Machine Learning Approaches for Assessing Risk Factors of Adrenal Insufficiency in Patients Undergoing Immune Checkpoint Inhibitor Therapy.

Adrenal insufficiency is a rare, yet life-threatening immune-related adverse event of immune checkpoint inhibitors (ICIs). This study aimed to establish a risk scoring system for adrenal insufficiency in patients receiving anti-programmed cell death 1 (PD-1) or anti-programmed cell death-ligand 1 (PD-L1) agents. Moreover, several machine learning methods were utilized to predict such complications. This study included 209 ICI-treated patients from July 2015 to February 2021, excluding those with prior adrenal insufficiency, previous steroid therapy, or incomplete data to ensure data integrity. Patients were continuously followed up at Gyeongsang National University Hospital, with morning blood samples taken for basal cortisol level measurements, facilitating a comprehensive analysis of their adrenal insufficiency risk. Using a chi-squared test and logistic regression model, we derived the odds ratio and adjusted odds ratio (AOR) through univariate and multivariable analyses. This study utilized machine learning algorithms, such as decision trees, random forests, support vector machines (SVM), and logistic regression to predict adrenal insufficiency in patients treated with ICIs. The performance of each algorithm was evaluated using metrics like accuracy, sensitivity, specificity, precision, and the area under the receiver operating characteristic curve (AUROC), ensuring rigorous assessment and reproducibility. A risk scoring system was developed from the multivariable and machine learning analyses. In a multivariable analysis, proton pump inhibitors (PPIs) (AOR 4.5), and α-blockers (AOR 6.0) were significant risk factors for adrenal insufficiency after adjusting for confounders. Among the machine learning models, logistic regression and elastic net showed good predictions, with AUROC values of 0.75 (0.61-0.90) and 0.76 (0.64-0.89), respectively. Based on multivariable and machine learning analyses, females (1 point), age ≥ 65 (1 point), PPIs (1 point), α-blockers (2 points), and antipsychotics (3 points) were integrated into the risk scoring system. From the logistic regression curve, patients with 0, 1, 2, 4, 5, and 6 points showed approximately 1.1%, 2.8%, 7.3%, 17.6%, 36.8%, 61.3%, and 81.2% risk for adrenal insufficiency, respectively. The application of our scoring system could prove beneficial in patient assessment and clinical decision-making while administering PD-1/PD-L1 inhibitors.

Experience of surgical treatment in a granular cell tumor in the qscending colon: a case report.

We report a case about successful surgical treatment of a granular cell tumor in the ascending colon. A 36-year-old man underwent screening colonoscopy. An endoscopic examination revealed a 10-mm yellowish and hemispheric mass in the ascending colon, and lower endoscopic ultrasonography revealed a hypoechoic-to-isoechoic mass invaded the submucosal layer. The mass was suspected to be a colonic carcinoid tumor. Based on the preoperative evaluation, endoscopic complete resection was considered difficult. Therefore, the lesion was removed via laparoscopic right hemicolectomy. Histological examination revealed that the tumor consisted of nests of polygonal cells with abundant granular eosinophilic cytoplasm. Immunohistochemical staining revealed diffuse positivity for S100 and CD68. Therefore, the tumor was diagnosed as a granular cell tumor. We suggest that surgical resection should be considered if it is located in the thin-walled ascending colon prone to perforation, difficult to rule out malignant tumor due to submucosal invasion, or to remove endoscopically.

How is leadership maintained? A longitudinal mediation model linking informal leadership to upward voice through peer advice seeking.

Whereas scholars have identified individual antecedents of emerging as an informal leader among one's peers, our research seeks to understand how established informal leaders maintain their leadership status. Guided by principles from expectation states theory, we predict that being seen as an informal leader in a workgroup motivates other members to seek one out for work-related advice and, accordingly, facilitates the informal leader's engaging in upward voice directed toward the formal leader. Upward influence on behalf of the group may, in turn, reinforce leadership status among peers. Cross-lagged panel analysis of four-wave survey responses from 375 employees in 63 duty-free shops supported our hypotheses. Advice network centrality partially mediated the positive relationship between informal leadership and supervisor ratings of upward voice. The time-lagged effect of informal leadership on peer advice seeking was stronger among employees in a more central position of the friendship network. However, the theorized effect of upward voice on subsequent informal leadership received more limited support. Our research identifies a mechanism that stabilizes workgroup leadership structure. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Association between Genetic Polymorphism of GSTP1 and Toxicities in Patients Receiving Platinum-Based Chemotherapy: A Systematic Review and Meta-Analysis.

Platinum-based chemotherapy regimens have been proven to be effective in various cancers; however, considerable toxicities may develop and can even lead to treatment discontinuation. Diverse factors may influence adverse treatment events, with pharmacogenetic variations being one prime example. Polymorphisms within the glutathione S-transferase pi 1 (GSTP1) gene may especially alter enzyme activity and, consequently, various toxicities in patients receiving platinum-based chemotherapy. Due to a lack of consistency in the degree of elevated complication risk, we performed a systematic literature review and meta-analysis to determine the level of platinum-associated toxicity in patients with the GSTP1 rs1695 polymorphism. We conducted a systematic search for eligible studies published before January 2022 from PubMed, Web of Science, and EMBASE based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to evaluate the strength of the association between the rs1695 polymorphism and various toxicities. Ten eligible studies met the inclusion criteria. The pooled ORs for hematological toxicity and neutropenia in the patients with the variant (G) allele were 1.7- and 2.6-times higher than those with the AA genotype (95% CI 1.06-2.73 and 1.07-6.35), respectively. In contrast, the rs1695 polymorphism resulted in a 44% reduced gastrointestinal toxicity compared to wild-type homozygotes. Our study found that the GSTP1 rs1695 polymorphism was significantly correlated with platinum-induced toxicities. The study also revealed that rs1695 expression exhibited tissue-specific patterns and thus yielded opposite effects in different tissues. A personalized chemotherapy treatment based on these polymorphisms may be considered for cancer patients in the future.

Elevated Risk of Fluoropyrimidine-Associated Toxicity in European Patients with DPYD Genetic Polymorphism: A Systematic Review and Meta-Analysis.

Background: Fluoropyrimidine is widely used owing to its clinical efficacy, however, patients with dihydropyrimidine dehydrogenase (DPD) deficiency can experience fluoropyrimidine-associated toxicity. The dihydropyrimidine dehydrogenase (DPYD) gene encodes DPD, and studies suggest that DPYD polymorphisms can result in DPD deficiency. Since there is not a complete consistency of how much the risk of complication is elevated, we aimed to conduct a systematic literature review and a meta-analysis to provide the risk of fluoropyrimidine-associated toxicity in patients with DPYD rs1801160 polymorphism. Methods: We searched for qualifying studies published before October 2021 from PubMed, Web of Science, and EMBASE based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to evaluate the strength of the association between rs1801160 polymorphism and toxicities. A sensitivity analysis using the leave-one-out method was performed on the overall toxicity. Results: The pooled OR for overall toxicity in the patients with A allele was elevated 1.73 times higher than those with the GG genotype (95% CI 1.44-2.07). Sensitivity analysis yielded similar results, showing the robustness of the result. Subjects with variants showed a 2.37-fold increased hematological toxicity (95% CI 1.48-3.81); especially a 1.87-fold increased neutropenia compared to patients with wildtype (95% CI 1.49-2.34). Patients with A allele revealed 1.22 times higher gastrointestinal toxicity compared to those with GG genotype (95% CI 0.93-1.61), and among gastrointestinal toxicity, the risk of diarrhea was elevated 1.43 times higher in those with variants than patients with wildtype (95% CI 1.12-1.83). Conclusions: rs1801160 polymorphism is associated with elevated fluoropyrimidine-associated toxicity. Therefore, rs1801160 can be a potential candidate for DPD deficiency screening prior to fluoropyrimidine-based regimen.

Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models.

Targets of immune checkpoint inhibitors (ICIs) regulate immune homeostasis and prevent autoimmunity by downregulating immune responses and by inhibiting T cell activation. Although ICIs are widely used in immunotherapy because of their good clinical efficacy, they can also induce autoimmune-related adverse events. Thyroid-related adverse events are frequently associated with anti-programmed cell death 1 (PD-1) or anti-programmed cell death-ligand 1 (PD-L1) agents. The present study aims to investigate the factors associated with thyroid dysfunction in patients receiving PD-1 or PD-L1 inhibitors and to develop various machine learning approaches to predict complications. A total of 187 patients were enrolled in this study. Logistic regression analysis was conducted to investigate the association between such factors and adverse events. Various machine learning methods were used to predict thyroid-related complications. After adjusting for covariates, we found that smoking history and hypertension increase the risk of thyroid dysfunction by approximately 3.7 and 4.1 times, respectively (95% confidence intervals (CIs) 1.338-10.496 and 1.478-11.332, p = 0.012 and 0.007). In contrast, patients taking opioids showed an approximately 4.0-fold lower risk of thyroid-related complications than those not taking them (95% CI 1.464-11.111, p = 0.007). Among the machine learning models, random forest showed the best prediction, with an area under the receiver operating characteristic of 0.770 (95% CI 0.648-0.883) and an area under the precision-recall of 0.510 (95%CI 0.357-0.666). Hence, this study utilized various machine learning models for prediction and showed that factors such as smoking history, hypertension, and opioids are associated with thyroid-related adverse events in cancer patients receiving PD-1/PD-L1 inhibitors.

Combination of Niraparib, Cisplatin and Twist Knockdown in Cisplatin-Resistant Ovarian Cancer Cells Potentially Enhances Synthetic Lethality through ER-Stress Mediated Mitochondrial Apoptosis Pathway.

Poly (ADP-ribose) polymerase 1 inhibitors (PARPi) are used to treat recurrent ovarian cancer (OC) patients due to greater survival benefits and minimal side effects, especially in those patients with complete or partial response to platinum-based chemotherapy. However, acquired resistance of platinum-based chemotherapy leads to the limited efficacy of PARPi monotherapy in most patients. Twist is recognized as a possible oncogene and contributes to acquired cisplatin resistance in OC cells. In this study, we show how Twist knockdown cisplatin-resistant (CisR) OC cells blocked DNA damage response (DDR) to sensitize these cells to a concurrent treatment of cisplatin as a platinum-based chemotherapy agent and niraparib as a PARPi on in vitro two-dimensional (2D) and three-dimensional (3D) cell culture. To investigate the lethality of PARPi and cisplatin on Twist knockdown CisR OC cells, two CisR cell lines (OV90 and SKOV3) were established using step-wise dose escalation method. In addition, in vitro 3D spheroidal cell model was generated using modified hanging drop and hydrogel scaffolds techniques on poly-2-hydroxylethly methacrylate (poly-HEMA) coated plates. Twist expression was strongly correlated with the expression of DDR proteins, PARP1 and XRCC1 and overexpression of both proteins was associated with cisplatin resistance in OC cells. Moreover, combination of cisplatin (Cis) and niraparib (Nira) produced lethality on Twist-knockdown CisR OC cells, according to combination index (CI). We found that Cis alone, Nira alone, or a combination of Cis+Nira therapy increased cell death by suppressing DDR proteins in 2D monolayer cell culture. Notably, the combination of Nira and Cis was considerably effective against 3D-cultures of Twist knockdown CisR OC cells in which Endoplasmic reticulum (ER) stress is upregulated, leading to initiation of mitochondrial-mediated cell death. In addition, immunohistochemically, Cis alone, Nira alone or Cis+Nira showed lower ki-67 (cell proliferative marker) expression and higher cleaved caspase-3 (apoptotic marker) immuno-reactivity. Hence, lethality of PARPi with the combination of Cis on Twist knockdown CisR OC cells may provide an effective way to expand the therapeutic potential to overcome platinum-based chemotherapy resistance and PARPi cross resistance in OC.

Increased Extracellular Adenosine in Radiotherapy-Resistant Breast Cancer Cells Enhances Tumor Progression through A2AR-Akt-ß-Catenin Signaling.

Recently, we found that the expressions of adenosine (ADO) receptors A2AR and A2BR and the ectonucleotidase CD73 which is needed for the conversion of adenosine triphosphate (ATP) to adenosine diphosphate (ADP) and the extracellular ADO level are increased in TNBC MDA-MB-231 cells and RT-R-MDA-MB-231 cells compared to normal cells or non-TNBC cells. The expression of A2AR, but not A2BR, is significantly upregulated in breast cancer tissues, especially TNBC tissues, compared to normal epithelial tissues. Therefore, we further investigated the role of ADO-activated A2AR and its signaling pathway in the progression of RT-R-TNBC. ADO treatment induced MDA-MB-231 cell proliferation, colony formation, and invasion, which were enhanced in RT-R-MDA-MB-231 cells in an A2AR-dependent manner. A2AR activation by ADO induced AKT phosphorylation and then ß-catenin, Snail, and vimentin expression, and these effects were abolished by A2AR-siRNA transfection. In an in vivo animal study, compared to 4T1-injected mice, RT-R-4T1-injected mice exhibited significantly increased tumor growth and lung metastasis, which were decreased by A2AR-knockdown. The upregulation of phospho-AKT, ß-catenin, Snail, and vimentin expression in mice injected with RT-R-4T1 cells was also attenuated in mice injected with RT-R-4T1-A2AR-shRNA cells. These results suggest that A2AR is significantly upregulated in BC tissues, especially TNBC tissues, and ADO-mediated A2AR activation is involved in RT-R-TNBC invasion and metastasis through the AKT-ß-catenin pathway.

Recommendations for pathologic practice using digital pathology: consensus report of the Korean Society of Pathologists.

Digital pathology (DP) using whole slide imaging (WSI) is becoming a fundamental issue in pathology with recent advances and the rapid development of associated technologies. However, the available evidence on its diagnostic uses and practical advice for pathologists on implementing DP remains insufficient, particularly in light of the exponential growth of this industry. To inform DP implementation in Korea, we developed relevant and timely recommendations. We first performed a literature review of DP guidelines, recommendations, and position papers from major countries, as well as a review of relevant studies validating WSI. Based on that information, we prepared a draft. After several revisions, we released this draft to the public and the members of the Korean Society of Pathologists through our homepage and held an open forum for interested parties. Through that process, this final manuscript has been prepared. This recommendation contains an overview describing the background, objectives, scope of application, and basic terminology; guidelines and considerations for the hardware and software used in DP systems and the validation required for DP implementation; conclusions; and references and appendices, including literature on DP from major countries and WSI validation studies.

Single GaAs Nanowire/Graphene Hybrid Devices Fabricated by a Position-Controlled Microtransfer and an Imprinting Technique for an Embedded Structure.

We developed a new technique to fabricate single nanowire devices with reliable graphene/nanowire contacts using a position-controlled microtransfer and an embedded nanowire structure in a planar junction configuration. A thorough study of electrical properties and fabrication challenges of single p-GaAs nanowire/graphene devices was carried out in two different device configurations: (1) a graphene bottom-contact device where the nanowire-graphene contact junction is formed by transferring a nanowire on top of graphene and (2) a graphene top-contact device where the nanowire-graphene contact junction is formed by transferring graphene on top of an embedded nanowire. For the graphene top-contact devices, graphene-nanowire-metal devices, where graphene is used as one electrode and metal is the other electrode to a nanowire, and graphene-nanowire-graphene devices, where both electrodes to a nanowire are graphene, were investigated and compared with conventional metal/p-GaAs nanowire devices. Conventional metal/p-GaAs nanowire contact devices were further investigated in embedded and nonembedded nanowire device configurations. A significantly improved current in the embedded device configuration is explained with a "parallel resistors model" where the high-resistance parts with the metal-semiconductor Schottky contact and the low-resistance parts with noncontacted facets of the hexagonal nanowires are taken into consideration. Consistently, the nonembedded nanowire structure is found to be depleted much easier than the embedded nanowires from which an estimation for a fully depleted condition has also been established.

GaN/AlGaN Nanocolumn Ultraviolet Light-Emitting Diode Using Double-Layer Graphene as Substrate and Transparent Electrode.

The many outstanding properties of graphene have impressed and intrigued scientists for the last few decades. Its transparency to light of all wavelengths combined with a low sheet resistance makes it a promising electrode material for novel optoelectronics. So far, no one has utilized graphene as both the substrate and transparent electrode of a functional optoelectronic device. Here, we demonstrate the use of double-layer graphene as a growth substrate and transparent conductive electrode for an ultraviolet light-emitting diode in a flip-chip configuration, where GaN/AlGaN nanocolumns are grown as the light-emitting structure using plasma-assisted molecular beam epitaxy. Although the sheet resistance is increased after nanocolumn growth compared with pristine double-layer graphene, our experiments show that the double-layer graphene functions adequately as an electrode. The GaN/AlGaN nanocolumns are found to exhibit a high crystal quality with no observable defects or stacking faults. Room-temperature electroluminescence measurements show a GaN related near bandgap emission peak at 365 nm and no defect-related yellow emission.

Estimates of African Dust Deposition Along the Trans-Atlantic Transit Using the Decade-long Record of Aerosol Measurements from CALIOP, MODIS, MISR, and IASI.

Deposition of mineral dust into ocean fertilizes ecosystems and influences biogeochemical cycles and climate. In-situ observations of dust deposition are scarce, and model simulations depend on the highly parameterized representations of dust processes with few constraints. By taking advantage of satellites' routine sampling on global and decadal scales, we estimate African dust deposition flux and loss frequency (LF, a ratio of deposition flux to mass loading) along the trans-Atlantic transit using the three-dimensional distributions of aerosol retrieved by spaceborne lidar (CALIOP) and radiometers (MODIS, MISR, and IASI). On the basis of a ten-year (2007-2016) and basin scale average, the amount of dust deposition into the tropical Atlantic Ocean is estimated at 136 - 222 Tg yr-1. The 65-83% of satellite-based estimates agree with the in-situ climatology within a factor of 2. The magnitudes of dust deposition are highest in boreal summer and lowest in fall, whereas the interannual variability as measured by the normalized standard deviation with mean is largest in spring (28-41%) and smallest (7-15%) in summer. The dust deposition displays high spatial heterogeneity, revealing that the meridional shifts of major dust deposition belts are modulated by the seasonal migration of the intertropical convergence zone (ITCZ). On the basis of the annual and basin mean, the dust LF derived from the satellite observations ranges from 0.078 to 0.100 d-1, which is lower than model simulations by up to factors of 2 to 5. The most efficient loss of dust occurs in winter, consistent with the higher possibility of low-altitude transported dust in southern trajectories being intercepted by rainfall associated with the ITCZ. The satellite-based estimates of dust deposition can be used to fill the geographical gaps and extend time span of in-situ measurements, study the dust-ocean interactions, and evaluate model simulations of dust processes.

Robust Inductive Matrix Completion Strategy to Explore Associations Between LincRNAs and Human Disease Phenotypes.

Over the past few years, it has been established that a number of long intergenic non-coding RNAs (lincRNAs) are linked to a wide variety of human diseases. The relationship among many other lincRNAs still remains as puzzle. Validation of such link between the two entities through biological experiments is expensive. However, piles of information about the two are becoming available, thanks to the High Throughput Sequencing (HTS) platforms, Genome Wide Association Studies (GWAS), etc., thereby opening opportunity for cutting-edge machine learning and data mining approaches. However, there are only a few in silico lincRNA-disease association inference tools available to date, and none of these utilizes side information of both the entities. The recently developed Inductive Matrix Completion (IMC) technique provides a recommendation platform among two entities considering respective side information. But, the formulation of IMC is incapable of handling noise and outliers that may present in the dataset, while data sparsity consideration is another issue with the standard IMC method. Thus, a robust version of IMC is needed that can solve these two issues. As a remedy, in this paper, we propose Robust Inductive Matrix Completion (RIMC) using l2,1 norm loss function as well as l2,1 norm based regularization. We applied RIMC to the available association data between human lincRNAs and OMIM disease phenotypes as well as a diverse set of side information about the lincRNAs and the diseases. Our method performs better than the state-of-the-art methods in terms of precision@k and recall@k at the top- k disease prioritization to the subject lincRNAs. We also demonstrate that RIMC is equally effective for querying about novel lincRNAs, as well as predicting rank of a newly known disease for a set of well-characterized lincRNAs. Availability: All the supporting datasets are available at the publicly accessible URL located at http://biomecis.uta.edu/~ashis/res/RIMC/.

Effects of low dose ionizing radiation on DNA damage-caused pathways by reverse-phase protein array and Bayesian networks.

Ionizing radiation (IR) causing damages to Deoxyribonucleic acid (DNA) constitutes a broad range of base damage and double strand break, and thereby, it induces the operation of relevant signaling pathways such as DNA repair, cell cycle control, and cell apoptosis. The goal of this paper is to study how the exposure to low dose radiation affects the human body by observing the signaling pathway associated with Ataxia Telangiectasia mutated (ATM) using Reverse-Phase Protein Array (RPPA) and isogenic human Ataxia Telangiectasia (A-T) cells under different amounts and durations of IR exposure. In order to verify which proteins could be involved in a DNA damage-caused pathway, only proteins that highly interact with each other under IR are selected by using correlation coefficient. The pathway inference is derived from learning Bayesian networks in combination with prior knowledge such as Protein-Protein Interactions (PPIs) and signaling pathways from well-known databases. Learning Bayesian networks is based on a score and search scheme that provides the highest scored network structure given a score function, and the prior knowledge is included in the score function as a prior probability by using Dempster-Shafer theory (DST). In this way, the inferred network can be more likely to be similar to already discovered pathways and consistent with confirmed PPIs for more reliable inference. The experimental results show which proteins are involved in signaling pathways under IR, how the inferred pathways are different under low and high doses of IR, and how the selected proteins regulate each other in the inferred pathways. As our main contribution, overall results confirm that low dose IR could cause DNA damage and thereby induce and affect related signaling pathways such as apoptosis, cell cycle, and DNA repair.

Development of High-Resolution Dynamic Dust Source Function -A Case Study with a Strong Dust Storm in a Regional Model.

A high-resolution dynamic dust source has been developed in the NASA Unified-Weather Research and Forecasting (NU-WRF) model to improve the existing coarse static dust source. In the new dust source map, topographic depression is in 1-km resolution and surface bareness is derived using the Normalized Difference Vegetation Index (NDVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS). The new dust source better resolves the complex topographic distribution over the Western United States where its magnitude is higher than the existing, coarser resolution static source. A case study is conducted with an extreme dust storm that occurred in Phoenix, Arizona in 02-03 UTC July 6, 2011. The NU-WRF model with the new high-resolution dynamic dust source is able to successfully capture the dust storm, which was not achieved with the old source identification. However the case study also reveals several challenges in reproducing the time evolution of the short-lived, extreme dust storm events.

Stable solution to l 2,1-based robust inductive matrix completion and its application in linking long noncoding RNAs to human diseases.

BACKGROUNDS: A large number of long intergenic non-coding RNAs (lincRNAs) are linked to a broad spectrum of human diseases. The disease association with many other lincRNAs still remain as puzzle. Validation of such links between the two entities through biological experiments are expensive. However, a plethora lincRNA-data are available now, thanks to the High Throughput Sequencing (HTS) platforms, Genome Wide Association Studies (GWAS), etc, which opens the opportunity for cutting-edge machine learning and data mining approaches to extract meaningful relationships among lincRNAs and diseases. However, there are only a few in silico lincRNA-disease association inference tools available to date, and none of them utilizes side information of both the entities simultaneously in a single framework. METHODS: The recently developed Inductive Matrix Completion (IMC) technique provides a recommendation platform among two entities considering respective side information about them. However, the formulation of IMC is incapable of handling noise and outliers that may be present in the datasets, while data sparsity consideration is another issue with the standard IMC method. Thus, a robust version of IMC is needed that can solve the two issues. As a remedy, in this paper, we propose Stable Robust Inductive Matrix Completion (SRIMC) that utilizes the l 2,1 norm based regularization to optimize the objective function with a unique 2-step stable solution approach. RESULTS: We applied SRIMC to the available association data between human lincRNAs and OMIM disease phenotypes as well as a diverse set of side information about the lincRNAs and the diseases. The method performs better than the state-of-the-art methods in terms of p r e c i s i o n @ k and r e c a l l @ k at the top-k disease prioritization to the subject lincRNAs. We also demonstrate that SRIMC is equally effective for querying about novel lincRNAs, as well as predicting rank of a newly known disease for a set of well-characterized lincRNAs. CONCLUSIONS: With the experimental results and computational evaluation, we show that SRIMC is robust in handling datasets with noise and outliers as well as dealing with novel lincRNAs and disease phenotypes.

Multi-Block Bipartite Graph for Integrative Genomic Analysis.

Human diseases involve a sequence of complex interactions between multiple biological processes. In particular, multiple genomic data such as Single Nucleotide Polymorphism (SNP), Copy Number Variation (CNV), DNA Methylation (DM), and their interactions simultaneously play an important role in human diseases. However, despite the widely known complex multi-layer biological processes and increased availability of the heterogeneous genomic data, most research has considered only a single type of genomic data. Furthermore, recent integrative genomic studies for the multiple genomic data have also been facing difficulties due to the high-dimensionality and complexity, especially when considering their intra- and inter-block interactions. In this paper, we introduce a novel multi-block bipartite graph and its inference methods, MB2I and sMB2I, for the integrative genomic study. The proposed methods not only integrate multiple genomic data but also incorporate intra/inter-block interactions by using a multi-block bipartite graph. In addition, the methods can be used to predict quantitative traits (e.g., gene expression, survival time) from the multi-block genomic data. The performance was assessed by simulation experiments that implement practical situations. We also applied the method to the human brain data of psychiatric disorders. The experimental results were analyzed by maximum edge biclique and biclustering, and biological findings were discussed.

Low frequency noise in single GaAsSb nanowires with self-induced compositional gradients.

Due to bandgap tunability, GaAsSb nanowires (NWs) have received a great deal of attention for a variety of optoelectronic device applications. However, electrical and optical properties of GaAsSb are strongly affected by Sb-related defects and scattering from surface states and/or defects, which can limit the performance of GaAsSb NW devices. Thus, in order to utilize the GaAsSb NWs for high performance electronic and optoelectronic devices, it is required to study the material and interface properties (e.g. the interface trap density) in the GaAsSb NW devices. Here, we investigate the low frequency noise in single GaAsSb NWs with self-induced compositional gradients. The current noise spectral density of the GaAsSb NW device showed a typical 1/f noise behavior. The Hooge's noise parameter and the interface trap density of the GaAsSb NW device were found to be ∼2.2 × 10(-2) and ∼2 × 10(12) eV(-1) cm(-2), respectively. By applying low frequency noise measurements, the noise equivalent power, a key figure of merit of photodetectors, was calculated. The observed low frequency noise properties can be useful as guidance for quality and reliability of GaAsSb NW based electronic devices, especially for photodetectors.

Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders.

BACKGROUND: Inferring gene regulatory networks is one of the most interesting research areas in the systems biology. Many inference methods have been developed by using a variety of computational models and approaches. However, there are two issues to solve. First, depending on the structural or computational model of inference method, the results tend to be inconsistent due to innately different advantages and limitations of the methods. Therefore the combination of dissimilar approaches is demanded as an alternative way in order to overcome the limitations of standalone methods through complementary integration. Second, sparse linear regression that is penalized by the regularization parameter (lasso) and bootstrapping-based sparse linear regression methods were suggested in state of the art methods for network inference but they are not effective for a small sample size data and also a true regulator could be missed if the target gene is strongly affected by an indirect regulator with high correlation or another true regulator. RESULTS: We present two novel network inference methods based on the integration of three different criteria, (i) z-score to measure the variation of gene expression from knockout data, (ii) mutual information for the dependency between two genes, and (iii) linear regression-based feature selection. Based on these criterion, we propose a lasso-based random feature selection algorithm (LARF) to achieve better performance overcoming the limitations of bootstrapping as mentioned above. CONCLUSIONS: In this work, there are three main contributions. First, our z score-based method to measure gene expression variations from knockout data is more effective than similar criteria of related works. Second, we confirmed that the true regulator selection can be effectively improved by LARF. Lastly, we verified that an integrative approach can clearly outperform a single method when two different methods are effectively jointed. In the experiments, our methods were validated by outperforming the state of the art methods on DREAM challenge data, and then LARF was applied to inferences of gene regulatory network associated with psychiatric disorders.

Fast Contour-Tracing Algorithm Based on a Pixel-Following Method for Image Sensors.

Contour pixels distinguish objects from the background. Tracing and extracting contour pixels are widely used for smart/wearable image sensor devices, because these are simple and useful for detecting objects. In this paper, we present a novel contour-tracing algorithm for fast and accurate contour following. The proposed algorithm classifies the type of contour pixel, based on its local pattern. Then, it traces the next contour using the previous pixel's type. Therefore, it can classify the type of contour pixels as a straight line, inner corner, outer corner and inner-outer corner, and it can extract pixels of a specific contour type. Moreover, it can trace contour pixels rapidly because it can determine the local minimal path using the contour case. In addition, the proposed algorithm is capable of the compressing data of contour pixels using the representative points and inner-outer corner points, and it can accurately restore the contour image from the data. To compare the performance of the proposed algorithm to that of conventional techniques, we measure their processing time and accuracy. In the experimental results, the proposed algorithm shows better performance compared to the others. Furthermore, it can provide the compressed data of contour pixels and restore them accurately, including the inner-outer corner, which cannot be restored using conventional algorithms.