The Weight-Based Feature Selection (WBFS) Algorithm Classifies Lung Cancer Subtypes Using Proteomic Data.

Feature selection plays an important role in improving the performance of classification or reducing the dimensionality of high-dimensional datasets, such as high-throughput genomics/proteomics data in bioinformatics. As a popular approach with computational efficiency and scalability, information theory has been widely incorporated into feature selection. In this study, we propose a unique weight-based feature selection (WBFS) algorithm that assesses selected features and candidate features to identify the key protein biomarkers for classifying lung cancer subtypes from The Cancer Proteome Atlas (TCPA) database and we further explored the survival analysis between selected biomarkers and subtypes of lung cancer. Results show good performance of the combination of our WBFS method and Bayesian network for mining potential biomarkers. These candidate signatures have valuable biological significance in tumor classification and patient survival analysis. Taken together, this study proposes the WBFS method that helps to explore candidate biomarkers from biomedical datasets and provides useful information for tumor diagnosis or therapy strategies.

Catalytic selectivity of nanorippled graphene.

Experiments have shown that nanoscale ripples in a graphene membrane exhibit unexpectedly high catalytic activity with respect to hydrogen dissociation. Nonetheless, the catalytic selectivity of nanorippled graphene remains unknown, which is an equally important property for assessing a catalyst's potential and its fit-for-purpose applications. Herein, we examine the catalytic selectivity of nanorippled graphene using a model reaction of molecular hydrogen with another simple but double-bonded molecule, oxygen, and comparing the measurement results with those from splitting of hydrogen molecules. We show that although nanorippled graphene exhibits a high catalytic activity toward hydrogen dissociation, the activity for catalyzing the hydrogen-oxygen reaction is quite low, translating into a strong catalytic selectivity. The latter reaction involves the reduction of oxygen molecules by the dissociated hydrogen adatoms, which requires additional energy cost and practically determines the selectivity. In this sense, the well-established information about reactions in general of atomic hydrogen with many other species in the literature could potentially predict the selectivity of nanorippled graphene as a catalyst. Our work provides implications for the catalytic properties of nanorippled graphene, especially its selectivity. The results would be important for its extension to a wider range of reactions and for designer technologies involving hydrogen.

Recent trends in the transfer of graphene films.

Recent years have witnessed advances in chemical vapor deposition growth of graphene films on metal foils with fine scalability and thickness controllability. However, challenges for obtaining wrinkle-free, defect-free and large-area uniformity remain to be tackled. In addition, the real commercial applications of graphene films still require industrially compatible transfer techniques with reliable performance of transferred graphene, excellent production capacity, and suitable cost. Transferred graphene films, particularly with a large area, still suffer from the presence of transfer-related cracks, wrinkles and contaminants, which would strongly deteriorate the quality and uniformity of transferred graphene films. Potential applications of graphene films include moisture barrier films, transparent conductive films, electromagnetic shielding films, and optical communications; such applications call different requirements for the performance of transferred graphene, which, in turn, determine the suitable transfer techniques. Besides the reliable transfer process, automatic machines should be well developed for the future batch transfer of graphene films, ensuring the repeatability and scalability. This mini-review provides a summary of recent advances in the transfer of graphene films and offers a perspective for future directions of transfer techniques that are compatible for industrial batch transfer.

Tunable Adhesion for All-Dry Transfer of 2D Materials Enabled by the Freezing of Transfer Medium.

The real applications of chemical vapor deposition (CVD)-grown graphene films require the reliable techniques for transferring graphene from growth substrates onto application-specific substrates. The transfer approaches that avoid the use of organic solvents, etchants, and strong bases are compatible with industrial batch processing, in which graphene transfer should be conducted by dry exfoliation and lamination. However, all-dry transfer of graphene remains unachievable owing to the difficulty in precisely controlling interfacial adhesion to enable the crack- and contamination-free transfer. Herein, through controllable crosslinking of transfer medium polymer, the adhesion is successfully tuned between the polymer and graphene for all-dry transfer of graphene wafers. Stronger adhesion enables crack-free peeling of the graphene from growth substrates, while reduced adhesion facilitates the exfoliation of polymer from graphene surface leaving an ultraclean surface. This work provides an industrially compatible approach for transferring 2D materials, key for their future applications, and offers a route for tuning the interfacial adhesion that would allow for the transfer-enabled fabrication of van der Waals heterostructures.

Suppression of the coffee-ring effect by self-assembling graphene oxide and monolayer titania.

The in situ self-assembly of two types of typical two-dimensional (2D) nanomaterials (i.e., graphene oxide (GO) and monolayer titania (TO)) is realized using a simple drop-casting method. Within the as-prepared hybrid films, the GO and TO nanosheets arrange alternately into a lamellar structure. Notably, the hybridization of GO and TO suppresses the formation of coffee-rings when drop-cast, which is attributed to the strong interactions between the GO and TO nanosheets. Finally, the mechanism for the in situ hybridization of these two types of nanosheets into heterogeneous lamellar films and the suppression of the coffee-ring effect are discussed. These results demonstrate the potential applications of drop-cast hybrid films for high-quality membrane deposition from liquid phases.

Using feature selection and Bayesian network identify cancer subtypes based on proteomic data.

The Cancer Proteome Atlas (TCPA) project collects reverse-phase protein arrays (RPPA)-based proteome datasets from nearly 8000 samples across 32 cancer types. This study aims to investigate the pan-cancer proteome signature and identify cancer subtypes of glioma, kidney cancer, and lung cancer based on TCPA data. We first visualized the tumor clustering models using t-distributed stochastic neighbour embedding (t-SNE) and bi-clustering heatmap. Then, three feature selection methods (pyHSICLasso, XGBoost, and Random Forest) were performed to select protein features for classifying cancer subtypes in training dataset, and the LibSVM algorithm was empolyed to test classification accuracy in the validation dataset. Clustering analysis revealed that different kinds of tumors have relatively distinct proteomic profiling based on tissue or origin. We identified 20, 10, and 20 protein features with the highest accuracies in classifying subtypes of glioma, kidney cancer, and lung cancer, respectively. The predictive abilities of the selected proteins were confirmed by receiving operating characteristic (ROC) analysis. Finally, the Bayesian network was utilized to explore the protein biomarkers that have direct causal relationships with cancer subtypes. Overall, we highlight the theoretical and technical applications of machine learning based feature selection approaches in the analysis of high-throughput biological data, particularly for cancer biomarker research. SIGNIFICANCE: Functional proteomics is a powerful approach for characterizing cell signaling pathways and understanding their phenotypic effects on cancer development. The TCPA database provides a platform to explore and analyze TCGA pan-cancer RPPA-based protein expression. With the advent of the RPPA technology, the availability of high-throughput data in TCPA platform has made it possible to use machine learning methods to identify protein biomarkers and further differentiate subtypes of cancer based on proteomic data. In this study, we highlight the role of feature selection and Bayesian network in discovery protein biomarker for classifying cancer subtypes based on functional proteomic data. The application of machine learning methods in the analysis of high-throughput biological data, particularly for cancer biomarker researches, which have potential clinical values in developing individualized treatment strategies.

Identification of gene signatures for COAD using feature selection and Bayesian network approaches.

The combination of TCGA and GTEx databases will provide more comprehensive information for characterizing the human genome in health and disease, especially for underlying the cancer genetic alterations. Here we analyzed the gene expression profile of COAD in both tumor samples from TCGA and normal colon tissues from GTEx. Using the SNR-PPFS feature selection algorithms, we discovered a 38 gene signatures that performed well in distinguishing COAD tumors from normal samples. Bayesian network of the 38 genes revealed that DEGs with similar expression patterns or functions interacted more closely. We identified 14 up-DEGs that were significantly correlated with tumor stages. Cox regression analysis demonstrated that tumor stage, STMN4 and FAM135B dysregulation were independent prognostic factors for COAD survival outcomes. Overall, this study indicates that using feature selection approaches to select key gene signatures from high-dimensional datasets can be an effective way for studying cancer genomic characteristics.

A hybrid feature selection algorithm and its application in bioinformatics.

Feature selection is an independent technology for high-dimensional datasets that has been widely applied in a variety of fields. With the vast expansion of information, such as bioinformatics data, there has been an urgent need to investigate more effective and accurate methods involving feature selection in recent decades. Here, we proposed the hybrid MMPSO method, by combining the feature ranking method and the heuristic search method, to obtain an optimal subset that can be used for higher classification accuracy. In this study, ten datasets obtained from the UCI Machine Learning Repository were analyzed to demonstrate the superiority of our method. The MMPSO algorithm outperformed other algorithms in terms of classification accuracy while utilizing the same number of features. Then we applied the method to a biological dataset containing gene expression information about liver hepatocellular carcinoma (LIHC) samples obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). On the basis of the MMPSO algorithm, we identified a 18-gene signature that performed well in distinguishing normal samples from tumours. Nine of the 18 differentially expressed genes were significantly up-regulated in LIHC tumour samples, and the area under curves (AUC) of the combination seven genes (ADRA2B, ERAP2, NPC1L1, PLVAP, POMC, PYROXD2, TRIM29) in classifying tumours with normal samples was greater than 0.99. Six genes (ADRA2B, PYROXD2, CACHD1, FKBP1B, PRKD1 and RPL7AP6) were significantly correlated with survival time. The MMPSO algorithm can be used to effectively extract features from a high-dimensional dataset, which will provide new clues for identifying biomarkers or therapeutic targets from biological data and more perspectives in tumor research.

Highly efficient and selective extraction of gold by reduced graphene oxide.

Materials capable of extracting gold from complex sources, especially electronic waste (e-waste), are needed for gold resource sustainability and effective e-waste recycling. However, it remains challenging to achieve high extraction capacity and precise selectivity if only a trace amount of gold is present along with other metallic elements . Here we report an approach based on reduced graphene oxide (rGO) which provides an ultrahigh capacity and selective extraction of gold ions present in ppm concentrations (>1000 mg of gold per gram of rGO at 1 ppm). The excellent gold extraction performance is accounted to the graphene areas and oxidized regions of rGO. The graphene areas spontaneously reduce gold ions to metallic gold, and the oxidized regions allow good dispersibility of the rGO material so that efficient adsorption and reduction of gold ions at the graphene areas can be realized. By controlling the protonation of the oxidized regions of rGO, gold can be extracted exclusively, without contamination by the other 14 co-existing elements typically present in e-waste. These findings are further exploited to demonstrate recycling gold from real-world e-waste with good scalability and economic viability, as exemplified by using rGO membranes in a continuous flow-through process.

Recent progress on exploring exceptionally high and anisotropic H+/OH- ion conduction in two-dimensional materials.

Ion conducting membranes/electrolytes have been employed extensively in some important industrial and biological systems, especially in fuel cells, water electrolyzers, gas separation, sensors and biological selective ion transport, acting as one of the core components and sometimes directly determining the device performance. However, the traditional polymeric proton exchange membranes (PEMs)/anion exchange membranes (AEMs) suffer from highly toxic preparation procedures, poor thermal and chemical stabilities, and unsatisfactory ion conductivities. This has triggered researchers worldwide to explore alternative inorganic building blocks with high ion conductivities and stabilities from the new materials library, hoping to solve the above long-lasting problems. The recent burgeoning research on two-dimensional (2D) materials has unveiled exceptionally high ionic conductivities, which raises the feasibility of fabricating high-performance nanosheet-based ion conductors/membranes. In this perspective, the recent advances in measuring and understanding the exceptionally high and anisotropic H+/OH- ion conductivities of representative 2D materials, e.g. graphene oxide (GO), vermiculite and layered double hydroxide (LDH) nanosheets, are reviewed. In particular, regarding the anisotropic ionic conduction in 2D nanosheets, possible design strategies and technological innovations for fabricating macroscopic nanosheet-based ionic conductors/membranes are proposed for maximizing the high in-plane conduction, which may serve to guide future development of high-performance industrial and biological systems relying on H+/OH- conducting membranes.

Single-layer nanosheets with exceptionally high and anisotropic hydroxyl ion conductivity.

When the dimensionality of layered materials is reduced to the physical limit, an ultimate two-dimensional (2D) anisotropy and/or confinement effect may bring about extraordinary physical and chemical properties. Layered double hydroxides (LDHs), bearing abundant hydroxyl groups covalently bonded within 2D host layers, have been proposed as inorganic anion conductors. However, typical hydroxyl ion conductivities for bulk or lamellar LDHs, generally up to 10-3 S cm-1, are considered not high enough for practical applications. We show that single-layer LDH nanosheets exhibited exceptionally high in-plane conductivities approaching 10-1 S cm-1, which were the highest among anion conductors and comparable to proton conductivities in commercial proton exchange membranes (for example, Nafion). The in-plane conductivities were four to five orders of magnitude higher than the cross-plane or cross-membrane values of restacked LDH nanosheets. This 2D superionic transport characteristic might have great promises in a variety of applications including alkaline fuel cells and water electrolysis.

Recent Developments in Graphene-Based Membranes: Structure, Mass-Transport Mechanism and Potential Applications.

Significant achievements have been made on the development of next-generation filtration and separation membranes using graphene materials, as graphene-based membranes can afford numerous novel mass-transport properties that are not possible in state-of-art commercial membranes, making them promising in areas such as membrane separation, water desalination, proton conductors, energy storage and conversion, etc. The latest developments on understanding mass transport through graphene-based membranes, including perfect graphene lattice, nanoporous graphene and graphene oxide membranes are reviewed here in relation to their potential applications. A summary and outlook is further provided on the opportunities and challenges in this arising field. The aspects discussed may enable researchers to better understand the mass-transport mechanism and to optimize the synthesis of graphene-based membranes toward large-scale production for a wide range of applications.

Development of efficient electrocatalysts via molecular hybridization of NiMn layered double hydroxide nanosheets and graphene.

Ni(2+)Mn(3+) layered double hydroxide (LDH) nanoplatelets have been hydrothermally synthesized in a homogeneous precipitation of mixed Ni(2+)/Mn(2+) salts at a molar ratio of 2 : 1 via the hydrolysis of hexamethylenetetramine (HMT) and in situ oxidation with H2O2. After anion-exchange, NiMn LDH was exfoliated into unilamellar nanosheets. Subsequent flocculation of NiMn LDH nanosheets with (reduced) graphene oxide (GO/rGO) into superlattice composites was achieved and further tested as electrocatalysts for oxygen evolution reaction (OER). The face-to-face heteroassembly of NiMn LDH nanosheets with conductive rGO at an alternating sequence resulted in a small overpotential of 0.26 V and a Tafel slope of 46 mV per decade, which is much superior to as-exfoliated nanosheets. The analyses of electrochemical activity surface area (ECSA) and impedance spectra clearly indicated that the superlattice structure was ideal in facilitating the migration/transfer of the charge and reactants, revealing the electrochemical energetics and mechanism behind the synergistic effect arising from molecular hybridization. The proof of concept toward total water splitting using the newly developed hybrid electrocatalyst was demonstrated by an electrolysis cell powered by a single AA battery.

Intrinsic high water/ion selectivity of graphene oxide lamellar membranes in concentration gradient-driven diffusion.

Although graphene oxide lamellar membranes (GOLMs) are effective in blocking large organic molecules and nanoparticles for nanofiltration and ultrafiltration, water desalination with GOLM is challenging, with seriously controversial results. Here, a combined experimental and molecular dynamics simulation study shows that intrinsic high water/ion selectivity of GOLM was achieved in concentration gradient-driven diffusion, showing great promise in water desalination. However, in pressure-driven filtration the salt rejection was poor. This study unveils a long-overlooked reason behind the controversy in water desalination with GOLM and further provides a fundamental understanding on the in-depth mechanism concerning the strong correlation of water/ion selectivity with the applied pressure and GO nanochannel length. Our calculations and experiments show that the applied pressure weakened the water-ion interactions in GO nanochannels and reduced their permeation selectivity, while the length of nanochannels dominated the mass transport processes and the ion selectivity. The new insights presented here may open up new opportunities for the optimization of GOLMs in this challenging area.

Ultrafast liquid water transport through graphene-based nanochannels measured by isotope labelling.

Based on isotope labelling, we found that liquid water can afford an ultrafast permeation through graphene-based nanochannels with a diffusion coefficient 4-5 orders of magnitude greater than in the bulk case. When dissolving ions in sources, the diffusion coefficient of ions through graphene channels lies in the same order of magnitude as water, while the ion diffusion is slightly faster than water, indicating that the ions are mainly transported by water flows and the delicate interactions between ions and nanocapillary walls also take effect in the accelerated ion transportation.

Structure evolution of graphene oxide during thermally driven phase transformation: is the oxygen content really preserved?

A mild annealing procedure was recently proposed for the scalable enhancement of graphene oxide (GO) properties with the oxygen content preserved, which was demonstrated to be attributed to the thermally driven phase separation. In this work, the structure evolution of GO with mild annealing is closely investigated. It reveals that in addition to phase separation, the transformation of oxygen functionalities also occurs, which leads to the slight reduction of GO membranes and furthers the enhancement of GO properties. These results are further supported by the density functional theory based calculations. The results also show that the amount of chemically bonded oxygen atoms on graphene decreases gradually and we propose that the strongly physisorbed oxygen species constrained in the holes and vacancies on GO lattice might be responsible for the preserved oxygen content during the mild annealing procedure. The present experimental results and calculations indicate that both the diffusion and transformation of oxygen functional groups might play important roles in the scalable enhancement of GO properties.

Realizing synchronous energy harvesting and ion separation with graphene oxide membranes.

A synchronous ion separation and electricity generation process has been developed using G-O membranes. In addition to the size effect proposed prevsiouly, the separation of ions can be attributed to the different interactions between ions and G-O membranes; the generation of electricity is due to the confinement of G-O membranes, and the mobility difference of ions. Efficient energy transduction has been achieved with G-O membranes, converting magnetic, thermal and osmotic energy to electricity, distinguishing this material from other commercial semi-permeable membranes. Our study indicated that G-O membranes could find potential applications in the purification of wastewater, while producing electricity simultaneously. With G-O membranes, industrial magnetic leakage and waste heat could also be used to produce electricity, affording a superior approach for energy recovery.

Electro- and magneto-modulated ion transport through graphene oxide membranes.

The control of ion trans-membrane transport through graphene oxide (GO) membranes is achieved by electric and magnetic fields. Electric field can either increase or decrease the ion transport through GO membranes depending on its direction, and magnetic field can enhance the ion penetration monotonically. When electric field is applied across GO membrane, excellent control of ion fluidic flows can be done. With the magnetic field, the effective anchoring of ions is demonstrated but the modulation of the ion flowing directions does not occur. The mechanism of the electro- and magneto-modulated ion trans-membrane transport is investigated, indicating that the electric fields dominate the ion migration process while the magnetic fields tune the structure of nanocapillaries within GO membranes. Results also show that the ion selectivity of GO membranes can be tuned with the electric fields while the transport of ions can be enhanced synchronously with the magnetic fields. These excellent properties make GO membranes promising in areas such as field-induced mass transport control and membrane separation.

Vertical junction photodetectors based on reduced graphene oxide/silicon Schottky diodes.

Reduced graphene oxide (RGO) has been employed as an electrode for a series of vertically structured photodetectors. Compared with mechanically exfoliated or chemical vapor deposited graphene, RGO possesses more oxygen containing groups and defects, which are proved to be favorable to enhance the performance of photodetectors. As a matter of fact, RGO with different reduction levels can be readily obtained by varying the annealing temperature. The synthesis procedures for the RGO material are suitable for large scale production and its performance can be effectively improved by functionalization or element doping. For RGO-based devices, the Schottky junction properties and photoelectric conversion have been investigated, primarily by analyzing their current-voltage characteristics. Subsequently, the ON/OFF ratio, responsivity and detectivity of the photodetectors were closely examined, proving that the RGO material could be effectively utilized as the electrode material; also, their relationship with the RGO reduction levels has also been explored. By analyzing the response/recovery speed of the RGO-based photodetectors, we have studied the effects of oxygen-containing functional groups and crystalline defects on the photoelectric conversion.