Dynamic Study on Water State and Water Migration during Gluten-Starch Model Dough Development under Different Gluten Protein Contents.

Mixing is crucial for dough quality. The gluten content influences water migration in dough development and properties, leading to quality changes in dough-based products. Understanding how the gluten protein content influences water migration during dough development is necessary for dough processing. A compound flour with different gluten protein contents (GPCs, 10-26%, w/w) was used to study the dough farinograph parameters and water migration during dough development. According to the farinograph test of the gluten-starch model dough, the GPC increases the water absorption and the strength of the dough. Water migration was determined via low-field nuclear magnetic resonance (LF-NMR). With the increase in GPC, the gluten protein increases the binding ability of strongly bound water and promotes the transformation of weakly bound water. However, inappropriate GPC (10% and 26%, w/w) results in the release of free water, which is caused by damage to the gluten network according to the microstructure result. Moreover, the changes in proteins' secondary structures are related to the migration of weakly bound water. Therefore, weakly bound water plays an important role in dough development. Overall, these results provide a theoretical basis for the optimization of dough processing.

Research on Fault Prediction Method of Elevator Door System Based on Transfer Learning.

The elevator door system plays a crucial role in ensuring elevator safety. Fault prediction is an invaluable tool for accident prevention. By analyzing the sound signals generated during operation, such as component wear and tear, the fault of the system can be accurately determined. This study proposes a GNN-LSTM-BDANN deep learning model to account for variations in elevator operating environments and sound signal acquisition methods. The proposed model utilizes the historical sound data from other elevators to predict the remaining useful life (RUL) of the target elevator door system. Firstly, the opening and closing sounds of other elevators is collected, followed by the extraction of relevant sound signal characteristics including A-weighted sound pressure level, loudness, sharpness, and roughness. These features are then transformed into graph data with geometric structure representation. Subsequently, the Graph Neural Networks (GNN) and long short-term memory networks (LSTM) are employed to extract deeper features from the data. Finally, transfer learning based on the improved Bhattacharyya Distance domain adversarial neural network (BDANN) is utilized to transfer knowledge learned from historical sound data of other elevators to predict RUL for the target elevator door system effectively. Experimental results demonstrate that the proposed method can successfully predict potential failure timeframes for different elevator door systems.

Prevalence and factors associated with dynapenia among middle-aged and elderly people in rural southern China.

To estimate the prevalence of dynapenia and examine potential risk factors for dynapenia using a sample of rural middle-aged and elderly Chinese. A cross-sectional study of 253 Chinese adults aged 50 years and older was conducted from June to August in 2022 in Nanjing. A questionnaire was used to collect data on all socioeconomic variables. Body weight, height, body fat percentage, grip strength, waist circumference, calf circumference, and gait speed were measured. The prevalence of dynapenia was 69.6 %, 62.3 % in men and 72.7 % in women respectively. Binary logistic regressions indicated significant associations between dynapenia and age (odds ratio [OR] = 2.59; 95 % confidence interval [CI] 1.63, 4.12; p < 0.001), educational level (OR = 0.55; 95 % CI 0.38, 0.80; p = 0.002). Dynapenia was prevalent among rural middle-aged and elderly people in southern China. Age and lower education level were both associated with dynapenia. Nutrition and physical activity should be strongly recommended as important strategies to maintain and improve muscle strength.

Temperature-Immune, Wide-Range Flexible Robust Pressure Sensors for Harsh Environments.

Flexible pressure sensors possess vast potential for various applications such as new energy batteries, aerospace engines, and rescue robots owing to their exceptional flexibility and adaptability. However, the existing sensors face significant challenges in maintaining long-term reliability and environmental resilience when operating in harsh environments with variable temperatures and high pressures (∼MPa), mainly due to possible mechanical mismatch and structural instability. Here, we propose a composite scheme for a flexible piezoresistive pressure sensor to improve its robustness by utilizing material design of near-zero temperature coefficient of resistance (TCR), radial gradient pressure-dividing microstructure, and flexible interface bonding process. The sensing layer comprising multiwalled carbon nanotubes (MWCNTs), graphite (GP), and thermoplastic polyurethane (TPU) was optimized to achieve a near-zero temperature coefficient of resistance over a temperature range of 25-70 °C, while the radial gradient microstructure layout based on pressure division increases the range of pressure up to 2 MPa. Furthermore, a flexible interface bonding process introduces a self-soluble transition layer by direct-writing TPU bonding solution at the bonding interface, which enables the sensor to achieve signal fluctuations as low as 0.6% and a high interface strength of up to 1200 kPa. Moreover, it has been further validated for its capability of monitoring the physiological signals of athletes as well as the long-term reliable environmental resilience of the expansion pressure of the power cell. This work demonstrates that the proposed scheme sheds new light on the design of robust pressure sensors for harsh environments.

High Triglyceride-Glucose Index Is Associated with Poor Prognosis in Patients with Acute Pancreatitis.

BACKGROUND: Acute pancreatitis (AP) is a common gastrointestinal disease worldwide. Severe acute pancreatitis (SAP) is characterized as persistent organ failure with a mortality rate as high as 20-30%. Early assessment of the severity and screening out possible SAP is of great significance. Given that there is still a lack of both convenient and practical tools for evaluating SAP, we conducted this study to explore the association between TyG index and acute pancreatitis prognosis. METHODS: A total of 353 in-patients diagnosed with acute pancreatitis in the Second Hospital of Shandong University were retrospectively enrolled from January 2018 to November 2021 in this study. According to the Atlanta Classification, they were divided into two groups based on the AP severity. Demographic information and clinical materials were retrospectively collected. The TyG index calculation formula is as follows: ln [fasting triglycerides (mg/dL) × fasting plasma glucose (mg/dL)/2]. Statistical analyses were performed using SPSS software (IBM version 22.0) and Medcalc software. Multivariable logistic regression analyses were used to investigate independent predictors for SAP. ROC curve was plotted to assess the predictive ability and cutoffs of TyG index. RESULTS: A total of 353 AP patients were respectively enrolled in this study, of which 47 suffered from SAP. Compared with the non-SAP group, TyG index was significantly higher in the SAP group (10.44 ± 1.55 vs 9.33 ± 1.44, P < 0.001). Multivariate logistic regression analysis showed that TyG index was an independent risk factor for SAP (OR 1.835, 95% CI 1.380-2.442 P < 0.001), with a cutoff of 8.76 for non-HTG/AAP and 11.81 for HTG/AAP by ROC curve. TyG index of patients who suffered from SIRS, OF, APFC, and ANC was higher than those without (P < 0.05). CONCLUSIONS: The triglyceride-glucose index is an independent risk factor for SAP. High TyG index is closely related to SAP and AP-related complications.

Blind Turbo Equalization of Short CPM Bursts for UAV-Aided Internet of Things.

With the surge of Internet of Things (IoT) applications using unmanned aerial vehicles (UAVs), there is a huge demand for an excellent complexity/power efficiency trade-off and channel fading resistance at the physical layer. In this paper, we consider the blind equalization of short-continuous-phase-modulated (CPM) burst for UAV-aided IoT. To solve the problems of the high complexity and poor convergence of short-burst CPM blind equalization, a novel turbo blind equalization algorithm is proposed based on establishing a new expectation-maximization Viterbi (EMV) algorithm and turbo scheme. Firstly, a low complexity blind equalization algorithm is obtained by applying the soft-output Lazy Viterbi algorithm within the EM algorithm iteration. Furthermore, a set of initializers that achieves a high global convergence probability is designed by the blind channel-acquisition (BCA) method. Meanwhile, a soft information iterative process is used to improve the system performance. Finally, the convergence, bit error rate, and real-time performance of iterative detection can be further improved effectively by using improved exchange methods of extrinsic information and the stopping criterion. The analysis and simulation results show that the proposed algorithm achieves a good blind equalization performance and low complexity.

MRI-Based Optimization Design of the Pre-Spinal Route of Contralateral C7 Nerve Transfer for Spastic Arm Paralysis.

Purpose: The prespinal route of contralateral cervical 7 nerve transfer developed by Prof. Wendong Xu helps realize the direct anastomosis of the bilateral cervical 7 nerves. However, 20% of operations still require a nerve graft, which leads to an unfavorable prognosis. This study aims to explore the optimized prespinal route with MRI to further improve the prognosis. Methods: The current study enrolled 30 patients who suffered from central spastic paralysis of an upper limb and who underwent contralateral cervical 7 nerve transfer via Prof. Xu's prespinal route through the anterior edge of the contralateral longus colli. MRI images were used to analyze the route length, vertebral artery exposure, and contralateral cervical 7 nerve included angle. Three prespinal routes were virtually designed and analyzed. The selected optimal route was applied to another 50 patients with central spastic paralysis of an upper limb for contralateral cervical 7 nerve transfer. Results: By the interventions on the 30 patients, the middle and posterior routes were shorter than the anterior route in length, but with no statistical difference between the two routes. Of 30 contralateral vertebral arteries, 26 were located at the posterior medial edge of the longus colli. The average included angles of the anterior, middle, and posterior routes were 108.02 ± 7.89°, 95.51 ± 6.52°, and 72.48 ± 4.65°, respectively. According to these data, the middle route was optimally applied to 50 patients, in whom the rate of nerve transplantation was only 4%, and no serious complications such as vertebral artery or brachial plexus injury occurred. Conclusion: The low rate of nerve transplantation in 50 patients and the absence of any serious complications in these cases suggests that the middle route is the optimal one.

Identifying changes in soybean protein properties during high-moisture extrusion processing using dead-stop operation.

To provide additional evidence and better understand the high-moisture texturing extrusion process of soybean protein isolate (SPI), the physico-chemical changes of SPI during extrusion were investigated. SPI in the extruder feeding zone, barrel zones 1 (80 °C) to 4 (135 °C), and the cooling die (80 °C) were obtained from a dead-stop operation. The lowest values associated with the denaturation enthalpy and the extractable protein occurred in zone 3 (150 °C). The minimum level of the protein subunit content was identified in zone 4. The highest value of the average protein molar mass occurred in zone 3. The ß-sheet ratio in the protein increased, and the unordered ratio decreased after extrusion. The surface hydrophobicity of the protein decreased with water injection in zone 1; however, it increased in zone 2 (110 °C). Overall, SPI undergoes swelling, denaturation, aggregation, and depolymerization due to water injection, heating, and shearing during high-moisture extrusion.

Floral morphology and phenology of Sassafras tzumu (Lauraceae).

BACKGROUND: Sassafras has been considered to belong to trib. Laureae of Lauraceae and has been assumed to have unisexual flowers. However, recent molecular phylogenetic studies have consistently suggested that Sassafras does not belong to the trib. Laureae but to Cinnamomeae and that it is nested within Cinnamomum. A recent morphological study revealed that one of the Asian species, S. randaiense, possesses bisexual flowers that are plesiomorphic in the family Lauraceae. As reports on the flower structure of the second Asian species, S. tzumu, have been contradictory, we wanted to ascertain if it has bisexual flowers or not. If the flowers were bisexual, could earlier reports that they were unisexual have been based on dichogamous flowering? RESULTS: In this study, we investigated two populations of S. tzumu. We found that this species has determinate botryoid racemes, and possesses bisexual flowers. Among the three extant species, S. tzumu is more similar to its sister species S. randaiense but markedly different from the American S. albidum: the two Asian species possess bisexual flowers while the American species has unisexual flowers. The bisexual flower of S. tzumu is protogynous, and shows two phenological phases typical of Lauraceae: 1) in a flower, the pistil functions first, the stigma is fresh and white, stamens of the outer two whorls are spreading, anthers do not open, and the staminodes secrete nectar at this stage; 2) in the second phase, the stigma becomes brown, staminodes are withered, stamens of the third whorl stand up and surround the pistil, glands of the third whorl of stamens secrete nectar, and the anthers open and release pollen. CONCLUSIONS: The similarity of racemose inflorescences between Sassafras and some members of Laureae were caused by parallel evolution; the racemose inflorescence of ancestral Sassafras originated from the thyrsoid-cymose inflorescence in Cinnamomum. The Asian species S. tzumu and S. randaiense possess bisexual flowers with two phenological phases, the American S. albidum evolved unisexual flowers independently from other clades with unisexual flowers in the Lauraceae, i.e., the Laureae, Alseodaphnopsis in the Perseeae and the unisexual clade in the Ocotea complex of the Cinnamomeae.

Quality Differences between Fresh and Dried Buckwheat Noodles Associated with Water Status and Inner Structure.

Buckwheat noodles are mainly sold in the form of fresh and dried noodles in China. Among the noodles with varied proportions of extruded buckwheat flour (20% to 80%), the cooking or textural qualities of fresh and dried buckwheat noodles (FBN and DBN, respectively) were significantly different, and FBN showed a lower cooking loss and breakage ratio and were more elastic than DBN. FBN-20% showed the highest sensory score, followed by DBN-50%. The mechanisms causing the quality differences were investigated using water mobility and the internal structures of the noodles were investigated with low-field nuclear magnetic resonance and scanning electron microscopy, respectively. Compared with FBN, DBN showed a denser internal structure, which explained its higher hardness. The water within FBN and DBN was mainly in the form of softly bound water and tightly bound water, respectively. FBN with highly mobile softly bound water (longer T 22) and a more uniform internal structure had a lower breakage ratio, whereas the trends of water relation with texture properties were different for FBN and DBN. The drying process and added extruded buckwheat flour together contributed to the varied cooking and textural properties.

In Situ Raman Study of CO Electrooxidation on Pt(hkl) Single-Crystal Surfaces in Acidic Solution.

The adsorption and electrooxidation of CO molecules at well-defined Pt(hkl) single-crystal electrode surfaces is a key step towards addressing catalyst poisoning mechanisms in fuel cells. Herein, we employed in situ electrochemical shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) coupled with theoretical calculation to investigate CO electrooxidation on Pt(hkl) surfaces in acidic solution. We obtained the Raman signal of top- and bridge-site adsorbed CO* molecules on Pt(111) and Pt(100). In contrast, on Pt(110) surfaces only top-site adsorbed CO* was detected during the entire electrooxidation process. Direct spectroscopic evidence for OH* and COOH* species forming on Pt(100) and Pt(111) surfaces was afforded and confirmed subsequently via isotope substitution experiments and DFT calculations. In summary, the formation and adsorption of OH* and COOH* species plays a vital role in expediting the electrooxidation process, which relates with the pre-oxidation peak of CO electrooxidation. This work deepens knowledge of the CO electrooxidation process and provides new perspectives for the design of anti-poisoning and highly effective catalysts.

Parallel isotope differential modeling for instationary 13C fluxomics at the genome scale.

BACKGROUND: A precise map of the metabolic fluxome, the closest surrogate to the physiological phenotype, is becoming progressively more important in the metabolic engineering of photosynthetic organisms for biofuel and biomass production. For photosynthetic organisms, the state-of-the-art method for this purpose is instationary 13C fluxomics, which has arisen as a sibling of transcriptomics or proteomics. Instationary 13C data processing requires solving high-dimensional nonlinear differential equations and leads to large computational and time costs when its scope is expanded to a genome-scale metabolic network. RESULT: Here, we present a parallelized method to model instationary 13C labeling data. The elementary metabolite unit (EMU) framework is reorganized to allow treating individual mass isotopomers and breaking up of their networks into strongly connected components (SCCs). A variable domain parallel algorithm is introduced to process ordinary differential equations in a parallel way. 15-fold acceleration is achieved for constant-step-size modeling and ~ fivefold acceleration for adaptive-step-size modeling. CONCLUSION: This algorithm is universally applicable to isotope granules such as EMUs and cumomers and can substantially accelerate instationary 13C fluxomics modeling. It thus has great potential to be widely adopted in any instationary 13C fluxomics modeling.

Massive MIMO-Based Distributed Signal Detection in Multi-Antenna Wireless Sensor Networks.

For massive multiple-input multiple-output (MIMO) distributed wireless sensor networks, this paper investigates the role of multi-antenna sensors in improving network perception performance. First, we construct a distributed multi-antenna sensor network based on massive MIMO. By using the anti-fading characteristics of multi-antennas, it is better to achieve accurate detection than the single-antenna sensor network. Based on this, we derive a closed-loop expression for the detection probability of the best detector. Then, we consider the case that the sensor power resources are limited, and thus we want to use finite power to achieve higher detection probability. For this reason, the power was optimized by the alternating direction method of multipliers (ADMM). Moreover, we also prove that only statistical channel state is needed in large-scale antenna scenarios, which avoid the huge overhead of channel state information. Finally, according to the simulation results, the multi-antenna sensor network has better detection performance than the single-antenna sensor network which demonstrates the improved performance of the proposed schemes and also validates the theoretical findings.

Effects of flour dynamic viscosity on the quality properties of buckwheat noodles.

Buckwheat noodles processing with higher ratio of buckwheat flour is very challenging while using conventional noodle processing techniques. The mechanism of how extruded buckwheat flours can increase the addition ratio, modify the noodle cooking, and eating qualities were explored. The molecular structures of extruded buckwheat flours with different dynamic viscosities were analyzed using size-exclusion chromatography, and the water status/distribution, morphologies and starch/protein distribution of buckwheat noodles with raw or extruded buckwheat flours (RBN/MBNs) were investigated using low-field nuclear magnetic resonance, scanning electron microscopy and Fourier transform infrared spectroscopy microscope. MBNs showed lower breakage ratio, higher hardness, elasticity, and total sensory score than those of RBN. In addition, medium-dynamic-viscosity extruded buckwheat flour contributed to a continuous gluten network and even starch/protein distribution of noodles, with better cooking and eating properties. Extruded buckwheat flour with dynamic viscosity higher than 7.37 Pa s functioned as adhesives, which can hold starch granules and other components within flours together.

A genome-scale metabolic network alignment method within a hypergraph-based framework using a rotational tensor-vector product.

Biological network alignment aims to discover important similarities and differences and thus find a mapping between topological and/or functional components of different biological molecular networks. Then, the mapped components can be considered to correspond to both their places in the network topology and their biological attributes. Development and evolution of biological network alignment methods has been accelerated by the rapidly increasing availability of such biological networks, yielding a repertoire of tens of methods based upon graph theory. However, most biological processes, especially the metabolic reactions, are more sophisticated than simple pairwise interactions and contain three or more participating components. Such multi-lateral relations are not captured by graphs, and computational methods to overcome this limitation are currently lacking. This paper introduces hypergraphs and association hypergraphs to describe metabolic networks and their potential alignments, respectively. Within this framework, metabolic networks are aligned by identifying the maximal Z-eigenvalue of a symmetric tensor. A shifted higher-order power method was utilized to identify a solution. A rotational strategy has been introduced to accelerate the tensor-vector product by 250-fold on average and reduce the storage cost by up to 1,000-fold. The algorithm was implemented on a spark-based distributed computation cluster to significantly increase the convergence rate further by 50- to 80-fold. The parameters have been explored to understand their impact on alignment accuracy and speed. In particular, the influence of initial value selection on the stationary point has been simulated to ensure an accurate approximation of the global optimum. This framework was demonstrated by alignments among the genome-wide metabolic networks of Escherichia coli MG-1655 and Halophilic archaeon DL31. To our knowledge, this is the first genome-wide metabolic network alignment at both the metabolite level and the enzyme level. These results demonstrate that it can supply quite a few valuable insights into metabolic networks. First, this method can access the driving force of organic reactions through the chemical evolution of metabolic network. Second, this method can incorporate the chemical information of enzymes and structural changes of compounds to offer new way defining reaction class and module, such as those in KEGG. Third, as a vertex-focused treatment, this method can supply novel structural and functional annotation for ill-defined molecules. The related source code is available on request.

δ2H of wheat and soil water in different growth stages and their application potentialities as fingerprints of geographical origin.

The study aims to investigate whether stable hydrogen isotopic ratio (δ2H) in wheat and soil water can be used as the fingerprint for geographical origin of wheat. Wheat was planted in three different regions in China across two years. The δ2H values were determined for soil water in three growth periods, and rainwater, groundwater, and defatted wheat in the maturity stage. The δ2H values both in soil water and defatted wheat showed significant differences among different regions and the changing trend of δ2H value in wheat was consistent with that in soil water and rainwater, but different from that in groundwater. The δ2H values in soil water in the depth of 0-20cm in the maturity stage were positively correlated to the δ2H values of defatted wheat (y=0.205x-52.628, r2=0.645) and could be used as the potential indicator for tracing wheat geographical origin.

Multilevel Structure of Wheat Starch and Its Relationship to Noodle Eating Qualities.

Although the processing and eating qualities of noodles are largely related to the quality and quantity of wheat protein (gluten), the importance of starch, a major ingredient of wheat flour, is often overlooked. Recent developments on the multilevel structural model of starch have brought new insights into the role of starch for better processing and noodle eating qualities. With critical analysis and discussion, this review outlines the comprehensive relationships between the multilevel (molecular, crystalline, and granular) starch structure, noodle eating qualities, and related physicochemical properties. Further, the major and minor structural features of wheat starch and their contributions toward noodle quality are summarized and presented as a schematic diagram, which shows the effects of starch structure on cooked noodles. These features provide new insights for the scientific community, as well as industry, into the role of starch, along with gluten, on the quality of noodles.

Combination of the (87)Sr/(86)Sr ratio and light stable isotopic values (δ(13)C, δ(15)N and δD) for identifying the geographical origin of winter wheat in China.

This study aims to investigate whether isotopic signatures can be used to develop reliable fingerprints for discriminating the geographical origin of Chinese winter wheat, and to evaluate the discrimination effects of δ(13)C, δ(15)N and δD, alone or with (87)Sr/(86)Sr. In this study, the values of δ(13)C, δ(15)N and δD, and the (87)Sr/(86)Sr ratios of wheat and provenance soils from three regions were determined. Significant differences were found in all parameters of wheat and (87)Sr/(86)Sr in soil extract (reflecting the bioavailable fraction of soil) among different regions. A significantly positive correlation was observed between the (87)Sr/(86)Sr ratios of wheat and soil extracts. An overall correct classification rate of 77.8% was obtained for discriminating wheat from three regions based on light stable isotopes (δ(13)C, δ(15)N, and δD). The correct classification rate of 98.1% could be obtained with the combination of the (87)Sr/(86)Sr ratio and the light stable isotopic values.

Complex Neural Network Models for Time-Varying Drazin Inverse.

Two complex Zhang neural network (ZNN) models for computing the Drazin inverse of arbitrary time-varying complex square matrix are presented. The design of these neural networks is based on corresponding matrix-valued error functions arising from the limit representations of the Drazin inverse. Two types of activation functions, appropriate for handling complex matrices, are exploited to develop each of these networks. Theoretical results of convergence analysis are presented to show the desirable properties of the proposed complex-valued ZNN models. Numerical results further demonstrate the effectiveness of the proposed models.