Identifying gray matter alterations in Cushing's disease using machine learning: An interpretable approach.

BACKGROUND: Cushing's Disease (CD) is a rare clinical syndrome characterized by excessive secretion of adrenocorticotrophic hormone, leading to significant functional and structural brain alterations as observed in Magnetic Resonance Imaging (MRI). While traditional statistical analysis has been widely employed to investigate these MRI changes in CD, it has lacked the ability to predict individual-level outcomes. PURPOSE: To address this problem, this paper has proposed an interpretable machine learning (ML) framework, including model-level assessment, feature-level assessment, and biology-level assessment to ensure a comprehensive analysis based on structural MRI of CD. METHODS: The ML framework has effectively identified the changes in brain regions in the stage of model-level assessment, verified the effectiveness of these altered brain regions to predict CD from normal controls in the stage of feature-level assessment, and carried out a correlation analysis between altered brain regions and clinical symptoms in the stage of biology-level assessment. RESULTS: The experimental results of this study have demonstrated that the Insula, Fusiform gyrus, Superior frontal gyrus, Precuneus, and the opercular portion of the Inferior frontal gyrus of CD showed significant alterations in brain regions. Furthermore, our study has revealed significant correlations between clinical symptoms and the frontotemporal lobes, insulin, and olfactory cortex, which also have been confirmed by previous studies. CONCLUSIONS: The ML framework proposed in this study exhibits exceptional potential in uncovering the intricate pathophysiological mechanisms underlying CD, with potential applicability in diagnosing other diseases.

Proteomic Profiling of Messenger Ribonucleoproteins in Mouse Tissues Based on Formaldehyde Cross-Linking.

Messenger ribonucleoprotein particles (mRNPs) are vital for tissue-specific gene expression via mediating posttranscriptional regulations. However, proteomic profiling of proteins in mRNPs, i.e., mRNA-associated proteins (mRAPs), has been challenging at the tissue level. Herein, we report the development of formaldehyde cross-linking-based mRNA-associated protein profiling (FAXRAP), a chemical strategy that enables the identification of mRAPs in both cultured cells and intact mouse organs. Applying FAXRAP, tissue-specific mRAPs were systematically profiled in the mouse liver, kidney, heart, and brain. Furthermore, brain mRAPs in Parkinson's disease (PD) mouse model were investigated, which revealed a global decrease of mRNP assembly in the brain of mice with PD. We envision that FAXRAP will facilitate uncovering the posttranscriptional regulation networks in various biological systems.

Leading officials' audits of natural-resource assets and local environmental attention: evidence of word frequency analysis from Chinese local government work reports.

Drawing on the textual contents of the Chinese local government work reports, this paper focuses on the impact of leading officials' audits of natural-resource assets on the local environmental attention allocation. The study has found that during the pilot period of audits, compared with the non-pilot cities, the local government in the pilot cities has concentrated more on the ecological environment field and has placed greater emphasis on the actual environmental governance. The results of mechanism analysis show that differences in the personal and take-office characteristics of local officials, may lead to different promotion pressure and performance evaluation modes, and then have a different impact on local environmental attention. The test results of consequence analysis show that the environmental attention in the pilot cities can be further transformed into environmental administrative and financial governance behavior. The paper discusses the policy effect and internal mechanism of leading officials' audits of natural-resource assets from the perspective of local governments' environmental attention, providing empirical evidence for China to carry out the top-level design of environmental policy based on the idea of optimizing officials' performance evaluation mechanism.

How does technological innovation affect carbon emission efficiency in the Yellow River Economic Belt: the moderating role of government support and marketization.

The Yellow River Economic Belt (YREB) is a fundamental ecological protection barrier for China. Its carbon pollution issues are currently severe owing to the extensive energy consumption and unsatisfactory industrial constructions. In this context, this paper estimates carbon emission efficiency (CEE) based on the panel data from 56 cities in the YREB during the period 2006-2019 and analyzes its spatial distribution characteristics. Additionally, the spatial Durbin model (SDM) is utilized to examine the effect of technological innovation (TI) on CEE as a result of the moderating effects of government support (GS) and marketization (MA), respectively. The results indicated that (i) in the YREB, CEE exhibited significant spatial autocorrelation characteristics; (ii) TI negatively affected local CEE; (iii) the moderating effect of local GS on the relationship between TI and CEE in the local area was negative, but its spatial spillover effect was still not significant; (iv) the moderating effect of local MA on the relationship between TI and CEE in the local area was also negative, but positive in the surrounding areas. Based on the empirical analysis, a series of policy suggestions are proposed to improve the YREB's CEE.

On physical analysis of topological indices via curve fitting for natural polymer of cellulose network.

Plant materials are processed in a variety of ways to produce biologically active compounds. Cellulose (natural polymer) has the ability to deliver physiologically active compounds to organ targets that have been extracted by CO 2 . Researchers have recently become interested in polymers that can transport biologically active compounds into human bodies. For appropriately selecting bearers of biologically active chemicals, knowledge of the thermodynamic properties of cellulose is required. In QSPR/QSAR modelling, which provides the theoretical and optimum foundation for costly experimental drug discovery, molecular descriptors are extremely important. In this article, we investigated a natural polymer of cellulose network which has interesting pharmacological applications, outstanding characteristics, and a novel molecular structure. We plan to look into and compute a variety of closed-form formulas of various K-Banhatti indices along with their respective K-Banhatti entropies and the heat of formation. The numerical and graphical characterization of computed results was combined with curve fitting between calculated thermodynamic properties and topological indices. This presentation will provide a complete description of potentially important thermodynamic features that could be useful in modifying the structure of natural polymer of cellulose network CN y x .

A carbon tax or a subsidy? Policy choice when a green firm competes with a high carbon emitter.

Choosing pollution control instrument is an important environmental policy decision. Carbon taxes and subsidies for emissions reductions are two commonly used environmental policies. In practice, the government may be restricted to use only one policy instrument. In this paper, we compare the social welfare effect between policies of a carbon tax and a subsidy. We show that as the marginal environmental damage of the high carbon product increases, the control instrument should change from a subsidy policy to a carbon tax policy. It also turns out that Bertrand competition does not always incur a higher social welfare than Cournot competition when the government intervenes with a pollution control policy.

On eccentricity-based entropy measures for dendrimers.

The eccentricity-based entropy inspired by Shannon's entropy approach is the information-theoretic quantity to figure out the structural information of complex networks. The investigation for advance biomedical utilization of dendrimers has improved the synthesis of radical based molecules. Categorically, attaining radical dendrimers has initiated their use in different fields such as anti-tumor agents and as magnetic resonance imaging. The use of radical dendrimers has increased the possibility of establishing new kinds of devices based on para-magnetic axioms of organic radicals. In this article, we discussed dendrimer based on cyclotriphosphazene ( N 3 P 3 ) which has balanced edge groups and these are examined by EPR temperature spectrum. Firstly, we computed eccentricity-based indices and then we computed eccentricity based entropies by developing an acquaintance between these indices and their entropies. Moreover we presented our computed result numerically and graphically which leads to good importance of our contribution.

Metabolic RNA labeling for probing RNA dynamics in bacteria.

Metabolic labeling of RNAs with noncanonical nucleosides that are chemically active, followed by chemoselective conjugation with imaging probes or enrichment tags, has emerged as a powerful method for studying RNA transcription and degradation in eukaryotes. However, metabolic RNA labeling is not applicable for prokaryotes, in which the complexity and distinctness of gene regulation largely remain to be explored. Here, we report 2'-deoxy-2'-azidoguanosine (AzG) as a noncanonical nucleoside compatible with metabolic labeling of bacterial RNAs. With AzG, we develop AIR-seq (azidonucleoside-incorporated RNA sequencing), which enables genome-wide analysis of transcription upon heat stress in Escherichia coli. Furthermore, AIR-seq coupled with pulse-chase labeling allows for global analysis of bacterial RNA degradation. Finally, we demonstrate that RNAs of mouse gut microbiotas can be metabolically labeled with AzG in living animals. The AzG-enabled metabolic RNA labeling should find broad applications in studying RNA biology in various bacterial species.

9-Azido Analogues of Three Sialic Acid Forms for Metabolic Remodeling of Cell-Surface Sialoglycans.

Neu5Ac, Neu5Gc, and KDN are three forms of sialic acids in vertebrates that possess distinct biological functions. Herein, we report the synthesis and metabolic incorporation of the 9-azido analogues of three sialic acid forms in mammalian cells. The incorporated sialic acid analogues enable fluorescent imaging of cell-surface sialoglycans and proteomic profiling of sialoglycoproteins. Furthermore, we apply them to metabolically engineer cell surfaces with sialoglycans terminated with distinct sialic acids or their 9-azido analogues. The remodeled cells expressing specific cell-surface sialoglycoforms show distinct binding affinity toward subtilase cytotoxin (SubAB), a toxin secreted by Shiga toxigenic Escherichia coli. The 9-azido analogues of sialic acid forms developed in this work provide a versatile tool for metabolic remodeling of cell-surface properties and modulating pathogen-host interactions.

Capture and Identification of RNA-binding Proteins by Using Click Chemistry-assisted RNA-interactome Capture (CARIC) Strategy.

A comprehensive identification of RNA-binding proteins (RBPs) is key to understanding the posttranscriptional regulatory network in cells. A widely used strategy for RBP capture exploits the polyadenylation [poly(A)] of target RNAs, which mostly occurs on eukaryotic mature mRNAs, leaving most binding proteins of non-poly(A) RNAs unidentified. Here we describe the detailed procedures of a recently reported method termed click chemistry-assisted RNA-interactome capture (CARIC), which enables the transcriptome-wide capture of both poly(A) and non-poly(A) RBPs by combining the metabolic labeling of RNAs, in vivo UV cross-linking, and bioorthogonal tagging.

Transcriptome-wide discovery of coding and noncoding RNA-binding proteins.

Transcriptome-wide identification of RNA-binding proteins (RBPs) is a prerequisite for understanding the posttranscriptional gene regulation networks. However, proteomic profiling of RBPs has been mostly limited to polyadenylated mRNA-binding proteins, leaving RBPs on nonpoly(A) RNAs, including most noncoding RNAs (ncRNAs) and pre-mRNAs, largely undiscovered. Here we present a click chemistry-assisted RNA interactome capture (CARIC) strategy, which enables unbiased identification of RBPs, independent of the polyadenylation state of RNAs. CARIC combines metabolic labeling of RNAs with an alkynyl uridine analog and in vivo RNA-protein photocross-linking, followed by click reaction with azide-biotin, affinity enrichment, and proteomic analysis. Applying CARIC, we identified 597 RBPs in HeLa cells, including 130 previously unknown RBPs. These newly discovered RBPs can likely bind ncRNAs, thus uncovering potential involvement of ncRNAs in processes previously unknown to be ncRNA-related, such as proteasome function and intermediary metabolism. The CARIC strategy should be broadly applicable across various organisms to complete the census of RBPs.

Mechanistic Investigation and Multiplexing of Liposome-Assisted Metabolic Glycan Labeling.

Metabolic labeling of glycans with bioorthogonal reporters has been widely used for glycan imaging and glycoproteomic profiling. One of the intrinsic limitations of metabolic glycan labeling is the lack of cell-type selectivity. The recently developed liposome-assisted bioorthogonal reporter (LABOR) strategy provides a promising means to overcome this limitation, but the mechanism of LABOR has not been investigated in detail. In this work, we performed a mechanistic study on LABOR and explored its multiplexing capability. Our studies support an endocytosis-salvage mechanism. The ligand-targeted liposomes encapsulating azidosugars are internalized into the endosome via the receptor-mediated endocytosis. Unlike the conventional drug delivery, LABOR does not rely on the endosomal escape pathways. Rather, the liposomes are allowed to enter the lysosome, inside which the azidosugars are released from the liposomes. The released azidosugars then intercept the salvage pathways of monosaccharides and get transported into the cytosol by lysosomal sugar transporters. Based on this mechanism, we expanded the scope of LABOR by evaluating a series of ligand-receptor pairs for targeting sialoglycans in various cell types. Different ligand types including small molecules, antibodies, aptamers, and peptides could be easily implemented into LABOR. Finally, we demonstrated that the sialoglycans in two distinct cell populations in a co-cultured system could be selectively labeled with two distinct chemical reporters by performing a multiplexed LABOR labeling.

Artificial Cysteine S-Glycosylation Induced by Per-O-Acetylated Unnatural Monosaccharides during Metabolic Glycan Labeling.

The unexpected, non-enzymatic S-glycosylation of cysteine residues in various proteins by per-O-acetylated monosaccharides is described. This artificial S-glycosylation greatly compromises the specificity and validity of metabolic glycan labeling in living cells by per-O-acetylated azido and alkynyl sugars, which has been overlooked in the field for decades. It is demonstrated that the use of unacetylated unnatural sugars can avoid the artifact formation and a corrected list of O-GlcNAcylated proteins and O-GlcNAc sites in HeLa cells has been assembled by using N-azidoacetylgalactosamine (GalNAz).

Constructing a Watts-Strogatz network from a small-world network with symmetric degree distribution.

Though the small-world phenomenon is widespread in many real networks, it is still challenging to replicate a large network at the full scale for further study on its structure and dynamics when sufficient data are not readily available. We propose a method to construct a Watts-Strogatz network using a sample from a small-world network with symmetric degree distribution. Our method yields an estimated degree distribution which fits closely with that of a Watts-Strogatz network and leads into accurate estimates of network metrics such as clustering coefficient and degree of separation. We observe that the accuracy of our method increases as network size increases.

Immunization against the Spread of Rumors in Homogenous Networks.

Since most rumors are harmful, how to control the spread of such rumors is important. In this paper, we studied the process of "immunization" against rumors by modeling the process of rumor spreading and changing the termination mechanism for the spread of rumors to make the model more realistic. We derived mean-field equations to describe the dynamics of the rumor spread. By carrying out steady-state analysis, we derived the spreading threshold value that must be exceeded for the rumor to spread. We further discuss a possible strategy for immunization against rumors and obtain an immunization threshold value that represents the minimum level required to stop the rumor from spreading. Numerical simulations revealed that the average degree of the network and parameters of transformation probability significantly influence the spread of rumors. More importantly, the simulations revealed that immunizing a higher proportion of individuals is not necessarily better because of the waste of resources and the generation of unnecessary information. So the optimal immunization rate should be the immunization threshold.

Targeted imaging and proteomic analysis of tumor-associated glycans in living animals.

Although it has been well known that dynamic changes in glycosylation are associated with tumor progression, it remains challenging to selectively visualize the cancer glycome in vivo. Herein, a strategy for the targeted imaging of tumor-associated glycans by using ligand-targeted liposomes encapsulating azidosugars is described. The intravenously injected liposomal nanoparticles selectively bound to the cancer-cell-specific receptors and installed azides into the melanoma glycans in a xenograft mouse model in a tissue-specific manner. Subsequently, a copper-free click reaction was performed in vivo to chemoselectively conjugate the azides with a near-infrared fluorescent dye. The glycosylation dynamics during tumor growth were monitored by in vivo fluorescence imaging. Furthermore, the newly synthesized sialylated glycoproteins were enriched during tumor growth and identified by glycoproteomics. Compared with the labeling methods using free azidosugars, this method offers improved labeling efficiency and high specificity and should facilitate the elucidation of the functional role of glycans in cancer biology.

A model for interprovincial air pollution control based on futures prices.

UNLABELLED: Based on the current status of research on tradable emission rights futures, this paper introduces basic market-related assumptions for China's interprovincial air pollution control problem. The authors construct an interprovincial air pollution control model based on futures prices: the model calculated the spot price of emission rights using a classic futures pricing formula, and determined the identities of buyers and sellers for various provinces according to a partitioning criterion, thereby revealing five trading markets. To ensure interprovincial cooperation, a rational allocation result for the benefits from this model was achieved using the Shapley value method to construct an optimal reduction program and to determine the optimal annual decisions for each province. Finally, the Beijing-Tianjin-Hebei region was used as a case study, as this region has recently experienced serious pollution. It was found that the model reduced the overall cost of reducing SO2 pollution. Moreover, each province can lower its cost for air pollution reduction, resulting in a win-win solution. Adopting the model would therefore enhance regional cooperation and promote the control of China's air pollution. IMPLICATIONS: The authors construct an interprovincial air pollution control model based on futures prices. The Shapley value method is used to rationally allocate the cooperation benefit. Interprovincial pollution control reduces the overall reduction cost of SO2. Each province can lower its cost for air pollution reduction by cooperation.

Bifunctional unnatural sialic acids for dual metabolic labeling of cell-surface sialylated glycans.

Sialic acid analogues containing a unique chemical functionality or chemical reporter have been metabolically incorporated into sialylated glycans. This process, termed metabolic glycan labeling, has emerged as a powerful tool for studying sialylation as well as other types of glycosylation. Currently, this technique can install only a single functionality. Here we describe a strategy for dual labeling of sialylated glycans using a new class of bifunctional sialic acid analogues containing two distinct chemical reporters at the N-acyl and C9 positions. These bifunctional unnatural sialic acids were metabolically incorporated into cellular glycans, where the two chemical reporters exerted their distinct functions. This approach expands the capability of metabolic glycan labeling to probe sialylation and glycan-protein interactions.