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The global economic downturn due to the pandemic has resulted in shrinking the digital market in big economies such as the US and China. After the pandemic, many of the major tech and internet-based companies had to take action to reverse their the declining balance sheets and look for ways to financially rebound. Illustrated in this paper is how these digital giants in China could further advance their economy by minimising their paid manpower by working with/under the education system and inventing new temporary intern positions as semi-(im)material labour to expand their workforce. Based on our ethnographic work and interviews with interns, we elucidate the case of a Chinese tech intern, which exemplifies what we refer to as involution. Involution is a process by which the new generation is induced to accept a much more precarious economy as a result of nominal pay, yet is able to nevertheless survive, meet their daily needs and dwell in big cities, rather than engaging a radical change or revolution.

Research has identified volatility transmission from the oil market to the tanker freight market through external shocks. However, in the presence of intricate nonlinear structures, the academic literature often encounters difficulties in identifying cycles and their correlations across various timescales. This paper provides a multi-market analysis to comprehend the information from shock effects on different tanker routes and multi-peak fitting. Under different shock regimes, crude oil market and tanker freight rate shocks could transit bi-directly. When events occur, the crude oil market prices the expectations. However, when the actual performance of the future market differs from the traders’ predictions of the future market, a price gap exists. Generally, the trade opportunity is tough to catch up on because only partial information can be found. In this study, we investigate the volatility connection of multi-markets and shock effects to clarify previously undisclosed information using multi-peak analysis. The information gathered and double-checked by cargo markets, crude oil supply-demand balance, and tanker freight prices of various tanker types could assist us in identifying prospective trends and investment opportunities. The volatility of each market, as well as the correlation of multi-markets, gives insights to crude oil dealers, tanker market participants, and energy regulators.

Job-seeking is always an inescapable challenge for graduates. It may take a lot of time to find satisfying jobs due to the information gap between students who need satisfying offers and enterprises which ask for proper candidates. Although campus recruiting and job advertisements on the Internet could provide partial information, it is still not enough to help students and enterprises know each other and effectively match a graduate with a job. To narrow the information gap, we propose to recommend jobs for graduates based on historical employment data. Specifically, we construct a heterogeneous information network to characterize the relations between students, enterprises and industries. And then, we propose a meta-path based graph neural network, namely GraphRecruit, to further learn both latent student and enterprise portrait representations. The designed meta-paths connect students with their preferred enterprises and industries from different aspects. Also, we apply genetic algorithm optimization for meta-path selection according to application scenarios to enhance recommendation suitability and accuracy. To show the effectiveness of GraphRecruit, we collect five-year employment data and conduct extensive experiments comparing GraphRecruit with 4 classical baselines. The results demonstrate the superior performance of the proposed method.

BackgroundThe progress in Colorectal cancer (CRC) screening and management has resulted in an unprecedented caseload for histopathological diagnosis. While artificial intelligence (AI) presents a potential solution, the predominant emphasis on slide-level aggregation performance without thorough verification of cancer in each location, impedes both explainability and transparency. Effectively addressing these challenges is crucial to ensuring the reliability and efficacy of AI in histology applications.MethodIn this study, we created an innovative AI algorithm using transfer learning from a polyp segmentation model in endoscopy. The algorithm precisely localized CRC targets within 0.25 mm² grids from whole slide imaging (WSI). We assessed the CRC detection capabilities at this fine granularity and examined the influence of AI on the diagnostic behavior of pathologists. The evaluation utilized an extensive dataset comprising 858 consecutive patient cases with 1418 WSIs obtained from an external center.ResultsOur results underscore a notable sensitivity of 90.25% and specificity of 96.60% at the grid level, accompanied by a commendable area under the curve (AUC) of 0.962. This translates to an impressive 99.39% sensitivity at the slide level, coupled with a negative likelihood ratio of

IntroductionColorectal cancer (CRC) presents a substantial challenge characterized by unacceptably high mortality and morbidity, primarily attributed to delayed diagnosis and reliance on palliative care. The immune response of the host plays a pivotal role in carcinogenesis, with IL-38 emerging as a potential protective factor in CRC. However, the precise involvement of IL-38 among various leucocytes, its interactions with PD-1/PD-L1, and its impact on metastasis require further elucidation.ResultsOur investigation revealed a significant correlation between IL-38 expression and metastasis, particularly concerning survival and interactions among diverse leucocytes within draining lymph nodes. In the mesentery lymph nodes, we observed an inverse correlation between IL-38 expression and stages of lymph node invasions (TNM), invasion depth, distance, and differentiation. This aligns with an overall survival advantage associated with higher IL-38 expression in CRC patients’ nodes compared to lower levels, as well as elevated IL-38 expression on CD4+ or CD8+ cells. Notably, a distinct subset of patients characterized by IL-38high/PD-1low expression exhibited superior survival outcomes compared to other combinations.DiscussionOur findings demonstrate that IL-38 expression in colorectal regional nodes from CRC patients is inversely correlated with PD-1/PD-L1 but positively correlated with infiltrating CD4+ or CD8+ lymphocytes. The combined assessment of IL-38 and PD-1 expression in colorectal regional nodes emerges as a promising biomarker for predicting the prognosis of CRC.

IntroductionWound healing poses a clinical challenge in diabetes mellitus (DM) due to compromised host immunity. CD64, an IgG-binding Fcgr1 receptor, acts as a pro-inflammatory mediator. While its presence has been identified in various inflammatory diseases, its specific role in wound healing, especially in DM, remains unclear.ObjectivesWe aimed to investigate the involvement of CD64 in diabetic wound healing using a DM animal model with CD64 KO mice.MethodsFirst, we compared CD64 expression in chronic skin ulcers from human DM and non-DM skin. Then, we monitored wound healing in a DM mouse model over 10 days, with or without CD64 KO, using macroscopic and microscopic observations, as well as immunohistochemistry.ResultsCD64 expression was significantly upregulated (1.25-fold) in chronic ulcerative skin from DM patients compared to non-DM individuals. Clinical observations were consistent with animal model findings, showing a significant delay in wound healing, particularly by day 7, in CD64 KO mice compared to WT mice. Additionally, infiltrating CD163+ M2 macrophages in the wounds of DM mice decreased significantly compared to non-DM mice over time. Delayed wound healing in DM CD64 KO mice correlated with the presence of inflammatory mediators.ConclusionCD64 seems to play a crucial role in wound healing, especially in DM conditions, where it is associated with CD163+ M2 macrophage infiltration. These data suggest that CD64 relies on host immunity during the wound healing process. Such data may provide useful information for both basic scientists and clinicians to deal with diabetic chronic wound healing.

With the rapid development of a social economy, the yearly increase in air conditioning load in the winter and summer seasons may bring serious challenges to the safe and economic operation of the power grid during the peak period of electricity consumption. So, how we reasonably adjust the set temperature of air conditioning so as to cut down the load during peak periods is very important. In this paper, considering the thermal inertia of air-conditioned buildings and the adaptability of human thermal comfort to temperature changes, the air conditioning load is regarded as virtual energy storage, the air conditioning temperature adjustment range for different users is determined based on the adaptive thermal comfort model of different geographic locations and climatic conditions, and a compensation mechanism is set up based on air conditioning users’ level of participation. Then, an optimal scheduling strategy for a microgrid was constructed with the objectives of user satisfaction, carbon emissions, and microgrid operation benefits, as well as regulating the users’ electricity consumption behavior, and the strategy was solved by using a multi-objective JAYA algorithm. Finally, winter and summer are used as case studies to analyze the results, which demonstrate that regulating the virtual energy storage of air conditioning can effectively improve the economy and environmental friendliness of a microgrid operation and reduce the cost of electricity consumption for the users, taking into account the comfort of the users.

BackgroundPeople usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations.MethodsIn this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potential predictor variable data were derived from outdoor monitoring stations and meteorological databases. A ten-fold cross-validation was conducted to examine the performance of all proposed models.ResultsThe final predictor variables incorporated in the MLR model were outdoor PM2.5 concentration, type of place, season, wind direction, surface wind speed, hour, precipitation, air pressure, and relative humidity. The ten-fold cross-validation results indicated that both models constructed had good predictive performance, with the determination coefficients (R2) of RFR and MLR were 72.20 and 60.35%, respectively. Generally, the RFR model had better predictive performance than the MLR model (RFR model developed using the same predictor variables as the MLR model, R2 = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables.ConclusionIn this research, hourly average indoor PM2.5 concentration prediction models based on multiple types of places were developed for the first time. Both the MLR and RFR models based on easily accessible indicators displayed promising predictive performance, in which the machine learning domain RFR model outperformed the classical MLR model, and this result suggests the potential application of RFR algorithms for indoor air pollutant concentration prediction.

This paper uses a large-scale nationally representative dataset to examine the nonlinear effect of income on mental health. To investigate their causal relationship, the exogenous impact of automation on income is utilized as the instrument variable (IV). In addition, to explore their nonlinear relationship, both income and its quadratic term are included in regressions. It is found that the impact of income on mental health is U-shaped rather than linear. The turning point (7.698) of this nonlinear relation is near the midpoint of the income interval ([0, 16.113]). This suggests that depression declines as income increases at the lower-income level. However, beyond middle income, further increases in income take pronounced mental health costs, leading to a positive relationship between the two factors. We further exclude the possibility of more complex nonlinear relationships by testing higher order terms of income. In addition, robustness checks, using other instrument variables and mental health indicators, different IV models and placebo analysis, all support above conclusions. Heterogeneity analysis demonstrates that males, older workers, ethnic minorities and those with lower health and socioeconomic status experience higher levels of depression. Highly educated and urban residents suffer from greater mental disorders after the turning point. Religious believers and Communist Party of China members are mentally healthier at lower income levels, meaning that religious and political beliefs moderate the relationship between income and mental health.

Abstract Background and aims Colorectal cancer (CRC) is a major killer. Host immunity is important in tumorigenesis. Direct comparison among IL-36α, IL-36β and IL-36γ in the prognosis of CRC is unclear. Methods CRC tissue arrays were generated from colorectostomy samples with TNM stage, invasion depth and the demography of these patients (n = 185). Using immunohistochemistry/histopathology, IL-36α, IL-36β and IL-36γ were determined, in comparison to non-cancer tissues. Results A significant association was observed between colonic IL-36α, IL-36β or IL-36γ and the presence of cancer (with all P

This paper examines how AI at work impacts on-the-job learning, shedding light on workers’ reactions to the groundbreaking AI technology. Based on theoretical analysis, six hypotheses are proposed regarding three aspects of AI’s influence on on-the-job learning. Empirical results demonstrate that AI significantly inhibits people’s on-the-job learning and this conclusion holds true in a series of robustness and endogeneity checks. The impact mechanism is that AI makes workers more pessimistic about the future, leading to burnout and less motivation for on-the-job learning. In addition, AI’s replacement, mismatch, and deskilling effects decrease people’s income while extending working hours, reducing their available financial resources and disposable time for further learning. Moreover, it has been found that AI’s impact on on-the-job learning is more prominent for older, female and less-educated employees, as well as those without labor contracts and with less job autonomy and work experience. In regions with more intense human–AI competition, more labor-management conflicts, and poorer labor protection, the inhibitory effect of AI on further learning is more pronounced. In the context of the fourth technological revolution driving forward the intelligent transformation, findings of this paper have important implications for enterprises to better understand employee behaviors and to promote them to acquire new skills to achieve better human–AI teaming.

This study investigates the dynamic trading network structure of the international crude oil and gas market from year 2012 to 2017. We employed the dynamical similarity analysis at different time scales by inducing a multiscale embedding for dimensionality reduction. This analysis quantifies the effect of a global event on the dependencies and correlation stability at both the country and world level, which covers the top 53 countries. The response of China’s trading structure toward events after the unexpected 2014 price drop is compared with other major traders. China, as the world’s largest importing country, lacks strong stability under global events and could be greatly affected by a supply shortage, especially in the gas market. The trend of multi-polarization on the market share gives a chance for China to construct closer relationships with more stable exporters and join in the trade loop of major countries to improve its position in the energy trading networks. The hidden features of trade correlation may provide a deeper understanding of the robustness of relationship and risk resistance.

Cultural tightness-looseness represents the degree to which a particular culture possesses strong behavioral norms, and the degree to which members of that culture are likely to sanction individuals who deviate from those norms. While tightness-looseness has been quantified for a large and growing number of countries around the world, there are many countries where a tightness-looseness score has yet to be determined, thus impeding the inclusion of those countries in cross-cultural research with a tightness-looseness focus. There is a dearth of research on cultural tightness-looseness in South America in particular. We report results from a national survey of 1,265 Ecuadorian residents which provided quantification of the relatively tight culture of Ecuador.

The fluorescent advanced glycation end products (AGEs) and two typical AGEs, Nε-carboxymethyl-lysine (CML) and Nε-carboxyethyl-lysine (CEL) were identified in Pleurotus eryngii chips using fluorescence spectroscopy and ultra-performance liquid chromatography-triple quadrupole tandem mass spectrometry, respectively. Additionally, the effect of frying temperatures and times on the quality of Pleurotus eryngii chips was investigated. The results showed that the Pleurotus eryngii chips underwent browning during the frying process, with the color changing from white to golden yellow. The color intensified as the air-frying time and temperature increased. The crispy slices had a significantly (P

Starch is the main source of energy for the human body. Natural products such as procyanidin (PC) and protocatechuic acid (PCA) can reduce postp'ran'dial blood sugar by controlling the digestion of starch. The inhibition effects of PC and PCA on α-amylase and α-glucosidase were studied using inhibition rate determination, Lineweaver-Burk double reciprocal diagram, ultraviolet spectroscopy, fluorescence quenching, equivalent mapping, and Fourier transform infrared spectroscopy. The results showed that the IC50 values of PC for α-amylase and α-glucosidase inhibition were 0.0524 and 0.0106 mg/mL, respectively, and those of PCA were 1.9426 and 1.0667 mg/mL respectively, which showed that PCA had better inhibition ability. The Lineweaver-Burk double reciprocal diagram showed that the inhibition of PC and PCA on α-amylase was a mixed inhibition, and the inhibition on α-glucosidase was non-competitive inhibition and competitive inhibition, respectively. The Binding of PC and PCA with α-amylase and α-glucosidase mainly depended on hydrophobic and hydrogen bonds, and further altered the enzyme structure by changing the microenvironment of amino acid residues. The inhibitory effects on α-glucosidase were the best when the ratio of PC and PCA was 1:21, and the inhibitory effects on α-amylase were optimal at 1:37. When the ratio of PC and PCA was 1:21, CI was less than 1, which showed a synergistic inhibitory effect on α-glucosidase. When the compounding ratio was 1:37, CI was less than 1, which showed a synergistic inhibitory effect on α-amylase. This study revealed the inhibitory mechanism of the combined use of PC and PCA on starch-digesting enzymes and would provide a theoretical basis for the development of natural hypoglycemic dietary supplements as health food excipients or drugs.

ABSTRACTLentinan (LNT) is extracted from the fruiting body of Lentinus edodes. Its active ingredient is β−1,3-dextran and has many biological activities, but its rheological characteristics and antibacterial mechanism when mixed with saliva are rarely reported. In this study, the LNT and artificial saliva (AS) mixture was used to investigate the rheological analysis and antibacterial mechanism against Streptococcus mutans (S. mutans). The results showed that the apparent viscosity of the mixture was positively correlated with the concentration, showing a shear thinning behavior and a “non-Newtonian fluid” with a pseudoplastic behavior with the increase of shear rate. In dynamic rheology, the 8% and 6% mixtures became a gel trend and viscoelastic properties, showing elasticity of weak gel at low frequencies and liquid-like viscosity at high frequencies. Moreover, the compound could damage the structures of S. mutans and increased the leakage of intracellular macromolecules, electrical conductivity, and activities of alkaline phosphatase (AKP) and β-galactoside but inhibited acid production, fluorescence intensity, and ATP synthesis by decreasing the levels of glucokinase (glk), acetate kinase (ackA), pyruvate kinase (pykf), phosphoglycerate kinase (pgk), 6-phosphofructokinase (pfk) in the glycolysis pathway. These results indicate that LNT plays a dominant role in the oral processing and antibacterial mechanism against S. mutans. It can provide an excellent theoretical basis for studying LNT as a functional food raw material.

In recent years, the environmental quality of Guangxi Province has been deteriorating, and the pollution of the "three wastes" has directly affected the happiness index of contemporary people's lives and the quality of life of future generations. Therefore, it has certain practical significance to explore the source and path of environmental pollution. This paper draws the environmental Kuznets curve based on the spatial panel model of the relationship between environmental pollution and economic growth in various regions of Guangxi Province. We found that the overall, eastern and central wastewater discharges and per capita GDP in Guangxi Province all have a monotonous increasing curve, while the western and northeastern regions have inverted U-shaped and U-shaped curves, respectively. The sulphur dioxide emissions in various regions of Guangxi Province and the per capita real GDP are in an inverted N-shaped curve, and both have passed the second inflection point. The relationship between industrial waste gas emissions and per capita GDP, except for the northeastern region, which is inverted-N, has a monotonic increasing curve in other regions. The relationship between the emission of smoke and dust and per capita GDP in Guangxi Province has a monotonous decreasing curve except for the northeastern region which is inverted N-shaped. The relationship between industrial solid waste discharge and per capita GDP in Guangxi Province has a monotonic increasing curve except for the U-shaped northeast region. The results show that the economic growth in various regions of Guangxi Province has caused serious environmental pollution, especially wastewater, industrial waste gas, and industrial solid waste pollution. [ABSTRACT FROM AUTHOR]

Research background. Sparassis latifolia polysaccharides can regulate lipids and cholesterol in serum and liver. However, little is known about the regulation mechanism of the polysaccharides on cholesterol metabolism and especially the causal relationship with gut microbiota regulation. This study will provide a theoretical basis for the cholesterol-lowering mechanism of S. latifolia polysaccharides and further development of functional foods. Experimental approach. In this study, we investigated how the regulation mechanism of Sparassis latifolia polysaccharides affects intestinal cholesterol metabolism in high-fat and high-cholesterol diet-fed rats. Briefly, enzymatic colorimetric microplate assay was used to determine the concentration of faecal bile acid. Gas chromatography-mass spectrometry was used to detect the content of cholesterol and alcohol in faeces. Haematoxylin and eosin staining method was applied to observe the changes in the structure of the small intestine tissue. The related gene expressions in jejunum and ileum were detected by real-time fluorescent quantitative polymerase chain reaction. The related protein expressions in jejunum were studied by using Western blot. High-throughput sequencing was used to detect the intestinal flora changes of the caecal contents. Gas chromatography-mass spectrometry was applied to detect the concentration of short-chain fatty acids in the caecal content. Results and conclusions. The results showed that Sparassis latifolia polysaccharides could improve the intestinal morphological structure and physiological indices in rats fed high-fat and high-cholesterol diet. Moreover, it could improve intestinal cholesterol metabolism disorder induced by high-fat and high-cholesterol diets via the reduction of the expression of HMGCR, NPC1L1, ACAT2, MTP, ASBT and IBABP mRNA or protein, increasing ABCG8 mRNA expression. In addition, it could also increase the relative abundance of Bacteroides, Butyricicoccus, Parabacteroides, Parasutteerella and Alloprevotella and the short-chain fatty acid concentration, to comprehensively regulate the intestinal cholesterol metabolism. The metabolomics analysis found that Sparassis latifolia polysaccharides could affect lipid, carbohydrate and other related metabolites. Some biomarkers associated with cholesterol metabolism correlated significantly with the abundance of specific intestinal microbiota. Novelty and scientific contribution. These findings indicate that Sparassis latifolia polysaccharides could attenuate intestinal cholesterol metabolism disorder, correlating with modulating gut microbiota and improving host metabolism. They provide theoretical support for the development of Sparassis latifolia as a new food resource.

Reliable photovoltaic(PV) forecasting can provide important data support for power system operation, which is the key to realize the large-scale consumption of solar energy resources. PV forecasting task becomes crucial to ensure power system stability and economic operation. This paper reviews the existing research of PV forecasting methods from the perspective of multi-temporal scale and multi-spatial scale. Firstly, according to the forecasting process, demand, temporal and spatial scale, the forecasting methods are classified and the evaluation indicators involved in the research are listed. Secondly, based on the temporal scale of PV power generation, the results are combed through the three kind of scale of ultra-short-term, short-term and medium and long-term prediction. Thirdly, on each kind of temporal scale, the results are subdivided into single-site prediction and regional prediction to sort out in detail. Finally, the results are analyzed on the basis of the predicted temporal scale, spatial scale and input data. It has been observed that most recent papers highlight the importance of short-term predictions. The machine learning method shows excellent nonlinear description ability in short-term prediction, the prediction results are satisfactory. The spatial average effect of regional prediction reduces the variability of solar energy, the prediction results are reliable.

BackgroundSparassis latifolia (S. latifolia) is a precious edible fungus with multiple biological activities. To date, no study has been investigated the underlying molecular mechanism of immunoregulation caused by the neutral polysaccharide of S. latifolia.Materials and methodsTo investigate immunomodulatory mechanism of S. latifolia neutral polysaccharide (SLNP), SLNP was obtained from S. latifolia and its structure, immune receptors and regulation mechanism were studied.ResultsS. latifolia neutral polysaccharide consisted of arabinose, galactose, glucose, xylose, and mannose with a molar ratio of 6:12:63:10:5. SLNP was a pyran polysaccharide with a relative molecular weight of 3.2 × 105 Da. SLNP promoted the proliferation of RAW264.7, which further induced the secretions of nitric oxide, TNF-α, IL-6, and IFN-β, and upregulated the immune receptor TLR4 expression. Moreover, SLNP increased remarkably the levels of TRAF6, IRF3, JNK, ERK, p38, and p38 mRNA and protein mediated by TLR4.ConclusionS. latifolia neutral polysaccharide regulated the immune function of RAW264.7 through MyD88-dependent and -independent signaling pathways mediated by TLR4 receptor, which suggests that SLNP is a new immunomodulator.

Abstract Background Sparassis crispa polysaccharides (SCPs) have multiple pharmacological activities. Fermentation characteristics of SCPs and its effects on the intestinal microbes in mice remain inconclusive. Results In this study, SCPs were fermented by the human feces and used to administer the Kunming mice to explore the fermentation characteristics of SCPs in the intestinal tract and the effects on the intestinal microbes in mice. Results from in vitro experiments revealed that SCPs were utilized by intestinal microbiota to produce short-chain fatty acids (SCFAs). The specific monosaccharide composition of SCPs determines which SCFAs are produced. Furthermore, the colon index and villi length of the SCPs-treated mice were significantly higher compared with the control group. In addition, SCPs exhibited beneficial effect on the relative abundance and diversity of dominant bacteria in the intestinal tract, such as increasing Bacteroidetes/Firmicutes ratio and up-regulating SCFA-producing bacteria, including Bacteroidales_S24-7_group, Alloprevotella, Alistipes, Bacteroides, Butyricimonas, Parabacteroides, Lachnospiraceae_NK4A136_group and Oscillibacter. SCPs increased the abundance of genes in carbohydrate, amino acid, and energy metabolism. Conclusion Our results indicate SCPs can improve the physiological indices of the colon in mice, which is likely to be associated with the increase in the relative abundance and diversity of SCFA-producing bacteria and SCFAs level produced by intestinal microbiota. Graphic abstract

This study aimed to evaluate the effects of high-voltage pulsed electric fields (HPEF) and transglutaminase (TGase) cross-clinking on the physicochemical and rheological properties of Pleurotus eryngii protein (PEP). The results showed that HPEF increased α-helixes and β-turns but decreased β-folds. A HPEF at 1500 V/cm maximized the free sulfhydryl content and solubility of PEP. TGase formed high-molecular-weight polymers in PEP. TGase at 0.25% maximized the free sulfhydryl groups, particle size, and solubility; shifted the maximum absorption wavelength from 343 nm to 339 nm and 341 nm; increased α-helixes and β-turns and decreased β-folds; and showed better rheological properties. Compared with TGase cross-linking, HPEF-1500 V/cm and 1% TGase significantly reduced the free sulfhydryl groups, particle size, and solubility, produced more uniform network structures, and improved the rheological properties. These results suggest that HPEF can increase the cross-linking of TGase and improve rheological properties of TGase-cross-linked PEP by affecting the physicochemical properties.

Dietary supplements have improved the prevention of insulin resistance and metabolic diseases, which became a research hotspot in food science and nutrition. Obesity and insulin resistance, caused by a high-fat diet, eventually result in severe metabolic diseases, can be prevented with the dietary supplement D-chiro-inositol (DCI). In this work, we isolated mice primary hepatocytes with palmitic acid stimulation and DCI was applied to compare and contrast its effects of in primary hepatocyte biology. Before and after intervention with DCI, we used RNA-Seq technology to establish a primary hepatocyte transcriptome gene profile. We found that both PA and DCI cause a wide variation in gene expression. Particularly, we found that DCI plays critical role in this model by acting on glycolysis and gluconeogenesis. Overall, we generated extensive transcripts from primary hepatocytes and uncovered new functions and gene targets for DCI.

The computed tomography of chemiluminescence (CTC) technique is an advanced volumetric imaging technique that can resolve the internal structure of a flame and has found extensive applications in spatiotemporally resolved measurements of complex combustion phenomena. Despite its extensive application, certain improvements are still long-desired to further enhance its performance and reduce measurement uncertainties. Based on the Beer–Lambert law, this work introduces absorption correction into the CTC technique to account for the impact of signal attenuation in the tomographic algorithm. A reverse ray tracing method with distortion correction is proposed to calculate the absorption length within each cubic voxel and the corresponding absorption term, which has been ignored by previous works. To mathematically resolve the coupling process between chemiluminescence attenuation and accumulation during the signal trapping, an alternating-iteration reconstruction algorithm is developed. The results from a proof-of-concept experiment prove the good fidelity of the absorption-corrected CTC technique in solving the 3D distribution of chemiluminescence. [ABSTRACT FROM AUTHOR]