Intracortical remodelling increases in highly loaded bone after exercise cessation.

Resorption within cortices of long bones removes excess mass and damaged tissue and increases during periods of reduced mechanical loading. Returning to high-intensity exercise may place bones at risk of failure due to increased porosity caused by bone resorption. We used point-projection X-ray microscopy images of bone slices from highly loaded (metacarpal, tibia) and minimally loaded (rib) bones from 12 racehorses, 6 that died during a period of high-intensity exercise and 6 that had a period of intense exercise followed by at least 35 days of rest prior to death, and measured intracortical canal cross-sectional area (Ca.Ar) and number (N.Ca) to infer remodelling activity across sites and exercise groups. Large canals that are the consequence of bone resorption (Ca.Ar >0.04 mm2 ) were 1.4× to 18.7× greater in number and area in the third metacarpal bone from rested than exercised animals (p = 0.005-0.008), but were similar in number and area in ribs from rested and exercised animals (p = 0.575-0.688). An intermediate relationship was present in the tibia, and when large canals and smaller canals that result from partial bony infilling (Ca.Ar >0.002 mm2 ) were considered together. The mechanostat may override targeted remodelling during periods of high mechanical load by enhancing bone formation, reducing resorption and suppressing turnover. Both systems may work synergistically in rest periods to remove excess and damaged tissue.

Classification of racehorse limb radiographs using deep convolutional neural networks.

Purpose: To assess the capability of deep convolutional neural networks to classify anatomical location and projection from a series of 48 standard views of racehorse limbs. Materials and methods: Radiographs (N = 9504) of horse limbs from image sets made for veterinary inspections by 10 independent veterinary clinics were used to train, validate and test (116, 40 and 42 radiographs, respectively) six deep learning architectures available as part of the open source machine learning framework PyTorch. The deep learning architectures with the best top-1 accuracy had the batch size further investigated. Results: Top-1 accuracy of six deep learning architectures ranged from 0.737 to 0.841. Top-1 accuracy of the best deep learning architecture (ResNet-34) ranged from 0.809 to 0.878, depending on batch size. ResNet-34 (batch size = 8) achieved the highest top-1 accuracy (0.878) and the majority (91.8%) of misclassification was due to laterality error. Class activation maps indicated that joint morphology, not side markers or other non-anatomical image regions, drove the model decision. Conclusions: Deep convolutional neural networks can classify equine pre-import radiographs into the 48 standard views including moderate discrimination of laterality, independent of side marker presence.

The precedence effect in spatial hearing manifests in cortical neural population responses.

BACKGROUND: To localize sound sources accurately in a reverberant environment, human binaural hearing strongly favors analyzing the initial wave front of sounds. Behavioral studies of this "precedence effect" have so far largely been confined to human subjects, limiting the scope of complementary physiological approaches. Similarly, physiological studies have mostly looked at neural responses in the inferior colliculus, the main relay point between the inner ear and the auditory cortex, or used modeling of cochlear auditory transduction in an attempt to identify likely underlying mechanisms. Studies capable of providing a direct comparison of neural coding and behavioral measures of sound localization under the precedence effect are lacking. RESULTS: We adapted a "temporal weighting function" paradigm previously developed to quantify the precedence effect in human for use in laboratory rats. The animals learned to lateralize click trains in which each click in the train had a different interaural time difference. Computing the "perceptual weight" of each click in the train revealed a strong onset bias, very similar to that reported for humans. Follow-on electrocorticographic recording experiments revealed that onset weighting of interaural time differences is a robust feature of the cortical population response, but interestingly, it often fails to manifest at individual cortical recording sites. CONCLUSION: While previous studies suggested that the precedence effect may be caused by early processing mechanisms in the cochlea or inhibitory circuitry in the brainstem and midbrain, our results indicate that the precedence effect is not fully developed at the level of individual recording sites in the auditory cortex, but robust and consistent precedence effects are observable only in the auditory cortex at the level of cortical population responses. This indicates that the precedence effect emerges at later cortical processing stages and is a significantly "higher order" feature than has hitherto been assumed.

Interaural time difference tuning in the rat inferior colliculus is predictive of behavioral sensitivity.

While a large body of literature has examined the encoding of binaural spatial cues in the auditory midbrain, studies that ask how quantitative measures of spatial tuning in midbrain neurons compare with an animal's psychoacoustic performance remain rare. Researchers have tried to explain deficits in spatial hearing in certain patient groups, such as binaural cochlear implant users, in terms of declines in apparent reductions in spatial tuning of midbrain neurons of animal models. However, the quality of spatial tuning can be quantified in many different ways, and in the absence of evidence that a given neural tuning measure correlates with psychoacoustic performance, the interpretation of such finding remains very tentative. Here, we characterize ITD tuning in the rat inferior colliculus (IC) to acoustic pulse train stimuli with varying envelopes and at varying rates, and explore whether quality of tuning correlates behavioral performance. We quantified both mutual information (MI) and neural d' as measures of ITD sensitivity. Neural d' values paralleled behavioral ones, declining with increasing click rates or when envelopes changed from rectangular to Hanning windows, and they correlated much better with behavioral performance than MI. Meanwhile, MI values were larger in an older, more experienced cohort of animals than in naive animals, but neural d' did not differ between cohorts. However, the results obtained with neural d' and MI were highly correlated when ITD values were coded simply as left or right ear leading, rather than specific ITD values. Thus, neural measures of lateralization ability (e.g. d' or left/right MI) appear to be highly predictive of psychoacoustic performance in a two-alternative forced choice task.

Detection of Disease-Specific Parent Cells Via Distinct Population of Nano-Vesicles by Machine Learning.

BACKGROUND: The diagnosis and prognosis of pathological conditions, such as age-related macular degeneration (AMD) and cancer still need improvement. AMD is primarily caused due to the dysfunction of retinal pigment epithelium (RPE), whereas endothelial cells (ECs) play one of the major roles in angiogenesis; an important process which occurs in malignant progression of cancer. Several reports suggested the augmented release of nano-vesicles under pathological conditions, including from RPE as well as cancer-associated ECs, which take part in various biological processes, including intercellular communication in disease progression. Importantly, these nano-vesicles are around 30-1000 nm and carry the fingerprint of their initiating parent cells (IPCs). Therefore, these nano-vesicles could be utilized as the diagnostic tool for AMD and cancer, respectively. However, the analysis of nano-vesicles for biomarker study is confounded by their extensive heterogeneous nature. METHODS: To confront this challenge, we utilized artificial intelligence (AI) based machine learning (ML) algorithms such as support vector machine (SVM) and decision tree model on the dataset of nano-vesicles from RPE and ECs cell lines with low dimensionality. RESULTS: Overall, Gaussian SVM demonstrated the highest prediction accuracy of the IPCs of nano-vesicles, among all the chosen SVM classifiers. Additionally, the bagged tree showed the highest prediction among the chosen decision tree-based classifiers. CONCLUSION: Therefore, the overall bagged tree showed the best performance for the prediction of IPCs of nanovesicles, suggesting the applicability of AI-based prediction approach in diagnosis and prognosis of pathological conditions, including non-invasive liquid biopsy via various biofluids-derived nano-vesicles.