Resolving the influence of lignin on soil organic matter decomposition with mechanistic models and continental-scale data.

Confidence in model estimates of soil CO2 flux depends on assumptions regarding fundamental mechanisms that control the decomposition of litter and soil organic carbon (SOC). Multiple hypotheses have been proposed to explain the role of lignin, an abundant and complex biopolymer that may limit decomposition. We tested competing mechanisms using data-model fusion with modified versions of the CN-SIM model and a 571-day laboratory incubation dataset where decomposition of litter, lignin, and SOC was measured across 80 soil samples from the National Ecological Observatory Network. We found that lignin decomposition consistently decreased over time in 65 samples, whereas in the other 15 samples, lignin decomposition subsequently increased. These "lagged-peak" samples can be predicted by low soil pH, high extractable Mn, and fungal community composition as measured by ITS PC2 (the second principal component of an ordination of fungal ITS amplicon sequences). The highest-performing model incorporated soil biogeochemical factors and daily dynamics of substrate availability (labile bulk litter:lignin) that jointly represented two hypotheses (C substrate limitation and co-metabolism) previously thought to influence lignin decomposition. In contrast, models representing either hypothesis alone were biased and underestimated cumulative decomposition. Our findings reconcile competing hypotheses of lignin decomposition and suggest the need to precisely represent the role of lignin and consider soil metal and fungal characteristics to accurately estimate decomposition in Earth-system models.

One-Week-Ahead Prediction of Cyanobacterial Harmful Algal Blooms in Iowa Lakes.

Cyanobacterial harmful algal blooms (CyanoHABs) pose serious risks to inland water resources. Despite advancements in our understanding of associated environmental factors and modeling efforts, predicting CyanoHABs remains challenging. Leveraging an integrated water quality data collection effort in Iowa lakes, this study aimed to identify factors associated with hazardous microcystin levels and develop one-week-ahead predictive classification models. Using water samples from 38 Iowa lakes collected between 2018 and 2021, feature selection was conducted considering both linear and nonlinear properties. Subsequently, we developed three model types (Neural Network, XGBoost, and Logistic Regression) with different sampling strategies using the nine selected variables (mcyA_M, TKN, % hay/pasture, pH, mcyA_M:16S, % developed, DOC, dewpoint temperature, and ortho-P). Evaluation metrics demonstrated the strong performance of the Neural Network with oversampling (ROC-AUC 0.940, accuracy 0.861, sensitivity 0.857, specificity 0.857, LR+ 5.993, and 1/LR- 5.993), as well as the XGBoost with downsampling (ROC-AUC 0.944, accuracy 0.831, sensitivity 0.928, specificity 0.833, LR+ 5.557, and 1/LR- 11.569). This study exhibited the intricacies of modeling with limited data and class imbalances, underscoring the importance of continuous monitoring and data collection to improve predictive accuracy. Also, the methodologies employed can serve as meaningful references for researchers tackling similar challenges in diverse environments.

Indoor Air Quality Analysis Using Deep Learning with Sensor Data.

Indoor air quality analysis is of interest to understand the abnormal atmospheric phenomena and external factors that affect air quality. By recording and analyzing quality measurements, we are able to observe patterns in the measurements and predict the air quality of near future. We designed a microchip made out of sensors that is capable of periodically recording measurements, and proposed a model that estimates atmospheric changes using deep learning. In addition, we developed an efficient algorithm to determine the optimal observation period for accurate air quality prediction. Experimental results with real-world data demonstrate the feasibility of our approach.

Molecular approaches for the detection and monitoring of microbial communities in bioaerosols: A review.

Bioaerosols significantly affect atmospheric processes while they undergo long-range vertical and horizontal transport and influence atmospheric chemistry and physics and climate change. Accumulating evidence suggests that exposure to bioaerosols may cause adverse health effects, including severe disease. Studies of bioaerosols have primarily focused on their chemical composition and largely neglected their biological composition and the negative effects of biological composition on ecosystems and human health. Here, current molecular methods for the identification, quantification, and distribution of bioaerosol agents are reviewed. Modern developments in environmental microbiology technology would be favorable in elucidation of microbial temporal and spatial distribution in the atmosphere at high resolution. In addition, these provide additional supports for growing evidence that microbial diversity or composition in the bioaerosol is an indispensable environmental aspect linking with public health.

Anti-allodynic effect of intrathecal processed Aconitum jaluense is associated with the inhibition of microglial activation and P2X7 receptor expression in spinal cord.

BACKGROUND: For their analgesic and anti-arthritic effects, Aconitum species have been used in folk medicine in some East Asian countries. Although their analgesic effect is attributed to its action on voltage-dependent sodium channels, they also suppress purinergic receptor expression in dorsal root ganglion neurons in rats with neuropathic pain. In vitro study also demonstrated that the Aconitum suppresses ATP-induced P2X7 receptor (P2X7R)-mediated inflammatory responses in microglial cell lines. Herein, we examined the effect of intrathecal administration of thermally processed Aconitum jaluense (PA) on pain behavior, P2X7R expression and microglial activation in a rat spinal nerve ligation (SNL) model. METHODS: Mechanical allodynia induced by L5 SNL in Sprague-Dawley rats was measured using the von Frey test to evaluate the effect of intrathecal injection of PA. Changes in the expression of P2X7R in the spinal cord were examined using RT-PCR and Western blot analysis. In addition, the effect of intrathecal PA on microglial activation was evaluated by immunofluorescence. RESULTS: Intrathecal PA attenuated mechanical allodynia in a dose-dependent manner showing both acute and chronic effects with 65 % of the maximal possible effect. The expression and production of spinal P2X7R was increased five days after SNL, but daily intrathecal PA injection significantly inhibited the increase to the level of naïve animals. Immunofluorescence of the spinal cord revealed a significant increase in P2X7R expression and activation of microglia in the dorsal horn, which was inhibited by intrathecal PA treatment. P2X7R co-localized with microglia marker, but not neurons. CONCLUSIONS: Intrathecal PA exerts anti-allodynic effects in neuropathic pain, possibly by suppressing P2X7R production and expression as well as reducing microglial activation in the spinal cord.

Adaptive control of the packet transmission period with solar energy harvesting prediction in wireless sensor networks.

A number of research works has studied packet scheduling policies in energy scavenging wireless sensor networks, based on the predicted amount of harvested energy. Most of them aim to achieve energy neutrality, which means that an embedded system can operate perpetually while meeting application requirements. Unlike other renewable energy sources, solar energy has the feature of distinct periodicity in the amount of harvested energy over a day. Using this feature, this paper proposes a packet transmission control policy that can enhance the network performance while keeping sensor nodes alive. Furthermore, this paper suggests a novel solar energy prediction method that exploits the relation between cloudiness and solar radiation. The experimental results and analyses show that the proposed packet transmission policy outperforms others in terms of the deadline miss rate and data throughput. Furthermore, the proposed solar energy prediction method can predict more accurately than others by 6.92%.

Modified dovetail-plasty in scar revision.

Scar revision is one of the fundamental techniques in the field of plastic and reconstructive surgery. Local flaps, such as a Z-plasty, W-plasty, or geometric broken-line closure, have been used for scar revision. Camouflaging a scar during scar revision for marginal scars from skin grafts and flaps, trapdoor scars, and linear scars is difficult. We describe our experience with the use of modified dovetail-plasty for scar revision in these difficult areas. Our study group consisted of 28 cases among 22 patients (9 males and 13 females) with a mean age of 33.6 years (range, 6-61 years). The conspicuous scars were located on the face (50%) and extremities (50%). The authors designed Y-shaped incision lines to relax the skin tension lines on one side of the excision line and trapezoid incision lines on the other side. There were 16 follow-up operations performed over 6 months after the initial operation among a total of 22 patients. There were scar depressions (2 patients) and a hypertrophic scar (1 patient) at the interval area between the dovetail flaps. A diffuse hypertrophic scar occurred in 1 patient with a dorsal foot scar. The overall success rates of the procedure as assessed by the surgeons were as follows: excellent (75%), good (12.4%), fair (6.3%), and poor (6.3%). This new local flap can achieve an inconspicuous scar using a blurred scar line and reducing tension. The authors recommend a modified dovetail-plasty for the revision of trapdoor scars and scars under excessive tension.

Luteolibacter yonseiensis sp. nov., isolated from activated sludge using algal metabolites.

A Gram-negative, rod-shaped, aerobic bacterial strain, designated EBTL01(T), was isolated from activated sludge by using metabolites of microalgae Ankistrodesmus gracilis SAG278-2. Phylogenetic analyses based on 16S rRNA gene sequence showed that strain EBTL01(T) belongs to the family Verrucomicrobiaceae, class Verrucomicrobiae, and is related most closely to Luteolibacter pohnpeiensis A4T-83(T) (95.5 % sequence similarity) and Luteolibacter algae A5J-41-2(T) (95.2 %). The G+C content of the genomic DNA of strain EBTL01(T) was 56.3 mol% and the menaquinone MK-9 was detected as the predominant quinone. Major fatty acid components were iso-C14 : 0, C16 : 1ω7c and C16 : 0. The amino acids of the cell-wall peptidoglycan contained muramic acid and meso-diaminopimelic acid. These profile results supported the affiliation of strain EBTL01(T) to the genus Luteolibacter. On the other hand, based on chemotaxonomic properties and phenotypic characteristics, strain EBTL01(T) could be clearly differentiated from its phylogenetic neighbours. Therefore, strain EBTL01(T) represents a novel species of the genus Luteolibacter, for which the name Luteolibacter yonseiensis sp. nov. is proposed. The type strain is EBTL01(T) ( = KCTC 23678(T) = JCM 18052(T)).

Development of an Angiography Guide Indicator for Stereotactic Radiosurgery for High Precision.

BACKGROUND: Stereotactic radiosurgery (SRS) is effectively used for treating various cerebrovascular diseases, including arteriovenous malformations. As image-based surgery is the gold standard technique in SRS, the quality of stereotactic angiography images greatly influences the surgical approach for cerebrovascular diseases. Despite several studies in the relevant literature, research on auxiliary devices, including angiography indicators used for cerebrovascular disease surgeries, is limited. Thus, the development of angiographic indicators may provide meaningful data for stereotactic surgery. METHODS: A centerline was drawn, and a guideline was attached such that the "+" and "X" centers of the existing angiography guide indicator intersect. Further, a guideline wire connecting "+" and "X" was fixed using a tape. Based on the presence or absence of the guide indicator, angiography anterior-posterior (AP) and lateral (LAT) images were taken 10 times each, and statistical analysis was performed. RESULTS: The average and standard deviation of the conventional AP and LAT indicators were 10.22 ± 0.53 mm and 9.02 ± 0.33 mm, and those of the developed AP and LAT indicators were 10.3 ± 0.57 mm and 8.92 ± 0.23 mm, respectively. CONCLUSIONS: The results confirmed that the use of the lead indicator developed in this study provides higher accuracy and precision compared to that of the use of the conventional indicator. Furthermore, the developed guide indicator may provide meaningful information during SRS.

Microbiome differences in sugarcane and metabolically engineered oilcane accessions and their implications for bioenergy production.

Oilcane is a metabolically engineered sugarcane (Saccharum spp. hybrid) that hyper-accumulates lipids in its vegetable biomass to provide an advanced feedstock for biodiesel production. The potential impact of hyper-accumulation of lipids in vegetable biomass on microbiomes and the consequences of altered microbiomes on plant growth and lipid accumulation have not been explored so far. Here, we explore differences in the microbiome structure of different oilcane accessions and non-modified sugarcane. 16S SSU rRNA and ITS rRNA amplicon sequencing were performed to compare the characteristics of the microbiome structure from different plant compartments (leaf, stem, root, rhizosphere, and bulk soil) of four greenhouse-grown oilcane accessions and non-modified sugarcane. Significant differences were only observed in the bacterial microbiomes. In leaf and stem microbiomes, more than 90% of the entire microbiome of non-modified sugarcane and oilcane was dominated by similar core taxa. Taxa associated with Proteobacteria led to differences in the non-modified sugarcane and oilcane microbiome structure. While differences were observed between multiple accessions, accession 1566 was notable in that it was consistently observed to differ in its microbial membership than other accessions and had the lowest abundance of taxa associated with plant-growth-promoting bacteria. Accession 1566 is also unique among oilcane accessions in that it has the highest constitutive expression of the WRI1 transgene. The WRI1 transcription factor is known to contribute to significant changes in the global gene expression profile, impacting plant fatty acid biosynthesis and photomorphogenesis. This study reveals for the first time that genetically modified oilcanes associate with distinct microbiomes. Our findings suggest potential relationships between core taxa, biomass yield, and TAG in oilcane accessions and support further research on the relationship between plant genotypes and their microbiomes.

Fabrication of p-type 2D single-crystalline transistor arrays with Fermi-level-tuned van der Waals semimetal electrodes.

High-performance p-type two-dimensional (2D) transistors are fundamental for 2D nanoelectronics. However, the lack of a reliable method for creating high-quality, large-scale p-type 2D semiconductors and a suitable metallization process represents important challenges that need to be addressed for future developments of the field. Here, we report the fabrication of scalable p-type 2D single-crystalline 2H-MoTe2 transistor arrays with Fermi-level-tuned 1T'-phase semimetal contact electrodes. By transforming polycrystalline 1T'-MoTe2 to 2H polymorph via abnormal grain growth, we fabricated 4-inch 2H-MoTe2 wafers with ultra-large single-crystalline domains and spatially-controlled single-crystalline arrays at a low temperature (~500 °C). Furthermore, we demonstrate on-chip transistors by lithographic patterning and layer-by-layer integration of 1T' semimetals and 2H semiconductors. Work function modulation of 1T'-MoTe2 electrodes was achieved by depositing 3D metal (Au) pads, resulting in minimal contact resistance (~0.7 kΩ·µm) and near-zero Schottky barrier height (~14 meV) of the junction interface, and leading to high on-state current (~7.8 µA/µm) and on/off current ratio (~105) in the 2H-MoTe2 transistors.

Contrasting geochemical and fungal controls on decomposition of lignin and soil carbon at continental scale.

Lignin is an abundant and complex plant polymer that may limit litter decomposition, yet lignin is sometimes a minor constituent of soil organic carbon (SOC). Accounting for diversity in soil characteristics might reconcile this apparent contradiction. Tracking decomposition of a lignin/litter mixture and SOC across different North American mineral soils using lab and field incubations, here we show that cumulative lignin decomposition varies 18-fold among soils and is strongly correlated with bulk litter decomposition, but not SOC decomposition. Climate legacy predicts decomposition in the lab, and impacts of nitrogen availability are minor compared with geochemical and microbial properties. Lignin decomposition increases with some metals and fungal taxa, whereas SOC decomposition decreases with metals and is weakly related with fungi. Decoupling of lignin and SOC decomposition and their contrasting biogeochemical drivers indicate that lignin is not necessarily a bottleneck for SOC decomposition and can explain variable contributions of lignin to SOC among ecosystems.

Venadaparib Is a Novel and Selective PARP Inhibitor with Improved Physicochemical Properties, Efficacy, and Safety.

PARP inhibitors have been approved by the FDA for use in the treatment of patients with ovarian, breast, pancreatic, and prostate cancers. PARP inhibitors show diverse suppressive effects on PARP family members and PARP-DNA trapping potency. These properties are associated with distinct safety/efficacy profiles. Here, we report the nonclinical characteristics of venadaparib (also known as IDX-1197 or NOV140101), a novel potent PARP inhibitor. The physiochemical properties of venadaparib were analyzed. Furthermore, the efficacy of venadaparib against PARP enzymes, PAR formation, and PARP trapping activities, and growth inhibition of cell lines with BRCA mutations were evaluated. Ex vivo and in vivo models were also established to study pharmacokinetics/pharmacodynamics, efficacy, and toxicity. Venadaparib specifically inhibits PARP-1 and -2 enzymes. Oral administration of venadaparib HCl at doses above 12.5 mg/kg significantly reduced tumor growth in the OV_065 patient-derived xenograft model. Intratumoral PARP inhibition remained at over 90% until 24 hours after dosing. Venadaparib had wider safety margins than olaparib. Notably, venadaparib showed favorable physicochemical properties and superior anticancer effects in homologous recombination-deficient in vitro and in vivo models with improved safety profiles. Our results suggest the possibility of venadaparib as a next-generation PARP inhibitor. On the basis of these findings, phase Ib/IIa studies on the efficacy and safety of venadaparib have been initiated.

Nocardioides daejeonensis sp. nov., a denitrifying bacterium isolated from sludge in a sewage-disposal plant.

Strain MJ31(T), a gram-reaction-positive, aerobic, rod-shaped, non-motile bacterium, was isolated from a sludge sample collected at the Daejeon sewage-disposal plant, in South Korea, and characterized in order to determine its taxonomic position. Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain MJ31(T) belonged to the genus Nocardioides, appearing most closely related to Nocardioides dubius KSL-104(T) (98.6 % sequence similarity), Nocardioides jensenii DSM 20641(T) (97.6 %), Nocardioides daedukensis MDN22(T) (97.2 %) and Nocardioides mesophilus MSL-22(T) (97.0 %). The chemotaxonomic properties of strain MJ31(T) were consistent with those of the genus Nocardioides: MK-8(H(4)) was the predominant menaquinone, iso-C(16 : 0), iso-C(17 : 0) and C(18 : 1)ω9c were the predominant cellular fatty acids, and the cell-wall peptidoglycan was based on LL-2,6-diaminopimelic acid. The genomic DNA G+C content of strain MJ31(T) was 71.2 mol%. Some differential phenotypic properties and low DNA-DNA relatedness values (<28 %) with the type strains of closely related species indicated that strain MJ31(T) represents a novel species, for which the name Nocardioides daejeonensis sp. nov. is proposed. The type strain is MJ31(T) ( = KCTC 19772(T) = JCM 16922(T)).

Response of Total (DNA) and Metabolically Active (RNA) Microbial Communities in Miscanthus × Giganteus Cultivated Soil to Different Nitrogen Fertilization Rates.

Miscanthus × giganteus is a promising high-yielding perennial plant to meet growing bioenergy demands; however, the degree to which the soil microbiome affects its nitrogen cycling and subsequently, biomass yield remains unclear. In this study, we hypothesize that contributions of metabolically active soil microbial membership may be underestimated with DNA-based approaches. We assessed the response of the soil microbiome to nitrogen availability in terms of both DNA and RNA soil microbial communities from the Long-term Assessment of Miscanthus Productivity and Sustainability (LAMPS) field trial. DNA and RNA were extracted from 271 samples, and 16S small subunit (SSU) rRNA amplicon sequencing was performed to characterize microbial community structure. Significant differences were observed in the resulting soil microbiomes and were best explained by the sequencing library of origin, either DNA or RNA. Similar numbers of membership were detected in DNA and RNA microbial communities, with more than 90% of membership shared. However, the profile of dominant membership within DNA and RNA differed, with varying proportions of Actinobacteria and Proteobacteria and Firmicutes and Proteobacteria. Only RNA microbial communities showed seasonal responses to nitrogen fertilization, and these differences were associated with nitrogen-cycling bacteria. The relative abundance of bacteria associated with nitrogen cycling was 7-fold higher in RNA than in DNA, and genes associated with denitrifying bacteria were significantly enriched in RNA, suggesting that these bacteria may be underestimated with DNA-only approaches. Our findings indicate that RNA-based SSU characterization can be a significant and complementing resource for understanding the role of soil microbiomes in bioenergy crop production. IMPORTANCEMiscanthus × giganteus is a promising candidate for bioeconomy cropping systems; however, it remains unclear how the soil microbiome supplies nitrogen to this low-input crop. DNA-based techniques are used to provide community characterization, but may miss important metabolically active taxa. By analyzing both DNA- and actively transcribed RNA-based microbial communities, we found that nitrogen cycling taxa in the soil microbiome may be underestimated using only DNA-based approaches. Accurately understanding the role of microbes and how they cycle nutrients is important for the development of sustainable bioenergy crops, and RNA-based approaches are recommended as a complement to DNA approaches to better understand the microbial, plant, and management interactions.

Identification of Survival-Specific Genes in Clear Cell Renal Cell Carcinoma Using a Customized Next-Generation Sequencing Gene Panel.

PURPOSE: Although mutations are associated with carcinogenesis, little is known about survival-specific genes in clear cell renal cell carcinoma (ccRCC). We developed a customized next-generation sequencing (NGS) gene panel with 156 genes. The purpose of this study was to investigate whether the survival-specific genes we found were present in Korean ccRCC patients, and their association with clinicopathological findings. MATERIALS AND METHODS: DNA was extracted from the formalin-fixed, paraffin-embedded tissue of 22 ccRCC patients. NGS was performed using our survival-specific gene panel with an Illumina MiSeq. We analyzed NGS data and the correlations between mutations and clinicopathological findings and also compared them with data from the Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) and Renal Cell Cancer-European Union (RECA-EU). RESULTS: We found a total of 100 mutations in 37 of the 156 genes (23.7%) in 22 ccRCC patients. Of the 37 mutated genes, 11 were identified as clinicopathologically significant. Six were novel survival-specific genes (ADAMTS10, CARD6, NLRP2, OBSCN, SECISBP2L, and USP40), and five were top-ranked mutated genes (AKAP9, ARID1A, BAP1, KDM5C, and SETD2). Only CARD6 was validated as an overall survival-specific gene in this Korean study (p = 0.04, r = -0.441), TCGA-KIRC cohort (p = 0.0003), RECA-EU (p = 0.0005). The 10 remaining gene mutations were associated with clinicopathological findings; disease-free survival, mortality, nuclear grade, sarcomatoid component, N-stage, sex, and tumor size. CONCLUSIONS: We discovered 11 survival-specific genes in ccRCC using data from TCGA-KIRC, RECA-EU, and Korean patients. We are the first to find a correlation between CARD6 and overall survival in ccRCC. The 11 genes, including CARD6, NLRP2, OBSCN, and USP40, could be useful diagnostic, prognostic, and therapeutic markers in ccRCC.

Atomic transistors based on seamless lateral metal-semiconductor junctions with a sub-1-nm transfer length.

The edge-to-edge connected metal-semiconductor junction (MSJ) for two-dimensional (2D) transistors has the potential to reduce the contact length while improving the performance of the devices. However, typical 2D materials are thermally and chemically unstable, which impedes the reproducible achievement of high-quality edge contacts. Here we present a scalable synthetic strategy to fabricate low-resistance edge contacts to atomic transistors using a thermally stable 2D metal, PtTe2. The use of PtTe2 as an epitaxial template enables the lateral growth of monolayer MoS2 to achieve a PtTe2-MoS2 MSJ with the thinnest possible, seamless atomic interface. The synthesized lateral heterojunction enables the reduced dimensions of Schottky barriers and enhanced carrier injection compared to counterparts composed of a vertical 3D metal contact. Furthermore, facile position-selected growth of PtTe2-MoS2 MSJ arrays using conventional lithography can facilitate the design of device layouts with high processability, while providing low contact resistivity and ultrashort transfer length on wafer scales.

Forecasting the Walking Assistance Rehabilitation Level of Stroke Patients Using Artificial Intelligence.

Cerebrovascular accidents (CVA) cause a range of impairments in coordination, such as a spectrum of walking impairments ranging from mild gait imbalance to complete loss of mobility. Patients with CVA need personalized approaches tailored to their degree of walking impairment for effective rehabilitation. This paper aims to evaluate the validity of using various machine learning (ML) and deep learning (DL) classification models (support vector machine, Decision Tree, Perceptron, Light Gradient Boosting Machine, AutoGluon, SuperTML, and TabNet) for automated classification of walking assistant devices for CVA patients. We reviewed a total of 383 CVA patients' (1623 observations) prescription data for eight different walking assistant devices from five hospitals. Among the classification models, the advanced tree-based classification models (LightGBM and tree models in AutoGluon) achieved classification results of over 90% accuracy, recall, precision, and F1-score. In particular, AutoGluon not only presented the highest predictive performance (almost 92% in accuracy, recall, precision, and F1-score, and 86.8% in balanced accuracy) but also demonstrated that the classification performances of the tree-based models were higher than that of the other models on its leaderboard. Therefore, we believe that tree-based classification models have potential as practical diagnosis tools for medical rehabilitation.

Walk-weighted subsequence kernels for protein-protein interaction extraction.

BACKGROUND: The construction of interaction networks between proteins is central to understanding the underlying biological processes. However, since many useful relations are excluded in databases and remain hidden in raw text, a study on automatic interaction extraction from text is important in bioinformatics field. RESULTS: Here, we suggest two kinds of kernel methods for genic interaction extraction, considering the structural aspects of sentences. First, we improve our prior dependency kernel by modifying the kernel function so that it can involve various substructures in terms of (1) e-walks, (2) partial match, (3) non-contiguous paths, and (4) different significance of substructures. Second, we propose the walk-weighted subsequence kernel to parameterize non-contiguous syntactic structures as well as semantic roles and lexical features, which makes learning structural aspects from a small amount of training data effective. Furthermore, we distinguish the significances of parameters such as syntactic locality, semantic roles, and lexical features by varying their weights. CONCLUSIONS: We addressed the genic interaction problem with various dependency kernels and suggested various structural kernel scenarios based on the directed shortest dependency path connecting two entities. Consequently, we obtained promising results over genic interaction data sets with the walk-weighted subsequence kernel. The results are compared using automatically parsed third party protein-protein interaction (PPI) data as well as perfectly syntactic labeled PPI data.

An Improved Approach to Identify Bacterial Pathogens to Human in Environmental Metagenome.

The identification of bacterial pathogens to humans is critical for environmental microbial risk assessment. However, current methods for identifying pathogens in environmental samples are limited in their ability to detect highly diverse bacterial communities and accurately differentiate pathogens from commensal bacteria. In the present study, we suggest an improved approach using a combination of identification results obtained from multiple databases, including the multilocus sequence typing (MLST) database, virulence factor database (VFDB), and pathosystems resource integration center (PATRIC) databases to resolve current challenges. By integrating the identification results from multiple databases, potential bacterial pathogens in metagenomes were identified and classified into eight different groups. Based on the distribution of genes in each group, we proposed an equation to calculate the metagenomic pathogen identification index (MPII) of each metagenome based on the weighted abundance of identified sequences in each database. We found that the accuracy of pathogen identification was improved by using combinations of multiple databases compared to that of individual databases. When the approach was applied to environmental metagenomes, metagenomes associated with activated sludge were estimated with higher MPII than other environments (i.e., drinking water, ocean water, ocean sediment, and freshwater sediment). The calculated MPII values were statistically distinguishable among different environments (p<0.05). These results demonstrate that the suggested approach allows more for more accurate identification of the pathogens associated with metagenomes.