Erk709#Conspiracy theory 101:
1. Come up with a #ConspiracyTheory
2. Tell everyone
3. Remember, whenever anyone gets shocked, upset or concerned, it's proof they are in on it!
4. Jump back to #1
#humor #foilHat Non-anonymous #Q (remember #StarTrek? #idiots)
Reddit Tech VN Bot"Bẫy đèn xanh" trong self-hosted: Dịch vụ "chạy" chưa chắc đã "sẵn sàng"!
🔍 Lỗi phổ biến: Container hiển thị "healthy" nhưng vẫn gặp sự cố ngầm do:
- Database nhận kết nối nhưng chưa khởi tạo xong
- Reverse proxy cache lỗi 502 khi upstream chưa sẵn sàng
- Ứng dụng khởi động trước khi mount volume
💡 Giải pháp: Thiết lập healthcheck chi tiết + điều kiện phụ thuộc (depends_on) để đảm bảo các service đã THỰC SỰ hoạt động.
#SelfHosted #DevOps #RaceCondition #CloudComputing
#TựHost #Q
flypapers📰 "Geometric developmental principles for the emergence of brain-like weighted and directed neuronal networks"
https://arxiv.org/abs/2601.05021
#Physics.Bio-Ph #Drosophila #Q-Bio.Nc
https://arxiv.org/abs/2601.05021
#Physics.Bio-Ph #Drosophila #Q-Bio.Nc
Geometric developmental principles for the emergence of brain-like weighted and directed neuronal networks
Brain networks exhibit remarkable structural properties, including high local clustering, short path lengths, and heavy-tailed weight and degree distributions. While these features are thought to enable efficient information processing with minimal wiring costs, the fundamental principles that generate such complex network architectures across species remain unclear. Here, we analyse single-neuron resolution connectomes across five species (C. Elegans, Platynereis, Drosophila M., zebrafish and mouse) to investigate the fundamental wiring principles underlying brain network formation. We show that distance-dependent connectivity alone produces small-world networks, but fails to generate heavy-tailed distributions. By incorporating weight-preferential attachment, which arises from spatial clustering of synapses along neurites, we reproduce heavy-tailed weight distributions while maintaining small-world topology. Adding degree-preferential attachment, linked to the extent of dendritic and axonal arborization, enables the generation of heavy-tailed degree distributions. Through systematic parameter exploration, we demonstrate that the combination of distance dependence, weight-preferential attachment, and degree-preferential attachment is sufficient to reproduce all characteristic properties of empirical brain networks. Our results reveal that activity-independent geometric constraints during neural development can account for the conserved architectural principles observed across evolutionarily distant species, suggesting universal mechanisms governing neural circuit assembly.
Rxiv mechanobio📰 "Cell size control in bacteria is modulated through extrinsic noise, single-cell- and population-growth"
https://arxiv.org/abs/2601.05193 #Cond-Mat.Stat-Mech #Q-Bio.Pe #Dynamics #Q-Bio.Cb #Cell
https://arxiv.org/abs/2601.05193 #Cond-Mat.Stat-Mech #Q-Bio.Pe #Dynamics #Q-Bio.Cb #Cell
Cell size control in bacteria is modulated through extrinsic noise, single-cell- and population-growth
Living cells maintain size homeostasis by actively compensating for size fluctuations. Here, we present two stochastic maps that unify phenomenological models by integrating fluctuating single-cell growth rates and size-dependent noise mechanisms with cell size control. One map is applicable to mother machine lineages and the other to lineage trees of exponentially-growing cell populations, which reveals that population dynamics alter size control measured in mother machine experiments. For example, an adder can become more sizer-like or more timer-like at the population level depending on the noise statistics. Our analysis of bacterial data identifies extrinsic noise as the dominant mechanism of size variability, characterized by a quadratic conditional variance-mean relationship for division size across growth conditions. This finding contradicts the reported independence of added size relative to birth size but is consistent with the adder property in terms of the independence of the mean added size. Finally, we derive a trade-off between population-growth-rate gain and division-size noise. Correlations between size control quantifiers and single-cell growth rates inferred from data indicate that bacteria prioritize a narrow division-size distribution over growth rate maximisation.
nico
📰 "Functional classification of metabolic networks"
https://arxiv.org/abs/2503.14437 #Physics.Bio-Ph #Dynamics #Q-Bio.Mn #Matrix
https://arxiv.org/abs/2503.14437 #Physics.Bio-Ph #Dynamics #Q-Bio.Mn #Matrix
Functional classification of metabolic networks
Chemical reaction networks underpin biological and physical phenomena across scales, from microbial interactions to planetary atmosphere dynamics. Bacterial communities exhibit complex competitive interactions for resources, human organs and tissues demonstrate specialized biochemical functions, and planetary atmospheres can display diverse organic and inorganic chemical processes. Despite their complexities, comparing these networks methodically remains a challenge due to the vast underlying degrees of freedom. In biological systems, comparative genomics has been pivotal in tracing evolutionary trajectories and classifying organisms via DNA sequences. However, purely genomic classifications often fail to capture functional roles within ecological systems. Metabolic changes driven by nutrient availability highlight the need for classification schemes that integrate metabolic information. Here we introduce and apply a computational framework for a classification scheme of organisms that compares matrix representations of chemical reaction networks using the Grassmann distance, corresponding to measuring distances between the nullspaces of stoichiometric matrices. Applying this framework to human gut microbiome data confirms that metabolic distances are distinct from phylogenetic distances, underscoring the limitations of genetic information in metabolic classification. Importantly, our analysis of metabolic distances reveals functional groups of organisms enriched or depleted in specific metabolic processes and shows robustness to metabolically silent genetic perturbations. The generalizability of metabolic Grassmann distances is illustrated by application to chemical reaction networks in human tissue and planetary atmospheres, highlighting its potential for advancing functional comparisons across diverse chemical reaction systems.
COREandCO🎤On est allé jusqu'en Bretagne pour rencontrer Houle qui ont répondu à nos nombreuses questions...
L'interview complète ➡️https://www.coreandco.fr/interviews/houle-august-2025-438.html
Miguel¡Primer #Q&A (preguntas y respuestas) de Ubuntu Touch del año! Conoce qué es un Q&A y las formas de participar. @ubports.bsky.social #UbuntuTouch #UBports #LinuxMobile
https://www.innerzaurus.com/qa-de-ubuntu-touch-que-son-y-como-hacer-oir-tu-voz/
https://www.innerzaurus.com/qa-de-ubuntu-touch-que-son-y-como-hacer-oir-tu-voz/
📰 "A New Framework for Explainable Rare Cell Identification in Single-Cell Transcriptomics Data"
https://arxiv.org/abs/2601.01358 #Dynamics #Q-Bio.Gn #Cs.Lg #Cell
https://arxiv.org/abs/2601.01358 #Dynamics #Q-Bio.Gn #Cs.Lg #Cell
A New Framework for Explainable Rare Cell Identification in Single-Cell Transcriptomics Data
The detection of rare cell types in single-cell transcriptomics data is crucial for elucidating disease pathogenesis and tissue development dynamics. However, a critical gap that persists in current methods is their inability to provide an explanation based on genes for each cell they have detected as rare. We identify three primary sources of this deficiency. First, the anomaly detectors often function as "black boxes", designed to detect anomalies but unable to explain why a cell is anomalous. Second, the standard analytical framework hinders interpretability by relying on dimensionality reduction techniques, such as Principal Component Analysis (PCA), which transform meaningful gene expression data into abstract, uninterpretable features. Finally, existing explanation algorithms cannot be readily applied to this domain, as single-cell data is characterized by high dimensionality, noise, and substantial sparsity. To overcome these limitations, we introduce a framework for explainable anomaly detection in single-cell transcriptomics data which not only identifies individual anomalies, but also provides a visual explanation based on genes that makes an instance anomalous. This framework has two key ingredients that are not existed in current methods applied in this domain. First, it eliminates the PCA step which is deemed to be an essential component in previous studies. Second, it employs the state-of-art anomaly detector and explainer as the efficient and effective means to find each rare cell and the relevant gene subspace in order to provide explanations for each rare cell as well as the typical normal cell associated with the rare cell's closest normal cells.
📰 "The spontaneous emergence of leaders and followers in a mathematical model of cranial neural crest cell migration"
https://arxiv.org/abs/2601.00374 #CellMigration #Q-Bio.Cb #Dynamics #Cell
https://arxiv.org/abs/2601.00374 #CellMigration #Q-Bio.Cb #Dynamics #Cell
The spontaneous emergence of leaders and followers in a mathematical model of cranial neural crest cell migration
Many agent-based mathematical models of cranial neural crest cell (CNCC) migration impose a binary phenotypic partition of cells into either leaders or followers. In such models, the movement of leader cells at the front of collectives is guided by local chemoattractant gradients, while follower cells behind leaders move according to local cell-cell guidance cues. Although such model formulations have yielded many insights into the mechanisms underpinning CNCC migration, they rely on fixed phenotypic traits that are difficult to reconcile with evidence of phenotypic plasticity in vivo. A later agent-based model of CNCC migration aimed to address this limitation by allowing cells to adaptively combine chemotactic and cell-cell guidance cues during migration. In this model, cell behaviour adapts instantaneously in response to environmental cues, which precludes the identification of a persistent subset of cells as leader-like over biologically relevant timescales, as observed in vivo. Here, we build on previous leader-follower and adaptive phenotype models to develop a polarity-based agent-based model of CNCC migration, in which all cells evolve according to identical rules, interact via a pairwise interaction potential, and carry polarity vectors that evolve according to a dynamical system driven by time-averaged exposure to chemoattractant gradients. Numerical simulations of this model show that a leader-follower phenotypic partition emerges spontaneously from the underlying collective dynamics of the model. Furthermore, the model reproduces behaviour that is consistent with experimental observations of CNCC migration in the chick embryo. Thus, we provide an experimentally consistent, mechanistically-grounded mathematical model that captures the emergence of leader and follower cell phenotypes without their imposition a priori.