Managing the sequence-specificity of antisense oligonucleotides in drug discovery

All drugs perturb the expression of many genes in the cells that are exposed to them. These gene expression changes can be divided into effects resulting from engaging the intended target and effects resulting from engaging unintended targets. For antisense oligonucleotides, developments in bioinformatics algorithms, and the quality of sequence databases, allow oligonucleotide sequences to be analyzed computationally, in terms of the predictability of their interactions with intended and unintended RNA targets. Applying these tools enables selection of sequence-specific oligonucleotides where no- or only few unintended RNA targets are expected. To evaluate oligonucleotide sequence-specificity experimentally, we recommend a transcriptomics protocol where two or more oligonucleotides targeting the same RNA molecule, but with entirely different sequences, are evaluated together. This helps to clarify which changes in cellular RNA levels result from downstream processes of engaging the intended target, and which are likely to be related to engaging unintended targets. As required for all classes of drugs, the toxic potential of oligonucleotides must be evaluated in cell- and animal models before clinical testing. Since potential adverse effects related to unintended targeting are sequence-dependent and therefore species-specific, in vitro toxicology assays in human cells are especially relevant in oligonucleotide drug discovery

Zircon U-Pb age constraints on NW Himalayan exhumation from the Laxmi Basin, Arabian Sea

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Zhou, P., Stockli, D. F., Ireland, T., Murray, R. W., & Clift, P. D. Zircon U-Pb age constraints on NW Himalayan exhumation from the Laxmi Basin, Arabian Sea. Geochemistry Geophysics Geosystems, 23(1), (2022): e2021GC010158, https://doi.org/10.1029/2021GC010158.The Indus Fan, located in the Arabian Sea, contains the bulk of the sediment eroded from the Western Himalaya and Karakoram. Scientific drilling in the Laxmi Basin by the International Ocean Discovery Program recovered a discontinuous erosional record for the Indus River drainage dating back to at least 9.8 Ma, and with a single sample from 15.6 Ma. We dated detrital zircon grains by U-Pb geochronology to reconstruct how erosion patterns changed through time. Long-term increases in detrital zircon U-Pb components of 750–1,200 and 1,500–2,300 Ma record increasing preferential erosion of the Himalaya relative to the Karakoram between 8.3–7.0 and 5.9–5.7 Ma. The average contribution of Karakoram-derived sediment to the Indus Fan fell from 70% of the total at 8.3–7.0 Ma to 35% between 5.9 and 5.7 Ma. An increase in the contribution of 1,500–2,300 Ma zircons starting between 2.5 and 1.6 Ma indicates significant unroofing of the Inner Lesser Himalaya (ILH) by that time. The trend in zircon age spectra is consistent with bulk sediment Nd isotope data. The initial change in spatial erosion patterns at 7.0–5.9 Ma occurred during a time of drying climate in the foreland. The increase in ILH erosion postdated the onset of dry-wet glacial-interglacial cycles suggesting some role for climate control. However, erosion driven by rising topography in response to formation of the ILH thrust duplex, especially during the Pliocene, also played an important role, while the influence of the Nanga Parbat Massif to the total sediment flux was modest.This work was partially funded by a grant from the USSSP, as well as additional funding from the Charles T. McCord Chair in petroleum geology at LSU, and the Chevron (Gulf) Centennial professorship and the UTChron Laboratory at the University of Texas

Characterization of population heterogeneity in a model biotechnological process using Pseudomonas putida

Biotechnological processes are distinguished from classical chemistry by employing bio-molecules or whole cells as the catalytic element, providing unique reaction mechanisms with unsurpassed specificity. Whole cells are the most versatile \''factories\'' for natural or non-natural products, however, the conversion of e.g. hydrophobic substrates can quickly become cytotoxic. One host organism with the potential to handle such conditions is the gram-negative bacterium Pseudomonas putida, which distinguishes itself by solvent tolerance, metabolic flexibility, and genetic amenability. However, whole cell bioconversions are highly complex processes. A typical bottleneck compared to classical chemistry is lower yield and reproducibility owing to cell-to-cell variability. The intention of this work was therefore to characterize a model producer strain of P. putida KT2440 on the single cell level to identify non-productive or impaired subpopulations. Flow cytometry was used in this work to discriminate subpopulations regarding DNA content or productivity, and further mass spectrometry or digital PCR was employed to reveal differences in protein composition or plasmid copy number. Remarkably, productivity of the population was generally bimodally distributed comprising low and highly producing cells. When these two subpopulations were analyzed by mass spectrometry, only few metabolic changes but fundamental differences in stress related proteins were found. As the source for heterogeneity remained elusive, it was hypothesized that cell cycle state may be related to production capacity of the cells. However, subpopulations of one, two, or higher fold DNA content were virtually identical providing no clear hints for regulatory differences. On the quest for heterogeneity the loss of genetic information came into focus. A new work flow using digital PCR was created to determine the absolute number of DNA copies per cell and, finally, lack of expression could be attributed to loss of plasmid in non-producing cells. The average plasmid copy number was shown to be much lower than expected (1 instead of 10-20). In conclusion, this work established techniques for the quantification of proteins and DNA in sorted subpopulations, and by these means provided a highly detailed picture of heterogeneity in a microbial population

miRNA detection and analysis from high-throughput small RNA sequencing data

Small RNAs (sRNAs) are a broad class of short regulatory non-coding RNAs. microRNAs (miRNAs) are a special class of -21-22 nucleotide sRNAs which are derived from a stable hairpin-like secondary structure. miRNAs have critical gene regulatory functions and are involved in many pathways including developmental timing, organogenesis and development in both plants and animals. Next generation sequencing (NGS) technologies, which are often used for identifying miRNAs, are continuously evolving, generating datasets containing millions of sRNAs, which has led to new challenges for the tools used to predict miRNAs from such data. There are several tools for miRNA detection from NGS datasets, which we review in this thesis, identifying a number of potential shortcomings in their algorithms. In this thesis, we present a novel miRNA prediction algorithm, miRCat2. Our algorithm is more robust to variations in sequencing depth due to the fact that it compares aligned sRNA reads to a random uniform distribution to detect peaks in the input dataset, using a new entropy-based approach. Then it applies filters based on the miRNA biogenesis on the read alignment and on the computed secondary structure. Results show that miRCat2 has a better specificity-sensitivity trade-off than similar tools, and its predictions also contains a larger percentage of sequences that are downregulated in mutants in the miRNA biogenesis pathway. This con�rms the validity of novel predictions, which may lead to new miRNA annotations, expanding and contributing to the field of sRNA research

miRNA detection and analysis from high-throughput small RNA sequencing data

Small RNAs (sRNAs) are a broad class of short regulatory non-coding RNAs. microRNAs (miRNAs) are a special class of -21-22 nucleotide sRNAs which are derived from a stable hairpin-like secondary structure. miRNAs have critical gene regulatory functions and are involved in many pathways including developmental timing, organogenesis and development in both plants and animals. Next generation sequencing (NGS) technologies, which are often used for identifying miRNAs, are continuously evolving, generating datasets containing millions of sRNAs, which has led to new challenges for the tools used to predict miRNAs from such data. There are several tools for miRNA detection from NGS datasets, which we review in this thesis, identifying a number of potential shortcomings in their algorithms. In this thesis, we present a novel miRNA prediction algorithm, miRCat2. Our algorithm is more robust to variations in sequencing depth due to the fact that it compares aligned sRNA reads to a random uniform distribution to detect peaks in the input dataset, using a new entropy-based approach. Then it applies filters based on the miRNA biogenesis on the read alignment and on the computed secondary structure. Results show that miRCat2 has a better specificity-sensitivity trade-off than similar tools, and its predictions also contains a larger percentage of sequences that are downregulated in mutants in the miRNA biogenesis pathway. This con�rms the validity of novel predictions, which may lead to new miRNA annotations, expanding and contributing to the field of sRNA research

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

Field Measurements in Determining Incumbent Spectrum Utilization and Protection Criteria in Wireless Co-existence Studies

Studies of spectrum sharing and co-existence between different wireless communication systems are important, as the current aim is to optimize their spectrum utilization and shift from static exclusive spectrum allocation to more dynamic co-existence of different systems within same frequency bands. The main goal of this thesis is to provide measurement methodologies for obtaining realistic results in modeling incumbent spectrum utilization and in determining incumbent protection criteria. The following research questions are considered in this thesis: Q1) How should field measurements be conducted and used to model incumbent spectrum utilization? Q2) How should field measurements be conducted and used to determine protection criteria for incumbents in a co-existence scenario with mobile broadband? and Q3) Which licensing methods and technological solutions are feasible to enable spectrum sharing in frequency bands with incumbents? To answer to Q1, this thesis describes the development of a spectrum observatory network concept created through international collaboration and presents measurement methodologies, which allow to obtain realistic spectrum occupancy data over geographical areas using interference map concept. A cautious approach should be taken in making strong conclusions from previous single fixed location spectrum occupancy studies, and measurements covering larger geographical areas might be needed if the measurement results are to be used in making spectrum management decisions. The field interference measurements considered in Q2 are not covered well in the current research literature. The measurements are expensive to conduct as they require substantial human resources, test network infrastructure, professional level measurement devices and radio licenses. However, field measurements are needed to study and verify hypotheses from computer simulations or theoretical analyses in realistic operating conditions, as field measurement conditions can not or are not practical to be adequately modeled in simulations. This thesis proposes measurement methodologies to obtain realistic results from field interference measurements, taking into account the propagation environments and external sources of interference. Less expensive simulations and laboratory measurements should be used both to aid in the planning of field measurements and to complement the results obtained from field measurements. Q3 is investigated through several field interference measurement campaigns to determine incumbent protection criteria and by analyzing the spectrum observatory data to determine the occupancy and trends in incumbent spectrum utilization. The field interference measurement campaigns have been conducted in real TV White Space, LTE Supplemental Downlink and Licensed Shared Access test network environments, and the obtained measurement results have been contributed to the development of the European spectrum regulation. In addition, field measurements have been conducted to contribute to the development and technical validation of the spectrum sharing frameworks. This thesis also presents an overview of the current status and possible directions in spectrum sharing. In conclusion, no single spectrum sharing method can provide universally optimal efficiency in spectrum utilization. Thus, an appropriate spectrum sharing framework should be chosen taking into account both the spectrum utilization of the current incumbents and the future needs in wireless communications.Siirretty Doriast

Machine Learning for Unmanned Aerial System (UAS) Networking

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale. With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring. This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS

Convergent communication, sensing and localization in 6g systems: An overview of technologies, opportunities and challenges

Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust

Spectrum Sharing, Latency, and Security in 5G Networks with Application to IoT and Smart Grid

The surge of mobile devices, such as smartphones, and tables, demands additional capacity. On the other hand, Internet-of-Things (IoT) and smart grid, which connects numerous sensors, devices, and machines require ubiquitous connectivity and data security. Additionally, some use cases, such as automated manufacturing process, automated transportation, and smart grid, require latency as low as 1 ms, and reliability as high as 99.99\%. To enhance throughput and support massive connectivity, sharing of the unlicensed spectrum (3.5 GHz, 5GHz, and mmWave) is a potential solution. On the other hand, to address the latency, drastic changes in the network architecture is required. The fifth generation (5G) cellular networks will embrace the spectrum sharing and network architecture modifications to address the throughput enhancement, massive connectivity, and low latency. To utilize the unlicensed spectrum, we propose a fixed duty cycle based coexistence of LTE and WiFi, in which the duty cycle of LTE transmission can be adjusted based on the amount of data. In the second approach, a multi-arm bandit learning based coexistence of LTE and WiFi has been developed. The duty cycle of transmission and downlink power are adapted through the exploration and exploitation. This approach improves the aggregated capacity by 33\%, along with cell edge and energy efficiency enhancement. We also investigate the performance of LTE and ZigBee coexistence using smart grid as a scenario. In case of low latency, we summarize the existing works into three domains in the context of 5G networks: core, radio and caching networks. Along with this, fundamental constraints for achieving low latency are identified followed by a general overview of exemplary 5G networks. Besides that, a loop-free, low latency and local-decision based routing protocol is derived in the context of smart grid. This approach ensures low latency and reliable data communication for stationary devices. To address data security in wireless communication, we introduce a geo-location based data encryption, along with node authentication by k-nearest neighbor algorithm. In the second approach, node authentication by the support vector machine, along with public-private key management, is proposed. Both approaches ensure data security without increasing the packet overhead compared to the existing approaches