Explore the vast range of titles available.

The scope of this book covers the modeling and forecast of renewable energy and operation and planning of power system with renewable energy integration. The first part presents mathematical theories of stochastic mathematics; the second presents modelling and analytic techniques for renewable energy generation; the third provides solutions on how to handle the uncertainty of renewable energy in power system operation. It includes advanced stochastic unit commitment models to acquire the optimal generation schedule under uncertainty, efficient algorithms to calculate the probabilistic power, and an efficient operation strategy for renewable power plants participating in electricity markets.

The research field of systems biology has greatly advanced and, as a result, the concept of network pharmacology has been developed. This advancement, in turn, has shifted the paradigm from a \"one-target, one-drug\" mode to a \"network-target, multiple-component-therapeutics\" mode. Network pharmacology is more effective for establishing a \"compound-protein/gene-disease\" network and revealing the regulation principles of small molecules in a high-throughput manner. This approach makes it very powerful for the analysis of drug combinations, especially Traditional Chinese Medicine (TCM) preparations. In this work, we first summarized the databases and tools currently used for TCM research. Second, we focused on several representative applications of network pharmacology for TCM research, including studies on TCM compatibility, TCM target prediction, and TCM network toxicology research. Third, we compared the general statistics of several current TCM databases and evaluated and compared the search results of these databases based on 10 famous herbs. In summary, network pharmacology is a rational approach for TCM studies, and with the development of TCM research, powerful and comprehensive TCM databases have emerged but need further improvements. Additionally, given that several diseases could be treated by TCMs, with the mediation of gut microbiota, future studies should focus on both the microbiome and TCMs to better understand and treat microbiome-related diseases.

This study aimed to evaluate the safety and efficacy of camrelizumab combined with chemotherapy during preoperative neoadjuvant therapy in patients with locally advanced resectable esophageal squamous cell carcinoma (ESCC) of clinical Stages II and III. The patients received camrelizumab plus chemotherapy regimen on Day 1 for up to three to four cycles (3 weeks per cycle). The probabilities of overall survival (OS) were 55.6% at 12 months and 35.6% at 18 months (45 patients). The disease‐free survival (DFS) rates were 70.0% at 12 months and 63.3% at 18 months (30 patients). The median OS and DFS were not reached. The proportion of patients at postneoadjuvant pathological tumor stages ypT0, ypT2, and ypT3 were 10 (33.3%), 14 (46.7%), and 6 (20.0%), respectively, and those at stages ypN0 and ypN1 were 19 (63.3%) and 11 (36.7%), respectively. Additionally, the pathological complete response rate was 33.3% (95% confidence interval [CI]: 0.154–0.512), and the major pathologic response rate was 46.7% (95% CI: 0.277–0.656). Grade ≥3 adverse events (AEs) were reported in five patients (11.1%), with vomiting being the most common AE (three patients; 3.3%). Other common AEs of any grade included decreased lymphocyte count (48.9%), reactive capillary endothelial proliferation (46.7%), decreased white blood cell count (40.0%), anemia (31.1%), and vomiting (31.1%). The combination of camrelizumab and neoadjuvant chemotherapy in patients with locally advanced resectable ESCC demonstrated promising efficacy and acceptable safety.

BackgroundLiver hepatocellular carcinoma (LIHC) is a challenging and deadly cancer with poor prognosis and treatment options. Despite recent advances in genomics and immunotherapies, a deeper understanding of the molecular mechanisms underlying LIHC survival is crucial to identify novel therapeutic targets. One promising area of research is the tumor microbiome, a complex community of microbes found in tumors and surrounding tissue. However, the intricate relationships between microbial profiles and host gene expressions that drive the development of LIHC and influence patient survival remain unclear.MethodsTo address this challenge, we developed ASD-LIHC (autoencoder-based subtypes detector for LIHC), a semi-supervised deep learning framework that extracts survival-related features from tumor microbiome and transcriptome data to differentiate LIHC patients into survival subtypes based on their overall survival time (IDDF2023-ABS-0080 Figure 1a). We tested our framework on a cohort of 360 LIHC patients from The Cancer Genome Atlas (TCGA) database.ResultsUsing ASD-LIHC, we identified two statistically distinct survival subtypes in these LIHC patients. Our framework provided improved risk-stratification (log-rank test, P = 8.12E-6) compared to traditional PCA methods (log-rank test, P = 0.87), accurately predicted survival subtypes, and identified important biomarkers for classifying survival subtypes, which are likely not sensitive about clinical stages (IDDF2023-ABS-0080 Figure 1b). Furthermore, our analysis revealed that the high-risk group had more cancer-related pathways compared to the low-risk group, and we identified potential pathways of interaction between microbes and genes that may play a role in LIHC survival (IDDF2023-ABS-0080 Figure 1c,d). For instance, Arcobacter,Methylocella, and Isoptericola may regulate host survival through interactions with host genes enriched in critical signaling pathways in cancer, particularly the HIF-1 signaling pathway, indicating these species as potential therapy targets to improve LIHC patient prognosis.ConclusionsOverall, our study sheds light on the complex interplay between microbes and genes in LIHC survival and has important implications for its monitoring, management, prevention, and treatment. Our findings may guide the development of specific treatments and future drug design, ultimately improving outcomes for patients with this devastating disease.Abstract IDDF2023-ABS-0080 Figure 1

Summary Glycyrrhiza uralensis Fisch is a medicinal plant widely used to treat multiple diseases in Europe and Asia, and its efficacy largely depends on liquiritin and glycyrrhizic acid. The regulatory pattern responsible for the difference in efficacy between wild and cultivated G. uralensis remains largely undetermined. Here, we collected roots and rhizosphere soils from wild (WT) G. uralensis as well as those farmed for 1 year (C1) and 3 years (C3), generated metabolite and transcript data for roots, microbiota data for rhizospheres and conducted comprehensive multi‐omics analyses. We updated gene structures for all 40 091 genes in G. uralensis, and based on 52 differentially expressed genes, we charted the route‐map of both liquiritin and glycyrrhizic acid biosynthesis, with genes BAS, CYP72A154 and CYP88D6 critical for glycyrrhizic acid biosynthesis being significantly expressed higher in wild G. uralensis than in cultivated G. uralensis. Additionally, multi‐omics network analysis identified that Lysobacter was strongly associated with CYP72A154, which was required for glycyrrhizic acid biosynthesis. Finally, we developed a holistic multi‐omics regulation model that confirmed the importance of rhizosphere microbial community structure in liquiritin accumulation. This study thoroughly decoded the key regulatory mechanisms of liquiritin and glycyrrhizic acid, and provided new insights into the interactions of the plant's key metabolites with its transcriptome, rhizosphere microbes and environment, which would guide future cultivation of G. uralensis. The transcripts, metabolite profiles and rhizosphere microbial communities of wild and cultivated Glycyrrhiza uralensis Fisch were characterized and compared. The accumulations of both liquiritin and glycyrrhizic acid in wild G. uralensis were higher than those of cultivated G. uralensis. Significant gene expression differences were observed for G. uralensis with different growth status (wild and cultivated), especially for those genes involved in biosynthesis of liquiritin and glycyrrhizic acid. Rhizosphere microbial community structures have profound influences on the accumulation of liquiritin for G. uralensis.

With the advancement of systems biology research, we have already seen great progress in pharmacology studies, especially in network pharmacology. Network pharmacology has been proven to be effective for establishing the “compounds-proteins/genes-diseases” network, and revealing the regulation principles of small molecules in a high-throughput manner, thus would be very effective for the analysis of drug combinations, especially for TCM preparations. In this work, we have proposed the TCM-Mesh system, which records TCM-related information collected from various resources and could serve for network pharmacology analysis for TCM preparations in a high-throughput manner (http://mesh.tcm.microbioinformatics.org/). Currently, the database contains 6,235 herbs, 383,840 compounds, 14,298 genes, 6,204 diseases, 144,723 gene-disease associations, 3,440,231 pairs of gene interactions, 163,221 side effect records and 71 toxic records, and web-based software construct a network between herbs and treated diseases, which will help to understand the underlying mechanisms for TCM preparations at molecular levels. We have used 1,293 FDA-approved drugs, as well as compounds from an herbal material Panax ginseng and a patented drug Liuwei Dihuang Wan (LDW) for evaluating our database. By comparison of different databases, as well as checking against literature, we have demonstrated the completeness, effectiveness, and accuracy of our database.

While high sulfur loading has been pursued as a key parameter to build realistic high-energy lithium-sulfur batteries, less attention has been paid to the cathode porosity, which is much higher in sulfur/carbon composite cathodes than in traditional lithium-ion battery electrodes. For high-energy lithium-sulfur batteries, a dense electrode with low porosity is desired to minimize electrolyte intake, parasitic weight, and cost. Here we report the profound impact on the discharge polarization, reversible capacity, and cell cycling life of lithium-sulfur batteries by decreasing cathode porosities from 70 to 40%. According to the developed mechanism-based analytical model, we demonstrate that sulfur utilization is limited by the solubility of lithium-polysulfides and further conversion from lithium-polysulfides to Li 2 S is limited by the electronically accessible surface area of the carbon matrix. Finally, we predict an optimized cathode porosity to maximize the cell level volumetric energy density without sacrificing the sulfur utilization. For high-energy lithium-sulfur batteries, a dense electrode with low porosity is desired to minimize electrolyte intake, parasitic weight, and cost. Here the authors show the impact of porosity on the performance of lithium-sulfur batteries and reveal the mechanism through analytical modeling.

Pteromalidae and Eulophidae are predominant and abundant taxa within Chalcidoidea (Hymenoptera: Apocrita). These taxa are found in diverse ecosystems, ranging from basin deserts (200 m) to alpine grasslands (4500 m). Mitochondria, cellular powerhouses responsible for energy production via oxidative phosphorylation, are sensitive to various environmental factors such as extreme cold, hypoxia, and intense ultraviolet radiation characteristic of alpine regions. Whether the molecular evolution of mitochondrial genes in these parasitoids corresponds to changes in the energy requirements and alpine environmental adaptations remains unknown. In this study, we performed a comparative analysis of mitochondrial protein-coding genes from 11 alpine species of Pteromalidae and Eulophidae, along with 18 lowland relatives, including 16 newly sequenced species. We further examined the codon usage preferences (RSCU, ENC-GC3s, neutrality, and PR2 bias plot) in these mitochondrial protein-coding sequences and conducted positive selection analysis based on their Bayesian phylogenetic relationships, and identified positive selection sites in the ATP6 , ATP8 , COX1 , COX3 , and CYTB genes, emphasizing the crucial role of mitochondrial gene adaptive evolution in the adaptation of Pteromalidae and Eulophidae to alpine environments. The phylogenetically independent contrast (PIC) analysis results verified the ω ratio of 13 PCGs from Pteromalidae and Eulophidae increased with elevation, and results from generalized linear model confirm that ATP6 , ATP8 , COX3 , and ND1 are closely correlated with temperature-related environmental factors. This research not only enriched the molecular data of endemic alpine species but also underscores the significance of mitochondrial genes in facilitating the adaptation of these minor parasitoids to plateau habitats.

The pandemic of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to >35 million confirmed cases and >1 million fatalities worldwide. SARS-CoV-2 mainly replicates in human airway epithelia in COVID-19 patients. In this study, we used in vitro cultures of polarized human bronchial airway epithelium to model SARS-CoV-2 replication for a period of 21 to 51 days. We discovered that in vitro airway epithelial cultures endure a long-lasting SARS-CoV-2 propagation with recurrent peaks of progeny virus release at an interval of approximately 7 to 10 days. Our study also revealed that SARS-CoV-2 infection causes airway epithelia damage with disruption of tight junction function and loss of cilia. Importantly, SARS-CoV-2 exhibits a polarity of infection in airway epithelium only from the apical membrane; it infects ciliated and goblet cells but not basal and club cells. Furthermore, the productive infection of SARS-CoV-2 requires a high viral load of over 2.5 × 10 5 virions per cm 2 of epithelium. Our study highlights that the proliferation of airway basal cells and regeneration of airway epithelium may contribute to the recurrent infections. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replicates throughout human airways. The polarized human airway epithelium (HAE) cultured at an airway-liquid interface (HAE-ALI) is an in vitro model mimicking the in vivo human mucociliary airway epithelium and supports the replication of SARS-CoV-2. Prior studies characterized only short-period SARS-CoV-2 infection in HAE. In this study, continuously monitoring the SARS-CoV-2 infection in HAE-ALI cultures for a long period of up to 51 days revealed that SARS-CoV-2 infection was long lasting with recurrent replication peaks appearing between an interval of approximately 7 to 10 days, which was consistent in all the tested HAE-ALI cultures derived from 4 lung bronchi of independent donors. We also identified that SARS-CoV-2 does not infect HAE from the basolateral side, and the dominant SARS-CoV-2 permissive epithelial cells are ciliated cells and goblet cells, whereas virus replication in basal cells and club cells was not detected. Notably, virus infection immediately damaged the HAE, which is demonstrated by dispersed zonula occludens-1 (ZO-1) expression without clear tight junctions and partial loss of cilia. Importantly, we identified that SARS-CoV-2 productive infection of HAE requires a high viral load of >2.5 × 10 5 virions per cm 2 of epithelium. Thus, our studies highlight the importance of a high viral load and that epithelial renewal initiates and maintains a recurrent infection of HAE with SARS-CoV-2. IMPORTANCE The pandemic of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to >35 million confirmed cases and >1 million fatalities worldwide. SARS-CoV-2 mainly replicates in human airway epithelia in COVID-19 patients. In this study, we used in vitro cultures of polarized human bronchial airway epithelium to model SARS-CoV-2 replication for a period of 21 to 51 days. We discovered that in vitro airway epithelial cultures endure a long-lasting SARS-CoV-2 propagation with recurrent peaks of progeny virus release at an interval of approximately 7 to 10 days. Our study also revealed that SARS-CoV-2 infection causes airway epithelia damage with disruption of tight junction function and loss of cilia. Importantly, SARS-CoV-2 exhibits a polarity of infection in airway epithelium only from the apical membrane; it infects ciliated and goblet cells but not basal and club cells. Furthermore, the productive infection of SARS-CoV-2 requires a high viral load of over 2.5 × 10 5 virions per cm 2 of epithelium. Our study highlights that the proliferation of airway basal cells and regeneration of airway epithelium may contribute to the recurrent infections.