Environmental study and stress-related biomarkers modifications in a crew during analog astronaut mission EMMPOL 6.

PURPOSE: Human presence in space is increasingly frequent, but we must not forget that it is a hostile environment. We aimed to study the characteristics of experimental scenarios, to obtain data on human response to isolation, disruption of circadian rhythm and high levels of psychophysical stress. METHODS: In these experiments, we evaluated stress response in five young healthy subjects inside an earth-based moon-settlement-like habitat during a 1-week long analog astronaut mission. Wearable devices were used to monitor daily step count of the subjects, physical activity, heart rate during physical exercise and at rest, and sleep parameters. From saliva and urine samples collected every day at awakening, we studied oxy-inflammation biomarkers and hormones (stress and appetite) were studied too. RESULTS: At the end of the week, all subjects revealed an increase in oxidative stress and cortisol levels but no inflammation biomarkers variations, in conjunction with increasing time/daily exercise. Furthermore, a significant decrease in hours of sleep/day, sleep quality, and REM phase of sleep was recorded and correlated with the increase of reactive oxygen species. CONCLUSION: Oxidative stress increased in a short period of time and may be attributed to the influence of psychological stress during confinement, as well as increased exercise and decreased amount of sleep. On a long-term basis, this could impact performance.

Far-red light photoacclimation in a desert Chroococcidiopsis strain with a reduced FaRLiP gene cluster and expression of its chlorophyll f synthase in space-resistant isolates.

Introduction: Some cyanobacteria can use far-red light (FRL) to drive oxygenic photosynthesis, a phenomenon known as Far-Red Light Photoacclimation (FaRLiP). It can expand photosynthetically active radiation beyond the visible light (VL) range. Therefore, it holds promise for biotechnological applications and may prove useful for the future human exploration of outer space. Typically, FaRLiP relies on a cluster of ~20 genes, encoding paralogs of the standard photosynthetic machinery. One of them, a highly divergent D1 gene known as chlF (or psbA4), is the synthase responsible for the formation of the FRL-absorbing chlorophyll f (Chl f) that is essential for FaRLiP. The minimum gene set required for this phenotype is unclear. The desert cyanobacterium Chroococcidiopsis sp. CCMEE 010 is unusual in being capable of FaRLiP with a reduced gene cluster (15 genes), and it lacks most of the genes encoding FR-Photosystem I. Methods: Here we investigated whether the reduced gene cluster of Chroococcidiopsis sp. CCMEE 010 is transcriptionally regulated by FRL and characterized the spectral changes that occur during the FaRLiP response of Chroococcidiopsis sp. CCMEE 010. In addition, the heterologous expression of the Chl f synthase from CCMEE 010 was attempted in three closely related desert strains of Chroococcidiopsis. Results: All 15 genes of the FaRLiP cluster were preferentially expressed under FRL, accompanied by a progressive red-shift of the photosynthetic absorption spectrum. The Chl f synthase from CCMEE 010 was successfully expressed in two desert strains of Chroococcidiopsis and transformants could be selected in both VL and FRL. Discussion: In Chroococcidiopsis sp. CCME 010, all the far-red genes of the unusually reduced FaRLiP cluster, are transcriptionally regulated by FRL and two closely related desert strains heterologously expressing the chlF010 gene could grow in FRL. Since the transformation hosts had been reported to survive outer space conditions, such an achievement lays the foundation toward novel cyanobacteria-based technologies to support human space exploration.

AOP report: Development of an adverse outcome pathway for deposition of energy leading to learning and memory impairment.

Understanding radiation-induced non-cancer effects on the central nervous system (CNS) is essential for the risk assessment of medical (e.g., radiotherapy) and occupational (e.g., nuclear workers and astronauts) exposures. Herein, the adverse outcome pathway (AOP) approach was used to consolidate relevant studies in the area of cognitive decline for identification of research gaps, countermeasure development, and for eventual use in risk assessments. AOPs are an analytical construct describing critical events to an adverse outcome (AO) in a simplified form beginning with a molecular initiating event (MIE). An AOP was constructed utilizing mechanistic information to build empirical support for the key event relationships (KERs) between the MIE of deposition of energy to the AO of learning and memory impairment through multiple key events (KEs). The evidence for the AOP was acquired through a documented scoping review of the literature. In this AOP, the MIE is connected to the AO via six KEs: increased oxidative stress, increased deoxyribonucleic acid (DNA) strand breaks, altered stress response signaling, tissue resident cell activation, increased pro-inflammatory mediators, and abnormal neural remodeling that encompasses atypical structural and functional alterations of neural cells and surrounding environment. Deposition of energy directly leads to oxidative stress, increased DNA strand breaks, an increase of pro-inflammatory mediators and tissue resident cell activation. These KEs, which are themselves interconnected, can lead to abnormal neural remodeling impacting learning and memory processes. Identified knowledge gaps include improving quantitative understanding of the AOP across several KERs and additional testing of proposed modulating factors through experimental work. Broadly, it is envisioned that the outcome of these efforts could be extended to other cognitive disorders and complement ongoing work by international radiation governing bodies in their review of the system of radiological protection.

The Future of Artificial Intelligence in Surgery.

Until recently, innovations in surgery were largely represented by extensions or augmentations of the surgeon's perception. This includes advancements such as the operating microscope, tumor fluorescence, intraoperative ultrasound, and minimally invasive surgical instrumentation. However, introducing artificial intelligence (AI) into the surgical disciplines represents a transformational event. Not only does AI contribute substantively to enhancing a surgeon's perception with such methodologies as three-dimensional anatomic overlays with augmented reality, AI-improved visualization for tumor resection, and AI-formatted endoscopic and robotic surgery guidance. What truly makes AI so different is that it also provides ways to augment the surgeon's cognition. By analyzing enormous databases, AI can offer new insights that can transform the operative environment in several ways. It can enable preoperative risk assessment and allow a better selection of candidates for procedures such as organ transplantation. AI can also increase the efficiency and throughput of operating rooms and staff and coordinate the utilization of critical resources such as intensive care unit beds and ventilators. Furthermore, AI is revolutionizing intraoperative guidance, improving the detection of cancers, permitting endovascular navigation, and ensuring the reduction in collateral damage to adjacent tissues during surgery (e.g., identification of parathyroid glands during thyroidectomy). AI is also transforming how we evaluate and assess surgical proficiency and trainees in postgraduate programs. It offers the potential for multiple, serial evaluations, using various scoring systems while remaining free from the biases that can plague human supervisors. The future of AI-driven surgery holds promising trends, including the globalization of surgical education, the miniaturization of instrumentation, and the increasing success of autonomous surgical robots. These advancements raise the prospect of deploying fully autonomous surgical robots in the near future into challenging environments such as the battlefield, disaster areas, and even extraplanetary exploration. In light of these transformative developments, it is clear that the future of surgery will belong to those who can most readily embrace and harness the power of AI.

Navigating the frontier of drug-like chemical space with cutting-edge generative AI models.

Deep generative models (GMs) have transformed the exploration of drug-like chemical space (CS) by generating novel molecules through complex, nontransparent processes, bypassing direct structural similarity. This review examines five key architectures for CS exploration: recurrent neural networks (RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), normalizing flows (NF), and Transformers. It discusses molecular representation choices, training strategies for focused CS exploration, evaluation criteria for CS coverage, and related challenges. Future directions include refining models, exploring new notations, improving benchmarks, and enhancing interpretability to better understand biologically relevant molecular properties.

Considering radial basis function neural network for effective solution generation in metaheuristic algorithms.

In many engineering optimization problems, the number of function evaluations is severely limited by the time or cost constraints. These limitations present a significant challenge in the field of global optimization, because existing metaheuristic methods typically require a substantial number of function evaluations to find optimal solutions. This paper presents a new metaheuristic optimization algorithm that considers the information obtained by a radial basis function neural network (RBFNN) in terms of the objective function for guiding the search process. Initially, the algorithm uses the maximum design approach to strategically distribute a set of solutions across the entire search space. It then enters a cycle in which the RBFNN models the objective function values from the current solutions. The algorithm identifies and uses key neurons in the hidden layer that correspond to the highest objective function values to generate new solutions. The centroids and standard deviations of these neurons guide the sampling process, which continues until the desired number of solutions is reached. By focusing on the areas of the search space that yield high objective function values, the algorithm avoids exhaustive solution evaluations and significantly reduces the number of function evaluations. The effectiveness of the method is demonstrated through a comparison with popular metaheuristic algorithms across several test functions, where it consistently outperforms existing techniques, delivers higher-quality solutions, and improves convergence rates.

LONEStar: The Lunar Flashlight Optical Navigation Experiment.

This paper documents the results from the highly successful Lunar flashlight Optical Navigation Experiment with a Star tracker (LONEStar). Launched in December 2022, Lunar Flashlight (LF) was a NASA-funded technology demonstration mission. After a propulsion system anomaly prevented capture in lunar orbit, LF was ejected from the Earth-Moon system and into heliocentric space. NASA subsequently transferred ownership of LF to Georgia Tech to conduct an unfunded extended mission to demonstrate further advanced technology objectives, including LONEStar. From August to December 2023, the LONEStar team performed on-orbit calibration of the optical instrument and a number of different OPNAV experiments. This campaign included the processing of nearly 400 images of star fields, Earth and Moon, and four other planets (Mercury, Mars, Jupiter, and Saturn). LONEStar provided the first on-orbit demonstrations of heliocentric navigation using only optical observations of planets. Of special note is the successful in-flight demonstration of (1) instantaneous triangulation with simultaneous sightings of two planets with the LOST algorithm and (2) dynamic triangulation with sequential sightings of multiple planets.

Automatic lithology identification in meteorite impact craters using machine learning algorithms.

Identifying lithologies in meteorite impact craters is an important task to unlock processes that have shaped the evolution of planetary bodies. Traditional methods for lithology identification rely on time-consuming manual analysis, which is costly and limits the efficiency of rapid decision-making. This paper utilizes different machine learning algorithms namely Random Forest, Decision Tree, K Nearest Neighbors, and Logistic Regression with Grid Search to classify rock lithologies using data from the Bosumtwi impact crater in Ghana. A repeated stratified k-fold cross-validation method is applied to Grid Search to select the best combination of hyperparameters. The findings demonstrate that the Random Forest algorithm achieves the most promising results in classifying lithologies in the meteorite impact crater with an accuracy score of 86.89%, a recall score of 84.88%, a precision score of 87.21%, and an F1 score of 85.48%. The findings also suggest that more high-quality data has the potential to further increase the accuracy scores of the machine learning algorithm. In conclusion, this study demonstrates the significant potential of machine learning techniques to revolutionize lithology identification in meteorite impact craters, thus paving the way for their influential role in future space exploration endeavors.

Technology Selection for Inline Topography Measurement with Rover-Borne Laser Spectrometers.

This work studies enhancing the capabilities of compact laser spectroscopes integrated into space-exploration rovers by adding 3D topography measurement techniques. Laser spectroscopy enables the in situ analysis of sample composition, aiding in the understanding of the geological history of extraterrestrial bodies. To complement spectroscopic data, the inclusion of 3D imaging is proposed to provide unprecedented contextual information. The morphological information aids material characterization and hence the constraining of rock and mineral histories. Assigning height information to lateral pixels creates topographies, which offer a more complete spatial dataset than contextual 2D imaging. To aid the integration of 3D measurement into future proposals for rover-based laser spectrometers, the relevant scientific, rover, and sample constraints are outlined. The candidate 3D technologies are discussed, and estimates of performance, weight, and power consumptions guide the down-selection process in three application examples. Technology choice is discussed from different perspectives. Inline microscopic fringe-projection profilometry, incoherent digital holography, and multiwavelength digital holography are found to be promising candidates for further development.

AOP report: Development of an adverse outcome pathway for deposition of energy leading to cataracts.

Cataracts are one of the leading causes of blindness, with an estimated 95 million people affected worldwide. A hallmark of cataract development is lens opacification, typically associated not only with aging but also radiation exposure as encountered by interventional radiologists and astronauts during the long-term space mission. To better understand radiation-induced cataracts, the adverse outcome pathway (AOP) framework was used to structure and evaluate knowledge across biological levels of organization (e.g., macromolecular, cell, tissue, organ, organism and population). AOPs identify a sequence of key events (KEs) causally connected by key event relationships (KERs) beginning with a molecular initiating event to an adverse outcome (AO) of relevance to regulatory decision-making. To construct the cataract AO and retrieve evidence to support it, a scoping review methodology was used to filter, screen, and review studies based on the modified Bradford Hill criteria. Eight KEs were identified that were moderately supported by empirical evidence (e.g., dose-, time-, incidence-concordance) across the adjacent (directly linked) relationships using well-established endpoints. Over half of the evidence to justify the KER linkages was derived from the evidence stream of biological plausibility. Early KEs of oxidative stress and protein modifications had strong linkages to downstream KEs and could be the focus of countermeasure development. Several identified knowledge gaps and inconsistencies related to the quantitative understanding of KERs which could be the basis of future research, most notably directed to experiments in the range of low or moderate doses and dose-rates, relevant to radiation workers and other occupational exposures.

Secondary proton buildup in space radiation shielding.

The risk posed by prolonged exposure to space radiation represents a significant obstacle to long-duration human space exploration. Of the ion species present in the galactic cosmic ray spectrum, relativistic protons are the most abundant and as such are a relevant point of interest with regard to the radiation protection of space crews involved in future long-term missions to the Moon, Mars, and beyond. This work compared the shielding effectiveness of a number of standard and composite materials relevant to the design and development of future spacecraft or planetary surface habitats. Absorbed dose was measured using Al2O3:C optically stimulated luminescence dosimeters behind shielding targets of varying composition and depth using the 1 GeV nominal energy proton beam available at the NASA Space Radiation Laboratory at the Brookhaven National Laboratory in New York. Absorbed dose scored from computer simulations performed using the multi-purpose Monte Carlo radiation transport code FLUKA agrees well with measurements obtained via the shielding experiments. All shielding materials tested and modeled in this study were unable to reduce absorbed dose below that measured by the (unshielded) front detector, even after depths as large as 30 g/cm2. These results could be noteworthy given the broad range of proton energies present in the galactic cosmic ray spectrum, and the potential health and safety hazard such space radiation could represent to future human space exploration.

Flare: An FPGA-Based Full Precision Low Power CNN Accelerator with Reconfigurable Structure.

Convolutional neural networks (CNNs) have significantly advanced various fields; however, their computational demands and power consumption have escalated, posing challenges for deployment in low-power scenarios. To address this issue and facilitate the application of CNNs in power constrained environments, the development of dedicated CNN accelerators is crucial. Prior research has predominantly concentrated on developing low precision CNN accelerators using code generated from high-level synthesis (HLS) tools. Unfortunately, these approaches often fail to efficiently utilize the computational resources of field-programmable gate arrays (FPGAs) and do not extend well to full precision scenarios. To overcome these limitations, we integrate vector dot products to unify the convolution and fully connected layers. By treating the row vector of input feature maps as the fundamental processing unit, we balance processing latency and resource consumption while eliminating data rearrangement time. Furthermore, an accurate design space exploration (DSE) model is established to identify the optimal design points for each CNN layer, and dynamic partial reconfiguration is employed to maximize each layer's access to computational resources. Our approach is validated through the implementation of AlexNet and VGG16 on 7A100T and ZU15EG platforms, respectively. We achieve an average convolutional layer throughput of 28.985 GOP/s and 246.711 GOP/s for full precision. Notably, the proposed accelerator demonstrates remarkable power efficiency, with a maximum improvement of 23.989 and 15.376 times compared to current state-of-the-art FPGA implementations.

Understanding the complexities of space anaemia in extended space missions: revelations from microgravitational odyssey.

Space travel exposes astronauts to several environmental challenges, including microgravity and radiation exposure. To overcome these stressors, the body undergoes various adaptations such as cardiovascular deconditioning, fluid shifts, metabolic changes, and alterations in the state of the bone marrow. Another area of concern is the potential impact of these adaptations on erythrocyte and haemoglobin concentrations, which can lead to what is commonly referred to as space anaemia or microgravity-induced anaemia. It is known that anaemia may result in impaired physical and cognitive performance, making early detection and management crucial for the health and wellbeing of astronauts during extended space missions. However, the effects and mechanisms of space anaemia are not fully understood, and research is underway to determine the extent to which it poses a challenge to astronauts. Further research is needed to clarify the long-term effects of microgravity on the circulatory system and to investigate possible solutions to address spaceflight-induced anaemia. This article reviews the potential link between spaceflight and anaemia, based on existing evidence from simulated studies (e.g., microgravity and radiation studies) and findings from spaceflight studies (e.g., International Space Station and space shuttle missions).

Radium deposition in human brain tissue: A Geant4-DNA Monte Carlo toolkit study.

NASA has encouraged studies on 226Ra deposition in the human brain to investigate the effects of exposure to alpha particles with high linear energy transfer, which could mimic some of the exposure astronauts face during space travel. However, this approach was criticized, noting that radium is a bone-seeker and accumulates in the skull, which means that the radiation dose from alpha particles emitted by 226Ra would be heavily concentrated in areas close to cranial bones rather than uniformly distributed throughout the brain. In the high background radiation areas of Ramsar, Iran, extremely high levels of 226Ra in soil contribute to a large proportion of the inhabitants' radiation exposure. A prospective study on Ramsar residents with a calcium-rich diet was conducted to improve the dose uniformity due to 226Ra throughout the cerebral and cerebellar parenchyma. The study found that exposure of the human brain to alpha particles did not significantly affect working memory but was significantly associated with increased reaction times. This finding is crucial because astronauts on deep space missions may face similar cognitive impairments due to exposure to high charge and energy particles. The current study was aimed to evaluate the validity of the terrestrial model using the Geant4 Monte Carlo toolkit to simulate the interactions of alpha particles and representative cosmic ray particles, acknowledging that these radiation types are only a subset of the complete space radiation environment.

Recent Advances and Challenges in Polymer-Based Materials for Space Radiation Shielding.

Space exploration requires the use of suitable materials to protect astronauts and structures from the hazardous effects of radiation, in particular, ionizing radiation, which is ubiquitous in the hostile space environment. In this scenario, polymer-based materials and composites play a crucial role in achieving effective radiation shielding while providing low-weight and tailored mechanical properties to spacecraft components. This work provides an overview of the latest developments and challenges in polymer-based materials designed for radiation-shielding applications in space. Recent advances in terms of both experimental and numerical studies are discussed. Different approaches to enhancing the radiation-shielding performance are reported, such as integrating various types of nanofillers within polymer matrices and optimizing the materials design. Furthermore, this review explores the challenges in developing multifunctional materials that are able to provide radiation protection. By summarizing the state-of-the-art research and identifying emerging trends, this review aims to contribute to the ongoing efforts to identify polymer materials and composites that are most useful to protect human health and spacecraft performance in the harsh radiation conditions that are typically found during missions in space.

Research and Verification of a Novel Interferometry Method by Joint Processing of Downlink Pseudo-Noise Ranging and DOR Signals for Deep Space Exploration.

The remarkably long distances covered by deep space probes result in extremely weak downlink signals, which poses great challenges for ground measurement systems. In the current climate, improving the comprehensive utilization of downlink signal power to increase the detection distance or enhance the measurement accuracy is of great significance in deep space exploration. Facing this problem, we analyze the delta Differential One-way Range (ΔDOR) error budget of the X-band of the China Deep Space Network (CDSN). Then, we propose a novel interferometry method that detunes one group of DOR beacons and reuses the clock components of regenerative pseudo-code ranging signals for interferometry delay estimation. The primary advantage of this method is its ability to enhance the power utilization efficiency of downlink signals, thereby facilitating more efficient tracking and measurement without necessitating additional design requirements for deep space transponders. Finally, we analyze and verify the correctness and effectiveness of our proposed method using measured data from CDSN. Our results indicate that the proposed method can save approximately 13% of the downlink signal power and increase the detection distance by about 6.25% using typical modulation parameters. Furthermore, if the relative power of other signal components remains unchanged, the power of the DOR tone can be directly increased by more than 100%, improving the deep space exploration ability more significantly.

Cultivation of Chroococcidiopsis thermalis Using Available In Situ Resources to Sustain Life on Mars.

The cultivation of cyanobacteria by exploiting available in situ resources represents a possible way to supply food and oxygen to astronauts during long-term crewed missions on Mars. Here, we evaluated the possibility of cultivating the extremophile cyanobacterium Chroococcidiopsis thermalis CCALA 050 under operating conditions that should occur within a dome hosting a recently patented process to produce nutrients and oxygen on Mars. The medium adopted to cultivate this cyanobacterium, named Martian medium, was obtained using a mixture of regolith leachate and astronauts' urine simulants that would be available in situ resources whose exploitation could reduce the mission payload. The results demonstrated that C. thermalis can grow in such a medium. For producing high biomass, the best medium consisted of specific percentages (40%vol) of Martian medium and a standard medium (60%vol). Biomass produced in such a medium exhibits excellent antioxidant properties and contains significant amounts of pigments. Lipidomic analysis demonstrated that biomass contains strategic lipid classes able to help the astronauts facing the oxidative stress and inflammatory phenomena taking place on Mars. These characteristics suggest that this strain could serve as a valuable nutritional resource for astronauts.

Neutrinos to Astrovirology: Signatures.

In the 20th century, the concept of terrestrial life's unity was solidified, and the 21st century saw the emergence and establishment of astrovirology. To date, life originating beyond Earth has not been identified. The singular instance where NASA investigated potential microfossils in Martian ejecta found on Earth has since been refuted. This report suggests that a more comprehensive discussion and analysis of life's biosignatures and communication methods are essential. Such approaches are crucial not only to avoid overlooking the possible existence of extra-terrestrial intelligence (ETI) but also to prevent potential human infections that could arise from extra-terrestrial contact. In addition terrestrial infections by microorganism that originally derived from Earth and were returned, require investigation due to potential mutations and subsequent increased pathogenicity.

Estimation of Distribution Algorithm for Grammar-Guided Genetic Programming.

Genetic variation operators in grammar-guided genetic programming are fundamental to guide the evolutionary process in search and optimization problems. However, they show some limitations, mainly derived from an unbalanced exploration and local-search trade-off. This article presents an estimation of distribution algorithm for grammar-guided genetic programming to overcome this difficulty and thus increase the performance of the evolutionary algorithm. Our proposal employs an extended dynamic stochastic context-free grammar to encode and calculate the estimation of the distribution of the search space from some promising individuals in the population. Unlike traditional estimation of distribution algorithms, the proposed approach improves exploratory behavior by smoothing the estimated distribution model. Therefore, this algorithm is referred to as SEDA, smoothed estimation of distribution algorithm. Experiments have been conducted to compare overall performance using a typical genetic programming crossover operator, an incremental estimation of distribution algorithm, and the proposed approach after tuning their hyperparameters. These experiments involve challenging problems to test the local search and exploration features of the three evolutionary systems. The results show that grammar-guided genetic programming with SEDA achieves the most accurate solutions with an intermediate convergence speed.