Evaluation of Water Ammonium Ion Test Kit and Its Feasibility for the Analysis of Protein in Food

The traditional method for the determination of protein in food needs the operations of digestion, distillation, absorption, and titration; therefore, it is complicated and time-consuming and requires professional personnel. Is there a more convenient and faster detection method that can directly determine the ammonium ions in protein digestion solution to obtain the protein content of food and avoid the distillation–absorption–titration process? The feasibility of water ammonium ion test kits for food protein rapid detection was discussed here. After digestion, the protein in food transforms into ammonium ions in the digestion solution. Because of the variety of food, there are many different inorganic ions left in the food digestion solution, and at the same time, digestion agents are added in the digestion process and become potential interference factors in ammonium determination. Therefore, the detection accuracy of ammonium test kits needs to be evaluated first, including their anti-interference ability. The standard curve of ammonium was established by the test kit. When the ammonium concentration was 0.00–2.50 mg/L, the absorbance at 620 nm was linearly related to the ammonium concentration, the determination coefficient R² was 0.9995, and the detection limit of this method was 0.01 mg/L. The influences of temperature, pH value, and reaction time on the test kit method were discussed. The precision was 0.90–3.33%; the repeatability was 1.71–4.86%; and the recovery rate of tap water, river water, and sea water was controlled within 90–103%. The anti-interference ability of the evaluated test kit was better than that of the national standard detection method. The test kit, combined with sample pretreatment and protein conversion formula, was used to detect protein in different types of food (milk powder, rice flour, wheat flour, soy, banana, milk, fish food, chicken food, and dog food). The results showed that there were no significant differences (ρ > 0.05) between the national method and the test kit method. The ammonium ion test kit method shortened the determination time and had higher sensitivity, showing its potential for the rapid determination of food protein.     

Rapid Identification for the Pterocarpus Bracelet by Three-Step Infrared Spectrum Method

In order to explore a rapid identification method for the anti-counterfeit of commercial high value collections, a three-step infrared spectrum method was used for the pterocarpus collection identification to confirm whether a commercial pterocarpus bracelet (PB) was made from the precious species of Pterocarpus santalinus (P. santalinus). In the first step, undertaken by Fourier transform infrared spectroscopy (FTIR) spectrum, the absorption peaks intensity of PB was slightly higher than that of P. santalinus only at 1594 cm⁻¹, 1205 cm⁻¹, 1155 cm⁻¹ and 836 cm⁻¹. In the next step of second derivative IR spectra (SDIR), the FTIR features of the tested samples were further amplified, and the peaks at 1600 cm⁻¹, 1171 cm⁻¹ and 1152 cm⁻¹ become clearly defined in PB. Finally, by means of two-dimensional correlation infrared (2DIR) spectrum, it revealed that the response of holocellulose to thermal perturbation was stronger in P. santalinus than that in PB mainly at 977 cm⁻¹, 1008 cm⁻¹, 1100 cm⁻¹, 1057 cm⁻¹, 1190 cm⁻¹ and 1214 cm⁻¹, while the aromatic functional groups of PB were much more sensitive to the thermal perturbation than those of P. santalinus mainly at 1456 cm⁻¹, 1467 cm⁻¹, 1518 cm⁻¹, 1558 cm⁻¹, 1576 cm⁻¹ and 1605 cm⁻¹. In addition, fluorescence microscopy was used to verify the effectiveness of the above method for wood identification and the results showed good consistency. This study demonstrated that the three-step IR method could provide a rapid and effective way for the anti-counterfeit of pterocarpus collections.     

Toxic Effect of Fullerene and Its Derivatives upon the Transmembrane β2-Adrenergic Receptors

Numerous experiments have revealed that fullerene (C₆₀) and its derivatives can bind to proteins and affect their biological functions. In this study, we explored the interaction between fullerine and the β₂-adrenergic receptor (β₂AR). The MD simulation results show that fullerene binds with the extracellular loop 2 (ECL2) and intracellular loop 2 (ICL2) of β₂AR through hydrophobic interactions and π–π stacking interactions. In the C₆₀_in1 trajectory, due to the π–π stacking interactions of fullerene molecules with PHE and PRO residues on ICL2, ICL2 completely flipped towards the fullerene direction and the fullerene moved slowly into the lipid membrane. When five fullerene molecules were placed on the extracellular side, they preferred to stack into a stable fullerene cluster (a deformed tetrahedral aggregate), and had almost no effect on the structure of β₂AR. The hydroxyl groups of fullerene derivatives (C₆₀(OH)_X, X represents the number of hydroxyl groups, X = 4, 8) can form strong hydrogen bonds with the ECL2, helix6, and helix7 of β₂AR. The hydroxyl groups firmly grasp the β₂AR receptor like several claws, blocking the binding entry of ligands. The simulation results show that fullerene and fullerene derivatives may have a significant effect on the local structure of β₂AR, especially the distortion of helix4, but bring about no great changes within the overall structure. It was found that C₆₀ did not compete with ligands for binding sites, but blocked the ligands’ entry into the pocket channel. All the above observations suggest that fullerene and its derivatives exhibit certain cytotoxicity.     

Deep Multilabel Multilingual Document Learning for Cross-Lingual Document Retrieval

Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the word level and then represent documents via word embeddings, which leads to insufficient structure information. In this work, the cross-lingual comparison at the document level is achieved through the cross-lingual semantic space. Our method, MDL (deep multilabel multilingual document learning), leverages a six-layer fully connected network to project cross-lingual documents into a shared semantic space. The semantic distances can be calculated when the cross-lingual documents are transformed into embeddings in semantic space. The supervision signals are automatically extracted from the data and then used to construct the semantic space via a linear classifier. The ambiguity of manual labels could be avoided and the multilabel supervision signals can be acquired instead of a single label. The representation of the semantic space is enriched by multilabel supervision signals, which improves the discriminative ability of the embeddings. The MDL is easy to extend to other fields since it does not depend on specific data. Furthermore, MDL is more efficient than the models training all languages jointly, since each language is trained individually. Experiments on Wikipedia data showed that the proposed method outperforms the state-of-the-art cross-lingual document retrieval methods.     

Effects of Different Extraction Methods on Vanilla Aroma

To establish the analytic conditions for examining the aroma quality of vanilla pods, we compared different extraction methods and identified a suitable option. We utilized headspace solid-phase microextraction (HS-SPME), steam distillation (SD), simultaneous steam distillation (SDE) and alcoholic extraction combined with gas chromatography (GC) and gas chromatography–mass spectrometry (GC-MS) to identify volatile components of vanilla pods. A total of 84 volatile compounds were identified in this experiment, of which SDE could identify the most volatile compounds, with a total of 51 species, followed by HS-SPME, with a total of 28 species. Ten volatile compounds were identified by extraction with a minimum of 35% alcohol. HS-SPME extraction provided the highest total aroma peak areas, and the peak areas of aldehydes, furans, alcohols, monoterpenes and phenols compounds were several times higher than those of the other extraction methods. The results showed that the two technologies, SDE and HS-SPME, could be used together to facilitate analysis of vanilla pod aroma.     

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.     

Tailoring Pore Structure and Morphologies in Covalent Organic Frameworks for Xe/Kr Capture and Separation

As a rising star among porous solid materials, covalent organic frameworks(COFs) with excellent properties including but not limit to facilely controllable structure, high porosity, and multi-chemical functionality represent significant potential for efficient ¹²⁷Xe/⁸⁵Kr capture and separation. In this study, through tuning the length of the organic ligands, two-dimensional(2D) COF mate-rials with identical connection group but different pore properties, denoted as ATFG-COF and TpPa-COF with AA-stacking model and TpBD-COF with AB-stacking model were synthesized and tested for Kr and Xe adsorption for the first time. Adsorption measurements indicate that the narrower pore apertures and higher porosity are conducive for COF materials to capture Xe and Kr. Furthermore, the Henry's constant, isosteric heat of adsorption(Q_st), and ideal adsorbed solution theory(IAST) selectivity of ATFG-COF, the pore size of which is closest to the kinetic diameter of the Xe atom(0.41 nm) among 2D COF materials, were carried out based on the single component sorption isotherms. The results illustrate that the high isosteric heat values of Xe/Kr adsorption on ATFG-COF are 25 and 16 kJ/mol at room temperature, respectively. Henry's law predicts that the selectivity factor of Xe to Kr is 6.07, consistent with the adsorption selectivity(ca. 6) calculated based on the IAST.     

Precise electro-reduction of alkyl halides for radical defluorinative alkylation

Reported here is a precise electro-reduction strategy for radical defluorinative alkylation towards the synthesis of gem-difluoroalkenes from α-trifluoromethylstyrenes. According to the redox-potential difference of the radical precursors, direct or indirect electrolysis is respectively adopted to realize the precise reduction. An easy-to-handle, catalyst-and metal-free condition is developed for the reduction of alkyl radical precursors that are generally easier to be reduced than α-trifluorome...     

高比探测率和高速石墨烯/n-GaAs复合结构的光电探测器

混合结构的石墨烯/半导体光电晶体管因其超高的响应度而备受关注.然而,该类光电晶体管通过源-漏电极测试得到的比探测率(D．)容易受到1/f噪声的限制.本文制备了混合结构的石墨烯/GaAs光电探测器,通过源-栅电极测得D*大约为1.82X1011 Jones,与通过源-漏电极测量相比,D'提高了约500倍.这可归因于界面上...