Label-free metabolic fingerprinting of motile mammalian spermatozoa with subcellular resolution
Bibliographic record
Abstract
Acquisition The fluorescence lifetime images of mouse sperm cells ex vivo were acquired using a Carl Zeiss LSM880 NLO microscope (Carl Zeiss, Germany) coupled to a (\(690-1040\,\textrm{nm} \)) Ti:Saphire laser system (Chameleon Ultra II, Coherent) pulsing at 80 MHz and a TCSPC card (HydraHarp 400, PicoQuant). Sperm samples were kept in an Okolab chamber at \(37^{\circ}\) and \(5\%\,\textrm{CO}_2\). The samples were excited with a laser of \(740\,\textrm{nm}\) wavelength using a \(40\, \times \, 1.1 \) NA water immersion objective. A dichroic mirror (\(690\,\textrm{nm} \)) was used to separate the fluorescence signal from the excitation laser. NAD(P)H fluorescence was collected through a \(460 (\pm20)\,\textrm{nm}\) filter by a hybrid detector (HPM-100-40, Becker & Hickl). An additional filter was used to block near-infrared light. The FLIM data were recorded using the SymPhoTime 64 software (PicoQuant) using 16 ps TCSPC sampling (length of the time slot). 16 time frames with dimension \(512\times512\) were acquired, with the scanning speed \(5\, \mu \textrm{s/pixel}\) (giving full acquisition time of 1 minute and 23 seconds). Export Custom software, called TTTR Data Analysis, was used to open the files and adjust them for export. Each file is a composition of 16 time frames of the same scene. The sperm were not immobilised, each scene was acquired multiple times sequentially, thus, each time frame may include the sperm in a different position. Therefore, some of the sperm may be blurry. For this dataset we provide only one time frame of the field from the selected data, trying to pick the time frame with the least movement of the sperm. Manual Annotation Masks for the training, validation and test dataset were manually prepared in the binary editor of NIS-Elements by Fitore Kusari. Two binary masks are provided for each image, for the heads and the mitochondrial midpiece of the sperm. Final dataset size is summarized in: Images Midpiece Objects Head Objects Train 235 17952 8620 Validation 16 1448 678 Test 30 2507 1167 Sum 281 21907 10465
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".