Rate Adaptation Research Articles

An efficient and robust ABC approach to infer the rate and strength of adaptation.

Inferring the effects of positive selection on genomes remains a critical step in characterizing the ultimate and proximate causes of adaptation across species, and quantifying positive selection remains a challenge due to the confounding effects of many other evolutionary processes. Robust and efficient approaches for adaptation inference could help characterize the rate and strength of adaptation in nonmodel species for which demographic history, mutational processes, and recombination patterns are not currently well-described. Here, we introduce an efficient and user-friendly extension of the McDonald-Kreitman test (ABC-MK) for quantifying long-term protein adaptation in specific lineages of interest. We characterize the performance of our approach with forward simulations and find that it is robust to many demographic perturbations and positive selection configurations, demonstrating its suitability for applications to nonmodel genomes. We apply ABC-MK to the human proteome and a set of known virus interacting proteins (VIPs) to test the long-term adaptation in genes interacting with viruses. We find substantially stronger signatures of positive selection on RNA-VIPs than DNA-VIPs, suggesting that RNA viruses may be an important driver of human adaptation over deep evolutionary time scales.

Demographic feedbacks during evolutionary rescue can slow or speed adaptive evolution.

Populations declining toward extinction can persist via genetic adaptation in a process called evolutionary rescue. Predicting evolutionary rescue has applications ranging from conservation biology to medicine, but requires understanding and integrating the multiple effects of a stressful environmental change on population processes. Here we derive a simple expression for how generation time, a key determinant of the rate of evolution, varies with population size during evolutionary rescue. Change in generation time is quantitatively predicted by comparing how intraspecific competition and the source of maladaptation each affect the rates of births and deaths in the population. Depending on the difference between two parameters quantifying these effects, the model predicts that populations may experience substantial changes in their rate of adaptation in both positive and negative directions, or adapt consistently despite severe stress. These predictions were then tested by comparison to the results of individual-based simulations of evolutionary rescue, which validated that the tolerable rate of environmental change varied considerably as described by analytical results. We discuss how these results inform efforts to understand wildlife disease and adaptation to climate change, evolution in managed populations and treatment resistance in pathogens.

Multiple correlation filters with gaussian constraint for fast online tracking

The correlation filter based online tracking methods usually can achieve high real-time performance due to the leverage of the well-known FFT. However, they are also apt to generate the “corrupted training samples” in scenarios with complex background, which will trigger the model drift and deteriorate the tracking accuracy rapidly. The existing methods usually consider this problem from certain aspect and none of them has mined the potential of combining multiple formulas. In this paper, we propose the Output Constraint Transfer - Discriminative Scale Space Tracking (OCT-DSST) algorithm, which has taken full consideration of multiple channel feature, multiple filters, kernel trick, memory with incremental learning, and the self-supervision mechanism. We re-formulate the online tracking process by combining all formulas above in a unified framework. The so obtained adaptive learning rate can better exploit the feedback information coming from the intermediate tracking results, and effectively mitigate the corrupted sample problem. The experimental results on the OTB-50/100 and the VOT2016 datasets reveal that the proposed method is comparative to most state-of-the-arts algorithms, and can increase the accuracy by 2% and the success rate by 1.7%, compared to the traditional DSST method.

Lasting dynamic effects of the psychedelic 2,5-dimethoxy-4-iodoamphetamine ((±)-DOI) on cognitive flexibility.

Psychedelic drugs can aid fast and lasting remission from various neuropsychiatric disorders, though the underlying mechanisms remain unclear. Preclinical studies suggest serotonergic psychedelics enhance neuronal plasticity, but whether neuroplastic changes can also be seen at cognitive and behavioural levels is unexplored. Here we show that a single dose of the psychedelic 2,5-dimethoxy-4-iodoamphetamine ((±)-DOI) affects structural brain plasticity and cognitive flexibility in young adult mice beyond the acute drug experience. Using ex vivo magnetic resonance imaging, we show increased volumes of several sensory and association areas one day after systemic administration of 2 mgkg-1 (±)-DOI. We then demonstrate lasting effects of (±)-DOI on cognitive flexibility in a two-step probabilistic reversal learning task where 2 mgkg-1 (±)-DOI improved the rate of adaptation to a novel reversal in task structure occurring one-week post-treatment. Strikingly, (±)-DOI-treated mice started learning from reward omissions, a unique strategy not typically seen in mice in this task, suggesting heightened sensitivity to previously overlooked cues. Crucially, further experiments revealed that (±)-DOI's effects on cognitive flexibility were contingent on the timing between drug treatment and the novel reversal, as well as on the nature of the intervening experience. (±)-DOI's facilitation of both cognitive adaptation and novel thinking strategies may contribute to the clinical benefits of psychedelic-assisted therapy, particularly in cases of perseverative behaviours and a resistance to change seen in depression, anxiety, or addiction. Furthermore, our findings highlight the crucial role of time-dependent neuroplasticity and the influence of experiential factors in shaping the therapeutic potential of psychedelic interventions for impaired cognitive flexibility.

Effects of governmental support on clinical nurse educators in South Korea: A repeated cross-sectional study.

To assess the effectiveness of the Clinical Nurse Educator Support Project and offer valuable insights for supporting nursing education. Allocating clinical nursing educators is crucial for supporting novice nurses' transition into the clinical setting and improving their performance. In 2019, the Ministry of Health and Welfare in South Korea implemented the Clinical Nurse Educator Support Project, which involves governmental financial support for the employment of clinical nurse educators. This study employed a repeated cross-sectional design to assess the project outcomes. Following the framework of the Kirkpatrick Evaluation Model, secondary data from annual self-program evaluation reports were analyzed to assess program satisfaction, clinical adaptation, and turnover rates of novice nurses. The "Strengthening the Reporting of Observational Studies in Epidemiology checklist" guided the reporting of the study. The project played a pivotal role in enhancing the quality of nursing education. Novice nurses' program satisfaction and clinical adaptation consistently remained high or exhibited an increase. The project led to a decrease in turnover rate among novice nurses, while the coronavirus 2019 pandemic resulted in increased turnover rates due to limited clinical practice opportunities for nursing students. Government support for clinical nurse educators has positively impacted the institutionalization of nursing education. The pressing need is to prioritize not only the enhancement of nursing education quality and the improvement of nurses' working conditions but also the development of healthcare policies and programs to effectively respond to unforeseen challenges and crises. Government and healthcare institutions must collaborate to strengthen clinical education, crucial for novice nurses' clinical adaptation. Prioritizing the improvement of nursing education quality and nurses' working conditions is essential. Continuous research and evaluation of the Clinical Nurse Educator Support Project is imperative to assess its impact and make necessary adjustments.

Attacking Robot Vision Models Efficiently Based on Improved Fast Gradient Sign Method

The robot vision model is the basis for the robot to perceive and understand the environment and make correct decisions. However, the security and stability of robot vision models are seriously threatened by adversarial examples. In this study, we propose an adversarial attack algorithm, RMS-FGSM, for robot vision models based on root-mean-square propagation (RMSProp). RMS-FGSM uses an exponentially weighted moving average (EWMA) to reduce the weight of the historical cumulative squared gradient. Additionally, it can suppress the gradient growth based on an adaptive learning rate. By integrating with the RMSProp, RMS-FGSM is more likely to generate optimal adversarial examples, and a high attack success rate can be achieved. Experiments on two datasets (MNIST and CIFAR-100) and several models (LeNet, Alexnet, and Resnet-101) show that the attack success rate of RMS-FGSM is higher than the state-of-the-art methods. Above all, our generated adversarial examples have a smaller perturbation than those generated by existing methods under the same attack success rate.

Temperature Adaptation of Aquatic Bacterial Community Growth Is Faster in Response to Rising than to Falling Temperature

Bacteria are key organisms in energy and nutrient cycles, and predicting the effects of temperature change on bacterial activity is important in assessing global change effects. A changing in situ temperature will affect the temperature adaptation of bacterial growth in lake water, both long term in response to global change, and short term in response to seasonal variations. The rate of adaptation may, however, depend on whether temperature is increasing or decreasing, since bacterial growth and turnover scale with temperature. Temperature adaptation was studied for winter (in situ temperature 2.5 °C) and summer communities (16.5 °C) from a temperate lake in Southern Sweden by exposing them to a temperature treatment gradient between 0 and 30 °C in ~ 5 °C increments. This resulted mainly in a temperature increase for the winter and a decrease for the summer community. Temperature adaptation of bacterial community growth was estimated as leucine incorporation using a temperature Sensitivity Index (SI, log growth at 35 °C/4 °C), where higher values indicate adaptation to higher temperatures. High treatment temperatures resulted in higher SI within days for the winter community, resulting in an expected level of community adaptation within 2 weeks. Adaptation for the summer community was also correlated to treatment temperature, but the rate of adaption was slower. Even after 5 weeks, the bacterial community had not fully adapted to the lowest temperature conditions. Thus, during periods of increasing temperature, the bacterial community will rapidly adapt to function optimally, while decreasing temperature may result in long periods of non-optimal functioning.

Abstract PR006: Evolution of evolvability

Abstract The purpose of this work is to better understand how the rate of phenotypic adaptation of cells in a tumor affects its ability to survive treatment. Drug resistance is an ongoing problem for maintaining a sustained response in treating advanced cancers. Regardless of their lineage, heritable phenotypic adaptions may be beneficial for the tumor to survive stepwise changes in the environment during tumor growth. There may be further benefit for cells to develop the trait of being adaptable - to respond to large shifts in the microenvironment, survive metastasis to a new organ, and thrive when treatments are applied. Genomic instability and phenotypic redundancy may aid in adaptations, but there may be limits. With too much mutation and deviation from the parental phenotype, cells lose important regulatory and maintenance functions necessary to survive. So is there an optimal rate of evolvability? We use an off-lattice agent-based model to investigate how the rate of change through phenotypic trait space affects tumor growth and response to treatment. We assign cells with different combinations of proliferation rates and migration speeds and test how the variance from the parental cell on division affects the selection of trait combinations over time. During growth, heterogeneity is gained quickly and space matters less if evolvability is forced to continually increase, but there is more selection for proliferative phenotypes and slower evolvability if its rate is allowed to drift freely. We found that tradeoffs generally imposed a greater selection force leading to faster recurrence and the coexistence of proliferation or migration specialists. We compared responses to continuous, intermittent, and adaptive treatment schedules of an anti-proliferative drug. At some point with treatment, there is an optimal evolvability, as larger increases in diversity allows for cells to fall back into a sensitive state and be killed by treatment. We aim to design better treatment strategies that work for tumors with different levels of evolvability by understanding how the rate of tumor evolution affects growth and treatment response. Citation Format: Jill A. Gallaher, Maximilian Strobl, Jeffrey West, Mark Roberston-Tessi, Alexander R. A. Anderson. Evolution of evolvability [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR006.

Neuroanatomical, electrophysiological, and morphological characterization of melanin-concentrating hormone cells coexpressing cocaine- and amphetamine-regulated transcript.

Melanin-concentrating hormone (MCH) cells in the hypothalamus regulate fundamental physiological functions like energy balance, sleep, and reproduction. This diversity may be ascribed to the neurochemical heterogeneity among MCH cells. One prominent subpopulation of MCH cells coexpresses cocaine- and amphetamine-regulated transcript (CART), and as MCH and CART can have opposing actions, MCH/CART+ and MCH/CART- cells may differentially modulate behavioral outcomes. However, it is not known if there are differences in the cellular properties underlying their functional differences; thus, we compared the neuroanatomical, electrophysiological, and morphological properties of MCH cells in male and female Mch-cre;L10-Egfp reporter mice. Half of MCH cells expressed CART and were most prominent in the medial hypothalamus. Whole-cell patch-clamp recordings revealed differences in their passive and active membrane properties in a sex-dependent manner. Female MCH/CART+ cells had lower input resistances, but male cells largely differed in their firing properties. All MCH cells increased firing when stimulated, but their firing frequency decreases with sustained stimulation. MCH/CART+ cells showed stronger spike rate adaptation than MCH/CART- cells. The kinetics of excitatory events at MCH cells also differed by cell type, as the rising rate of excitatory events was slower at MCH/CART+ cells. By reconstructing the dendritic arborization of our recorded cells, we found no sex differences, but male MCH/CART+ cells had less dendritic length and fewer branch points. Overall, distinctions in topographical division and cellular properties between MCH cells add to their heterogeneity and help elucidate their response to stimuli or effect on modulating their respective neural networks.

A Comparative Study and Analysis of LoRaWAN Performance in NS3

Long Range Wide Area Network (LoRaWAN) is a widely adopted Internet of Things (IoT) protocol due to its high range and lower energy consumption. LoRaWAN utilizes Adaptive Data Rate (ADR) for efficient resource (e.g., spreading factor and transmission power) management. The ADR manages these two resource parameters on the network server side and end device side. This paper focuses on analyzing the ADR and Gaussian ADR performance of LoRaWAN. We have performed NS3 simulation under a static scenario by varying the antenna height. The simulation results showed that antenna height has a significant impact on the packet delivery ratio. Higher antenna height (e.g., 50 m) has shown an improved packet success ratio when compared with lower antenna height (e.g., 10 m) in static and mobility scenarios. Based on the results, it is suggested to use the antenna at higher allevation for successful packet delivery

Multiplicative update rules for accelerating deep learning training and increasing robustness

Even nowadays, where Deep Learning (DL) has achieved state-of-the-art performance in a wide range of research domains, accelerating training and building robust DL models remains a challenging task. To this end, generations of researchers have pursued developing robust methods for training DL architectures that can be less sensitive to weight distributions, model architectures, and loss landscapes. However, such methods are limited to adaptive learning rate optimizers, initialization schemes, and clipping gradients without investigating the fundamental rule of parameters update. Although multiplicative updates have contributed significantly to the early development of machine learning and hold strong theoretical claims, to the best of our knowledge, this is the first work that investigates them in a unified manner in the context of DL optimization. In this work, we propose an optimization framework that fits a wide range of optimization algorithms and enables one to apply alternative update rules. To this end, we propose a novel multiplicative update rule and we extend their capabilities by combining it with a traditional additive update term, under a novel hybrid update method. We claim that the proposed framework accelerates training while leading to more robust models in contrast to traditionally used additive update rule, and we experimentally demonstrate its effectiveness in a wide range of task and optimization methods. Such tasks range from convex and non-convex optimization to difficult image classification benchmarks applying a wide range of traditionally used optimization methods and Deep Neural Network (DNN) architectures, providing quantitative and qualitative experimental results.

An FPGA-Based Adaptive Solution for Synchronous Configuration in UART Communication

To address the challenges associated with data reception failures, which arise due to abrupt alterations in the baud rate and the data format of the transmission signal in traditional Universal Asynchronous Receiver-Transmitter (UART) scenarios, this study presents an adaptive UART parameter solution underpinned by Field-Programmable Gate Array (FPGA) technology. Initially, this manuscript elucidates the underlying principles of UART communication and delineates the pivotal constituents of the UART protocol. Subsequently, it offers an in-depth analysis, from the transmission perspective, of how specific parameters influence the effectiveness of the UART protocol. Based on the software and hardware embodiments associated with the dual primary functions – baud rate adaptation and data format adaptation – this paper extensively details the flow chart for baud rate adaptation, the circuitry for edge detection, methodologies for baud rate computation, and the parameter eigenvalue approach. Following this, Verilog, a hardware description language, is employed for coding and subsequent simulation within the Quartus II environment. Empirical evidence corroborates the efficacy of the proposed design in achieving synchronous configuration between the baud rate and the data format. The findings of this research bear substantial implications for augmenting both the adaptability and dependability of UART communication systems.

Variation in temperature of peak trait performance constrains adaptation of arthropod populations to climatic warming

The capacity of arthropod populations to adapt to long-term climatic warming is currently uncertain. Here we combine theory and extensive data to show that the rate of their thermal adaptation to climatic warming will be constrained in two fundamental ways. First, the rate of thermal adaptation of an arthropod population is predicted to be limited by changes in the temperatures at which the performance of four key life-history traits can peak, in a specific order of declining importance: juvenile development, adult fecundity, juvenile mortality and adult mortality. Second, directional thermal adaptation is constrained due to differences in the temperature of the peak performance of these four traits, with these differences expected to persist because of energetic allocation and life-history trade-offs. We compile a new global dataset of 61 diverse arthropod species which provides strong empirical evidence to support these predictions, demonstrating that contemporary populations have indeed evolved under these constraints. Our results provide a basis for using relatively feasible trait measurements to predict the adaptive capacity of diverse arthropod populations to geographic temperature gradients, as well as ongoing and future climatic warming.

Predictive reinforcement learning in non-stationary environments using weighted mixture policy

Reinforcement Learning (RL) within non-stationary environments presents a formidable challenge. In some applications, anticipating abrupt alterations in the environment model might be possible. The existing literature lacks a framework that proactively harnesses such predictions to enhance reward optimization. This paper introduces an innovative methodology designed to preemptively leverage these predictions, thereby maximizing the overall achieved performance. This is executed by formulating a novel approach that generates a weighted mixture policy from both the optimal policies of the prevailing and forthcoming models. To ensure safe learning, an adaptive learning rate is derived to facilitate training of the weighted mixture policy. This theoretically guarantees monotonic performance improvement at each update during training. Empirical trials focus on a model-free predictive reference tracking scenario involving piecewise constant references. Through the utilization of the cart–pole position control problem, it is demonstrated that the proposed algorithm surpasses prior techniques such as context Q-learning and RL with context detection algorithms in non-stationary environments. Moreover, the algorithm outperforms the application of individual optimal policies derived from each observed environment model (i.e., policies not utilizing predictions).

Time-Allocation Adaptive Data Rate: An Innovative Time-Managed Algorithm for Enhanced Long-Range Wide-Area Network Performance

Currently, a variety of Low-Power Wide-Area Network (LPWAN) technologies offer diverse solutions for long-distance communication. Among these, Long-Range Wide-Area Network (LoRaWAN) has garnered considerable attention for its widespread applications in the Internet of Things (IoT). Nevertheless, LoRaWAN still faces the challenge of channel collisions when managing dense node communications, a significant bottleneck to its performance. Addressing this issue, this study has developed a novel “time allocation adaptive Data Rate” (TA-ADR) algorithm for network servers. This algorithm dynamically adjusts the spreading factor (SF) and transmission power (TP) of LoRa (Long Range) nodes and intelligently schedules transmission times, effectively reducing the risk of data collisions on the same frequency channel and significantly enhancing data transmission efficiency. Simulations in a dense LoRaWAN network environment, encompassing 1000 nodes within a 480 m × 480 m range, demonstrate that compared to the ADR+ algorithm, our proposed algorithm achieves substantial improvements of approximately 30.35% in data transmission rate, 24.57% in energy consumption, and 31.25% in average network throughput.

‘Ease of adaptation’ predicts preferred spectacle prescriptions better than visual acuity: a retrospective analysis

ABSTRACT Clinical relevance Factors predicting patient acceptance of a new spectacle prescription need to be determined to make optimal prescribing decisions. Background Clinicians usually prescribe for best visual acuity. However, for some patients, a partial change is prescribed to ease adaptation, despite providing suboptimal visual acuity. This study seeks to develop an understanding of which factors predict patient preference between spectacle prescriptions by using a retrospective analysis to compare ease of adaptation, subjective quality of distance vision and optimal distance visual acuity. Methods A retrospective analysis utilised a 196-patient data set in which participants wore two prescriptions, one based on the subjective refraction of an optometrist modified by judgement and one on autorefractor results modified for ease of adaptation by an algorithm. Spectacles were worn for 3 weeks each, and participants responded to questions about which prescription they preferred and their quality of distance vision and ease of adaptation (on a 0–10 scale) with each prescription. A logistic regression analysed which variables predicted whether participants responded yes or no to the question ‘If you had purchased these spectacles for $100 (US$200 adjusted to 2023 value), would you be happy with them?’ Results There was a significant difference between the preferred and non-preferred prescriptions for the subjective quality of distance vision rating (medians 9 vs. 8; Z = –7.80, p < 0.0001) and ease of adaptation rating (medians 8 vs. 5; Z = –8.32, p < 0.0001) but the distance binocular visual acuity was not significantly different (both means = –0.09 logMAR; Z = –0.60, p = 0.55). Of all participants, 94% preferred the prescription deemed easier to adapt to but only 59% preferred the prescription with better subjective quality of distance vision and best visual acuity. Conclusion Distance visual acuity was not found to be a useful predictor of participant preference to a new prescription and is likely over-relied upon in practice. The results support the adjustment of the subjective prescription where appropriate to aid patient adaptation and comfort.

An improved indirect adaptive neural control performance based on MOPSO approach: An experimental validation via a transesterification reactor

This paper proposes an indirect adaptive control method based on recurrent neural networks. To achieve satisfactory closed-loop performances, a neural emulator (NE) and a neural controller (NC) adapting rates are established using the multiobjective particle swarm optimization (MOPSO) algorithm. The proposed MOPSO algorithm has been designed to minimize, simultaneously, two separated objective functions: the emulation and the tracking errors. The proposed approach guarantees that the NE tracks the system dynamics within a short time window. Consequently, it provides for the suggested control structure useful information to synthesize optimal adaptive rates of the NE and NC. To validate the effectiveness of the proposed MOPSO algorithm, a numerical example and an experimental validation on a chemical reactor are proposed.

Automatic mode-locked fiber laser based on adaptive genetic algorithm

A modified adaptive genetic algorithm (GA) is proposed and implemented in a mode-locked erbium-doped fiber laser (EDFL) based on nonlinear polarization rotation. The algorithm combines GAs with adaptive crossover and mutation rates. As the number of iterations increases during the solving operation, the crossover and mutation rates are dynamically adjusted with the change in the fitness function value. A stretched pulse was automatically generated when the modified adaptive GA was continuously optimized to converge the intracavity polarization state to the target state. The average number of generations required to adjust from other states to stretched pulse locking is two, which is better than that afforded by the general GA. The evolution curves of the mode-locked EDFL with different crossover and mutation rates were investigated, and the results indicate the effectiveness of the proposed modified adaptive GA. This study proposes a new scheme for rapid polarization searching in automatic mode-locked fiber lasers.

Health and equity implications of individual adaptation to air pollution in a changing climate

Future climate change can cause more days with poor air quality. This could trigger more alerts telling people to stay inside to protect themselves, with potential consequences for health and health equity. Here, we study the change in US air quality alerts over this century due to fine particulate matter (PM2.5), who they may affect, and how they may respond. We find air quality alerts increase by over 1 mo per year in the eastern United States by 2100 and quadruple on average. They predominantly affect areas with high Black populations and leakier homes, exacerbating existing inequalities and impacting those less able to adapt. Reducing emissions can offer significant annual health benefits ($5,400 per person) by mitigating the effect of climate change on air pollution and its associated risks of early death. Relying on people to adapt, instead, would require them to stay inside, with doors and windows closed, for an extra 142 d per year, at an average cost of $11,000 per person. It appears likelier, however, that people will achieve minimal protection without policy to increase adaptation rates. Boosting adaptation can offer net benefits, even alongside deep emission cuts. New adaptation policies could, for example: reduce adaptation costs; reduce infiltration and improve indoor air quality; increase awareness of alerts and adaptation; and provide measures for those working or living outdoors. Reducing emissions, conversely, lowers everyone's need to adapt, and protects those who cannot adapt. Equitably protecting human health from air pollution under climate change requires both mitigation and adaptation.

Impact of platelet-derived mitochondria transfer in the metabolic profiling and progression of metastatic MDA-MB-231 human triple-negative breast cancer cells.

Introduction: An active role of platelets in the progression of triple-negative breast cancer (TNBC) cells has been described. Even the role of platelet-derived extracellular vesicles on the migration of MDA-MB-231 cells has been reported. Interestingly, upon activation, platelets release functional mitochondria into the extracellular environment. However, the impact of these platelet-derived mitochondria on the metabolic properties of MDA-MB-231 cells remains unclear. Methods: MDA-MB-231 and MDA-MB-231-Rho-0 cells were co-cultured with platelets, which were isolated from donor blood. Mitochondrial transfer was assessed through confocal microscopy and flow cytometry, while metabolic analyses were conducted using a Seahorse XF HS Mini Analyzer. The mito-chondrial DNA (mtDNA) copy number was determined via quantitative PCR (qPCR) following platelet co-culture. Finally, cell proliferation and colony formation assay were performed using crystal violet staining. Results and Discussion: We have shown that platelet-derived mitochondria are internalized by MDA-MB-231 cells in co-culture with platelets, increasing ATP production, oxygen (O2) consumption rate (OCR), cell proliferation, and metabolic adaptability. Additionally, we observed that MDA-MB-231 cells depleted from mtDNA restore cell proliferation in uridine/pyruvate-free cell culture medium and mitochondrial O2 consumption after co-culture with platelets, indicating a reconstitution of mtDNA facilitated by platelet-derived mitochondria. In conclusion, our study provides new insights into the role of platelet-derived mitochondria in the metabolic adaptability and progression of metastatic MDA-MB-231 TNBC cells.