Vladimir Savić
Gopi Adusumilli 
IndiGo parent swings to Rs 2,537 crore loss in Q4 from Rs 3,067 crore profit #IndiGo #Rs #Q #socialnewsxyz
IndiGo parent swings to Rs 2,537 crore loss in Q4 from Rs 3,067 crore profit - Social News XYZ
Mumbai, May 29 (SocialNews.XYZ) InterGlobe Aviation, the parent company of IndiGo, on Friday reported a consolidated net loss of Rs 2,537 crore for the March quarter (Q4) of FY26, compared with a net profit of... - Social News XYZ
Rxiv mechanobio📰 "PACE: Geometry-Aware Bridge Transport for Single-Cell Trajectory Inference"
https://arxiv.org/abs/2605.18587 #Dynamics #Q-Bio.Gn #Cs.Lg #Cell
https://arxiv.org/abs/2605.18587 #Dynamics #Q-Bio.Gn #Cs.Lg #Cell
PACE: Geometry-Aware Bridge Transport for Single-Cell Trajectory Inference
Single-cell trajectory inference from destructive time-course snapshots is fundamentally ill-posed: neither cross-time cell correspondences nor continuous trajectories are observed, so the snapshot distributions alone do not uniquely determine the underlying dynamics. Existing optimal transport and flow-based methods typically couple cells by Euclidean proximity at observed clock times, which can misalign trajectories when development is asynchronous and cells sampled at the same experimental time occupy different latent pseudotime stages. We propose PACE, a trajectory inference framework that recovers geometry-consistent continuous transport dynamics from destructive time-course snapshots through three coupled components. First, PACE constructs a state- and time-dependent anisotropic Riemannian metric that assigns low transport cost along locally supported tangent directions while penalizing normal velocity components. Second, it alternates between refining cross-time couplings under the induced path-action cost and fitting endpoint-preserving neural bridges between adjacent snapshots. Third, it distills the learned bridge dynamics into a global continuous-time velocity field over cellular states. Across seven controlled and biological datasets covering nine held-out reconstruction experiments, PACE achieves the strongest overall reconstruction performance, reducing MMD, Wasserstein-1 distance, and Wasserstein-2 distance by 23.7% on average relative to the strongest competing baseline. PACE also improves RNA-velocity alignment by 15.4% on an embryoid body differentiation benchmark, without requiring explicit cell pairing, lineage tracing, or RNA-velocity supervision during training. Code is available at https://github.com/AI4Science-WestlakeU/PACE.
📰 "Collective gene dynamics leave signatures of decision landscapes in cell fate coordinates"
https://arxiv.org/abs/2506.04219 #Cond-Mat.Stat-Mech #Cond-Mat.Dis-Nn #Dynamics #Q-Bio.Cb #Cell
https://arxiv.org/abs/2506.04219 #Cond-Mat.Stat-Mech #Cond-Mat.Dis-Nn #Dynamics #Q-Bio.Cb #Cell
Collective gene dynamics leave signatures of decision landscapes in cell fate coordinates
Multicellular organisms contain a wide variety of highly specialized cell types. The consistency and robustness of developmental trajectories suggest that complex gene regulatory networks effectively act as low-dimensional cell fate landscapes. Prior work inspired by dynamical systems theory argues that cell fate transitions fall into universal decision-making classes, but the theory connecting these geometric landscapes to high-dimensional gene expression space is still in its infancy. Here, we introduce a phenomenological model that identifies experimental signatures of decision-making classes in single-cell RNA-sequencing time-series data. The model combines low-dimensional gradient-like dynamics with high-dimensional Hopfield networks to capture the interplay between cell fate, gene expression, and signaling. We apply the framework to experimental mouse data on maturing lung alveolar cells and lineage-traced hematopoietic differentiation and show that the measured cell fate dynamics are consistent with developmental landscapes containing intermediate progenitors and saddle points. We further show that the framework can be used to understand spatial patterning and cell fate organization, focusing on Notch signaling in lung airways. Together, these results provide evidence that collective transcriptomic dynamics carry signatures of landscape features associated with universal decision-making classes.
エロ本セールナビセール中！
モンスター少女との痴情
#FANZA #Q #塚沢 #GEN #木谷椎 #椋木尋 #宇行日和 #忍桐ずん #柚子鉄線 #葛城ゆう #ねこぱんつ #みなこなみ #大和田朋輝
https://hnaportal.net/ebsn/?filter=FANZA+b165aisat00422
モンスター少女との痴情
#FANZA #Q #塚沢 #GEN #木谷椎 #椋木尋 #宇行日和 #忍桐ずん #柚子鉄線 #葛城ゆう #ねこぱんつ #みなこなみ #大和田朋輝
https://hnaportal.net/ebsn/?filter=FANZA+b165aisat00422
セール中！
モンスター少女との劣情
#FANZA #Q #塚沢 #GEN #音音丸 #つむじぃ #宇行日和 #祭野薙刀 #ねこぱんつ #みなこなみ #昇龍亭圓楽
https://hnaportal.net/ebsn/?filter=FANZA+b165aisat00488
モンスター少女との劣情
#FANZA #Q #塚沢 #GEN #音音丸 #つむじぃ #宇行日和 #祭野薙刀 #ねこぱんつ #みなこなみ #昇龍亭圓楽
https://hnaportal.net/ebsn/?filter=FANZA+b165aisat00488
セール中！
モンスター少女への欲情
#FANZA #Q #塚沢 #GEN #ぉとぉ #ぷりてゐ #石野鐘音 #あましょく #ぶーちゃん #パルコ長嶋 #大和田朋輝 #林田虎之助
https://hnaportal.net/ebsn/?filter=FANZA+b165aisat00517
モンスター少女への欲情
#FANZA #Q #塚沢 #GEN #ぉとぉ #ぷりてゐ #石野鐘音 #あましょく #ぶーちゃん #パルコ長嶋 #大和田朋輝 #林田虎之助
https://hnaportal.net/ebsn/?filter=FANZA+b165aisat00517
Japan Pop Newshttps://www.wacoca.com/news/2846295/ イラン外務省「米が停戦違反」 中東は米軍の盾ではないとハメネイ師 | ロイター ##war #AE #AMERS #Asia #ASXPAC #BH #Com #CWP #Def #DEST:NOJPTPM #DEST:NOJPWDM #DEST:NOJPZTM #dip #EMEA #EMEAE #EMRG #FERT #FRGT #Gen #IL #in #INDS #IR #JFOR #JLN #KW #MEAST #MEASTE #MTPIX #MTVID #NAMER #NEWS1 #NUCL #om #PK #POL #POTUS #Q&A #SA #SASIA #SEAFT #SECUR #SWASIA #TGLF #TOPCMB #TOPNWS #TPT #TRAN #TRN #US #wash #WorldNews
"It is hard to imagine a more stupid or more dangerous way of making decisions than by putting those decisions in the hands of people who pay no price for being wrong."
-- Thomas Sowell #q
📰 "Learning dynamical systems with biochemically informed neural ordinary differential equations"
https://arxiv.org/abs/2605.24170 #Dynamics #Q-Bio.Qm #Math.Ds #Matrix #Cs.Lg
https://arxiv.org/abs/2605.24170 #Dynamics #Q-Bio.Qm #Math.Ds #Matrix #Cs.Lg
Learning dynamical systems with biochemically informed neural ordinary differential equations
Ordinary differential equation models of biochemical reactions are often formulated as stoichiometric systems in which the dynamics arise from a collection of interacting processes. A central challenge is that the functional form of each process is rarely known a priori and may be difficult to infer from data. We propose biochemically informed neural ordinary differential equations (BINODEs), a neural-ODE framework that retains the stoichiometric structure of mechanistic models while representing individual processes by neural networks. In BINODEs, the outputs of neural network processes (NNPs) are mapped to state derivatives through a linear layer analogous to a stoichiometric matrix. This architecture allows biological side information, such as process-specific inputs, sign constraints, and monotonicity assumptions, to be built directly into the model. We characterize the approximation properties of NNPs for several standard biochemical rate laws and show that the proposed framework recovers both trajectories and process-level structure in Monod, Lotka--Volterra, pharmacokinetic, and ultradian endocrine models. These results suggest that BINODEs offer a useful compromise between mechanistic interpretability and data-driven flexibility for modeling partially known biochemical or biological dynamical systems.