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Skeletal deformities are typical AD-HIES manifestations, which are mainly caused by heterozygous and loss-of-function mutations in Signal transducer and activator of transcription 3 (STAT3). However, the mechanism is still unclear and the treatment strategy is limited. Herein, we reported that the mice with Stat3 deletion in osteoblasts, but not in osteoclasts, induced AD-HIES-like skeletal defects, including craniofacial malformation, osteoporosis, and spontaneous bone fracture. Mechanistic analyses revealed that STAT3 in cooperation with Msh homeobox 1(MSX1) drove osteoblast differentiation by promoting Distal-less homeobox 5( Dlx5) transcription. Furthermore, pharmacological activation of STAT3 partially rescued skeletal deformities in heterozygous knockout mice, while inhibition of STAT3 aggravated bone loss. Taken together, these data show that STAT3 is critical for modulating skeletal development and maintaining bone homeostasis through STAT3-indcued osteogenesis and suggest it may be a potential target for treatments. Autosomal dominant hyper-immunoglobulin E syndrome (AD-HIES) is associated with mutations in STAT3, and clinical manifestations include skeletal deformities. Here, the authors show that inactivation of STAT3 in osteoblast induces AD-HIES-like skeletal defects by impairing osteogenesis, and show that pharmacological STAT3 activation rescues the phenotype.

Displacement self-sensing active magnetic bearings (AMBs) have garnered significant attention from both academia and industry for their potential to reduce cost, enable system integration, and enhance reliability. While numerous self-sensing methodologies have been researched, the field lacks a unified framework for discussing their theoretical foundation and practical applicability. This paper analyzes and summarizes various displacement self-sensing methods, deriving the underlying principles and essence of these techniques, and clarifying the intrinsic interconnections of different schemes. The process of self-sensing is constructed through two steps: online inductance estimation and electromagnetic inductance modeling. A novel framework is then proposed, categorizing online inductance estimation, with dedicated discussion on modeling and handling critical nonlinearity like magnetic saturation and the eddy current effect. Furthermore, this review conducts a systematic comparative analysis, evaluating prevalent schemes against key performance metrics such as robustness, stability, signal-to-noise ratio (SNR), and system complexity. Finally, persistent challenges and future research trends are discussed. This review provides a valuable reference for both researchers and engineers when selecting and implementing self-sensing technologies for AMB systems.

The cyanobacterium Synechococcus elongatus is a model organism for the study of circadian rhythms. It is naturally competent for transformation—that is, it takes up DNA from the environment, but the underlying mechanisms are unclear. Here, we use a genome-wide screen to identify genes required for natural transformation in S. elongatus , including genes encoding a conserved Type IV pilus, genes known to be associated with competence in other bacteria, and others. Pilus biogenesis occurs daily in the morning, while natural transformation is maximal when the onset of darkness coincides with the dusk circadian peak. Thus, the competence state in cyanobacteria is regulated by the circadian clock and can adapt to seasonal changes of day length. The cyanobacterium Synechococcus elongatus is a model organism for the study of circadian rhythms, and is naturally competent for transformation. Here, Taton et al. identify genes required for natural transformation in this organism, and show that the coincidence of circadian dusk and darkness regulates the competence state in different day lengths.

As SARS-CoV-2 Omicron and other variants of concern (VOCs) continue spreading worldwide, development of antibodies and vaccines to confer broad and protective activity is a global priority. Here, we report on the identification of a special group of nanobodies from immunized alpaca with potency against diverse VOCs including Omicron subvariants BA.1, BA.2 and BA.4/5, SARS-CoV-1, and major sarbecoviruses. Crystal structure analysis of one representative nanobody, 3-2A2-4, discovers a highly conserved epitope located between the cryptic and the outer face of the receptor binding domain (RBD), distinctive from the receptor ACE2 binding site. Cryo-EM and biochemical evaluation reveal that 3-2A2-4 interferes structural alteration of RBD required for ACE2 binding. Passive delivery of 3-2A2-4 protects K18-hACE2 mice from infection of authentic SARS-CoV-2 Delta and Omicron. Identification of these unique nanobodies will inform the development of next generation antibody therapies and design of pan-sarbecovirus vaccines. The authors identify nanobodies from immunized alpaca with broadly neutralizing activity against SARS-CoV-1, SARS-CoV-2 variants, and major sarbecoviruses. One representative nanobody binds to a highly conserved epitope on RBD and protects K18-hACE2 mice from Omicron and Delta infection.

Background China is the largest producer of sweet potato in the world, accounting for 57.0% of the global output. Germplasm resources are the basis for promoting innovations in the seed industry and ensuring food security. Individual and accurate identification of sweet potato germplasm is an important part of conservation and efficient utilization. Results In this study, nine pairs of simple sequence repeat molecular markers and 16 morphological markers were used to construct genetic fingerprints for sweet potato individual identification. Combined with basic information, typical phenotypic photographs, genotype peak graphs, and a two-dimensional code for detection and identification were generated. Finally, a genetic fingerprint database containing 1021 sweet potato germplasm resources in the “National Germplasm Guangzhou Sweet Potato Nursery Genebank in China” was constructed. Genetic diversity analysis of the 1021 sweet potato genotypes using the nine pairs of simple sequence repeat markers revealed a narrow genetic variation range of Chinese native sweet potato germplasm resources, and Chinese germplasm was close to that from Japan and the United States, far from that from the Philippines and Thailand, and the furthest from that from Peru. Sweet potato germplasm resources from Peru had the richest genetic diversity, supporting the view that Peru is the center of origin and domestication of sweet potato varieties. Conclusions Overall, this study provides scientific guidance for the conservation, identification, and utilization of sweet potato germplasm resources and offers a reference to facilitate the discovery of important genes to boost sweet potato breeding.

With the development of Internet technologies, the shipping industry has also entered the Industry 4.0 era, which is the era of using information technology to promote industrial change. Group decision making (GDM), as one of the key methods in decision science, can be used to obtain optimal solutions by aggregating the opinions of experts on several alternatives, and it has been applied to many fields to optimize the decision-making process. This paper provides an overview and analysis of the specific applications of GDM methods in Shipping Industry 4.0, and discusses future developments and research directions. First, the existing relevant literature is analyzed using bibliometrics. Then, the general procedure of GDM is investigated: opinion/preference representation, consensus measure, feedback mechanism, and the selection of alternatives. Next, the specific applications of GDM methods in Shipping Industry 4.0 are summarized. Lastly, possible future directions are discussed to advance this area of research.

It was recently proposed that olfaction evolved to aid navigation. Consistent with this hypothesis, olfactory identification and spatial memory are linked to overlapping brain areas which include the orbitofrontal cortex and hippocampus. However, the relationship between these two processes has never been specifically investigated. Here, we show that olfactory identification covaries with spatial memory in humans. We also found that the cortical thickness of the left medial orbitofrontal cortex, and the volume of the right hippocampus, predict both olfactory identification and spatial memory. Finally, we demonstrate deficits in both olfactory identification and spatial memory in patients with lesions of the medial orbitofrontal cortex. Our findings reveal an intrinsic relationship between olfaction and spatial memory that is supported by a shared reliance on the hippocampus and medial orbitofrontal cortex. This relationship may find its roots in the parallel evolution of the olfactory and hippocampal systems. Olfaction, the sense of smell, may have originally evolved to aid navigation in space, but there is no direct evidence of a link between olfaction and navigation in humans. Here the authors show that olfaction and spatial memory abilities are correlated and rely on similar brain regions in humans.

In the age of intelligence, information technology (IT) education has become the focus of attention in the education sector. However, traditional teaching methods fall short in motivating students and fostering higher-order thinking and have difficulty providing a personalized learning experience. Although AI chatbots can provide instant feedback as an innovative teaching tool, it is still challenging to fully enhance learner engagement. Based on this, this study developed a digital game-based AI chatbot system to enhance students’ learning experience through digital game-based learning strategies. This study utilized a quasi-experimental design with the experimental group using a digital game-based AI chatbot and the control group using a traditional AI chatbot. A comparison was made between the two groups concerning student learning performance in IT courses, higher-order thinking (including problem-solving, computational thinking, and creativity), learning motivation, and flow experience. In addition, the behavioral patterns of high-achieving and low-achieving students in the experimental group were analyzed. The results showed that the experimental group was significantly better than the control group in academic performance, problem-solving, computational thinking, learning motivation, and flow experience, but there was no significant difference in creativity tendency. Behavioral pattern analysis showed that high-achieving students in the experimental group showed more systematic learning strategies, while low-achieving students relied more on immediate feedback and external help, but both high- and low-achieving groups were able to actively talk to the AI chatbot and actively explore problem-solving strategies in the digital game. Therefore, AI chatbots based on digital games can be effectively used in IT courses to help students construct knowledge and develop higher-order thinking.

Parallel multisite recordings in the visual cortex of trained monkeys revealed that the responses of spatially distributed neurons to natural scenes are ordered in sequences. The rank order of these sequences is stimulus-specific and maintained even if the absolute timing of the responses is modified by manipulating stimulus parameters. The stimulus specificity of these sequences was highest when they were evoked by natural stimuli and deteriorated for stimulus versions in which certain statistical regularities were removed. This suggests that the response sequences result from a matching operation between sensory evidence and priors stored in the cortical network. Decoders trained on sequence order performed as well as decoders trained on rate vectors but the former could decode stimulus identity from considerably shorter response intervals than the latter. A simulated recurrent network reproduced similarly structured stimulus-specific response sequences, particularly once it was familiarized with the stimuli through non-supervised Hebbian learning. We propose that recurrent processing transforms signals from stationary visual scenes into sequential responses whose rank order is the result of a Bayesian matching operation. If this temporal code were used by the visual system it would allow for ultrafast processing of visual scenes. How the brain analyzes complex visual scenes within a fraction of a second remains poorly understood. Here, the authors suggest that this might be accomplished through the use of a temporal code by exploiting the sequence order of responses generated in networks of recurrently coupled neurons that harbor the priors of natural image statistics.

Condition monitoring is used to assess the reliability and equipment efficiency of wind turbines. Feature extraction is an essential preprocessing step to achieve a high level of performance in condition monitoring. However, the fluctuating conditions of wind turbines usually cause sudden variations in the monitored features, which may lead to an inaccurate prediction and maintenance schedule. In this scenario, this article proposed a novel methodology to detect the multiple levels of faults of rolling bearings in variable operating conditions. First, signal decomposition was carried out by variational mode decomposition (VMD). Second, the statistical features were calculated and extracted in the time domain. Meanwhile, a permutation entropy analysis was conducted to estimate the complexity of the vibrational signal in the time series. Next, feature selection techniques were applied to achieve improved identification accuracy and reduce the computational burden. Finally, the ranked feature vectors were fed into machine learning algorithms for the classification of the bearing defect status. In particular, the proposed method was performed over a wide range of working regions to simulate the operational conditions of wind turbines. Comprehensive experimental investigations were employed to evaluate the performance and effectiveness of the proposed method.