Encapsulation and Evolution of Polyynes Inside Single-Walled Carbon Nanotubes.

Polyyne is an sp-hybridized linear carbon chain (LCC) with alternating single and triple carbon-carbon bonds. Polyyne is very reactive; thus, its structure can be easily damaged through a cross-linking reaction between the molecules. The longer the polyyne is, the more unstable it becomes. Therefore, it is difficult to directly synthesize long polyynes in a solvent. The encapsulation of polyynes inside carbon nanotubes not only stabilizes the molecules to avoid cross-linking reactions, but also allows a restriction reaction to occur solely at the ends of the polyynes, resulting in long LCCs. Here, by controlling the diameter of single-walled carbon nanotubes (SWCNTs), polyynes were filled with high yield below room temperature. Subsequent annealing of the filled samples promoted the reaction between the polyynes, leading to the formation of long LCCs. More importantly, single chiral (6,5) SWCNTs with high purity were used for the successful encapsulation of polyynes for the first time, and LCCs were synthesized by coalescing the polyynes in the (6,5) SWCNTs. This method holds promise for further exploration of the synthesis of property-tailored LCCs through encapsulation inside different chiral SWCNTs.

B cell receptor repertoire analysis in primary Sjogren's syndrome salivary glands identifies repertoire features associated with clinical activity.

BACKGROUND: Primary Sjogren's syndrome (pSS) is a complex autoimmune disease featuring damage to salivary and lacrimal glands, with the possibility of manifestations across multiple organs. Antibody-producing B cells have long been appreciated to play a significant role in pSS pathogenesis, with a number of autoreactive antibody species having been identified to be elevated in pSS patients. While several studies have attempted to characterize the BCR repertoires of peripheral blood B cells in pSS patients, much remains unknown about the repertoire characteristics of gland-infiltrating B cells. METHODS: Through paired scRNAseq and scBCRseq, we profiled the BCR repertoires of both infiltrating and circulating B cells in a small cohort of patients. We further utilize receptor reconstruction analyses to further investigate repertoire characteristics in a wider cohort of pSS patients previously profiled through RNAseq. RESULTS: Via integrated BCR and transcriptome analysis of B cell clones, we generate a trajectory progression pattern for infiltrated memory B cells in pSS. We observe significant differences in BCR repertoires between the peripheral blood and labial gland B cells of pSS patients in terms of relative expansion, isotype usage, and BCR clustering. We further observe significant decreases in IgA2 isotype usage among pSS patient labial and parotid gland B cells these analyses relative to controls as well as a positive correlation between kappa/lambda light chain usage and clinical disease activity. CONCLUSIONS: Through BCR repertoire analysis of pSS patient salivary glands, we identify a number of novel repertoire characteristics that may serve as useful indicators of clinical disease and disease activity. By collecting these BCR repertoires into an accessible database, we hope to also enable comparative analysis of patient repertoires in pSS and potentially other autoimmune disorders.

Community assembly and microbial interactions in an alkaline vanadium tailing pond.

Intensive development of vanadium-titanium mines leads to an increasing discharge of vanadium (V) into the environment, imposing potential risks to both environmental system and public health. Microorganisms play a key role in the biogeochemical cycling of V, influencing its transformation and distribution. In addition, the characterization of microbial community patterns serves to assess potential threats imposed by elevated V exposure. However, the impact of V on microbial community remains largely unknown in alkaline V tailing areas with a substantial amounts of V accumulation and nutrient-poor conditions. This study aims to explore the characteristics of microbial community in a wet tailing pond nearby a large-scale V mine. The results reveal V contamination in both water (0.60 mg/L) and sediment tailings (340 mg/kg) in the tailing pond. Microbial community diversity shows distinctive pattern between environmental metrices. Genera with the functional potential of metal reduction\resistance, nitrogen metabolism, and carbon fixation have been identified. In this alkaline V tailing pond, V and pH are major drivers to induce community variation, particularly for functional bacteria. Stochastic processes primarily govern the assemblies of microbial community in the water samples, while deterministic process regulate the community assemblies of sediment tailings. Moreover, the co-occurrence network pattern unveils strong selective pattern for sediment tailings communities, where genera form a complex network structure exhibiting strong competition for limited resource. These findings reveal the patterns of microbial adaptions in wet vanadium tailing ponds, providing insightful guidelines to mitigate the negative impact of V tailing and develop sustainable management for mine-waste reservoir.

The Rapid Identification and Evaluation of the Resonant Noise of a Cooling Module Based on the Frequency Difference Sensitivity Method.

The energy amplification factor transmitted from the excitation source to the response end cannot be identified quickly and accurately using the method of obtaining modal frequency combined with damping through modal frequency resonance. As a result, the above method cannot be used to further evaluate the effect of structural improvement. In this paper, a frequency difference sensitivity method is proposed in order to improve the efficiency of the above identification and evaluation processes while also guaranteeing accuracy. Firstly, a theoretical model suitable for the damping and rigid-body-mode frequency range of the cooling module is established according to the frequency difference between the excitation frequency, .system mode frequency, and vibration response sensitivity. Then, the accuracy and effect of the model are studied from the perspective of a simulation and experiment. The model can be used to identify and evaluate the resonance problem of the vehicle cooling module quickly and accurately. The energy amplification factor of the fan to the passive end of the cooling module can be efficiently predicted using the frequency difference sensitivity method proposed in this paper while ensuring accuracy. The quantized noise-reduction effect of the interior before and after the improvement can be efficiently predicted and evaluated using said model. The literature shows that the resonance of a vehicle cooling module can be identified and evaluated quickly and accurately via the frequency difference sensitivity method, which can overcome the problems that the conventional modal frequency resonance method encounters in identifying and evaluating quickly.

Quasi-Static Modelling of a Full-Channel Effective Magnetorheological Damper with Trapezoidal Magnetic Rings.

Magnetorheological damper (MRD) has been successfully applied to vehicle suspension systems as an intelligent core component. Most conventional MRDs have closed rectangle-shaped magnetic circuits, resulting in a short effective working length and negligible damping force. To address the above issues, a novel full-channel effective MRD with trapezoidal magnetic rings (FEMRD_TMR) is proposed. The trapezoidal magnetic ring can shunt the magnetic circuit, distributing it evenly along the damping channel and increasing the effective working length. Additionally, which has the same variation trend as the magnetic flux through it, makes the magnetic induction intensity distribution more uniform to reduce the magnetic saturation problem. Theoretically analyzing the damping characteristics of the FEMRD_TMR, a quasi-static model is developed to forecast the output damping force. The structural design of MRD is challenging since conventional quasi-static models rely on the yield stress of magnetorheological fluid (MRF) to reflect the rheological property, which cannot be directly observed and is challenging to calculate. The Takagi-Sugeno (T-S) fuzzy neural network and a unique magnetic circuit computation are offered as a novel quasi-static modeling approach to address the issue. The MRF's yield stress is linearized into magnetic induction intensity functions by the T-S fuzzy neural network and then converted into the MRD's structural size by the special magnetic circuit calculation. Therefore, the proposed quasi-static model can directly reflect the relationship between the damping force and structure size, simplifying MRD's structure design. The novel quasi-static model is shown to be more straightforward and understandable than the conventional Bingham quasi-static model and to have approximately accurate damping force prediction when compared to experimental data.

Screening single-cell trajectories via continuity assessments for cell transition potential.

Advances in single-cell sequencing and data analysis have made it possible to infer biological trajectories spanning heterogeneous cell populations based on transcriptome variation. These trajectories yield a wealth of novel insights into dynamic processes such as development and differentiation. However, trajectory analysis relies on an assumption of trajectory continuity, and experimental limitations preclude some real-world scenarios from meeting this condition. The current lack of assessment metrics makes it difficult to ascertain if/when a given trajectory deviates from continuity, and what impact such a divergence would have on inference accuracy is unclear. By analyzing simulated breaks introduced into in silico and real single-cell data, we found that discontinuity caused precipitous drops in the accuracy of trajectory inference. We then generate a simple scoring algorithm for assessing trajectory continuity, and found that continuity assessments in real-world cases of intestinal stem cell development and CD8 + T cells differentiation efficiently identifies trajectories consistent with empirical knowledge. This assessment approach can also be used in cases where a priori knowledge is lacking to screen a pool of inferred lineages for their adherence to presumed continuity, and serve as a means for weighing higher likelihood trajectories for validation via empirical studies, as exemplified by our case studies in psoriatic arthritis and acute kidney injury. This tool is freely available through github at qingshanni/scEGRET.

Suit the Remedy to the Case-The Effectiveness of COVID-19 Nonpharmaceutical Prevention and Control Policies Based on Individual Going-Out Behavior.

Nonpharmaceutical policies for epidemic prevention and control have been extensively used since the outbreak of COVID-19. Policies ultimately work by limiting individual behavior. The aim of this paper is to evaluate the effectiveness of policies by combining macro nonpharmaceutical policies with micro-individual going-out behavior. For different going out scenarios triggered by individual physiological safety needs, friendship needs, and family needs, this paper categorizes policies with significant differences in intensity, parameterizes the key contents of the policies, and simulates and analyzes the effectiveness of the policies in different going-out scenarios with simulation methods. The empirical results show that enhancing policy intensity can effectively improve policy effectiveness. Among different types of policies, restricting the times of going out is more effective. Further, the effect of controlling going out based on physiological safety needs is better than other needs. We also evaluate the policy effectiveness of 26 global countries or regions. The results show that the policy effectiveness varies among 26 countries or regions. The quantifiable reference provided by this study facilitates decision makers to establish policy and practices for epidemic prevention and control.

A Social Media Infodemic-Based Prediction Model for the Number of Severe and Critical COVID-19 Patients in the Lockdown Area.

Accurately predicting the number of severe and critical COVID-19 patients is critical for the treatment and control of the epidemic. Social media data have gained great popularity and widespread application in various research domains. The viral-related infodemic outbreaks have occurred alongside the COVID-19 outbreak. This paper aims to discover trustworthy sources of social media data to improve the prediction performance of severe and critical COVID-19 patients. The innovation of this paper lies in three aspects. First, it builds an improved prediction model based on machine learning. This model helps predict the number of severe and critical COVID-19 patients on a specific urban or regional scale. The effectiveness of the prediction model, shown as accuracy and satisfactory robustness, is verified by a case study of the lockdown in Hubei Province. Second, it finds the transition path of the impact of social media data for predicting the number of severe and critical COVID-19 patients. Third, this paper provides a promising and powerful model for COVID-19 prevention and control. The prediction model can help medical organizations to realize a prediction of COVID-19 severe and critical patients in multi-stage with lead time in specific areas. This model can guide the Centers for Disease Control and Prevention and other clinic institutions to expand the monitoring channels and research methods concerning COVID-19 by using web-based social media data. The model can also facilitate optimal scheduling of medical resources as well as prevention and control policy formulation.

DNA-guided lattice remodeling of carbon nanotubes.

Covalent modification of carbon nanotubes is a promising strategy for engineering their electronic structures. However, keeping modification sites in registration with a nanotube lattice is challenging. We report a solution using DNA-directed, guanine (G)-specific cross-linking chemistry. Through DNA screening we identify a sequence, C3GC7GC3, whose reaction with an (8,3) enantiomer yields minimum disorder-induced Raman mode intensities and photoluminescence Stokes shift, suggesting ordered defect array formation. Single-particle cryo-electron microscopy shows that the C3GC7GC3 functionalized (8,3) has an ordered helical structure with a 6.5 angstroms periodicity. Reaction mechanism analysis suggests that the helical periodicity arises from an array of G-modified carbon-carbon bonds separated by a fixed distance along an armchair helical line. Our findings may be used to remodel nanotube lattices for novel electronic properties.

Arbuscular Mycorrhizal Fungi and Glomalin Play a Crucial Role in Soil Aggregate Stability in Pb-Contaminated Soil.

With the rapid development of industrialization and urbanization, soil contamination with heavy metal (HM) has gradually become a global environmental problem. Lead (Pb) is one of the most abundant toxic metals in soil and high concentrations of Pb can inhibit plant growth, harm human health, and damage soil properties, including quality and stability. Arbuscular mycorrhizal fungi (AMF) are a type of obligate symbiotic soil microorganism forming symbiotic associations with most terrestrial plants, which play an essential role in the remediation of HM-polluted soils. In this study, we investigated the effects of AMF on the stability of soil aggregates under Pb stress in a pot experiment. The results showed that the hyphal density (HLD) and spore density (SPD) of the AMF in the soil were significantly reduced at Pb stress levels of 1000 mg kg−1 and 2000 mg kg−1. AMF inoculation strongly improved the concentration of glomalin-related soil protein (GRSP). The percentage of soil particles >2 mm and 2−1 mm in the AMF-inoculation treatment was higher than that in the non-AMF-inoculation treatment, while the Pb stress increased the percentage of soil particles <0.053 mm and 0.25−0.53 mm. HLD, total glomalin-related soil protein (T-GRSP), and easily extractable glomalin-related soil protein (EE-GRSP) were the three dominant factors regulating the stability of the soil aggregates, based on the random forest model analysis. Furthermore, the structural equation modeling analysis indicated that the Pb stress exerted an indirect effect on the soil-aggregate stability by regulating the HLD or the GRSP, while only the GRSP had a direct effect on the mean weight diameter (MWD) and geometric mean diameter (GMD). The current study increases the understanding of the mechanism through which soil degradation is caused by Pb stress, and emphasizes the crucial importance of glomalin in maintaining the soil-aggregate stability in HM-contaminated ecosystems.