The Intersection of cerebral cholesterol metabolism and Alzheimer's disease: Mechanisms and therapeutic prospects.

Alzheimer's disease (AD) is a common neurodegenerative disease in the elderly, the exact pathogenesis of which remains incompletely understood, and effective preventive and therapeutic drugs are currently lacking. Cholesterol plays a vital role in cell membrane formation and neurotransmitter synthesis, and its abnormal metabolism is associated with the onset of AD. With the continuous advancement of imaging techniques and molecular biology methods, researchers can more accurately explore the relationship between cholesterol metabolism and AD. Elevated cholesterol levels may lead to vascular dysfunction, thereby affecting neuronal function. Additionally, abnormal cholesterol metabolism may affect the metabolism of ß-amyloid protein, thereby promoting the onset of AD. Brain cholesterol levels are regulated by multiple factors. This review aims to deepen the understanding of the subtle relationship between cholesterol homeostasis and AD, and to introduce the latest advances in cholesterol-regulating AD treatment strategies, thereby inspiring readers to contemplate deeply on this complex relationship. Although there are still many unresolved important issues regarding the risk of brain cholesterol and AD, and some studies may have opposite conclusions, further research is needed to enrich our understanding. However, these findings are expected to deepen our understanding of the pathogenesis of AD and provide important insights for the future development of AD treatment strategies targeting brain cholesterol homeostasis.

Spatiotemporal heterogeneity and long-term impact of meteorological, environmental, and socio-economic factors on scrub typhus in China from 2006 to 2018.

BACKGROUND: Large-scale outbreaks of scrub typhus combined with its emergence in new areas as a vector-borne rickettsiosis highlight the ongoing neglect of this disease. This study aims to explore the long-term changes and regional leading factors of scrub typhus in China, with the goal of providing valuable insights for disease prevention and control. METHODS: This study utilized a Bayesian space-time hierarchical model (BSTHM) to examine the spatiotemporal heterogeneity of scrub typhus and analyze the relationship between environmental factors and scrub typhus in southern and northern China from 2006 to 2018. Additionally, a GeoDetector model was employed to assess the predominant influences of geographical and socioeconomic factors in both regions. RESULTS: Scrub typhus exhibits a seasonal pattern, typically occurring during the summer and autumn months (June to November), with a peak in October. Geographically, the high-risk regions, or hot spots, are concentrated in the south, while the low-risk regions, or cold spots, are located in the north. Moreover, the distribution of scrub typhus is influenced by environment and socio-economic factors. In the north and south, the dominant factors are the monthly normalized vegetation index (NDVI) and temperature. An increase in NDVI per interquartile range (IQR) leads to a 7.580% decrease in scrub typhus risk in northern China, and a 19.180% increase in the southern. Similarly, of 1 IQR increase in temperature reduces the risk of scrub typhus by 10.720% in the north but increases it by 15.800% in the south. In terms of geographical and socio-economic factors, illiteracy rate and altitude are the key determinants in the respective areas, with q-values of 0.844 and 0.882. CONCLUSIONS: These results indicated that appropriate climate, environment, and social conditions would increase the risk of scrub typhus. This study provided helpful suggestions and a basis for reasonably allocating resources and controlling the occurrence of scrub typhus.

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems.

Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

Tuberculosis-targeted next-generation sequencing and machine learning: An ultrasensitive diagnostic strategy for paucibacillary pulmonary tuberculosis and tuberculous meningitis.

BACKGROUND: Existing diagnostic approaches for paucibacillary tuberculosis (TB) are limited by the low sensitivity of testing methods and difficulty in obtaining suitable samples. METHODS: An ultrasensitive TB diagnostic strategy was established, integrating efficient and specific TB targeted next-generation sequencing and machine learning models, and validated in clinical cohorts to test plasma cfDNA, cerebrospinal fluid (CSF) DNA collected from tuberculous meningitis (TBM) and pediatric pulmonary TB (PPTB) patients. RESULTS: In the detection of 227 samples, application of the specific thresholds of CSF DNA (AUC = 0.974) and plasma cfDNA (AUC = 0.908) yielded sensitivity of 97.01 % and the specificity of 95.65 % in CSF samples and sensitivity of 82.61 % and specificity of 86.36 % in plasma samples, respectively. In the analysis of 44 paired samples from TBM patients, our strategy had a high concordance of 90.91 % (40/44) in plasma cfDNA and CSF DNA with both sensitivity of 95.45 % (42/44). In the PPTB patient, the sensitivity of the TB diagnostic strategy yielded higher sensitivity on plasma specimen than Xpert assay on gastric lavage (28.57 % VS. 15.38 %). CONCLUSIONS: Our TB diagnostic strategy provides greater detection sensitivity for paucibacillary TB, while plasma cfDNA as an easily collected specimen, could be an appropriate sample type for PTB and TBM diagnosis.

Genome-Wide Association Study of Arsenic Accumulation in Polished Rice.

The accumulation of arsenic (As) in rice poses a significant threat to food safety and human health. Breeding rice varieties with low As accumulation is an effective strategy for mitigating the health risks associated with arsenic-contaminated rice. However, the genetic mechanisms underlying As accumulation in rice grains remain incompletely understood. We evaluated the As accumulation capacity of 313 diverse rice accessions grown in As-contaminated soils with varying As concentrations. Six rice lines with low As accumulation were identified. Additionally, a genome-wide association studies (GWAS) analysis identified 5 QTLs significantly associated with As accumulation, with qAs4 being detected in both of the experimental years. Expression analysis demonstrated that the expression of LOC_Os04g50680, which encodes an MYB transcription factor, was up-regulated in the low-As-accumulation accessions compared to the high-As-accumulation accessions after As treatment. Therefore, LOC_Os04g50680 was selected as a candidate gene for qAs4. These findings provide insights for exploiting new functional genes associated with As accumulation and facilitating the development of low-As-accumulation rice varieties through marker-assisted breeding.

When Broad Learning System Meets Label Noise Learning: A Reweighting Learning Framework.

Broad learning system (BLS) is a novel neural network with efficient learning and expansion capacity, but it is sensitive to noise. Accordingly, the existing robust broad models try to suppress noise by assigning each sample an appropriate scalar weight to tune down the contribution of noisy samples in network training. However, they disregard the useful information of the noncorrupted elements hidden in the noisy samples, leading to unsatisfactory performance. To this end, a novel BLS with adaptive reweighting (BLS-AR) strategy is proposed in this article for the classification of data with label noise. Different from the previous works, the BLS-AR learns for each sample a weight vector rather than a scalar weight to indicate the noise degree of each element in the sample, which extends the reweighting strategy from sample level to element level. This enables the proposed network to precisely identify noisy elements and thus highlight the contribution of informative ones to train a more accurate representation model. Thanks to the separability of the model, the proposed network can be divided into several subnetworks, each of which can be trained efficiently. In addition, three corresponding incremental learning algorithms of the BLS-AR are developed for adding new samples or expanding the network. Substantial experiments are conducted to explicate the effectiveness and robustness of the proposed BLS-AR model.

Plug-and-Play Priors for Multi-Shot Compressive Hyperspectral Imaging.

Multi-shot coded aperture snapshot spectral imaging (CASSI) uses multiple measurement snapshots to encode the three-dimensional hyperspectral image (HSI). Increasing the number of snapshots will multiply the number of measurements, making CASSI system more appropriate for detailed spatial or spectrally rich scenes. However, the reconstruction algorithms still face the challenge of being ineffective or inflexible. In this paper, we propose a plug-and-play (PnP) method that uses denoiser as priors for multi-shot CASSI. Specifically, the proposed PnP method is based on the primal-dual algorithm with linesearch (PDAL), which makes it flexible and can be used for any multi-shot CASSI mechanisms. Furthermore, a new subspaced-based nonlocal reweighted low-rank (SNRL) denoiser is presented to utilize the global spectral correlation and nonlocal self-similarity priors of HSI. By integrating the SNRL denoiser into PnP-PDAL, we show the balloons ( 512×512×31 ) in CAVE dataset recovered from two snapshots compressive measurements with MPSNR above 50 dB. Experimental results demonstrate that our proposed method leads to significant improvements compared to the current state-of-the-art methods.

Glycerol photocatalytic oxidation to higher value-added compounds via bismuth oxyhalide photocatalysts.

Bismuth oxyhalides (BiOX) including BiOCl, BiOBr, and BiOI, were well synthesized using solvothermal technique and then used in the aqueous phase photooxidation of glycerol as a catalyst. The as-synthesized BiOBr could achieve the highest glycerol transformation of around 85.6% in 8 h under ultraviolet light (UV) irradiation among as-synthesized BiOXs. Moreover, the BiOBr/TiO2 heterojunction was also prepared through an ethylene glycol-assisted solvothermal process. This new BiOBr/TiO2 heterostructure exhibited excellent photocatalytic activity (97.4%) for the oxidation of glycerol compared with pure BiOBr (74%) under ultraviolet light irradiation at 6 h. This obtained behavior was confirmed by more produced OH• radicals of BiOBr/TiO2.

China's dietary changes would increase agricultural blue and green water footprint.

Agriculture accounts for 61 % of fresh water consumption in China. Although population and diet have a significant impact on water consumption, little is known about the reasons for and extent of their influence. Changes in the blue and green water footprint of 20 agricultural sectors in 31 Chinese provinces were estimated in 5 scenarios by applying the environmentally expanded multi-regional input-output model. The water footprint network is strongly interconnected, with over 50 % of the provinces characterized as net importers of the blue water footprint, 70 % of the total blue and green water footprint imports in developed provinces, and 65 % of the total blue and green water footprint exports in developing provinces, with the flow distribution driven and dominated by economically developed provinces. The findings also highlighted that the impact of population change on the water footprint is insignificant, contributing 0.51 % and 5.78 % to the reduction of the water footprint in 2030 and 2050, respectively. The impact of simultaneous changes in the population and dietary structure on the water footprint was higher than population changes and lower than dietary structure changes. The main force driving changes in the water footprint was changes in the dietary structure, which resulted in a two-fold effect on the water footprint. First, it has increased the blue and green water footprint by 33 % and 12 %, respectively, thus aggravating the coercive impact on water resources on the production side. Second, it has led to a change in the main contributing sectors for the blue and green water footprint from cereals to fruits, vegetables, and potatoes. Therefore, when the population is changing and optimizing its dietary structures, a greater focus must be placed on threats and pressures to water resources. This will result in better scientific management and more efficient use of water resources.

[Genetic modification of acetogens and optimization of fermentation process in C1-gas bioconversion].

The current linear economy model relies on fossil energy and increases CO2 emissions, which contributes to global warming and environmental pollution. Therefore, there is an urgent need to develop and deploy technologies for carbon capture and utilization to establish a circular economy. The use of acetogens for C1-gas (CO and CO2) conversion is a promising technology due to high metabolic flexibility, product selectivity, and diversity of the products including chemicals and fuels. This review focuses on the physiological and metabolic mechanisms, genetic and metabolic engineering modifications, fermentation process optimization, and carbon atom economy in the process of C1-gas conversion by acetogens, with the aim to facilitate the industrial scale-up and carbon negative production through acetogen gas fermentation.

Longitudinal Changes in Milk Microorganisms in the First Two Months of Lactation of Primiparous and Multiparous Cows.

The present experiment was carried out to analyze the longitudinal changes in milk microorganisms. For this purpose, milk samples were collected from 12 healthy cows (n = 96; six primiparous cows and six multiparous cows) at eight different time points. The characteristics and variations in microbial composition were analyzed by 16S rRNA gene high-throughput sequencing. In the primiparous group, higher and more stable alpha diversity was observed in transitional and mature milk compared with the colostrum, with no significant difference in alpha diversity at each time point in the multiparous group. Proteobacteria, Firmicutes, Bacteroidota, and Actinobacteriota were the most dominant phyla, and Pseudomonas, UCG-005, Acinetobacter, Vibrio, Lactobacillus, Bacteroides, Serratia, Staphylococcus, and Glutamicibacter were the most dominant genera in both primiparous and multiparous cow milk. Some typically gut-associated microbes, such as Bacteroides, UCG-005, and Rikenellaceae_RC9_gut_group, etc., were enriched in the two groups. Biomarker taxa with the day in time (DIM) were identified by a random forest algorithm, with Staphylococcus showing the highest degree of interpretation, and the difference in milk microbiota between the two groups was mainly reflected in 0 d-15 d. Additionally, network analysis suggested that there were bacteria associated with the total protein content in milk. Collectively, our results disclosed the longitudinal changes in the milk microbiota of primiparous and multiparous cows, providing further evidence in dairy microbiology.

A Review of Convex Clustering From Multiple Perspectives: Models, Optimizations, Statistical Properties, Applications, and Connections.

Traditional partition-based clustering is very sensitive to the initialized centroids, which are easily stuck in the local minimum due to their nonconvex objectives. To this end, convex clustering is proposed by relaxing K -means clustering or hierarchical clustering. As an emerging and excellent clustering technology, convex clustering can solve the instability problems of partition-based clustering methods. Generally, convex clustering objective consists of the fidelity and the shrinkage terms. The fidelity term encourages the cluster centroids to estimate the observations and the shrinkage term shrinks the cluster centroids matrix so that their observations share the same cluster centroid in the same category. Regularized by the lpn -norm ( pn ∈ {1,2,+∞} ), the convex objective guarantees the global optimal solution of the cluster centroids. This survey conducts a comprehensive review of convex clustering. It starts with the convex clustering as well as its nonconvex variants and then concentrates on the optimization algorithms and the hyperparameters setting. In particular, the statistical properties, the applications, and the connections of convex clustering with other methods are reviewed and discussed thoroughly for a better understanding the convex clustering. Finally, we briefly summarize the development of convex clustering and present some potential directions for future research.

Liquid-Phase Dehydration of Glycerol to Acrolein with ZSM-5-Based Catalysts in the Presence of a Dispersing Agent.

Liquid-phase dehydration of glycerol to acrolein was investigated with solid acid catalysts, including H-ZSM-5, H3PO4-modified H-ZSM-5, H3PW12O40·14H2O and Cs2.5H0.5PW12O40, in the presence of sulfolane ((CH2)4SO2) as a dispersing agent under atmospheric pressure N2 in a batch reactor. High weak-acidity H-ZSM-5, high temperatures and high-boiling-point sulfolane improved the activity and selectivity for the production of acrolein through suppressing the formation of polymers and coke and promoting the diffusion of glycerol and products. Brønsted acid sites were soundly demonstrated to be responsible for dehydration of glycerol to acrolein by infrared spectroscopy of pyridine adsorption. Brønsted weak acid sites favored the selectivity to acrolein. Combined catalytic and temperature-programmed desorption of ammonia studies revealed that the selectivity to acrolein increased as the weak-acidity increased over the ZSM-5-based catalysts. The ZSM-5-based catalysts produced a higher selectivity to acrolein, while the heteropolyacids resulted in a higher selectivity to polymers and coke.

Modal-Regression-Based Broad Learning System for Robust Regression and Classification.

A novel neural network, namely, broad learning system (BLS), has shown impressive performance on various regression and classification tasks. Nevertheless, most BLS models may suffer serious performance degradation for contaminated data, since they are derived under the least-squares criterion which is sensitive to noise and outliers. To enhance the model robustness, in this article we proposed a modal-regression-based BLS (MRBLS) to tackle the regression and classification tasks of data corrupted by noise and outliers. Specifically, modal regression is adopted to train the output weights instead of the minimum mean square error (MMSE) criterion. Moreover, the l2,1 -norm-induced constraint is used to encourage row sparsity of the connection weight matrix and achieve feature selection. To effectively and efficiently train the network, the half-quadratic theory is used to optimize MRBLS. The validity and robustness of the proposed method are verified on various regression and classification datasets. The experimental results demonstrate that the proposed MRBLS achieves better performance than the existing state-of-the-art BLS methods in terms of both accuracy and robustness.

A chromosome-level genome assembly reveals that a bipartite gene cluster formed via an inverted duplication controls monoterpenoid biosynthesis in Schizonepeta tenuifolia.

Biosynthetic gene clusters (BGCs) are regions of a genome where genes involved in a biosynthetic pathway are in proximity. The origin and evolution of plant BGCs as well as their role in specialized metabolism remain largely unclear. In this study, we have assembled a chromosome-scale genome of Japanese catnip (Schizonepeta tenuifolia) and discovered a BGC that contains multiple copies of genes involved in four adjacent steps in the biosynthesis of p-menthane monoterpenoids. This BGC has an unprecedented bipartite structure, with mirrored biosynthetic regions separated by 260 kilobases. This bipartite BGC includes identical copies of a gene encoding an old yellow enzyme, a type of flavin-dependent reductase. In vitro assays and virus-induced gene silencing revealed that this gene encodes the missing isopiperitenone reductase. This enzyme evolved from a completely different enzyme family to isopiperitenone reductase from closely related Mentha spp., indicating convergent evolution of this pathway step. Phylogenomic analysis revealed that this bipartite BGC has emerged uniquely in the S. tenuifolia lineage and through insertion of pathway genes into a region rich in monoterpene synthases. The cluster gained its bipartite structure via an inverted duplication. The discovered bipartite BGC for p-menthane biosynthesis in S. tenuifolia has similarities to the recently described duplicated p-menthane biosynthesis gene pairs in the Mentha longifolia genome, providing an example of the convergent evolution of gene order. This work expands our understanding of plant BGCs with respect to both form and evolution, and highlights the power of BGCs for gene discovery in plant biosynthetic pathways.

Structure and physicochemical properties of low digestible Euryale ferox Salisb. seed starch.

BACKGROUND: Euryale ferox Salisb. is widely grown in China and Southeast Asia as a grain crop and medicinal plant. The composition, morphology, structure, physicochemical properties, thermal properties, and in vitro digestibility of North Euryale ferox seeds starch (NEFS), hybrid Euryale ferox seeds starch (HEFS), and South Euryale ferox seeds starch (SEFS) were studied. RESULT: Of the varieties that were studied, the amylose content of NEFS (23.03%) was the highest. Starch granules of each variety were smooth, sharp, small, and had an average diameter of 2 µm. All three varieties were A-type crystals with crystallinity ranging from 26.42% to 28.17%. The degree of double helix and the short-range order ranged from 1.9006 to 2.5324 and 1.4294 to 1.6006, respectively. The high proportion of C1 region in NEFS (17.74%) and HEFS (17.66%) were found. Thermodynamic properties in North Euryale ferox seeds included the highest onset temperature (To ) (71.43 °C), peak temperature (Tp ) (76.60 °C), conclusion temperature (Tc ) (82.77 °C), enthalpy of gelatinization (ΔH) (12.64 J g-1 ), and peak viscosity (1514 mPa·s). All three varieties maintained a low level of in vitro digestibility, with the highest resistant starch (RS) content (29.57%), the lowest rapidly digestible starch (RDS) content (27.07%), and the slowest hydrolysis kinetic constant (0.0303) in NEFS. CONCLUSION: The results revealed that the low digestibility of NEFS was attributable to compact granules, high crystallinity, high degree of order, and strong thermal stability. These digestive, physicochemical, and thermodynamic properties provide information for the future application of Euryale ferox seed starch in the food industry. © 2022 Society of Chemical Industry.

Self-Paced Broad Learning System.

Broad learning system (BLS), an efficient neural network with a flat structure, has received a lot of attention due to its advantages in training speed and network extensibility. However, the conventional BLS adopts the least square loss, which treats each sample equally and thus is sensitivity to noise and outliers. To address this concern, in this article we propose a self-paced BLS (SPBLS) model by incorporating the novel self-paced learning (SPL) strategy into the network for noisy data regression. With the assistance of the SPL criterion, the model output is used as feedback to learn appropriate priority weight to readjust the importance of each sample. Such a reweighting strategy can help SPBLS to distinguish samples from "easy" to "difficult" in model training, equipping the model robust to noise and outliers while maintaining the characteristics of the original system. Moreover, two incremental learning algorithms associated to SPBLS have also been developed, with which the system can be updated quickly and flexibly without retraining the entire model when new training samples are added or the network needs to be expanded. Experiments conducted on various datasets demonstrate that the proposed SPBLS can achieve satisfying performance for noisy data regression.

Modal Regression-Based Graph Representation for Noise Robust Face Hallucination.

Manifold learning-based face hallucination technologies have been widely developed during the past decades. However, the conventional learning methods always become ineffective in noise environment due to the least-square regression, which usually generates distorted representations for noisy inputs they employed for error modeling. To solve this problem, in this article, we propose a modal regression-based graph representation (MRGR) model for noisy face hallucination. In MRGR, the modal regression-based function is incorporated into graph learning framework to improve the resolution of noisy face images. Specifically, the modal regression-induced metric is used instead of the least-square metric to regularize the encoding errors, which admits the MRGR to robust against noise with uncertain distribution. Moreover, a graph representation is learned from feature space to exploit the inherent typological structure of patch manifold for data representation, resulting in more accurate reconstruction coefficients. Besides, for noisy color face hallucination, the MRGR is extended into quaternion (MRGR-Q) space, where the abundant correlations among different color channels can be well preserved. Experimental results on both the grayscale and color face images demonstrate the superiority of MRGR and MRGR-Q compared with several state-of-the-art methods.

Molecular Engineering of Peripheral Substitutions to Construct Efficient Acridine Core-Based Hole Transport Materials for Perovskite Solar Cells.

The development of highly efficient hole transport materials (HTMs) for perovskite solar cells (PSCs) has been a hot research topic. Acridine and its derivatives are gradually utilized as new blocks for optoelectronic applications, which stems from its rigid conjugated structure, shedding a new light on this old molecule. Meanwhile, its application in PSCs as a HTM has not been well explored, and the efficiency of 9,10-dihydroacridine (ACR)-based HTMs is relatively low. In this work, we conduct a systematic modulation of the peripheral substituents for ACR core building block-based HTMs and investigate the effects of the electron-donating ability and π-conjugation of peripheral groups on the photovoltaic performance of the corresponding HTMs. It is found that the peripheral groups with a weaker electron-donating ability and stronger π-conjugation are more suitable for the acridine core, which itself has a stronger electron-donating ability. Through molecular engineering, the newly developed HTM ACR-PhDM achieves an impressive power conversion efficiency of 23.5%. Our work lays the foundation for the design and development of efficient HTMs in the future.