A neural network model for constructing endophenotypes of common complex diseases: an application to male young-onset hypertension microarray data

Motivation: Identification of disease-related genes using high-throughput microarray data is more difficult for complex diseases as compared with monogenic ones. We hypothesized that an endophenotype derived from transcriptional data is associated with a set of genes corresponding to a pathway cluster. We assumed that a complex disease is associated with multiple endophenotypes and can be induced by their up/downregulated gene expression patterns. Thus, a neural network model was adopted to simulate the gene–endophenotype–disease relationship in which endophenotypes were represented by hidden nodes

A Power Model for Routers: Modeling Alpha 21364 and InfiniBand Routers

As interconnection networks proliferate to many new applications, a low-latency high-throughput fabric is no longer sufficient. Applications are becoming power-constrained. In this paper, we propose an architectural-level power model for interconnection network routers that will allow researchers and designers to easily factor in power when exploring architectural trade-offs. We applied our model to two commercial routers -- the integrated Alpha 21364 router and the IBM 8-port 12X InfiniBand router, and show that the different micro-architectures lead to vastly different power consumption and distribution estimates

Data from: Fishing-induced changes in adult length are mediated by skipped-spawning

Elucidating fishing effects on fish population dynamics is a critical step toward sustainable fisheries management. Despite previous studies that have suggested age or size truncation in exploited fish populations, other aspects of fishing effects on population demography, e.g., via altering life histories and density, have received less attention. Here, we investigated the fishing effects altering adult demography via shifting reproductive trade-offs in the iconic, overexploited, Pacific bluefin tuna Thunnus orientalis. We found that, contrary to our expectation, mean lengths of catch increased over time in longline fisheries. On the other hand, mean catch lengths for purse seine fisheries did not show such increasing trends. We hypothesized that the size-dependent energetic cost of the spawning migration and elevated fishing mortality on the spawning grounds potentially drive size-dependent skipped spawning for adult tuna, mediating the observed changes in the catch lengths. Using eco-genetic individual-based modeling, we demonstrated that fishing-induced evolution of skipped spawning and size truncation interacted to shape the observed temporal changes in mean catch lengths for tuna. Skipped spawning of the small adults led to increased mean catch lengths for the longline fisheries, while truncation of small adults by the purse seines could offset such a pattern. Our results highlight the eco-evolutionary dynamics of fishing effects on population demography and caution against using demographic traits as a basis for fisheries management of the Pacific bluefin tuna as well as other migratory species

Life histories determine divergent population trends for fishes under climate warming

Most marine fish species express life-history changes across temperature gradients, such as faster growth, earlier maturation, and higher mortality at higher temperature. However, such climate-driven effects on life histories and population dynamics remain unassessed for most fishes. For 332 Indo-Pacific fishes, we show positive effects of temperature on body growth (but with decreasing asymptotic length), reproductive rates (including earlier age-at-maturation), and natural mortality for all species, with the effect strength varying among habitat-related species groups. Reef and demersal fishes are more sensitive to temperature changes than pelagic and bathydemersal fishes. Using a life table, we show that the combined changes of life histories upon increasing temperature tend to facilitate population growth for slow life-history populations, but reduce it for fast life-history ones. Within our data, lower proportions (25–30%) of slow life-history fishes but greater proportions of fast life-history fishes (42–60%) show declined population growth rates under 1 °C warming. Together, these findings suggest prioritizing sustainable management for fast life-history species

Farmland Trace Metal Contamination and Management Model—Model Development and a Case Study in Central Taiwan

In this study, the water quality of the irrigation system and concentration of trace metals in the sediments were combined to establish a farmland trace metal contamination and management model (FTM_CMM). The purpose of this model was to clarify the main sources of the trace metals that have caused the contamination of paddy soil in central Taiwan. The results of the model simulation showed that the trace metals in the paddy soil mainly came from the irrigation water and especially from the sediments in the irrigation channels. The contribution of the sediments in the irrigation channel to the individual trace metals in the paddy soil ranged from 56% to 72% as the contributions for Cr, Cu, Ni, and Zn were 72%, 68%, 56%, and 62%, respectively. The trace metal species according to their concentration in the contaminated soil ranked in the order of Zn > Cr ≈ Cu > Ni, which is about the same as in the channel sediment. During the simulation process, Cr, Cu, Ni, and Zn exceeded the control standards for farmlands in the 18th, 12th, 13th, and 17th years, respectively. This highlights that, in addition to the management of irrigation water quality, the management of trace metal contaminated sediment in adjacent canal irrigation systems is also an important part of the prevention of trace metal contamination in farmland

Farmland Trace Metal Contamination and Management Model—Model Development and a Case Study in Central Taiwan

In this study, the water quality of the irrigation system and concentration of trace metals in the sediments were combined to establish a farmland trace metal contamination and management model (FTM_CMM). The purpose of this model was to clarify the main sources of the tracemetals that have caused the contamination of paddy soil in central Taiwan. The results of the model simulation showed that the trace metals in the paddy soil mainly came from the irrigation water and especially from the sediments in the irrigation channels. The contribution of the sediments in the irrigation channel to the individual trace metals in the paddy soil ranged from 56% to 72% as the contributions for Cr, Cu, Ni, and Zn were 72%, 68%, 56%, and 62%, respectively. The trace metal species according to their concentration in the contaminated soil ranked in the order of Zn > Cr Cu > Ni, which is about the same as in the channel sediment. During the simulation process, Cr, Cu, Ni, and Zn exceeded the control standards for farmlands in the 18th, 12th, 13th, and 17th years, respectively. This highlights that, in addition to the management of irrigation water quality, the management of trace metal contaminated sediment in adjacent canal irrigation systems is also an important part of the prevention of trace metal contamination in farmland

BFspawner_mean_length_data

This file contains the time series of mean catch length data of adult Pacific bluefin tuna based on three fisheries: Taiwanese longline (TWL), Japanese coastal longline (JCL), and Japanese purse seine (JPS). The unit of length is cm. FYEAR: fishing year. Description of the sampling methods is available in the Material and Methods of the paper and supplemental material Table S1

Integration of Clinical and CT-Based Radiomic Features for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Systemic Therapy in Breast Cancer

The purpose of the present study was to examine the potential of a machine learning model with integrated clinical and CT-based radiomics features in predicting pathologic complete response (pCR) to neoadjuvant systemic therapy (NST) in breast cancer. Contrast-enhanced CT was performed in 329 patients with breast tumors (n = 331) before NST. Pyradiomics was used for feature extraction, and 107 features of seven classes were extracted. Feature selection was performed on the basis of the intraclass correlation coefficient (ICC), and six ICC thresholds (0.7–0.95) were examined to identify the feature set resulting in optimal model performance. Clinical factors, such as age, clinical stage, cancer cell type, and cell surface receptors, were used for prediction. We tried six machine learning algorithms, and clinical, radiomics, and clinical–radiomics models were trained for each algorithm. Radiomics and clinical–radiomics models with gray level co-occurrence matrix (GLCM) features only were also built for comparison. The linear support vector machine (SVM) regression model trained with radiomics features of ICC ≥0.85 in combination with clinical factors performed the best (AUC = 0.87). The performance of the clinical and radiomics linear SVM models showed statistically significant difference after correction for multiple comparisons (AUC = 0.69 vs. 0.78; p < 0.001). The AUC of the radiomics model trained with GLCM features was significantly lower than that of the radiomics model trained with all seven classes of radiomics features (AUC = 0.85 vs. 0.87; p = 0.011). Integration of clinical and CT-based radiomics features was helpful in the pretreatment prediction of pCR to NST in breast cancer

Versatile Grafting Approaches to Functionalizing Individually Dispersed Graphene Nanosheets Using RAFT Polymerization and Click Chemistry

Developing powerful and reliable strategies to covalently functionalize graphene for efficient grafting and achieving precise interface control remains challenging due to the strong interlayer cohesive energy and the surface inertia of graphene. Here, we present versatile and efficient grafting strategies to functionalize graphene nanosheets. An alkyne-bearing graphene core was used to prepare polymer-functionalized graphene using ‘grafting to’ and ‘grafting from’ strategies in combination with reversible chain transfer and click chemistry. The use of the ‘grafting to’ approach allows full control over limited length grafted polymer chains, while permitting a high grafting density to a single graphene face, resulting in good solubility and processability. The ‘grafting from’ approach offers complementary advantages, such as the grafting of high molecular weight polymer chains and a better coverage ratio on the graphene surface; however, the extra steps introduced, the presence of initiating groups, and difficulty in controlling the grafted polymer lead to decreased processability. Various types of polymer chains have been successful covalently tethered to graphene nanosheets using these two approaches, producing various molecular brushes with multifunctional arms resulting in water-soluble, oil-soluble, acidic, basic, polar, apolar, and variously functionalized polymers. This work describes versatile methodologies, using the ‘grafting to’ and ‘grafting from’ approaches, for the preparation of individually dispersed graphene nanosheets having the desirable properties described