Clinical application of two types of Hook-Wire needle localization procedures for pulmonary small nodule biopsy.

BACKGROUND: With the widespread use of low-dose spiral computed tomography (LDCT) and increasing awareness of personal health, the detection rate of pulmonary nodules is steadily rising. OBJECTIVE: To evaluate the success rate and safety of two different models of Hook-Wire needle localization procedures for pulmonary small nodule biopsy. METHODS: Ninety-four cases with a total of 97 pulmonary small nodules undergoing needle localization biopsy were retrospectively analyzed. The cases were divided into two groups: Group A, using breast localization needle steel wire (Bard Healthcare Science Co., Ltd.); Group B, using disposable pulmonary nodule puncture needle (SensCure Biotechnology Co., Ltd.). All patients underwent video-assisted thoracoscopic surgery (VATS) for nodule removal on the same day after localization and biopsy. The puncture localization operation time, success rate, complications such as pulmonary hemorrhage, pneumothorax, hemoptysis, and postoperative comfort were observed and compared. RESULTS: In Group A, the average localization operation time for 97 nodules was 15.47 ± 5.31 minutes, with a success rate of 94.34%. The complication rate was 71.69% (12 cases of pneumothorax, 35 cases of pulmonary hemorrhage, 2 cases of hemoptysis), and 40 cases of post-localization discomfort were reported. In Group B, the average localization operation time was 25.32 ± 7.83 minutes, with a 100% success rate. The complication rate was 29.55% (3 cases of pneumothorax, 15 cases of pulmonary hemorrhage, 0 cases of hemoptysis), and 3 cases reported postoperative discomfort. According to the data analysis in this study, Group B had a lower incidence of puncture-related complications than Group A, along with a higher success rate and significantly greater postoperative comfort. CONCLUSIONS: The disposable pulmonary nodule puncture needle is safer and more effective in pulmonary small nodule localization biopsy, exhibiting increased comfort compared to the breast localization needle. Additionally, the incidence of complications is significantly lower.

De novo Phased Genome Assembly, Annotation and Population Genotyping of Alectoris Chukar.

The Alectoris Chukar (chukar) is the most geographically widespread partridge species in the world, demonstrating exceptional adaptability to diverse ecological environments. However, the scarcity of genetic resources for chukar has hindered research into its adaptive evolution and molecular breeding. In this study, we have sequenced and assembled a high-quality, phased chukar genome that consists of 31 pairs of relatively complete diploid chromosomes. Our BUSCO analysis reported a high completeness score of 96.8% and 96.5%, with respect to universal single-copy orthologs and a low duplication rate (0.3% and 0.5%) for two assemblies. Through resequencing and population genomic analyses of six subspecies, we have curated invaluable genotype data that underscores the adaptive evolution of chukar in response to both arid and high-altitude environments. These data will significantly contribute to research on how chukars adaptively evolve to cope with desertification and alpine climates.

Electrophysiological representations of multivariate human emotion experience.

ABSTRACTDespite the fact that human daily emotions are co-occurring by nature, most neuroscience studies have primarily adopted a univariate approach to identify the neural representation of emotion (emotion experience within a single emotion category) without adequate consideration of the co-occurrence of different emotions (emotion experience across different emotion categories simultaneously). To investigate the neural representations of multivariate emotion experience, this study employed the inter-situation representational similarity analysis (RSA) method. Researchers used an EEG dataset of 78 participants who watched 28 video clips and rated their experience on eight emotion categories. The EEG-based electrophysiological representation was extracted as the power spectral density (PSD) feature per channel in the five frequency bands. The inter-situation RSA method revealed significant correlations between the multivariate emotion experience ratings and PSD features in the Alpha and Beta bands, primarily over the frontal and parietal-occipital brain regions. The study found the identified EEG representations to be reliable with sufficient situations and participants. Moreover, through a series of ablation analyses, the inter-situation RSA further demonstrated the stability and specificity of the EEG representations for multivariate emotion experience. These findings highlight the importance of adopting a multivariate perspective for a comprehensive understanding of the neural representation of human emotion experience.

Breeding Behavior, Nestling Growth, and Begging Behavior in the Plain Laughingthrush (Garrulax davidi): Implications for Parent-Offspring Conflict.

Investigation on food allocation among nestlings of altricial birds is crucial in understanding parent-offspring conflicts within avian families. However, there is no consensus in empirical studies regarding whether parents or offspring determine the food allocation pattern within a brood. In the Plain Laughingthrush (Garrulax davidi), we examine the relationship between parental feeding strategies and nestling begging behaviors. Due to hatching asynchrony, larger nestlings have a competitive advantage in food acquisition over their smaller brood-mates; nevertheless, if the initial food-receivers were already satiated and did not immediately consume the food, parents would retrieve the food and re-allocate it to another nestling. This re-feeding tactic employed by parents reduced the likelihood of early-hatched nestlings monopolizing the food solely due to their larger body size. Our findings indicate that parents primarily allocated food based on nestling begging intensity, while their re-feeding tactic is determined by whether the first food-receivers have consumed the food. To date, our research demonstrates that while parental food allocation primarily hinges on the begging intensity of the nestlings, the decision to re-feed is contingent upon whether the initial recipients of the food ingest it immediately.

Soil texture and microorganisms dominantly determine the subsoil carbonate content in the permafrost-affected area of the Tibetan Plateau.

Under climate warming conditions, storage and conversion of soil inorganic carbon (SIC) play an important role in regulating soil carbon (C) dynamics and atmospheric CO2 content in arid and semi-arid areas. Carbonate formation in alkaline soil can fix a large amount of C in the form of inorganic C, resulting in soil C sink and potentially slowing global warming trends. Therefore, understanding the driving factors affecting carbonate mineral formation can help better predict future climate change. Till date, most studies have focused on abiotic drivers (climate and soil), whereas a few examined the effects of biotic drivers on carbonate formation and SIC stock. In this study, SIC, calcite content, and soil microbial communities were analyzed in three soil layers (0-5 cm, 20-30 cm, and 50-60 cm) on the Beiluhe Basin of Tibetan Plateau. Results revealed that in arid and semi-arid areas, SIC and soil calcite content did not exhibit significant differences among the three soil layers; however, the main factors affecting the calcite content in different soil layers are different. In the topsoil (0-5 cm), the most important predictor of calcite content was soil water content. In the subsoil layers 20-30 cm and 50-60 cm, the ratio of bacterial biomass to fungal biomass (B/F) and soil silt content, respectively, had larger contributions to the variation of calcite content than the other factors. Plagioclase provided a site for microbial colonization, whereas Ca2+ contributed in bacteria-mediated calcite formation. This study aims to highlight the importance of soil microorganisms in managing soil calcite content and reveals preliminary results on bacteria-mediated conversion of organic to inorganic C.

The EEG microstate representation of discrete emotions.

Understanding how human emotions are represented in our brain is a central question in the field of affective neuroscience. While previous studies have mainly adopted a modular and static perspective on the neural representation of emotions, emerging research suggests that emotions may rely on a distributed and dynamic representation. The present study aimed to explore the EEG microstate representations for nine discrete emotions (Anger, Disgust, Fear, Sadness, Neutral, Amusement, Inspiration, Joy and Tenderness). Seventy-eight participants were recruited to watch emotion eliciting videos with their EEGs recorded. Multivariate analysis revealed that different emotions had distinct EEG microstate features. By using the EEG microstate features in the Neutral condition as the reference, the coverage of C, duration of C and occurrence of B were found to be the top-contributing microstate features for the discrete positive and negative emotions. The emotions of Disgust, Fear and Joy were found to be most effectively represented by EEG microstate. The present study provided the first piece of evidence of EEG microstate representation for discrete emotions, highlighting a whole-brain, dynamical representation of human emotions.

Soil texture influences soil bacterial biomass in the permafrost-affected alpine desert of the Tibetan plateau.

Under warm climate conditions, permafrost thawing results in the substantial release of carbon (C) into the atmosphere and potentially triggers strong positive feedback to global warming. Soil microorganisms play an important role in decomposing organic C in permafrost, thus potentially regulating the ecosystem C balance in permafrost-affected regions. Soil microbial community and biomass are mainly affected by soil organic carbon (SOC) content and soil texture. Most studies have focused on acidic permafrost soil (pH < 7), whereas few examined alkaline permafrost-affected soil (pH > 7). In this study, we analyzed soil microbial communities and biomass in the alpine desert and steppe on the Tibetan plateau, where the soil pH values were approximately 8.7 ± 0.2 and 8.5 ± 0.1, respectively. Our results revealed that microbial biomass was significantly associated with mean grain size (MGS) and SOC content in alkaline permafrost-affected soils (p < 0.05). In particular, bacterial and fungal biomasses were affected by SOC content in the alpine steppe, whereas bacterial and fungal biomasses were mainly affected by MGS and SOC content, respectively, in the alpine desert. Combined with the results of the structural equation model, those findings suggest that SOC content affects soil texture under high pH-value (pH 8-9) and that soil microbial biomass is indirectly affected. Soils in the alpine steppe and desert are dominated by plagioclase, which provides colonization sites for bacterial communities. This study aimed to highlight the importance of soil texture in managing soil microbial biomass and demonstrate the differential impacts of soil texture on fungal and bacterial communities in alkaline permafrost-affected regions.

Effects of Climate Change on the Habitat of the Leopard (Panthera pardus) in the Liupanshan National Nature Reserve of China.

Climate change affects animal populations by affecting their habitats. The leopard population has significantly decreased due to climate change and human disturbance. We studied the impact of climate change on leopard habitats using infrared camera technology in the Liupanshan National Nature Reserve of Jingyuan County, Ningxia Hui Autonomous Region, China, from July 2017 to October 2019. We captured 25 leopard distribution points over 47,460 camera working days. We used the MAXENT model to predict and analyze the habitat. We studied the leopard's suitable habitat area and distribution area under different geographical scales in the reserve. Changes in habitat area of leopards under the rcp2.6, rcp4.5, and rcp8.5 climate models in Guyuan in 2050 were also studied. We conclude that the current main factors affecting suitable leopard habitat area were vegetation cover and human disturbance. The most critical factor affecting future suitable habitat area is rainfall. Under the three climate models, the habitat area of the leopard decreased gradually because of an increase in carbon dioxide concentration. Through the prediction of the leopard's distribution area in the Liupanshan Nature Reserve, we evaluated the scientific nature of the reserve, which is helpful for the restoration and protection of the wild leopard population.

Gut Microbial Profile in Asymptomatic Gallstones.

There are few studies on the changes of gut microbiota in patients with gallstones, especially in patients with asymptomatic gallstones, and there are some deficiencies in these studies, for instance, the effects of metabolic factors on gut microbiota are not considered. Here, we selected 30 asymptomatic gallstone patients from the survey population, and 30 controls according to the age and BMI index matching principle. The 16SrDNA technology was used to detect and compare the structural differences in the gut microbiota between the two groups. Compared with healthy controls, the abundance of gut microbiota in patients with gallstones increased significantly, while the microbiota diversity decreased. At the level of phylum, both groups were dominated by Firmicutes, Bacteroides, Proteobacteria, and Actinobacteria. At the genus level, there were 15 species with significant differences in abundance between the two groups. Further subgroup analysis found that only unclassified Lactobacillales showed differences in the intestines of gallstones patients with hypertension, non-alcoholic fatty liver disease, or patients with elevated BMI (≧24). The structure of gut microbiota in patients with gallstones changed significantly, and this might be related to the occurrence of gallstones, rather than metabolic factors such as hypertension, non-alcoholic fatty liver disease, and obesity.

Cytosolic peptides encoding CaV1 C-termini downregulate the calcium channel activity-neuritogenesis coupling.

L-type Ca2+ (CaV1) channels transduce channel activities into nuclear signals critical to neuritogenesis. Also, standalone peptides encoded by CaV1 DCT (distal carboxyl-terminus) act as nuclear transcription factors reportedly promoting neuritogenesis. Here, by focusing on exemplary CaV1.3 and cortical neurons under basal conditions, we discover that cytosolic DCT peptides downregulate neurite outgrowth by the interactions with CaV1's apo-calmodulin binding motif. Distinct from nuclear DCT, various cytosolic peptides exert a gradient of inhibitory effects on Ca2+ influx via CaV1 channels and neurite extension and arborization, and also the intermediate events including CREB activation and c-Fos expression. The inhibition efficacies of DCT are quantitatively correlated with its binding affinities. Meanwhile, cytosolic inhibition tends to facilitate neuritogenesis indirectly by favoring Ca2+-sensitive nuclear retention of DCT. In summary, DCT peptides as a class of CaV1 inhibitors specifically regulate the channel activity-neuritogenesis coupling in a variant-, affinity-, and localization-dependent manner.

Brain-inspired global-local learning incorporated with neuromorphic computing.

There are two principle approaches for learning in artificial intelligence: error-driven global learning and neuroscience-oriented local learning. Integrating them into one network may provide complementary learning capabilities for versatile learning scenarios. At the same time, neuromorphic computing holds great promise, but still needs plenty of useful algorithms and algorithm-hardware co-designs to fully exploit its advantages. Here, we present a neuromorphic global-local synergic learning model by introducing a brain-inspired meta-learning paradigm and a differentiable spiking model incorporating neuronal dynamics and synaptic plasticity. It can meta-learn local plasticity and receive top-down supervision information for multiscale learning. We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors. It achieves significantly higher performance than single-learning methods. We further implement the model in the Tianjic neuromorphic platform by exploiting algorithm-hardware co-designs and prove that the model can fully utilize neuromorphic many-core architecture to develop hybrid computation paradigm.

Are microbes and metabolites influencing the parental consumption of nestlings' feces in gray-backed shrikes?

The behavioral video recordings of the gray-backed shrike Lanius tephronotus revealed that parent birds eat the feces produced by their nestlings. "Parental nutrition hypothesis" attributes the origin of this behavior to nutrition-recovery and cost-saving, respectively. However, the presence of usable nutrients in the nestlings' feces is unknown because of traditional technology. In this study, we analyzed all the metabolites and the variations in the diversity and content of microbes in the feces of gray-backed shrike nestlings. We aimed to report the changes in microbes and metabolites with the age of nestlings and point out that the parent birds that eat the feces may gain potential nutrition benefits. The results showed that the relative abundances of Proteobacteria, Firmicutes, and Bacteroidota, changed significantly when the nestlings were 6 days old. The relative abundances of 6 probiotics, which are involved in digestion, metabolism, and immunity-related physiological functions, decreased in the nestlings' feces gradually with age; therefore, these probiotics may be obtained by parent birds upon ingestion of the feces of young nestlings. Among the metabolites that were detected, 20 were lipids and some had a role in anti-parasitic functions and wound healing; however, their relative contents decreased with age. These beneficial substances in the nestlings' feces may stimulate the parents to swallow the feces. Moreover, there were many aromatic metabolites in the newly hatched nestlings' feces, but the content of bitter metabolites increased as they grew up. Therefore, our results are in accordance with the nutritional hypothesis.

Deep geometric representations for modeling effects of mutations on protein-protein binding affinity.

Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial role in protein engineering and drug design. In this study, we develop GeoPPI, a novel structure-based deep-learning framework to predict the change of binding affinity upon mutations. Based on the three-dimensional structure of a protein, GeoPPI first learns a geometric representation that encodes topology features of the protein structure via a self-supervised learning scheme. These representations are then used as features for training gradient-boosting trees to predict the changes of protein-protein binding affinity upon mutations. We find that GeoPPI is able to learn meaningful features that characterize interactions between atoms in protein structures. In addition, through extensive experiments, we show that GeoPPI achieves new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on six benchmark datasets. Moreover, we show that GeoPPI can accurately estimate the difference of binding affinities between a few recently identified SARS-CoV-2 antibodies and the receptor-binding domain (RBD) of the S protein. These results demonstrate the potential of GeoPPI as a powerful and useful computational tool in protein design and engineering. Our code and datasets are available at: https://github.com/Liuxg16/GeoPPI.

The duration of withdrawal affects the muscarinic signaling in the nucleus accumbens after chronic morphine exposure in neonatal rats.

The infants experience withdrawal from opiates, and time-dependent adaptations in neuronal activity of nucleus accumbens (NAc) may be crucial for this process. A key adaptation is an increased release of acetylcholine. The present study investigates muscarinic acetylcholine receptors (mAChRs) functions in the NAc at short-term (SWT) and long-term (LWT) withdrawal time following chronic morphine exposure in neonatal rats. The inhibitory role of presynaptic mAChRs activation in spontaneous excitatory postsynaptic currents (sEPSCs) in medium spiny neurons was decreased at LWT but not at SWT. Whereas, the excitatory role of post/extrasynaptic mAChRs activation in membrane currents was reduced at LWT but enhanced at SWT. Furthermore, the inhibitory effect of acute morphine on post/extrasynaptic mAChRs-mediated inward currents was enhanced at SWT but not at LWT. These results suggest that withdrawal from morphine leads to downregulation of presynaptic and post/extrasynaptic mAChRs functions in the NAc, which may coregulate the development of withdrawal in neonates.NEW & NOTEWORTHY We investigated for the first time how the duration of withdrawal affects mAChRs functions in the nucleus accumbens in neonatal rats. Compared with short-term withdrawal time, rats showed downregulation of presynaptic and post/extrasynaptic mAChRs functions during long-term withdrawal time. Our finding introduces a new possible correlation between the mAChRs dysfunction in the nucleus accumbens and the development of withdrawal in neonates.

An effective self-supervised framework for learning expressive molecular global representations to drug discovery.

How to produce expressive molecular representations is a fundamental challenge in artificial intelligence-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for modeling molecular data. However, previous supervised approaches usually suffer from the scarcity of labeled data and poor generalization capability. Here, we propose a novel molecular pre-training graph-based deep learning framework, named MPG, that learns molecular representations from large-scale unlabeled molecules. In MPG, we proposed a powerful GNN for modelling molecular graph named MolGNet, and designed an effective self-supervised strategy for pre-training the model at both the node and graph-level. After pre-training on 11 million unlabeled molecules, we revealed that MolGNet can capture valuable chemical insights to produce interpretable representation. The pre-trained MolGNet can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of drug discovery tasks, including molecular properties prediction, drug-drug interaction and drug-target interaction, on 14 benchmark datasets. The pre-trained MolGNet in MPG has the potential to become an advanced molecular encoder in the drug discovery pipeline.

Projections from D2 Neurons in Different Subregions of Nucleus Accumbens Shell to Ventral Pallidum Play Distinct Roles in Reward and Aversion.

The nucleus accumbens shell (NAcSh) plays an important role in reward and aversion. Traditionally, NAc dopamine receptor 2-expressing (D2) neurons are assumed to function in aversion. However, this has been challenged by recent reports which attribute positive motivational roles to D2 neurons. Using optogenetics and multiple behavioral tasks, we found that activation of D2 neurons in the dorsomedial NAcSh drives preference and increases the motivation for rewards, whereas activation of ventral NAcSh D2 neurons induces aversion. Stimulation of D2 neurons in the ventromedial NAcSh increases movement speed and stimulation of D2 neurons in the ventrolateral NAcSh decreases movement speed. Combining retrograde tracing and in situ hybridization, we demonstrated that glutamatergic and GABAergic neurons in the ventral pallidum receive inputs differentially from the dorsomedial and ventral NAcSh. All together, these findings shed light on the controversy regarding the function of NAcSh D2 neurons, and provide new insights into understanding the heterogeneity of the NAcSh.

Riboexp: an interpretable reinforcement learning framework for ribosome density modeling.

Translation elongation is a crucial phase during protein biosynthesis. In this study, we develop a novel deep reinforcement learning-based framework, named Riboexp, to model the determinants of the uneven distribution of ribosomes on mRNA transcripts during translation elongation. In particular, our model employs a policy network to perform a context-dependent feature selection in the setting of ribosome density prediction. Our extensive tests demonstrated that Riboexp can significantly outperform the state-of-the-art methods in predicting ribosome density by up to 5.9% in terms of per-gene Pearson correlation coefficient on the datasets from three species. In addition, Riboexp can indicate more informative sequence features for the prediction task than other commonly used attribution methods in deep learning. In-depth analyses also revealed the meaningful biological insights generated by the Riboexp framework. Moreover, the application of Riboexp in codon optimization resulted in an increase of protein production by around 31% over the previous state-of-the-art method that models ribosome density. These results have established Riboexp as a powerful and useful computational tool in the studies of translation dynamics and protein synthesis. Availability: The data and code of this study are available on GitHub: https://github.com/Liuxg16/Riboexp. Contact:zengjy321@tsinghua.edu.cn; songsen@tsinghua.edu.cn.

TrimNet: learning molecular representation from triplet messages for biomedicine.

MOTIVATION: Computational methods accelerate drug discovery and play an important role in biomedicine, such as molecular property prediction and compound-protein interaction (CPI) identification. A key challenge is to learn useful molecular representation. In the early years, molecular properties are mainly calculated by quantum mechanics or predicted by traditional machine learning methods, which requires expert knowledge and is often labor-intensive. Nowadays, graph neural networks have received significant attention because of the powerful ability to learn representation from graph data. Nevertheless, current graph-based methods have some limitations that need to be addressed, such as large-scale parameters and insufficient bond information extraction. RESULTS: In this study, we proposed a graph-based approach and employed a novel triplet message mechanism to learn molecular representation efficiently, named triplet message networks (TrimNet). We show that TrimNet can accurately complete multiple molecular representation learning tasks with significant parameter reduction, including the quantum properties, bioactivity, physiology and CPI prediction. In the experiments, TrimNet outperforms the previous state-of-the-art method by a significant margin on various datasets. Besides the few parameters and high prediction accuracy, TrimNet could focus on the atoms essential to the target properties, providing a clear interpretation of the prediction tasks. These advantages have established TrimNet as a powerful and useful computational tool in solving the challenging problem of molecular representation learning. AVAILABILITY: The quantum and drug datasets are available on the website of MoleculeNet: http://moleculenet.ai. The source code is available in GitHub: https://github.com/yvquanli/trimnet. CONTACT: xjyao@lzu.edu.cn, songsen@tsinghua.edu.cn.

Exploring EEG microstates for affective computing: decoding valence and arousal experiences during video watching.

Investigating the electroencephalography (EEG) correlates of human emotional experiences has attracted increasing interest in the field of affective computing. Substantial progress has been made during the past decades, mainly by using EEG features extracted from localized brain activities. The present study explored a brain network-based feature defined by EEG microstates for a possible representation of emotional experiences. A publicly available and widely used benchmarking EEG dataset called DEAP was used, in which 32 participants watched 40 one-minute music videos with their 32channel EEG recorded. Four quasi-stable prototypical microstates were obtained, and their temporal parameters were extracted as features. In random forest regression, the microstate features showed better performances for decoding valence (model fitting mean squared error (MSE) = 3.85±0.28 and 4.07 ± 0.30, respectively, p = 0.022) and comparable performances for decoding arousal (MSE = 3.30±0.30 and 3.41 ±0.31, respectively, p = 0.169), as compared to conventional spectral power features. As microstate features describe neural activities from a global spatiotemporal dynamical perspective, our findings demonstrate a possible new mechanism for understanding human emotion and provide a promising type of EEG feature for affective computing.

A hybrid and scalable brain-inspired robotic platform.

Recent years have witnessed tremendous progress of intelligent robots brought about by mimicking human intelligence. However, current robots are still far from being able to handle multiple tasks in a dynamic environment as efficiently as humans. To cope with complexity and variability, further progress toward scalability and adaptability are essential for intelligent robots. Here, we report a brain-inspired robotic platform implemented by an unmanned bicycle that exhibits scalability of network scale, quantity and diversity to handle the changing needs of different scenarios. The platform adopts rich coding schemes and a trainable and scalable neural state machine, enabling flexible cooperation of hybrid networks. In addition, an embedded system is developed using a cross-paradigm neuromorphic chip to facilitate the implementation of diverse neural networks in spike or non-spike form. The platform achieved various real-time tasks concurrently in different real-world scenarios, providing a new pathway to enhance robots' intelligence.