The organization of the semantic network as reflected by the neural correlates of six semantic dimensions.

Multiple sensory-motor and non-sensory-motor dimensions have been proposed for semantic representation, but it remains unclear how the semantic system is organized along them in the human brain. Using naturalistic fMRI data and large-scale semantic ratings, we investigated the overlaps and dissociations between the neural correlates of six semantic dimensions: vision, motor, socialness, emotion, space, and time. Our findings revealed a more complex semantic atlas than what is predicted by current neurobiological models of semantic representation. Brain regions that are selectively sensitive to specific semantic dimensions were found both within and outside the brain networks assumed to represent multimodal general and/or abstract semantics. Overlaps between the neural correlates of different semantic dimensions were mainly found inside the default mode network, concentrated in the left anterior superior temporal gyrus and angular gyrus, which have been proposed as two connector hubs that bridge the multimodal experiential semantic system and the language-supported semantic system.

Butyrate Alleviates High-Fat-Induced Metabolic Disorders Partially through Increasing Systematic Glutamine.

Obesity has emerged as a worldwide epidemic. Both butyrate and glutamine counteract obesity-related metabolic disorders; however, whether and how they synergistically cooperate with each other remains a mystery. In the study, a high-fat diet (HFD, 60% calories from fat) was used to develop a model of obesity-related metabolic disorder and compared with administrated saline and sodium butyrate (SB, 300 mg/kg body weight) daily by gavage. Compared with HFD counterparts, oral administration of SB in mice exhibited significantly reduced body weight and fat mass and decreased hepatic triglyceride content. The targeted mass spectrum revealed that SB restored serum contents of glutamine, which were significantly decreased by HFD. Furthermore, SB significantly elevated the expression of glutamine synthetase (GS, encoded by GLUL) in the liver, accompanied by more enrichment of H3K27ac modifications within its promoter. In summary, the study verified the contribution of elevated glutamine to the beneficial effects of butyrate on metabolic disorders induced by a high-fat diet, providing a novel pathway for understanding how butyrate benefits metabolic homeostasis.

Two new flavanones from Polytrichum commune Hedw.

Seven flavanones, including two new compounds coupled with styryl units, communins C (1) and D (2), as well as five known compounds, were isolated from Polytrichum commune Hedw. The planar structures of all compounds were determined using extensive spectroscopic analysis. The absolute configurations of two new compounds were assigned by comparing experimental ECD with calculated ECD. The cytotoxicity of all isolates against HCT-116, BGC803, MCF7 and PANC-1 cell lines was evaluated. Communin D exhibited significant cytotoxic activity on BGC803 cells with an IC50 value of 9.3 µM.

A large dataset of semantic ratings and its computational extension.

Evidence from psychology and cognitive neuroscience indicates that the human brain's semantic system contains several specific subsystems, each representing a particular dimension of semantic information. Word ratings on these different semantic dimensions can help investigate the behavioral and neural impacts of semantic dimensions on language processes and build computational representations of language meaning according to the semantic space of the human cognitive system. Existing semantic rating databases provide ratings for hundreds to thousands of words, which can hardly support a comprehensive semantic analysis of natural texts or speech. This article reports a large database, the Six Semantic Dimension Database (SSDD), which contains subjective ratings for 17,940 commonly used Chinese words on six major semantic dimensions: vision, motor, socialness, emotion, time, and space. Furthermore, using computational models to learn the mapping relations between subjective ratings and word embeddings, we include the estimated semantic ratings for 1,427,992 Chinese and 1,515,633 English words in the SSDD. The SSDD will aid studies on natural language processing, text analysis, and semantic representation in the brain.

Two new benzophenones from the moss Pogonatum spinulosum.

Two new benzophenone derivatives (1 and 2), named Pogonatone C and pogonatone D, were isolated from the moss Pogonatum spinulosum. Their structures were elucidated by spectroscopic data analyses. The cytotoxicity of compounds for HepG2, HCT-116, A-549 and PANC-1 cells line was also evaluated by using the MTT method. Pogonatone C (1) displays high cytotoxicity on PANC-1 cell with IC50 value of 9.2 µM.

An fMRI Dataset for Concept Representation with Semantic Feature Annotations.

The neural representation of concepts is a focus of many cognitive neuroscience studies. Prior works studying concept representation with neural imaging data have been largely limited to concrete concepts. The use of relatively small and constrained sets of stimuli leaves open the question of whether the findings can generalize other concepts. We share an fMRI dataset in which 11 participants thought of 672 individual concepts, including both concrete and abstract concepts. The concepts were probed using words paired with images in which the words were selected to cover a wide range of semantic categories. Furthermore, according to the componential theories of concept representation, we collected the 54 semantic features of the 672 concepts comprising sensory, motor, spatial, temporal, affective, social, and cognitive experiences by crowdsourcing annotations. The quality assessment results verify this as a high-quality neuroimaging dataset. Such a dataset is well suited to study how the brain represents different semantic features and concepts, creating the essential condition to investigate the neural representation of individual concepts.

A synchronized multimodal neuroimaging dataset for studying brain language processing.

We present a synchronized multimodal neuroimaging dataset for studying brain language processing (SMN4Lang) that contains functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data on the same 12 healthy volunteers while the volunteers listened to 6 hours of naturalistic stories, as well as high-resolution structural (T1, T2), diffusion MRI and resting-state fMRI data for each participant. We also provide rich linguistic annotations for the stimuli, including word frequencies, syntactic tree structures, time-aligned characters and words, and various types of word and character embeddings. Quality assessment indicators verify that this is a high-quality neuroimaging dataset. Such synchronized data is separately collected by the same group of participants first listening to story materials in fMRI and then in MEG which are well suited to studying the dynamic processing of language comprehension, such as the time and location of different linguistic features encoded in the brain. In addition, this dataset, comprising a large vocabulary from stories with various topics, can serve as a brain benchmark to evaluate and improve computational language models.

Food spending in the United States during the first year of the COVID-19 pandemic.

The COVID-19 pandemic brought about a significant increase in the unemployment rate and a decline in consumer income. At the same time, the public health responses to the pandemic, such as lockdowns and business closures, disrupted the food supply chain. These pandemic-driven changes could lead to a shift in food spending behaviors and potentially exacerbate the food insecurity situation. Leveraging the nationally representative dataset from the 2017-2020 consumer expenditure surveys, we employ a two-part model to assess the changes in weekly household spending on total food, food-at-home (FAH), and food-away-from-home (FAFH) between the pre-pandemic and pandemic period in the United States. Our finding shows a predicted marginal decline in FAFH expenditure by 33.7% but an increase in FAH spending by 6.9% during the pandemic. The increase in FAH spending could not fully offset the decrease in FAFH spending, leading to a decline in total food spending by 12.6%. The results could provide a basis for future studies on food insecurity, nutrient intake, and healthy consumption during the pandemic.

The Dynamic Relationship among Bank Credit, House Prices and Carbon Dioxide Emissions in China.

This paper explores the dynamic relationship among bank credit, house prices and carbon dioxide emissions in China by systematically analyzing related data from January 2000 to December 2019 with the help of the time-varying parameter vector autoregression with stochastic volatility (TVP-SV-VAR) model and the Bayesian DCC-GARCH model. Empirical results show the expansion of bank credit significantly drives up house prices and increases carbon dioxide emissions in mosttimes. The rise in house prices inhibits the expansion of bank credit but increases carbon dioxide emissions and aggravates environment pollution, and that the increase in carbon dioxide is helpful to stimulate bank credit expansion and house price rise. In addition, bank credit and house prices are most relevant, followed by bank credit and carbon dioxide emissions, then by house prices and carbon dioxide emissions. Therefore, we believe that in order to stabilize skyrocketing house prices, restrain carbon dioxide emissions, and secure a stable and healthy macro-economy, the government should strengthen management of bank credit, and effectively control its total volume.

Therapeutic impact of BET inhibitor BI 894999 treatment: backtranslation from the clinic.

BACKGROUND: BET inhibitors have been tested in several clinical trials where, despite encouraging preclinical results, substantial clinical benefit in monotherapy remains limited. This work illustrates the translational challenges and reports new data around the novel BET inhibitor, BI 894999. At clinically achievable concentrations, mechanistic studies were carried out to study pathway modulation and rational drug combinations. METHODS: BRD-NUT fusions are oncogenic drivers in NUT carcinoma (NC). The effects of BI 894999 on proliferation, chromatin binding and pathway modulation were studied in NC in vitro. These studies were complemented by efficacy studies either as a single agent or in combination with the clinical p300/CBP inhibitor CCS1477. RESULTS: Based on the modelling of preclinical and clinical data, we proposed and implemented a new clinical scheduling regimen. This led to plasma levels sufficient to fully dislodge BRD-NUT from chromatin and to sustained and pronounced pharmacodynamic (PD) modulation of HEXIM1 and HIST2H2BF. Platelet counts in patient blood samples were improved compared to previous schedules. Rational combination studies of BI 894999 performed at clinically meaningful concentrations led to tumour regressions in all NC xenograft models tested. CONCLUSIONS: BI 894999 holds significant potential as a combination drug and CCS1477 p300/CBP inhibitor is a promising partner for future clinical trials.

Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method.

In recent years, surface electromyography (sEMG)-based human-computer interaction has been developed to improve the quality of life for people. Gesture recognition based on the instantaneous values of sEMG has the advantages of accurate prediction and low latency. However, the low generalization ability of the hand gesture recognition method limits its application to new subjects and new hand gestures, and brings a heavy training burden. For this reason, based on a convolutional neural network, a transfer learning (TL) strategy for instantaneous gesture recognition is proposed to improve the generalization performance of the target network. CapgMyo and NinaPro DB1 are used to evaluate the validity of our proposed strategy. Compared with the non-transfer learning (non-TL) strategy, our proposed strategy improves the average accuracy of new subject and new gesture recognition by 18.7% and 8.74%, respectively, when up to three repeated gestures are employed. The TL strategy reduces the training time by a factor of three. Experiments verify the transferability of spatial features and the validity of the proposed strategy in improving the recognition accuracy of new subjects and new gestures, and reducing the training burden. The proposed TL strategy provides an effective way of improving the generalization ability of the gesture recognition system.

The brain network in support of social semantic accumulation.

Some studies have indicated that a specific 'social semantic network' represents the social meanings of words. However, studies of the comprehension of complex materials, such as sentences and narratives, have indicated that the same network supports the online accumulation of connected semantic information. In this study, we examined the hypothesis that this network does not simply represent the social meanings of words but also accumulates connected social meanings from texts. We defined the social semantic network by conducting a meta-analysis of previous studies on social semantic processing and then examined the effects of social semantic accumulation using a functional Magnetic Resonance Imaging (fMRI) experiment. Two important findings were obtained. First, the social semantic network showed a stronger social semantic effect in sentence and narrative reading than in word list reading, indicating the amplitude of social semantic activation can be accumulated in the network. Second, the activation of the social semantic network in sentence and narrative reading can be better explained by the holistic social-semantic-richness rating scores of the stimuli than by those of the constitutive words, indicating the social semantic contents can be integrated in the network. These two findings convergently indicate that the social semantic network supports the accumulation of connected social meanings.

Neural Encoding and Decoding With Distributed Sentence Representations.

Building computational models to account for the cortical representation of language plays an important role in understanding the human linguistic system. Recent progress in distributed semantic models (DSMs), especially transformer-based methods, has driven advances in many language understanding tasks, making DSM a promising methodology to probe brain language processing. DSMs have been shown to reliably explain cortical responses to word stimuli. However, characterizing the brain activities for sentence processing is much less exhaustively explored with DSMs, especially the deep neural network-based methods. What is the relationship between cortical sentence representations against DSMs? What linguistic features that a DSM catches better explain its correlation with the brain activities aroused by sentence stimuli? Could distributed sentence representations help to reveal the semantic selectivity of different brain areas? We address these questions through the lens of neural encoding and decoding, fueled by the latest developments in natural language representation learning. We begin by evaluating the ability of a wide range of 12 DSMs to predict and decipher the functional magnetic resonance imaging (fMRI) images from humans reading sentences. Most models deliver high accuracy in the left middle temporal gyrus (LMTG) and left occipital complex (LOC). Notably, encoders trained with transformer-based DSMs consistently outperform other unsupervised structured models and all the unstructured baselines. With probing and ablation tasks, we further find that differences in the performance of the DSMs in modeling brain activities can be at least partially explained by the granularity of their semantic representations. We also illustrate the DSM's selectivity for concept categories and show that the topics are represented by spatially overlapping and distributed cortical patterns. Our results corroborate and extend previous findings in understanding the relation between DSMs and neural activation patterns and contribute to building solid brain-machine interfaces with deep neural network representations.

Discovery and proteomics analysis of effective compounds in Valeriana jatamansi jones for the treatment of anxiety.

ETHNOPHARMACOLOGICAL RELEVANCE: Zhizhu Xiang (ZZX for short) is the root and rhizome of Valeriana jatamansi Jones, which is a Traditional Chinese Medicine (TCM) used to treat various mood disorders for more than 2000 years, especially anxiety. However, there have been few investigations to clarify the compounds in ZZX for the treatment of anxiety. AIM OF THE STUDY: Our previous study has identified five anti-anxiety components, including hesperidin, isochlorogenic acid A, isochlorogenic acid B and isochlorogenic acid C and chlorogenic acid, from extract of ZZX. In order to find the optimal combination and the underlying mechanism of these five components in the treatment of anxiety disorder, researches were designed based on uniform design method and proteomic technology. MATERIALS AND METHODS: The samples with different proportion and content of the five active components were arranged by uniform design method. Then a mathematical model was formulated using partial least square method and stepwise regression analysis. Moreover, the empty bottle stress-induced anxiety rat model was established, and the anti-anxiety effect was recorded by the unconditioned reflex elevated maze test and the open field test. In addition, the isobaric tags for relative and absolute quantitation (iTRAQ) technique, along with the multidimensional liquid chromatography and high-resolution mass spectrometry were applied in proteomic study. At last, the result of proteomic analysis was further confirmed by Western blot. RESULTS: The optimal combination of the components from the extract of ZZX was 1.153 mg/kg hesperidin, 2.197 mg/kg Isochlorogenic acid A, 0.699 mg/kg Isochlorogenic acid B and 1.249 mg/kg Chlorogenic acid. Total 6818 proteins were identified using proteomic analysis and 80 differentially expressed proteins were used for further bioinformatic analysis. These proteins were involved in the neuroactive ligand-receptor interaction, protein digestion and absorption, cholesterol metabolism, Chagas disease, and AGE/RAGE signaling pathway. CONCLUSIONS: The composition and proportion of anti-anxiety components in extract of ZZX was disclosed, and there was an anti-anxiety effect for the combined components of flavonoids and phenolic acids. Through proteomic analysis and Western blot, it was found that the effective components of extract of ZZX can exert synergistic anti-anxiety effects via the regulation of multi-signaling pathways. These findings could provide a preliminary research basis for the development of new low-toxic, efficient, stable and controllable anti-anxiety drugs.

Anxiolytic Effect of Alcohol-Water Extracted Suanzaoren-Wuweizi Herb-Pair by Regulating ECS-BDNF-ERK Signaling Pathway Expression in Acute Restraint Stress Male Rats.

Herb-pairs are the basic units of composition in Chinese herbal formulae, where the bridge linking Chinese medicine and prescription consists of two Chinese medicine herbs. The Suanzaoren-Wuweizi herb-pair (SWHP) is commonly used as a sedative or tranquilizer. SWHP has been demonstrated to exert an antianxiety effect in animal models of anxiety. However, little information about its mechanism is available and the effects of SWHP have not been investigated. This study examined the effects of SWHP on ameliorating anxiety-like behaviors by regulating endocannabinoids system (ECS)-brain-derived neurotrophic factor (BDNF)-extracellular regulated protein kinases (ERK) signaling pathway expression, induced by restraint stress (RS) procedures. The antianxiety effects of SWHP on RS rats were then examined through the open-field test (OF) and the elevated plus maze test (EPM). The concentration of BNDF, ERK1/2, p-ERK1/2, cAMP-response element binding protein (CREB), and p-CREB expression in the prefrontal cortex and hippocampus of the rats was then measured by western blot. The number of positive cells of CB1 and CB2 in the rats' hippocampus CA1 region was measured by immunohistochemistry. These results gave compelling evidence that SWHP could modify anxiety-like behaviors of RS rats through regulation of the ECS-BDNF-ERK signaling pathway. Our study demonstrated that SWHP improved anxiety-like behaviors in RS rat models by regulating the ECS-BDNF-ERK signaling pathway. The findings indicate that SWHP may have a therapeutic application in the RS model of anxiety disorder, which proposes a potential new direction for research into anxiety disorders regarding mechanisms and the development of novel antianxiety drugs.

P-glycoprotein overactivity in epileptogenic developmental lesions measured in vivo using (R)-[11 C]verapamil PET.

OBJECTIVE: Overexpression of the drug transporter P-glycoprotein (P-gp) is thought to be involved in drug-resistance in epilepsy by extrusion of antiepileptic drugs (AEDs). We used positron emission tomography (PET) and the P-gp substrate radiotracer (R)-[11 C]verapamil (VPM) together with the third-generation P-gp inhibitor tariquidar (TQD) to evaluate P-gp function in individuals with drug-resistant epileptogenic developmental lesions. METHODS: Twelve healthy controls (7 male, median age 45, range 35-55 years), and two patients with epileptogenic developmental lesions (2 male, aged 24 and 62 years) underwent VPM-PET scans before and 60 minutes after a 30-minute infusion of 2 and 3 mg/kg TQD. The influx rate constant, VPM-K1 , was estimated from the first 10 minutes of dynamic data using a single-tissue compartment model with a VPM plasma input function. Statistical parametric mapping (SPM) analysis was used to compare individual patients with the healthy controls. RESULTS: At baseline, SPM voxel-based analysis revealed significantly lower uptake of VPM corresponding to the area of the epileptogenic developmental lesion compared to 12 healthy controls (P < .048). This was accentuated following P-gp inhibition with TQD. After TQD, the uptake of VPM was significantly lower in the area of the epileptogenic developmental lesion compared to controls (P < .002). SIGNIFICANCE: This study provides further evidence of P-gp overactivity in patients with drug-resistant epilepsy, irrespective of the type of lesion. Identifying P-gp overactivity as an underlying contributor to drug-resistance in individual patients will enable novel treatment strategies aimed at overcoming or reversing P-gp overactivity.

Dissociating the neural correlates of the sociality and plausibility effects in simple conceptual combination.

Neuroimaging studies have indicated that a brain network distributed in the supramodal cortical regions of the frontal, temporal, and parietal lobes plays a central role in conceptual processing. The activation of this network is modulated by two orthogonal dimensions in conceptual processing-the semantic features of individual concepts and the meaningfulness of conceptual combinations-but it remains unclear how the network is functionally organized along these two dimensions. In this fMRI study, we focused on two specific factors, i.e. the social semantic richness of words and the semantic plausibility of word combinations, along the two dimensions. In literature, the distributions of the effects of the two factors are very similar, but have not been rigorously compared in one study. We orthogonally manipulated the two factors in a phrase comprehension task and found a clear dissociation between their effects. The combination of these results with our previous findings reveals three adjacently distributed subnetworks of the supramodal semantic network, associated with the sociality effect, imageability effect, and semantic plausibility effect, respectively. Further analysis of the resting-state functional connectivity data indicated that the functional dissociation among the three subnetworks is associated with their underlying intrinsic connectivity structures.