Active Sites and Their Individual Turnover Frequencies for Ethylene Hydrogenation on Reduced Graphene Aerogel.

Graphene aerogel (GA) was reduced at various temperatures to prepare a series of reduced graphene aerogels (rGAs) with different surface characteristics. Detailed characterization demonstrated that an increase in the thermal reduction temperature leads to an increase in surface area accompanied by an increase in surface density of defect sites formed by the removal of the oxygen-containing functional groups. rGA samples were then tested for ethylene hydrogenation under identical conditions. A comparison of catalytic performances of each catalyst demonstrated that the rGA sample prepared by reduction in Ar at 900 °C (rGA-900) provides the highest performance compared with others prepared at lower temperatures. Next, we analyzed the per-gram activity of each catalyst as a sum of individual contributions from different defect sites quantified by Raman spectroscopy and CHNS-O analysis to determine the individual turnover frequencies (TOFs) of each active site. This analysis identified polyene-like structures and interstitial defects associated with amorphous sp2 bonded carbon atoms as the dominant active sites responsible for hydrogenation. A comparison of their TOFs further indicated that the polyene-like structures provide approximately ten times higher TOF compared to those associated with the amorphous carbon defects. These results, identifying the dominant active centers and quantifying their corresponding TOFs, provide opportunities toward the rational design of GA-based carbocatalysts.

Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup.

OBJECTIVE: Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. PROCESS: A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. CONCLUSIONS: Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.

Evaluating CH4/N2 Separation Performances of Hundreds of Thousands of Real and Hypothetical MOFs by Harnessing Molecular Modeling and Machine Learning.

Considering the large abundance and diversity of metal-organic frameworks (MOFs), evaluating the gas adsorption and separation performance of the entire MOF material space using solely experimental techniques or brute-force computer simulations is impractical. In this study, we integrated high-throughput molecular simulations with machine learning (ML) to explore the potential of both synthesized, the real MOFs, and computer-generated, the hypothetical MOFs (hypoMOFs), for adsorption-based CH4/N2 separation. CH4/N2 mixture adsorption data obtained from molecular simulations were used to train the ML models that could accurately predict gas uptakes of 4612 real MOFs. These models were then transferred to two distinct databases consisting of 98 601 hypoMOFs and 587 anion-pillared hypoMOFs to examine their CH4/N2 mixture separation performances using various adsorbent evaluation metrics. The top adsorbents were identified for vacuum swing adsorption (VSA) and pressure swing adsorption (PSA) conditions and examined in detail to gain molecular insights into their structural and chemical properties. Results revealed that the hypoMOFs offered high CH4 selectivities, up to 14.8 and 13.6, and high working capacities, up to 3.1 and 5.8 mol/kg, at VSA and PSA conditions, respectively, and many of the hypoMOFs could outperform the real MOFs. Our approach offers a rapid and accurate assessment of the mixture adsorption and separation properties of MOFs without the need for computationally demanding simulations. Our results for the best adsorbents will be useful in accelerating the experimental efforts for the design of novel MOFs that can achieve high-performance CH4/N2 separation.

Composite of MIL-101(Cr) with a Pyrrolidinium-Based Ionic Liquid Providing High CO2 Selectivity.

Capturing CO2 selectively from flue gas and natural gas addresses the criteria of a sustainable society. In this work, we incorporated an ionic liquid (IL) (1-methyl-1-propyl pyrrolidinium dicyanamide, [MPPyr][DCA]) into a metal organic framework (MOF), MIL-101(Cr), by wet impregnation and characterized the resulting [MPPyr][DCA]/MIL-101(Cr) composite in deep detail to identify the interactions between [MPPyr][DCA] molecules and MIL-101(Cr). Consequences of these interactions on the CO2/N2, CO2/CH4, and CH4/N2 separation performance of the composite were examined by volumetric gas adsorption measurements complemented by the density functional theory (DFT) calculations. Results showed that the composite offers remarkably high CO2/N2 and CH4/N2 selectivities of 19,180 and 1915 at 0.1 bar and 15 °C corresponding to 1144- and 510-times improvements, respectively, as compared to the corresponding selectivities of pristine MIL-101(Cr). At low pressures, these selectivities reached practically infinity, making the composite completely CO2-selective over CH4 and N2. The CO2/CH4 selectivity was improved from 4.6 to 11.7 at 15 °C and 0.001 bar, yielding a 2.5-times improvement, attributed to the high affinity of [MPPyr][DCA] toward CO2, validated by the DFT calculations. These results offer broad opportunities for the design of composites where ILs are incorporated into the pores of MOFs for high performance gas separation applications to address the environmental challenges.

MMR Deficiency Defines Distinct Molecular Subtype of Breast Cancer with Histone Proteomic Networks.

Mismatch repair (MMR) alterations are important prognostic and predictive biomarkers in a variety of cancer subtypes, including colorectal and endometrial. However, in breast cancer (BC), the distinction and clinical significance of MMR are largely unknown. This may be due in part to the fact that genetic alterations in MMR genes are rare and only seen to occur in around 3% of BCs. In the present study, we analyzed TCGA data using a multi-sample protein-protein interaction (PPI) analysis tool, Proteinarium, and showed a distinct separation between specific MMR-deficient and -intact networks in a cohort of 994 BC patients. In the PPI networks specific to MMR deficiency, highly connected clusters of histone genes were identified. We also found the distribution of MMR-deficient BC to be more prevalent in HER2-enriched and triple-negative (TN) BC subtypes compared to luminal BCs. We recommend defining MMR-deficient BC by next-generation sequencing (NGS) when any somatic mutation is detected in one of the seven MMR genes.

Integrating Molecular Simulations with Machine Learning Guides in the Design and Synthesis of [BMIM][BF4]/MOF Composites for CO2/N2 Separation.

Considering the existence of a large number and variety of metal-organic frameworks (MOFs) and ionic liquids (ILs), assessing the gas separation potential of all possible IL/MOF composites by purely experimental methods is not practical. In this work, we combined molecular simulations and machine learning (ML) algorithms to computationally design an IL/MOF composite. Molecular simulations were first performed to screen approximately 1000 different composites of 1-n-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF4]) with a large variety of MOFs for CO2 and N2 adsorption. The results of simulations were used to develop ML models that can accurately predict the adsorption and separation performances of [BMIM][BF4]/MOF composites. The most important features that affect the CO2/N2 selectivity of composites were extracted from ML and utilized to computationally generate an IL/MOF composite, [BMIM][BF4]/UiO-66, which was not present in the original material data set. This composite was finally synthesized, characterized, and tested for CO2/N2 separation. Experimentally measured CO2/N2 selectivity of the [BMIM][BF4]/UiO-66 composite matched well with the selectivity predicted by the ML model, and it was found to be comparable, if not higher than that of all previously synthesized [BMIM][BF4]/MOF composites reported in the literature. Our proposed approach of combining molecular simulations with ML models will be highly useful to accurately predict the CO2/N2 separation performances of any [BMIM][BF4]/MOF composite within seconds compared to the extensive time and effort requirements of purely experimental methods.

Clinical features of patients with MTAP-deleted bladder cancer.

Advanced urothelial carcinoma continues to have a dismal prognosis despite several new therapies in the last 5 years. FGFR2 and FGFR3 mutations and fusions, PD-L1 expression, tumor mutational burden, and microsatellite instability are established predictive biomarkers in advanced urothelial carcinoma. Novel biomarkers can optimize the sequencing of available treatments and improve outcomes. We describe herein the clinical and pathologic features of patients with an emerging subtype of bladder cancer characterized by deletion of the gene MTAP encoding the enzyme S-Methyl-5'-thioadenosine phosphatase, a potential biomarker of response to pemetrexed. We performed a retrospective analysis of 61 patients with advanced urothelial carcinoma for whom demographics, pathologic specimens, next generation sequencing, and clinical outcomes were available. We compared the frequency of histology variants, upper tract location, pathogenic gene variants, tumor response, progression free survival (PFS) and overall survival (OS) between patients with tumors harboring MTAP deletion (MTAP-del) and wild type tumors (MTAP-WT). A propensity score matching of 5 covariates (age, gender, presence of variant histology, prior surgery, and prior non-muscle invasive bladder cancer) was calculated to compensate for disparity when comparing survival in these subgroups. Non-supervised clustering analysis of differentially expressed genes between MTAP-del and MTAP-WT urothelial carcinomas was performed. MTAP-del occurred in 19 patients (31%). Tumors with MTAP-del were characterized by higher prevalence of squamous differentiation (47.4 vs 11.9%), bone metastases (52.6 vs 23.5%) and lower frequency of upper urinary tract location (5.2% vs 26.1%). Pathway gene set enrichment analysis showed that among the genes upregulated in the MTAP-del cohort, at least 5 were linked to keratinization (FOXN1, KRT33A/B, KRT84, RPTN) possibly contributing to the higher prevalence of squamous differentiation. Alterations in the PIK3 and MAPK pathways were more frequent when MTAP was deleted. There was a trend to inferior response to chemotherapy among MTAP-del tumors, but no difference in the response to immune checkpoint inhibitors or enfortumab. Median progression free survival after first line therapy (PFS1) was 5.5 months for patients with MTAP-WT and 4.5 months for patients with MTAP-del (HR = 1.30; 95% CI, 0.64-2.63; P = 0.471). There was no difference in the time from metastatic diagnosis to death (P = 0.6346). Median OS from diagnosis of localized or de novo metastatic disease was 16 months (range 1.5-60, IQR 8-26) for patients with MTAP-del and 24.5 months (range 3-156, IQR 16-48) for patients with MTAP-WT (P = 0.0218), suggesting that time to progression to metastatic disease is shorter in MTAP-del patients. Covariates did not impact significantly overall survival on propensity score matching. In conclusion, MTAP -del occurs in approximately 30% of patients with advanced urothelial carcinoma and defines a subgroup of patients with aggressive features, such as squamous differentiation, frequent bone metastases, poor response to chemotherapy, and shorter time to progression to metastatic disease.

Identification of Heme Oxygenase-1 as a Putative DNA-Binding Protein.

Heme oxygenase-1 (HO-1) is a rate-limiting enzyme in degrading heme into biliverdin and iron. HO-1 can also enter the nucleus and regulate gene transcription independent of its enzymatic activity. Whether HO-1 can alter gene expression through direct binding to target DNA remains unclear. Here, we performed HO-1 CHIP-seq and then employed 3D structural modeling to reveal putative HO-1 DNA binding domains. We identified three probable DNA binding domains on HO-1. Using the Proteinarium, we identified several genes as the most highly connected nodes in the interactome among the HO-1 gene binding targets. We further demonstrated that HO-1 modulates the expression of these key genes using Hmox1 deficient cells. Finally, mutation of four conserved amino acids (E215, I211, E201, and Q27) within HO-1 DNA binding domain 1 significantly increased expression of Gtpbp3 and Eif1 genes that were identified within the top 10 binding hits normalized by gene length predicted to bind this domain. Based on these data, we conclude that HO-1 protein is a putative DNA binding protein, and regulates targeted gene expression. This provides the foundation for developing specific inhibitors or activators targeting HO-1 DNA binding domains to modulate targeted gene expression and corresponding cellular function.

Influence of ionic liquids on the electronic environment of atomically dispersed Ir on (MgO)(100).

Recently, ionic liquids (ILs) have been used as ligands for single-site Ir(CO)2 complexes bound to metal-oxide supports because of their electron-donor/acceptor capacities. The combined effects of supports and ILs as ligands may pave the way to the tuning of the surrounding electronic properties to increase electron-donor/acceptor efficiency in metal-oxide supported Ir(CO)2 complexes. Herein, we have used Density Functional Theory to model Ir(CO)2 complexes bound to MgO supports with and without the presence of an IL to explain the role of ILs in modifying the electronic structure of the supported complex. Comparison of the ν(CO) band stretching frequencies with experimental results has led to the rationalization of the factors driving the interactions between the IL, the support, and the catalyst as well as the justification of the methodology for further studies.

Protein interaction networks define the genetic architecture of preterm birth.

The likely genetic architecture of complex diseases is that subgroups of patients share variants in genes in specific networks sufficient to express a shared phenotype. We combined high throughput sequencing with advanced bioinformatic approaches to identify such subgroups of patients with variants in shared networks. We performed targeted sequencing of patients with 2 or 3 generations of preterm birth on genes, gene sets and haplotype blocks that were highly associated with preterm birth. We analyzed the data using a multi-sample, protein-protein interaction (PPI) tool to identify significant clusters of patients associated with preterm birth. We identified shared protein interaction networks among preterm cases in two statistically significant clusters, p < 0.001. We also found two small control-dominated clusters. We replicated these data on an independent, large birth cohort. Separation testing showed significant similarity scores between the clusters from the two independent cohorts of patients. Canonical pathway analysis of the unique genes defining these clusters demonstrated enrichment in inflammatory signaling pathways, the glucocorticoid receptor, the insulin receptor, EGF and B-cell signaling, These results support a genetic architecture defined by subgroups of patients that share variants in genes in specific networks and pathways which are sufficient to give rise to the disease phenotype.

A novel alkali activated magnesium silicate as an effective and mechanically strong adsorbent for methylene blue removal.

A novel, cheap, and easy-to-synthesize sepiolite-based alkali-activated material (Sep-AAM), synthesized by the reaction of a magnesium silicate source, sepiolite, with sodium silicate solution, demonstrating high mechanical strength and methylene blue (MB) removal performance is introduced. Kinetics data indicated that MB adsorption occurs through pseudo-second-order adsorption kinetics model, while the Langmuir isotherm model provided a better fit to adsorption isotherms. The Sep-AAM provided a removal capacity of 99.92 mg g-1 at 50 °C, setting a new benchmark value among the materials used for this purpose. Thermodynamical parameters indicated that the adsorption of MB onto Sep-AAM was endothermic and the interaction between Sep-AAM and MB included weak chemical bonding. Regenerability of the Sep-AAM in powder and monolith forms was confirmed up to four-cycles. Structural parameters determined by several characterization tools demonstrated that the surface hydroxyl groups are responsible for the superior MB adsorption performance. The mechanical strength measurements showed that Sep-AAM in monolith form displayed a remarkable compressive strength value of 40 MPa. To establish a new approach forward on the development of AAMs for wastewater treatment, this study shows that sepiolite can effectively be utilized and Sep-AAM provides a sustainable solution for dye removal with advanced mechanical properties.

Proteomic analysis of a murine model of lung hypoplasia induced by oligohydramnios.

Severe oligohydramnios (OH) due to prolonged loss of amniotic fluid can cause pulmonary hypoplasia. Animal model of pulmonary hypoplasia induced by amniotic fluid drainage is partly attributed to changes in mechanical compression of the lung. Although numerous studies on OH-model have demonstrated changes in several individual proteins, however, the underlying mechanisms for interrupting normal lung development in response to a decrease of amniotic fluid volume are not fully understood. In this study, we used a proteomic approach to explore differences in the expression of a wide range of proteins after induction of OH in a mouse model of pulmonary hypoplasia to find out the signaling/molecular pathways involved in fetal lung development. Liquid chromatography-massspectromery/mass spectrometry analysis found 474 proteins that were differentially expressed in OH-induced hypoplastic lungs in comparison to untouched (UnT) control. Among these proteins, we confirmed the downregulation of AKT1, SP-D, and CD200, and provided proof-of-concept for the first time about the potential role that these proteins could play in fetal lung development.

Transcription Profiles Associated with Inducible Adhesion in Candida parapsilosis.

Candida parapsilosis has emerged as a frequent cause of invasive candidiasis with increasing evidence of unique biological features relative to C. albicans As it adapts to conditions within a mammalian host, rapid changes in gene expression are necessary to facilitate colonization and persistence in this environment. Adhesion of the organism to biological surfaces is a key first step in this process and is the focus of this study. Building on previous observations showing the importance of a member of the ALS gene family in C. parapsilosis adhesion, three clinical isolates were cultured under two conditions that mimic the mammalian host and promote adhesion, incubation at 37°C in tissue culture medium 199 or in human plasma. Transcriptional profiles using RNA-seq were obtained in these adhesion-inducing conditions and compared to profiles following growth in yeast media that suppress adhesion to identify gene expression profiles associated with adhesion. Overall gene expression profiles among the three strains were similar in both adhesion-inducing conditions and distinct from adhesion-suppressing conditions. Pairwise analysis among the three growth conditions identified 133 genes that were differentially expressed at a cutoff of ±4-fold, with the most upregulated genes significantly enriched in iron acquisition and transmembrane transport, while the most downregulated genes were enriched in oxidation-reduction processes. Gene family enrichment analysis identified gene families with diverse functions that may have an important role in this important step for colonization and disease.IMPORTANCE Invasive Candida infections are frequent complications of the immunocompromised and are associated with substantive morbidity and mortality. Although C. albicans is the best-studied species, emerging infections by non-albicans Candida species have led to increased efforts to understand aspects of their pathogenesis that are unique from C. albicansC. parapsilosis is a frequent cause of invasive infections, particularly among premature infants. Recent efforts have identified important virulence mechanisms that have features distinct from C. albicansC. parapsilosis can exist outside a host environment and therefore requires rapid modifications when it encounters a mammalian host to prevent its clearance. An important first step in the process is adhesion to host surfaces. This work takes a global, nonbiased approach to investigate broad changes in gene expression that accompany efficient adhesion. As such, biological pathways and individual protein targets are identified that may be amenable to manipulation to reduce colonization and disease from this organism.

Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia.

Preeclampsia is a hypertensive disorder of pregnancy, which complicates up to 15% of US deliveries. It is an idiopathic disorder associated with several different phenotypes. We sought to determine if the genetic architecture of preeclampsia can be described by clusters of patients with variants in genes in shared protein interaction networks. We performed a case-control study using whole exome sequencing on early onset preeclamptic mothers with severe clinical features and control mothers with uncomplicated pregnancies between 2016 and 2020. A total of 143 patients were enrolled, 61 women with early onset preeclampsia with severe features based on ACOG criteria, and 82 control women at term, matched for race and ethnicity. A network analysis and visualization tool, Proteinarium, was used to confirm there are clusters of patients with shared gene networks associated with severe preeclampsia. The majority of the sequenced patients appear in two significant clusters. We identified one case dominant and one control dominant cluster. Thirteen genes were unique to the case dominated cluster. Among these genes, LAMB2, PTK2, RAC1, QSOX1, FN1, and VCAM1 have known associations with the pathogenic mechanisms of preeclampsia. Using bioinformatic analysis, we were able to identify subsets of patients with shared protein interaction networks, thus confirming our hypothesis about the genetic architecture of preeclampsia.

Proteinarium: Multi-sample protein-protein interaction analysis and visualization tool.

We posit the likely architecture of complex diseases is that subgroups of patients share variants in genes in specific networks which are sufficient to give rise to a shared phenotype. We developed Proteinarium, a multi-sample protein-protein interaction (PPI) tool, to identify clusters of patients with shared gene networks. Proteinarium converts user defined seed genes to protein symbols and maps them onto the STRING interactome. A PPI network is built for each sample using Dijkstra's algorithm. Pairwise similarity scores are calculated to compare the networks and cluster the samples. A layered graph of PPI networks for the samples in any cluster can be visualized. To test this newly developed analysis pipeline, we reanalyzed publicly available data sets, from which modest outcomes had previously been achieved. We found significant clusters of patients with unique genes which enhanced the findings in the original study.

Towards complete elucidation of structural factors controlling thermal stability of IL/MOF composites: Effects of ligand functionalization on MOFs.

In this work, we incorporated an ionic liquid (IL), 1-n-butyl-3-methylimidazolium methyl sulfate ([BMIM][MeSO4]) into two different metal organic frameworks (MOFs), UiO-66, and its amino-functionalized counterpart, NH2-UiO-66, to investigate the effects of ligand-functionalization on the thermal stability limits of IL/MOF composites. The as-synthesized IL/MOF composites were characterized in detail by combining X-ray diffraction (XRD), scanning electron microscopy (SEM), Brunauer-Emmett-Teller analysis (BET), X-ray fluorescence (XRF), infrared spectroscopies (FTIR), and their thermal stability limits were determined by thermogravimetric analysis (TGA). Characterization data confirmed the successful incorporation of the IL into each MOF and indicated the presence of direct interactions between them. A comparison of the interactions in [BMIM][MeSO4]-incorporated UiO-66 and NH2-UiO-66 with those in their 1-butyl-3-methylimidazolium hexafluorophosphate ([BMIM][PF6])-incorporated counterparts showed that the hydrophilic IL, [BMIM][MeSO4], interacts with the 1,4-benzenedicarboxylate (BDC) ligand of the UiO-66, while the hydrophobic IL, [BMIM][PF6], is interacting with the joints where zirconium metal cluster coordinates with BDC ligand. The TGA data demonstrated that the composite with the ligand-functionalized MOF, NH2-UiO-66, exhibited a lower percentage decrease in the maximum tolerable temperature compared to those of IL/UiO-66 composites. Moreover, it is discovered that when the IL is hydrophilic, its hydrogen bonding ability can be utilized to designate an interaction site on MOF's ligand structure which will lead to a lower reduction in thermal stability limits. These results provide insights for the rational design of IL/MOF composites and contribute towards the complete elucidation of structural factors controlling the thermal stability.

Mechanical stretch regulates the expression of specific miRNA in extracellular vesicles released from lung epithelial cells.

The underlying mechanism of normal lung organogenesis is not well understood. An increasing number of studies are demonstrating that extracellular vesicles (EVs) play critical roles in organ development by delivering microRNAs (miRNA) to neighboring and distant cells. miRNAs are important for fetal lung growth; however, the role of miRNA-EVs (miRNAs packaged inside the EVs) during fetal lung development is unexplored. The aim of this study was to examine the expression of miRNA-EVs in MLE-12, a murine lung epithelial cell line subjected to mechanical stretch in vitro with the long-term goal to investigate their potential role in the fetal lung development. Both cyclic and continuous mechanical stretch regulate miRNA differentially in EVs released from MLE-12 and intracellularly, demonstrating that mechanical signals regulate the expression of miRNA-EVs in lung epithelial cells. These results provide a proof-of-concept for the potential role that miRNA-EVs could play in the development of fetal lung.

Enhanced Water Purification Performance of Ionic Liquid Impregnated Metal-Organic Framework: Dye Removal by [BMIM][PF6]/MIL-53(Al) Composite.

We incorporated a water-stable ionic liquid (IL), 1-butyl-3-methylimidazolium hexafluorophosphate, [BMIM][PF6], into a water-stable metal-organic framework (MOF), MIL-53(Al), to generate the [BMIM][PF6]/MIL-53(Al) composite. This composite was examined for water purification by studying its capacity for methylene blue (MB) and methyl orange (MO) removal from aqueous solutions having either single dye or a mixture of both. Data illustrated that the removal efficiency and the maximum adsorption capacity of MIL-53(Al) were increased several times upon [BMIM][PF6] incorporation. For instance, within 1 min, 10 mg of pristine MIL-53(Al) adsorbed 23.3% MB from 10 mg/L of MB solution, while [BMIM][PF6]/MIL-53(Al) composite was adsorbed 82.3% MB in an identical solution. In the case of MO, 10 mg of pristine MIL-53(Al) achieved 27.8 and 53.6% MO removal from 10 mg/L of MO solution, while [BMIM][PF6]/MIL-53(Al) composite removed 61.4 and 99.2% within 5 min and 3 h, respectively. Moreover, upon [BMIM][PF6] incorporation, the maximum MB and MO adsorption capacities of the pristine MOF were increased from 84.5 to 44 mg/g to 204.9 to 60 mg/g, respectively. The adsorption of dyes in pristine MIL-53(Al) and [BMIM][PF6]/MIL-53(Al) followed a pseudo-second-order kinetic model and a Langmuir isotherm model. In a mixture of both dyes, the IL/MOF composite showed a doubled MB selectivity after the IL incorporation. The composite was successfully regenerated at least two times after its use in water purification to remove MB, MO, and their mixtures. Infrared (IR) spectra indicated that the MB/MO adsorption occurs on [BMIM][PF6]/MIL-53(Al) by electrostatic interactions, hydrogen bonding, and π-π interactions. These results showed that [BMIM][PF6]/MIL-53(Al) composite is a highly promising material for efficient water purification.

Successful long-term limb salvage using cephalic and small saphenous vein grafts: A case report.

In this case report, we present a patient scheduled for operation due to critical leg ischemia in whom a bilateral great saphenous vein (GSV) had already been used during previous cardiac and peripheral vascular surgeries. The patient underwent femorofemoral crossover bypass from left to right with a small saphenous vein and right femoropopliteal bypass with cephalic vein (CV) during the same session. Distal pulses became palpable, and symptoms regressed dramatically following the operation. A control computed tomographic angiography scan revealed no signs of graft stenosis 32 months after the surgery. Despite the recent advances in synthetic graft materials, small saphenous and CVs should be remembered as alternative long-standing conduits in the absence of the GSV.

Machine learning approach to literature mining for the genetics of complex diseases.

To generate a parsimonious gene set for understanding the mechanisms underlying complex diseases, we reasoned it was necessary to combine the curation of public literature, review of experimental databases and interpolation of pathway-associated genes. Using this strategy, we previously built the following two databases for reproductive disorders: The Database for Preterm Birth (dbPTB) and The Database for Preeclampsia (dbPEC). The completeness and accuracy of these databases is essential for supporting our understanding of these complex conditions. Given the exponential increase in biomedical literature, it is becoming increasingly difficult to manually maintain these databases. Using our curated databases as reference data sets, we implemented a machine learning-based approach to optimize article selection for manual curation. We used logistic regression, random forests and neural networks as our machine learning algorithms to classify articles. We examined features derived from abstract text, annotations and metadata that we hypothesized would best classify articles with genetically relevant content associated to the disorder of interest. Combinations of these features were used build the classifiers and the performance of these feature sets were compared to a standard 'Bag-of-Words'. Several combinations of these genetic based feature sets outperformed 'Bag-of-Words' at a threshold such that 95% of the curated gene set obtained from the original manual curation of all articles were extracted from the articles classified by machine learning as 'considered'. The performance was superior in terms of the reduction of required manual curation and two measures of the harmonic mean of precision and recall. The reduction in workload ranged from 0.814 to 0.846 for the dbPTB and 0.301 to 0.371 for the dbPEC. Additionally, a database of metadata and annotations is generated which allows for rapid query of individual features. Our results demonstrate that machine learning algorithms can identify articles with relevant data for databases of genes associated with complex diseases.