Study on the structural characteristics and emulsifying properties of chickpea protein isolate-citrus pectin conjugates prepared by Maillard reaction.

Chickpea protein isolate (CPI) typically exhibits limited emulsifying properties under various food processing conditions, including pH variations, different salt concentrations, and elevated temperatures, which limits its applications in the food industry. In this study, CPI-citrus pectin (CP) conjugates were prepared through the Maillard reaction to investigate the influence of various CP concentrations on the structural and emulsifying properties of CPI. With the CPI/CP ratio of 1:2, the degree of graft reached 35.54 %, indicating the successful covalent binding between CPI and CP. FT-IR and intrinsic fluorescence spectroscopy analyses revealed alterations in the secondary and tertiary structures of CPI after glycosylation modification. The solubility of CPI increased from 81.39 % to 89.59 % after glycosylation. Moreover, freshly prepared CPI emulsions showed an increase in interfacial protein adsorption (70.33 % to 92.71 %), a reduction in particle size (5.33 µm to 1.49 µm), and a decrease in zeta-potential (-34.9 mV to -52.5 mV). Simultaneously, the long-term stability of the emulsions was assessed by employing a LUMiSizer stability analyzer. Furthermore, emulsions prepared with CPI:CP 1:2 exhibited excellent stability under various environmental stressors. In conclusion, the results of this study demonstrate that the glycosylation is a valuable approach to improve the emulsifying properties of CPI.

Structure Design of Polymer-Based Films for Passive Daytime Radiative Cooling.

Passive daytime radiative cooling (PDRC), a cooling method that needs no additional energy, has become increasingly popular in recent years. The combination of disordered media and polymeric photonics will hopefully lead to the large-scale fabrication of high-performance PDRC devices. This work aims to study two typical PDRC structures, the randomly distributed silica particle (RDSP) structure and the porous structure, and systematically investigates the effects of structural parameters (diameter D, volume fraction fv, and thickness t) on the radiative properties of the common plastic materials. Through the assistance of the metal-reflective layer, the daytime cooling power Pnet of the RDSP structures is slightly higher than that of the porous structures. Without the metal-reflective layer, the porous PC films can still achieve good PDRC performance with Pnet of 86 W/m2. Furthermore, the effective thermal conductivity of different structures was evaluated. The single-layer porous structure with optimally designed architecture can achieve both good optical and insulating performance, and it is the structure with the most potential in PDRC applications. The results can provide guidelines for designing high-performance radiative cooling films.

Inactivated SARS-CoV-2 vaccines elicit immunogenicity and T-cell responses in people living with HIV.

BACKGROUNDS: To date, the effects of SARS-CoV-2 vaccines on people living with HIV (PLWH) were mainly focused on messenger RNA (mRNA) and adenovirus vector-based vaccines, and little is known about the effects of inactivated virus-based vaccine. This study was designed to determine the effects of inactivated SARS-CoV-2 vaccines on PLWH. METHODS: Twenty-four HIV-positive individuals and 24 healthy donors (HD) were respectively recruited from Malipo Country People's Hospital and community in Kunming city. Enumeration of lymphocyte and CD4+CD45RO+ memory T cells were evaluated by flow cytometry. Competitive ELISA was used to measure the level of Anti-SARS-CoV-2 neutralization antibody. Spearman or Pearson correlation analysis was used to analyze the relationship between laboratory indicators and neutralization antibodies in PLWH. T-cell responses (Th1, Th2, Th17, Treg) and intracellular expression of cytokines (IL-2 and TNF-α) in CD4 or CD8 were induced by spike protein in SARS-CoV-2 (SARS-2-S) and further measured by intracellular staining. RESULTS: CD4, B cells, CD4+CD45RO+ memory T cells in peripheral blood of PLWH are dramatically decreased in comparison with HD. Importantly, PLWH display comparable neutralizing antibody positive rate to HD after inoculation with inactivated SARS-CoV-2 vaccine. However, PLWH showed weaker responses to vaccines exhibited by lower levels of neutralizing antibodies. Correlation analysis shows that this is possibly caused by low number of CD4 and B cells. Furthermore, SARS-2-S-induced Th2 and Th17 responses are also decreased in PLWH, while no influences on Treg and other cytokines (IL-2, TNF-α and IFN-γ) observed. CONCLUSIONS: PLWH and HD have comparable neutralizing antibodies positive rates, but PLWH display weaker responses to inactivated SARS-CoV-2 vaccines in magnitude, which suggests that a booster dose or dose adjustment are required for HIV-infected individuals, especially for those with lower counts of CD4 T and B cells.

Label-Noise Robust Domain Adaptation.

Domain adaptation aims to correct the classifiers when faced with distribution shift between source (training) and target (test) domains. State-of-the-art domain adaptation methods make use of deep networks to extract domain-invariant representations. However, existing methods assume that all the instances in the source domain are correctly labeled; while in reality, it is unsurprising that we may obtain a source domain with noisy labels. In this paper, we are the first to comprehensively investigate how label noise could adversely affect existing domain adaptation methods in various scenarios. Further, we theoretically prove that there exists a method that can essentially reduce the side-effect of noisy source labels in domain adaptation. Specifically, focusing on the generalized target shift scenario, where both label distribution PY and the class-conditional distribution P X|Y can change, we discover that the denoising Conditional Invariant Component (DCIC) framework can provably ensures (1) extracting invariant representations given examples with noisy labels in the source domain and unlabeled examples in the target domain and (2) estimating the label distribution in the target domain with no bias. Experimental results on both synthetic and real-world data verify the effectiveness of the proposed method.

Ultrafast miniature fiber-tip Fabry-Perot humidity sensor with thin graphene oxide diaphragm.

We demonstrate here an ultrafast, miniature, and high-performance fiber-tip Fabry-Perot (F-P) humidity sensor with ∼300 nm-thick graphene oxide (GO) diaphragm suspended onto the end face of a capillary tube with an inner diameter of 50 µm and a cavity length of ∼100 µm. The sensitivity to relative humidity (RH) spanning from ∼10%RH to ∼90%RH was examined based on the wavelength shift in the interference spectrum. Due to the intrinsic hydrophilicity of the porous GO membrane, the developed sensor exhibited an average wavelength variation of ∼0.2 nm/%RH, which indicated a relatively broad and readily detectable RH linear measurement range. More prominently, an ultrahigh response time of 60 ms was achieved over other alternative F-P humidity sensors previously reported, to our knowledge.

An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption.

In this paper, we study the mixture proportion estimation (MPE) problem in a new setting: given samples from the mixture and the component distributions, we identify the proportions of the components in the mixture distribution. To address this problem, we make use of a linear independence assumption, i.e., the component distributions are independent from each other, which is much weaker than assumptions exploited in the previous MPE methods. Based on this assumption, we propose a method (1) that uniquely identifies the mixture proportions, (2) whose output provably converges to the optimal solution, and (3) that is computationally efficient. We show the superiority of the proposed method over the state-of-the-art methods in two applications including learning with label noise and semi-supervised learning on both synthetic and real-world datasets.

Room-Temperature Pressure-Induced Optically-Actuated Fabry-Perot Nanomechanical Resonator with Multilayer Graphene Diaphragm in Air.

We demonstrated a miniature and in situ ~13-layer graphene nanomechanical resonator by utilizing a simple optical fiber Fabry-Perot (F-P) interferometric excitation and detection scheme. The graphene film was transferred onto the endface of a ferrule with a 125-µm inner diameter. In contrast to the pre-tension induced in membrane that increased quality (Q) factor to ~18.5 from ~3.23 at room temperature and normal pressure, the limited effects of air damping on resonance behaviors at 10-2 and 105 Pa were demonstrated by characterizing graphene F-P resonators with open and micro-air-gap cavities. Then in terms of optomechanical behaviors of the resonator with an air micro-cavity configuration using a polished ferrule substrate, measured resonance frequencies were increased to the range of 509-542 kHz from several kHz with a maximum Q factor of 16.6 despite the lower Knudsen number ranging from 0.0002 to 0.0006 in damping air over a relative pressure range of 0-199 kPa. However, there was the little dependence of Q on resonance frequency. Note that compared with the inferior F-P cavity length response to applied pressures due to interfacial air leakage, the developed F-P resonator exhibited a consistent fitted pressure sensitivity of 1.18 × 105 kHz³/kPa with a good linearity error of 5.16% in the tested range. These measurements shed light on the pre-stress-dominated pressure-sensitive mechanisms behind air damping in in situ F-P resonant sensors using graphene or other 2D nanomaterials.