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Background: Several respiratory infections outbreaks have been observed in mainland China after reduction of non-pharmaceutical interventions. Other countries have seen increases in respiratory infections outside typical seasons post-COVID-19, warranting investigation into underlying causes. Methods: We established monitoring networks for suspected respiratory infection in 14 tertiary hospitals nationwide. PCR for SARS-CoV-2, influenza A and B were performed on 3708 respiratory specimens and deep sequencing were conducted to identify co-infections or newly emerging microbes in 2023. Viral evolutionary analysis was completed. We retrospectively detected serum antibody level for various respiratory pathogens from 4324 adults without respiratory infections over 7 years to observe its dynamic curves. Findings: SARS-CoV-2 and influenza A were the main pathogens during outbreaks in 2023, bacterial-virus and bacterial-bacterial co-infections were most detected, but community co-infections didn't significantly increase pneumonia incidence. Different SARS-CoV-2 and influenza variants were present in different outbreaks, and no novel pathogens were found. The epidemiological patterns of influenza A, COVID-19 and etc. were altered, exhibiting characteristics of being ''staggered'' compared to most global regions, and potentially led to ''overlapping prevalence''. Binding antibody testing showed regular fluctuation, without significant decrease against common respiratory pathogens in adults. Influenza A antibody stimulation was attenuated during the 2023 outbreak. Conclusions: ''Misaligned'' alteration in seasonal respiratory disease patterns possibly caused combined epidemics, leading to cases spike in China, 2023. In adults, antibody levels didn't show significant decline, but reduced immune response to influenza during 2020–2023 emphasizes the need for consistent vaccination during pandemics.

Abstract Radiative cooling is a zero-energy technology that enables subambient cooling by emitting heat into outer space (~3 K) through the atmospheric transparent windows. However, existing designs typically focus only on the main atmospheric transparent window (8–13 μm) and ignore another window (16–25 μm), under-exploiting their cooling potential. Here, we show a dual-selective radiative cooling design based on a scalable thermal emitter, which exhibits selective emission in both atmospheric transparent windows and reflection in the remaining mid-infrared and solar wavebands. As a result, the dual-selective thermal emitter exhibits an ultrahigh subambient cooling capacity (~9 °C) under strong sunlight, surpassing existing typical thermal emitters (≥3 °C cooler) and commercial counterparts (as building materials). Furthermore, the dual-selective sample also exhibits high weather resistance and color compatibility, indicating a high practicality. This work provides a scalable and practical radiative cooling design for sustainable thermal management.

Abstract The lateral hypothalamus (LH) is involved in feeding behavior and defense responses by interacting with different brain structures, including the Ventral Tegmental Area (VTA). Emerging evidence indicates that LH-glutamatergic neurons infrequently synapse on VTA-dopamine neurons but preferentially establish multiple synapses on VTA-glutamatergic neurons. Here, we demonstrated that LH-glutamatergic inputs to VTA promoted active avoidance, long-term aversion, and escape attempts. By testing feeding in the presence of a predator, we observed that ongoing feeding was decreased, and that this predator-induced decrease in feeding was abolished by photoinhibition of the LH-glutamatergic inputs to VTA. By VTA specific neuronal ablation, we established that predator-induced decreases in feeding were mediated by VTA-glutamatergic neurons but not by dopamine or GABA neurons. Thus, we provided evidence for an unanticipated neuronal circuitry between LH-glutamatergic inputs to VTA-glutamatergic neurons that plays a role in prioritizing escape, and in the switch from feeding to escape in mice.

Abstract The development of genetically encoded dopamine sensors such as dLight has provided a new approach to measuring slow and fast dopamine dynamics both in brain slices and in vivo, possibly enabling dopamine measurements in areas like the dorsolateral striatum (DLS) where previously such recordings with fast-scan cyclic voltammetry (FSCV) were difficult. To test this, we first evaluated dLight photometry in mouse brain slices with simultaneous FSCV and found that both techniques yielded comparable results, but notable differences in responses to dopamine transporter inhibitors, including cocaine. We then used in vivo fiber photometry with dLight in mice to examine responses to cocaine in DLS. We also compared dopamine responses during Pavlovian conditioning across the striatum. We show that dopamine increases were readily detectable in DLS and describe transient dopamine kinetics, as well as slowly developing signals during conditioning. Overall, our findings indicate that dLight photometry is well suited to measuring dopamine dynamics in DLS.

Summary: The ventral tegmental area (VTA) has been proposed to play a role in pain, but the brain structures modulating VTA activity in response to nociceptive stimuli remain unclear. Here, we demonstrate that the lateral preoptic area (LPO) glutamate neurons relay nociceptive information to the VTA. These LPO glutamatergic neurons synapsing on VTA neurons respond to nociceptive stimulation and conditioned stimuli predicting nociceptive stimulation and also mediate aversion. In contrast, LPO GABA neurons synapsing in the VTA mediate reward. By ultrastructural quantitative synaptic analysis, ex vivo electrophysiology, and functional neuroanatomy we identify a complex circuitry between LPO glutamatergic and GABAergic neurons and VTA dopaminergic, GABAergic, and glutamatergic neurons. We conclude that LPO glutamatergic neurons play a causal role in the processing of nociceptive stimuli and in relaying information about nociceptive stimuli. The pathway from LPO glutamatergic neurons to the VTA represents an unpredicted interface between peripheral nociceptive information and the limbic system.

Abstract Carbon nanotube fibers (CNTFs) are endowed with excellent mechanical, electrical, and thermal properties and are considered promising candidates in numerous cutting‐edge fields. However, the inherent black color of CNTFs hinders their practical application in fields with high aesthetic requirements such as wearable devices and smart textiles. Due to the smooth surface and chemical inertness, CNTFs are hard to be dyed by conventional chemical dyes or colorful inks. Herein, we realize a structural coloration of CNTFs by coating them with two metal oxide layers via atomic layer deposition. The three elements of color, that is, hue, saturation, and brightness, can be controlled by adjusting the types and thickness of each oxide layer. Colorful CNTFs with wide color gamut and high saturation are achieved through different combinations. A film interference model is also established to reveal the mechanism of the structural coloration, which is a comprehensive result of thin‐film interference and surface roughness briefly. The calculated reflectance well fits the measured results by introducing surface roughness parameters. Moreover, the colored CNTFs are not iridescent because of retinal signal delay, which will further expand their applications.

Abstract In real life, either the subjective factors of speakers or the objective environment degrades the performance of automatic speech recognition (ASR). This study focuses on one of the subjective factors, accented speech, and attempts to build a multi‐accent ASR system to solve the degradation caused by different accents, one of whose characteristic is the low resource. To deal with the challenge of the low‐resource data and the different speech styles, a wav2vec‐MoE (mixture of experts) is proposed to adapt the wav2vec 2.0 for multi‐accent ASR. In the wav2vec‐MoE, a domain MoE is developed by introducing pseudo‐domain information in the pre‐training stage, where the domain denotes a collection of speech varied by the same influence factors. The MoE is trained with two strategies according to the proposed domain mismatch assessment between unlabeled speech and target speech, without requiring any explicit domain information. Experiments show that the wav2vec‐MoE achieves a 14.69% relative word error rate reduction (WERR) on the AESRC2020 accent dataset and an 8.79% relative WERR on the Common Voice English dataset.

Aiming at the force-tracking error phenomenon of impedance control in an unknown surface environment, an adaptive variable-damping impedance control algorithm is proposed, and the stability and convergence of the algorithm are deduced. An adaptive-law selection rule is proposed to aim at the phenomenon that the adaptive parameters are too large to cause the system oscillation and overshoot and too small to cause the adaptive line variation in the curved surface environment. Finally, experiments conclude that the impedance control based on the adaptive variable-damping algorithm has a better force-tracking effect than the ordinary impedance control in the curved surface environment where the contact surface between the end-effector of the manipulator and the atmosphere is unknown.

Magnesium (Mg) metals have been widely used in various fields as one of the most promising lightweight structural materials. However, the low corrosion resistance and poor mechanical properties restrict its applications. Surface treatments are common approach to enhance the mechanical strength and corrosion resistance of Mg metals. Among them, laser surface treatment generates novel tissues and structures in situ on the sample surface, thereby improving properties of mechanical strength and corrosion resistance. We briefly describe the changes in surface organization that arise after laser treatment of Mg surfaces, as well as the creation of structures such as streaks, particles, holes, craters, etc., and provide an overview of the reasons for the alterations. The effect of laser processing on wettability, hardness, friction wear, degradation, biocompatibility and mechanical properties were reviewed. At last, the limitations and development trend of laser treatment on Mg metals research were further pointed out.

Guided by system science, we propose a cognitive model based on graph theory and explore personalized recommendation algorithms based on a deep knowledge point tracking model by integrating the learning characteristics, prior knowledge, and learning ability of learners. Recommendation of the knowledge point is provided by combining the deep knowledge point tracking model and cognitive model, and personalized curriculum recommendation is provided by combining a knowledge point tracking model and graph theory. A dynamic personalized learning path is recommended by combining the knowledge point network and a student model. Then, teaching resources are recommended, and learning efficiency is improved.

Differential privacy (DP) defines privacy protection by promising quantified indistinguishability between individuals who consent to share their privacy-sensitive information and those who do not. DP aims to deliver this promise by including well-crafted elements of random noise in the published data, and thus there is an inherent tradeoff between the degree of privacy protection and the ability to utilize the protected data. Currently, several open-source tools have been proposed for DP provision. To the best of our knowledge, there is no comprehensive study for comparing these open-source tools with respect to their ability to balance DP’s inherent tradeoff as well as the use of system resources. This work proposes an open-source evaluation framework for privacy protection solutions and offers evaluation for OpenDP Smartnoise, Google DP, PyTorch Opacus, Tensorflow Privacy, and Diffprivlib. In addition to studying their ability to balance the above tradeoff, we consider discrete and continuous attributes by quantifying their performance under different data sizes. Our results reveal several patterns that developers should have in mind when selecting tools under different application needs and criteria. This evaluation survey can be the basis for an improved selection of open-source DP tools and quicker adaptation of DP.

In the containment control problem of multi-agent systems (MASs), the convergence of followers is always a potential threat to the security of system operations. From the perspective of system topology, the inherently non-linear properties of the algebraic connectivity of the follower2follower (F2F) network, combined with the influence of the leader2follower (L2F) topology on the system, make it difficult to design the convergence positions of the followers through mere mathematical analysis. Therefore, in the background of temporary networking tasks for large-scale systems, to achieve the goal of forecasting the performance of the whole system when networking is only completed with local information, this paper investigates the application and effectiveness of recurrent neural networks (RNNs) in the containment control system performance identification, thus improving the efficiency of system networking while ensuring system security. Two types of identification models based on two types of neural networks (NNs), MLP and standard RNN are developed, according to the range of information required for performance identification. Evaluation of the models is carried out by means of the coefficient of determination (R2) as well as the root-mean-square error (RMSE). The results show that each model may produce a better forecasting accuracy than the other models in specific cases, with models based on the standard RNN possessing smaller errors. With the proposed method, model identification can be achieved, but in-depth development of the model in further studies is still necessary to the extent the accuracy of the model.

Klebsiella pneumoniae is a gram-negative bacterium that can cause many diseases in hospitals and communities. Intestinal K. pneumoniae infections are relatively rare. Most K. pneumoniae infections begin with the colonization of the gastrointestinal system. In this study, clinically isolated K. pneumoniae strains were used to infect intestinal epithelial Caco-2 cells to study the possible intestinal translocation mechanism of K. pneumoniae. We found that of the three K. pneumoniae strains tested, KP1821 exhibited the strongest adhesive and invasive abilities and that the adhesion to Caco-2 intestinal epithelial cells was affected by the acidic environment of the stomach. Transcriptome sequencing revealed the involvement of molecules associated with the extracellular matrix and cell adhesion, inflammatory response, calcium ion and transforming growth factor β (TGF-β) signaling pathways, and other abnormalities in biological processes and cell signaling pathways. Additionally, tolloid-like protein 1 (TLL1) was significantly upregulated. Knocking down TLL1 with shRNA significantly reduced KP1821's ability to invade and adhere to intestinal epithelial cells. TLL1 is involved in the activation of the TGF-β signaling pathway. Inhibition of this pathway using the inhibitor SB431542 induced significantly reduced adhesion and invasion capabilities of KP1821. Our findings demonstrate that TLL1 participates in K. pneumoniae adhesion and invasion of intestinal epithelial cells by activating the TGF-β signaling pathway.

The objective of the study was to investigate the levels of plasma exosome-derived fragile site-associated tumor suppressor (FATS) and evaluate its prognostic predictive ability in ovarian cancer (OC) patients. Exosome-rich fractions were isolated from the plasma of 90 patients with OC enrolled in this study. The levels of plasma exosome-derived FATS were detected by ELISA. The levels of exosome-derived FATS in OC patients were significantly lower as compared to the healthy controls (P < 0.001). The levels of plasma exosome-derived FATS were higher in OC patients with low grade (1/2), and Federation International of Gynecology and Obstetrics (FIGO) Stages I/II than those in high grade (3/4) and Stages III/IV of the disease (p = 0.003; p < 0.001), respectively. The levels of plasma exosome-derived FATS were significantly higher in OC patients with no lymph node metastasis or no ascites as compared to those with lymph node metastasis or ascites, respectively (both p < 0.001). The levels of plasma exosome-derived FATS were higher in OC patients having CA-125 below 35 U/ml as compared to those with CA-125 greater than 35 U/ml (p < 0.001). Among all enrolled OC patients, both 5-DFS and 5-OS were shorter in patients with lower plasma exosome-derived FATS levels than those with higher levels (both p < 0.001). The area under the receiver operating characteristic curve of plasma exosome-derived FATS was 0.85 (95% CI: 0.76-0.91) for 5-DFS and 0.91 (95% CI: 0.83-0.96) for 5-OS prediction in patients with OC. Plasma exosome-derived FATS levels in OC patients were significantly downregulated. Low levels of plasma exosome-derived FATS had a significant relationship with FIGO Stages III/IV, high grade, ascites, higher levels of CA-125, lymph node metastasis, and prognosis of OC patients. Thus, our findings may provide insights for the development of a new strategy OC treatment.

Sepsis is a systemic inflammatory response syndrome caused by infection, with high incidence and mortality. Therefore, it is necessary to carry out an effective anti-infection treatment. In this work, we designed and synthesized red blood cell (RBC) membrane-coated PLGA nanoparticles named γ3-RBCNPs, which target the highly expressed intercellular adhesion molecule-1 (ICAM-1) at the site of infection through the γ3 peptide on its surface and kill the Klebsiella pneumoniae through ciprofloxacin encapsulated in its core. In addition, the homogenous RBC membrane coated on the surface of the nanoparticles helps them avoid immune surveillance and prolong the circulation time of the drug in the body. We found that the γ3-RBCNPs target human umbilical vein endothelial cells (HUVECs) activated by TNF-α in vitro and the infected lung of mice in the sepsis model very well. In vitro evaluation suggested that γ3-RBCNPs have a low risk of acute hemolysis and are less likely to be engulfed by macrophages. In vivo evaluation showed that γ3-RBCNPs has a long half-life and good bio-safety. More importantly, we confirmed that γ3-RBCNPs have the good antibacterial and anti-infection ability in vivo and in vitro. Our research provides a new strategy for the nano-drug treatment of Klebsiella pneumoniae-induced sepsis.

In the past 20 years, clinicians have shifted away from relying solely on clinicopathologic indicators toward increasing use of multigene expression assays in guiding treatment decisions regarding adjuvant chemotherapy for early-stage hormone receptor (HR)-positive, HER2-negative breast cancer. Oncotype DX Recurrence Score (RS) is one of the most widely used multigene assays when considering indications for adjuvant chemotherapy, and guidelines have recently incorporated its use in women with early HR-positive HER2-negative breast cancer and up to three positive lymph nodes. While multiple retrospective and prospective clinical studies have demonstrated that most women with a low- to mid-range RS (0–25) can safely forgo chemotherapy, premenopausal women remain an important subgroup for which recommendations based on RS are ill-defined. The majority of patients included in clinical trials and retrospective analyses validating the use of RS have been postmenopausal women. In the subgroup of premenopausal women with HR-positive HER2-negative breast cancer, studies indicate that traditional clinicopathologic methods for assessing risk continue to be powerful tools when combined with RS to predict benefit from chemotherapy. This suggests that there is an element of uncaptured risk inherent to the premenopausal state that evades characterization by RS alone. This review describes the evidence that has supported the recommendation of RS in clinical guidelines, specifically focusing on data for its current use in premenopausal women. We review available data regarding the impact of the menstrual cycle on hormonally regulated gene expression, which may drive variations in the RS. Further research on the reliability and interpretation of the RS in the premenopausal subgroup is necessary and represents a gap in knowledge of how the RS should be applied in premenopausal women.

Background: Stroke is a leading cause of morbidity and mortality. Over the past decade, plasma D-dimer levels have emerged as a biomarker for predicting stroke outcome. However, no consensus in the literature currently exists concerning its utility for predicting post-stroke functional outcome and mortality.Objective: To systematically review the effectiveness of plasma D-dimer levels for predicting functional outcome and mortality following stroke.Methods: Five academic databases were screened according to PRISMA guidelines for eligible studies. With these studies, we conducted a random-effect meta-analysis to evaluate the impact of plasma D-dimer levels for predicting functional outcome and mortality post-stroke. We also conducted subgroup analyses to evaluate differences in predictive capacity for different stroke subtypes.Results: Nineteen studies were included, containing data on 5,781 stroke patients (mean age: 65.26 ± 6.4 years). Overall methodological quality for the included studies was high. Meta-analysis showed that increased D-dimer levels were predictive of worsened functional outcomes (Hazard ratio: 2.19, 95% CI: 1.63–2.93) and elevated overall mortality (2.29, 1.35–3.88). Subgroup analysis showed that plasma D-dimer levels were more predictive of poorer functional outcomes for ischemic (2.08, 1.36–3.18) stroke as compared to intracerebral hemorrhage (2.62, 1.65–4.17). We also noted that predictive capacity was similar when it came to mortality in patients with cryptogenic ischemic stroke (2.65, 0.87–8.08) and intracerebral hemorrhage (2.63, 1.50–4.59).Conclusion: The study provides preliminary evidence concerning the capacity of plasma D-dimer levels for predicting functional outcomes and mortality following stroke and reports that higher D-dimer levels of are associated with poorer functional outcomes and higher mortality.

A compound external magnetic field (EMF) can be used to prevent weld bead defects by controlling the metal transfer and arc behaviors during high-speed gas metal arc welding. A unified model containing the filler metal, arc, weld pool, and compound EMF was established to study the effect of the compound EMF on the welding process. The heat and mass transfer among the filler metal, arc, and weld pool under the action of the EMF was numerically analyzed. The simulation results showed that the high-temperature region in the molten pool was reduced and the temperature distribution was more uniform after the EMF was introduced. The speed of the forward liquid metal flow in the front of the molten pool increased due to the forward inclination of the filler metal and arc plasma, and a small metal bulge formed at the leading edge of the molten pool. Because of the periodic swing of the arc and the droplet, the transverse filling of the liquid metal strengthened. The generated additional electromagnetic force inside the weld pool was analyzed, and it had limited impact on the weld pool behavior.

Based on comprehensive analysis of core, well logging, seismic and production data, the multi-scale reservoir space, reservoir types, spatial shape and distribution of fractures and caves, and the configuration relationship with production wells in fracture-cavity carbonate reservoirs were studied systematically, the influence of them on the distribution of residual oil was analyzed, and the main controlling factors mode of residual oil distribution after water flooding was established. Enhanced oil recovery methods were studied considering the development practice of Tahe oilfield. Research shows that the main controlling factors of residual oil distribution after water flooding in fracture-cavity carbonate reservoirs can be classified into four categories: local high point, insufficient well control, flow channel shielding and weak hydrodynamic. It is a systematic project to improve oil recovery in fracture-cavity carbonate reservoirs. In the stage of natural depletion, production should be well regulated to prevent bottom water channeling. In the early stage of waterflooding, injection-production relationship should be constructed according to reservoir type, connectivity and spatial location to enhance control and producing degree of waterflooding and minimize remaining oil. In the middle and late stage, according to the main controlling factors and distribution characteristics of remaining oil after water flooding, remaining oil should be tapped precisely by making use of gravity differentiation and capillary force imbibition, enhancing well control, disturbing the flow field and so on. Meanwhile, backup technologies of reservoir stimulation, new injection media, intelligent optimization etc. should be developed, smooth shift from water injection to gas injection should be ensured to maximize oil recovery. Key words: carbonate rock, fracture-cavity reservoir, multiple scale, remaining oil distribution, main controlling factor, enhanced oil recovery

Spatial scale is a fundamental issue for geographical phenomena because the size of the spatial unit adopted for analysis can have a significant effect on aggregated spatial data and the corresponding analytical results. There exists much research on the scale issue of spatial distribution data while a few has paid attention to the scale effect on spatial interactions. Big geo-data with micro-level individual movements provide an unprecedented opportunity to explore spatial interactions from the bottom up and to understand the scale effect from the perspective of interaction patterns. In this paper, we introduced an empirical framework to explore spatial interaction data across scales. By incorporating two data sets of taxi trajectories in Beijing and Shanghai, we aggregated the taxi O-D trips under multiple regular grids with different cell sizes and analyzed the properties of spatial interactions in three aspects: the statistical distribution, the distance decay mechanism, and the structure pattern. Our research demonstrated the feasibility of analyzing spatial interactions from a multi-scale view and offered a basic empirical framework for future geographical research that is interested in scale and spatial interactions.

Summary: The lateral habenula (LHb) is a brain structure that participates in cognitive and emotional processing and has been implicated in several mental disorders. Although one of the largest inputs to the LHb originates in the lateral preoptic area (LPO), little is known about how the LPO participates in the regulation of LHb function. Here, we provide evidence that the LPO exerts bivalent control over the LHb through the convergent transmission of LPO glutamate and γ-aminobutyric acid (GABA) onto single LHb neurons. In vivo, both LPO-glutamatergic and LPO-GABAergic inputs to the LHb are activated by aversive stimuli, and their predictive cues yet produce opposing behaviors when stimulated independently. These results support a model wherein the balanced response of converging LPO-glutamate and LPO-GABA are necessary for a normal response to noxious stimuli, and an imbalance in LPO→LHb glutamate or GABA results in the type of aberrant processing that may underlie mental disorders. : Barker et al. show that distinct populations of lateral preoptic area glutamate and GABA neurons synapse together on single lateral habenula neurons and find that this “convergent neurotransmission” allows preoptic area neurons to exert bivalent control over single lateral habenula neurons and drive opposing motivational states. Keywords: preoptic, habenula, reward, aversion, synapse, glutamate, GABA, stress, calcium imaging, optogenetics, electron microscopy

Summary: Dorsal raphe (DR) serotonin neurons provide a major input to the ventral tegmental area (VTA). Here, we show that DR serotonin transporter (SERT) neurons establish both asymmetric and symmetric synapses on VTA dopamine neurons, but most of these synapses are asymmetric. Moreover, the DR-SERT terminals making asymmetric synapses on VTA dopamine neurons coexpress vesicular glutamate transporter 3 (VGluT3; transporter for accumulation of glutamate for its synaptic release), suggesting the excitatory nature of these synapses. VTA photoactivation of DR-SERT fibers promotes conditioned place preference, elicits excitatory currents on mesoaccumbens dopamine neurons, increases their firing, and evokes dopamine release in nucleus accumbens. These effects are blocked by VTA inactivation of glutamate and serotonin receptors, supporting the idea of glutamate release in VTA from dual DR SERT-VGluT3 inputs. Our findings suggest a path-specific input from DR serotonergic neurons to VTA that promotes reward by the release of glutamate and activation of mesoaccumbens dopamine neurons. : Wang et al. identify the ultrastructural and molecular characteristics of synaptic connectivity between DR serotonin neurons and VTA dopamine neurons and determined the role of these connections in behavior. Their results indicate that the DR→VTA serotonergic neurons promote reward by the co-release of glutamate and activation of mesoaccumbens dopamine neurons. Keywords: dorsal raphe nucleus, ventral tegmental area, serotonin, VGluT3, reward

Accurate and fast determination of blood component concentration is very essential for the efficient diagnosis of patients. This paper proposes a nonlinear regression method with high-dimensional space mapping for blood component spectral quantitative analysis. Kernels are introduced to map the input data into high-dimensional space for nonlinear regression. As the most famous kernel, Gaussian kernel is usually adopted by researchers. More kernels need to be studied for each kernel describes its own high-dimensional feature space mapping which affects regression performance. In this paper, eight kernels are used to discuss the influence of different space mapping to the blood component spectral quantitative analysis. Each kernel and corresponding parameters are assessed to build the optimal regression model. The proposed method is conducted on a real blood spectral data obtained from the uric acid determination. Results verify that the prediction errors of proposed models are more precise than the ones obtained by linear models. Support vector regression (SVR) provides better performance than partial least square (PLS) when combined with kernels. The local kernels are recommended according to the blood spectral data features. SVR with inverse multiquadric kernel has the best predictive performance that can be used for blood component spectral quantitative analysis.

SiO2-supported silyl chromate catalyst is an important industrial catalyst for production of high grade HDPE pipe materials. The control of the short chain branch (SCB) distribution using this catalyst system is still a great challenge. In this work, ethylene and 1-hexene copolymers were synthesized using SiO2-supported silyl chromate catalyst combined with triisobutylaluminium (TIBA), triethylaluminium (TEA) and mixed TIBA/TEA at molar ratio 1:1 (TIBA/TEA/1:1) as three different Al-alkyl co-catalysts.The temperature rising elution fractionation (TREF) and successive self-nucleation and annealing (SSA, by DSC) methods were combined to analyze the short chain branch distribution (SCBD) of these ethylene/1-hexene copolymers. The results showed that different types of co-catalyst had a great influence on SCBD of ethylene/1-hexene copolymers. The copolymer produced with TIBA showed better SCBD than the copolymer produced with TEA, and the copolymer produced with TIBA/ TEA/1:1 showed a SCBD in between those with TIBA and TEA.

During brain development, neural stem cells (NSCs) receive on-or-off signals important for regulating their amplification and reaching adequate neuron density. However, how a coordinated regulation of intracellular pathways and genetic programs is achieved has remained elusive. Here, we found that the embryonic (e) CSF contains 10¹² nanoparticles/ml (77 nm diameter), some of which were identified as exosome nanovesicles that contain evolutionarily conserved molecules important for coordinating intracellular pathways. eCSF nanovesicles collected from rodent and human embryos encapsulate protein and microRNA components of the insulin-like growth factor (IGF) signaling pathway. Supplementation of eCSF nanovesicles to a mixed culture containing eNSCs activated the IGF-mammalian target of rapamycin complex 1 (mTORC1) pathway in eNSCs and expanded the pool of proliferative eNSCs. These data show that the eCSF serves as a medium for the distribution of nanovesicles, including exosomes, and the coordinated transfer of evolutionary conserved molecules that regulate eNSC amplification during corticogenesis.