Preparation of Edible Corn Starch Phosphate with Highly Reactive Sodium Tripolyphosphate in the Absence of Catalyst

Purpose: To prepare edible corn starch phosphate under optimized experimental conditions.Methods: Edible corn starch phosphate was prepared via the reaction of starch with active sodium tripolyphosphate. Reaction efficiency and viscosity were used as indices to optimize experimental conditions. Freeze-thaw stability and transparency of starch phosphate and native starch were comparatively studied.Results: Starch phosphate with optimal combined phosphate content (0.39 %) was obtained under optimized conditions: reaction duration, 90 min; temperature, 160 oC; pH, 5.0; and phosphate, 1.5 g. Starch phosphate with optimal viscosity (230 cp) was obtained under different conditions: reaction duration, 120 min; temperature, 140 oC; pH, 6.0; and phosphate, 1.5 g. Significant differences (p < 0.05) were observed in syneresis and paste transparency of starch phosphate and native starch.Conclusion: Edible corn starch phosphate has been successfully prepared under optimized experimental conditions whose freeze-thaw stability and paste transparency has obvious improvement compared with native starch.Keywords: Starch phosphate, Combined phosphate, Sodium tripolyphosphate, Syneresis, Paste efficienc

Quantum frequency conversion and single-photon detection with lithium niobate nanophotonic chips

In the past few years, the lithium niobate on insulator (LNOI) platform has revolutionized lithium niobate materials, and a series of quantum photonic chips based on LNOI have shown unprecedented performances. Quantum frequency conversion (QFC) photonic chips, which enable quantum state preservation during frequency tuning, are crucial in quantum technology. In this work, we demonstrate a low-noise QFC process on an LNOI nanophotonic platform designed to connect telecom and near-visible bands with sum-frequency generation by long-wavelength pumping. An internal conversion efficiency of 73% and an on-chip noise count rate of 900 counts per second (cps) are achieved. Moreover, the on-chip preservation of quantum statistical properties is verified, showing that the QFC chip is promising for extensive applications of LNOI integrated circuits in quantum information. Based on the QFC chip, we construct an upconversion single-photon detector with the sum-frequency output spectrally filtered and detected by a silicon single-photon avalanche photodiode, demonstrating the feasibility of an upconversion single-photon detector on-chip with a detection efficiency of 8.7% and a noise count rate of 300 cps. The realization of a low-noise QFC device paves the way for practical chip-scale QFC-based quantum systems in heterogeneous configurations.Comment: 8pages, 6 figures, 1 tabl

Reference Range for Thyroid Function during Twin Pregnancies

Background The correct reference range for maternal thyroid function during pregnancy is essential for making an accurate diagnosis of thyroid disease and delivering proper interventions in pregnant women. But there is still no universal standard for this in women with a twin pregnancy. Objective To determine a rational reference range for maternal thyroid function during twin pregnancies. Methods Healthy pregnant women who underwent antenatal examination in Obstetric Clinic, Beijing Friendship Hospital, Capital Medical University from January 2009 to September 2019 were retrospectively selected, including 352 with a twin pregnancy (twin group) , and 988 with a singleton pregnancy (singleton group) . Clinical and laboratory data were collected. The lower and upper limits for determining normal maternal thyroid function during twin pregnancies were the 2.5 (P2.5) and 97.5 (P97.5) percentiles of TSH and FT4. Clinical hyperthyroidism was defined as TSH<P2.5 (total TSH) and FT4>P97.5 (total FT4) . Clinical hypothyroidism was defined as TSH>P97.5 (total TSH) and FT4<P2.5 (total FT4) . Subclinical hypothyroidism was diagnosed by TSH>P97.5 and P2.5≤FT4≤P97.5. Low T4 syndrome was diagnosed by P2.5 (total TSH) ≤TSH≤P97.5 (total TSH) and FT4<P2.5 (total FT4) . FT4 and TSH levels in the early, middle and late pregnancy were compared between singleton and twin groups. Prevalence of thyroid function abnormalities in the early, middle and late pregnancy was in twin group was recorded and analyzed. Results Three hundred and fifty-two pregnant women with a twin pregnancy and 988 with a singleton pregnancy were finally included. The average FT4 level in the twin group was higher than that of the singleton group regardless of the stage of pregnancy (P<0.05) . The average TSH level in the twin group was lower in the early pregnancy, but was higher in late pregnancy compared with that of singleton group (P<0.05) . For maternal thyroid function during a twin pregnancy, the determined normal FT4 in the early, middle and late pregnancy expressed as median and interquartile range M (P2.5, P97.5) was 〔11.84 (7.95, 26.73) 〕, 〔8.24 (5.53, 18.58) 〕, 〔8.37 (5.80, 15.79) 〕pmol/L, respectively, and the determined normal TSH in the three stages of pregnancy was〔0.67 (0.03, 3.99) 〕, 〔1.44 (0.06, 4.79) 〕, 〔2.43 (0.41, 6.92) 〕mU/L, respectively. In the twin group, the prevalence of hyperthyroidism, clinical hypothyroidism, subclinical hypothyroidism, and low T4 syndrome was 0, 0.28% (1/352) , 4.83% (17/352) and 3.98% (14/352) , respectively, by the above-mentioned criteria for diagnosing thyroid disease in a twin pregnancy, and that of the four diseases was 8.24% (29/352) , 0, 15.91% (56/352) and 1.99% (7/352) , respectively, by the criteria for diagnosing thyroid disease in a singleton pregnancy. Conclusion In this study, the recommended reference ranges of FT4 in the early, middle and late stages of pregnancy were 7.95-26.73, 5.53-18.58 and 5.80-15.79 pmol/L, respectively, and the reference ranges of TSH were 0.03-3.99, 0.06-4.79 and 0.41-6.92 mU/L, respectively. Based on the FT4 and TSH standards of the pregnant women with twin pregnancies obtained in our laboratory as the reference standards, the incidence of thyroid dysfunction detected in the pregnant women with twin pregnancies is low, which is consistent with relevant literature reports. The FT4 and TSH standard range of single pregnancy obtained in our laboratory may lead to overdiagnosis of hyperthyroidism and subclinical hypothyroidism in pregnant women of twin pregnancy. So it is necessary to establish specific reference intervals for pregnant women with twin pregnancies based on the FT4 and TSH standard ranges obtained in our laboratory

CSR and new product development performance in transition economies: Roles of internal capabilities, external networks, and dysfunctional competition

Despite abundant research on corporate social responsibility (CSR) outcomes, few studies have examined whether CSR contributes to new product development (NPD) performance. Existing studies argue that CSR increases firms’ innovation activities and launches more new products, however, these arguments have yet to fully extend to the understanding of underlying mechanisms in the CSR-NPD relationship and how CSR fosters NPD performance in different scenarios, especially in transition economies. Building on the resource-based view, social capital theory, and structuration theory, this paper hypothesizes that the CSR influence on NPD performance is mediated by internal capabilities and external networks and moderated by dysfunctional competition. Using empirical data of 219 small and medium-sized firms in China, we find that CSR not only directly increases firms’ NPD performance but also has positive effects on internal capabilities and external networks, which in turn improve NPD performance. Moreover, as one of the most representative institutional environments in transition economies, dysfunctional competition strengthens the effects of CSR on internal capabilities and external networks. This research contributes to the CSR and NPD literature in transition economies and offers strategic guidance to innovating firms in transition economies on how to foster and benefit from CSR initiatives, NPD managers should comprehend the coevolution of CSR, innovation, and institutional environments in transition economies

Cross-Domain Classification Based on Frequency Component Adaptation for Remote Sensing Images

Cross-domain scene classification requires the transfer of knowledge from labeled source domains to unlabeled target domain data to improve its classification performance. This task can reduce the labeling cost of remote sensing images and improve the generalization ability of models. However, the huge distributional gap between labeled source domains and unlabeled target domains acquired by different scenes and different sensors is a core challenge. Existing cross-domain scene classification methods focus on designing better distributional alignment constraints, but are under-explored for fine-grained features. We propose a cross-domain scene classification method called the Frequency Component Adaptation Network (FCAN), which considers low-frequency features and high-frequency features separately for more comprehensive adaptation. Specifically, the features are refined and aligned separately through a high-frequency feature enhancement module (HFE) and a low-frequency feature extraction module (LFE). We conducted extensive transfer experiments on 12 cross-scene tasks between the AID, CLRS, MLRSN, and RSSCN7 datasets, as well as two cross-sensor tasks between the NWPU-RESISC45 and NaSC-TG2 datasets, and the results show that the FCAN can effectively improve the model’s performance for scene classification on unlabeled target domains compared to other methods

Dietary supplementation with selenium nanoparticles-enriched Lactobacillus casei ATCC 393 alleviates intestinal barrier dysfunction of mice exposed to deoxynivalenol by regulating endoplasmic reticulum stress and gut microbiota

Deoxynivalenol (DON), a secondary product of Fusarium metabolism, is common in wheat, corn, barley and other grain crops, posing a variety of adverse effects to environment, food safety, human and animal health. The absorption of DON mainly occurs in the proximal part of the small intestine, which can induce intestinal mucosal epithelial injury, and ultimately affect the growth performance and production performance of animals. This study was conducted to investigate the protective effects of selenium nanoparticles (SeNPs)-enriched Lactobacillus casei ATCC 393 (L. casei ATCC 393) on intestinal barrier function of C57BL/6 mice exposed to DON and its association with endoplasmic reticulum stress (ERS) and gut microbiota. The results showed that DON exposure increased the levels of interleukin-6 (IL-6) and interleukin-8 (IL-8), decreased the levels of interleukin-10 (IL-10) and transforming growth factor beta (TGF-β), caused a redox imbalance and intestinal barrier dysfunction, decreased the mRNA levels of endoplasmic reticulum- resident selenoproteins, activated ERS-protein kinase R-like endoplasmic reticulum kinase (PERK) signaling pathway, altered the composition of the gut microbiota and decreased short-chain fatty acids (SCFAs) content. Dietary supplementation with SeNPs-enriched L. casei ATCC 393 can effectively protect the integrity of intestinal barrier function by reducing inflammatory response, enhancing the antioxidant capacity, up-regulating the mRNA levels of endoplasmic reticulum-resident selenoproteins, inhibiting the activation of PERK signaling pathway, reversing gut microbiota dysbiosis and increasing the content of SCFAs in mice exposed to DON. In conclusion, dietary supplementation with SeNPs-enriched L. casei ATCC 393 effectively alleviated intestinal barrier dysfunction induced by DON in C57BL/6 mice, which may be closely associated with the regulation of ERS and gut microbiota

A Semantic Spatial Structure-Based Loop Detection Algorithm for Visual Environmental Sensing

Loop closure detection is an important component of the Simultaneous Localization and Mapping (SLAM) algorithm, which is utilized in environmental sensing. It helps to reduce drift errors during long-term operation, improving the accuracy and robustness of localization. Such improvements are sorely needed, as conventional visual-based loop detection algorithms are greatly affected by significant changes in viewpoint and lighting conditions. In this paper, we present a semantic spatial structure-based loop detection algorithm. In place of feature points, robust semantic features are used to cope with the variation in the viewpoint. In consideration of the semantic features, which are region-based, we provide a corresponding matching algorithm. Constraints on semantic information and spatial structure are used to determine the existence of loop-back. A multi-stage pipeline framework is proposed to systematically leverage semantic information at different levels, enabling efficient filtering of potential loop closure candidates. To validate the effectiveness of our algorithm, we conducted experiments using the uHumans2 dataset. Our results demonstrate that, even when there are significant changes in viewpoint, the algorithm exhibits superior robustness compared to that of traditional loop detection methods