scholarly journals Towards Best Practice in Hair Metabolomic Studies: Systematic Investigation on the Impact of Hair Length and Color

Metabolites ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 381
Author(s):  
Lisa Eisenbeiss ◽  
Tina M. Binz ◽  
Markus R. Baumgartner ◽  
Thomas Kraemer ◽  
Andrea E. Steuer
Keyword(s):  
Large Scale ◽  
Best Practice ◽  
Data Interpretation ◽  
Systematic Investigation ◽  
Hair Color ◽  
Large Scale Analysis ◽  
Retrospective Monitoring ◽  
Hair Samples ◽  
Endogenous Metabolites ◽  
The Impact

Untargeted metabolomic studies are used for large-scale analysis of endogenous compounds. Due to exceptional long detection windows of incorporated substances in hair, analysis of hair samples for retrospective monitoring of metabolome changes has recently been introduced. However, information on the general behavior of metabolites in hair samples is scarce, hampering correct data interpretation so far. The presented study aimed to investigate endogenous metabolites depending on hair color and along the hair strand and to propose recommendations for best practice in hair metabolomic studies. A metabolite selection was analyzed using untargeted data acquisition in genuine hair samples from different hair colors and after segmentation in 3 cm segments. Significant differences in metabolites among hair colors and segments were found. In conclusion, consideration of hair color and hair segments is necessary for hair metabolomic studies and, subsequently, recommendations for best practice in hair metabolomic studies were proposed.

Large Scale Analysis of MASCOT Results Using a Mass Accuracy-Based THreshold (MATH) Effectively Improves Data Interpretation

2005 ◽  
Vol 4 (4) ◽  
pp. 1353-1360 ◽  
Author(s):  
Paul A. Rudnick ◽  
Yueju Wang ◽  
Erin Evans ◽  
Cheng S. Lee ◽  
Brian M. Balgley
Keyword(s):  
Large Scale ◽  
Data Interpretation ◽  
Mass Accuracy ◽  
Scale Analysis ◽  
Large Scale Analysis

scholarly journals Toward a science of translational science

2017 ◽  
Vol 1 (4) ◽  
pp. 253-255 ◽  
Author(s):  
Caleb Smith ◽  
Roohi Baveja ◽  
Teri Grieb ◽  
George A. Mashour
Keyword(s):  
Translational Research ◽  
Large Scale ◽  
Network Science ◽  
Systematic Investigation ◽  
Industrial Engineering ◽  
Translational Science ◽  
Exponential Increase ◽  
Large Scale Networks ◽  
The Impact

Translational research as a discipline has experienced explosive growth over the last decade as evidenced by significant federal investment and the exponential increase in related publications. However, narrow project-focused or process-based measurement approaches have resulted in insufficient techniques to measure the translational progress of institutions or large-scale networks. A shift from traditional industrial engineering approaches to systematic investigation using the techniques of scientometrics and network science will be required to assess the impact of investments in translational research.

A Large-scale Empirical Analysis of Ransomware Activities in Bitcoin

2022 ◽  
Vol 16 (2) ◽  
pp. 1-29
Author(s):  
Kai Wang ◽  
Jun Pang ◽  
Dingjie Chen ◽  
Yu Zhao ◽  
Dapeng Huang ◽  
...  
Keyword(s):  
Empirical Analysis ◽  
Large Scale ◽  
Classification Model ◽  
Scale Analysis ◽  
Clustering Method ◽  
Fine Grained ◽  
Large Scale Analysis ◽  
The Impact ◽  
Transfer Trajectories

Exploiting the anonymous mechanism of Bitcoin, ransomware activities demanding ransom in bitcoins have become rampant in recent years. Several existing studies quantify the impact of ransomware activities, mostly focusing on the amount of ransom. However, victims’ reactions in Bitcoin that can well reflect the impact of ransomware activities are somehow largely neglected. Besides, existing studies track ransom transfers at the Bitcoin address level, making it difficult for them to uncover the patterns of ransom transfers from a macro perspective beyond Bitcoin addresses. In this article, we conduct a large-scale analysis of ransom payments, ransom transfers, and victim migrations in Bitcoin from 2012 to 2021. First, we develop a fine-grained address clustering method to cluster Bitcoin addresses into users, which enables us to identify more addresses controlled by ransomware criminals. Second, motivated by the fact that Bitcoin activities and their participants already formed stable industries, such as Darknet and Miner , we train a multi-label classification model to identify the industry identifiers of users. Third, we identify ransom payment transactions and then quantify the amount of ransom and the number of victims in 63 ransomware activities. Finally, after we analyze the trajectories of ransom transferred across different industries and track victims’ migrations across industries, we find out that to obscure the purposes of their transfer trajectories, most ransomware criminals (e.g., operators of Locky and Wannacry) prefer to spread ransom into multiple industries instead of utilizing the services of Bitcoin mixers. Compared with other industries, Investment is highly resilient to ransomware activities in the sense that the number of users in Investment remains relatively stable. Moreover, we also observe that a few victims become active in the Darknet after paying ransom. Our findings in this work can help authorities deeply understand ransomware activities in Bitcoin. While our study focuses on ransomware, our methods are potentially applicable to other cybercriminal activities that have similarly adopted bitcoins as their payments.

scholarly journals Graphing Ecotoxicology: The MAGIC Graph for Linking Environmental Data on Chemicals

Data ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 34 ◽  
Author(s):  
Sascha Bub ◽  
Jakob Wolfram ◽  
Sebastian Stehle ◽  
Lara Petschick ◽  
Ralf Schulz
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
Environmental Data ◽  
Relational Data ◽  
Chemical Information ◽  
Graph Database ◽  
Data Systems ◽  
Large Scale Analysis ◽  
Multiple Data ◽  
The Impact

Assessing the impact of chemicals on the environment and addressing subsequent issues are two central challenges to their safe use. Environmental data are continuously expanding, requiring flexible, scalable, and extendable data management solutions that can harmonize multiple data sources with potentially differing nomenclatures or levels of specificity. Here, we present the methodological steps taken to construct a rule-based labeled property graph database, the “Meta-analysis of the Global Impact of Chemicals” (MAGIC) graph, for potential environmental impact chemicals (PEIC) and its subsequent application harmonizing multiple large-scale databases. The resulting data encompass 16,739 unique PEICs attributed to their corresponding chemical class, stereo-chemical information, valid synonyms, use types, unique identifiers (e.g., Chemical Abstract Service registry number CAS RN), and others. These data provide researchers with additional chemical information for a large amount of PEICs and can also be publicly accessed using a web interface. Our analysis has shown that data harmonization can increase up to 98% when using the MAGIC graph approach compared to relational data systems for datasets with different nomenclatures. The graph database system and its data appear more suitable for large-scale analysis where traditional (i.e., relational) data systems are reaching conceptional limitations.

A Large-Scale Analysis of the Semantic Password Model and Linguistic Patterns in Passwords

2021 ◽  
Vol 24 (3) ◽  
pp. 1-21
Author(s):  
Rafael Veras ◽  
Christopher Collins ◽  
Julie Thorpe
Keyword(s):  
Neural Network ◽  
Graphical Models ◽  
Large Scale ◽  
Network Models ◽  
Neural Network Models ◽  
Part Of Speech ◽  
Large Scale Analysis ◽  
Linguistic Patterns ◽  
High Level ◽  
The Impact

In this article, we present a thorough evaluation of semantic password grammars. We report multifactorial experiments that test the impact of sample size, probability smoothing, and linguistic information on password cracking. The semantic grammars are compared with state-of-the-art probabilistic context-free grammar ( PCFG ) and neural network models, and tested in cross-validation and A vs. B scenarios. We present results that reveal the contributions of part-of-speech (syntactic) and semantic patterns, and suggest that the former are more consequential to the security of passwords. Our results show that in many cases PCFGs are still competitive models compared to their latest neural network counterparts. In addition, we show that there is little performance gain in training PCFGs with more than 1 million passwords. We present qualitative analyses of four password leaks (Mate1, 000webhost, Comcast, and RockYou) based on trained semantic grammars, and derive graphical models that capture high-level dependencies between token classes. Finally, we confirm the similarity inferences from our qualitative analysis by examining the effectiveness of grammars trained and tested on all pairs of leaks.

New insights into the fungal diversity of cranberry fruit rot in Québec farms through a large-scale molecular analysis

Plant Disease ◽  
2021 ◽  
Author(s):  
Matteo Conti ◽  
Benjamin Cinget ◽  
Caroline Labbe ◽  
Yanick Asselin ◽  
Richard R Bélanger
Keyword(s):  
Relative Abundance ◽  
Large Scale ◽  
Cultural Practices ◽  
Genetic Resistance ◽  
Fungal Species ◽  
Fruit Rot ◽  
Molecular Diagnostic ◽  
Scale Analysis ◽  
Large Scale Analysis ◽  
The Impact

Cranberry fruit rot (CFR) pathogens are widely reported in the literature but performing large-scale analysis of their presence inside fruit has always been challenging. In this study, a new molecular diagnostic tool, capable of identifying simultaneously 12 potential fungal species causing CFR, was exploited to better define the impact of CFR across cranberry fields in Québec. For this purpose, 126 fields and 7,825 fruit were sampled in three cranberry farms distributed throughout the province and subjected to comparative analyses of fungal presence and abundance according to cultural practices, sampling times and cranberry cultivars. All 12 pathogens were detected throughout the study but, as a first major finding, the analyses revealed that four species, Godronia cassandrae, Colletotrichum fructivorum, Allantophomopsis cytisporea, and Coleophoma empetri were consistently predominant regardless of the parameters studied. Conventional productions versus organic ones showed a significant reduction in fungal richness and relative abundance. Interestingly, Monilinia oxycocci was found almost exclusively in organic productions indicating that fungicides had a strong and persistent effect on its population. Surprisingly, there were no significant differences in fungal relative abundance or species richness between fruit sampled at harvest or in storage, suggesting that there may not exist a clear distinction between field and storage rot, as it was previously thought. Comparative analysis of fungal species found on eight different cranberry cultivars indicated that they were all infected by the same fungi, but could not rule out differences in genetic resistance. This large-scale analysis allows us to draw an exhaustive picture of CFR in Québec and provides new information with respect to its management.

scholarly journals Global-Scale Metabolomic Profiling of Human Hair for Simultaneous Monitoring of Endogenous Metabolome, Short- and Long-Term Exposome

2021 ◽  
Vol 9 ◽  
Author(s):  
Ying Chen ◽  
Jian Guo ◽  
Shipei Xing ◽  
Huaxu Yu ◽  
Tao Huan
Keyword(s):  
High Performance ◽  
High Resolution Mass Spectrometry ◽  
Global Scale ◽  
Short Term ◽  
Experimental Conditions ◽  
Hair Samples ◽  
Wide Range ◽  
Endogenous Metabolites ◽  
The Impact

Hair is a unique biological matrix that adsorbs short-term exposures (e. g., environmental contaminants and personal care products) on its surface and also embeds endogenous metabolites and long-term exposures in its matrix. In this work, we developed an untargeted metabolomics workflow to profile both temporal exposure chemicals and endogenous metabolites in the same hair sample. This analytical workflow begins with the extraction of short-term exposures from hair surfaces through washing. Further development of mechanical homogenization extracts endogenous metabolites and long-term exposures from the cleaned hair. Both solutions of hair wash and hair extract were analyzed using ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS)-based metabolomics for global-scale metabolic profiling. After analysis, raw data were processed using bioinformatic programs recently developed specifically for exposome research. Using optimized experimental conditions, we detected a total of 10,005 and 9,584 metabolic features from hair wash and extraction samples, respectively. Among them, 274 and 276 features can be definitively confirmed by MS2 spectral matching against spectral library, and an additional 3,356 and 3,079 features were tentatively confirmed as biotransformation metabolites. To demonstrate the performance of our hair metabolomics, we collected hair samples from three female volunteers and tested their hair metabolic changes before and after a 2-day exposure exercise. Our results show that 645 features from wash and 89 features from extract were significantly changed from the 2-day exposure. Altogether, this work provides a novel analytical approach to study the hair metabolome and exposome at a global scale, which can be implemented in a wide range of biological applications for a deeper understanding of the impact of environmental and genetic factors on human health.

scholarly journals Abusers indoors and coronavirus outside: an examination of public discourse about COVID-19 and family violence on Twitter using machine learning

2020 ◽  
Author(s):  
Jia Xue ◽  
Junxiang Chen ◽  
Chen Chen ◽  
Ran Hu ◽  
Tingshao Zhu
Keyword(s):  
Machine Learning ◽  
Domestic Violence ◽  
Family Violence ◽  
Social Services ◽  
Public Discourse ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Large Scale Analysis ◽  
Protective Orders ◽  
The Impact

Purpose: This brief report aims to provide the first large-scale analysis of public discourse regarding family violence and the COVID-19 pandemic on Twitter. Method: We analyzed 301,606 Tweets related to family violence and COVID-19 from April 12 to July 16, 2020, for this study. We used the machine learning approach, Latent Dirichlet Allocation, and identified salient themes, topics, and representative Twitter examples. Results: We extracted nine themes on family violence and COVID-19 pandemic, including (1) the Impact of COVID-19 on family violence (e.g., rising rates, hotline calls increased, murder & homicide); (2) the types (e.g., child abuse, domestic violence, sexual violence) and (3) forms of family violence (e.g., physical aggression, coercive control); (4) risk factors of family violence (e.g., alcohol abuse, financial constraints, gun, quarantine); (5) victims of family violence (e.g., LGBTQ, women, and women of color, children); (6) social services of family violence (e.g., hotlines, social workers, confidential services, shelters, funding); (7) law enforcement response (e.g., 911 calls, police arrest, protective orders, abuse reports); (8) Social movement/ awareness (e.g., support victims, raise awareness); and (9) domestic violence-related news (e.g., Tara Reade, Melissa Derosa). Conclusions: The COVID-19 has an impact on family violence. This report overcomes the limitation of existing scholarship that lacks data for consequences of COVID-19 on family violence. We contribute to the understanding of family violence during the pandemic by providing surveillance in Tweets, which is essential to identify potentially effective policy programs in offering targeted support for victims and survivors and preparing for the next wave.

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