Explore the vast range of titles available.

Quantum biology is the study of quantum effects on biochemical mechanisms and biological function. We show that the biological production of reactive oxygen species (ROS) in live cells can be influenced by coherent electron spin dynamics, providing a new example of quantum biology in cellular regulation. ROS partitioning appears to be mediated during the activation of molecular oxygen (O 2 ) by reduced flavoenzymes, forming spin-correlated radical pairs (RPs). We find that oscillating magnetic fields at Zeeman resonance alter relative yields of cellular superoxide (O 2 •− ) and hydrogen peroxide (H 2 O 2 ) ROS products, indicating coherent singlet-triplet mixing at the point of ROS formation. Furthermore, the orientation-dependence of magnetic stimulation, which leads to specific changes in ROS levels, increases either mitochondrial respiration and glycolysis rates. Our results reveal quantum effects in live cell cultures that bridge atomic and cellular levels by connecting ROS partitioning to cellular bioenergetics.

Increased generation of reactive oxygen species (ROS) and an altered redox status have long been observed in cancer cells, suggesting that ROS might be involved in the development of these cells. However, recent studies suggest that inducing an excess of ROS in cancer cells can be exploited for therapeutic benefits. Cancer cells in advanced stage tumors frequently exhibit multiple genetic alterations and high oxidative stress, suggesting that it might be possible to preferentially modulate the development of these cells by controlling their ROS production. Low levels of ROS are also important for the development and survival of normal cells. In this manuscript, we present data on the influence of the suppression of the Earth's magnetic field (low level magnetic fields or LLF) which magnitudes range from 0.2 µT to 2 µT on the modulation of hydrogen peroxide (H(2)O(2)) in human fibrosarcoma cancer cell line HT1080, pancreatic AsPC-1 cancer cell line, and bovine pulmonary artery endothelial cells (PAEC) exposed to geomagnetic field (control; 45 µT-60 µT). Reduction of the Earth's magnetic field suppressed H(2)O(2) production in cancer cells and PAEC. The addition of catalase and superoxide dismutase (SOD) mimetic MnTBAP inhibited the magnetic field effect. Modulating ROS production by magnetic fields may open new venues of biomedical research and therapeutic strategies.

Cryptochromes are widespread blue‐light absorbing flavoproteins with important signaling roles. In plants they mediate de‐etiolation, developmental and stress responses resulting from interaction with downstream signaling partners such as transcription factors and components of the proteasome. Recently, it has been shown that Arabidopsis cry1 activation by blue light also results in direct enzymatic conversion of molecular oxygen (O₂) to reactive oxygen species (ROS) and hydrogen peroxide (H₂O₂) in vitro. Here we explored whether direct enzymatic synthesis of ROS by Arabidopsis cry1 can play a physiological role in vivo. ROS formation resulting from cry1 expression was measured by fluorescence assay in insect cell cultures and in Arabidopsis protoplasts from cryptochrome mutant seedlings. Cell death was determined by colorimetric assay. We found that ROS formation results from cry1 activation and induces cell death in insect cell cultures. In plant protoplasts, cryptochrome activation results in rapid increase in ROS formation and cell death. We conclude that ROS formation by cryptochromes may indeed be of physiological relevance and could represent a novel paradigm for cryptochrome signaling.

Recently, it has been reported that mitochondria possess a novel pathway for nitric oxide (NO) synthesis. This pathway is induced when cells experience hypoxia, is nitrite (NO₂⁻)-dependent, is independent of NO synthases, and is catalyzed by cytochrome c oxidase (Cco). It has been proposed that this mitochondrially produced NO is a component of hypoxic signaling and the induction of nuclear hypoxic genes. In this study, we examine the NO₂⁻-dependent NO production in yeast engineered to contain alternative isoforms, Va or Vb, of Cco subunit V. Previous studies have shown that these isoforms have differential effects on oxygen reduction by Cco, and that their genes (COX5a and COX5b, respectively) are inversely regulated by oxygen. Here, we find that the Vb isozyme has a higher turnover rate for NO production than the Va isozyme and that the Vb isozyme produces NO at much higher oxygen concentrations than the Va isozyme. We have also found that the hypoxic genes CYC7 and OLE1 are induced to higher levels in a strain carrying the Vb isozyme than in a strain carrying the Va isozyme. Together, these results demonstrate that the subunit V isoforms have differential effects on NO₂⁻-dependent NO production by Cco and provide further support for a role of Cco in hypoxic signaling. These findings also suggest a positive feedback mechanism in which mitochondrially produced NO induces expression of COX5b, whose protein product then functions to enhance the ability of Cco to produce NO in hypoxic/anoxic cells.

This paper presents a methodology for training a Deep Learning model aimed at flight management tasks in a fixed-wing unmanned aerial vehicle (UAV), specifically autopilot control and GPS prediction. In this formulation, sensor data and the most recent GPS signal are first processed by an LSTM to produce an initial coordinate prediction. This preliminary estimate is then merged with additional sensor inputs and passed to an MLP, which replaces the conventional autopilot algorithm by generating the control commands for real-time navigation. The approach is particularly valuable in scenarios where the aircraft must follow a predetermined route—such as surveillance operations—or maintain a remote ground link under varying GPS availability. The study focuses on Class I UAVs; however, the proposed methodology can be adapted to larger classes (II and III) by adjusting sensor configurations and network parameters. To collect training data, a small fixed-wing aircraft was instrumented to record kinematic and control inputs, which then served as inputs to the neural network. Despite the limited sensor suite and the use of an open-source flight controller (SpeedyBee), the flexibility of the proposed approach allows for easy adaptation to more complex UAVs equipped with additional sensors, potentially improving prediction accuracy. The performance of the neural network, implemented in PyTorch, was evaluated by comparing its predicted data with actual flight logs. In addition, the method has been shown to be robust to both short and prolonged GPS outages, as it relies on waypoint-based navigation along previously explored routes, ensuring reliable performance in known operational contexts.

This paper aims to describe the methodology used in the design and manufacture of a fixed-wing aircraft manufactured using additive techniques together with the implementation of technology based on solar panels. The main objective is increasing the autonomy and range of the UAV’s autonomous missions. Moreover, one of the main targets is to improve the capabilities of the aeronautical industry towards sustainable aircrafts and to acquire better mechanical properties owing to the use of additive technologies and new printing materials. Further, a lower environmental impact could be achieved through the use of renewable energies. Material extrusion (MEX) technology may be able to be used for the manufacture of stronger and lighter parts by using gyroids as the filling of the printed material. The paper proposes the use of minimal surfaces for the reinforcement of the UAV aircraft wings. This type of surface was never used because it is not possible to manufacture it using conventional techniques. The rapid growth of additive technologies led to many expectations for new design methodologies in the aeronautical industry. In this study, mechanical tests were carried out on specimens manufactured with different geometries to address the design and manufacture of a UAV as a demonstrator. In addition, to carry out the manufacture of the prototype, a 3D printer with a movable bench similar to a belt, that allows for the manufacture of parts without limitations in the Z axis, was tested. The parts manufactured with this technique can be structurally improved, and it is possible to avoid manufacturing multiple prints of small parts of the aircraft that will have to be glued later, decreasing the mechanical properties of the UAV. The conceptual design and manufacturing of a solar aircraft, SolarÍO, using additive technologies, is presented. A study of the most innovative 3D printers was carried out that allowed for the manufacture of parts with an infinite Z-axis and, in addition, a filler based on minimal surfaces (gyroids) was applied, which considerably increased the mechanical properties of the printed parts. Finally, it can be stated that in this article, the potential of the additive manufacturing as a new manufacturing process for small aircrafts and for the aeronautical sector in the future when new materials and more efficient additive manufacturing processes are already developed is demonstrated.

Some ecofeminist scholars have argued that being a feminist entails being a contextual vegan. Donna Haraway has opposed this position and received extensive critique. Yet no one, to my knowledge, has systematically studied how Haraway's theory can enrich ecofeminist vegan literature. To this end, I first establish the method of analysis, and/or framework, I use to read Haraway's work, what I call, interconstitutionality. Next, I delineate the limitations of Haraway's thinking insofar as it assumes a position of human dominion over animals. I then explore some aspects of Haraway's theory that can enrich ecofeminist vegan scholarship and provide insights to go beyond the limits of Haraway's corpus regarding: (1) the entanglements and embodied vulnerabilities that constitute human and non-human animals; (2) the agency of animals and the importance of curiosity and respect in leading just lives with other than human animals; (3) the ethical relevance of otherness, difference, and vulnerability at multiple scales: subject, community/herd, species, and cross-species (e.g., there are shared vulnerabilities between beings who are pregnant regardless of the species they belong to); and (4) the unavoidable violence that human existence entails. The text closes by affirming an ecofeminist non-anthropocentric vegan ontology and ethico-politics that aspires to overcome human dominion over animals.

Background Cognitive impairment and depressive symptoms are highly prevalent after Intracerebral Hemorrhage (ICH). We leveraged Latent Profile Analysis (LPA) to identify profiles for cognitive decline and depression onset after ICH. We also investigated differences in clinical, genetic and neuroimaging characteristics across patients’ profiles. Methods We analyzed data from the ICH study conducted at Massachusetts General Hospital between January 1998 and December 2019. We collected information from electronical health records, follow-up interviews, CT and MRI imaging, and APOE genotype. We conducted LPA and multinomial logistic regression analyses to: 1) identify distinct profiles for cognitive decline and depression onset after ICH; 2) identify clinical, neuroimaging and genetic factors predicting individuals’ likelihood to express a specific profile. Results We followed 784 ICH survivors for a median of 45.8 months. We identified four distinct profiles in cognitive and depressive symptoms after ICH: low depression and dementia risk, early-onset depression and dementia, late-onset depression and dementia, high depression with low dementia risk. Cerebral small vessel disease severity and APOE genotype were specifically associated with the late-onset profile (both p <  0.05). Acute hematoma characteristics (size, intraventricular extension) and functional disability were specifically associated with the early-onset profile (all p <  0.05). Conclusion We identified four distinct profiles for cognitive and depressive symptoms after ICH, each displaying specific associations with individual patients’ clinical, genetic and neuroimaging data. These associations reflect separate biological mechanisms influencing dementia and depression risk after ICH. Our findings support employing LPA in future ICH studies, and is likely applicable to stroke survivors at large.

Large Format Additive Manufacturing (LFAM) has gained prominence in the aerospace and automotive industries, where topology optimization has become crucial. LFAM facilitates the layer-by-layer production of sizeable industrial components in carbon fiber (CF) reinforced polymers, however 3D printing at large scales results in warpage generation. Printed components are deformed as residual stresses generated due to thermal gradients between adjacent layers. This paper tackles the problem at two different scales: the micro and macroscale. Initially, the microstructure characterization of the thermoplastic ABS matrix composite material enriched with 20% short CF is used in the development of numerical models to understand the mechanical behavior of the studied material. Numerical modeling is performed simultaneously by means of Mean-Field (MF) homogenization methods and Finite Element Analysis (FEA). Outcomes validated with corrected experimental mechanical testing results show a discrepancy in the elastic modulus of 7.8% with respect to FE multi-layer analysis. Micro-level results are coupled with the a macroscopic approach to reproduce the LFAM process, demonstrating the feasibility of the tool in the development of a Digital Twin (DT).

We propose a novel Bayesian hierarchical structure of state-space surplus production models that accommodate multiple catch per unit effort (CPUE) data of various fisheries exploiting the same stock. The advantage of this approach in data-limited stock assessment is the possibility of borrowing strength among different data sources to estimate reference points useful for management decisions. The model is applied to thirteen years of data from seven fisheries of the lebranche mullet (Mugil liza) southern population, distributed along the southern and southeastern shelf regions of Brazil. The results indicate that this modelling strategy is useful and has room for extensions. There are reasons for concern about the sustainability of the mullet stock, although the wide posterior credibility intervals for key reference points preclude conclusive statistical evidence at this time