DDN3.0: determining significant rewiring of biological network structure with differential dependency networks.

MOTIVATION: Complex diseases are often caused and characterized by misregulation of multiple biological pathways. Differential network analysis aims to detect significant rewiring of biological network structures under different conditions and has become an important tool for understanding the molecular etiology of disease progression and therapeutic response. With few exceptions, most existing differential network analysis tools perform differential tests on separately learned network structures that are computationally expensive and prone to collapse when grouped samples are limited or less consistent. RESULTS: We previously developed an accurate differential network analysis method-differential dependency networks (DDN), that enables joint learning of common and rewired network structures under different conditions. We now introduce the DDN3.0 tool that improves this framework with three new and highly efficient algorithms, namely, unbiased model estimation with a weighted error measure applicable to imbalance sample groups, multiple acceleration strategies to improve learning efficiency, and data-driven determination of proper hyperparameters. The comparative experimental results obtained from both realistic simulations and case studies show that DDN3.0 can help biologists more accurately identify, in a study-specific and often unknown conserved regulatory circuitry, a network of significantly rewired molecular players potentially responsible for phenotypic transitions. AVAILABILITY AND IMPLEMENTATION: The Python package of DDN3.0 is freely available at https://github.com/cbil-vt/DDN3. A user's guide and a vignette are provided at https://ddn-30.readthedocs.io/.

Rational design of efficient deep eutectic solvents for HCl absorption through their competitive H-bonding interactions.

The design of an efficient absorbent is the premise for recovery and resource utilization of hydrogen chloride (HCl) from its industrial tail gases. Herein, a series of 1-butyl-3-methylimidazolium chloride (BmimCl) based-deep eutectic solvents (DESs) were designed and the solubility behavior for HCl was studied in terms of their structure, basicity, free volume, intermolecular interaction energy, and absorption enthalpy. The relationship between the interaction energy and the phase change in the HCl dissolution process was explored in detail. BmimCl-TAA (thioacetamide) (1 : 1) shows high reversible solubility due to its high free volume, suitable absorption enthalpy, and closer H-bonding (HB) interactions between BmimCl and TAA or HCl. The dissolution mechanism of HCl and the dynamic evolution of the HB network were verified by FT-IR and NMR spectra and quantum chemical calculations. The results show that it is the competitive HB interaction that promotes the dissolution of HCl, reduces the absorption enthalpy, and renders a reversible absorption. Compared with BmimCl, the absorption enthalpy of HCl in BmimCl-TAA (1 : 1) is reduced by 25% and the reversible solubility increased 150%. The reversible solubility of HCl in BmimCl-TAA (1 : 1) is as high as 0.51 g g-1 (1.76 mol mol-1) at 303.15 K and 101.3 kPa, and the absorbent can be regenerated facilely by heating under reduced pressure. This work provides new insights into the rational design of DES for efficient and reversible absorption of HCl and other polar gases.

Alkynyl carbon materials as novel and efficient sorbents for the adsorption of mercury(II) from wastewater.

For the first time, a series of alkynyl carbon materials (ACMs) were prepared via the mechanochemical reaction of CaC2 with six polyhalogenated precursors, namely CCl4, C2Cl6, C2Cl4, C6Cl6, C6Br6, and C14H4Br10 (ACM-1, ACM-2, ACM-3, ACM-4, ACM-5, and ACM-6, respectively) and used for the adsorptive removal of mercury from aqueous solutions. Based on preliminary investigations, the adsorption of mercury on ACM-5 was studied in depth. Specifically, the effect of pH on mercury adsorptivity, adsorption kinetics, thermodynamics, isotherms, and recyclability was studied. The adsorptivity of mercury on ACMs was found to be closely related to the hydrocarbon precursor, specific surface area of sorbent, and the alkynyl content. ACM-5 showed the best performance and is among the best raw carbonaceous sorbents reported so far, with a Langmuir saturated adsorption capacity of 191.9mgg-1. The promising mercury adsorption performance mainly arises from the strong Lewis soft acid-soft base interactions between the alkynyl groups and mercury ions. The adsorption isotherms could be satisfactorily correlated with the Langmuir equation. The results show that the ACMs can be used as efficient sorbents for the removal of mercury and may also be useful for the adsorption of other heavy metals.

Uncertainty Quantification and Interpretability for Clinical Trial Approval Prediction.

Background: Clinical trial is a crucial step in the development of a new therapy (e.g., medication) and is remarkably expensive and time-consuming. Forecasting the approval of clinical trials accurately would enable us to circumvent trials destined to fail, thereby allowing us to allocate more resources to therapies with better chances. However, existing approval prediction algorithms did not quantify the uncertainty and provide interpretability, limiting their usage in real-world clinical trial management. Methods: This paper quantifies uncertainty and improves interpretability in clinical trial approval predictions. We devised a selective classification approach and integrated it with the Hierarchical Interaction Network, the state-of-the-art clinical trial prediction model. Selective classification, encompassing a spectrum of methods for uncertainty quantification, empowers the model to withhold decision-making in the face of samples marked by ambiguity or low confidence. This approach not only amplifies the accuracy of predictions for the instances it chooses to classify but also notably enhances the model's interpretability. Results: Comprehensive experiments demonstrate that incorporating uncertainty markedly enhances the model's performance. Specifically, the proposed method achieved 32.37%, 21.43%, and 13.27% relative improvement on area under the precision-recall curve over the base model (Hierarchical Interaction Network) in phase I, II, and III trial approval predictions, respectively. For phase III trials, our method reaches 0.9022 area under the precision-recall curve scores. In addition, we show a case study of interpretability that helps domain experts to understand model's outcome. The code is publicly available at https://github.com/Vincent-1125/Uncertainty-Quantification-on-Clinical-Trial-Outcome-Prediction. Conclusion: Our approach not only measures model uncertainty but also greatly improves interpretability and performance for clinical trial approval prediction.

ABDS: a bioinformatics tool suite for analyzing biologically diverse samples.

Bioinformatics software tools are essential to identify informative molecular features that define different phenotypic sample groups. Among the most fundamental and interrelated tasks are missing value imputation, signature gene detection, and differential pattern visualization. However, many commonly used analytics tools can be problematic when handling biologically diverse samples if either informative missingness possess high missing rates with mixed missing mechanisms, or multiple sample groups are compared and visualized in parallel. We developed the ABDS tool suite specifically for analyzing biologically diverse samples. Collectively, a mechanism-integrated group-wise pre-imputation scheme is proposed to retain informative missingness associated with signature genes, a cosine-based one-sample test is extended to detect group-silenced signature genes, and a unified heatmap is designed to display multiple sample groups. We describe the methodological principles and demonstrate the effectiveness of three analytics tools under targeted scenarios, supported by comparative evaluations and biomedical showcases. As an open-source R package, ABDS tool suite complements rather than replaces existing tools and will allow biologists to more accurately detect interpretable molecular signals among phenotypically diverse sample groups.

ABDS: tool suite for analyzing biologically diverse samples.

Motivation: Analytics tools are essential to identify informative molecular features about different phenotypic groups. Among the most fundamental tasks are missing value imputation, signature gene detection, and expression pattern visualization. However, most commonly used analytics tools may be problematic for characterizing biologically diverse samples when either signature genes possess uneven missing rates across different groups yet involving complex missing mechanisms, or multiple biological groups are simultaneously compared and visualized. Results: We develop ABDS tool suite tailored specifically to analyzing biologically diverse samples. Mechanism-integrated group-wise imputation is developed to recruit signature genes involving informative missingness, cosine-based one-sample test is extended to detect enumerated signature genes, and unified heatmap is designed to comparably display complex expression patterns. We discuss the methodological principles and demonstrate the conceptual advantages of the three software tools. We also showcase the biomedical applications of these individual tools. Implemented in open-source R scripts, ABDS tool suite complements rather than replaces the existing tools and will allow biologists to more accurately detect interpretable molecular signals among diverse phenotypic samples. Availability and implementation: The R Scripts of ABDS tool suite is freely available at https://github.com/niccolodpdu/ABDS.

Alkynyl functionalized MoS2 mesoporous materials with superb adsorptivity for heavy metal ions.

Effective elimination of heavy metal ions from water is an arduous task for their toxic effects to aquatic ecosystem and human health. Herein, a novel alkynyl functionalized molybdenum disulfide (C-MoS2) is fabricated via mechanochemical method with well interlayered spacing, meso porosity, and high surface area (~211 m2g-1). Mineral MoS2 was first peeled mechanically and oxidized in situ to MoS2-xOx, and then reduced by ball milling with CaC2 to form the C-MoS2 composite. The as-obtained C-MoS2 shows extraordinary adsorptivity for heavy metal ions, viz. 1194 mg-Hg g-1 (Hg(NO3)2 solution, pH= 5, 303.15 K, equilibrium Hg(II) concentration Ce= 36.9 µg·g-1, ionic strength I= 17.2 mmolL-1), and 442.3 mg-Pbg-1 (Pb(NO3)2 solution, pH= 5, 303.15 K, equilibrium Pb(II) concentration Ce= 46.9µgg-1, I= 5.8 mmolL-1), respectively, along with excellent recyclability, representing one of the best sorbents till now. The adsorption isotherms of Hg(II) followed the Langmuir model and the adsorption kinetics followed the pseudo-second-order model. The adsorption is an endothermic and entropy driven spontaneous process. The excellent adsorption performance of C-MoS2 is attributed to its very high S-content, availability, and soft acid-base interaction with mercury and lead anions. The C-MoS2 is an advanced sorbent for Hg(II) and Pb(II) with excellent adsorption performance and recyclability.

Cosbin: cosine score-based iterative normalization of biologically diverse samples.

Motivation: Data normalization is essential to ensure accurate inference and comparability of gene expression measures across samples or conditions. Ideally, gene expression data should be rescaled based on consistently expressed reference genes. However, to normalize biologically diverse samples, the most commonly used reference genes exhibit striking expression variability and size-factor or distribution-based normalization methods can be problematic when the amount of asymmetry in differential expression is significant. Results: We report an efficient and accurate data-driven method-Cosine score-based iterative normalization (Cosbin)-to normalize biologically diverse samples. Based on the Cosine scores of cross-condition expression patterns, the Cosbin pipeline iteratively eliminates asymmetric differentially expressed genes, identifies consistently expressed genes, and calculates sample-wise normalization factors. We demonstrate the superior performance and enhanced utility of Cosbin compared with six representative peer methods using both simulation and real multi-omics expression datasets. Implemented in open-source R scripts and specifically designed to address normalization bias due to significant asymmetry in differential expression across multiple conditions, the Cosbin tool complements rather than replaces the existing methods and will allow biologists to more accurately detect true molecular signals among diverse phenotypic groups. Availability and implementation: The R scripts of Cosbin pipeline are freely available at https://github.com/MinjieSh/Cosbin. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

COT: an efficient and accurate method for detecting marker genes among many subtypes.

Motivation: Ideally, a molecularly distinct subtype would be composed of molecular features that are expressed uniquely in the subtype of interest but in no others-so-called marker genes (MGs). MG plays a critical role in the characterization, classification or deconvolution of tissue or cell subtypes. We and others have recognized that the test statistics used by most methods do not exactly satisfy the MG definition and often identify inaccurate MG. Results: We report an efficient and accurate data-driven method, formulated as a Cosine-based One-sample Test (COT) in scatter space, to detect MG among many subtypes using subtype expression profiles. Fundamentally different from existing approaches, the test statistic in COT precisely matches the mathematical definition of an ideal MG. We demonstrate the performance and utility of COT on both simulated and real gene expression and proteomics data. The open source Python/R tool will allow biologists to efficiently detect MG and perform a more comprehensive and unbiased molecular characterization of tissue or cell subtypes in many biomedical contexts. Nevertheless, COT complements not replaces existing methods. Availability and implementation: The Python COT software with a detailed user's manual and a vignette are freely available at https://github.com/MintaYLu/COT. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Data-driven detection of subtype-specific differentially expressed genes.

Among multiple subtypes of tissue or cell, subtype-specific differentially-expressed genes (SDEGs) are defined as being most-upregulated in only one subtype but not in any other. Detecting SDEGs plays a critical role in the molecular characterization and deconvolution of multicellular complex tissues. Classic differential analysis assumes a null hypothesis whose test statistic is not subtype-specific, thus can produce a high false positive rate and/or lower detection power. Here we first introduce a One-Versus-Everyone Fold Change (OVE-FC) test for detecting SDEGs. We then propose a scaled test statistic (OVE-sFC) for assessing the statistical significance of SDEGs that applies a mixture null distribution model and a tailored permutation test. The OVE-FC/sFC test was validated on both type 1 error rate and detection power using extensive simulation data sets generated from real gene expression profiles of purified subtype samples. The OVE-FC/sFC test was then applied to two benchmark gene expression data sets of purified subtype samples and detected many known or previously unknown SDEGs. Subsequent supervised deconvolution results on synthesized bulk expression data, obtained using the SDEGs detected from the independent purified expression data by the OVE-FC/sFC test, showed superior performance in deconvolution accuracy when compared with popular peer methods.

Induced neural stem cell grafts exert neuroprotection through an interaction between Crry and Akt in a mouse model of closed head injury.

BACKGROUND: Recently, growing evidence has indicated an important role of the complement system, a crucial component of immunity, in mediating neuroinflammation and promoting neuronal apoptosis following closed head injury (CHI). We previously reported that transplanted induced neural stem cells (iNSCs) pre-treated with CHI mouse serum could enhance complement receptor type 1-related protein y (Crry) expression and ameliorate complement-mediated damage in mouse CHI models. However, the mechanism underlying the elevated levels of Crry expression remains elusive. METHODS: CHI models were established using a standardized weight-drop device. We collected CHI mouse serum at 12 h post-trauma. RT-QPCR assay, western blot analysis, complement deposition assay, Akt inhibition assay, flow cytometry, cell transplantation, and functional assay were utilized to clarify the mechanism of Crry expression in iNSCs receiving CHI mouse serum treatment. RESULTS: We observed dramatic increases in the levels of Crry expression and Akt activation in iNSCs receiving CHI mouse serum treatment. Remarkably, Akt inhibition led to the reduction of Crry expression in iNSCs. Intriguingly, the treatment of iNSC-derived neurons with recombinant complement receptor 2-conjugated Crry (CR2-Crry), which inhibits all complement pathways, substantially enhanced Crry expression and Akt activation in neurons after CHI mouse serum treatment. In subsequent in vitro experiments of pre-treatment of iNSCs with CR2-Crry, we observed significant increases in the levels of Crry expression and Akt activation in iNSCs and iNSC-derived astrocytes and neurons post-treatment with CHI mouse serum. Additionally, an in vivo study showed that intracerebral-transplanted iNSCs pre-treated with CR2-Crry markedly enhanced Crry expression in neurons and protected neurons from complement-dependent damage in the brains of CHI mice. CONCLUSION: INSCs receiving CR2-Crry pre-treatment increased the levels of Crry expression in iNSCs and iNSC-derived astrocytes and neurons and attenuated complement-mediated injury following CHI.

Preparation of C-MOx nanocomposite for efficient adsorption of heavy metal ions via mechanochemical reaction of CaC2 and transitional metal oxides.

Three nanocomposites of carbon and MnOx (C-Mns) were prepared via mechanochemical reaction of CaC2 with excessive MnO2 in a planetary ball mill. Their structure and composition were analyzed by XPS, Raman, FT-IR, XRD, N2 adsorption-desorption, SEM and TEM, respectively, and their adsorption performance for heavy metal ions was studied. In addition, a series of nanocomposites of carbon and transition metal oxides (C-MOx) were prepared by ball milling CaC2 with excessive TiO2, V2O5, Fe2O3, CuO, MoO3, Co2O3, and CrO3, respectively, and their adsorptivity was evaluated. C-Mn1 is a micro-mesoporous sorbent with rich MnOx, alkynyl and oxygenated groups, and moderate specific area (∼180 m2 g-1), showing excellent Pb2+ adsorption with its saturated adsorptivity being 404.4 mg-Pb g-1. Further, it is also effective for other heavy metals (Hg2+, Cd2+, Cr3+, Zn2+ and Cu2+). Some C-MOx show even better adsorptivity for Pb2+ and Hg2+, being superior to most of the advanced carbon-based sorbents. We reported herein a facile method for preparing a new kind of C-MOx nanocomposites for the efficient adsorption of heavy metal ions from wastewater.

Polyacetylene carbon materials: facile preparation using AlCl3 catalyst and excellent electrochemical performance for supercapacitors.

Polyacetylene (PA) was synthesized for the first time under mild conditions via polymerization of acetylene in n-octane with AlCl3 as a catalyst, whereby a series of PA-derived carbon materials were obtained. Their composition and structure were characterized and their electrochemical performance was evaluated systematically. It is found that acetylene gas at 1 MPa can polymerize explosively at room temperature under catalysis of AlCl3, forming acetylene black-like PA and a great amount of H2, while in the presence of n-octane solvent, acetylene polymerizes smoothly at higher temperature (30 to 300 °C), forming PA with a H(CH[double bond, length as m-dash]CH) n H structure. A series of PA-derived carbon materials are obtained by treating PA with KOH at 800 °C. The as-synthesizzed PA-100-KOH exhibits a high specific surface area (∼2500 m2 g-1), high specific capacitance (241 F g-1 at a current density of 0.1 A g-1 and 143 F g-1 at 5 A g-1), low AC resistance, and good cycling stability with 91.7% maintenance of capacity after 2000 cycles at a current density of 2 A g-1. This paper provides a new method for the facile synthesis of PA and a novel carbon source for supercapacitor electrode materials with excellent electrochemical performance and practical application.

Aldol condensation of refluxing acetone on CaC2 achieves efficient coproduction of diacetone alcohol, mesityl oxide and isophorone.

Efficient production of diacetone alcohol (DAA), mesityl oxide (MO) and isophorone (IP) is important for the high value utilization of acetone. For this, a novel process is proposed for the aldol condensation of acetone by refluxing on CaC2, whereby 95% of total selectivity of (DAA, MO and IP) and 85% of acetone conversion are achieved simultaneously under mild conditions (56 °C, 100 kPa). This result is superior to the previously reported ones, which may be ascribed to the extremely high catalytic capacity of CaC2 for the aldol condensation and instant separation of the target products from the catalyst to suppress the unwanted successive condensations. Moreover, the target products may be adjusted by operation conditions. Lower temperature and higher reflux rate favour the production of DAA and MO; MO and IP are favoured otherwise. The present process integrates several functions into one unit, i.e. the basic catalysis of calcium carbide, aldol condensation of acetone, instant distillation separation of the condensates, and the hydrolysis of CaC2 to C2H2, which makes it feasible to coproduce C2H2 and high value derivatives of acetone simultaneously.

Induced neural stem cell-derived astrocytes modulate complement activation and mediate neuroprotection following closed head injury.

The complement system is a crucial component of immunity, and its activation has critical roles in neuroinflammatory response and cellular damage following closed head injury (CHI). We previously demonstrated that systemically injected induced neural stem cells (iNSCs) could modulate complement activation to ameliorate neuronal apoptosis in mouse CHI models. However, it remains unknown whether iNSC derivatives can regulate complement activation. In the present study, after CHI mouse serum treatment, we found dramatic decreases in the cellular viabilities of differentiated iNSCs. Interestingly, following CHI mouse serum treatment, the death of astrocytes derived from iNSCs which were pre-treated with CHI mouse serum was significantly decreased. Meanwhile, the deposition of C3 (C3d) and C5b-9 in these astrocytes was substantially reduced. Remarkably, we detected increased expression of complement receptor type 1-related protein y (Crry) in these astrocytes. Moreover, these astrocytes could reduce the numbers of apoptotic neurons via Crry expression post-CHI mouse serum treatment. Additionally, intracerebral-transplanted iNSCs, pre-treated with CHI mouse serum, significantly increased the levels of Crry expression in astrocytes to reduce the accumulation of C3d and C9 and the death of neurons in the brains of CHI mice. In summary, iNSCs receiving CHI mouse serum pre-treatment could enhance the expression of Crry in iNSC-derived astrocytes to modulate complement activation and mediate neuroprotection following CHI.

Efficient destruction of hexachlorobenzene by calcium carbide through mechanochemical reaction in a planetary ball mill.

Mechanochemical destruction (MCD) is a good alternative to traditional incineration for the destruction of persistent organic pollutants (POPs), like hexachlorobenzene (HCB), and the key is to find an efficient co-milling reagent. Toward this aim, HCB was milled with various reagents in a planetary ball mill at room temperature, and CaC2 was found to be the best one. HCB can be destroyed completely within 20 min at a mass ratio of CaC2/HCB = 0.9 and a rotation speed of 300 rpm. The ground samples were characterized by X-ray diffraction, X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy. The results show that the destruction products are nonhazardous CaCl2 and carbon material with both crystalline and amorphous structures. On these bases, possible reaction pathways were proposed. Considering its excellent efficiency and safety, CaC2 may be the most feasible co-milling regent for MCD treatment of HCB. Further, the results are instructive for the destruction of other POPs.

Synthesis and Supercapacitor Application of Alkynyl Carbon Materials Derived from CaC2 and Polyhalogenated Hydrocarbons by Interfacial Mechanochemical Reactions.

The discovery of new carbon materials and the reactive activation of CaC2 are challenging subjects. In this study, a series of alkynyl carbon materials (ACMs) were synthesized by the interfacial mechanochemical reaction of CaC2 with four typical polyhalogenated hydrocarbons. Their properties and structures were characterized, and their electrochemical performances were examined. The reaction was rapid and efficient arising from the intense mechanical activation of CaC2. The ACMs are micro-mesoporous materials with distinct layered structure, specific graphitization degree, and clear existence of sp-C. In addition, the ACMs exhibit high specific capacitance in the range of 57-133 F g-1 and thus can be ideal candidates for active materials used in supercapacitors. The results may imply an alternative synthesis of carbon allotropes, as well as an efficient approach for the activation of CaC2.

Systemic Administration of Induced Neural Stem Cells Regulates Complement Activation in Mouse Closed Head Injury Models.

Complement activation plays important roles in the pathogenesis of central nervous system (CNS) diseases. Patients face neurological disorders due to the development of complement activation, which contributes to cell apoptosis, brain edema, blood-brain barrier dysfunction and inflammatory infiltration. We previously reported that induced neural stem cells (iNSCs) can promote neurological functional recovery in closed head injury (CHI) animals. Remarkably, we discovered that local iNSC grafts have the potential to modulate CNS inflammation post-CHI. In this study, we aimed to explore the role of systemically delivered iNSCs in complement activation following CNS injury. Our data showed that iNSC grafts decreased the levels of sera C3a and C5a and down-regulated the expression of C3d, C9, active Caspase-3 and Bax in the brain, kidney and lung tissues of CHI mice. Furthermore, iNSC grafts decreased the levels of C3d+/NeuN+, C5b-9+/NeuN+, C3d+/Map2+ and C5b-9+/Map2+ neurons in the injured cortices of CHI mice. Subsequently, we explored the mechanisms underlying these effects. With flow cytometry analysis, we observed a dramatic increase in complement receptor type 1-related protein y (Crry) expression in iNSCs after CHI mouse serum treatment. Moreover, both in vitro and in vivo loss-of-function studies revealed that iNSCs could modulate complement activation via Crry expression.