Apple trees being deciduous trees, shed leaves each year which is preceded by the change in color of leaves from green to yellow (also known as senescence) during the fall season. The rate and timing of color change are affected by the number of factors including nitrogen (N) deficiencies. The green color of leaves is highly dependent on the chlorophyll content, which in turn depends on the nitrogen concentration in the leaves. The assessment of the leaf color can give vital information on the nutrient status of the tree. The use of a machine vision based system to capture and quantify these timings and changes in leaf color can be a great tool for that purpose. \par This study is based on data collected during the fall of 2021 and 2023 at a commercial orchard using a ground-based stereo-vision sensor for five weeks. The point cloud obtained from the sensor was segmented to get just the tree in the foreground. The study involved the segmentation of the trees in a natural background using point cloud data and quantification of the color using a custom-defined metric, \textit{yellowness index}, varying from $-1$ to $+1$ ($-1$ being completely green and $+1$ being completely yellow), which gives the proportion of yellow leaves on a tree. The performance of K-means based algorithm and gradient boosting algorithm were compared for \textit{yellowness index} calculation. The segmentation method proposed in the study was able to estimate the \textit{yellowness index} on the trees with $R^2 = 0.72$. The results showed that the metric was able to capture the gradual color transition from green to yellow over the study duration. It was also observed that the trees with lower nitrogen showed the color transition to yellow earlier than the trees with higher nitrogen. The onset of color transition during both years aligned with the $29^{th}$ week post-full bloom.

Artificial intelligence technology helped researchers identify a trio of chemicals that target faulty cells linked to age-related health issues, including certain cancers and Alzheimer's disease. The algorithm comb through a library of more than 4,300 chemical compounds and identified 21 drug candidates that could prompt cell senescence. This is a phenomenon in which faulty cells stop multiplying but do not die off as they should and continue to release chemicals that can trigger inflammation. Of those 21 candidates, the scientists zeroed in on three compounds – ginkgetin, oleandrin, and periplocin – which were able to remove defective cells without harming healthy ones when tested on human cells. AI is increasingly becoming a fixture in medical and scientific research, able to sift through mountains of dense data far faster than a human ever could to aid in the diagnosis of and treatment for diseases.

Cellular senescence is an important factor in aging and many age-related diseases, but understanding its role in health is challenging due to the lack of exclusive or universal markers. Using neural networks, we predict senescence from the nuclear morphology of human fibroblasts with up to 95% accuracy, and investigate murine astrocytes, murine neurons, and fibroblasts with premature aging in culture. After generalizing our approach, the predictor recognizes higher rates of senescence in p21-positive and ethynyl-2’-deoxyuridine (EdU)-negative nuclei in tissues and shows an increasing rate of senescent cells with age in H&E-stained murine liver tissue and human dermal biopsies. Evaluating medical records reveals that higher rates of senescent cells correspond to decreased rates of malignant neoplasms and increased rates of osteoporosis, osteoarthritis, hypertension and cerebral infarction. In sum, we show that morphological alterations of the nucleus can serve as a deep learning predictor of senescence that is applicable across tissues and species and is associated with health outcomes in humans. Senescent cells are typically identified by a combination of senescence-associated markers, and the phenotype is heterogeneous. Here, using deep neural networks, Heckenbach et al. show that nuclear morphology can be used to predict cellular senescence in images of tissues and cell cultures.

Aging (spelled ageing in British English) is the process of becoming older, that involves a series of functional changes that appear over time and are not the result of illness or accident, but occur as a consequence of accumulating disorders in the body's structure and functions. It is an unpreventable chronological, social and biological process and is genetically determined and environmentally modulated. Let's see now how aging and life expectancy are affected. In the case of mammals, life expectancy varies hugely and it ranges from 3–4 years in small rodents to as long as 150–200 years in bowhead whales. As for us humans, we can potentially live for one hundred and twenty years, and just now an international research team has identified more than 2,000 new genes linked to longevity in humans (linked to DNA repair, coagulation and inflammatory response) during an evolutionary comparative genomic study that included 57 species of mammals.

Death has long been overlooked in evolutionary algorithms. Recent research has shown that death (when applied properly) can benefit the overall fitness of a population and can outperform sub-sections of a population that are "immortal" when allowed to evolve together in an environment [1]. In this paper, we strive to experimentally determine whether death is an adapted trait and whether this adaptation can be used to enhance our implementations of conventional genetic algorithms. Using some of the most widely accepted evolutionary death and aging theories, we observed that senescent death (in various forms) can lower the total run-time of genetic algorithms, increase the optimality of a solution, and decrease the variance in an algorithm's performance. We believe that death-enhanced genetic algorithms can accomplish this through their unique ability to backtrack out of and/or avoid getting trapped in local optima altogether.