Browse Articles

Estimating of contact area among carbon nanofibers in nanocomposites by the features of network, tunnel and interphase

Optimal disk packing of chloroplasts in plant cells

Photosynthesis is essential for ecosystem survival, but while plants require light, excessive exposure can damage cells. Chloroplasts, photosynthetic organelles, respond via self-organized motion within cells to optimize light absorption. These disk-shaped organelles must balance two competing needs: dense packing to enhance absorption under dim light and rapid spatial rearrangement to avoid damage from excess light. Using microscopy, we show that plant cell shape and chloroplast size achieve both goals: dense monolayer packing for optimal absorption in low light and sidewall packing for light avoidance. We present a theoretical model using random close packing simulations of polydispersed hard disks in rectangular boxes and find optimal cell shapes that match plant cell measurements. Our findings highlight how particle packing principles under confinement enable light adaptation in plants, offering insights into organelle organization under confinement, a physical challenge relevant across biological systems.

Transformer-based representation learning for robust gene expression modeling and cancer prognosis

Correction to Supporting Information for Milkman et al., A megastudy of text-based nudges encouraging patients to get vaccinated at an upcoming doctor’s appointment

Ultrastructural evaluation of lithium-induced autophagic and mitochondrial stress in 3D endometrial and neuroblastoma spheroids

Abstract Lithium chloride (LiCl), a widely used mood stabilizer, has been reported to modulate selective autophagy pathways, including mitophagy. However, its ultrastructural effects in three-dimensional (3D) tumor models remain incompletely characterized. In this study, we examined the subcellular alterations induced by LiCl in 3D spheroid cultures derived from Ishikawa endometrial cancer and SH-SY5Y neuroblastoma cells. Spheroids were treated with 1, 10, or 50 mM LiCl and analyzed using transmission electron microscopy (TEM). The analysis revealed double-membrane-bound vesicles surrounding degenerating mitochondria, along with cytoplasmic vacuolization and membrane remodeling. These morphological features are suggestive of mitophagic activity, accompanied by stress-related ultrastructural remodeling. Although molecular validation (e.g., LC3B or PINK1/Parkin Western blotting) was not performed, the observed ultrastructural profiles are consistent with organelle-selective autophagy. These findings underscore the dose-dependent cellular responses to LiCl and support the value of 3D cancer spheroids as models to explore non-canonical autophagy-related stress pathways. Future studies incorporating molecular markers such as LC3B, PINK1, Parkin, and Lamin B1 will be essential to confirm these observations.

Parallel shifts in differential gene expression reveal convergent miniaturization in fishes

Body size variation in vertebrates is a complex polygenic trait, tightly correlated with numerous aspects of a species’ biology, ecology, and physiology. Miniaturization, the extreme reduction of adult body size, is a common phenomenon across the Tree of Life, yet the mechanisms underlying this process are poorly understood. Here, we investigate the molecular basis of body size evolution in goby fishes, a clade encompassing some of the smallest vertebrates on Earth. We generate a genome-wide phylogeny for 162 Gobioidei species and perform comparative transcriptomics across three clades with repeated instances of miniaturization and large-bodied forms. We identified 54 differentially expressed one-to-one orthologs between miniature and large-bodied species. These genes reveal distinct functional profiles, suggesting that regulation of cell numbers is a key mechanism governing body size control. Miniature species consistently overexpress growth inhibitors like CDKN1B and ING2 , associated with tighter cell cycle regulation and decreased proliferation rates, while large-bodied species upregulate growth-promoting genes such as TGFB3 , linked to tissue development and growth signaling. These enriched functional pathways, conserved since the Eocene (50 Ma), suggest macroevolutionary convergence in size regulation over deep time. Our findings provide insights into how size determination is governed at a genetic level and highlights the importance of exploring these factors in nonmodel organisms to uncover the fundamental processes regulating vertebrate body size evolution.

Sex-based differences in mortality among a large cohort of hospitalized patients with RT-PCR-confirmed SARS-CoV-2 infection at five different pandemic waves in Northern Iran

Abstract The COVID-19 pandemic has highlighted significant sex-based disparities in mortality, with men often experiencing higher death rates than women. These disparities vary across regions, time, COVID-19 waves, and viral variants (e.g., Alpha, Delta, Omicron). This study aims to analyze sex-specific mortality trends across pandemic waves and COVID-19 related mortality determinants using reverse transcriptase polymerase chain reaction (RT-PCR) confirmed cases. This retrospective cohort study was conducted on a data registry consisting of 44,544 hospitalized COVID-19 patients with positive RT-PCR from 2019 to 2021. Using SPSS version 26, a multiple logistic regression model with adjustment for potential confounders was conducted to calculate the odds ratio (OR) and 95% confidence interval (95%CI) of COVID-19 related death in each variable, both in the total population and separately sorted by sex. A dose-responsive relationship between age, number of comorbidities, and death was observed in the multiple logistic regression model. Certain comorbidities such as diabetes, cancer, cardiac diseases, chronic neurological diseases, and COPD were significantly associated with death. Males were at 17% higher risk of death compared to women (OR: 1.17, 95%CI: 1.09–1.25, P  < 0.001) after adjustment for confounders. Compared to the fifth peak, females had 2.35 (95%CI: 1.91–2.89), 1.34 (95%CI: 1.17–1.53), 1.08 (95%CI: 0.95–1.23), and 0.76 (95%CI: 0.65–0.89) times odds of death, whereas males had 2.50 (95%CI: 2.07–3.01), 1.20 (95%CI: 1.05–1.37), 0.98 (95%CI: 0.86–1.12), and 0.78 (95%CI: 0.66–0.91) folds odds of death in the first, second, third, and fourth peaks, respectively. Our results showed that men had higher odds of mortality overall, but there were no significant differences at each peak separately. Also, age and the number of comorbidities demonstrated a significant association with mortality, with possible dose-responsive behavior.

Linker histone regulates the myeloid versus lymphoid bifurcation of multipotent hematopoietic stem and progenitors

Myeloid-biased differentiation of multipotent hematopoietic stem and progenitor cells (HSPCs) occurs with aging or exhaustion. The molecular mechanism(s) responsible for this fate bias remain unclear. Here, we report that linker histone regulates HSPC fate choice at the lymphoid versus myeloid bifurcation. Linker histones package nucleosomes and compact chromatin. HSPCs expressing a doxycycline (dox)-inducible H1.0 transgene favor the lymphoid fate, display strengthened nucleosome organization, and reduced chromatin accessibility at subsets of genomic regions. The genomic regions showing reduced chromatin accessibility host many known marker genes of myeloid-biased HSCs. The transcription factor Hlf is located in one of the most differentially closed regions, whose chromatin accessibility and gene expression are reduced in H1.0 high HSPCs. Failure to reduce Hlf expression in multipotential HSPCs abrogates the H1.0-endowed lymphoid potential. Furthermore, HSPCs display aspartyl protease–dependent H1.0 decreases, especially in response to interferon alpha (IFNα). Aspartyl protease inhibitors preserve endogenous H1.0 levels and promote the lymphoid fate of wild type HSPCs. Thus, our work elucidates a molecular scenario of how myeloid bias arises and uncovers a point of intervention for correcting myeloid skewed hematopoiesis.

Prototype-oriented class-conditional clustering transport for unsupervised domain adaptation

Quantum dynamics of C7N− and C10H− anions in collision with H2 at interstellar medium conditions

We present quantum calculations for two of the longest linear anions, recently detected in the interstellar environments: C7N− and C10H−, in collision with the most abundant neutral partner in the same environment: the H2 molecule. The interaction forces are obtained from accurate ab initio calculations for the two partners as rigid rotors, generating a dense grid of potential energy values in four dimensions. The potential energy surface is, in turn, fitted by using high-level neural network procedures, and multipolar expansion coefficients are obtained to provide input for calculations of the collision-induced rotational energy transfer processes at the temperatures of interstellar environments. Cross sections are used to generate state-to-state inelastic rate coefficients up to 50 K. The results for the cases of ortho- and para-H2 as collision partners are discussed and analyzed.

Dual-site cooperation for synergistic optimization of the band structure and spin state to facilitate C–N coupling reaction

The emerging electrocatalytic C–N coupling reaction provides an attractive route toward green urea synthesis, but a lack of in-depth insight into the catalytic mechanism and the geometric/electronic configurations that determine the key C- and N-coupling intermediates formation hampers the exploration of efficient catalysts. Herein, we design a bimetallic oxide (Fe-Mo-O) with dual active sites of Fe and Mo for the adsorption and activation of NO 2 − and CO 2 , respectively. Constructing dual-metal catalyst leads to an upshift of the d-band center and the generation of an intermediate-spin Fe center, which not only favors the selective conversion of *CO 2 into the key intermediate *CO on Mo sites, but also facilitates the adsorption and reduction of NO 2 − on Fe sites. Operando characterizations and theoretical calculations together elucidate that urea generation is associated with the formation of *CONH 2 intermediate by coupling *CO and *NH 2 on the alternating Mo and intermediate-spin Fe active sites, ultimately synergistically lowering the C–N coupling energy barrier. Specifically, the Fe-Mo-O catalyst delivers a high urea yield rate of 681.8 μg h −1 mg −1 cat. and an excellent Faradaic efficiency of 60% at −0.5 V (vs. RHE). Furthermore, a C–N coupling paired with a glycerol oxidation system allows for energy-saving electrochemical coproduction of urea and formic acid. Our findings offer a feasible strategy to develop cutting-edge electrocatalysts for urea synthesis by active site design and electronic structure regulation.

Determinants of preterm birth among newborns in resource limited settings

Partially active polymer barrier crossing retains kink mechanism similar to a long passive polymer

Using Brownian dynamics simulations (BD), we study two-dimensional (2D) barrier crossing of a long passive self-avoiding polymer that becomes active upon reaching the trans side, mimicking biomolecular translocation into nonthermally active regions across membrane pores. We find an analytical time-dependent kink solution or soliton-like solution for a passive Rouse polymer with N monomers crossing a one-dimensional asymmetric barrier, where the average translocation time, ⟨tc⟩ ∼ N. This manifests a kink, a polymer conformation stretched over the barrier that moves along the chain backbone at constant speed, opposite to the translocation direction. The analytical result agrees with our simulation results for passive phantom and self-avoiding polymers crossing a 2D barrier within the l ≪ Rg ≪ L limit, where Rg is the radius of gyration in its free state, l is its Kuhn length, and L is the barrier width. Within the same limit, the partially active self-avoiding polymer with varying self-propulsion forces follows a similar time-dependent kink mechanism at higher trans side monomer activities, which facilitate translocation by pulling the cis side chain segments. Interestingly, for all geometrical limits, the kink mechanism is retained by the partially active polymer at high self-propulsion forces when unbiased. In contrast, the passive self-avoiding polymer translocation deviates from the kink motion as ⟨tc⟩ ∼ Nα, α ∼ 2–2.5, irrespective of the limit of L when unbiased and l ≈ L ≤ Rg in driven translocations. The mechanism provides insights into translocations relevant to living matter and nanotechnology.

Full interhemispheric integration sustained by a fraction of posterior callosal fibers

The dynamic integration of the lateralized and specialized capacities of the two cerebral hemispheres constitutes a hallmark feature of human brain function. This interhemispheric exchange of information critically depends upon the corpus callosum. Classical anatomical descriptions of callosal organization outline a topographic gradient from front to back, such that specific transcallosal fibers support distinct aspects of integrated brain function. Here, we present a challenge to this conventional model. Using neuroimaging data obtained from a new cohort of adult corpus callosotomy patients, we leverage modern network neuroscience techniques to show that full interhemispheric integration can be achieved via a small proportion of posterior callosal fibers. Partial callosotomy patients with spared callosal fibers retained widespread patterns of interhemispheric functional connectivity and showed no signs of behavioral disconnection, even with only 1 cm of the splenium intact. Conversely, only complete callosotomy patients demonstrated sweeping disruptions of interhemispheric network architectures, aligning with disconnection syndromes long-thought to reflect diminished information propagation and communication across the brain. These findings motivate an evolving mechanistic understanding of synchronized interhemispheric neural activity for large-scale human brain function and behavior.

Mixed convolutional classification method for hyperspectral images based on spatial spectrum orthogonal constraints and bidirectional attention mechanism

Machine learning workflow for analysis of high-dimensional order parameter space: A case study of polymer crystallization from molecular dynamics simulations

Currently, identification of crystallization pathways in polymers is being carried out using molecular simulation-based data on a preset cutoff point on a single order parameter (OP) to define nucleated or crystallized regions. Aside from sensitivity to the cutoff, each of these OPs introduces its own systematic biases. In this study, an integrated machine learning workflow is presented to quantify accurately crystallinity in polymeric systems using atomistic molecular dynamics simulation data. Each atom is represented by a high-dimensional feature vector that combines geometric, thermodynamic-like, and symmetry-based descriptors. Low-dimensional embeddings are employed to expose latent structural fingerprints within atomic environments. Subsequently, unsupervised clustering on the embeddings is used to identify crystalline and amorphous atoms with high fidelity. After generating high-quality labels with multidimensional data, we use supervised learning techniques to identify a minimal set of order parameters that can fully capture this label. Various tests were conducted to reduce the feature set, and it is shown that using only three order parameters, namely q6, S̄i, and p2, is sufficient to recreate the crystallization labels with great accuracy. Based on these observed OPs, the crystallinity index (C-index) is introduced as the logistic regression model’s probability of crystallinity. This measure remains bimodal at all stages of the process and achieves >98% classification performance. Notably, a model trained on one or a few snapshots enables efficient on-the-fly computation of crystallinity. Lastly, we demonstrate how the optimal C-index fit evolves during various stages of crystallization, supporting the hypothesis that entropy dominates early nucleation, while q6 gains relevance in the later stages. This workflow yields a data-driven strategy for OP selection and provides a generalizable metric to monitor structural transformations in large-scale polymer simulations.

Descattering and image restoration with a transformer-based neural network in deep tissue imaging

Imaging biological structures deep inside tissues is crucial but challenging due to common light scattering. This study proposes a multiattention network that directly maps degraded scattering two-photon excitation fluorescence (TPEF) images to high-quality scattering-free images, thereby computationally extending the imaging depth for TPEF without requiring complex optical additions. The model relies solely on simulated data rather than well-registered real data pairs, and is trained to descatter and restore hidden spatial information at greater depths. Quantitative evaluations on simulated fluorescent beads and vasculature show significant performance improvements in peak signal-to-noise ratio (23 to 29 dB) and structural similarity index (23×) compared to the raw data. We also apply the framework to various ex vivo and in vivo experiments, achieving clear visualization of lipid droplets up to a depth of 1,300 μm and of vascular structure and astrocytes up to 950 μm and 500 μm, respectively, in live mouse brains at lower excitation powers.

Berberine alleviates the proliferation and metastasis of ESCA by promoting CCDC18-AS1 expression based on bioinformatics and in vitro experimental verification

From heteropolymer stiffness distributions to effective homopolymers. II. Conformational analysis of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are characterized by a lack of defined secondary and tertiary structures and are thus well-suited for descriptions within polymer theory. However, the intrinsic heterogeneity of proteins, stemming from their diverse amino acid building blocks, introduces local variations in chain stiffness, which can impact conformational behavior at larger scales. To investigate this effect, we developed a heterogeneous worm-like chain model in which the local persistence length follows a Gaussian distribution. We demonstrate that these heterogeneous chains can be effectively mapped to homogeneous chains with a single effective persistence length. To assess whether this mapping can be extended to naturally occurring IDPs, we performed simulations using various coarse-grained IDP models, finding that the simulated IDPs have similar shapes compared to the corresponding homogeneous and heterogeneous worm-like chains. However, the IDPs are systematically larger than ideal worm-like chains, yet slightly more compact when excluded volume interactions are considered. We attribute these differences to intramolecular interactions between non-bonded monomers, which our theoretical models do not account for.

Therapeutic IgG- and IgM-specific proteases disarm the acetylcholine receptor autoantibodies that drive myasthenia gravis pathology

Myasthenia gravis (MG) is an autoimmune disorder caused mainly by autoantibodies against the acetylcholine receptor (AChR), leading to muscle weakness. While treatments targeting AChR autoantibodies benefit many, some patients remain refractory, highlighting the need for personalized therapies. This study evaluates the therapeutic potential of S-1117, a pan-IgG-specific protease, in AChR autoantibody-mediated pathology. Using live cell-based assays, we examined AChR-specific monoclonal IgG autoantibodies (mAbs) and patient-derived serum samples for their effects on receptor binding, blockade, internalization, and complement activation, before and after treatment with S-1117. S-1117 effectively removed the crystallizable fragment (Fc)γ from both mAbs and serum IgG, impairing Fcγ-mediated complement activation in both soluble and antigen-bound forms. In cases with partial complement reduction, AChR-specific IgM contributed to complement deposition. AChR-IgM acted in concert with IgG in some patients to enhance complement deposition, while acting as main complement driver in others. An IgM-specific protease completely suppressed the pathogenic effects of AChR-IgM in two independent patient cohorts. These findings highlight the therapeutic potential of S-1117 in neutralizing AChR-IgG Fcγ-mediated effector functions and reveal an MG subset driven by IgM pathology. Our study shows that targeting both IgG- and IgM-mediated mechanisms with therapeutic proteases provides an approach to MG treatment and establishes a framework for patient stratification based on disease mechanisms, advancing precision medicine in MG.