Environmental Signals Research Articles

Unobtrusive measurement of cognitive load and physiological signals in uncontrolled environments

While individuals fail to assess their mental health subjectively in their day-to-day activities, the recent development of consumer-grade wearable devices has enormous potential to monitor daily workload objectively by acquiring physiological signals. Therefore, this work collected consumer-grade physiological signals from twenty-four participants, following a four-hour cognitive load elicitation paradigm with self-chosen tasks in uncontrolled environments and a four-hour mental workload elicitation paradigm in a controlled environment. The recorded dataset of approximately 315 hours consists of electroencephalography, acceleration, electrodermal activity, and photoplethysmogram data balanced across low and high load levels. Participants performed office-like tasks in the controlled environment (mental arithmetic, Stroop, N-Back, and Sudoku) with two defined difficulty levels and in the uncontrolled environments (mainly researching, programming, and writing emails). Each task label was provided by participants using two 5-point Likert scales of mental workload and stress and the pairwise NASA-TLX questionnaire. This data is suitable for developing real-time mental health assessment methods, conducting research on signal processing techniques for challenging environments, and developing personal cognitive load assistants.

Toward an AI Era: Advances in Electronic Skins.

Electronic skins (e-skins) have seen intense research and rapid development in the past two decades. To mimic the capabilities of human skin, a multitude of flexible/stretchable sensors that detect physiological and environmental signals have been designed and integrated into functional systems. Recently, researchers have increasingly deployed machine learning and other artificial intelligence (AI) technologies to mimic the human neural system for the processing and analysis of sensory data collected by e-skins. Integrating AI has the potential to enable advanced applications in robotics, healthcare, and human-machine interfaces but also presents challenges such as data diversity and AI model robustness. In this review, we first summarize the functions and features of e-skins, followed by feature extraction of sensory data and different AI models. Next, we discuss the utilization of AI in the design of e-skin sensors and address the key topic of AI implementation in data processing and analysis of e-skins to accomplish a range of different tasks. Subsequently, we explore hardware-layer in-skin intelligence before concluding with an analysis of the challenges and opportunities in the various aspects of AI-enabled e-skins.

Characterizing the nonmonotonic behavior of mutual information along biochemical reaction cascades.

Cells sense environmental signals and transmit information intracellularly through changes in the abundance of molecular components. Such molecular abundances can be measured in single cells and exhibit significant heterogeneity in clonal populations even in identical environments. Experimentally observed joint probability distributions can then be used to quantify the covariability and mutual information between molecular abundances along signaling cascades. However, because stationary state abundances along stochastic biochemical reaction cascades are not conditionally independent, their mutual information is not constrained by the data-processing inequality. Here, we report the conditions under which the mutual information between stationary state abundances increases along a cascade of biochemical reactions. This nonmonotonic behavior can be intuitively understood in terms of noise propagation and time-averaging stochastic fluctuations that are short-lived compared to an extrinsic signal. Our results reemphasize that mutual information measurements of stationary state distributions of cellular components may be of limited utility for characterizing cellular signaling processes because they do not measure information transfer.

Physics-informed neural network for volumetric sound field reconstruction of speech signals

Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based approaches, particularly in sound field reconstruction. Physics-informed neural networks (PINNs) have emerged as a novel framework, bridging the gap between data-driven and model-based techniques for addressing physical phenomena governed by partial differential equations. This paper introduces a PINN-based approach for the recovery of arbitrary volumetric acoustic fields. The network incorporates the wave equation to impose a regularization on signal reconstruction in the time domain. This methodology enables the network to learn the physical law of sound propagation and allows for the complete characterization of the sound field based on a limited set of observations. The proposed method’s efficacy is validated through experiments involving speech signals in a real-world environment, considering varying numbers of available measurements. Moreover, a comparative analysis is undertaken against state-of-the-art frequency domain and time domain reconstruction methods from existing literature, highlighting the increased accuracy across the various measurement configurations.

Advances in Plant Auxin Biology: Synthesis, Metabolism, Signaling, Interaction with Other Hormones, and Roles under Abiotic Stress.

Auxin is a key hormone that regulates plant growth and development, including plant shape and sensitivity to environmental changes. Auxin is biosynthesized and metabolized via many parallel pathways, and it is sensed and transduced by both normal and atypical pathways. The production, catabolism, and signal transduction pathways of auxin primarily govern its role in plant growth and development, and in the response to stress. Recent research has discovered that auxin not only responds to intrinsic developmental signals, but also mediates various environmental signals (e.g., drought, heavy metals, and temperature stresses) and interacts with hormones such as cytokinin, abscisic acid, gibberellin, and ethylene, all of which are involved in the regulation of plant growth and development, as well as the maintenance of homeostatic equilibrium in plant cells. In this review, we discuss the latest research on auxin types, biosynthesis and metabolism, polar transport, signaling pathways, and interactions with other hormones. We also summarize the important role of auxin in plants under abiotic stresses. These discussions provide new perspectives to understand the molecular mechanisms of auxin's functions in plant development.

Molecular Targets in Streptococcus pyogenes for the Development of Anti-Virulence Agents.

Streptococcus pyogenes, commonly known as Group A Streptococcus (GAS), is a significant human pathogen responsible for a wide range of diseases, from mild pharyngitis to severe conditions such as necrotizing fasciitis and toxic shock syndrome. The increasing antibiotic resistance, especially against macrolide antibiotics, poses a challenge to the effective treatment of these infections. This paper reviews the current state and mechanisms of antibiotic resistance in S. pyogenes. Furthermore, molecular targets for developing anti-virulence agents, which aim to attenuate virulence rather than killing it outright, are explored. This review specifically focuses on virulence regulators, proteins that coordinate the expression of multiple virulence factors in response to environmental signals, playing a crucial role in the pathogen's ability to cause disease. Key regulatory systems, such as RopB, Mga, CovRS, and the c-di-AMP signaling system, are discussed for their roles in modulating virulence gene expression. Additionally, potential molecular target sites for the development of anti-virulence agents are suggested. By concentrating on these regulatory pathways, it is proposed that anti-virulence strategies could enhance the effectiveness of existing antibiotics and reduce the selective pressure that drives the development of resistance.

Expression of AtNF-YB1 activates early flowering, showing potential in breeding hybrid rice.

The nuclear factor Y (NF-Y) has been shown to be involved in plant growth and development in response to various environmental signals. However, the integration of these mechanisms into breeding practices for new cultivars has not been extensively investigated. In this study, the Arabidopsis gene AtNF-YB1 was introduced into rice, including inbred Kasalath and the hybrids Jinfeng × Chenghui 727 and Jinfeng × Chuanhui 907. The obtained transgenic rice showed early flowering under both natural long day (NLD) and natural short day (NSD) conditions. For the inbred Kasalath, the transgenic lines clearly showed a shorter plant height and lower grain yield, with a decrease in spike length and grain number but more productive panicles. However, the hybrids with AtNF-YB1 had much smaller or even zero reduction in spike length and grain number and more productive panicles. Thus, maintained or even increased grain yields of the transgenic hybrids were recorded under the NLD conditions. Quantitative PCR analysis indicated that the rice flowering initiation pathways were early activated via the suppression of Ghd7 induction in the transgenic rice. RNA-Seq further demonstrated that three pathways related to plant photosynthesis were markedly upregulated in both Jinfeng B and the hybrid Jinfeng × Chuanhui 907 with AtNF-YB1 expression. Moreover, physiological experiments showed an upregulation of photosynthetic rates in the transgenic lines. Taken together, this study suggests that AtNF-YB1 expression in rice not only induces early flowering but also benefits photosynthesis, which might be used to develop hybrid varieties with early ripening.

Oxygen-dependent regulation of F-box proteins in Toxoplasma gondii is mediated by Skp1 glycosylation

A dynamic proteome is required for cellular adaption to changing environments including levels of O2, and the SKP1/CULLIN-1/F-box protein/RBX1 (SCF) family of E3 ubiquitin ligases contributes importantly to proteasome-mediated degradation. We examine, in the apicomplexan parasite Toxoplasma gondii, the influence on the interactome of SKP1 by its novel glycan attached to a hydroxyproline generated by PHYa, the likely ortholog of the HIFα PHD2 oxygen-sensor of human host cells. Strikingly, the representation of several putative F-box proteins (FBPs) is substantially reduced in PHYaΔ parasites grown in fibroblasts. One, FBXO13, is a predicted lysyl hydroxylase related to the human JmjD6 oncogene except for its F-box domain. The abundance of FBXO13, epitope-tagged at its genetic locus, was reduced in PHYaΔ parasites thus explaining its diminished presence in the SKP1 interactome. A similar effect was observed for FBXO14, a cytoplasmic protein of unknown function that may have co-evolved with PHYa in apicomplexans. Similar findings in glycosylation-mutant cells, rescue by proteasomal inhibitors, and unchanged transcript levels, suggested the involvement of the SCF in their degradation. The effect was selective, because FBXO1 was not affected by loss of PHYa. These findings are physiologically significant because the effects were phenocopied in parasites reared at 0.5% O2. Modest impact on steady-state SKP1 modification levels suggests that effects are mediated during a lag phase in hydroxylation of nascent SKP1. The dependence of FBP abundance on O2-dependent SKP1 modification likely contributes to the reduced virulence of PHYaΔ parasites owing to impaired ability to sense O2 as an environmental signal.

Deciphering the influence of NaCl on social behaviour of Bacillus subtilis

Various environmental signals, such as temperature, pH, nutrient levels, salt content and the presence of other microorganisms, can influence biofilm’s development and dynamics. However, the innate mechanisms that govern at the molecular and cellular levels remain elusive. Here, we report the impact of physiologically relevant concentrations of NaCl on biofilm formation and the associated differences in an undomesticated natural isolate of Bacillus subtilis . NaCl exposure and its uptake by bacterial cells induced substantial changes in the architecture of pellicle biofilm and an upsurge in the expansion of biofilm colonies on agar surfaces. We have observed the upregulation of genes involved in motility and the downregulation of genes involved in the biosynthesis of extracellular matrix components through the transcription factor sigD, suggesting the possible underlying mechanisms. To further support these observations, we have used Δ sigD and Δ srfAC null mutants, which showed compromised NaCl-induced effects. Our results indicate that NaCl induces a lifestyle shift in B. subtilis from a sessile biofilm state to an independent unicellular motile state. Overall, we present evidence that NaCl can reprogramme gene expression and alter cellular morphology and the state of cells to adapt to motility, which facilitates the expansion of bacterial colonies.

Influence of Neltuma flexuosa and rainfall event on the germination of Leptochloa crinita and Pappophorum caespitosum in two different locations along a stress gradient in the central Monte desert

AbstractIn drylands, grass germination occurs after a large rainfall event. However, trees influence the water requirements for germination either by decreasing the rate of evaporation from soil surface or by interception rainfall with their canopy. The aim of this work was to evaluate the role of Neltuma flexuosa tree on the germination of Leptochloa crinita and Pappophorum caespitosum grasses in response to precipitation events of different sizes. The experiment was conducted in two locations within the Monte desert, Argentina: a semi‐arid location with an average annual precipitation of 345 mm and an arid location with an average annual precipitation of 156 mm. Six microsites were established under N. flexuosa canopies and six microsites in open areas at both locations. Within each microsite, 35 seeds of each grass species were exposed to precipitation events of varying sizes. This procedure was repeated 14 times across two growing seasons. Germinated seeds were counted in the laboratory. Generalized linear mixed models were then applied to evaluate the effects of accumulated precipitation and the microsite on the grass germination in both locations. Results showed that N. flexuosa did not influence the germination of either grass species in the arid location. However, in the semi‐arid location, N. flexuosa facilitated the germination of P. caespitosum only. Large precipitation events would act as an environmental signal for these perennial grasses, allowing them to germinate in favorable years regardless of the microsite or the location.

Leveraging the science of early life predictability to inform policies promoting child health

Addressing the tremendous burden of early-life adversity requires constructive dialogues between scientists and policy makers to improve population health. Whereas dialogues focused on several aspects of early-life adversity have been initiated, discussion of an underrecognized form of adversity that has been observed across multiple contexts and cultures is only now emerging. Here we provide evidence for “why unpredictability?”, including: 1. Evidence that exposures to unpredictability affect child neurodevelopment, with influences that persist into adulthood. 2. The existence of a translational non-human animal model of exposure to early life unpredictability that can be capitalized upon to causally probe neurobiological mechanisms. 3. Evidence that patterns of signals in the early environment promote brain maturation across species. 4. The uneven distribution of unpredictability across demographic populations that illuminates a possible focal point for enhancing health equity. We then outline the potential of unpredictability in terms of the “what”; that is, how might the concept of unpredictability be leveraged to inform policy? We emphasize the importance of interdisciplinary and community partnerships to the success of this work and describe our community-engaged research project. Finally, we highlight opportunities for the science of unpredictability to inform policies in areas such as screening, immigration, criminal justice, education, childcare, child welfare, employment, healthcare and housing.

Research on the safety risks of microwave irradiation on motion balance perception in electric power environments

To the microwave irradiation safety hazards caused by the co-construction of towers in smart grids, this paper investigates the effects of microwave irradiation in the power environment on the biological motion balance perception function. Firstly, simulation of microwave signals in the electric power environment, i.e., low-frequency harmonics and high-frequency carriers, were realized by signal modulation and applied in four types of behavior testing scenarios. Then, determining rats as target organisms to replace workers and randomly dividing into groups in proportion: open field, rotating rod fatigue, beam walking and forced swimming. Configuring radar with various parameters to match the electric power irradiation scene and stimulate rats, monitoring the abnormal behavior by image processing module, including posture, motion trajectory, distance, and other features. The experimental result showed that exposed to microwaves induce rats motor ability decline, balance perception imbalance, together with paralysis within long-term exposure, and its locomotor activity, coordination, posture control and reaction time all exhibit varying degrees of weakening. These findings indicate that microwave irradiation in electric power environment may pose significant health and safety risks for worker.

Automatic extraction of transcriptional regulatory interactions of bacteria from biomedical literature using a BERT-based approach.

Transcriptional regulatory networks (TRNs) give a global view of the regulatory mechanisms of bacteria to respond to environmental signals. These networks are published in biological databases as a valuable resource for experimental and bioinformatics researchers. Despite the efforts to publish TRNs of diverse bacteria, many of them still lack one and many of the existing TRNs are incomplete. In addition, the manual extraction of information from biomedical literature ("literature curation") has been the traditional way to extract these networks, despite this being demanding and time-consuming. Recently, language models based on pretrained transformers have been used to extract relevant knowledge from biomedical literature. Moreover, the benefit of fine-tuning a large pretrained model with new limited data for a specific task ("transfer learning") opens roads to address new problems of biomedical information extraction. Here, to alleviate this lack of knowledge and assist literature curation, we present a new approach based on the Bidirectional Transformer for Language Understanding (BERT) architecture to classify transcriptional regulatory interactions of bacteria as a first step to extract TRNs from literature. The approach achieved a significant performance in a test dataset of sentences of Escherichia coli (F1-Score: 0.8685, Matthew's correlation coefficient: 0.8163). The examination of model predictions revealed that the model learned different ways to express the regulatory interaction. The approach was evaluated to extract a TRN of Salmonella using 264 complete articles. The evaluation showed that the approach was able to accurately extract 82% of the network and that it was able to extract interactions absent in curation data. To the best of our knowledge, the present study is the first effort to obtain a BERT-based approach to extract this specific kind of interaction. This approach is a starting point to address the limitations of reconstructing TRNs of bacteria and diseases of biological interest. Database URL: https://github.com/laigen-unam/BERT-trn-extraction.

UWB/MEMS IMU integrated positioning method based on NLOS angle discrimination and MAP constraints

A dynamic nonline-of-sight (NLOS) angle discrimination method is proposed to address the insufficiency of current research on the NLOS error characteristics of ultrawideband (UWB) signals in dynamic environments as well as the problem that UWB signals frequently suffer from occlusion, leading to poor or impossible localization. The experimental results indicate that the degree of UWB signal occlusion increases as the horizontal angle decreases, and when the horizontal angle is less than 167°, the UWB ranging error is so large that no ranging value is available. On this basis, a tightly integrated UWB/MEMS IMU positioning algorithm incorporating NLOS angle discrimination and map constraints is proposed; it employs horizontal angles to discriminate UWB ranging values in NLOS environments in accordance with the dynamic NLOS characteristics of UWB signals to assign better weights to UWB observations. Through comparative analysis of the results from both groups of experiments, the algorithm achieved northward, eastward, and planar positioning errors of 0.189 m and 0.126 m, 0.119 m and 0.134 m, 0.243 m and 0.211 m, respectively. Compared to the Robust Adaptive Kalman Filtering algorithm, the positional accuracy in the plane improved by 22.9% and 28.5%, respectively.

Malassezia responds to environmental pH signals through the conserved Rim/Pal pathway.

During mammalian colonization and infection, microorganisms must be able to rapidly sense and adapt to changing environmental conditions including alterations in extracellular pH. The fungus-specific Rim/Pal signaling pathway is one process that supports microbial adaptation to alkaline pH. This cascading series of interacting proteins terminates in the proteolytic activation of the highly conserved Rim101/PacC protein, a transcription factor that mediates microbial responses that favor survival in neutral/alkaline pH growth conditions, including many mammalian tissues. We identified the putative Rim pathway proteins Rim101 and Rra1 in the human skin colonizing fungus Malassezia sympodialis. Gene deletion by transconjugation and homologous recombination revealed that Rim101 and Rra1 are required for M. sympodialis growth at higher pH. In addition, comparative transcriptional analysis of the mutant strains compared to wild-type suggested mechanisms for fungal adaptation to alkaline conditions. These pH-sensing signaling proteins are required for optimal growth in a murine model of atopic dermatitis, a pathological condition associated with increased skin pH. Together, these data elucidate both conserved and phylum-specific features of microbial adaptation to extracellular stresses.IMPORTANCEThe ability to adapt to host pH has been previously associated with microbial virulence in several pathogenic fungal species. Here we demonstrate that a fungal-specific alkaline response pathway is conserved in the human skin commensal fungus Malassezia sympodialis (Ms). This pathway is characterized by the pH-dependent activation of the Rim101/PacC transcription factor that controls cell surface adaptations to changing environmental conditions. By disrupting genes encoding two predicted components of this pathway, we demonstrated that the Rim/Pal pathway is conserved in this fungal species as a facilitator of alkaline pH growth. Moreover, targeted gene mutation and comparative transcriptional analysis support the role of the Ms Rra1 protein as a cell surface pH sensor conserved within the basidiomycete fungi, a group including plant and human pathogens. Using an animal model of atopic dermatitis, we demonstrate the importance of Ms Rim/Pal signaling in this common inflammatory condition characterized by increased skin pH.

Resin acid δ13C and δ18O as indicators of intra-seasonal physiological and environmental variability.

Understanding the dynamics of δ13C and δ18O in modern resin is crucial for interpreting (sub)fossilized resin records and resin production dynamics. We measured the δ13C and δ18O offsets between resin acids and their precursor molecules in the top-canopy twigs and breast-height stems of mature Pinus sylvestris trees. We also investigated the physiological and environmental signals imprinted in resin δ13C and δ18O at an intra-seasonal scale. Resin δ13C was c. 2‰ lower than sucrose δ13C, in both twigs and stems, likely due to the loss of 13C-enriched C-1 atoms of pyruvate during isoprene formation and kinetic isotope effects during diterpene synthesis. Resin δ18O was c. 20‰ higher than xylem water δ18O and c. 20‰ lower than δ18O of water-soluble carbohydrates, possibly caused by discrimination against 18O during O2-based diterpene oxidation and 35%-50% oxygen atom exchange with water. Resin δ13C and δ18O recorded a strong signal of soil water potential; however, their overall capacity to infer intraseasonal environmental changes was limited by their temporal, within-tree and among-tree variations. Future studies should validate the potential isotope fractionation mechanisms associated with resin synthesis and explore the use of resin δ13C and δ18O as a long-term proxy for physiological and environmental changes.

Extraction of Underwater Acoustic Signals across Sea–Air Media Using Butterworth Filtering

Direct wireless communication through sea–air media is essential for constructing an integrated communication network that spans space, air, land, and sea. The amplitude of acoustically induced micromotion surface waves is much smaller than the noise interference in complex sea states, making the accurate extraction of these signals from the raw signals detected by an FMCW millimeter-wave radar a major challenge. In this paper, Butterworth filtering is used to extract underwater acoustic signals from the surface waves detected by radar. The physical processes of the channel were simulated theoretically and verified experimentally. The results demonstrate a fitting coefficient of 0.99 between the radar-detected water surface waves and the simulation outcomes, enabling the effective elimination of noise interference and the extraction of acoustically induced micromotion signals in environments with a signal-to-noise ratio (SNR) of −20 dB to −10 dB. Experiments modifying frequency and linear frequency modulation have verified that the usable frequency range for underwater acoustic signals is at least 400 Hz, meeting the frequency requirements of Binary Frequency Shift Keying (2FSK) modulation encoding methods. This research confirms the accuracy of the simulation results and the feasibility of filtering and extracting underwater acoustic signals, providing a theoretical basis and an experimental foundation for building cross-media communication links.

Circadian and environmental signal integration in a natural population of Arabidopsis

Plants sense and respond to environmental cues during 24 h fluctuations in their environment. This requires the integration of internal cues such as circadian timing with environmental cues such as light and temperature to elicit cellular responses through signal transduction. However, the integration and transduction of circadian and environmental signals by plants growing in natural environments remains poorly understood. To gain insights into 24 h dynamics of environmental signaling in nature, we performed a field study of signal transduction from the nucleus to chloroplasts in a natural population of Arabidopsis halleri. Using several modeling approaches to interpret the data, we identified that the circadian clock and temperature are key regulators of this pathway under natural conditions. We identified potential time-delay steps between pathway components, and diel fluctuations in the response of the pathway to temperature cues that are reminiscent of the process of circadian gating. We found that our modeling framework can be extended to other signaling pathways that undergo diel oscillations and respond to environmental cues. This approach of combining studies of gene expression in the field with modeling allowed us to identify the dynamic integration and transduction of environmental cues, in plant cells, under naturally fluctuating diel cycles.

Primary multistep phosphorelay activation comprises both cytokinin and abiotic stress responses: insights from comparative analysis of Brassica type-A response regulators.

Multistep phosphorelay (MSP) signaling integrates hormonal and environmental signals to control both plant development and adaptive responses. Type-A RESPONSE REGULATOR (RRA) genes, the downstream members of the MSP cascade and cytokinin primary response genes, are thought to mediate primarily the negative feedback regulation of (cytokinin-induced) MSP signaling. However, transcriptional data also suggest the involvement of RRA genes in stress-related responses. By employing evolutionary conservation with the well-characterized Arabidopsis thaliana RRA genes, we identified five and 38 novel putative RRA genes in Brassica oleracea and Brassica napus, respectively. Our phylogenetic analysis suggests the existence of gene-specific selective pressure, maintaining the homologs of ARR3, ARR6, and ARR16 as singletons during the evolution of Brassicaceae. We categorized RRA genes based on the kinetics of their cytokinin-mediated up-regulation and observed both similarities and specificities in this type of response across Brassicaceae species. Using bioinformatic analysis and experimental data demonstrating the cytokinin and abiotic stress responsiveness of the A. thaliana-derived TCSv2 reporter, we unveil the mechanistic conservation of cytokinin- and stress-mediated up-regulation of RRA genes in B. rapa and B. napus. Notably, we identify partial cytokinin dependency of cold stress-induced RRA transcription, thus further demonstrating the role of cytokinin signaling in crop adaptive responses.

Non-interference detection of digital signals in transmission line utilizing quantum sensor of nitrogen-vacancy centers

Digital signals play an extremely important role in the integration and multi-functionality of industrial instrumentation. With the expansion of industrial application scenarios, non-interference detection of digital signals in certain extreme environments has become the need of the hour. This paper demonstrates a method for non-interference Detection of transmission line digital signals based on nitrogen-vacancy (NV) centers in diamond. The optical readability and the sensitivity to magnetic fields of the NV centers are utilized to characterize the current-induced weak magnetic field signals by fluorescence signals in a confined space. By combining quantum sensing technology, quantum imaging technology and electromagnetic induction technology through NV centers, it is able to realize high sensitivity and high precision multi-point measurement and dynamic monitoring of electrical signals. The temporal resolution of the digital signal was evaluated by regulating the duration of the input current in the transmission line, and the lowest transmission current in a single transmission line and two transmission lines in a 1.2 mm × 1.2 mm imaging field of view was explored. The experimental results show that the time resolution of the detected digital signals is up to 30 μs. This research highlights the potential value of NV centers in digital signal processing and provides new research directions for detecting digital signals in extreme environments.