil and pure hydrocarbon fractions by some wild bacterial and fungal species

The use of biodegradation as a method for cleaning up soil that has been contaminated by spilt petroleum can be an effective strategy. So, this study investigated the existence of the wild microorganism in soil contaminated with oil and study their ability to degrade petroleum in vitro. Nineteen samples were collected from various locations near Taq Taq (TTOPCO) natural seeps in the Kurdistan Region of Iraq. Morphological, cultural, biochemical tests and molecular identification were used to identify the microbial communities, in addition, spore texture and the colour of the fungal isolates were investigated on the fungal isolates. Out of the19 samples, 17 indigenous bacterial strains and 5 fungal strains were successfully isolated. From the absorption spectrophotometry, Bacillus anthracis, Bacillus cereus, Achromobacter sp. and Pseudomonas aeruginosa for the bacterial isolates grew well on a minimal salt medium supplemented with 1% crude oil. Results showed that these isolates mentioned above had a strong ability to degrade crude oil by reducing the colour of 2,6-dichlorophenol indophenol (DCPIP) from deep blue to colourless. However, for the fractions of hydrocarbon, the bacterial isolates failed and did not affect the colour of any of the fractions. The results for fungi showed that Aspergillus lentulus and Rhizopus arrhizus had a strong ability to degrade both crude oil and fraction F1 by reducing the colour of DCPIP. Each fungal isolates also had a great tolerance to different concentrations of crude oil when grown on solid MSM. This study showed these microorganisms have a strong ability to degrade crude oil and can be used to clean up soil and the environment.

arxiv.org

Complex physical properties of an adaptive, self-organizing biological system. (arXiv:2107.13195v5 [q-bio.MN] UPDATED) arxiv.org/abs/2107.13195

Complex physical properties of an adaptive, self-organizing biological system

The physical interpretation of the functioning of the adaptive immune system, which has been thoroughly characterized on genetic and molecular levels, provides a unique opportunity to define an adaptive self-organizing biological system in its entirety. This paper describes a configuration space model of immune function, where directed chemical potentials of the system constitute a space of interactions. In the physical sense, the humoral adaptive immune system adjusts the chemical potential of all available antigenic molecules by tuning the chemical potential and organizing the network hierarchy of its sensor-effector molecules, antibodies. The coupling of sensors and effectors allows the system to adjust the thermodynamic activity of antigens and antibodies, while network organization helps minimize chemical potentials and maximize diversity. Mathematically the system couples the variance of Gaussian distributed interaction energies in its interaction space to the exponentially distributed chemical potentials of its effector molecules to maintain its stationary state. This process creates a scale-free network in interaction space, where absolute thermodynamic activity corresponds to node degree. In the thermodynamic interpretation, the system is an ensemble carrying out {mu}N work, adjusting chemical potentials according to the changes in the chemical potentials of the surroundings. The validity of the model is supported by identifying an interaction flexibility index, the corresponding variables in thermodynamics and network science, and by confirming its applicability to the humoral immune system. Overall, this statistical thermodynamics model of adaptive immunity describes how adaptive biological self-organization arises from the maintenance of a scale-free, directed interaction network with fractal topology.

arxiv.org

Relative vs absolute fitness in a population genetics model. How stronger selection may promote genetic diversity. (arXiv:2301.07762v1 [q-bio.PE]) arxiv.org/abs/2301.07762

Relative vs absolute fitness in a population genetics model. How stronger selection may promote genetic diversity

Since the foundation of population genetics, it is believed that directional selection should reduce genetic diversity. We present an exactly solvable population model which contradicts this intuition. The population is modelled as a cloud of particles evolving in a 1-dimensional fitness space (fitness wave). We show the existence of a phase transition which separates the parameter space into a weak and a strong selection regimes. We find that genetic diversity is highly non-monotone in the selection strength and, in contrast with the common intuition, our model predicts that genetic diversity is typically higher in the strong selection regime. This apparent paradox is resolved by observing that a higher selection strength increases the absolute fitness of the wave, but typically generate lower relative fitness between individuals within the wave. These findings entail that inferring the magnitude of natural selection from genetic data may raise some serious conceptual issues. Along the way, we uncover a new phase transition in front propagation. Namely, we show that the transition from weak to strong selection can be reformulated in terms of a transition from fully-pulled to semi-pulled waves. This transition is the pulled analog to the semi-pushed to fully-pushed regimes observed in noisy-FKPP travelling waves in the presence of Allee effect.

arxiv.org

Heterogeneous biological membranes regulate protein partitioning via fluctuating diffusivity. (arXiv:2301.07932v1 [cond-mat.soft]) arxiv.org/abs/2301.07932

Heterogeneous biological membranes regulate protein partitioning via fluctuating diffusivity

Cell membranes phase separate into ordered ${\rm L_o}$ and disordered ${\rm L_d}$ domains depending on their compositions. This membrane compartmentalization is heterogeneous and regulates the localization of specific proteins related to cell signaling and trafficking. However, it is unclear how the heterogeneity of the membranes affects the diffusion and localization of proteins in ${\rm L_o}$ and ${\rm L_d}$ domains. Here, using Langevin dynamics simulations coupled with the phase-field method (LDPF), we investigate submillisecond-scale diffusion and localization of proteins in heterogeneous biological membrane models showing phase separation into ${\rm L_o}$ and ${\rm L_d}$ domains. The diffusivity of proteins exhibits temporal fluctuations depending on the field composition. Increases in molecular concentrations and domain preference of the molecule induce subdiffusive behavior due to molecular collisions by crowding and confinement effects, respectively. Moreover, we quantitatively demonstrate that the protein partitioning into the ${\rm L_o}$ domain is determined by the difference in molecular diffusivity between domains, molecular preference of domain, and molecular concentration. These results pave the way for understanding how biological reactions caused by molecular partitioning may be controlled in heterogeneous media.

arxiv.org

Model-based assessment of sampling protocols for infectious disease genomic surveillance. (arXiv:2301.07951v1 [q-bio.PE]) arxiv.org/abs/2301.07951

Model-based assessment of sampling protocols for infectious disease genomic surveillance

Genomic surveillance of infectious diseases allows monitoring circulating and emerging variants and quantifying their epidemic potential. However, due to the high costs associated with genomic sequencing, only a limited number of samples can be analysed. Thus, it is critical to understand how sampling impacts the information generated. Here, we combine a compartmental model for the spread of COVID-19 (distinguishing several SARS-CoV-2 variants) with different sampling strategies to assess their impact on genomic surveillance. In particular, we compare adaptive sampling, i.e., dynamically reallocating resources between screening at points of entry and inside communities, and constant sampling, i.e., assigning fixed resources to the two locations. We show that adaptive sampling uncovers new variants up to five weeks earlier than constant sampling, significantly reducing detection delays and estimation errors. This advantage is most prominent at low sequencing rates. Although increasing the sequencing rate has a similar effect, the marginal benefits of doing so may not always justify the associated costs. Consequently, it is convenient for countries with comparatively few resources to operate at lower sequencing rates, thereby profiting the most from adaptive sampling. Finally, our methodology can be readily adapted to study undersampling in other dynamical systems.

arxiv.org

Complex Mapping between Neural Response Frequency and Hierarchical Linguistic Units in Natural Speech. (arXiv:2301.08011v1 [q-bio.NC]) arxiv.org/abs/2301.08011

Complex Mapping between Neural Response Frequency and Hierarchical Linguistic Units in Natural Speech

When listening to connected speech, the brain can extract multiple levels of linguistic units, such as syllables, words, and sentences. It has been hypothesized that the time scale of cortical activity encoding each linguistic unit is commensurate with the time scale of that linguistic unit in speech. Evidence for the hypothesis originally comes from studies using the frequency-tagging paradigm that presents each linguistic unit at a constant rate, and more recently extends to studies on natural speech. For natural speech, it is sometimes assumed that neural activity tracking the speech envelope near 1 Hz encodes phrase-level information while neural activity tracking the speech envelope around 4-5 Hz encodes syllable-level information. Here, however, it is demonstrated using simulations that a neural response that only tracks the onset of each syllable can strongly correlate with the speech envelope both around 4-5 Hz and below 1 Hz. Further analyses reveal that the 1-Hz correlation mainly originates from the pauses in connected speech. The results here suggest that a simple frequency-domain analysis cannot separate the neural tracking of different linguistic units in natural speech.

arxiv.org

Evolutionary dynamics of glucose-deprived cancer cells: insights from experimentally-informed mathematical modelling. (arXiv:2301.08023v1 [q-bio.CB]) arxiv.org/abs/2301.08023

Evolutionary dynamics of glucose-deprived cancer cells: insights from experimentally-informed mathematical modelling

Glucose is a primary energy source for cancer cells. Several lines of evidence support the idea that monocarboxylate transporters, such as MCT1, elicit metabolic reprogramming of cancer cells in glucose-poor environments, allowing them to reuse lactate, a byproduct of glucose metabolism, as an alternative energy source with serious consequences for disease progression. We employ a synergistic experimental and mathematical modelling approach to explore the evolutionary processes at the root of cancer cell adaptation to glucose deprivation, with particular focus on the mechanisms underlying the increase in MCT1 expression observed in glucose-deprived aggressive cancer cells. Data from in vitro experiments on breast cancer cells are used to inform and calibrate a mathematical model that comprises a partial integro-differential equation for the dynamics of a population of cancer cells structured by the level of MCT1 expression. Analytical and numerical results of this model suggest that environment-induced changes in MCT1 expression mediated by lactate-associated signalling pathways enable a prompt adaptive response of glucose-deprived cancer cells, whilst fluctuations in MCT1 expression due to epigenetic changes create the substrate for environmental selection to act upon, speeding up the selective sweep underlying cancer cell adaptation to glucose deprivation, and may constitute a long-term bet-hedging mechanism.

arxiv.org

Finding analytical approximations for discrete, stochastic, individual-based models of ecology. (arXiv:2301.08094v1 [q-bio.PE]) arxiv.org/abs/2301.08094

Finding analytical approximations for discrete, stochastic, individual-based models of ecology

Discrete time, spatially extended models play an important role in ecology, modelling population dynamics of species ranging from micro-organisms to birds. An important question is how 'bottom up', individual-based models can be approximated by 'top down' models of dynamics. Here, we study a class of spatially explicit individual-based models with contest competition: where species compete for space in local cells and then disperse to nearby cells. We start by describing simulations of the model, which exhibit large-scale discrete oscillations and characterise these oscillations by measuring spatial correlations. We then develop two new approximate descriptions of the resulting spatial population dynamics. The first is based on local interactions of the individuals and allows us to give a difference equation approximation of the system over small dispersal distances. The second approximates the long-range interactions of the individual-based model. These approximations capture demographic stochasticity from the individual-based model and show that dispersal stabilizes population dynamics. We calculate extinction probability for the individual-based model and show convergence between the local approximation and the non-spatial global approximation of the individual-based model as dispersal distance and population size simultaneously tend to infinity. Our results provide new approximate analytical descriptions of a complex bottom-up model and deepen understanding of spatial population dynamics.

arxiv.org

Modeling sustained transmission of Wolbachia among Anopheles mosquitoes: Implications for malaria control in Haiti. (arXiv:2301.08231v1 [q-bio.PE]) arxiv.org/abs/2301.08231

Modeling sustained transmission of Wolbachia among Anopheles mosquitoes: Implications for malaria control in Haiti

Wolbachia infection in Anopheles albimanus mosquitoes can render mosquitoes less capable of spreading malaria. We develop and analyze an ordinary differential equation model to evaluate the effectiveness of Wolbachia-based vector control strategies among wild Anopheles mosquitoes in Haiti. The model tracks the mosquito life stages, including egg, larva, and adult (male and female). It also accounts for critical biological effects, such as the maternal transmission of Wolbachia through infected females and cytoplasmic incompatibility, which effectively sterilizes uninfected females when they mate with infected males. We derived and interpreted dimensionless numbers, including the basic reproductive number and next-generation numbers. The proposed system presents backward bifurcation, which indicates a threshold infection that needs to be exceeded to establish a stable Wolbachia infection. The sensitivity analysis ranks the relative importance of the epidemiological parameters at the baseline. We simulate different intervention scenarios, including pre-release mitigation using larviciding and thermal fogging before the release, multiple releases of infected populations, and different release timing. Our simulations show that the most efficient approach to establishing Wolbachia is to release all the infected mosquitoes immediately after the pre-release mitigation process. Also, the model predicts that it is more efficient to release during the dry season than the wet season.

arxiv.org
Show older
Qoto Mastodon

QOTO: Question Others to Teach Ourselves
An inclusive, Academic Freedom, instance
All cultures welcome.
Hate speech and harassment strictly forbidden.