Loss-of-function mutations in Keratin 32 gene disrupt skin immune homeostasis in pityriasis rubra pilaris.

Pityriasis rubra pilaris (PRP) is an inflammatory papulosquamous dermatosis, characterized by hyperkeratotic follicular papules and erythematous desquamative plaques. The precise pathogenic mechanism underlying PRP remains incompletely understood. Herein, we conduct a case-control study involving a cohort of 102 patients with sporadic PRP and 800 healthy controls of Han Chinese population and identify significant associations (P = 1.73 × 10-6) between PRP and heterozygous mutations in the Keratin 32 gene (KRT32). KRT32 is found to be predominantly localized in basal keratinocytes and exhibits an inhibitory effect on skin inflammation by antagonizing the NF-κB pathway. Mechanistically, KRT32 binds to NEMO, promoting excessive K48-linked polyubiquitination and NEMO degradation, which hinders IKK complex formation. Conversely, loss-of-function mutations in KRT32 among PRP patients result in NF-κB hyperactivation. Importantly, Krt32 knockout mice exhibit a PRP-like dermatitis phenotype, suggesting compromised anti-inflammatory function of keratinocytes in response to external pro-inflammatory stimuli. This study proposes a role for KRT32 in regulating inflammatory immune responses, with damaging variants in KRT32 being an important driver in PRP development. These findings offer insights into the regulation of skin immune homeostasis by keratin and open up the possibility of using KRT32 as a therapeutic target for PRP.

ARBUR, a machine learning-based analysis system for relating behaviors and ultrasonic vocalizations of rats.

Deciphering how different behaviors and ultrasonic vocalizations (USVs) of rats interact can yield insights into the neural basis of social interaction. However, the behavior-vocalization interplay of rats remains elusive because of the challenges of relating the two communication media in complex social contexts. Here, we propose a machine learning-based analysis system (ARBUR) that can cluster without bias both non-step (continuous) and step USVs, hierarchically detect eight types of behavior of two freely behaving rats with high accuracy, and locate the vocal rat in 3-D space. ARBUR reveals that rats communicate via distinct USVs during different behaviors. Moreover, we show that ARBUR can indicate findings that are long neglected by former manual analysis, especially regarding the non-continuous USVs during easy-to-confuse social behaviors. This work could help mechanistically understand the behavior-vocalization interplay of rats and highlights the potential of machine learning algorithms in automatic animal behavioral and acoustic analysis.

Adaptive Gait Acquisition through Learning Dynamic Stimulus Instinct of Bipedal Robot.

Standard alternating leg motions serve as the foundation for simple bipedal gaits, and the effectiveness of the fixed stimulus signal has been proved in recent studies. However, in order to address perturbations and imbalances, robots require more dynamic gaits. In this paper, we introduce dynamic stimulus signals together with a bipedal locomotion policy into reinforcement learning (RL). Through the learned stimulus frequency policy, we induce the bipedal robot to obtain both three-dimensional (3D) locomotion and an adaptive gait under disturbance without relying on an explicit and model-based gait in both the training stage and deployment. In addition, a set of specialized reward functions focusing on reliable frequency reflections is used in our framework to ensure correspondence between locomotion features and the dynamic stimulus. Moreover, we demonstrate efficient sim-to-real transfer, making a bipedal robot called BITeno achieve robust locomotion and disturbance resistance, even in extreme situations of foot sliding in the real world. In detail, under a sudden change in torso velocity of -1.2 m/s in 0.65 s, the recovery time is within 1.5-2.0 s.

Targeted serum proteome profiling reveals nicotinamide adenine dinucleotide phosphate (NADPH)-related biomarkers to discriminate linear IgA bullous disorder from dermatitis herpetiformis.

Linear IgA bullous dermatosis (LABD) and dermatitis herpetiformis (DH) represent the major subtypes of IgA mediated autoimmune bullous disorders. We sought to understand the disease etiology by using serum proteomics. We assessed 92 organ damage biomarkers in LABD, DH, and healthy controls using the Olink high-throughput proteomics. The positive proteomic serum biomarkers were used to correlate with clinical features and HLA type. Targeted proteomic analysis of IgA deposition bullous disorders vs. controls showed elevated biomarkers. Further clustering and enrichment analyses identified distinct clusters between LABD and DH, highlighting the involvement of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase. Comparative analysis revealed biomarkers with distinction between LABD and DH and validated in the skin lesion. Finally, qualitative correlation analysis with DEPs suggested six biomarkers (NBN, NCF2, CAPG, FES, BID, and PXN) have better prognosis in DH patients. These findings provide potential biomarkers to differentiate the disease subtype of IgA deposition bullous disease.

Single Sequential Trajectory Optimization with Centroidal Dynamics and Whole-Body Kinematics for Vertical Jump of Humanoid Robot.

High vertical jumping motion, which enables a humanoid robot to leap over obstacles, is a direct reflection of its extreme motion capabilities. This article proposes a single sequential kino-dynamic trajectory optimization method to solve the whole-body motion trajectory for high vertical jumping motion. The trajectory optimization process is decomposed into two sequential optimization parts: optimization computation of centroidal dynamics and coherent whole-body kinematics. Both optimization problems converge on the common variables (the center of mass, momentum, and foot position) using cost functions while allowing for some tolerance in the consistency of the foot position. Additionally, complementarity conditions and a pre-defined contact sequence are implemented to constrain the contact force and foot position during the launching and flight phases. The whole-body trajectory, including the launching and flight phases, can be efficiently solved by a single sequential optimization, which is an efficient solution for the vertical jumping motion. Finally, the whole-body trajectory generated by the proposed optimized method is demonstrated on a real humanoid robot platform, and a vertical jumping motion of 0.5 m in height (foot lifting distance) is achieved.

Whole-Body Dynamics-Based Aerial Fall Trajectory Optimization and Landing Control for Humanoid Robot.

When humanoid robots work in human environments, falls are inevitable due to the complexity of such environments. Current research on humanoid robot falls has mainly focused on falls on the ground, with little research on humanoid robots falling from the air. In this paper, we employ an extended state variable formulation that directly maps from the high-level motion strategy space to the full-body joint space to optimize the falling trajectory in order to protect the robot when falling from the air. In order to mitigate the impact force generated by the robot's fall, during the aerial phase, we employ simple proportion differentiation (PD) control. In the landing phase, we optimize the optimal contact force at the contact point using the centroidal dynamics model. Based on the contact force, the changes to the end-effector positions are solved using a dual spring-damper model. In the simulation experiments, we conduct three comparative experiments, and the simulation results demonstrate that the robot can safely fall 1.5 m from the ground at a pitch angle of 45°. Finally, we experimentally validate the methods on an actual robot by performing a side-fall experiment. The experimental results show that the proposed trajectory optimization and motion control methods can provide excellent shock absorption for the impact generated when a robot falls.

High-precision dynamic torque control of high stiffness actuator for humanoids.

The high stiffness actuator (HSA), applied to each joint of an electrical driven humanoid robot, can directly affect the motion performance of the torque-controlled humanoid robots. For high control performance of HSA, a high-precision dynamic torque control (HDTC) is proposed. The HDTC consists of two phases: (1) A novel dynamic current control is used to linearize high stiffness actuator torque control system, which can estimate and compensate the nonlinear coupling parts; (2) An enhanced internal model control is designed to ensure high tracking accuracy in the system containing noisy torque signal and even numerical differentiation signals. Benefitting from dynamic current control and the enhanced internal model control, the proposed HDTC is accurate and adaptable. Finally, the superiority of the HDTC is verified with comparative experiments.

Spatio-temporal variation of bacterial community structure in two intertidal sediment types of Jiaozhou Bay.

The intertidal sediment environment is dynamic and the biofilm bacterial community within it must constantly adapt, but an understanding of the differences in the biofilm bacterial community within sediments of different types is still relatively limited. The semi-enclosed Jiaozhou Bay has a temperate monsoon climate, with strong currents at the mouth of the bay. In this study, the structure of the bacterial community in Jiaozhou Bay sediment biofilms are described using high-throughput 16 S rRNA gene sequencing and the effects of temporal change and different sediment environment types are discussed. Alpha diversity was significantly higher in sandy samples than in muddy samples. Sandy sediments with increased heterogeneity promote bacterial aggregation. Beta diversity analysis showed significant differences between sediment types and between stations. Proteobacteria and Acidobacteria were significantly more abundant at ZQ, while Campilobacterota was significantly more abundant at LC. The relative abundances of Bacteroidetes, Campilobacterota, Firmicutes, and Chloroflexi were significantly higher in the muddy samples, while Actinobacteria and Proteobacteria were higher in the sandy samples. There were different phylum-level biomarkers between sediment types at different stations. There were also different patterns of functional enrichment in biogeochemical cycles between sediment types and stations with the former having more gene families that differed significantly, highlighting their greater role in determining bacterial function. Bacterial amplicon sequence variant variation between months was less than KEGG ortholog variation between months, presumably the temporal change had an impact on shaping the intertidal sediment bacterial community, although this was less clear at the gene family level. Random forest prediction yielded a combination of 43 family-level features that responded well to temporal change, reflecting the influence of temporal change on sediment biofilm bacteria.

Online Running-Gait Generation for Bipedal Robots with Smooth State Switching and Accurate Speed Tracking.

Smooth state switching and accurate speed tracking are important for the stability and reactivity of bipedal robots when running. However, previous studies have rarely been able to synthesize these two capabilities online. In this paper, we present an online running-gait generator for bipedal robots that allows for smooth state switching and accurate speed tracking. Considering a fluctuating height nature and computational expediency, the robot is represented by a simplified variable-height inverted-pendulum (VHIP) model. In order to achieve smooth state switching at the beginning and end of running, a segmented zero moment point (ZMP) trajectory optimization is proposed to automatically provide a feasible and smooth center-of-mass (CoM) trajectory that enables the robot to stably start or stop running at the given speed. To accurately track online the desired speed during running, we propose an iterative algorithm to compute target footholds, which allows for the robot to follow the interactive desired speed after the next two steps. Lastly, a numerical experiment and the simulation of online variable speed running were performed with position-controlled bipedal robot BHR7P, and the results verified the effectiveness of the proposed methods.

Spatiotemporal Dynamics of Coastal Viral Community Structure and Potential Biogeochemical Roles Affected by an Ulva prolifera Green Tide.

The world's largest macroalgal green tide, caused by Ulva prolifera, has resulted in serious consequences for coastal waters of the Yellow Sea, China. Although viruses are considered to be one of the key factors in controlling microalgal bloom demise, understanding of the relationship between viral communities and the macroalgal green tide is still poor. Here, a Qingdao coastal virome (QDCV) time-series data set was constructed based on the metagenomic analysis of 17 DNA viromes along three coastal stations of the Yellow Sea, covering different stages of the green tide from Julian days 165 to 271. A total of 40,076 viral contigs were detected and clustered into 28,058 viral operational taxonomic units (vOTUs). About 84% of the vOTUs could not be classified, and 62% separated from vOTUs in other ecosystems. Green tides significantly influenced the spatiotemporal dynamics of the viral community structure, diversity, and potential functions. For the classified vOTUs, the relative abundance of Pelagibacter phages declined with the arrival of the bloom and rebounded after the bloom, while Synechococcus and Roseobacter phages increased, although with a time lag from the peak of their hosts. More than 80% of the vOTUs reached peaks in abundance at different specific stages, and the viral peaks were correlated with specific hosts at different stages of the green tide. Most of the viral auxiliary metabolic genes (AMGs) were associated with carbon and sulfur metabolism and showed spatiotemporal dynamics relating to the degradation of the large amount of organic matter released by the green tide. IMPORTANCE To the best of our knowledge, this study is the first to investigate the responses of viruses to the world's largest macroalgal green tide. It revealed the spatiotemporal dynamics of the unique viral assemblages and auxiliary metabolic genes (AMGs) following the variation and degradation of Ulva prolifera. These findings demonstrate a tight coupling between viral assemblages, and prokaryotic and eukaryotic abundances were influenced by the green tide.

Hybrid Momentum Compensation Control by Using Arms for Bipedal Dynamic Walking.

Biped robots swing their legs alternately to achieve highly dynamic walking, which is the basic ability required for them to perform tasks. However, swinging of the swinging leg in the air will disturb the interaction between the supporting leg and the ground and affect the upper body's balance during dynamic walking. To allow the robot to use its own intrinsic motion characteristics to maintain stable movement like a human when its lower limbs are affected by unknown disturbances during dynamic walking, the ability to use its arms to resist disturbances is essential. This article presents a hybrid momentum compensation control method for torque-controlled biped robots to adapt to unknown disturbances during dynamic walking. First, a hybrid angular momentum and linear momentum regulator is designed to compensate for the disturbance caused by the swinging leg. Second, based on real-time dynamic state changes of the legs, a mixed-momentum quadratic programming controller is designed to realize stable dynamic walking. The proposed method allows the force-controlled robot to maintain its balance while walking down an unknown platform, and it maintains good straightness in the forward direction of dynamic motion. The proposed method's effectiveness is verified experimentally on the BHR-B2 force-controlled biped robot platform.

A Friction-Driven Strategy for Agile Steering Wheel Manipulation by Humanoid Robots.

Vehicle driving can substantially enhance the maneuverability of humanoid robots. Agile steering wheel manipulation requires rapid rotation in narrow spaces such as a cab, serving as the foundation for increasing driving speed, especially in an obstacle avoidance scenario. Generally, there are 3 human driving strategies, "Hand-to-Hand," "Hand-over-Hand," and "One-Hand." Based on the human driving motion data, we quantitatively analyze these strategies from 3 aspects, motion range of joint combination, motion region of the shoulder, and velocity of the manipulation. Then, a friction-driven manipulation strategy using one hand is proposed utilizing the similarity between a humanoid robot and a driver (human). It effectively addresses the requirements of both a small range of motion and rapid manipulation. To prevent the deformation of the steering wheel caused by excessive force, we construct an operating force model specifically for the steering wheel. This model accurately describes the relationship between the rotation resistance and the state of the steering wheel. In addition, we propose a quadratic programming (QP)-based control framework to servo the robot to track the end-effector position and target wrench output by this model. Finally, the effectiveness of this paper is evaluated through an obstacle avoidance scenario, achieving a maximum rotation velocity of 3.14 rad/s.

Design of a Felid-like Humanoid Foot for Stability Enhancement.

The foot is an important part of humanoid robot locomotion that can help with shock absorption while making contact with the ground. The mechanism of the foot directly affects walking stability. A novel foot mechanism inspired by the toes of felids is proposed. The foot has four bionic modules with soft pads and sharp claws installed at the four corners of a flat foot. This foot can reduce the impact experienced during foot landing and increase the time that the foot is in contact with the ground, which can improve the adaptability of the robot to different ground surface conditions with different levels of stiffness. The main structure of the bionic module is a four-bar linkage consisting of a slide way and a spring. Furthermore, the length of the four-bar linkage and the posture of the claw during insertion into soft ground are optimized to improve the stability and buffering performance. The validity of the proposed foot mechanism has been proved in simulations.

Progress in 'taxonomic sufficiency' in aquatic biological investigations.

The 'taxonomic sufficiency' (TS) approach has been applied to algae, protists, invertebrates, and vertebrates, generally by aggregating species-level abundance data to a higher taxonomic level, where genus-level data are often highly correlated with species-level data and are a valid proxy level. The TS approach offers the possibility of a comparison of data from different geographical areas and highlights the effects of contaminants. The TS approach is stable in the face of different researchers and in the comparison of long-term biological survey data. The effectiveness of the TS approach may increase with increasing environmental gradients or spatial area. The TS approach should be avoided when the spatial area is small and small differences in species-level data are considered important, so as not to cancel out the distribution patterns specific to the local environment of the biological taxa.

Distributions and relationships of virio- and picoplankton in the epi-, meso- and bathypelagic zones of the Amundsen Sea, West Antarctica during the austral summer.

Virioplankton and picoplankton are the most abundant marine biological entities on earth and mediate biogeochemical cycles in the Southern Ocean. However, understanding of their distribution and relationships with environmental factors is lacking. Here, we report on their distribution and relationships with environmental factors at 48 stations from 112.5° to 150°W and 67° to 75.5°S in the Amundsen Sea of West Antarctica. The epipelagic stations were grouped into four clusters based on the virio- and picoplankton composition and abundance. Clusters three and four, which were associated with the ice-edge blooms in the coastal and Amundsen Sea Polynya (ASP) areas, had high abundances of autotrophic picoeukaryotes; this resulted in subsequent high abundances of heterotrophic prokaryotes and viruses. Cluster two stations were in open oceanic areas, where the abundances of autotrophic and heterotrophic picoplankton were low. Cluster one stations were located between the areas of blooms and the oceanic areas, which had a low abundance of heterotrophic prokaryotes and picoeukaryotes and a high abundance of virioplankton. The abundance of viruses was significantly correlated with the abundances of autotrophic picoeukaryotes and Chl-a concentration in oceanic areas, although this reflected a time-lag with autotrophic picoeukaryote and heterotrophic prokaryotes abundances in ice-edge bloom areas. The upwelling of Circumpolar Deep Water (CDW) might have induced the high abundance of autotrophic picoeukaryotes in the epipelagic zone, and the sinking particulate organic carbon (POC) might have induced the high abundance of heterotrophic prokaryotes and virioplankton in the meso- and bathypelagic zones. This study shows that the summer distribution of virio- and picoplankton in the Amundsen Sea of West Antarctica was mainly controlled by upwelling of the CDW and the timing of ice-edge blooms.

Upright and Crawling Locomotion and Its Transition for a Wheel-Legged Robot.

To face the challenge of adapting to complex terrains and environments, we develop a novel wheel-legged robot that can switch motion modes to adapt to different environments. The robot can perform efficient and stable upright balanced locomotion on flat roads and flexible crawling in low and narrow passages. For passing through low and narrow passages, we propose a crawling motion control strategy and methods for transitioning between locomotion modes of wheel-legged robots. In practical applications, the smooth transition between the two motion modes is challenging. By optimizing the gravity work of the body, the optimal trajectory of the center of mass (CoM) for the transition from standing to crawling is obtained. By constructing and solving an optimization problem regarding the posture and motion trajectories of the underactuated model, the robot achieves a smooth transition from crawling to standing. In experiments, the wheel-legged robot successfully transitioned between the crawling mode and the upright balanced moving mode and flexibly passed a low and narrow passage. Consequently, the effectiveness of the control strategies and algorithms proposed in this paper are verified by experiments.

Characteristics of zooplankton community structure and its relationship with environmental factors in the South Yellow Sea.

A total of 91 species of zooplankton were sampled from the South Yellow Sea in the summer and autumn of 2019. Copepods were the predominant arthropods. The summer sample (81) contained higher species diversity than the autumn (70). In both seasons, Calanus sinicus, Sagitta enflata, S. crassa, Doliolum denticulatum, and Macrura larva were dominant species. The average abundance of zooplankton in summer and autumn was 424.9 and 52.6 ind./m3, respectively, and the biomass was 207.7 and 107.6 mg/m3, respectively. The Shannon-Wiener index (H') in summer was higher than that in autumn, while the Pielou index (J') and Margalef index (D') were lower than those in autumn. BIOENV showed that water temperature (ST), dissolved oxygen (DO), turbidity (Tur), and tetrabromobisphenol A (TBBPA) affected the zooplankton community structure in the South Yellow Sea. The concentration of TBBPA increased from offshore to nearshore, with a significant negative impact on diversity in summer.