Journal: Microbiology Spectrum

Article Title: Microgravity effects on secondary metabolism of plant-affecting bacteria

doi: 10.1128/spectrum.02239-25

Figure Lengend Snippet: Ground-based hardware used to simulate microgravity on Earth. A rotatory cell culture system (RCCS) equipped with HARVs and clinostat vessels rotated at different rotational axes: horizontal rotation to simulate microgravity (left) and vertical rotation for traditional gravity (right).

Article Snippet: Both P. protegens and B. ambifaria were cultured using a clinostat ground-based microgravity simulation system (Synthecon).

Techniques: Cell Culture

Journal: Microbiology Spectrum

Article Title: Microgravity effects on secondary metabolism of plant-affecting bacteria

doi: 10.1128/spectrum.02239-25

Figure Lengend Snippet: Simulated microgravity affects the growth rate of P. protegens and B. ambifaria . ( A ) Growth curve of ( A ) P. protegens and ( B ) B. ambifaria under simulated microgravity (SMG), traditional gravity (TG), and orbital shaking (OS) control. X-axis values represent hours.

Article Snippet: Both P. protegens and B. ambifaria were cultured using a clinostat ground-based microgravity simulation system (Synthecon).

Techniques: Control

Journal: Microbiology Spectrum

Article Title: Microgravity effects on secondary metabolism of plant-affecting bacteria

doi: 10.1128/spectrum.02239-25

Figure Lengend Snippet: Relative production of select plant-promoting secondary metabolites under traditional gravity and simulated microgravity in P. protegens and B. ambifaria . ( A ) Pyoluteorin levels, ( B ) 2,4-DAPG levels, and ( C ) orfamide A levels on Day 1 and Day 3 in P. protegens cultured in TG and SMG conditions. ( D ) PQQ levels in B. ambifaria cultured in TG and SMG conditions on Day 1 and Day 3. Metabolite levels were measured via LC-MS/MS and are shown as maximum intensity values ( n = 3 biological replicates per condition). Bars show mean ± SD of three biological replicates ( n = 3), normalized to OD₆₀₀. Statistical significance was determined using a two-tailed Welch’s t -test; P < 0.05 (*), P < 0.01 (**), ns = not significant.

Article Snippet: Both P. protegens and B. ambifaria were cultured using a clinostat ground-based microgravity simulation system (Synthecon).

Techniques: Cell Culture, Liquid Chromatography with Mass Spectroscopy, Two Tailed Test

Journal: bioRxiv

Article Title: BioEngine: scalable execution and adaptation of bioimage AI through agent-readable interfaces

doi: 10.64898/2026.04.19.719496

Figure Lengend Snippet: a, Collaborative fine-tuning workflow. A biologist submits microscopy images to BioEngine, which runs Cellpose-SAM to produce initial segmentations. Annotators worldwide correct those masks through a browser-based tool at BioImage.io, and each annotation cycle adds new training images and triggers GPU fine-tuning on BioEngine with one click and no local installation. The progressively improved model is published to BioImage.IO and becomes immediately available through any BioEngine deployment, extending the shared model pool for the wider community. b, Segmentation montage showing Cellpose-SAM predictions on PlantSeg Arabidopsis lateral root nuclei (Movies 1–3) from baseline through four collaborative fine-tuning cycles, alongside ground-truth. Baseline Cellpose-SAM fails to detect most large plant nuclei. As more annotated slices accumulate across cycles, the model progressively recovers boundaries and separates adjacent objects. c, F1 score (IoU ≥ 0.5) on held-out test slices (± s.d., n = 3 slices per movie) increases consistently across all three movies with each annotation cycle, rising from a mean of 0.36 at baseline to 0.71 after four cycles (1,600 cumulative training epochs). d, An AI agent builds new BioEngine applications by generating the analysis workflow, deployment manifest, and web interface from a single plain-language prompt. The deployed EM Mitochondria Analyzer accepts EM image uploads and returns instance segmentations with morphological profiles on the Lucchi++ FIB-SEM benchmark (neural tissue, 5 nm/px), illustrating how BioEngine can be rapidly extended with new analysis capabilities without manual programming.

Article Snippet: Human Protein Atlas images and ground-truth annotations for model screening were obtained from the HPA Cell Image Segmentation Dataset ( https://www.proteinatlas.org ).

Techniques: Microscopy

Journal: npj Cardiovascular Health

Article Title: A view-flexible deep learning framework for automated analysis of 2D echocardiography

doi: 10.1038/s44325-025-00100-7

Figure Lengend Snippet: TTE transthoracic echocardiography, HCU handheld cardiac ultrasound, RH right heart, LVEF left ventricular ejection fraction.

Article Snippet: For this dataset, rather than the hierarchy of measurements described above, ground truth LVEF was determined using TomTec software (Image Arena v. 2.30.00; TomTec, Unterschleissheim, Germany) applied to A4C clips collected by expert sonographers.

Techniques: