Microscopical Evaluation of the Effects of High-Pressure Processing on Milk Casein Micelles

The effect of different high-pressure processing (HPP) treatments on casein micelles was analyzed through scanning electron microscopy (SEM) and a particle size distribution analysis. Raw whole and skim milk samples were subjected to HPP treatments at 400, 500 and 600 MPa for Come-Up Times (CUT) up to 15 min at ambient temperature. Three different phenomena were observed in the casein micelles: fragmentation, alterations to shape and agglomeration. The particle size distribution analysis determined that, as pressure and time treatment increased, the three phenomena intensified. First, the size of the casein micelles began to decrease as their fragmentation occurred. Subsequently, the casein micelles lost roundness, and their shape deformed. Finally, in the most intense treatments (higher pressures and/or longer times), the micelles fragments began to agglomerate, which resulted in an increase in their average diameter. Homogenization and defatting had no significant effect on the casein micelles; however, the presence of fat in whole milk samples was bioprotective, as the effects of the three phenomena appeared faster in treated skim milk samples. Through this study, it was concluded that the size and structure of casein micelles are greatly altered during high-pressure treatment. These results provide information that broadens the understanding of the changes induced on casein micelles by high-pressure treatments at room temperature.     

Water Status and Predictive Models of Moisture Content during Drying of Soybean Dregs Based on LF-NMR

To explore the drying characteristics of soybean dregs and a nondestructive moisture content test method, in this study, soybean dregs were dried with hot air (80 °C), the moisture content was measured using the drying method, water status was analyzed using low-field nuclear magnetic resonance (LF-NMR) and the moisture content prediction models were built and validated. The results revealed that the moisture contents of the soybean dregs were 0.57 and 0.01 g/g(w.b.), respectively, after drying for 5 and 7 h. The effective moisture diffusivity increased with the decrease in moisture content; it ranged from 5.27 × 10⁻⁹ to 6.96 × 10⁻⁸ m²·s⁻¹. Soybean dregs contained bound water (T₂₁), immobilized water (T₂₂) and free water (T₂₃ and T₂₃’). With the proceeding of drying, all of the relaxation peaks shifted left until a new peak (T₂₃’) appeared; then, the structure of soybean dregs changed, and the relaxation peaks reformed, and the peak shifted left again. The peak area may predict the moisture content of soybean dregs, and the gray values of images predict the moisture contents mainly composed of free water or immobilized water. The results may provide a reference for drying of soybean dregs and a new moisture detection method.     

Formalising the Pathways to Life Using Assembly Spaces

Assembly theory (referred to in prior works as pathway assembly) has been developed to explore the extrinsic information required to distinguish a given object from a random ensemble. In prior work, we explored the key concepts relating to deconstructing an object into its irreducible parts and then evaluating the minimum number of steps required to rebuild it, allowing for the reuse of constructed sub-objects. We have also explored the application of this approach to molecules, as molecular assembly, and how molecular assembly can be inferred experimentally and used for life detection. In this article, we formalise the core assembly concepts mathematically in terms of assembly spaces and related concepts and determine bounds on the assembly index. We explore examples of constructing assembly spaces for mathematical and physical objects and propose that objects with a high assembly index can be uniquely identified as those that must have been produced using directed biological or technological processes rather than purely random processes, thereby defining a new scale of aliveness. We think this approach is needed to help identify the new physical and chemical laws needed to understand what life is, by quantifying what life does.     

Effect of Different Processing Methods on the Chemical Constituents of Scrophulariae Radix as Revealed by 2D NMR-Based Metabolomics

Scrophulariae Radix (SR) is one of the oldest and most frequently used Chinese herbs for oriental medicine in China. Before clinical use, the SR should be processed using different methods after harvest, such as steaming, “sweating”, and traditional fire-drying. In order to investigate the difference in chemical constituents using different processing methods, the two-dimensional (2D) ¹H-¹³C heteronuclear single quantum correlation (¹H-¹³C HSQC)-based metabolomics approach was applied to extensively characterize the difference in the chemical components in the extracts of SR processed using different processing methods. In total, 20 compounds were identified as potential chemical markers that changed significantly with different steaming durations. Seven compounds can be used as potential chemical markers to differentiate processing by sweating, hot-air drying, and steaming for 4 h. These findings could elucidate the change of chemical constituents of the processed SR and provide a guide for the processing. In addition, our protocol may represent a general approach to characterizing chemical compounds of traditional Chinese medicine (TCM) and therefore might be considered as a promising approach to exploring the scientific basis of traditional processing of TCM.     

Impact of Tea Processing on Tryptophan,Melatonin, Phenolic and Flavonoid Contents in Mulberry (Morus alba L.) Leaves: Quantitative Analysis by LC-MS/MS

Mulberry (Morus alba L.) leaves from two cultivars, Yai-Burirum (YB) and Khunphai (KP), were prepared into green tea (GT) and black tea (BT). Compared to fresh leaf (FL) extract, GT and BT extracts were evaluated for their total phenolic and total flavonoid contents. Total phenolic content (TPCs) in all samples ranged between 129.93 and 390.89 mg GAE/g extract. The processing of tea decreased the levels of TPC when compared to FL extracts in both cultivars. The total flavonoid content (TFCs) in all samples was found in the range of 10.15–39.09 mg QE/g extract and TFCs in GT and BT extracts were higher than FL extracts. The change in tryptophan, melatonin, phenolic and flavonoid contents was investigated by liquid chromatography–mass spectroscopy (LC-MS). The results exhibited that tryptophan contents in all samples were detected in the range 29.54–673.72 µg/g extract. Both GT and BT extracts increased tryptophan content compared to FL extracts. BT extracts presented the highest amounts of tryptophan among others in both cultivars. Phenolic compounds were found in mulberry leaf extracts, including gallic acid, caffeic acid, gentisic acid, protocatechuic acid and chlorogenic acid. Chlorogenic acid presented the highest amount in all samples. Almost all phenolic acids were increased in the processed tea extracts except chlorogenic acid. Rutin was the only flavonoid that was detected in all extracts in the range 109.48–1009.75 mg/g extract. The change in phenolic and flavonoid compounds during tea processing resulted in the change in antioxidant capacities of the GT and BT extracts. All extracts presented acetylcholinesterase enzyme (AChE) inhibitory activity with IC₅₀ in the range 146.53–165.24 µg/mL. The processing of tea slightly increased the AChE inhibitory effect of GT and BT extracts. In conclusion, processed tea from mulberry leaves could serve as a new alternative functional food for health-concerned consumers because it could be a promising source of tryptophan, phenolics and flavonoids. Moreover, the tea extracts also had antioxidative and anti-AChE activities.     

Dynamics of Information Flow between the Chinese A-Share Market and the U.S. Stock Market: From the 2008 Crisis to the COVID-19 Pandemic Period

The relationship between the Chinese market and the US market is widely concerned by researchers and investors. This paper uses transfer entropy and local random permutation (LRP) surrogates to detect the information flow dynamics between two markets. We provide a detailed analysis of the relationship between the two markets using long-term daily and weekly data. Calculations show that there is an asymmetric information flow between the two markets, in which the US market significantly affects the Chinese market. Dynamic analysis based on weekly data shows that the information flow evolves, and includes three significant periods between 2004 and 2021. We also used daily data to analyze the dynamics of information flow in detail over the three periods and found that changes in the intensity of information flow were accompanied by major events affecting the market, such as the 2008 financial crisis and the COVID-19 pandemic period. In particular, we analyzed the impact of the S&P500 index on different industry indices in the Chinese market and found that the dynamics of information flow exhibit multiple patterns. This study reveals the complex information flow between two markets from the perspective of nonlinear dynamics, thereby helping to analyze the impact of major events and providing quantitative analysis tools for investment practice.     

On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI

Measuring the temporal complexity of functional MRI (fMRI) time series is one approach to assess how brain activity changes over time. In fact, hemodynamic response of the brain is known to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP). First, we compared two common choices of complexity measures, i.e., Hurst exponent and multiscale entropy, and observed a high spatial similarity between them. Second, we considered four tasks in the HCP dataset (Language, Motor, Social, and Working Memory) and found high task-specific complexity, even when the task design was regressed out. For the significance thresholding of brain complexity maps, we used a statistical framework based on graph signal processing that incorporates the structural connectome to develop the null distributions of fMRI complexity. The results suggest that the frontoparietal, dorsal attention, visual, and default mode networks represent stronger complex behaviour than the rest of the brain, irrespective of the task engagement. In sum, the findings support the hypothesis of fMRI temporal complexity as a marker of cognition.     

Multivariate Time Series Imputation: An Approach Based on Dictionary Learning

The problem addressed by dictionary learning (DL) is the representation of data as a sparse linear combination of columns of a matrix called dictionary. Both the dictionary and the sparse representations are learned from the data. We show how DL can be employed in the imputation of multivariate time series. We use a structured dictionary, which is comprised of one block for each time series and a common block for all the time series. The size of each block and the sparsity level of the representation are selected by using information theoretic criteria. The objective function used in learning is designed to minimize either the sum of the squared errors or the sum of the magnitudes of the errors. We propose dimensionality reduction techniques for the case of high-dimensional time series. For demonstrating how the new algorithms can be used in practical applications, we conduct a large set of experiments on five real-life data sets. The missing data (MD) are simulated according to various scenarios where both the percentage of MD and the length of the sequences of MD are considered. This allows us to identify the situations in which the novel DL-based methods are superior to the existing methods.     

Monte Carlo Simulation of Stochastic Differential Equation to Study Information Geometry

Information Geometry is a useful tool to study and compare the solutions of a Stochastic Differential Equations (SDEs) for non-equilibrium systems. As an alternative method to solving the Fokker–Planck equation, we propose a new method to calculate time-dependent probability density functions (PDFs) and to study Information Geometry using Monte Carlo (MC) simulation of SDEs. Specifically, we develop a new MC SDE method to overcome the challenges in calculating a time-dependent PDF and information geometric diagnostics and to speed up simulations by utilizing GPU computing. Using MC SDE simulations, we reproduce Information Geometric scaling relations found from the Fokker–Planck method for the case of a stochastic process with linear and cubic damping terms. We showcase the advantage of MC SDE simulation over FPE solvers by calculating unequal time joint PDFs. For the linear process with a linear damping force, joint PDF is found to be a Gaussian. In contrast, for the cubic process with a cubic damping force, joint PDF exhibits a bimodal structure, even in a stationary state. This suggests a finite memory time induced by a nonlinear force. Furthermore, several power-law scalings in the characteristics of bimodal PDFs are identified and investigated.     

Community Detection in Semantic Networks: A Multi-View Approach

The semantic social network is a complex system composed of nodes, links, and documents. Traditional semantic social network community detection algorithms only analyze network data from a single view, and there is no effective representation of semantic features at diverse levels of granularity. This paper proposes a multi-view integration method for community detection in semantic social network. We develop a data feature matrix based on node similarity and extract semantic features from the views of word frequency, keyword, and topic, respectively. To maximize the mutual information of each view, we use the robustness of L21-norm and F-norm to construct an adaptive loss function. On this foundation, we construct an optimization expression to generate the unified graph matrix and output the community structure with multiple views. Experiments on real social networks and benchmark datasets reveal that in semantic information analysis, multi-view is considerably better than single-view, and the performance of multi-view community detection outperforms traditional methods and multi-view clustering algorithms.