Chirality Engineering of Colloidal Copper Oxide Nanostructures for Tailored Spin-Polarized Catalysis.

The combination of the chiral concept and inorganic nanostructures holds great potential for significantly impacting catalytic processes and products. However, the synthesis of inorganic nanomaterials with engineered chiroptical activity and identical structure and size presents a substantial challenge, impeding exploration of the relationship between chirality (optical activity) and catalytic efficiency. Here, we present a facile wet-chemical synthesis for achieving intrinsic and tunable chiroptical activity within colloidal copper oxide nanostructures. These nanostructures exhibit strong spin-polarization selectivity compared with their achiral counterparts. More importantly, the ability to engineer chiroptical activity within the same type of chiral nanostructures allows for the manipulation of spin-dependent catalysis, facilitating a study of the connection between the chiroptical magnitude (asymmetric factor) and catalytic performance in inorganic nanostructures. Specifically, using these materials as model catalysts in a proof-of-concept catalytic reaction, we reveal a linear correlation between the asymmetric factor of chiral nanomaterials and the efficiency of the catalytic reaction. This work paves the way for the development of chiral inorganic nanosystems and their application in catalysis through chiroptical engineering.

Long-chain acyl-CoA synthetase 4-mediated mitochondrial fatty acid metabolism and dendritic cell antigen presentation.

OBJECTIVE: This study aims to investigate the role of Acyl-CoA synthetase 4 (ACSL4) in mediating mitochondrial fatty acid metabolism and dendritic cell (DC) antigen presentation in the immune response associated with asthma. METHODS: RNA sequencing was employed to identify key genes associated with mitochondrial function and fatty acid metabolism in DCs. ELISA was employed to assess the levels of fatty acid metabolism in DCs. Mitochondrial morphology was evaluated using laser confocal microscopy, structured illumination microscopy, and transmission electron microscopy. Flow cytometry and immunofluorescence were utilized to detect changes in mitochondrial superoxide generation in DCs, followed by immunofluorescence co-localization analysis of ACSL4 and the mitochondrial marker protein COXIV. Subsequently, pathological changes and immune responses in mouse lung tissue were observed. ELISA was conducted to measure the levels of fatty acid metabolism in lung tissue DCs. qRT-PCR and western blotting were employed to respectively assess the expression levels of mitochondrial-associated genes (ATP5F1A, VDAC1, COXIV, TFAM, iNOS) and proteins (ATP5F1A, VDAC1, COXIV, TOMM20, iNOS) in lung tissue DCs. Flow cytometry was utilized to analyze changes in the expression of surface antigens presented by DCs in lung tissue, specifically the MHCII molecule and the co-stimulatory molecules CD80/86. RESULTS: The sequencing results reveal that ACSL4 is a crucial gene regulating mitochondrial function and fatty acid metabolism in DCs. Inhibiting ACSL4 reduces the levels of fatty acid oxidases in DCs, increases arachidonic acid levels, and decreases A-CoA synthesis. Simultaneously, ACSL4 inhibition leads to an increase in mitochondrial superoxide production (MitoSOX) in DCs, causing mitochondrial rupture, vacuolization, and sparse mitochondrial cristae. In mice, ACSL4 inhibition exacerbates pulmonary pathological changes and immune responses, reducing the fatty acid metabolism levels within lung tissue DCs and the expression of mitochondria-associated genes and proteins. This inhibition induces an increase in the expression of MHCII antigen presentation molecules and co-stimulatory molecules CD80/86 in DCs. CONCLUSIONS: The research findings indicate that ACSL4-mediated mitochondrial fatty acid metabolism and dendritic cell antigen presentation play a crucial regulatory role in the immune response of asthma. This discovery holds promise for enhancing our understanding of the mechanisms underlying asthma pathogenesis and potentially identifying novel targets for its prevention and treatment.

Multicolor Circularly Polarized Luminescence from Inorganic Crystalline Nanostructures Induced by Atomic Chirality.

Circularly polarized luminescence (CPL) is well-studied in molecular systems but has been rarely reported in pure inorganic nanoscale crystals. Herein, we develop a family of pure inorganic rare-earth nanowires with robust and color-tunable CPL emissions. The chiral rare earth nanowires possess intrinsic atomic chirality with controlled handedness that is guided by the enantiomers with molecular chirality in the synthesis. By varying luminescent ions incorporated in the crystal lattice, color-tunable CPL can be achieved and is thermally robust, preserving emission over 300 °C, distinct from existing CPL-active materials. Moreover, as a proof of concept, we demonstrate that the synthesized nanostructures can be easily dispersed in a polymer matrix to enable transparent and flexible CPL films. This study opens up a promising avenue to design robust and tunable CPL materials helpful to the understanding of inorganic chiral information and capable of further applications in novel optoelectronic devices.

A Chinese verb semantic feature dataset (CVFD).

Language is an advanced cognitive function of humans, and verbs play a crucial role in language. To understand how the human brain represents verbs, it is critical to analyze what knowledge humans have about verbs. Thus, several verb feature datasets have been developed in different languages such as English, Spanish, and German. However, there is still a lack of a dataset of Chinese verbs. In this study, we developed a semantic feature dataset of 1140 Chinese Mandarin verbs (CVFD) with 11 dimensions including verb familiarity, agentive subject, patient, action effector, perceptual modality, instrumentality, emotional valence, action imageability, action complexity, action intensity, and the usage scenario of action. We calculated the semantic features of each verb and the correlation between dimensions. We also compared the difference between action, mental, and other verbs and gave some examples about how to use CVFD to classify verbs according to different dimensions. Finally, we discussed the potential applications of CVFD in the fields of neuroscience, psycholinguistics, cultural differences, and artificial intelligence. All the data can be found at https://osf.io/pv29z/ .

Guest-Regulated Generation of Reactive Oxygen Species from Porphyrin-Based Multicomponent Metallacages for Selective Photocatalysis.

The development of novel materials for highly efficient and selective photocatalysis is crucial for their practical applications. Herein, we employ the host-guest chemistry of porphyrin-based metallacages to regulate the generation of reactive oxygen species and further use them for the selective photocatalytic oxidation of benzyl alcohols. Upon irradiation, the sole metallacage (6) can generate singlet oxygen (1O2) effectively via excited energy transfer, while its complex with C70 (6⊃C70) opens a pathway for electron transfer to promote the formation of superoxide anion (O2⋅-), producing both 1O2 and O2⋅-. The addition of 4,4'-bipyridine (BPY) to complex 6⊃C70 forms a more stable complex (6⊃BPY) via the coordination of the Zn-porphyrin faces of 6 and BPY, which drives fullerenes out of the cavities and restores the ability of 1O2 generation. Therefore, benzyl alcohols are oxidized into benzyl aldehydes upon irradiation in the presence of 6 or 6⊃BPY, while they are oxidized into benzoic acids when 6⊃C70 is employed as the photosensitizing agent. This study demonstrates a highly efficient strategy that utilizes the host-guest chemistry of metallacages to regulate the generation of reactive oxygen species for selective photooxidation reactions, which could promote the utilization of metallacages and their related host-guest complexes for photocatalytic applications.

Direct Arylation of Silicon Nanocrystals with Hexadehydro-Diels-Alder-Derived Benzynes.

Colloidal silicon nanocrystals (SiNCs) have garnered significant interest in optoelectronics and biomedical applications. Direct arylation provides pathways to enhance the solution processability of particles and manipulate the photophysical and electronic properties of SiNCs. Unfortunately, existing methods employed to prepare aryl-functionalized SiNCs are based on organometallic coupling or transition-metal-catalyzed strategies, which require metal-based reagents for preactivation or the precursors and complicated post-treatment processes for product purification. Herein, we demonstrate a metal-free method that directly functionalizes SiNCs with aryl-based ligands. We design a series of benzyne derivatives formed from the thermal cyclization of predesigned alkynes, allowing efficient arylation on hydride-terminated silicon surfaces under mild conditions. These aryl-functionalized SiNCs exhibit strong blue emissions with nanosecond-scaled decay, suggesting the formation of a new radiative recombination channel on SiNC surfaces.

Interfacially Super-Assembled Benzimidazole Derivative-Based Mesoporous Silica Nanoprobe for Sensitive Copper (II) Detection and Biosensing in Living Cells.

Mesoporous silica nanoparticles (MSNs) functionalized with benzimidazole-derived fluorescent molecules (DHBM) are fabricated via a feasible interfacial superassembly strategy for the highly sensitive and selective detection of Cu2+ . DHBM-MSN exhibits an obvious quenching effect on Cu2+ in aqueous solutions, and the detection limit can be as low as 7.69×10-8  M. The DHBM-MSN solid-state sensor has good recyclability, and the silica framework can simultaneously improve the photostability of DHBM. Two mesoporous silica nanoparticles with different morphologies were specially designed to verify that nanocarriers with different morphologies do not affect the specific detectionability. The detection mechanism of the fluorescent probe was systematically elucidated by combining experimental results and density function theory calculations. Moreover, the detection system was successfully applied to detect Cu2+ in bovine serum, juice, and live cells. These results indicate that the DHBM-MSN fluorescent sensor holds great potential in practical and biomedical applications.

Clinical and genetic features of primary ciliary dyskinesia in a cohort of consecutive clinically suspect children in western China.

BACKGROUND: Primary ciliary dyskinesia (PCD) is a rare, inherited disorder of the motile cilia that exhibits genetic and clinical heterogeneity among different populations. PCD diagnosis remains challenging owing to the heterogeneity of associated clinical features and lack of a gold standard diagnostic test. OBJECTIVE: The aim of this study was to analyze the clinical and genetic characteristics of a group of children with clinically suspected PCD in one region of China, with the goal of providing a more robust knowledge base regarding the genetic stratification underlying this disease in Chinese populations. METHODS: We retrospectively analyzed the data from 38 patients with clinically suspected PCD who had undergone next-generation sequencing (NGS) between November 2016 and March 2021 in the respiratory department of a tertiary Children's hospital in Western China. The genetic features of the confirmed cases were summarized by reviewing data associated with other cohorts of Chinese children. RESULTS: Overall, 16 patients were ultimately diagnosed with PCD with a median age of 8.5 years. All patients presented with a chronic wet cough, 93.75% exhibited chronic or recurrent sinusitis/rhinitis, 43.75% experienced recurrent wheezing, 56.25% reported respiratory symptoms present since infancy, 31.25% had a history of neonatal respiratory distress (NRD), and 25% exhibited otitis media. Only 18.75% of these patients exhibited laterality defects. High frequencies of DNAH11 mutations were detected by integrating data from PCD patient cohorts in China. CONCLUSION: The high frequency of DNAH11 mutations may limit the utility of transmission electron microscopy (TEM) as a first-line approach to diagnosing PCD in China in the absence of other indicators.

Electrospinning Superassembled Mesoporous AIEgen-Organosilica Frameworks Featuring Diversified Forms and Superstability for Wearable and Washable Solid-State Fluorescence Smart Sensors.

Flexible optical sensors are widely studied and applied in many fields. However, developing highly stable and washable wearable sensors in optics is still facing significant challenges. Here, we demonstrate an AIEgen-organosilica framework (TPEPMO) hybrid nanostructure-based flexible optical sensor, which is prepared by a two-step co-condensation and electrospinning superassembly process. Organosilica precursors with aggregation-induced emission (AIE) features are covalently linked into periodic mesoporous organosilica (PMO) frameworks with high fluorescent efficiency due to the restriction of intramolecular motion. The three-dimensional space of ordered porous materials provides abundant reaction sites, allowing rapid and sensitive monitoring of analytes. TPEPMOs exhibit good properties as acidic pH fluorescent sensors with a pKa of 4.3. A flexible film is obtained by dispersing TPEPMO nanospheres in a poly(lactic-co-glycolic acid) (PLGA) and polyacrylonitrile (PAN) hybrid fibrous matrix (TPEPMO-CFs) using the electrospinning superassembly technique and is successfully served as an efficient fluorescent probe for the naked eye detection of ammonia gas and HCl vapor by emission changes. The fluorescence of TPEPMO-CFs can be reversed in the presence of volatile acidic/alkaline gas for more than five cycles, exhibiting excellent recyclability. In addition, TPEPMO-CF sensors show excellent washability and long-term photostability (fluorescence was maintained above 94% after washing 10 times). These stimuli-responsive AIEgen-organosilica frameworks featuring diversified forms and superstability for wearable and washable solid-state fluorescence exhibit great potential for smart gas sensors, wearable devices, and solid-state lighting applications.

Effect of statins on the risk of recurrent venous thromboembolism: A systematic review and meta-analysis.

BACKGROUND: Recent studies have suggested that statins may be associated with a lower risk of recurrent venous thromboembolism (VTE). METHODS: We systematically searched PubMed, Web of Science and Cochrane Library from inception until May 2020 to identify any eligible studies that reported the association between statin use and the risk of recurrent VTE, and conducted a comprehensive systematic review and meta-analysis (PROSPERO registration number: CRD42020190169) on this matter. RESULTS: A total of 14 observational studies were included for qualitative review and 12 of them qualified for meta-analyses. The main meta-analysis found that statin use was associated with a lower risk of disease recurrence among patients with VTE (pooled adjusted HR: 0.76, 95% CI: 0.69-0.83), which was robust in sensitivity analyses and free of significant publication bias. Additionally, such association was present when restricting to periods after anticoagulation withdrawal (pooled adjusted HR: 0.78, 95% CI: 0.70-0.88) and when separately analyzing recurrent deep vein thrombosis (pooled adjusted HR: 0.71, 95% CI: 0.62-0.81) and recurrent pulmonary embolism (pooled adjusted HR: 0.80, 95% CI: 0.66-0.97; P = 0.027). Furthermore, statin use in patients with VTE was also found to be associated with a lower risk of all-cause mortality (adjusted HR: 0.65, 95% CI: 0.56-0.77), and possibly an even lower risk of bleeding (adjusted HR: 0.88, 95% CI: 0.73-1.07), albeit not statistically significant. CONCLUSION: Statins have the potential to reduce recurrent events among patient with VTE. Randomized clinical trials to better explore the effect of statins in secondary prevention of VTE are warranted.

A water drop-shaped slingshot in plants: geometry and mechanics in the explosive seed dispersal of Orixa japonica (Rutaceae).

BACKGROUND AND AIMS: In angiosperms, many species disperse their seeds autonomously by rapid movement of the pericarp. The fruits of these species often have long rod- or long plate-shaped pericarps, which are suitable for ejecting seeds during fruit dehiscence by bending or coiling. However, here we show that fruit with a completely different shape can also rely on pericarp movement to disperse seeds explosively, as in Orixa japonica. METHODS: Fruit morphology was observed by hard tissue sectioning, scanning electron microscopy and micro-computed tomography, and the seed dispersal process was analysed using a high-speed camera. Comparisons were made of the geometric characteristics of pericarps before and after fruit dehiscence, and the mechanical process of pericarp movement was simulated with the aid of the finite element model. KEY RESULTS: During fruit dehydration, the water drop-shaped endocarp of O. japonica with sandwich structure produced two-way bending deformation and cracking, and its width increased more than three-fold before opening. Meanwhile the same shaped exocarp with uniform structure could only produce small passive deformation under relatively large external forces. The endocarp forced the exocarp to open by hygroscopic movement before seed launching, and the exocarp provided the acceleration for seed launching through a reaction force. CONCLUSIONS: Two layers of water drop-shaped pericarp in O. japonica form a structure similar to a slingshot, which launches the seed at high speed during fruit dehiscence. The results suggest that plants with explosive seed dispersal appear to have a wide variety of fruit morphology, and through a combination of different external shapes and internal structures, they are able to move rapidly using many sophisticated mechanisms.

Efficient solar photocatalytic hydrogen production using direct Z-scheme heterojunctions.

We report the preparation of a series of heterojunctions made of Ta3N5 and TiO2 nanoparticles that show good properties for photocatalytic hydrogen production. The composite photocatalyst with a light-response range up to 620 nm shows a hydrogen evolution rate of 250 µmol h-1. The apparent quantum efficiency at 330 nm can be as high as 46%. Particularly, normalized spectral studies indicate that the heterojunction is more active upon full-spectrum (without using optical filters) irradiation, and its activity is even superior  to the total activity exhibited upon UV-light irradiation (λ ≤ 420 nm) and visible-infrared light irradiation (λ ≥ 420 nm). Moreover, in situ photodeposition of platinum nanoparticles on the surface of the photocatalyst as well as the band alignment analysis demonstrate the Z-scheme mechanism associated with the photocatalytic process. Specifically, photogenerated electrons from TiO2 will rapidly combine with the photogenerated holes from Ta3N5 through interfacial charge transfer, leaving the more active electrons and holes in Ta3N5 and TiO2, respectively, to facilitate redox reactions. Basically, TiO2 is only UV-light active, while Ta3N5 can be activated under visible-light irradiation. In this case, a synergy effect, upon simultaneous UV-light excitation and visible-light excitation, can be achieved by full-spectrum irradiation, leading to a much higher photocatalytic activity. This work thus provides a favorable and upward direction for the establishment of heterojunctions for high-efficiency hydrogen production and solar energy applications.

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization.

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.

Consistency between nasopharyngeal aspirates and bronchoalveolar lavage fluid in pathogen detection in children with pneumonia: an analysis of 533 cases. / 533ä¾‹è‚ºç‚Žå„¿ç«¥é¼»å’½æŠ½å¸ç‰©å’Œæ”¯æ°”ç®¡è‚ºæ³¡çŒæ´—æ¶²çš„ç— åŽŸæ£€å‡ºä¸€è‡´æ€§åˆ†æž.

OBJECTIVES: To study the consistency between nasopharyngeal aspirates (NPA) and bronchoalveolar lavage fluid (BALF) in pathogen detection in children with pneumonia. METHODS: A retrospective analysis was performed on the data of pathogens detected in 533 children with pneumonia from February 2017 to March 2020. The paired McNemar's test was used to compare the difference in pathogen detection between NPA and BALF groups. The Kappa coefficient was used to analyze the consistency in pathogen detection between the two groups. RESULTS: NPA had a sensitivity of 28%, a specificity of 74%, a positive predictive value of 14%, and a negative predictive value of 91% in detecting bacteria, and a Kappa coefficient of 0.013 suggested poor consistency between NPA and BALF. NPA had a sensitivity of 52%, a specificity of 81%, a positive predictive value of 24%, and a negative predictive value of 94% in detecting viruses, and a Kappa coefficient of 0.213 suggested poor consistency between NPA and BALF. NPA had a sensitivity of 78%, a specificity of 71%, a positive predictive value of 49%, and a negative predictive value of 90% in detecting Mycoplasma pneumoniae, and a Kappa coefficient of 0.407 suggested moderate consistency between NPA and BALF. CONCLUSIONS: There is poor consistency between NPA and BALF in the detection of bacteria and viruses, and clinicians should be cautious in diagnosing lower respiratory tract infection based on bacteria or viruses detected in NPA. There is moderate consistency between NPA and BALF in the detection of Mycoplasma pneumoniae, suggesting that it may be reliable to diagnose lower respiratory tract infection based on Mycoplasma pneumoniae detected in NPA, while comprehensive judgment in combination with clinical conditions is needed.

Controlled Deposition of ZnS Nanoparticles on CuS Nanoplates for Visible-Light-Driven Photocatalytic H2 Production.

With giving property to guide and control charge carries at the nanoscale, fabrication of heterostructure photocatalyst with desirable spatial distribution has been significantly valued. In this study, by using CuS nanoplates as seeds, CuS@ZnS core-shell heterojunction photocatalysts with diverse morphologies were developed via controlled synthesis kinetics. Kinetic control was completed through manipulation of the injection rate of Zn2+ precursor with a syringe pump as well as the reaction temperature. It is found that the growth is determined by the deposition rate relative to the diffusion rate of the ZnS growth monomers. Specifically, at a high injection rate and a relatively low reaction temperature, ZnS monomers on the surface of the CuS nanoplate will be in a localized manner and tend to form island nanoparticles. On the contrary, when surface diffusion is facilitated at a lower injection rate and a higher reaction temperature, the morphology of the ZnS nanocrystals can be switched to flat ZnS layers covering the surface of CuS. In addition, the heterostructures have found with shape-dependent photocatalytic performance toward H2 evolution under visiblelight irradiation. The CuS@ZnS core-shell composites that possess ZnS islands exhibit the highest photocatalytic activity. The corresponding H2 generation rate reaches 6.3 µmol h-1 g-1, which is 37.9 times of that for CuS@ZnS core-shell nanostructure with flat surface. This work thus provides a powerful means for the rational design and synthesis of heterojunctions with spatially controlled distribution of the component.

Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO.

Rolling bearings are a vital and widely used component in modern industry, relating to the production efficiency and remaining life of a device. An effective and robust fault diagnosis method for rolling bearings can reduce the downtime caused by unexpected failures. Thus, a novel fault diagnosis method for rolling bearings by fine-sorted dispersion entropy and mutation sine cosine algorithm and particle swarm optimization (SCA-PSO) optimized support vector machine (SVM) is presented to diagnose a fault of various sizes, locations and motor loads. Vibration signals collected from different types of faults are firstly decomposed by variational mode decomposition (VMD) into sets of intrinsic mode functions (IMFs), where the decomposing mode number K is determined by the central frequency observation method, thus, to weaken the non-stationarity of original signals. Later, the improved fine-sorted dispersion entropy (FSDE) is proposed to enhance the perception for relationship information between neighboring elements and then employed to construct the feature vectors of different fault samples. Afterward, a hybrid optimization strategy combining advantages of mutation operator, sine cosine algorithm and particle swarm optimization (MSCAPSO) is proposed to optimize the SVM model. The optimal SVM model is subsequently applied to realize the pattern recognition for different fault samples. The superiority of the proposed method is assessed through multiple contrastive experiments. Result analysis indicates that the proposed method achieves better precision and stability over some relevant methods, whereupon it is promising in the field of fault diagnosis for rolling bearings.

A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm.

As crucial equipment during industrial manufacture, the health status of rotating machinery affects the production efficiency and device safety. Hence, it is of great significance to diagnose rotating machinery faults, which can contribute to guarantee the running stability and plan for maintenance, thus promoting production efficiency and economic benefits. For this purpose, a hybrid fault diagnosis model with entropy-based feature extraction and SVM optimized by a chaos quantum sine cosine algorithm (CQSCA) is developed in this research. Firstly, the state-of-the-art variational mode decomposition (VMD) is utilized to decompose the vibration signals into sets of components, during which process the preset parameter K is confirmed with the central frequency observation method. Subsequently, the permutation entropy values of all components are computed to constitute the feature vectors corresponding to different kind of signals. Later, the newly developed sine cosine algorithm (SCA) is employed and improved with chaotic initialization by a Duffing system and quantum technique to optimize the support vector machine (SVM) model, with which the fault pattern is recognized. Additionally, the availability of the optimized SVM with CQSCA was revealed in pattern recognition experiments. Finally, the proposed hybrid fault diagnosis approach was employed for engineering applications as well as contrastive analysis. The comparative results show that the proposed method achieved the best training accuracy 99.5% and best testing accuracy 97.89%. Furthermore, it can be concluded from the boxplots of different diagnosis methods that the stability and precision of the proposed method is superior to those of others.

[Changes of Serum Gonadal Hormones Levels During Musk-secreting Period and Estrus of Moschus berezovskii].

OBJECTIVE: To detect concentrations of serum gonadal hormones (testosterone, estradiol and progesterone) in musk-secreting period and estrus of Moschus berezovskii, and to study the association of serum gonadal hormones concentrations and musk-secreting. METHODS: The concentrations of serum gonadal hormones were detected with magnetic particle separation ELISA. RESULTS: During musk-secreting period, concentration changes of three serum gonadal hormones showed clear regularity, three crests occurred synchronously. Before musk-secreting period, testosterone, estradiol and progesterone concentrations were at its lower level, in prime musk-secreting period, they increased rapidly to respective highest peak; at later musk-secreting period, they quickly dropped to close to its previous levels before musk-secreting period. During estrus, serum testosterone concentration increased to lower peaks than that at later musk-secreting period. Estradiol remained at a low level and progesterone level was closed to zero. Serum testosterone concentrations in prime musk-secreting period were 114.4 ~ 190.5 times of estrus. During musk-secreting period, there were significant positive correlation among three serum gonadal hormone levels, a positive correlation between musk yield and serum testosterone levels, and negative correlation of musk yield with serum estradiol and progesterone levels as well as musk deer ages. CONCLUSION: Serum testosterone concentrations in prime musk-secreting period increase to the highest levels,which can provide reference in musk secretion induced by artificial means.

Chiral Plasmonic Hybrid Nanostructures: A Gateway to Advanced Chiroptical Materials.

Chirality introduces a new dimension of functionality to materials, unlocking new possibilities across various fields. When integrated with plasmonic hybrid nanostructures, this attribute synergizes with plasmonic and other functionalities, resulting in unprecedented chiroptical materials that push the boundaries of the system's capabilities. Recent advancements have illuminated the remarkable chiral light-matter interactions within chiral plasmonic hybrid nanomaterials, allowing for the harnessing of their tunable optical activity and hybrid components. These advancements have led to applications in areas such as chiral sensing, catalysis, and spin optics. Despite these promising developments, there remains a need for a comprehensive synthesis of the current state-of-the-art knowledge, as well as a thorough understanding of the construction techniques and practical applications in this field. This review begins with an exploration of the origins of plasmonic chirality and an overview of the latest advancements in the synthesis of chiral plasmonic hybrid nanostructures. Furthermore, representative emerging categories of hybrid nanomaterials are classified and summarized, elucidating their versatile applications. Finally, the review engages with the fundamental challenges associated with chiral plasmonic hybrid nanostructures and offer insights into the future prospects of this advanced field.

Genetic Programming for Document Classification: A Transductive Transfer Learning System.

Document classification is a challenging task to the data being high-dimensional and sparse. Many transfer learning methods have been investigated for improving the classification performance by effectively transferring knowledge from a source domain to a target domain, which is similar to but different from the source domain. However, most of the existing methods cannot handle the case that the training data of the target domain does not have labels. In this study, we propose a transductive transfer learning system, utilizing solutions evolved by genetic programming (GP) on a source domain to automatically pseudolabel the training data in the target domain in order to train classifiers. Different from many other transfer learning techniques, the proposed system pseudolabels target-domain training data to retrains classifiers using all target-domain features. The proposed method is examined on nine transfer learning tasks, and the results show that the proposed transductive GP system has better prediction accuracy on the test data in the target domain than existing transfer learning approaches including subspace alignment-domain adaptation methods, feature-level-domain adaptation methods, and one latest pseudolabeling strategy-based method.