Machine learning prediction of glioblastoma patient one-year survival

Glioblastoma (GBM) is a grade IV astrocytoma formed primarily from cancerous astrocytes and sustained by intense angiogenesis. GBM often causes non-specific symptoms, creating difficulty for diagnosis. This study aimed to utilize machine learning techniques to provide an accurate one-year survival prognosis for GBM patients using clinical and genomic data from the Chinese Glioma Genome Atlas. Logistic regression (LR), support vector machines (SVM), random forest (RF), and ensemble models were used to identify and select predictors for GBM survival and to classify patients into those with an overall survival (OS) of less than one year and one year or greater. With regards to overall survival, a significant (p \u3c 0.05, n = 175) correlation was found with age (negative), radiation treatment (positive), and chemotherapy treatment (positive). IDH1 mutation and 1p19q codeletion showed insignificant correlation with OS in this dataset. This potentially implies that IDH1 mutation alone, although important in secondary GBM prognosis, is insignificant for primary GBM prognosis. 1p19q codeletion also appeared to be insignificant for primary GBM prognosis when considered alone. The ensemble model had the highest overall accuracy, achieving a mean AUC score of 0.644 and an F1 score of 0.799

Compositional Data Analysis is necessary for simulating and analyzing RNA-Seq data

*Seq techniques (e.g. RNA-Seq) generate compositional datasets, i.e. the number of fragments sequenced is not proportional to the total RNA present. Thus, datasets carry only relative information, even though absolute RNA copy numbers are often of interest. Current normalization methods assume most features are not changing, which can lead to misleading conclusions when there are large shifts. However, there are few real datasets and no simulation protocols currently available that can directly benchmark methods when such large shifts occur. We present absSimSeq, an R package that simulates compositional data in the form of RNA-Seq reads. We tested several tools used for RNA-Seq differential analysis: sleuth, DESeq2, edgeR, limma, sleuth and ALDEx2 (which explicitly takes a compositional approach). For these tools, we compared their standard normalization to either “compositional normalization”, which uses log-ratios to anchor the data on a set of negative control features, or RUVSeq, another tool that directly uses negative control features. We show that common normalizations result in reduced performance with current methods when there is a large change in the total RNA per cell. Performance improves when spike-ins are included and used by a compositional approach, even if the spike-ins have substantial variation. In contrast, RUVSeq, which normalizes count data rather than compositional data, has poor performance. Further, we show that previous criticisms of spike-ins did not take into account the compositional nature of the data. We conclude that absSimSeq can generate more representative datasets for testing performance, and that spike-ins should be more broadly used in a compositional manner to minimize misleading conclusions from differential analyses

Compositional Data Analysis is necessary for simulating and analyzing RNA-Seq data

*Seq techniques (e.g. RNA-Seq) generate compositional datasets, i.e. the number of fragments sequenced is not proportional to the total RNA present. Thus, datasets carry only relative information, even though absolute RNA copy numbers are often of interest. Current normalization methods assume most features are not changing, which can lead to misleading conclusions when there are large shifts. However, there are few real datasets and no simulation protocols currently available that can directly benchmark methods when such large shifts occur. We present absSimSeq, an R package that simulates compositional data in the form of RNA-Seq reads. We tested several tools used for RNA-Seq differential analysis: sleuth, DESeq2, edgeR, limma, sleuth and ALDEx2 (which explicitly takes a compositional approach). For these tools, we compared their standard normalization to either “compositional normalization”, which uses log-ratios to anchor the data on a set of negative control features, or RUVSeq, another tool that directly uses negative control features. We show that common normalizations result in reduced performance with current methods when there is a large change in the total RNA per cell. Performance improves when spike-ins are included and used by a compositional approach, even if the spike-ins have substantial variation. In contrast, RUVSeq, which normalizes count data rather than compositional data, has poor performance. Further, we show that previous criticisms of spike-ins did not take into account the compositional nature of the data. We conclude that absSimSeq can generate more representative datasets for testing performance, and that spike-ins should be more broadly used in a compositional manner to minimize misleading conclusions from differential analyses

An Intronic Signal for Alternative Splicing in the Human Genome

An important level at which the expression of programmed cell death (PCD) genes is regulated is alternative splicing. Our previous work identified an intronic splicing regulatory element in caspase-2 (casp-2) gene. This 100-nucleotide intronic element, In100, consists of an upstream region containing a decoy 3′ splice site and a downstream region containing binding sites for splicing repressor PTB. Based on the signal of In100 element in casp-2, we have detected the In100-like sequences as a family of sequence elements associated with alternative splicing in the human genome by using computational and experimental approaches. A survey of human genome reveals the presence of more than four thousand In100-like elements in 2757 genes. These In100-like elements tend to locate more frequent in intronic regions than exonic regions. EST analyses indicate that the presence of In100-like elements correlates with the skipping of their immediate upstream exons, with 526 genes showing exon skipping in such a manner. In addition, In100-like elements are found in several human caspase genes near exons encoding the caspase active domain. RT-PCR experiments show that these caspase genes indeed undergo alternative splicing in a pattern predicted to affect their functional activity. Together, these results suggest that the In100-like elements represent a family of intronic signals for alternative splicing in the human genome

An Intronic Signal for Alternative Splicing in the Human Genome

An important level at which the expression of programmed cell death (PCD) genes is regulated is alternative splicing. Our previous work identified an intronic splicing regulatory element in caspase-2 (casp-2) gene. This 100-nucleotide intronic element, In100, consists of an upstream region containing a decoy 3′ splice site and a downstream region containing binding sites for splicing repressor PTB. Based on the signal of In100 element in casp-2, we have detected the In100-like sequences as a family of sequence elements associated with alternative splicing in the human genome by using computational and experimental approaches. A survey of human genome reveals the presence of more than four thousand In100-like elements in 2757 genes. These In100-like elements tend to locate more frequent in intronic regions than exonic regions. EST analyses indicate that the presence of In100-like elements correlates with the skipping of their immediate upstream exons, with 526 genes showing exon skipping in such a manner. In addition, In100-like elements are found in several human caspase genes near exons encoding the caspase active domain. RT-PCR experiments show that these caspase genes indeed undergo alternative splicing in a pattern predicted to affect their functional activity. Together, these results suggest that the In100-like elements represent a family of intronic signals for alternative splicing in the human genome.</p

Publisher Correction: Copper adparticle enabled selective electrosynthesis of n-propanol.

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Liver-Targeting of Interferon-Alpha with Tissue-Specific Domain Antibodies

PMCID: PMC3581439This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Therapy and Long-Term Prophylaxis of Vaccinia Virus Respiratory Infections in Mice with an Adenovirus-Vectored Interferon Alpha (mDEF201)

An adenovirus 5 vector encoding for mouse interferon alpha, subtype 5 (mDEF201) was evaluated for efficacy against lethal vaccinia virus (WR strain) respiratory infections in mice. mDEF201 was administered as a single intranasal treatment either prophylactically or therapeutically at doses of 106 to 108 plaque forming units/mouse. When the prophylactic treatment was given at 56 days prior to infection, it protected 90% of animals from death (100% protection for treatments given between 1–49 days pre-infection), with minimal weight loss occurring during infection. Surviving animals re-challenged with virus 22 days after the primary infection were protected from death, indicating that mDEF201 did not compromise the immune response against the initial infection. Post-exposure therapy was given between 6–24 h after vaccinia virus exposure and protection was afforded by a 108 dose of mDEF201 given at 24 h, whereas a 107 dose was effective up to 12 h. Comparisons were made of the ability of mDEF201, given either 28 or 1 day prior to infection, to inhibit tissue virus titers and lung infection parameters. Lung, liver, and spleen virus titers were inhibited to nearly the same extent by either treatment, as were lung weights and lung hemorrhage scores (indicators of pneumonitis). Lung virus titers were significantly (>100-fold) lower than in the placebo group, and the other infection parameters in mDEF201 treated mice were nearly at baseline. In contrast, viral titers and lung infection parameters were high in the placebo group on day 5 of the infection. These results demonstrate the long-acting prophylactic and treatment capacity of mDEF201 to combat vaccinia virus infections

Conformational adaptation of Asian macaque TRIMCyp directs lineage specific antiviral activity

TRIMCyps are anti-retroviral proteins that have arisen independently in New World and Old World primates. All TRIMCyps comprise a CypA domain fused to the tripartite domains of TRIM5α but they have distinct lentiviral specificities, conferring HIV-1 restriction in New World owl monkeys and HIV-2 restriction in Old World rhesus macaques. Here we provide evidence that Asian macaque TRIMCyps have acquired changes that switch restriction specificity between different lentiviral lineages, resulting in species-specific alleles that target different viruses. Structural, thermodynamic and viral restriction analysis suggests that a single mutation in the Cyp domain, R69H, occurred early in macaque TRIMCyp evolution, expanding restriction specificity to the lentiviral lineages found in African green monkeys, sooty mangabeys and chimpanzees. Subsequent mutations have enhanced restriction to particular viruses but at the cost of broad specificity. We reveal how specificity is altered by a scaffold mutation, E143K, that modifies surface electrostatics and propagates conformational changes into the active site. Our results suggest that lentiviruses may have been important pathogens in Asian macaques despite the fact that there are no reported lentiviral infections in current macaque populations

Observation of Coalescence Process of Silver Nanospheres During Shape Transformation to Nanoprisms

In this report, we observed the growth mechanism and the shape transformation from spherical nanoparticles (diameter ~6 nm) to triangular nanoprisms (bisector length ~100 nm). We used a simple direct chemical reduction method and provided evidences for the growth of silver nanoprisms via a coalescence process. Unlike previous reports, our method does not rely upon light, heat, or strong oxidant for the shape transformation. This transformation could be launched by fine-tuning the pH value of the silver colloidal solution. Based on our extensive examination using transmission electron microscopy, we propose a non-point initiated growth mechanism, which is a combination of coalescence and dissolution–recrystallization process during the growth of silver nanoprisms