An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Integrative clustering by non-negative matrix factorization can reveal coherent functional groups from gene profile data

Recent developments in molecular biology and tech- niques for genome-wide data acquisition have resulted in abun- dance of data to profile genes and predict their function. These data sets may come from diverse sources and it is an open question how to commonly address them and fuse them into a joint prediction model. A prevailing technique to identify groups of related genes that exhibit similar profiles is profile-based clustering. Cluster inference may benefit from consensus across different clustering models. In this paper we propose a technique that develops separate gene clusters from each of available data sources and then fuses them by means of non-negative matrix factorization. We use gene profile data on the budding yeast S. cerevisiae to demonstrate that this approach can successfully integrate heterogeneous data sets and yields high-quality clusters that could otherwise not be inferred by simply merging the gene profiles prior to clustering

Integrative clustering by non-negative matrix factorization can reveal coherent functional groups from gene profile data

Recent developments in molecular biology and tech- niques for genome-wide data acquisition have resulted in abun- dance of data to profile genes and predict their function. These data sets may come from diverse sources and it is an open question how to commonly address them and fuse them into a joint prediction model. A prevailing technique to identify groups of related genes that exhibit similar profiles is profile-based clustering. Cluster inference may benefit from consensus across different clustering models. In this paper we propose a technique that develops separate gene clusters from each of available data sources and then fuses them by means of non-negative matrix factorization. We use gene profile data on the budding yeast S. cerevisiae to demonstrate that this approach can successfully integrate heterogeneous data sets and yields high-quality clusters that could otherwise not be inferred by simply merging the gene profiles prior to clustering

Lipid Fingerprinting by Mass Spectrometry and Laser Light

Lipids have accompanied the emergence of life on Earth. Their tendency to form spherical assemblies in water afforded the separation of organisms from their environment while still allowing controlled exchange of substances between the inside and outside. Lipids enable cellular compartmentalization, modulate membrane proteins, and are involved in intra- and intercellular signaling. The multitude of biological functions is reflected in a tremendous structural diversity, which involves the frequent occurrence of isomers with very specific biological roles. Although highly relevant from a biological perspective, many lipid isomers cannot be distinguished by state-of-the-art mass spectrometry-based analytical techniques. This work explores the potential of coupling mass spectrometry with infrared spectroscopy to analyze lipid isomers and study lipid fragmentation mechanisms. Infrared spectroscopy adds a structural dimension to the mass spectrometric analysis and previously yielded promising results for other biomolecules. We recorded the first high-resolution infrared spectra of ionized lipids and glycolipids using helium nanodroplets as a cryogenic spectroscopic matrix. Isomeric glycolipids were found to be unambiguously distinguishable based on their diagnostic spectroscopic fingerprints. The high resolution and sensitivity of the technique enabled the relative quantification of low-abundant glycolipid isomers in biological samples by spectral deconvolution. A similar approach was used to analyze double bond isomers in lipids extracted from cancer cells, which revealed significant differences between cancer types. The position and geometry of C C bonds in sphingolipids and fatty acids were rendered visible by amines, which interact with the C C bond and induce diagnostic vibrations. Furthermore, infrared spectroscopy was employed in combination with computational chemistry to gain fundamental understanding of lipid fragmentation in tandem mass spectrometry. A key fragmentation mechanism used for the structural analysis of glycerolipids was thus unveiled and found to occur with high regioselectivity. In addition, unknown fragmentation pathways of vitamin E derivatives were identified by spectroscopy-based assignment of fragment structures. Overall, the coupling of mass spectrometry and infrared spectroscopy has proven to be a powerful approach to study lipid fragmentation mechanisms and to identify biologically relevant lipid isomers from minute sample amounts. The technique is expected to drive important advances in lipid analysis in the near future.Seit seiner Entstehung ist das Leben auf der Erde untrennbar mit Lipiden verbunden. Lipidmembranen ermöglichen die räumliche Abtrennung von Organellen innerhalb von Zellen, von Zellen innerhalb von Organismen und von Organismen in ihrer unbelebten Umgebung. Lipide beeinflussen zudem die Funktion von Membranproteinen und sind als Botenstoffe am intra- und interzellulären Informationsaustausch beteiligt. Die Vielzahl ihrer biologischen Funktionen spiegelt sich in der strukturellen Vielfalt der Lipide und dem häufigen Auftreten von Isomeren wider. Viele dieser Isomere, die oft sehr spezifische biologische Funktionen erfüllen, können mit modernen massenspektrometrischen Analysemethoden nicht unterschieden werden. Durch die Kopplung von Massenspektrometrie mit Infrarotspektroskopie werden neben der Masse von Molekülen auch strukturelle Informationen erhalten. Die Technik wurde bereits erfolgreich für andere Biomoleküle angewendet und wird in dieser Arbeit erstmals für die Analyse von isomeren Lipiden und die Untersuchung von Lipidfragmentierung eingesetzt. Für die Aufnahme von hochaufgelösten Infrarotspektren ionisierter Lipide und Glykolipide wurden Heliumtröpfchen als kryogene spektroskopische Matrix verwendet. Isomere Glykolipide konnten mit dieser Technik eindeutig voneinander unterschieden und in biologischen Proben durch Dekonvolution der Infrarotspektren quantifiziert werden. Eine ähnliche Methode wurde zur relativen Quantifizierung von Doppelbindungsisomeren in Lipidextrakten aus Krebszellen angewendet. Die Analyse zeigte eindeutige Unterschiede in der Isomerenverteilung zwischen verschiedenen Krebstypen. Die Position und Geometrie der C C Bindungen wurden mit Hilfe von Aminen bestimmt, die als Doppelbindungssensoren spezifische Wechselwirkungen mit der C C Bindung eingehen. Darüber hinaus wurde mittels Infrarotspektroskopie und quantenchemischen Methoden der regioselektive Mechanismus einer der wichtigsten Fragmentierungsreaktionen in der Analytik von Glycerolipiden aufgeklärt. Außerdem wurden Fragmentstrukturen von Vitamin E Derivaten spektroskopisch identifiziert und plausible Fragmentierungswege berechnet. Die Ergebnisse dieser Arbeit zeigen, dass mittels kryogener Infrarotspektroskopie ein grundlegendes Verständnis von Fragmentierungsmechanismen in der Lipidanalytik gewonnen werden kann. Zusätzlich können mit der Technik biologisch relevante Lipidisomere mit minimalem Probenverbrauch unterschieden werden. Die Kopplung von Massenspektrometrie mit Infrarotspektroskopie hat daher großes Potential, die nächsten Entwicklungen in der Lipidanalytik wesentlich voranzutreiben

Church and congregation in community mental health

This thesis reports an experiment made in an Edinburgh parish church to test two sets of hypotheses: (l) that regular church attenders are more dependency moti¬ vated in their job lives than persons chosen without reference to church e ttachmentsj and (2) that regular church attenders primarily seek the fulfilment of dependency needs through their church involvement. The thesis also contains a theological re¬ flection on this study and ends with concrete proposes for the ongoing life of the church.In the Introduction we presort a body of current thinking about the doctrine of thellalty. Our experience in parish ministry, hospital chaplaincy, as well as our study of some commonly held assumptions seemed to raise important questions about this material. Our questions will be clarified. Then we formulate a set of working hypotheses and explain the research design for gathering data in the study of those hypotheses. Finally, we develop our method of doing practical theology - a method to which we return in Chapter VI as we engage in a theological reflection on the outcomes.Chapter I is entitled "The Conceptual Framework." Here we present the Motivatlon-Hygiene Theory of mental health. This is the model with which our work will be done. A critivue is offered and the usefulness of K-H theory for our project is explained.Chapter II is entitled "The Method." Here we explain the method chosen to gather and analyze data pertinent to our hypotheses. The methods are discussed, a critique offered, and their usefulness for our purposes is indicated,'Chapter III is entitled "The Procedures." A pilot study which was conducted is reported in this chapter. Here we also present in detail the manner in which our subjects were selected from a list of "regular church attenders". Profiles are given of the church and of our subjects. Methods of contacting, Interviewing and recording our data are also explained.Chapter IV is entitled "The Results." In this chapter our "control group"is unstructured (to enable testing of the work setting). The chapter documents the results of our experiment for both sets of hypotheses with which we began.Chapter V is entitled "The Project Conclusions." After presenting the limitations of our work and clarifying certain issues relating to the purpose of our study, we develop some inferences of our findings. Our conclusions are summarized and we end with a note on the Important aspects of church life for "mental health".Chapter VI is entitled i'The Theological Reflection." It utilizes the twin principles of relating theology and psychology which were explained in the introduction, With this model serving as a structure, our data is juxtaposed to some elements of the theological anthropology of Ronald Gregor Smith, The lines of contact to the body of material about laity (from the Introduction) are drawn.The Epilogue presents some concrete proposals for the ongoing life of the church and offers suggestions for further research

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Mapping the Genomic Context of Mutagenesis

The accumulation of genomic mutations leads to the formation of cancer. For this reason, many efforts have been undertaken to characterise mutational processes in terms of their genomic imprints. A particularly successful approach is matrix-based mutational signature analysis, which identifies prototypical mutation patterns by applying non-negative matrix factorisation to catalogues of single nucleotide variants and other mutation types. However, mutagenesis is a multifaceted event that is affected by the genomic organisation of DNA and cellular processes such as transcription, replication, and DNA repair processes. Moreover, since many mutational processes also generate characteristic multi nucleotide variants, insertion and deletions, and structural variants, it appears valuable to jointly deconvolve broader mutational catalogues to better understand the complex nature of mutagenesis. In this thesis, I present TensorSignatures, an algorithm to learn mutational signatures jointly across different variant categories as well as their genomic localisation and properties. The analysis of 2,778 primary and 3,824 metastatic cancer genomes of the PCAWG consortium and the HMF cohort shows that practically all signatures operate dynamically in response to various genomic and epigenomic states. The analysis pins differential spectra of UV mutagenesis found in active and inactive chromatin to global genome nucleotide excision repair. TensorSignatures accurately characterises transcription-associated mutagenesis, which is detected in 7 different cancer types. The algorithm also extracts distinct signatures of replication- and double strand break repair-driven mutagenesis by APOBEC3A and 3B with differential numbers and length of mutation clusters. As a fourth example, TensorSignatures reproduces a signature of somatic hypermutation generating highly clustered variants around the transcription start sites of active genes in lymphoid leukaemia, distinct from a more general and less clustered signature of Polη-driven translesion synthesis found in a broad range of cancer types. Finally, I demonstrate TensorSignatures’ utility by applying it to multiple datasets in various collaboration projects. Taken together, TensorSignatures adds great detail and refines mutational signature analysis by jointly learning mutation patterns and their genomic determinants. This sheds light on the manifold influences that underlie mutagenesis and helps to pinpoint mutagenic influences which cannot easily be distinguished based on the mutation spectra alone. As mutational signature analysis is an essential element of the cancer genome analysis toolkit, TensorSignatures may help make the growing catalogues of mutational signatures more insightful by highlighting mutagenic mechanisms, or hypotheses thereof, to be investigated in greater depth

Automatic classification of communication logs into implementation stages via text analysis

Abstract Background To improve the quality, quantity, and speed of implementation, careful monitoring of the implementation process is required. However, some health organizations have such limited capacity to collect, organize, and synthesize information relevant to its decision to implement an evidence-based program, the preparation steps necessary for successful program adoption, the fidelity of program delivery, and the sustainment of this program over time. When a large health system implements an evidence-based program across multiple sites, a trained intermediary or broker may provide such monitoring and feedback, but this task is labor intensive and not easily scaled up for large numbers of sites. We present a novel approach to producing an automated system of monitoring implementation stage entrances and exits based on a computational analysis of communication log notes generated by implementation brokers. Potentially discriminating keywords are identified using the definitions of the stages and experts’ coding of a portion of the log notes. A machine learning algorithm produces a decision rule to classify remaining, unclassified log notes. Results We applied this procedure to log notes in the implementation trial of multidimensional treatment foster care in the California 40-county implementation trial (CAL-40) project, using the stages of implementation completion (SIC) measure. We found that a semi-supervised non-negative matrix factorization method accurately identified most stage transitions. Another computational model was built for determining the start and the end of each stage. Conclusions This automated system demonstrated feasibility in this proof of concept challenge. We provide suggestions on how such a system can be used to improve the speed, quality, quantity, and sustainment of implementation. The innovative methods presented here are not intended to replace the expertise and judgement of an expert rater already in place. Rather, these can be used when human monitoring and feedback is too expensive to use or maintain. These methods rely on digitized text that already exists or can be collected with minimal to no intrusiveness and can signal when additional attention or remediation is required during implementation. Thus, resources can be allocated according to need rather than universally applied, or worse, not applied at all due to their cost