Machine Learning Techniques for Characterizing IEEE 802.11b Encrypted Data Streams

As wireless networks become an increasingly common part of the infrastructure in industrialized nations, the vulnerabilities of this technology need to be evaluated. Even though there have been major advancements in encryption technology, security protocols and packet header obfuscation techniques, other distinguishing characteristics do exist in wireless network traffic. These characteristics include packet size, signal strength, channel utilization and others. Using these characteristics, windows of size 11, 31, and 51 packets are collected and machine learning (ML) techniques are trained to classify applications accessing the 802.11b wireless channel. The four applications used for this study included E-Mail, FTP, HTTP, and Print. Using neural networks and decision trees, the overall success (correct identification of applications) of the ML systems ranged from a low average of 65.8% for neural networks to a high of 85.9% for decision trees. These averages are a result of all classification attempts including the case where only one application is accessing the medium and also the unique combinations of two and three different applications

Correlation Analysis of Enzyme activities and Deconstruction of Ammonia-pretreated Switchgrass by Bacterial-fungal Communities

The mixed microbial communities that occur naturally on lignocellulosic feedstocks can provide feedstock-specific enzyme mixtures to saccharify lignocelluloses. Bacterial-fungal communities were enriched from switchgrass bales to deconstruct ammonia-pretreated switchgrass (DSG). Correlation analysis was carried out to elucidate the relationship between microbial decomposition of DSG by these communities, enzymatic activities produced and enzymatic saccharification of DSG using these enzyme mixtures. Results of the analysis showed that β glucosidase activities and xylosidase activities limited the extent of microbial deconstruction and enzymatic saccharification of DSG. The results also underlined the importance of ligninase activity for the enzymatic saccharification of pretreated lignocellulosic feedstock. The bacterial fungal communities developed in this research can be used to produce enzyme mixtures to deconstruct DSG, and the results from the correlation analysis can be used to optimize these enzyme mixtures for efficient saccharification of DSG to produce second-generation biofuels

Comparison of Electrical Moisture Meters for Baled Alfalfa Hay

A primary concern in producing quality alfalfa hay is moisture measurement. Some precision in moisture measurement is required since hay can be too wet, leading to dry matter and quality loss through mold; it can be too dry, leading to shatter loss during baling, handling and storage. Moisture measurement in hay can take many forms. One form of subjective (personal judgment) evaluation is brittleness of leaves and stems in the windrow or bale. Typical objective methods consist of electric meters with calibration curves and oven drying

A Practical Guide for Managing Interdisciplinary Teams: Lessons Learned from Coupled Natural and Human Systems Research

Interdisciplinary team science is essential to address complex socio-environmental questions, but it also presents unique challenges. The scientific literature identifies best practices for high-level processes in team science, e.g., leadership and team building, but provides less guidance about practical, day-to-day strategies to support teamwork, e.g., translating jargon across disciplines, sharing and transforming data, and coordinating diverse and geographically distributed researchers. This article offers a case study of an interdisciplinary socio-environmental research project to derive insight to support team science implementation. We evaluate the project’s inner workings using a framework derived from the growing body of literature for team science best practices, and derive insights into how best to apply team science principles to interdisciplinary research. We find that two of the most useful areas for proactive planning and coordinated leadership are data management and co-authorship. By providing guidance for project implementation focused on these areas, we contribute a pragmatic, detail-oriented perspective on team science in an effort to support similar projects

Different neural mechanisms within occipitotemporal cortex underlie repetition suppression across same and different-size faces.

Repetition suppression (RS) (or functional magnetic resonance imaging adaptation) refers to the reduction in blood oxygen level-dependent signal following repeated presentation of a stimulus. RS is frequently used to investigate the role of face-selective regions in human visual cortex and is commonly thought to be a "localized" effect, reflecting fatigue of a neuronal population representing a given stimulus. In contrast, predictive coding theories characterize RS as a consequence of "top-down" changes in between-region modulation. Differentiating between these accounts is crucial for the correct interpretation of RS effects in the face-processing network. Here, dynamic causal modeling revealed that different mechanisms underlie different forms of RS to faces in occipitotemporal cortex. For both familiar and unfamiliar faces, repetition of identical face images (same size) was associated with changes in "forward" connectivity between the occipital face area (OFA) and the fusiform face area (FFA) (OFA-to-FFA). In contrast, RS across image size was characterized by altered "backward" connectivity (FFA-to-OFA). In addition, evidence was higher for models in which information projected directly into both OFA and FFA, challenging the role of OFA as the input stage of the face-processing network. These findings suggest "size-invariant" RS to faces is a consequence of interactions between regions rather than being a localized effect

Prospective Molecular Profiling of Canine Cancers Provides a Clinically Relevant Comparative Model for Evaluating Personalized Medicine (PMed) Trials.

Background Molecularly-guided trials (i.e. PMed) now seek to aid clinical decision-making by matching cancer targets with therapeutic options. Progress has been hampered by the lack of cancer models that account for individual-to-individual heterogeneity within and across cancer types. Naturally occurring cancers in pet animals are heterogeneous and thus provide an opportunity to answer questions about these PMed strategies and optimize translation to human patients. In order to realize this opportunity, it is now necessary to demonstrate the feasibility of conducting molecularly-guided analysis of tumors from dogs with naturally occurring cancer in a clinically relevant setting. Methodology A proof-of-concept study was conducted by the Comparative Oncology Trials Consortium (COTC) to determine if tumor collection, prospective molecular profiling, and PMed report generation within 1 week was feasible in dogs. Thirty-one dogs with cancers of varying histologies were enrolled. Twenty-four of 31 samples (77%) successfully met all predefined QA/QC criteria and were analyzed via Affymetrix gene expression profiling. A subsequent bioinformatics workflow transformed genomic data into a personalized drug report. Average turnaround from biopsy to report generation was 116 hours (4.8 days). Unsupervised clustering of canine tumor expression data clustered by cancer type, but supervised clustering of tumors based on the personalized drug report clustered by drug class rather than cancer type. Conclusions Collection and turnaround of high quality canine tumor samples, centralized pathology, analyte generation, array hybridization, and bioinformatic analyses matching gene expression to therapeutic options is achievable in a practical clinical window (\u3c1 \u3eweek). Clustering data show robust signatures by cancer type but also showed patient-to-patient heterogeneity in drug predictions. This lends further support to the inclusion of a heterogeneous population of dogs with cancer into the preclinical modeling of personalized medicine. Future comparative oncology studies optimizing the delivery of PMed strategies may aid cancer drug development

College Student Mental Health: An Evaluation of the DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure

© 2018 American Psychological Association. The DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure was developed to aid in clinical decision-making for clients seeking psychiatric services and to facilitate empirical investigation of the dimensional nature of mental health issues. Preliminary evidence supports its utility with clinical samples. However, the brief, yet comprehensive structure of the DSM-5 Level 1 measure may benefit a high-risk population that is less likely to seek treatment. College students have high rates of hazardous substance use and co-occurring mental health symptoms, yet rarely seek treatment. Therefore, the current study evaluated the psychometric properties (i.e., construct and criterion-related validity) of the DSM-5 Level 1 measure with a large, diverse sample of non-treatment-seeking college/university students. Data from 7,217 college students recruited from 10 universities in 10 different states across the United States evidenced psychometric validation of the DSM-5 Level 1 measure. Specifically, we found acceptable internal consistency across multi-item DSM-5 domains and moderate to strong correlations among domains (internal validity). Further, several DSM-5 domains were positively associated with longer, validated measures of the same mental health construct and had similar strengths of associations with substance use outcomes compared to longer measures of the same construct (convergent validity). Finally, all DSM-5 domains were negatively associated with self-esteem and positively associated with other theoretically relevant constructs, such as posttraumatic stress (criterion-related validity). Taken together, the DSM-5 Level 1 measure appears to be a viable tool for evaluating psychopathology in college students. Several opportunities for clinical application and empirical investigation of the DSM-5 Level 1 measure are discussed

Tracing Glacial Meltwater From the Greenland Ice Sheet to the Ocean Using Gliders

The Greenland Ice Sheet (GrIS) is experiencing significant mass loss and freshwater discharge at glacier fronts. The freshwater input from Greenland will impact the physical properties of adjacent coastal seas, including important regions of deep water formation and contribute to global sea level rise. However, the biogeochemical impact of increasing freshwater discharge from the GrIS is less well constrained. Here, we demonstrate the use of bio-optical sensors on ocean gliders to track biogeochemical properties of meltwaters off southwest Greenland. Our results reveal that fresh, coastal waters, with an oxygen isotopic composition characteristic of glacial meltwater, are distinguished by a high optical backscatter and high levels of fluorescing dissolved organic matter (FDOM), representative of the overall colored dissolved organic matter pool. Reconstructions of geostrophic velocities are used to show that these particle and FDOM-enriched coastal waters cross the strong boundary currents into the Labrador Sea. Meltwater input into the Labrador Sea is likely driven by mesoscale processes, such as eddy formation and local bathymetric steering, in addition to wind-driven Ekman transport. Ocean gliders housing bio-optical sensors can provide the high-resolution observations of both dissolved and particulate glacially derived material that are needed to understand meltwater dispersal mechanisms and their sensitivity to future climatic change

Synergistic tumor suppression by combined inhibition of telomerase and CDKN1A

Tumor suppressor p53 plays an important role in mediating growth inhibition upon telomere dysfunction. Here, we show that loss of the p53 target gene cyclin-dependent kinase inhibitor 1A (CDKN1A, also known as p21WAF1/CIP1) increases apoptosis induction following telomerase inhibition in a variety of cancer cell lines and mouse xenografts. This effect is highly specific to p21, as loss of other checkpoint proteins and CDK inhibitors did not affect apoptosis. In telomerase, inhibited cell loss of p21 leads to E2F1- and p53-mediated transcriptional activation of p53-upregulated modulator of apoptosis, resulting in increased apoptosis. Combined genetic or pharmacological inhibition of telomerase and p21 synergistically suppresses tumor growth. Furthermore, we demonstrate that simultaneous inhibition of telomerase and p21 also suppresses growth of tumors containing mutant p53 following pharmacological restoration of p53 activity. Collectively, our results establish that inactivation of p21 leads to increased apoptosis upon telomerase inhibition and thus identify a genetic vulnerability that can be exploited to treat many human cancers containing either wild-type or mutant p53