Antimicrobial dichloroisocyanurate-salts for controlled release of chlorine.

Sodium dichloroisocyanurate (Na-DCC), a disinfectant known for rapid decomposition in water, loses its effectiveness with complete release of free available chlorine (FAC) in under an hour. To overcome this, a series of chlorine rich transition metal complexes/tetrabutylammonium (TBA) salts of DCC, including 2Na[Cu(DCC)4], 2Na[Fe(DCC)4], 2Na[Co(DCC)4]·6H2O, 2Na[Ni(DCC)4]·6H2O, and TBA[DCC]·4H2O have been developed for extended chlorine release studies. The DCC-salts are synthesized based on the metathesis reaction process and are characterized using IR, NMR, CHN analyses, TGA,DSC, and Lovi bond colorimeter. The DCC-salts displayed poor water solubility and low decomposition chlorine release profile compared to Na-DCC. The water solubility of DCC-salts was reduced by a factor of 5.37 to 2500 compared to Na-DCC. The decomposition release of FAC from DCC-salts has been studied over time in comparison to Na-DCC in distilled water using a Lovi-bond colorimeter. DCC-salts displayed controlled FAC release profiles that varied from 1-13 days depending on the type of metal/TBA unit in them, whereas the parent Na-DCC displayed complete FAC release in about 0.91 h. For a proof of concept, the controlled release of metal from one of the DCC-metal complex salts, i.e., copper from the Cu-DCC is also investigated with a function of time in distilled water at RT. The 100% release of copper from Cu-DCC was identified over a period of 10 days. In addition, the applicability of DCC-salts as excellent antiviral agents against the bacteriophage T4 and antibacterial agents against Erwinia, Pseudomonas aeruginosa PA014 (Gram-negative), and Staphylococcus epidermidis (Gram-positive) compared to Na-DCC has been demonstrated.

Understanding the Adhesion Mechanism of Hydroxyapatite-Binding Peptide.

Understanding the interactions between the protein collagen and hydroxyapatite is of high importance for understanding biomineralization and bone formation. Here, we undertook a reductionist approach and studied the interactions between a short peptide and hydroxyapatite. The peptide was selected from a phage-display library for its high affinity to hydroxyapatite. To study its interactions with hydroxyapatite, we performed an alanine scan to determine the contribution of each residue. The interactions of the different peptide derivatives were studied using a quartz crystal microbalance with dissipation monitoring and with single-molecule force spectroscopy by atomic force microscopy. Our results suggest that the peptide binds via electrostatic interactions between cationic moieties of the peptide and the negatively charged groups on the crystal surface. Furthermore, our findings show that cationic residues have a crucial role in binding. Using molecular dynamics simulations, we show that the peptide structure is a contributing factor to the adhesion mechanism. These results suggest that even small conformational changes can have a significant effect on peptide adhesion. We suggest that a bent structure of the peptide allows it to strongly bind hydroxyapatite. The results presented in this study improve our understanding of peptide adhesion to hydroxyapatite. On top of physical interactions between the peptide and the surface, peptide structure contributes to adhesion. Unveiling these processes contributes to our understanding of more complex biological systems. Furthermore, it may help in the design of de novo peptides to be used as functional groups for modifying the surface of hydroxyapatite.

A Comparative Study on Relieving Exercise-Induced Fatigue by Inhalation of Different Citrus Essential Oils.

Citrus essential oils (CEOs) possess physiological functions due to diverse aroma components. However, evidence for the effects of CEOs on exercise performance and exercise-induced fatigue is limited. The CEOs with discrepancies in components may exert different effects on the amelioration of exercise-induced fatigue. In this study, sweet orange (Citrus sinensis L.) essential oil (SEO), lemon (Citrus limon Osbeck) essential oil (LEO), and bergamot (Citrus bergamia Risso and Poit) essential oil (BEO) were chosen to explore the effect on amelioration of exercise-induced fatigue. Our results demonstrated that SEO and LEO increased the swimming time by 276% and 46.5%, while BEO did not. Moreover, the three CEOs exerted varying effects on mitigating exercise-induced fatigue via inhibiting oxidative stress, protecting muscle injury, and promoting glucose-dependent energy supply. Accordingly, BEO showed the best efficiency. Moreover, the GC-MS and Pearson correlation analysis of BEO showed that the contents of the major components, such as (±)-limonene (32.9%), linalyl butyrate (17.8%), and linalool (7.7%), were significantly positively correlated with relieving exercise-induced fatigue.

Antiviral Polymers Based on N-Halamine Polyurea.

N-halamines are a commonly applied class of antimicrobial agents used for a variety of applications relating to human health. Here, we present the modulation of the common polymers polyurea and polyguanidine with the N-halamine technology. The N-H bonds in either polymer were converted to N-Cl or N-Br bonds capable of releasing Cl+ or Br+ cations to aqueous media as antiviral agents. Controlled release of the oxidizing agents was monitored for a period of 4 weeks. Antiviral activity was evaluated against the T4 bacteriophage as well as against the highly stable plant virus belonging to the Tobamovirus genus, tomato brown rugose fruit virus. The incorporation of the N-halamine technology on commonly used polymers has effectively introduced antiviral functionality for a wide variety of potential applications.

Coassembly of C13-Dipeptides: Gelations from Solutions and Precipitations.

C13-dipeptides that did not gel on their own were found to form hydrogels when combined with mixtures (coassembly). At pH = 4.6, by mixing negatively charged C13-WD (C13-WD2- and/or C13-WD-) with C13-KW or C13-YK, where the side chain of K carried positive charge, two composite hydrogels with different mechanical properties were formed. The gels exhibited various fiber structures that would account for their individual functionalities. According to molecular dynamics computer simulations, the composite systems formed spherical micelles through hydrophobic interactions that further aggregate to form gels through electrostatic interactions. The electrostatic repulsions between C13-WD molecules were interfered by insertions of C13-KW or C13-YK molecules, which result in gel formation in the composite systems. The results of computer simulations well explained the experimental observations, which provided new insights into the design and selection strategies for peptide gelators.

A Comprehensive Study on Self-Assembly and Gelation of C13-Dipeptides-From Design Strategies to Functionalities.

Computational and experimental methods were applied to investigate the self-assembly and gelation of C13-dipeptides. A modified aggregation propensity (APS) was introduced to correlate the effects of side chains of amino acids on the tendency to aggregate. From the experimental results, the ranges of 0.156 < APS < 0.250 seemed to be a proper region for the C13-dipeptides to form hydrogels, while other molecules with higher or lower APS were insoluble or dissociated. As observed from molecular dynamics simulations, the C13-dipeptides first formed small aggregates through hydrophobic interactions and then rearranged through electrostatic attractions and hydrogen bonds for self-assembly. The C13-dipeptides tended to be antiparallel packed, as shown by hydrogen bonding analyses. Experimental observations and analyses on the structures of C13-dipeptide hydrogels matched the computational conclusions very well. From the five selected gelators, i.e., C13-GW, C13-VY, and C13-WT, strong π-π stacking was observed. For C13-WS, strong hydrogen bonding was found, and in C13-WY, both strong π-π interactions and hydrogen bonds were found. It takes around 90 min or longer for C13-dipeptides to form hydrogels, and those formed by C13-WY and C13-WS had weak water holding capacities, which might be due to strong intermolecular hydrogen bonding. From rheological studies, the C13-dipeptides formed strong chemical gels that were stabilized by strong interactions between the molecular aggregates. These gelators exhibit the potentials to be environmentally friendly substitutes for the common functionalized peptide gelators.

Analytic Study on the Characteristics of Infrared Spectrum of Oil Pool Flame.

In virtue of the severity and scale of the pollution caused by oil pool flame, space remote sensing can provide us a new way of monitoring in real time the oil pool flame pollution. Space remote sensing monitoring is based on the analysis of target spectrum characteristics. Due to lack of adequate researches on the characteristics of infrared spectrum of oil pool flame, this paper carries out the analytical study on flame spectrums of several types of oil, mixed oil and other combustible objects in outdoor space by establishing all-flame infrared testing system with the spectrum range of 1～14 µm. The results show that the spectrum curves of oil pool flame of 92# gasoline, 95# gasoline, 0# diesel, aviation kerosene and lube have similar features, that there exist characteristics emission peaks at the area of certain wave lengths­H2O characteristics emission peak for 1.1, 2.4, 2.8 and 6.3 µm, CO2 characteristics emission peak for 4.2 and 4.5 µm, C­H stretching vibration emission peak for 3.4 µm, and no obvious characteristics peak for spectrum curves of 6.3 µm and above; that there is no obvious difference in the spectrum of oil pool flame among the mixtures of 92# gasoline and 0# diesel at different proportions, that the comparison of the flame spectrum of 92# gasoline with that of wood and paper shows that there appears a characteristics emission peak at 3.4 µm; that though the flame spectrum of alcohol has similar radiated emission near 3.4 µm, the proportion of its radiation intensity to that of CO2 at 4.5 µm is far less than that for the flame spectrum of 92# gasoline; that the flame spectrum of honeycomb briquette is similar to that of gray body radiation. The differences in flame spectrum among all kinds of combustible materials are closely linked to their chemical compositions and burning reaction mechanisms. Comparative analysis on the spectrum characteristics at continuous area, intermission area and flue gas area shows that C­H stretching vibration peak only exists in continuous area, which proves that the emission peak is caused by the combustible reaction of oil and gas. This result is in line with the mechanism of oil pool combustion reaction. The experimental conclusion is of great significance in the remote-sensing recognition of oil pool flame based on the analysis of spectrum characteristics.

Detection of malicious nodes based on consortium blockchain.

With the development of technology, more and more devices are connected to the Internet. According to statistics, Internet of Things (IoT) devices have reached tens of billions of units, which forms a massive Internet of Things system. Social Internet of Things (SIoT) is an essential extension of the IoT system. Because of the heterogeneity present in the SIoT system and the limited resources available, it is facing increasing security issues, which hinders the interaction of SIoT information. Consortium chain combined with the trust problem in SIoT systems has gradually become an important goal to improve the security of SIoT data interaction. Detection of malicious nodes is one of the key points to solve the trust problem. In this article, we focus on the consortium chain network. According to the information characteristics of nodes on the consortium chain, it can be analyzed that the SIoT malicious node detection combined with the consortium chain network should have the privacy protection, subjectivity, uncertainty, lightweight, dynamic timeliness and so on. In response to the features above and the concerns of existing malicious node detection methods, we propose an algorithm based on inter-block delay. We employ unsupervised clustering algorithms, including K-means and DBSCAN, to analyze and compare the data set intercepted from the consortium chain. The results indicate that DBSCAN exhibits the best clustering performance. Finally, we transmit the acquired data onto the chain. We conclude that the proposed algorithm is highly effective in detecting malicious nodes on the combination of SIoT and consortium chain networks.

Fully automated measurement of femoral neck axis line in anatomical coordinate system using CT images.

The femoral neck is the most vulnerable site for fractures within the hip joint. Due to its complex three-dimensional structure and special blood supply mechanism, the treatment of femoral neck fractures is difficult and the healing probability is low. Using computers to accurately and automatically locate the femoral neck axis can detect the density of femoral neck, the neck-shaft angle and the anteversion angle, which effectively assists in the prevention and treatment of femoral neck fractures. Additionally, the traditional femoral neck axis positioning schemes have limitations in accuracy, automation and assistance to bone density measurement. Therefore, this paper proposes a new fully automatic femoral neck axis positioning method. First, the coronal plane's three-dimensional reconstruction highlights the details of the target bone, and then designs a coarse localization module based on multi-scale template matching to obtain the rough range of the femoral neck axis. Then, a detailed localization module based on the femoral neck virtual slices is used to obtain the contour centers and accurately locates the three-dimensional femoral neck axis. This method has been validated in comparison with the manual measurement method. Experimental results revealed that the extracted femoral neck axis in this study can achieve automation, ensure accuracy, and avoid subjective effects effectively and has the potential value to be applied in the prevention and treatment of femoral neck fractures.

[Hard and soft classification method of multi-spectral remote sensing image based on adaptive thresholds].

Hard and soft classification techniques are the conventional methods of image classification for satellite data, but they have their own advantages and drawbacks. In order to obtain accurate classification results, we took advantages of both traditional hard classification methods (HCM) and soft classification models (SCM), and developed a new method called the hard and soft classification model (HSCM) based on adaptive threshold calculation. The authors tested the new method in land cover mapping applications. According to the results of confusion matrix, the overall accuracy of HCM, SCM, and HSCM is 71.06%, 67.86%, and 71.10%, respectively. And the kappa coefficient is 60.03%, 56.12%, and 60.07%, respectively. Therefore, the HSCM is better than HCM and SCM. Experimental results proved that the new method can obviously improve the land cover and land use classification accuracy.

Dipeptide-polysaccharides hydrogels through co-assembly.

Self-assembling dipeptide hydrogels are attracting attention in food, materials, and biomedicine. However, there are still limitations such as weak hydrogel properties. Herein, we introduced two types of polysaccharides (Arabic gum and citrus pectin) into an alkyl-chain modified dipeptide (C13-tryptophan-tyrosine (C13-WY)) to generate co-assembled C13-WY-arabic gum and C13-WY-pectin hydrogels. The co-assembled hydrogels exhibited enhanced mechanical properties and stability. The G' value of C13-WY-arabic gum and C13-WY-pectin hydrogels was 3 and 10 times larger than that of C13-WY hydrogel, respectively. The addition of Arabic gum and citrus pectin led to the co-assembly and molecular rearrangement. Moreover, co-assembled hydrogels showed more ß-sheet structure and hydrogen bonds. Importantly, the self-/co-assembled hydrogels showed low cytotoxicity. We utilized these hydrogels for the encapsulation of docetaxel and they showed a high embedding rate and slow-release. Our findings provide a novel strategy for the development of stable supramolecular peptide hydrogels with good biocompatibility through simple co-assembly.

The influence of EEG channels and features significance on automatic detection of epileptic waves in MECT.

Modified Electric Convulsive Therapy (MECT) is an efficacious physical therapy in treating mental disorders. The occurrence of epilepsy is a crucial benchmark for evaluating therapeutic effectiveness. However, the medical field still lacks relevant research on automatically detecting epileptic waves in MECT. Therefore, this article proposes a novel automatic detection method of epileptic waves in MECT. In this article, EEG local features (time, frequency, and time-frequency domains) and global features (Pearson correlation coefficient) are combined for epileptic wave detection with SVM (Support Vector Machine). We researched the system with 15 EEG detection channels. The dataset under investigation contains EEG data from 22 patients who received MECT and presented with epileptic seizures. The results revealed that LA (Logarithm of Activity) feature exhibits the best classification significance. When epileptic waves appear, there is a decrease in the power ratio of delta waves and an increase in the power ratio of theta waves. Additionally, the complexity of EEG decreases while the correlation between EEG channels increases. The Cz, F4, and P3 channels exhibit the highest classification significance among all EEG channels. Furthermore, based on the channel classification significance, the EEG detection channels number can be reduced to 8. Similarly, based on the feature classification significance, the local feature number can be reduced from 9 to 3. These conclusions can improve detection efficiency and reduce the cost for MECT. Moreover, the method we proposed can effectively detect epileptic waves in MECT. This work can provide physicians with a reference for evaluating the effectiveness of MECT.

[Division of winter wheat yield estimation by remote sensing based on MODIS EVI time series data and spectral angle clustering].

Crop yield estimation division is the basis of crop yield estimation; it provides an important scientific basis for estimation research and practice. In the paper, MODIS EVI time-series data during winter wheat growth period is selected as the division data; JiangSu province is study area; A division method combined of advanced spectral angle mapping(SVM) and K-means clustering is presented, and tested in winter wheat yield estimation by remote sensing. The results show that: division method of spectral angle clustering can take full advantage of crop growth process that is reflected by MODIS time series data, and can fully reflect region differences of winter wheat that is brought by climate difference. Compared with the traditional division method, yield estimation result based on division result of spectral angle clustering has higher R2 (0.702 6 than 0.624 8) and lower RMSE (343.34 than 381.34 kg x hm(-2)), reflecting the advantages of the new division method in the winter wheat yield estimation. The division method in the paper only use convenient obtaining time-series remote sensing data of low-resolution as division data, can divide winter wheat into similar and well characterized region, accuracy and stability of yield estimation model is also very good, which provides an efficient way for winter wheat estimation by remote sensing, and is conducive to winter wheat yield estimation.

Sequence-Selected C13-Dipeptide Self-Assembled Hydrogels for Encapsulation of Lemon Essential Oil with Antibacterial Activity.

Supramolecular self-assembling peptide hydrogels are attracting attention. The switching of even one amino acid may lead to differences in structure and functions of peptide hydrogels. Herein, we investigate the effect of substitution of a single amino acid residue on the gelation properties of C13-dipeptide hydrogels. We show that four C13-R1Y (C13-VY, C13-FY, C13-WY, and C13-YY) can form hydrogels with drastically tunable rigidity (the G' values were 5.74, 0.16, 27.74, and 67.90 KPa, respectively). Moreover, C13-WY and C13-YY hydrogels with high stability and excellent mechanical properties formed ß-sheet nanofiber cross-linked networks. Furthermore, we applied four hydrogels into encapsulation of lemon essential oil (LEO). The peptide hydrogels had a high encapsulation rate and slowly released the LEO. Importantly, the LEO-loaded hydrogels showed enhanced antibacterial activity than free LEO. Our results clearly demonstrate the significance of side-chain interactions in determining hydrogel properties and their potential application in encapsulation for nutrition agents and hydrophobic drugs.

Thermal conditions and active substance stability affect the isomerization and degradation of lycopene.

This study evaluated the effects of thermal treatment conditions (100-120 °C, 0-5 h) and active substance stability on the isomerization and degradation of lycopene in the "Sanhong" pummelo (C. grandis cv. Sanhongmiyou) matrix. The results revealed that the degradation of (all-E)-lycopene and total lycopene in pummelo could be well fit by the first-order kinetic model under thermal treatment. The total Z-lycopene ratio was maintained at nearly 40 % (120 °C, 4 h). The behavior of lycopene was related to the degradation of active substances, such as the degradation of ascorbic acid and polysaccharides. The stability of polysaccharides was temperature-dependent, and with the increasing temperature (100-120 °C), a significant negative correlation was observed between the content of Z-isomers and polysaccharides. The enhancement of lycopene isomerization was attributed to the degradation of polysaccharides under thermal treatment. These findings facilitate the functional development and efficient utilization of lycopene in fruit processing.

Comparative Evaluation on the Bioaccessibility of Citrus Fruit Carotenoids In Vitro Based on Different Intake Patterns.

The intake pattern has a great impact on the bioaccessibility of carotenoids from citrus fruit. Here, we compared the bioaccessibility of carotenoids from fresh citrus fruit (FC), fresh citrus juice (FCJ), and not-from-concentrate citrus juice (NCJ) and analyzed the influencing factors. The results demonstrated that particle size, viscosity, and some active components of the samples during digestion are potential factors affecting the bioaccessibility of carotenoids. The total carotenoid bioaccessibility of NCJ (31.45 ± 2.58%) was significantly higher than that of FC (8.11 ± 0.43%) and FCJ (12.43 ± 0.49%). This work demonstrates that NCJ is an appropriate intake pattern to improve the bioaccessibility of carotenoids from citrus fruit. The findings also suggest that adjustment of food intake patterns is an effective way to improve the digestion and absorption of nutrients.

[Integration of soft and hard classifications using linear spectral mixture model and support vector machines].

This paper presents a new soft and hard classification. By analyzing the target objects in the image distribution, and calculating the adaptive threshold automatically, the image is divided into three regions: pure regions, non-target objects regions and mixed regions. For pure regions and non-target objects regions, hard classification method (support vector machine) is used to quickly extract classified results; For mixed regions, soft classification method (selective endmember for linear spectral mixture model) is used to extract the abundance of target objects. Finally, it generates an integrated soft and hard classification map. In order to evaluate the accuracy of this new method, it is compared with SVM and LSMM using ALOS image. The RMSE value of new method is 0.203, and total accuracy is 95.48%. Both overall accuracies and RMSE show that integration of hard and soft classification has a higher accuracy than single hard or soft classification. Experimental results prove that the new method can effectively solve the problem of mixed pixels, and can obviously improve image classification accuracy.

[Winter wheat area estimation with MODIS-NDVI time series based on parcel].

Several attributes of MODIS (moderate resolution imaging spectrometer) data, especially the short temporal intervals and the global coverage, provide an extremely efficient way to map cropland and monitor its seasonal change. However, the reliability of their measurement results is challenged because of the limited spatial resolution. The parcel data has clear geo-location and obvious boundary information of cropland. Also, the spectral differences and the complexity of mixed pixels are weak in parcels. All of these make that area estimation based on parcels presents more advantage than on pixels. In the present study, winter wheat area estimation based on MODIS-NDVI time series has been performed with the support of cultivated land parcel in Tongzhou, Beijing. In order to extract the regional winter wheat acreage, multiple regression methods were used to simulate the stable regression relationship between MODIS-NDVI time series data and TM samples in parcels. Through this way, the consistency of the extraction results from MODIS and TM can stably reach up to 96% when the amount of samples accounts for 15% of the whole area. The results shows that the use of parcel data can effectively improve the error in recognition results in MODIS-NDVI based multi-series data caused by the low spatial resolution. Therefore, with combination of moderate and low resolution data, the winter wheat area estimation became available in large-scale region which lacks completed medium resolution images or has images covered with clouds. Meanwhile, it carried out the preliminary experiments for other crop area estimation.

Antiviral Activity of Peptide-Based Assemblies.

The COVID-19 pandemic highlighted the importance of developing surfaces and coatings with antiviral activity. Here, we present, for the first time, peptide-based assemblies that can kill viruses. The minimal inhibitory concentration (MIC) of the assemblies is in the range tens of micrograms per milliliter. This value is 2 orders of magnitude smaller than the MIC of metal nanoparticles. When applied on a surface, by drop casting, the peptide spherical assemblies adhere to the surface and form an antiviral coating against both RNA- and DNA-based viruses including coronavirus. Our results show that the coating reduced the number of T4 bacteriophages (DNA-based virus) by 3 log, compared with an untreated surface and 6 log, when compared with a stock solution. Importantly, we showed that this coating completely inactivated canine coronavirus (RNA-based virus). This peptide-based coating can be useful wherever sterile surfaces are needed to reduce the risk of viral transmission.

[Farmland parcel extraction based on high resolution remote sensing image].

A new method of farmland parcel extraction from high resolution remote sensing image based on wavelet and watershed segmentation was proposed in the present paper. First, classification results were used to enhance the contrast of gray-scale value of typical pixels in the original image using the high resolution remote sensing image's spectral information. Second, wavelet transform and watershed segmentation were applied to the enhanced image, then improved region merger algorithm was used to solve the problem of over-segmentation. Finally, inverse wavelet transform was taken to get the reconstructed image, then Canny operator was introduced to add the edge information, and the result of farmland parcel segmentation was obtained. To validate the proposed approach, experiments on Quickbird images were performed, we rapidly extracted the farmland parcel from the test image, and the results had a high accuracy. Despite it had a lot to do in extracting the small size parcels, on the whole the method this paper proposed had a very good robustness. Compared with the traditional methods, it had the following advantages: (1) it was an automatic extraction method, did not need too much manual intervention, and could extract the large area of farmland parcels accurately and effectively. (2) It was a very good solution to the problem of over-segmentation by using improved region merger algorithm, and improved the accuracy of the extraction. All these indicated that the proposed approach was an effective farmland parcel extraction method based on high resolution remote sensing image.