Biology subjects

Grama, A.

Publications and source records attributed to Grama, A..

9 recordsLinked to original sources

bioRxiv2026

Soil microbial inoculants augment fertilizer performance across contrasting cropping systems in Rwanda

Smallholder farming systems in sub-Saharan Africa are constrained by declining soil fertility, erosion, and rising fertilizer costs, creating an urgent need for scalable inputs that sustain yields while maintaining soil health. While there is some evidence that microbial inoculants may offer a promising complement to conventional fertility management, field-scale evidence in tropical cereal and tuber systems remains limited. Here, we evaluated a multi-species inoculant composed of 20-22 Bacillus and Streptomyces species on potato and maize across four sites in Rwanda over two growing seasons (2025A and 2025B). Treatments included the inoculant applied at two rates (150 and 250 g ha-1), both alone and in combination with standard fertilization (inorganic fertilizer plus manure), alongside untreated and fertilized controls. Co-application of the inoculant with standard fertilization increased yield and plant biomass beyond fertilization alone, with gains of 6-51% for maize and 3-58% for potato. However, while the inoculant applied alone outperformed untreated controls, it generally did not match standard fertilization. Responses were strongest and most consistent for large-grade potato tubers, and application rate interacted with crop type, whereby the lower dose maximized marketable tuber yield, while maize showed a positive dose-response for grain and biomass. Yield increases were not accompanied by reductions in crop nutrient density, which was instead governed by site-level differences. Altogether, these results indicate that multi-species microbial inoculants are an effective complement to existing fertility practices that may offer, pending further research, a potential pathway to partial fertilizer replacement while sustaining productivity and nutritional quality in smallholder tropical agriculture.

ecology↗

bioRxiv2026

Solving High-Dimensional Population Balance Equations via Dynamics-Preserving Autoencoders

High-dimensional population balance equations (PBEs) provide a natural framework for modeling heterogeneous cell populations, but their direct numerical solution becomes computationally prohibitive when the internal state space contains many molecular variables. We propose a hybrid mechanistic-machine learning framework for reducing and simulating PBEs defined over high-dimensional intracellular coordinates. The cell population is described by a number density n(x, t), where x [isin] [R]N represents gene and protein states associated with macrophage activation. A dynamics-preserving autoencoder maps this state space to a low-dimensional latent coordinate z [isin] [R]d, with d << N, while retaining key qualitative features of the underlying gene regulatory network, including attractor structure and multistability. Mechanistic information from the original regulatory dynamics is used to construct interpretable drift and diffusion terms for the reduced latent-space PBE. The reduced PBE is solved using a stochastic Lagrangian particle representation, in which particles evolve according to stochastic differential equations (SDEs) corresponding to the latent drift and diffusion fields. The resulting latent-space solution is subsequently decoded and propagated back into the original state space to recover physically interpretable cellular dynamics. We demonstrate the framework on macrophage polarization under cytokine-dependent regulation, including gene knockout perturbations. Overall, the proposed framework provides a computationally tractable and mechanistically interpretable route for integrating single-cell genomic data with population balance models of cell-state dynamics.

systems biology↗

bioRxiv2026

Complex microbial consortia improve yield and physiological performance of leafy greens under deficit irrigation

O_LIWater scarcity is an increasing constraint on agricultural productivity, particularly for high-value vegetable crops. Deficit irrigation strategies can reduce water use but frequently compromise yield and crop physiological status. Scalable, biologically based tools are needed to sustain crop performance under reduced water input without reliance on additional agrochemical inputs. C_LIO_LIFive functionally diverse microbial consortia were assembled from taxa selected to support rhizosphere colonization, soil structural stabilization, and fungal-mediated nutrient and water foraging. Consortia were evaluated in greenhouse trials with lettuce (Lactuca sativa) and spinach (Spinacia oleracea) grown under full irrigation or a 30% deficit irrigation regime (70% of crop water requirement). Crop responses were assessed using yield, harvest delay, root length, wilting incidence, chlorophyll content, and Water Band Index. C_LIO_LIAcross both crops, microbial consortium treatments improved performance under deficit irrigation relative to untreated water-stressed controls. In lettuce, yield increased by 3-9%; in spinach, yield increased by 4-13%, with several treatments restoring performance to levels not significantly different from fully irrigated controls. C_LIO_LIMicrobial treatments reduced harvest delay by an average of three to four days, improved root length, lowered wilting incidence, and reduced WBI, indicating attenuation of plant water stress. In several cases, physiological responses under deficit irrigation approached those observed under full irrigation despite 30% lower water input. Higher application rates generally produced stronger responses, though this trend was not always statistically significant. C_LIO_LISynthesis and applications. Complex microbial consortia can buffer the physiological and yield penalties of deficit irrigation in leafy green crops. These findings demonstrate the potential of soil microbiome-based inoculants as scalable, biologically derived tools to improve agricultural water-use efficiency and resilience under increasing water scarcity. Practical adoption of such consortia could reduce irrigation demand in intensive vegetable production systems without proportional yield loss, supporting both economic and environmental sustainability goals in water-limited agricultural contexts. C_LI

microbiology↗

bioRxiv2026

Joint Modeling of Transcriptomic and Morphological Phenotypes for Generative Molecular Design

BackgroundEffectively translating data on complex cellular responses from transcriptomic and morphological measurements into molecular design remains a significant computational challenge. Existing generative methods operate on single modalities and condition on post-treatment measurements without leveraging paired control-treatment dynamics to capture perturbation effects. ResultsWe present Pert2Mol, a framework for multi-modal phenotype-to-structure generation that integrates transcriptomic and morphological features from paired control-treatment experiments. Pert2Mol employs bidirectional cross-attention between control and treatment states to capture perturbation dynamics, conditioning a rectified flow transformer that generates molecular structures along straight-line trajectories. We introduce Student-Teacher Self-Representation (SERE) learning to stabilize training in high-dimensional multi-modal spaces. On the Ginkgo Data Platform (GDP) dataset, Pert2Mol achieves Frechet ChemNet Distance of 4.996 compared to 7.343 for diffusion baselines and 59.114 for transcriptomics-only methods, while maintaining perfect molecular validity and appropriate physicochemical property distributions. The model demonstrates 84.7% scaffold diversity and 12.4 times faster generation than diffusion approaches with deterministic sampling suitable for hypothesis-driven validation. ConclusionsPert2Mol establishes a new paradigm for linking high-content phenotypic screening data with computational hypothesis generation in drug discovery: joint multi-modal perturbation modeling enables more accurate and structurally diverse molecular design than single-modality or diffusion-based approaches. Code and pretrained models are available at https://github.com/wangmengbo/Pert2Mol.

bioinformatics↗

bioRxiv2026

Generating Joint Transcriptomic and Morphological Responses to Drug Perturbations via Rectified Flow

MotivationPredicting cellular responses to drug perturbations requires capturing complex dependencies between transcriptomic and morphological changes. Existing approaches model these modalities in isolation, missing critical molecular-phenotypic relationships that occur simultaneously during drug treatment. No current method jointly predicts gene expression profiles and cellular morphology from chemical perturbations. ResultsWe introduce PertFlow, a unified computational frame-work that simultaneously predicts treatment gene expression (bulk RNA-seq) and generates cellular morphology (Cell Painting images) from control cellular states, conditioned on drug metadata. Evaluated on paired RNA-seq and imaging data from 3 cell lines and 40 compounds (17,242 samples), PertFlow achieves Pearson correlation of 0.780{+/-}0.264 for transcriptomic prediction and FID of 24.06 for morphological generation. Board-certified pathologists rated generated images with median similarity scores of 7.11-7.89/10. The model successfully recovers known drug mechanisms including microtubule disruption, DNA damage, and MAPK pathway inhibition, with gene enrichment analysis confirming activation of expected biological pathways (EMT, p53, apoptosis). AvailabilityCode and pretrained models will be available at https://github.com/wangmengbo/PertFlow.

bioinformatics↗

bioRxiv2025

AnnotateAnyCell: Open-Source AI Framework for Efficient Annotation in Digital Pathology

AO_SCPLOWBSTRACTC_SCPLOWManual annotation of histopathological whole slide images remains a critical bottleneck for computational pathology and clinical AI deployment, requiring prohibitive expert time at scale. Here we present an open-source semi-supervised framework combining active contrastive learning with iterative human-in-the-loop feedback for efficient cellular annotation and classification. The pipeline integrates Cellpose segmentation, UMAP-based latent space visualization, and contrastive learning with pseudolabel propagation, evaluated on five whole slide images of canine invasive urothelial carcinoma across low, intermediate, and high histological grades at 40x magnification. Latent space clustering-guided annotation required 47 minutes compared to 63 minutes for sequential annotation, a 25% reduction (95% CI 18-32%). Classification accuracy reached 96.3% {+/-} 1.2% for mitotic figures and 98.3% {+/-} 1.4% for nucleoli using 1,075 labeled samples, with nucleoli classification achieving 95.5% {+/-} 1.5% accuracy from only 215 samples. Inter-annotator agreement was high for chromatin ({kappa} = 1.00) and nucleoli ({kappa} = 0.95) but moderate for mitotic figures ({kappa} = 0.58) and nuclear shape ({kappa} = 0.36), reflecting intrinsic morphological ambiguity in these categories. This framework substantially reduces annotation burden while achieving expert-level accuracy for well-defined morphological features, providing a scalable path toward AI-assisted diagnostics in resource-constrained pathology settings.

pathology↗

bioRxiv2018

Wide-area all-optical neurophysiology in acute brain slices

Optical tools for simultaneous perturbation and measurement of neural activity open the possibility of mapping neural function over wide areas of brain tissue. However, spectral overlap of actuators and reporters presents a challenge for their simultaneous use, and optical scattering and out-of-focus fluorescence in tissue degrade resolution. To minimize optical crosstalk, we combined an optimized variant (eTsChR) of the most blue-shifted channelrhodopsin reported to-date with a nuclear-localized red-shifted Ca2+ indicator, H2B-jRGECO1a. To perform wide-area optically sectioned imaging in tissue, we designed a structured illumination technique that uses Hadamard matrices to encode spatial information. By combining these molecular and optical approaches we made wide-area maps, spanning cortex and striatum, of the effects of antiepileptic drugs on neural excitability and on the effects of AMPA and NMDA receptor blockers on functional connectivity. Together, these tools provide a powerful capability for wide-area mapping of neuronal excitability and functional connectivity in acute brain slices.

neuroscience↗

bioRxiv2018

DECODE-ing sparsity patterns in single-cell RNA-seq

An inherent challenge in interpreting single-cell transcriptomic data is the high frequency of zero values. This phenomenon has been attributed to both biological and technical sources, although the extent of the contribution of each remains unclear. Here, we show that the underlying gene presence/absence sparsity patterns are by themselves highly informative. We develop an algorithm, called DECODE, to assess the extent of joint presence/absence of genes across different cells, and to infer a gene dependency network. We show that this network captures biologically-meaningful pathways, cell-type specific modules, and connectivity patterns characteristic of complex networks. We develop a model that uses this network to discriminate biological vs. technical zeros, by exploiting each genes local network neighborhood. For inferred non-biological zeros, we build a predictive model that imputes the missing value of each gene based on activity patterns of its most informative neighbors. We show that our framework accurately infers gene-gene functional dependencies, pinpoints technical zeros, and predicts biologically-meaningful missing values in three diverse datasets.

bioinformatics↗

bioRxiv2016

Take ACTION to identify high-resolution cell types and associated transcriptional pathways

Single-cell transcriptomic data has the potential to radically redefine our view of cell type identity. Cells that were previously believed to be homogeneous are now clearly distinguishable in terms of their expression phenotype. Methods for automatically characterizing the functional identity of cells, and their associated properties, can be used to uncover processes involved in lineage differentiation as well as sub-typing cancer cells. They can also be used to suggest personalized therapies based on molecular signatures associated with pathology. We develop a new method, called ACTION, to infer the functional identity of cells from their transcriptional profile, classify them based on their dominant function, and reconstruct regulatory networks that are responsible for mediating their identity. Using ACTION, we identify novel Melanoma sub-types with differential survival rates and therapeutic responses, for which we provide biomarkers along with their underlying regulatory networks.

bioinformatics↗