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Tryptophan metabolism and gut microbiota form an integrated regulatory axis that impacts immunity, metabolism, and cancer. This review consolidated current knowledge on the bidirectional interactions between microbial tryptophan processing and the host. We focused on how the gut microbiome controls tryptophan breakdown via the indole, kynurenine, and serotonin pathways. Dysbiosis of the gut microbiota induces disruptions in tryptophan catabolism which contribute to disorders like inflammatory conditions, neuropsychiatric diseases, metabolic syndromes, and cancer. These disruptions affect immune homeostasis, neurotransmission, and gut-brain communication. Elucidating the mechanisms of microbial tryptophan modulation could enable novel therapeutic approaches like psychobiotics and microbiome-targeted dietary interventions. Overall, further research on the microbiota-tryptophan axis has the potential to revolutionize personalized diagnostics and treatments for improving human health.

In this paper, a kind of D-type photonic crystal fiber (PCF) sensor with an ultra-wide detection range based on micro-opening gold film coating is proposed. This sensor allows for the sensing detection of the refractive index (RI) of the analyte ranging from 1.30 to 1.42. However, the sensor coated with a micro-opening gold film only achieves an average wavelength sensitivity of 1489 nm/RIU in the x-polarization direction. To improve the performance of the sensor, an attempt was made to replace the micro-opening gold film with MoO 2 nanofilm. After simulation calculation, it was found that the RI detection range of the sensor using MoO 2 nanofilm became 1.33–1.39. Excitingly, the average wavelength sensitivity in the x-polarized direction reaches 17, 178 nm/RIU, which is 11.5 times better than the original sensor. This implies that the sensor is more sensitive to changes in the RI and can provide more accurate sensing and detection results. It has been demonstrated that the performance of a D-type PCF sensor can be significantly improved by using MoO 2 nanofilm. This improvement helps to expand the application domain of sensors and enhance the accuracy of sensing detection. We believe that this research result has important implications for the development of fiber sensor technologies. Graphical Abstract Ultra-wide refractive index detection range fiber optic sensor

Chlorella sorokiniana is an important industry microalga potential for biofuel production. Inoculum size is one of the important factors in algal large-scale culture, and has great effects on the growth, lipid accumulation and metabolism of microalgae. As the first barrier of cell contents, membrane plays a vital role in algal inoculum-related metabolism. The knowledge of phospholipids, the main membrane component and high accumulation of phospholipids as the major content of total lipids mass in some microalgae, is necessary to understand the role of membrane in cell growth and metabolism under different inoculum density. Profiling of C. sorokiniana phospholipids with LC-MS led to the identification of 119 phospholipid species. To discover the phospholipid molecules most related to change of inoculum sizes, Partial Least Squares Discriminant Analysis (PLS-DA) was employed and the results revealed that inoculum sizes significantly affected phospholipid profiling. Phosphatidylglycerol (PG), phosphatidyl- ethanolamine (PE) and several phosphatidylcholine (PC) species might play an important role under our experimental conditions. Further analysis of these biomarkers indicated that cell membrane status of C. sorokiniana might play an important role in the adaption to the inoculum sizes. And the culture with inoculum size of 1 × 10(6) cells mL(-1) presented the best membrane status with the highest content of PC and PG, and the lowest content of PE. We discovered that the inoculum size of 1 × 10(6) cells mL(-1) might provide the best growth condition for C. sorokiniana. Also we proposed that PG, PE and several PC may play an important role in inoculum-related metabolism in C. sorokiniana, which may work through thylakoid membrane and photosynthetic pathway. Thus this study would provide more potential targets for metabolic engineering to improve biofuel production and productivity in microalgae.

Vitamin A is a micronutrient critical for versatile biological functions and has been widely used in the food, cosmetics, pharmaceutical, and nutraceutical industries. Synthetic biology and metabolic engineering enable microbes, especially the model organism Saccharomyces cerevisiae (generally recognised as safe) to possess great potential for the production of vitamin A. Herein, we first generated a vitamin A-producing strain by mining β-carotene 15,15′-mono(di)oxygenase from different sources and identified two isoenzymes Mbblh and Ssbco with comparable catalytic properties but different catalytic mechanisms. Combinational expression of isoenzymes increased the flux from β-carotene to vitamin A metabolism. To modulate the vitamin A components, retinol dehydrogenase 12 from Homo sapiens was introduced to achieve more than 90 % retinol purity using shake flask fermentation. Overexpressing POS5Δ17 enhanced the reduced nicotinamide adenine dinucleotide phosphate pool, and the titer of vitamin A was elevated by almost 46 %. Multi-copy integration of the key rate-limiting step gene Mbblh further improved the synthesis of vitamin A. Consequently, the titer of vitamin A in the strain harbouring the Ura3 marker was increased to 588 mg/L at the shake-flask level. Eventually, the highest reported titer of 5.21 g/L vitamin A in S. cerevisiae was achieved in a 1-L bioreactor. This study unlocked the potential of S. cerevisiae for synthesising vitamin A in a sustainable and economical way, laying the foundation for the commercial-scale production of bio-based vitamin A. [Display omitted]

In this paper, the 2D material MoO 2 is innovatively chosen to replace traditional precious metals such as Au and Ag as the plasmonically excited material, and for the first time, it is combined with the extreme thermal optical material polydimethylsiloxane (PDMS). A D-type microstructured fiber is used as the optical information transmission medium and open sensing channel, and a surface plasmon resonance (SPR) effect based M O O 2 coated D-type microstructured fiber temperature sensor is constructed. The simulation results show that the temperature detection range of the proposed optical fiber sensor is 30℃ ~ 80℃, and the sensing range of resonance wavelength is near the communication band of 1550 nm. The sensor is very sensitive to temperature variations, in particular the average wavelength sensitivity is up to 9.217 nm/°C in the x-polarized direction and 9.443 nm/°C in the y-polarized direction. This means that the sensor can accurately measure small changes in ambient temperature and respond quickly to ensure stable system operation. Therefore, MoO 2 as a plasmonic sensing material and PDMS as a temperature sensing material have great potential for fiber sensing applications.

Natural gas hydrates, crystalline solids whose gas molecules are so compressed that they are denser than a typical fluid hydrocarbon, have extensive applications in the areas of climate change and the energy crisis. The hydrate deposit located in the Shenhu Area on the continental slope of the South China Sea is regarded as the most promising target for gas hydrate exploration in China. Samples taken at drilling site SH2 have indicated a high abundance of methane hydrate reserves in clay sediments. In the last few decades, with its relatively low energy cost, the depressurization gas recovery method has been generally regarded as technically feasible and the most promising one. For the purpose of a better acquaintance with the feasible field operational factors and processes which control the production behavior of a real 3D geological CH4-hydrate deposit, it is urgent to figure out the effects of the parameters such as well type, well spacing, bottom hole pressure, and perforation intervals on methane recovery. One years’ numerical simulation results show that under the condition of 3000 kPa constant bottom hole pressure, 1000 m well spacing, perforation in higher intervals and with one horizontal well, the daily peak gas rate can reach 4325.02 m3 and the cumulative gas volume is 1.291 × 106 m3. What’s more, some new knowledge and its explanation of the curve tendency and evolution for the production process are provided. Technically, one factor at a time design (OFAT) and an orthogonal design were used in the simulation to investigate which factors dominate the productivity ability and which is the most sensitive one. The results indicated that the order of effects of the factors on gas yield was perforation interval > bottom hole pressure > well spacing.

In quantum mechanics, a norm-squared wave function can be interpreted as the probability density that describes the likelihood of a particle to be measured in a given position or momentum. This statistical property is at the core of the fuzzy structure of microcosmos. Recently, hybrid neural structures raised intense attention, resulting in various intelligent systems with far-reaching influence. Here, we propose a probability-density-based deep learning paradigm for the fuzzy design of functional metastructures. In contrast to other inverse design methods, our probability-density-based neural network can efficiently evaluate and accurately capture all plausible metastructures in a high-dimensional parameter space. Local maxima in probability density distribution correspond to the most likely candidates to meet the desired performances. We verify this universally adaptive approach in but not limited to acoustics by designing multiple metastructures for each targeted transmission spectrum, with experiments unequivocally demonstrating the effectiveness and generalization of the inverse design.

Chlorella sorokiniana is an important industry microalga potential for biofuel production. Inoculum size is one of the important factors in algal large-scale culture, and has great effects on the growth, lipid accumulation and metabolism of microalgae. As the first barrier of cell contents, membrane plays a vital role in algal inoculum-related metabolism. The knowledge of phospholipids, the main membrane component and high accumulation of phospholipids as the major content of total lipids mass in some microalgae, is necessary to understand the role of membrane in cell growth and metabolism under different inoculum density. Profiling of C. sorokiniana phospholipids with LC-MS led to the identification of 119 phospholipid species. To discover the phospholipid molecules most related to change of inoculum sizes, Partial Least Squares Discriminant Analysis (PLS-DA) was employed and the results revealed that inoculum sizes significantly affected phospholipid profiling. Phosphatidylglycerol (PG), phosphatidyl- ethanolamine (PE) and several phosphatidylcholine (PC) species might play an important role under our experimental conditions. Further analysis of these biomarkers indicated that cell membrane status of C. sorokiniana might play an important role in the adaption to the inoculum sizes. And the culture with inoculum size of 1106 cells mL-1 presented the best membrane status with the highest content of PC and PG, and the lowest content of PE. We discovered that the inoculum size of 1106 cells mL-1 might provide the best growth condition for C. sorokiniana. Also we proposed that PG, PE and several PC may play an important role in inoculum-related metabolism in C. sorokiniana, which may work through thylakoid membrane and photosynthetic pathway. Thus this study would provide more potential targets for metabolic engineering to improve biofuel production and productivity in microalgae.

Food is foundational to human life, serving not only as a source of nourishment but also as a cornerstone of cultural identity and social interaction. As the complexity of global dietary needs and preferences grows, food intelligence is needed to enable food perception and reasoning for various tasks, ranging from recipe generation and dietary recommendation to diet-disease correlation discovery and understanding. Towards this goal, for powerful capabilities across various domains and tasks in Large Language Models (LLMs), we introduce Food-oriented LLM FoodSky to comprehend food data through perception and reasoning. Considering the complexity and typicality of Chinese cuisine, we first construct one comprehensive Chinese food corpus FoodEarth from various authoritative sources, which can be leveraged by FoodSky to achieve deep understanding of food-related data. We then propose Topic-based Selective State Space Model (TS3M) and the Hierarchical Topic Retrieval Augmented Generation (HTRAG) mechanism to enhance FoodSky in capturing fine-grained food semantics and generating context-aware food-relevant text, respectively. Our extensive evaluations demonstrate that FoodSky significantly outperforms general-purpose LLMs in both chef and dietetic examinations, with an accuracy of 67.2% and 66.4% on the Chinese National Chef Exam and the National Dietetic Exam, respectively. FoodSky not only promises to enhance culinary creativity and promote healthier eating patterns, but also sets a new standard for domain-specific LLMs that address complex real-world issues in the food domain. An online demonstration of FoodSky is available at http://222.92.101.211:8200.