A machine learning-based approach to predict energy layer for each field in spot-scanning proton arc therapy for lung cancer: A feasibility study.

BACKGROUND: Determining the optimal energy layer (EL) for each field, under considering both dose constraints and delivery efficiency, is crucial to promoting the development of proton arc therapy (PAT) technology. PURPOSE: This study aimed to explore the feasibility and potential clinical benefits of utilizing machine learning (ML) technique to automatically select EL for each field in PAT plans of lung cancer. METHODS: Proton Bragg peak position (BPP) was employed to characterize EL. The ground truth BPPs for each field were determined using the modified ELO-SPAT framework. Features in geometric, water-equivalent thicknesses (WET) and beamlet were defined and extracted. By analyzing the relationship between the extracted features and ground truth, a polynomial regression model with L2-norm regularization (Ridge regression) was constructed and trained. The performance of the regression model was reported as an error between the predictions and the ground truth. Besides, the predictions were used to make PAT plans (PAT_PRED). These plans were compared with those using the ground truth BPPs (PAT_TRUTH) and the mid-WET of the target volumes (PAT_MID) in terms of relative biological effectiveness-weighted dose (RWD) distributions. One hundred ten patients with lung cancer, a total of 7920 samples, were enrolled retrospectively, with 5940 cases randomly selected as the training set and the remaining 1980 cases as the testing set. Nine patients (648 samples) were collected additionally to evaluate the regression model in terms of plan quality and robustness. RESULTS: With regard to the prediction errors, the root mean squared errors and mean absolute errors between the ML-predicted and ground truth BPPs for the testing set were 9.165 and 6.572 mm, respectively, indicating differences of approximately two to three ELs. As for plan quality, the PAT_TRUTH and PAT_PRED plans performed similarly in terms of plan robustness, target coverage and organs at risk (OARs) protection, with differences smaller than 0.5 Gy(RBE). This trend was also observed for dose conformity and uniformity. The PAT_MID plans produced the lowest robustness index and lowest doses to OARs, along with the highest heterogeneity index, indicating better protection for OARs, improved plan robustness, but compromised dose homogeneity. Additionally, for relatively small tumor sizes, the PAT_MID plan demonstrated a notably poor dose conformity index. CONCLUSIONS: Within this cohort under investigation, our study demonstrated the feasibility of using ML technique to predict ELs for each field, offering a fast (within 2 s) and memory-efficient reduced way to select ELs for PAT plan.

A new sustainable filtration membrane extraction method based on nanoconfined liquid phase extraction: Determination of organochlorinated pesticides and pyrethroids in tea samples.

BACKGROUND: Pesticides are used in agricultural production for prevent and control crop diseases and pests, but it is easy to cause excessive pesticides residues in agricultural products, polluting the environment and endangering human health. Due to their unmatched and sustainable capabilities, nanoextraction procedures are becoming every day more important in Analytical Chemistry. In particular, nanoconfined liquid phase extraction has shown extraction capabilities toward polar, medium polar, and/or nonpolar substances, which can be easily modulated depending on the nanoconfined solvent used. Furthermore, this "green" technique showed excellent characteristics in terms of recoveries, extraction time (≤1 min), reliability, and versatility. (97) RESULTS: In this work, the advantages of this technique have been coupled with those of filtration membrane extraction, making use of carbon nanofibers (CnFs) growth on carbon microspheres (CµS). This substrate has been deposited on a filter, which combined with gas chromatographic mass spectrometry (GC-MS) analysis successfully employed for the nanoextraction of 30 pesticides (18 organochlorine and 12 pyrethroids) in tea samples. Under the optimized extraction conditions, the linear range with standard solutions was from 1 to 1000 ng mL-1 (R2 ≥ 0.99), the limit of detections in tea samples were in the range 0.56-17.98 µg kg-1. The accuracy of the developed method was evaluated by measuring the extraction recovery of the spiked tea samples, and recoveries between 74.41 % and 115.46 %. (119) SIGNIFICANCE: Considering the versatility of nanoconfined liquid phase extraction and the functionality of the filtration membrane extraction procedure, this new extraction method can be considered a powerful candidate for automatized high-throughput analyses of real samples. (34).

Exogenous Glucose Interferes with Antimicrobial-Mediated ROS Accumulation and Bacterial Death.

Glucose is widely used in the reconstitution of intravenous medications, which often include antimicrobials. How glucose affects antimicrobial activity has not been comprehensively studied. The present work reports that glucose added to bacteria growing in a rich medium suppresses the bactericidal but not the bacteriostatic activity of several antimicrobial classes, thereby revealing a phenomenon called glucose-mediated antimicrobial tolerance. Glucose, at concentrations corresponding to blood-sugar levels of humans, increased survival of Escherichia coli treated with quinolones, aminoglycosides, and cephalosporins with little effect on minimal inhibitory concentration. Glucose suppressed a ROS surge stimulated by ciprofloxacin. Genes involved in phosphorylated fructose metabolism contributed to glucose-mediated tolerance, since a pfkA deficiency, which blocks the formation of fructose-1,6-bisphosphate, eliminated protection by glucose. Disrupting the pentose phosphate pathway or the TCA cycle failed to alter glucose-mediated tolerance, consistent with an upstream involvement of phosphorylated fructose. Exogenous sodium pyruvate or sodium citrate reversed glucose-mediated antimicrobial tolerance. Both metabolites bypass the effects of fructose-1,6-bisphosphate, a compound known to scavenge hydroxyl radical and chelate iron, activities that suppress ROS accumulation. Treatment with these two compounds constitutes a novel way to mitigate the glucose-mediated antimicrobial tolerance that may exist during intravenous antimicrobial therapy, especially for diabetes patients.

Elevated Ozone Reduces the Quality of Tea Leaves but May Improve the Resistance of Tea Plants.

Tropospheric ozone (O3) pollution can affect plant nutritional quality and secondary metabolites by altering plant biochemistry and physiology, which may lead to unpredictable effects on crop quality and resistance to pests and diseases. Here, we investigated the effects of O3 (ambient air, Am; ambient air +80 ppb of O3, EO3) on the quality compounds and chemical defenses of a widely cultivated tea variety in China (Camellia sinensis cv. 'Baiye 1 Hao') using open-top chamber (OTC). We found that elevated O3 increased the ratio of total polyphenols to free amino acids while decreasing the value of the catechin quality index, indicating a reduction in leaf quality for green tea. Specifically, elevated O3 reduced concentrations of amino acids and caffeine but shows no impact on the concentrations of total polyphenols in tea leaves. Within individual catechins, elevated O3 increased the concentrations of ester catechins but not non-ester catechins, resulting in a slight increase in total catechins. Moreover, elevated O3 increased the emission of biogenic volatile organic compounds involved in plant defense against herbivores and parasites, including green leaf volatiles, aromatics, and terpenes. Additionally, concentrations of main chemical defenses, represented as condensed tannins and lignin, in tea leaves also increased in response to elevated O3. In conclusion, our results suggest that elevated ground-level O3 may reduce the quality of tea leaves but could potentially enhance the resistance of tea plants to biotic stresses.

TRPM7 in neurodevelopment and therapeutic prospects for neurodegenerative disease.

Neurodevelopment, a complex and highly regulated process, plays a foundational role in shaping the structure and function of the nervous system. The transient receptor potential melastatin 7 (TRPM7), a divalent cation channel with an α-kinase domain, mediates a wide range of cellular functions, including proliferation, migration, cell adhesion, and survival, all of which are essential processes in neurodevelopment. The global knockout of either TRPM7 or TRPM7-kinase is embryonically lethal, highlighting the crucial role of TRPM7 in development in vivo. Subsequent research further revealed that TRPM7 is indeed involved in various key processes throughout neurodevelopment, from maintaining pluripotency during embryogenesis to regulating gastrulation, neural tube closure, axonal outgrowth, synaptic density, and learning and memory. Moreover, a discrepancy in TRPM7 expression and/or function has been associated with neuropathological conditions, including ischemic stroke, Alzheimer's disease, and Parkinson's disease. Understanding the mechanisms of proper neurodevelopment may provide us with the knowledge required to develop therapeutic interventions that can overcome the challenges of regeneration in CNS injuries and neurodegenerative diseases. Considering that ion channels are the third-largest class targeted for drug development, TRPM7's dual roles in development and degeneration emphasize its therapeutic potential. This review provides a comprehensive overview of the current literature on TRPM7 in various aspects of neurodevelopment. It also discusses the links between neurodevelopment and neurodegeneration, and highlights TRPM7 as a potential therapeutic target for neurodegenerative disorders, with a focus on repair and regeneration.

Using molecular methods to delineate norovirus outbreaks: a systematic review.

Noroviruses are among the major causative agents of human acute gastroenteritis, and the nature of norovirus outbreaks can differ considerably. The number of single-nucleotide polymorphisms (SNPs) between strains is used to assess their relationships. There is currently no universally accepted cutoff value for clustering strains that define an outbreak or linking the individuals involved. This study was conducted to estimate the threshold value of genomic variations among related strains within norovirus outbreaks. We carried out a literature search in the PubMed and Web of Science databases. SNP rates were defined as the number of SNPs/sequence length (bp) × 100%. The Mann-Whitney U-test was used in comparisons of the distribution of SNP rates for different sequence regions, genogroups (GI and GII), transmission routes, and sequencing methods. A total of 25 articles reporting on 108 norovirus outbreaks were included. In 99.1% of the outbreaks, the SNP rates were below 0.50%, and in 89.8%, the SNP rates were under 0.20%. Outbreak strains showed higher SNP rates when the P2 domain was used for sequence analysis (Z = -2.652, p = 0.008) and when an NGS method was used (Z = -3.686, p < 0.001). Outbreaks caused by different norovirus genotypes showed no significant difference in SNP rates. Compared with person-to-person outbreaks, SNP rates were lower in common-source outbreaks, but no significant difference was found when differences in sequencing methods were taken into consideraton. SNP rates under 0.20% and 0.50% could be considered as the rigorous and relaxed threshold, respectively, of strain similarity within a norovirus outbreak. More data are needed to evaluate differences within and between various norovirus outbreaks.

Dynamics of the natural enemy community of Hyphantria cunea (Lepidoptera: Erebidae) in Dandong, China.

This study aims to explore the composition of natural enemy species in the fall webworm, Hyphantria cunea (Drury) population and the dynamics of its natural enemy community in Dandong, Liaoning Province, China, where it was first reported. We collected the natural enemy of eggs, larvae, and pupae of H. cunea on host trees at 12 survey sites from June 2019 to October 2020. The results showed that the community consists of 34 species: 20 predatory species, including 15 spiders and 5 insects, and 14 parasitic species, including 10 parasitic wasps and 4 parasitic flies. The top 3 dominant species based on the importance value index for both parasitic and predatory species were Pediobius pupariae > Chouioia cunea > Cotesia gregalis in the natural enemy community of H. cunea. Analysis of all 3 principal components by principal component analysis showed that Clubionidae sp. 1, Parena cavipennis, or other predators were the main factors affecting the natural enemy community. Analysis of the community structure parameters of the H. cunea natural enemy community in different developmental stages across generations revealed the following: (i) Compared with the degree of complexity of the egg and pupal stages, the larval stage was the highest. (ii) The complexity was determined by means of comprehensive evaluation: first-generation larvae in 2020 > first-generation larvae in 2019 > second-generation larvae in 2020 > second-generation larvae in 2019. These results clarify the dynamics of natural enemy species, coevolution with the host in the invaded habitat of H. cunea and development of biological control technologies.

Highly sensitive analysis of photoregulation and dynamic distribution of phytohormones based on nanoconfined liquid phase nanoextraction.

BACKGROUND: As a vital energy source, light is one of the most significant environmental signals for plants' growth and development. The crosstalk amongst phytohormones regulated by light exhibits quantitative dynamic changes, but methodologies to analyze their distribution during plant growth are still limited. Rapid, highly sensitive, low-invasive detection and simultaneous assessment of the levels of multiple classes of phytohormones have important phytology applications, however the existing sample pretreatment strategies remain intricate, laborious, and far from being developed for in vivo high-sensitivity testing. (81) RESULTS: We applied a nanoconfined liquid phase nanoextraction (NLPNE) technique based on acidified carbon nanofibers (ACNFs) in combination with LC-ESI-MS/MS for highly sensitive analysis of acidic phytohormones' photoregulation and dynamic distribution. In this system, the mass transfer ability of analytes entering the nanoconfined space is significantly improved given the nanoconfined effect. In particular, the accelerated and strong adsorption of alkaline compounds to the ACNFs surface provide minimum interference for acidic compounds (photosensitive phytohormones), which facilitates their simple, fast, and selective quantification with improved sensitivity. The ACNFs-NLPNE strategy achieved quantitative enrichment of multi-class phytohormones in less than 5 min, and detection limits down to 0.49 fg mL-1. Moreover, we monitored the phytohormone changes under red and blue monochromatic light with relative standard deviations <13.4 %. The results further indicated that short-time red light regulation promoted Lepidium sativum L. growth while blue light inhibited it. (141) SIGNIFICANCE: A nanoconfinement effect-based sample pretreatment platform was developed for monitoring photoregulation phytohormones dynamic distribution with higher sensitivity and stability. Our findings highlighted the importance of the NLPNE approach in providing an accurate plant crosstalk information at the molecular level, which opens a promising avenue for investigating internal hormonal responses to external stimuli. (52).

NhaA facilitates the maintenance of bacterial envelope integrity and the evasion of complement attack contributing to extraintestinal pathogenic Escherichia coli virulence.

Extraintestinal pathogenic Escherichia coli (ExPEC) is responsible for severe bloodstream infections in humans and animals. However, the mechanisms underlying ExPEC's serum resistance remain incompletely understood. Through the transposon-directed insertion-site sequencing approach, our previous study identified nhaA, the gene encoding a Na+/H+ antiporter, as a crucial factor for infection in vivo. In this study, we investigated the role of NhaA in ExPEC virulence utilizing both in vitro models and systemic infection models involving avian and mammalian animals. Genetic mutagenesis analysis revealed that nhaA deletion resulted in filamentous bacterial morphology and rendered the bacteria more susceptible to sodium dodecyl sulfate, suggesting the role of nhaA in maintaining cell envelope integrity. The nhaA mutant also displayed heightened sensitivity to complement-mediated killing compared to the wild-type strain, attributed to augmented deposition of complement components (C3b and C9). Remarkably, NhaA played a more crucial role in virulence compared to several well-known factors, including Iss, Prc, NlpI, and OmpA. Our findings revealed that NhaA significantly enhanced virulence across diverse human ExPEC prototype strains within B2 phylogroups, suggesting widespread involvement in virulence. Given its pivotal role, NhaA could serve as a potential drug target for tackling ExPEC infections.

Deep learning-based fast denoising of Monte Carlo dose calculation in carbon ion radiotherapy.

BACKGROUND: Plan verification is one of the important steps of quality assurance (QA) in carbon ion radiotherapy. Conventional methods of plan verification are based on phantom measurement, which is labor-intensive and time-consuming. Although the plan verification method based on Monte Carlo (MC) simulation provides a more accurate modeling of the physics, it is also time-consuming when simulating with a large number of particles. Therefore, how to ensure the accuracy of simulation results while reducing simulation time is the current difficulty and focus. PURPOSE: The purpose of this work was to evaluate the feasibility of using deep learning-based MC denoising method to accelerate carbon-ion radiotherapy plan verification. METHODS: Three models, including CycleGAN, 3DUNet and GhostUNet with Ghost module, were used to denoise the 1 × 106 carbon ions-based MC dose distribution to the accuracy of 1 × 108 carbon ions-based dose distribution. The CycleGAN's generator, 3DUNet and GhostUNet were all derived from the 3DUNet network. A total of 59 cases including 29 patients with head-and-neck cancers and 30 patients with lung cancers were collected, and 48 cases were randomly selected as the training set of the CycleGAN network and six cases as the test set. For the 3DUNet and GhostUNet models, the numbers of training set, validation set, and test set were 47, 6, and 6, respectively. Finally, the three models were evaluated qualitatively and quantitatively using RMSE and three-dimensional gamma analysis (3 mm, 3%). RESULTS: The three end-to-end trained models could be used for denoising the 1 × 106 carbon ions-based dose distribution, and their generalization was proved. The GhostUNet obtained the lowest RMSE value of 0.075, indicating the smallest difference between its denoised and 1 × 108 carbon ions-based dose distributions. The average gamma passing rate (GPR) between the GhostUNet denoising-based versus 1 × 108 carbon ions-based dose distributions was 99.1%, higher than that of the CycleGAN at 94.3% and the 3DUNet at 96.2%. Among the three models, the GhostUNet model had the fewest parameters (4.27 million) and the shortest training time (99 s per epoch) but achieved the best denoising results. CONCLUSION: The end-to-end deep network GhostUNet outperforms the CycleGAN, 3DUNet models in denoising MC dose distributions for carbon ion radiotherapy. The network requires less than 5 s to denoise a sample of MC simulation with few particles to obtain a qualitative and quantitative result comparable to the dose distribution simulated by MC with relatively large number particles, offering a significant reduction in computation time.

Sound processing in the cricket brain: evidence for a pulse duration filter.

The auditory system of female crickets allows them to specifically recognize and approach the species-specific male calling song, defined by sound pulses and silent intervals. Auditory brain neurons form a delay-line and coincidence detector network tuned to the pulse period of the male song. We analyzed the impact of changes in pulse duration on the behavior and the responses of the auditory neurons and the network. We confirm that the ascending neuron AN1 and the local neuron LN2 copy the temporal structure of the song. During ongoing long sound pulses, the delay-line neuron LN5 shows additional rebound responses and the coincidence detector neuron LN3 can generate additional bursts of activity, indicating that these may be driven by intrinsic oscillations of the network. Moreover, the response of the feature detector neuron LN4 is shaped by a combination of inhibitory and excitatory synaptic inputs, and LN4 responds even to long sound pulses with a short depolarization and burst of spikes, like to a sound pulse of natural duration. This response property of LN4 indicates a selective auditory pulse duration filter mechanism of the pattern recognition network, which is tuned to the duration of natural pulses. Comparing the tuning of the phonotactic behavior with the tuning of the local auditory brain neurons to the same test patterns, we find no evidence that a modulation of the phonotactic behavior is reflected at the level of the feature detector neurons. This rather suggests that steering to nonattractive pulse patterns is organized at the thoracic level.NEW & NOTEWORTHY Pulse period selectivity has been reported for the cricket delay-line and coincidence detector network, whereas pulse duration selectivity is evident from behavioral tests. Pulses of increasing duration elicit responses in the pattern recognition neurons, which do not parallel the behavioral responses and indicate additional processing mechanisms. Long sound pulses elicit rhythmic rebound activity and additional bursts, whereas the feature detector neuron reveals a pulse duration filter, expanding our understanding of the pattern recognition process.

A pilot study on improvements in attention function in major depressive disorder after 12 weeks of escitalopram monotherapy or combined treatment with agomelatine.

Objective: This study aimed to explore both impairments in attention function in patients with major depressive disorder (MDD) and the efficacy of escitalopram monotherapy or combination therapy with agomelatine. Methods: A total of 54 patients with MDD and 46 healthy controls (HCs) were included. Patients were treated with escitalopram for 12 weeks; those who presented with severe sleep impairments were also given agomelatine. Participants were evaluated using the Attention Network Test (ANT), which included tests of alerting, orienting, and executive control networks. Concentration, instantaneous memory, and resistance to information interference were tested using the digit span test, and the logical memory test (LMT) was used to evaluate abstract logical thinking. The Hamilton Depression Rating Scale-17 items, Hamilton Anxiety Rating Scale, and Pittsburgh Sleep Quality Index were used to assess depression, anxiety, and sleep quality, respectively. Patients with MDD were assessed at the end of weeks 0, 4, 8, and 12. HCs were assessed once at baseline. Results: Compared with HCs, patients with MDD showed significantly different alerting, orienting, and executive control functions of attention networks. Treatment with escitalopram alone or combined with agomelatine significantly improved LMT scores at the end of weeks 4, 8, and 12 and restored scores to the level of HCs at the end of week 8. Total Toronto Hospital Test of Alertness scores in patients with MDD increased significantly after 4 weeks of treatment. The ANT executive control reaction time in patients with MDD decreased significantly after 4 weeks of treatment, with this decrease lasting until the end of week 12, but scores did not return to the levels of HCs. Combined treatment with escitalopram and agomelatine led to more improvement in ANT orienting reaction time and was accompanied by a greater reduction of total scores on the Hamilton Depression Rating Scale-17 items and Hamilton Anxiety Rating Scale compared with escitalopram monotherapy. Conclusions: Patients with MDD showed overall impairments in three domains of attention networks as well as the LMT and a test of subjective alertness. Escitalopram monotherapy significantly improved the LMT scores and the executive control function scores in the ANT at the end of the fourth week of treatment, and the improvement was more extensive with combined escitalopram and agomelatine treatment.

Effects of Drought Stress on Non-Structural Carbohydrates in Different Organs of Cunninghamia lanceolata.

The Chinese fir Cunninghamia lanceolata (Lamb.) Hook. is an important timber conifer species in China. Much has been studied about Chinese fir, but the distribution of non-structural carbohydrates (NSCs) among different organs (needles, branch, stem, and roots) under drought stress remains poorly understood. In this study, we used one-year-old C. lanceolata plantlets to evaluate the effects of simulated drought under four water regimes, i.e., adequate water or control, light drought, moderate drought, and severe drought stress corresponding to 80%, 60%, 50%, and 40%, respectively of soil field maximum capacity on various NSCs in the needles, branch, stem and roots. The degree and duration of drought stress had significant effects on fructose, glucose, sucrose, soluble sugar, starch, and NSC content in various organs (p < 0.05). Fructose content increased in stem xylem, stem phloem, and leaves. Glucose and sucrose content declined in stem and branch xylem under light drought stress and moderate drought stress, and increased under severe drought stress conditions. Soluble sugars content declined, and starch content increased in leaf and branch phloem, but the latter could not compensate for soluble sugar consumption in the whole plant, and therefore, total NSCs decreased. Correlation analysis showed that a significant positive correlation existed in the soluble sugar content between leaves and roots, and between xylem and phloem in the stems and branches. Chinese fir appears to have different NSCs distribution strategies in response to drought stress, viz., allocating more soluble sugars to fine roots and increasing starch content in the needles, as well as ensuring osmosis to prevent xylem embolism. Our study may broaden the understanding of the various mechanisms that Chinese fir and other plants have to enhance their tolerance to drought stress.

Responses of Trollius chinensis to drought stress and rehydration: From photosynthetic physiology to gene expression.

Drought stress occurs more frequently in recent years due to the global climate change. Widely distributed in northern China, Mongolia, and Russia, Trollius chinensis Bunge has high medicinal and ornamental values and is often exposed to drought stress, while the mechanism underlying its drought response is still unclear. In this study, we applied 74-76% (control, CK), 49-51% (mild drought), 34-36% (moderate drought), and 19-21% (severe drought, SD) of the soil gravimetric water content to T. chinensis, and measured leaf physiological characteristics on the 0, 5th, 10th, 15th day after the soil reaching the set drought severities, and on the 10th day after rehydration. The results showed that many physiological parameters, such as chlorophyll contents, Fv/Fm, ΦPSⅡ, Pn, and gs decreased with the deepening of severity and duration of drought stress and recovered to some extent after rehydration. On the 10th day of drought stress, leaves in SD and CK were selected for RNA-Seq, and 1649 differentially expressed genes (DEGs) were found, including 548 up-regulated and 1101 down-regulated DEGs. Gene Ontology enrichment found that the DEGs were mainly enriched in catalytic activity and thylakoid. Koyto Encyclopedia of Genes and Genomes enrichment found that DEGs were enriched in some metabolic pathways such as carbon fixation and photosynthesis. Among them, the differential expression of genes related to photosynthesis process, ABA biosynthesis and signaling pathway, such as NCED, SnRK2, PsaD, PsbQ, and PetE, might explain why T. chinensis could tolerate and recover from as long as 15 days of severe drought conditions.

Hemodynamic alterations response to Chinese acupuncture therapy monitored by a custom near-infrared spectroscopy probe with an open hole.

Traditional acupuncture, a popular traditional Chinese medicine (TCM) technique, has demonstrated potential in relieving various ailments' symptoms. However, its black-box feedback loop model has limited proper evaluation and use by unskilled practitioners. This study aimed to analyze hemodynamic signals around acupoints during acupuncture to identify significant parameter changes. We designed hollow near-infrared spectroscopy (NIRS) probes for real-time measurements during acupuncture at acupoints, monitoring oxy-hemoglobin (HbO), deoxy-hemoglobin (Hb), and blood volume (HbT) changes. Acupuncture was performed on the "Xuehai" acupoint in 17 healthy subjects, with NIRS measuring hemodynamic alterations. Results showed significant and consistent increases in Hb and HbT around the acupoint, returning to baseline values after needle removal. One case of fainting revealed a significant Hb increase and HbO decrease. Acupuncture may induce tissue vasodilation and enhance oxygen consumption. This research provides a potential explanation for acupuncture's mechanism and emphasizes NIRS's potential in TCM.

Serotonin/GABA receptors modulate odor input to olfactory receptor neuron in locusts.

Introduction: Serotonin (5-hydroxytryptamine; 5-HT) and GABA (γ-aminobutyric acid) are involved in the regulation of behaviors in the central nervous system. However, it remains unclear whether they modulate olfaction in the peripheral nervous system, and how they modulate olfaction. Methods and results: One 5-HT receptor sequence (Lmig5-HT2) and one GABA receptor sequence (LmigGABAb) were identified in locust antennae by transcriptome analysis and polymerase chain reaction experiments. In situ hybridization localized Lmig5-HT2 to accessory cells, while LmigGABAb was localized to olfactory receptor neurons (ORNs) in locust chemosensilla. Single-unit electrophysiological recordings combined with RNA interference (RNAi) experiments indicated ORNs of locusts with knockdown of Lmig5-HT2 (ds-Lmig5-HT2) and LmigGABAb (ds-LmigGABAb) to some odors had significantly higher responses than wild-type and control locusts in the dose-dependent responses. Moreover, the gaps between the responses of ORNs of RNAi ones and those of wild-type and ds-GFP enlarged with an increase in concentrations of odors. Discussion: Taken together, our findings suggest that 5-HT, GABA, and their receptors exist in the insect peripheral nervous system and that they may function as negative feedback to ORNs and contribute to a fine-tuning mechanism for olfaction in the peripheral nervous system.

Paraventricular Thalamus Dynamically Modulates Aversive Memory via Tuning Prefrontal Inhibitory Circuitry.

The impact of stress on the formation and expression of memory is well studied, especially on the contributions of stress hormones. But how stress affects brain circuitry dynamically to modulate memory is far less understood. Here, we used male C57BL6/J mice in an auditory fear conditioning as a model system to examine this question and focused on the impact of stress on dorsomedial prefrontal cortex (dmPFC) neurons which play an important role in probabilistic fear memory. We found that paraventricular thalamus (PVT) neurons are robustly activated by acute restraining stress. Elevated PVT activity during probabilistic fear memory expression increases spiking in the dmPFC somatostatin neurons which in turn suppresses spiking of dmPFC parvalbumin (PV) neurons, and reverts the usual low fear responses associated with probabilistic fear memory to high fear. This dynamic and reversible modulation allows the original memory to be preserved and modulated during memory expression. In contrast, elevated PVT activity during fear conditioning impairs synaptic modifications in the dmPFC PV-neurons and abolishes the formation of probabilistic fear memory. Thus, PVT functions as a stress sensor to modulate the formation and expression of aversive memory by tuning inhibitory functions in the prefrontal circuitry.SIGNIFICANCE STATEMENT The impact of stress on cognitive functions, such as memory and executive functions, are well documented especially on the impact by stress hormone. However, the contributions of brain circuitry are far less understood. Here, we show that a circuitry-based mechanism can dynamically modulate memory formation and expression, namely, higher stress-induced activity in paraventricular thalamus (PVT) impairs the formation and expression of probabilistic fear memory by elevating the activity of somatostatin-neurons to suppress spiking in dorsomedial prefrontal parvalbumin (PV) neurons. This stress impact on memory via dynamic tuning of prefrontal inhibition preserves the formed memory but enables a dynamic expression of memory. These findings have implications for better stress coping strategies as well as treatment options including better drug targets/mechanisms.

Fibrillin microfibril structure identifies long-range effects of inherited pathogenic mutations affecting a key regulatory latent TGFß-binding site.

Genetic mutations in fibrillin microfibrils cause serious inherited diseases, such as Marfan syndrome and Weill-Marchesani syndrome (WMS). These diseases typically show major dysregulation of tissue development and growth, particularly in skeletal long bones, but links between the mutations and the diseases are unknown. Here we describe a detailed structural analysis of native fibrillin microfibrils from mammalian tissue by cryogenic electron microscopy. The major bead region showed pseudo eightfold symmetry where the amino and carboxy termini reside. On the basis of this structure, we show that a WMS deletion mutation leads to the induction of a structural rearrangement that blocks interaction with latent TGFß-binding protein-1 at a remote site. Separate deletion of this binding site resulted in the assembly of shorter fibrillin microfibrils with structural alterations. The integrin αvß3-binding site was also mapped onto the microfibril structure. These results establish that in complex extracellular assemblies, such as fibrillin microfibrils, mutations may have long-range structural consequences leading to the disruption of growth factor signaling and the development of disease.

Deep learning-assisted automated sewage pipe defect detection for urban water environment management.

A healthy sewage pipe system plays a significant role in urban water management by collecting and transporting wastewater and stormwater, which can be assessed by hydraulic model. However, sewage pipe defects have been observed frequently in recent years during regular pipe maintenance according to the captured interior videos of underground pipes by closed-circuit television (CCTV) robots. In this case, hydraulic model constructed based on a healthy pipe would produce large deviations with that in real hydraulic performance and even be out of work, which can result in unanticipated damages such as blockage collapse or stormwater overflows. Quick defect evaluation and defect quantification are the precondition to achieve risk assessment and model calibration of urban water management, but currently pipe defects assessment still largely relies on technicians to check the CCTV videos/images. An automated sewage pipe defect detection system is necessary to timely determine pipe issues and then rehabilitate or renew sewage pipes, while the rapid development of deep learning especially in recent five years provides a fantastic opportunity to construct automated pipe defect detection system by image recognition. Given the initial success of deep learning application in CCTV interpretation, the review (i) integrated the methodological framework of automated sewage pipe defect detection, including data acquisition, image pre-processing, feature extraction, model construction and evaluation metrics, (ii) discussed the state-of-the-art performance of deep learning in pipe defects classification, location, and severity rating evaluation (e.g., up to ~96 % of accuracy and 140 FPS of processing speed), and (iii) proposed risk assessment and model calibration in urban water management by considering pipe defects. This review introduces a novel practical application-oriented methodology including defect data acquisition by CCTV, model construction by deep learning, and model application, provides references for further improving accuracy and generalization ability of urban water management models in practical application.