Selective autophagy receptor p62/SQSTM1 inhibits TBK1-IRF7 innate immune pathway in triploid hybrid fish.

The production of type I interferon is tightly regulated to prevent excessive immune activation. However, the role of selective autophagy receptor SQSTM1 in this regulation in teleost remains unknown. In this study, we cloned the triploid fish SQSTM1 (3nSQSTM1), which comprises 1371 nucleotides, encoding 457 amino acids. qRT-PCR data revealed that the transcript levels of SQSTM1 in triploid fish were increased both in vivo and in vitro following spring viraemia of carp virus (SVCV) infection. Immunofluorescence analysis confirmed that 3nSQSTM1 was mainly distributed in the cytoplasm. Luciferase reporter assay results showed that 3nSQSTM1 significantly blocked the activation of interferon promoters induced by 3nMDA5, 3nMAVS, 3nTBK1, and 3nIRF7. Co-immunoprecipitation assays further confirmed that 3nSQSTM1 could interact with both 3nTBK1 and 3nIRF7. Moreover, upon co-transfection, 3nSQSTM1 significantly inhibited the antiviral activity mediated by TBK1 and IRF7. Mechanistically, 3nSQSTM1 decreased the TBK1 phosphorylation and its interaction with 3nIRF7, thereby suppressing the subsequent antiviral response. Notably, we discovered that 3nSQSTM1 also interacted with SVCV N and P proteins, and these viral proteins may exploit 3nSQSTM1 to further limit the host's antiviral innate immune responses. In conclusion, our study demonstrates that 3nSQSTM1 plays a pivotal role in negatively regulating the interferon signaling pathway by targeting 3nTBK1 and 3nIRF7.

NJmat: Data-Driven Machine Learning Interface to Accelerate Material Design.

Machine learning techniques have significantly transformed the way materials scientists conduct research. However, the widespread deployment of machine learning software in daily experimental and simulation research for materials and chemical design has been limited. This is partly due to the substantial time investment and learning curve associated with mastering the necessary codes and computational environments. In this paper, we introduce a user-friendly, data-driven machine learning interface featuring multiple "button-clicking" functionalities to streamline the design of materials and chemicals. This interface automates the processes of transforming materials and molecules, performing feature selection, constructing machine learning models, making virtual predictions, and visualizing results. Such automation accelerates materials prediction and analysis in the inverse design process, aligning with the time criteria outlined by the Materials Genome Initiative. With simple button clicks, researchers can build machine learning models and predict new materials once they have gathered experimental or simulation data. Beyond the ease of use, NJmat offers three additional features for data-driven materials design: (1) automatic feature generation for both inorganic materials (from chemical formulas) and organic molecules (from SMILES), (2) automatic generation of Shapley plots, and (3) automatic construction of "white-box" genetic models and decision trees to provide scientific insights. We present case studies on surface design for halide perovskite materials encompassing both inorganic and organic species. These case studies illustrate general machine learning models for virtual predictions as well as the automatic featurization and Shapley/genetic model construction capabilities. We anticipate that this software tool will expedite materials and molecular design within the scope of the Materials Genome Initiative, particularly benefiting experimentalists.

The domestic elder abuse in China: Scale development and psychometric properties.

An effective screening tool is essential to elder abuse research. Although several instruments have been developed in China to measure elder abuse, they present several limitations. The instrument development involved three components: (1) generating questionnaire items; (2) questionnaire testing and data collection in older adults; and (3) psychometric evaluation of the Domestic Elder Abuse Scale (DEAS). We collected questionnaire responses from 3725 community-dwelling Chinese older adults. The 26-item DEAS showed good reliability and validity across five dimensions: physical abuse, psychological abuse, financial exploitation, neglect, and abandonment. These five factors accounted for 78.432 % of the total variance, and model fitting results were acceptable. The Cronbach's alpha coefficient of the scale was 0.975, and the test-retest intraclass correlation coefficient (ICC) was 0.934 after 2 weeks. This study developed a five-dimension instrument to measure elder abuse, with good psychometric properties, which can play an essential role in community-based studies in China.

MC4R-dependent suppression of appetite by bone-derived lipocalin 2.

Bone has recently emerged as a pleiotropic endocrine organ that secretes at least two hormones, FGF23 and osteocalcin, which regulate kidney function and glucose homeostasis, respectively. These findings have raised the question of whether other bone-derived hormones exist and what their potential functions are. Here we identify, through molecular and genetic analyses in mice, lipocalin 2 (LCN2) as an osteoblast-enriched, secreted protein. Loss- and gain-of-function experiments in mice demonstrate that osteoblast-derived LCN2 maintains glucose homeostasis by inducing insulin secretion and improves glucose tolerance and insulin sensitivity. In addition, osteoblast-derived LCN2 inhibits food intake. LCN2 crosses the blood-brain barrier, binds to the melanocortin 4 receptor (MC4R) in the paraventricular and ventromedial neurons of the hypothalamus and activates an MC4R-dependent anorexigenic (appetite-suppressing) pathway. These results identify LCN2 as a bone-derived hormone with metabolic regulatory effects, which suppresses appetite in a MC4R-dependent manner, and show that the control of appetite is an endocrine function of bone.

Corrigendum: MC4R-dependent suppression of appetite by bone-derived lipocalin 2.

This corrects the article DOI: 10.1038/nature21697.

Longitudinal relationships among career adaptability, resilience, and career commitment in chinese nursing undergraduates: testing differences in career interest between cross-lagged models.

PURPOSE: Various physiological and psychological negative situations experienced by nurses as a result of COVID-19 pandemic have been shown to increase their perception of organizational difficulty and decrease their career commitment, thereby accelerating the turnover rate of nurses. Resilience and career adaptability have important influences on career commitment, so there is a need to evaluate the relationships between them and the underlying mechanisms. PATIENTS AND METHODS: Using a cross-lagged design, the Career Adaptability Scale, the Chinese version of career commitment, and Davidson's Resilience Scale as research methods, we studied 692 nursing students for two consecutive years to evaluate the relationship among career adaptability, resilience, and career commitment. RESULTS: Career adaptability at T1 substantially and positively predicts the career commitment at T2. Career adaptability and resilience are mutually predictive. No interaction is found between resilience and career commitment over time. There is a substantial difference in the cross-lagged relationship among career adaptability, resilience, and career commitment for low- and high-career interest. CONCLUSION: Our results show the importance of developing career commitment early on. Developing career adaptability, enhancing resilience, and increasing career interest in nursing students might help to increase career commitment.

Descriptor Design for Perovskite Material with Compatible Molecules via Language Model and First-Principles.

Directly applying big language models for material and molecular design is not straightforward, particularly for real-scenario cases, where experimental validation accuracy is required. In this study, we propose a multimode descriptor design method for materials prediction and analysis, leveraging the advantages of the natural language processing literature model and density functional theory (DFT) calculations with the assistance of the genetic algorithm (GA). A case study on prediction of aqueous photocurrents of multisolvent engineered halide perovskite CH3NH3PbI3 is performed, and the following-up validation experiments are carried out to demonstrate the improved accuracy of the multimode descriptors (an unprecedented experimental validation accuracy of 87.5% via the GA is achieved) for predicting aqueous photocurrents of perovskite materials (c.f. only 50% experimental accuracy for other common machine learning models). The improved experimental accuracy of the descriptors is attributed to the successful deployment of a language model incorporating concise scientific information from >1 million articles into molecular descriptors in combination with DFT calculations. The subsequent machine learning analysis suggests the importance of cation···π and crystallization in molecule-modified halide perovskite materials representing ontological and conceptual understanding. Importantly, the genetic process affords an accurate "white-box" model to describe the perovskite stability (accuracy = 90.2% for the test data set and 92.3% for the train data set) with the mathematical equation Stability=tanF2×F3×F1F2+F4+F5, where F1 ∼ F5 atomic-level structural and chemical details such as cation···π interactions and highest occupied molecular orbital levels. This study offers a feasible descriptor design route to accurately predict complex material properties, leveraging both language models and density functional theories.

Language Model-Assisted Machine Learning, Photoelectrochemical, and First-Principles Investigation of Compatible Solvents for a CH3NH3PbI3 Film in Water.

Machine learning and data-driven methods have attracted a significant amount of attention for the acceleration of the design of molecules and materials. In this study, a material design protocol based on multimode modeling that combines literature modeling, numerical data collection, textual descriptor design, genetic modeling, experimental validation, first-principles calculation, and theoretical efficiency calculation is proposed, with a case study on designing compatible complex solvent molecules for a halide perovskite film, which is notorious for optoelectronic deactivation under hostile conditions, especially in water. In the multimode modeling design process, the textual descriptors play the central role and store rich literature scientific knowledge, which starts from the construction of a high-dimension literature model based on scientific articles and is realized by a genetic algorithm for materials predictions. The prediction is substantiated by follow-up experiments and first-principles calculations, leading to the successful identification of effective molecular combinations delivering an unprecedented large aqueous photocurrent (increasing by 3 orders of magnitude compared with that of CH3NH3PbI3) and remarkable aqueous stability (improving from 36% to 89% after immersion in water) under the hostile condition. This study provides a practical route via multimode modeling for accelerating the design of molecule-modified and solution-processed materials in a real scenario.

Collaborative optimization of depot location, capacity and rolling stock scheduling considering maintenance requirements.

Generally, when optimizing a rolling stock schedule, the locations of the depots, or places in the network where the composition changes and maintenance occurs, are assumed known. The locations where maintenance is performed naturally influence the quality of any resulting rolling stock schedules. In this paper, the problem of selecting new depot locations and their corresponding capacities is considered. A two-stage mixed integer programming approach for rolling stock scheduling with maintenance requirements is extended to account for depot selection. First, a conventional flow-based model is solved, ignoring maintenance requirements, to obtain a variety of rolling stock schedules with multiple depot locations and capacity options. Then, a maintenance feasible rolling stock schedule can be obtained by solving a series of assignment problems by using the schedules found in the first stage. The proposed methodology is tested on real-life instances, and the numerical experiments of different operational scenarios are discussed.

Developing geriatric nursing micro-credentials for undergraduate nursing students based on training objectives: A modified Delphi study.

BACKGROUND: China's population has begun to age rapidly in the past several years and this trend is predicted to continue. In the face of this growing older population, the existing number of aged care personnel, especially medical care professionals, can hardly meet the demand for aged care services. AIM: To develop geriatric nursing micro-credentials (MCs) for undergraduate nursing students based on standardized training objectives and to specify the learning goals and course modules that correspond to each specific MC. DESIGN: Modified Delphi study. METHODS: An initial set of geriatric nursing MCs were developed based on the training objectives. Expert group discussion (n=13) reviewed the clarity and intelligibility of the statements' wording and supplemented the framework. A three-round Delphi survey (n=15) was then employed to obtain a consensus on the learning goals and course modules via an online questionnaire. Descriptive statistics were used to analyze the data. RESULTS: The final geriatric nursing MCs consisted of six courses, namely fundamentals of geriatric nursing (8 modules), geriatric sociology (6 modules), geriatric clinical nursing (3 modules), geriatric psychological nursing (8 modules), geriatric rehabilitation nursing (8 modules) and geriatric hospice care (10 modules). CONCLUSION: Nursing faculty can use the geriatric nursing MCs developed in this study to train current undergraduate nursing students to become backups for current, fully credentialed geriatric caregivers.

Degradation and regeneration inhibition of PCDD/Fs in incineration fly ash by low-temperature thermal technology.

Low-temperature thermal degradation of PCDD/Fs for incineration fly ash (IFA), as a novel and emerging technology approach, offers promising features of high degradation efficiency and low energy consumption, presenting enormous potential for application in IFA resource utilization processes. This review summarizes the concentrations, congener distributions, and heterogeneity characteristics of PCDD/Fs in IFA from municipal, medical, and hazardous waste incineration. A comparative analysis of five PCDD/Fs degradation technologies is conducted regarding their characteristics, industrial potential, and applicability. From the perspective of low-temperature degradation mechanisms, pathways to enhance PCDD/Fs degradation efficiency and inhibit their regeneration reactions are discussed in detail. Finally, the challenges to achieve low-temperature degradation of PCDD/Fs for IFA with high-efficiency are prospected. This review seeks to explore new opportunities for the detoxification and resource utilization of IFA by implementing more efficient and viable low-temperature degradation technologies.

PIAS1 S510G variant acts as a genetic modifier of spinocerebellar ataxia type 3 by selectively impairing mutant ataxin-3 proteostasis.

Dysregulated protein homeostasis, characterized by abnormal protein accumulation and aggregation, is a key contributor to the progression of neurodegenerative disorders such as Huntington's disease and spinocerebellar ataxia type 3 (SCA3). Previous studies have identified PIAS1 gene variants in patients with late-onset SCA3 and Huntington's disease. This study aims to elucidate the role of PIAS1 and its S510G variant in modulating the pathogenic mechanisms of SCA3. Through in vitro biochemical analyses and in vivo assays, we demonstrate that PIAS1 stabilizes both wild-type and mutant ataxin-3 (ATXN3). The PIAS1 S510G variant, however, selectively reduces the stability and SUMOylation of mutant ATXN3, thereby decreasing its aggregation and toxicity while maintaining the stability of wild-type ATXN3. This effect is mediated by a weakened interaction with the SUMO-conjugating enzyme UBC9 in the presence of mutant ATXN3. In Drosophila models, downregulation of dPIAS1 resulted in reduced levels of mutant ATXN3 and alleviated associated phenotypes, including retinal degeneration and motor dysfunction. Our findings suggest that the PIAS1 S510G variant acts as a genetic modifier of SCA3, highlighting the potential of targeting SUMOylation as a therapeutic strategy for this disease.

Accelerated Multisolvent Prediction for Aqueous Stable Halide Perovskite Materials.

Solvent treatment is critical to improving the stability of halide perovskite materials that suffer from notorious issues that inhibit their industrial deployment; however, the complicated perovskite virtual design space with different types of solvent modifiers is inaccessible to traditional trial-and-error methods. In this study, machine learning is employed to predict stable multiple solvent-modified perovskite films under hostile conditions, and a complicated quinary solvent system "DMSO + DMF + toluene + NMP + GBL" is effectively identified to significantly improve the optoelectronic stability of CH3NH3PbI3 in water. The "combinatorial solvent design" approach is realized by an extra tree machine learning model, which leads to a prediction dataset containing aqueous stability labels of 6720 new quinary solvent/perovskite systems. Importantly, the accuracy of the machine learning model is verified via photoelectrochemical experiments, achieving an experimental accuracy of 80%. A machine learning-predicted quinary solvent system offers significantly enhanced aqueous stability and 1000 times larger aqueous photocurrents, compared with the control CH3NH3PbI3 film under the same hostile conditions. This study demonstrates the efficacy of machine learning for solvent design toward stable halide perovskite materials under hostile conditions.

Monocular information for perceiving large egocentric distance: A comparison between monocularly blind patients and normally sighted observers.

The debate surrounding the advantages of binocular versus monocular vision has persisted for decades. This study aimed to investigate whether individuals with monocular vision loss could accurately and precisely perceive large egocentric distances in real-world environments, under natural viewing conditions, comparable to those with normal vision. A total of 49 participants took part in the study, divided into three groups based on their viewing conditions. Two experiments were conducted to assess the accuracy and precision of estimating egocentric distances to visual targets and the coordination of actions during blind walking. In Experiment 1, participants were positioned in both a hallway and a large open field, tasked with judging the midpoint of self-to-target distances spanning from 5 to 30 m. Experiment 2 involved a blind walking task, where participants attempted to walk towards the same targets without visual or environmental feedback at an unusually rapid pace. The findings revealed that perceptual accuracy and precision were primarily influenced by the environmental context, motion condition, and target distance, rather than the visual conditions. Surprisingly, individuals with monocular vision loss demonstrated comparable accuracy and precision in perceiving egocentric distances to that of individuals with normal vision.

Visual Crowding Reveals Field- and Axis-Specific Cortical Miswiring After Long-Term Axial Misalignment in Strabismic Patients Without Amblyopia.

Purpose: Inspired by physiological and neuroimaging findings that revealed squint-induced modification of cortical volume and visual receptive field in early visual areas, we hypothesized that strabismic eyes without amblyopia manifest an increase in critical spacing of visual crowding, an essential bottleneck on object recognition and reliable psychophysical index of cortical organization. Methods: We used real-time eye tracking to ensure gaze-contingent display and examined visual crowding in patients with horizontal concomitant strabismus (both esotropia and exotropia) but without amblyopia and age-matched normal controls. Results: Nineteen patients with exotropia (12 men, mean ± SD = 22.89 ± 7.82 years), 21 patients with esotropia (10 men, mean ± SD = 23.48 ± 6.95 years), and 14 age-matched normal controls (7 men, mean ± SD = 23.07 ± 1.07 years) participated in this study. We found that patients with strabismus without amblyopia showed significantly larger critical spacing with nasotemporal asymmetry in only the radial axis that related to the strabismus pattern, with exotropia exhibiting stronger temporal hemifield crowding and esotropia exhibiting stronger nasal hemifield crowding, in both the deviated and fixating eyes. Moreover, the magnitude of crowding change was related to the duration and degree of strabismic deviation. Conclusions: Using visual crowding as a psychophysical index of cortical organization, our study demonstrated significantly greater peripheral visual crowding with nasotemporal asymmetry in only the radial axis in patients with strabismus without amblyopia, indicating the existence of hemifield- and axis-specific miswiring of cortical processing in object recognition induced by long-term adaptation to ocular misalignment.

Cortical Reorganization After Optical Alignment in Strabismic Patients Outside of Critical Period.

Purpose: To measure visual crowding, an essential bottleneck on object recognition and reliable psychophysical index of cortex organization, in older children and adults with horizontal concomitant strabismus before and after strabismus surgery. Methods: Using real-time eye tracking to ensure gaze-contingent display, we examined the peripheral visual crowding effects in older children and adults with horizontal concomitant strabismus but without amblyopia before and after strabismus surgery. Patients were asked to discriminate the orientation of the central tumbling E target letter with flankers arranged along the radial or tangential axis in the nasal or temporal hemifield at different eccentricities (5° or 10°). The critical spacing value, which is the minimum space between the target and the flankers required for correct discrimination, was obtained for comparisons before and after strabismus surgery. Results: Twelve individuals with exotropia (6 males, 21.75 ± 7.29 years, mean ± SD) and 15 individuals with esotropia (6 males, 24.13 ± 5.96 years) participated in this study. We found that strabismic individuals showed significantly larger critical spacing with nasotemporal asymmetry along the radial axis that related to the strabismus pattern, with exotropes exhibiting stronger temporal field crowding and esotropes exhibiting stronger nasal field crowding before surgical alignment. After surgery, the critical spacing was reduced and rebalanced between the nasal and temporal hemifields. Furthermore, the postoperative recovery of stereopsis was associated with the extent of nasotemporal balance of critical spacing. Conclusions: We find that optical realignment (i.e., strabismus surgery) can normalize the enlarged visual crowding effects, a reliable psychophysical index of cortical organization, in the peripheral visual field of older children and adults with strabismus and rebalance the nasotemporal asymmetry of crowding, promoting the recovery of postoperative stereopsis. Our results indicated a potential of experience-dependent cortical organization after axial alignment even for individuals who are out of the critical period of visual development, illuminating the capacity and limitations of optics on sensory plasticity and emphasizing the importance of ocular correction for clinical practice.

Use of a tissue clearing technique combined with retrograde trans-synaptic viral tracing to evaluate changes in mouse retinorecipient brain regions following optic nerve crush.

Successful establishment of reconnection between retinal ganglion cells and retinorecipient regions in the brain is critical to optic nerve regeneration. However, morphological assessments of retinorecipient regions are limited by the opacity of brain tissue. In this study, we used an innovative tissue cleaning technique combined with retrograde trans-synaptic viral tracing to observe changes in retinorecipient regions connected to retinal ganglion cells in mice after optic nerve injury. Specifically, we performed light-sheet imaging of whole brain tissue after a clearing process. We found that pseudorabies virus 724 (PRV724) mostly infected retinal ganglion cells, and that we could use it to retrogradely trace the retinorecipient regions in whole tissue-cleared brains. Unexpectedly, PRV724-traced neurons were more widely distributed compared with data from previous studies. We found that optic nerve injury could selectively modify projections from retinal ganglion cells in the hypothalamic paraventricular nucleus, intergeniculate leaflet, ventral lateral geniculate nucleus, central amygdala, basolateral amygdala, Edinger-Westphal nucleus, and oculomotor nucleus, but not the superior vestibular nucleus, red nucleus, locus coeruleus, gigantocellular reticular nucleus, or facial nerve nucleus. Our findings demonstrate that the tissue clearing technique, combined with retrograde trans-synaptic viral tracing, can be used to objectively and comprehensively evaluate changes in mouse retinorecipient regions that receive projections from retinal ganglion cells after optic nerve injury. Thus, our approach may be useful for future estimations of optic nerve injury and regeneration.

Central 5-HTR2C in the Control of Metabolic Homeostasis.

The 5-hydroxytryptamine 2C receptor (5-HTR2C) is a class G protein-coupled receptor (GPCR) enriched in the hypothalamus and the brain stem, where it has been shown to regulate energy homeostasis, including feeding and glucose metabolism. Accordingly, 5-HTR2C has been the target of several anti-obesity drugs, though the associated side effects greatly curbed their clinical applications. Dissecting the specific neural circuits of 5-HTR2C-expressing neurons and the detailed molecular pathways of 5-HTR2C signaling in metabolic regulation will help to develop better therapeutic strategies towards metabolic disorders. In this review, we introduced the regulatory role of 5-HTR2C in feeding behavior and glucose metabolism, with particular focus on the molecular pathways, neural network, and its interaction with other metabolic hormones, such as leptin, ghrelin, insulin, and estrogens. Moreover, the latest progress in the clinical research on 5-HTR2C agonists was also discussed.

PERK in POMC neurons connects celastrol with metabolism.

ER stress and activation of the unfolded protein response in the periphery as well as the central nervous system have been linked to various metabolic abnormalities. Chemically lowering protein kinase R-like ER kinase (PERK) activity within the hypothalamus leads to decreased food intake and body weight. However, the cell populations required in this response remain undefined. In the current study, we investigated the effects of proopiomelanocortin-specific (POMC-specific) PERK deficiency on energy balance and glucose metabolism. Male mice deficient for PERK in POMC neurons exhibited improvements in energy balance on a high-fat diet, showing decreased food intake and body weight, independent of changes in glucose and insulin tolerances. The plant-based inhibitor of PERK, celastrol, increases leptin sensitivity, resulting in decreased food intake and body weight in a murine model of diet-induced obesity (DIO). Our data extend these observations by demonstrating that celastrol-induced improvements in leptin sensitivity and energy balance were attenuated in mice with PERK deficiency in POMC neurons. Altogether, these data suggest that POMC-specific PERK deficiency in male mice confers protection against DIO, possibly providing a new therapeutic target for the treatment of diabetes and metabolic syndrome.

Impacts of exercise interventions on different diseases and organ functions in mice.

Background: In recent years, much evidence has emerged to indicate that exercise can benefit people when performed properly. This review summarizes the exercise interventions used in studies involving mice as they are related to special diseases or physiological status. To further understand the effects of exercise interventions in treating or preventing diseases, it is important to establish a template for exercise interventions that can be used in future exercise-related studies. Methods: PubMed was used as the data resource for articles. To identify studies related to the effectiveness of exercise interventions for treating various diseases and organ functions in mice, we used the following search language: (exercise [Title] OR training [Title] OR physical activity [Title]) AND (mice [title/abstract] OR mouse [title/abstract] OR mus [title/abstract]). To limit the range of search results, we included 2 filters: one that limited publication dates to "in 10 years" and one that sorted the results as "best match". Then we grouped the commonly used exercise methods according to their similarities and differences. We then evaluated the effectiveness of the exercise interventions for their impact on diseases and organ functions in 8 different systems. Results: A total of 331 articles were included in the analysis procedure. The articles were then segmented into 8 systems for which the exercise interventions were used in targeting and treating disorders: motor system (60 studies), metabolic system (45 studies), cardio-cerebral vascular system (58 studies), nervous system (74 studies), immune system (32 studies), respiratory system (7 studies), digestive system (1 study), and the system related to the development of cancer (54 studies). The methods of exercise interventions mainly involved the use of treadmills, voluntary wheel-running, forced wheel-running, swimming, and resistance training. It was found that regardless of the specific exercise method used, most of them demonstrated positive effects on various systemic diseases and organ functions. Most diseases were remitted with exercise regardless of the exercise method used, although some diseases showed the best remission effects when a specific method was used. Conclusion: Our review strongly suggests that exercise intervention is a cornerstone in disease prevention and treatment in mice. Because exercise interventions in humans typically focus on chronic diseases, national fitness, and body weight loss, and typically have low intervention compliance rates, it is important to use mice models to investigate the molecular mechanisms underlying the health benefits from exercise interventions in humans.