Bovidae-based gelatin: Extractions method,physicochemical and functional properties,applications, and future trends

Gelatin is one of the most important multifunctional biopolymers and is widely used as an essential ingredient in food, pharmaceutical, and cosmetics. Porcine gelatin is regarded as the leading source of gelatin globally then followed by bovine gelatin. Porcine sources are favored over other sources since they are less expensive. However, porcine gelatin is religiously prohibited to be consumed by Muslims and the Jewish community. It is predicted that the global demand for gelatin will increase significantly in the future. Therefore, a sustainable source of gelatin with efficient production and free of disease transmission must be developed. The highest quality of Bovidae-based gelatin (BG) was acquired through alkaline pretreatment, which displayed excellent physicochemical and rheological properties. The utilization of mammalian- and plant-based enzyme significantly increased the gelatin yield. The emulsifying and foaming properties of BG also showed good stability when incorporated into food and pharmaceutical products. Manipulation of extraction conditions has enabled the development of custom-made gelatin with desired properties. This review highlighted the various modifications of extraction and processing methods to improve the physicochemical and functional properties of Bovidae-based gelatin. An in-depth analysis of the crucial stage of collagen breakdown is also discussed, which involved acid, alkaline, and enzyme pretreatment, respectively. In addition, the unique characteristics and primary qualities of BG including protein content, amphoteric property, gel strength, emulsifying and viscosity properties, and foaming ability were presented. Finally, the applications and prospects of BG as the preferred gelatin source globally were outlined.     

塔里木盆地塔西南坳陷柯东构造带甫沙4井原油来源及充注过程

甫沙4井位于塔里木盆地塔西南坳陷昆仑山前冲断带的柯东构造带上，北部和东部分别发育有柯克亚和柯东1井油气田。为研究甫沙4井原油来源与充注过程，对原油样品和连续抽提后的含油砂样各组分（游离态、束缚态、包裹体）进行GC、GC?MS和 GC?IRMS分析，与柯克亚凝析油气田油样进行油—油对比。结果表明：甫沙4井晚期充注原油组分具有C_29?32重排藿烷、重排甾烷和Ts相对含量高，C_27?29甾烷ααα 20R分布呈反“L”型，以及正构烷烃单体碳同位素值较低等特征，与柯克亚凝析油气田来源于二叠系普司格组（P_2?3p）烃源岩的主体原油（I类）地球化学特征一致。而早期充注的原油组分具有重排藿烷、重排甾烷和Ts相对含量较低，C_27?29甾烷ααα 20R分布呈“V”型，以及正构烷烃单体碳同位素值较高等特征，与柯克亚凝析油气田来源于中—下侏罗统湖相泥岩的II类原油地球化学特征一致。甫沙4井经历3个阶段成藏过程：①在上新世，二叠系烃源岩于生油晚期阶段生成的I类原油运移至柯克亚构造带或柯东构造带深部形成油藏；②在更新世早期，侏罗系烃源岩于生油早—中期生成的II类原油运移至甫沙4井白垩系储层；③在第四纪，强烈的构造作用使深部I类原油沿断裂调整进入甫沙4井白垩系储层。最终造成甫沙4井白垩系储层II类原油先充注，I类原油后充注的特殊现象。     

Simultaneous fractionation of multiple classes of polyphenols from honeybush tea using solid-phase extraction

The structural diversity of polyphenols and the inherent limitations of current extraction techniques pose a challenge to extract polyphenols using a simple and green method. Hence, in this study, a method was developed to simultaneously fractionate multiple classes of polyphenols by only varying ethanol-water solutions. Honeybush tea, which is rich in polyphenols, was selected as a model for this study. Solvent extraction followed by solid-phase extraction (SPE) was developed to obtain a polyphenol-rich fraction from six honeybush samples. Based on a gradient elution programme (10%, 30%, 50%, 70% and 90% (v/v) ethanol-water solution) of SPE, the Strata X cartridge showed a better recovery of most targeted polyphenols under 0.9 mL of the drying volume and 1 mL min⁻¹ of the dispensing speed. The elution programme for fractionating most polyphenols was as follows: single elution with 50% ethanol, followed by twice elution with 70% ethanol. The antioxidant capacity was used to analyse the differences among the polyphenol-rich fractions from six honeybush samples. Principal component analysis (PCA) revealed that unfermented C. genistoides (GG) has the greatest antioxidant capacity among the honeybush species studied. Additionally, mangiferin, isomangiferin and vicenin-2 were the main contributors to the antioxidant capacity in six honeybush fractions according to the correlation study.     

南美白对虾肝胰腺蛋白提取方法优化及无水保活环境胁迫对细胞凋亡蛋白的影响

为提高Western Blotting结果的可靠性，以蛋白溶出率和凝胶电泳蛋白条带完整性为指标，比较提取液种类、研磨方式、酶抑制剂种类及其体积分数和提取时间对南美白对虾肝胰腺蛋白提取效果的影响。采用优化后的提取方法获得高质量蛋白样品，并采用Western Blotting法分析无水环境胁迫后南美白对虾肝胰腺组织中细胞凋亡信号通路相关蛋白的表达水平。结果表明：RIPA裂解液作为提取溶剂所得的蛋白溶出率高于水提和磷酸盐缓冲液，电动匀浆和液氮研磨所得蛋白条带更完整，4%蛋白酶磷酸酶混合抑制剂能有效抑制肝胰腺内源酶引起的蛋白降解；采用Western Blotting法分析无水保活期间南美白对虾肝胰腺蛋白，发现低温诱导休眠的同时会引起细胞轻微凋亡，且凋亡水平呈应激时间依赖性增加，环境胁迫解除后有所回调。     

Enhanced wide-activated residual network for efficient and accurate image deblocking

To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity.     

一种植物油中苯并（a）芘含量测定的固相萃取前处理方法

为了建立一种准确性高、成本低的植物油中苯并(a)芘测定的样品前处理方法，基于GB 5009.7—2016以自制氧化铝柱为固相萃取柱，采用单因素试验对称样量、洗脱剂量、洗脱流速、吸附剂存放时间、吸附剂量这5个影响植物油中苯并（a）芘洗脱的因素进行研究，优化前处理条件，并对测定方法进行考察。结果表明：优化的前处理条件为称样量0.100 0 g、洗脱剂量120 mL、吸附剂（氧化铝）量22 g、洗脱流速1滴/2 s、吸附剂存放时间少于12周；方法检出限为0.2 μg/L，样品加标回收率为94.23%～100.00%，RSD为1.20%～7.36%；同一样品测定结果与SGS测定值接近，相对平均偏差为2.36%～3.50%。说明本试验方法测定结果准确，可应用于油脂企业植物油中痕量苯并（a）芘的测定。     

固相萃取结合气相色谱-三重四极杆质谱法测定方便面中16种多环芳烃

建立固相萃取-气相色谱-三重四极杆质谱测定方便面中16种多环芳烃的方法。方便面样品用正己烷和乙腈-丙酮提取,经Oasis HLB和Florisil柱两步净化,气相色谱-三重四极杆质谱检测。在试验浓度范围内,线性关系良好,各种多环芳烃的定量限在0.03μg/kg^0.54μg/kg之间。在不同的加标浓度下,加标回收率在75%~129%之间,相对标准偏差小于13.4%。该方法快速、准确、灵敏度高、重现性好,适用于大批量样品中多环芳烃的快速测定。     

基于语言特征自动获取的反问句识别方法

反问句是以疑问的形式表达强烈情感的修辞方式,对其有效识别可为自然语言处理中的情感分析任务提供技术支持。该文提出了一种基于语言特征自动获取的反问句识别方法。首先,利用标签注意机制,建立了一个数据驱动的特征抽取模型,用于获取与任务相关的词汇、句法结构、符号标记和话题等语言特征。其次,利用Bi-LSTM模型分别对句子和语言特征进行表示,两者的交互注意被用于获取句子的各个词和符号的注意力权重向量。该权重向量作用于句子的表示,用于构建一个强化语言特征的反问句识别模型。在中文微博数据集上的实验结果表明,提出的方法与之前的工作相比,反问句识别性能有显著提升。