Miracle-dz/UniFAR-FTUs huggingface dataset:
plasma: Partial LeAst Squares for Multiomics Analysis

Motivation The rapid growth in the number and application of high-throughput “omics” technologies has created a need for better methods to integrate multiomics data sets. Much progress has been made in developing unsupervised methods, but supervised methods have lagged behind. Results We develop a novel algorithm, plasma, to train and validate models to predict time-to-event outcomes from multiomics data sets. The model is built on using two layers of the existing partial least squares algorithm to first select components that covary with the outcome in order to construct a joint Cox proportional hazards model. We apply plasma to the lung squamous cell carcinoma (LUSC) data from The Cancer Genome Atlas (TCGA). Our model successfully separates an independent test data set into high risk and low risk patients (p = 0.0132). The performance of the joint multiomics model is superior to that of the individual omics data sets. It is also superior to the performance of an approach that uses an unsupervised method (Multi Omics Factor Analysis; MOFA) to find factors that might work as predictors. Many of the factors that contribute strongly to the plasma model can be justified from the biological literature. Availability and Implementation The plasma R package can be obtained from The Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/plasma/index.html. The latest version of the package can be obtained from R-Forge at https://r-forge.r-project.org/R/? group_id=174. Source code and data for the analysis presented here can be obtained from GitLab, at https://gitlab.com/krcoombes/plasma. Contact Email: kcoombes@augusta.edu Supplementary Information Supplementary material is available from Bioinformatics online.
Hypergraph Capacity with Applications to Matrix Multiplication

The capacity of a directed hypergraph is a particular numerical quantity associated with a hypergraph. It is of interest because of certain important connections to longstanding conjectures in theoretical computer science related to fast matrix multiplication and perfect hashing as well as various longstanding conjectures in extremal combinatorics. We give an overview of the concept of the capacity of a hypergraph and survey a few basic results regarding this quantity. Furthermore, we discuss the Lovasz number of an undirected graph, which is known to upper bound the capacity of the graph (and in practice appears to be the best such general purpose bound). We then elaborate on some attempted generalizations/modifications of the Lovasz number to undirected hypergraphs that we have tried. It is not currently known whether these attempted generalizations/modifications upper bound the capacity of arbitrary hypergraphs. An important method for proving lower bounds on hypergraph capacity is to exhibit a large independent set in a strong power of the hypergraph. We examine methods for this and show a barrier to attempts to usefully generalize certain of these methods to hypergraphs. We then look at cap sets: independent sets in powers of a certain hypergraph. We examine certain structural properties of them with the hope of finding ones that allow us to prove upper bounds on their size. Finally, we consider two interesting generalizations of capacity and use one of them to formulate several conjectures about connections between cap sets and sunflower-free sets.
Natural variation in the Caenorhabditis elegans egg-laying circuit modulates an intergenerational fitness trade-off

Evolutionary shifts from egg-laying (oviparity) to live birth (viviparity) are common across diverse taxa, and some species exhibit variability in egg-laying modes or an intermediate mode with eggs containing embryos at various stages of development. How such natural quantitative variation in egg retention arises through differences in genetics, behaviour, and physiology – and how this variation ultimately connects to variation in specific fitness components – remains poorly understood. Here, we investigate intraspecific variation in egg retention in the nematode Caenorhabditis elegans using a panel of 316 wild strains. We observe highly variable intra-uterine egg retention, with some strains exhibiting strongly reduced or increased retention with internal hatching. We identify multiple evolutionary origins of these phenotypic extremes and pinpoint candidate loci responsible for the observed variation. Analysis of a subset of strains confirms that natural variation in egg-laying behaviour contributes to differences in egg retention. Using neuromodulatory agents and CRISPR-Cas9-mediated genetic manipulation, we demonstrate that this behavioural variation arises from an evolutionarily divergent neuromodulatory architecture in the egg-laying circuitry. In addressing the question of why natural variation in C. elegans egg retention is maintained, we find that strong egg retention can reduce maternal fertility and survival due to detrimental hatching in utero. However, genotypes with strong egg retention may benefit from improved offspring protection against environmental insults and gain a competitive advantage with offspring exhibiting a shortened development time to reproductive maturity outside the uterus. The observed natural variation in egg-laying behaviour in C. elegans could therefore reflect modifications of a trade-off between alternative fitness components across generations. Our study reveals previously underappreciated diversity in the C. elegans egg-laying circuit and provides insights into the fitness consequences of this behavioural variation. We propose that intraspecific variation in nematode egg-laying behaviour offers an ideal system to identify molecular changes underlying evolutionary transitions between oviparity and viviparity in invertebrates.
Compact triband ACS-fed stepped monopole antenna with inverted-L slot for WLAN/WiMAX applications

A compact triband asymmetric coplanar strip (ACS)-fed stepped monopole antenna with inverted-L slot for wireless local area network (WLAN)/worldwide interoperability for microwave access (WiMAX) applications is proposed and investigated in this paper. The proposed antenna is composed of an ACS-fed stepped monopole and an inverted-L slot, which occupies a very compact size of 26.5 × 8 mm2 including the ground plane. By adjusting the dimensions of the stepped monopole, slot and ground plane, three desired resonant frequencies can be achieved and tuned independently. The antenna exhibits three resonances covering the 2.4/5.2/5.8 GHz WLAN bands and 3.5/5.5 GHz WiMAX bands. Details of the antenna design, simulation, and measurement results are presented and discussed. Furthermore, the proposed antenna shows nearly omnidirectional radiation characteristics and reasonable gains in the operating bands. The simple feeding structure, compact size, and uniplanar design make it easy to be integrated within the portable device for wireless communication.
Compartmentalisation proteomics revealed endolysosomal protein network changes in a goat model of atrial fibrillation

Endolysosomes (EL) are known for their role in regulating both intracellular trafficking and proteostasis. EL help facilitate elimination of damaged membrane and cytosolic proteins, protein aggregates, membranous organelles and also play an important role in calcium signalling. Despite the importance of EL, their specific role in cardiovascular disease is not well understood. In particular, it’s unclear how EL contribute to atrial pathology over longer time frames. To shed light on this question, we conducted a comprehensive analysis that involved proteomics, transcriptomics, integrated analysis, electron tomography, western blotting, and enzyme assays. To identify the role of EL in atrial fibrillation (AF), we applied a recently published organelle protein isolation method. We used this method to study biopsies from AF goat model and analyse the EL-specific proteins and pathways involved in this condition. Our results revealed the upregulation of the AMPK pathway and the expression of EL-specific proteins that were not found in whole tissue lysates (TL), including GAA, DYNLRB1, CLTB, SIRT3, CCT2, and muscle-specific HSPB2. We also observed structural anomalies, such as autophago-vacuole formation, irregularly shaped mitochondria, and glycogen deposition, which provide insights into the EL’s contribution to AF and related pathways and molecular mechanisms. Overall, our findings suggest that EL play an important role in the development of AF over longer time frames, and provide a more detailed understanding of the underlying molecular processes involved.
Evolution of flowering time through the asynchrony of pollen dispersal

The evolution of flowering time is often attributed to variation of pollinator rates over time. This study proposes that flowering time can evolve through siring success variation among individuals caused by differential pollen dispersal timing (a result of flowering time variation itself). Quantitative genetic models show that earlier flowering evolves under low pollen removal rates, high pollen deposition rates, and a slow decline in the fertilization ability of removed pollen. Variations in pollen dispersal timing also select for a stable variance in flowering time, which is larger when pollen removal rates are either very low or high, pollen deposition rates are moderate, and the fertilization ability of removed pollen declines more rapidly. Also, a model on the coevolution of flower longevity and flowering time predicts that under constant pollination rates, non-random mating results in a weak correlation between late flowering and longer-lived flowers. This baseline finding suggests that the observed correlation between late flowering and shorter flowering duration in nature is influenced by other factors, such as declining pollination rates during late-stage flowering. I discuss the role of altered pollination rates under climate change during flowering time evolution and the importance of distinguishing between pollen removal and deposition rates.
Iohexol-measured glomerular filtration rate and urinary biomarker changes between vancomycin and vancomycin plus piperacillin-tazobactam in a translational rat model

Recent clinical studies have reported additive nephrotoxicity with the combination of vancomycin and piperacillin-tazobactam. However, preclinical models have failed to replicate this finding. This study assessed differences in iohexol-measured glomerular filtration rate (GFR) and urinary injury biomarkers among rats receiving this antibiotic combination. Male Sprague-Dawley rats received either intravenous vancomycin, intraperitoneal piperacillin-tazobactam, or both for 96 hours. Iohexol-measured GFR was used to quantify real-time kidney function changes. Kidney injury was evaluated via the urinary biomarkers: kidney injury molecule-1 (KIM-1), clusterin, and osteopontin. Compared to the control, rats that received vancomycin had numerically lower GFR after drug dosing on day 3. Rats in this group also had elevations in urinary KIM-1 on experimental days 2 and 4. Increasing urinary KIM-1 was found to correlate with decreasing GFR on experimental days 1 and 3. Rats that received vancomycin+piperacillin-tazobactam did not exhibit worse kidney function or injury biomarkers compared to vancomycin alone. The combination of vancomycin+piperacillin-tazobactam does not cause additive nephrotoxicity in a translational rat model. Future clinical studies investigating this antibiotic combination should employ more sensitive biomarkers of kidney function and injury, similar to those utilized in this study.
Sparse Long Short-Term Memory Approach for Energy-Efficient Adaptive Cluster Fuzzy-based Controller in Wireless Sensor Network

Wireless Sensor Network (WSN) gadgets began with limited-scope WSNs and have expanded to larger-scope and Internet of Things -based WSNs. Clustering increases WSN activity. Before picking Cluster Heads (CHs), Nodes are clustered. Nodes in a clustered WSN, transmit CH environmental variables. Current sink-selection approaches presume unlimited data transfer. Multiple CHs using the same instance, might create complications. Insufficient drop-off space causes package loss. Sinks should have airy, less-restrictive regulators. SIS reduces sink nodes. Looser regulators choose the CH. R-recommendations are used to standardize. Reductions standardize regulators. These methods reduce energy, residual energy, First Node Dead, Half Nodes Dead, Last Node Dead, packet loss, and latency. Good throughput, latency, reliability, packet loss, durability, power consumption, and end-to-end delay are accomplished. The objective of this work is to examine the accessible clustering systems for further developing sensor network execution due to different plan limits and streamlining philosophies.
An h-adaptive solution of the spherical blast wave problem

Shock waves and contact discontinuities usually appear in compressible flows, requiring a fine mesh in order to achieve an acceptable accuracy of the numerical solution. The usage of a mesh adaptation strategy is convenient as uniform refinement of the whole mesh becomes prohibitive in three-dimensional (3D) problems. An unsteady h-adaptive strategy for unstructured finite element meshes is introduced. Non-conformity of the refined mesh and a bounded decrease in the geometrical quality of the elements are some features of the refinement algorithm. A 3D extension of the well-known refinement constraint for 2D meshes is used to enforce a smooth size transition among neighbour elements with different levels of refinement. A density-based gradient indicator is used to track discontinuities. The solution procedure is partially parallelised, i.e. the inviscid flow equations are solved in parallel with a finite element SUPG formulation with shock capturing terms while the adaptation of the mesh is sequentially performed. Results are presented for a spherical blast wave driven by a point-like explosion with an initial pressure jump of 105 atmospheres. The adapted solution is compared to that computed on a fixed mesh. Also, the results provided by the theory of self-similar solutions are considered for the analysis. In this particular problem, adapting the mesh to the solution accounts for approximately 4% of the total simulation time and the refinement algorithm scales almost linearly with the size of the problem.
Investigating Organizational Factors Associated with GDPR Noncompliance using Privacy Policies: A Machine Learning Approach

The General Data Protection Regulation (GDPR) came into effect in May 2018 to ensure and safeguard data subjects’ rights. This enactment profoundly shaped, among other things, data processing organizations’ privacy policies to comply with the GDPR’s transparency requirements—for compliance with the GDPR is compulsory. Nevertheless, despite the potential goodwill to change, complying with the GDPR can be challenging for some organizations, e.g., small and medium-sized enterprises, due to, for example, a lack of resources. This study explores what factors may correlate with GDPR-compliance practices in organizations by analyzing the corresponding privacy policies. The contribution of this study is twofold. First, we have devised a classification model using machine learning (ML) and natural language processing (NLP) techniques to assess the GDPR-compliance practices promised in privacy policies regarding the GDPR core privacy policy requirement of Purpose. Using this model, we have collected a data set of 8 614 organizations active in the European Union (EU) containing organizational information and GDPR-compliance promises derived from organizations’ privacy policies, as made publicly available. Our second contribution is an analysis of the resulting classification to identify organizational factors related to the disclosure of the GDPR core privacy policy requirement of Purpose in organizations’ privacy policies.
An Enhanced Smart Intelligent Detecting and Alerting System for Industrial Gas Leakage using IoT in Sensor Network

Leakage of any type of gas has emerged as a hazard in recent years, whether in a typical workplace, boiler, a commercial setting, a café, or a canteen, industries natural gas, coal, refinery products, crude oil, cement, electricity, steel, and fertilizers etc. In this article, I proposed a new invention of an IoT-based gas waste monitoring, leak detection, and alerting system. The research extends on the development of such an intelligent system that can intelligently preserve gas, ensure the safety precaution and pre vent accidents. The technique must be connected to the stove. The design incorporates ultrasonic sensors which identify bio gas leakage in circumstance with in square metre is usage area of industries or cooking place. The Proposed work smart intelligence system is cuts off the gas supply using an automated shutting off device. Whenever a gas leak is detected, consumers will be notified through SMS via GSM, location using GPRS, and will be able to resolve the situation as quickly as feasible. The flame sensor in the technology will supervise flame and fire. The buzzer starts to sound when a fire is detected. Aside from that, the system may save data on the cloud. With the help of this cloud storage solution, every user’s daily gas use may be traced. Finally, this approach will aid in recognizing per user natural gas consumption. The system has been examined and is capable of monitoring gas loss, evacuation, and sending an SMS notification and location aid to the user. The precipitate demonstrated its usefulness in conserving a considerable amount of domestically squandered gas.
TinyTricia – A Space-Optimized Patricia Trie For Transparent Access to Edge Computing Services

Multi-access Edge Computing (MEC) is an essential piece of 5G telecommunication systems to satisfy the challenging low-latency demands of future applications. Our previous publications argue that edge computing should be transparent to clients. We introduced an efficient solution to implement such a transparent approach, leveraging Software-Defined Networking and virtual IP+port addresses for registered edge services. A core component of our architecture is a Patricia Trie, which stores all our virtual IP+port addresses. Unfortunately, most implementations of Patricia Tries are not geared toward use cases with millions of keys where a low memory footprint becomes essential. In this paper, we present TinyTric$\dot{w}$, a space-efficient open-source implementation of a Patricia Trie for keys up to 256 bits. TinyTricia can keep track of up to half a billion (229) keys $\leq$57 bits and up to a quarter of a billion (228-1) keys $\leq$256 bits with tiny memory requirements. In the latter case, each key can have a data value of any type. Keys $\leq$57 bits require only 8 to 16 bytes per key (i.e., only 0 to 8 bytes overhead per 57-bit key); for keys $\geq$58 bits, add the key size (in whole bytes) to these values. Thus, for 16.78 million (224)57-bit keys, our solution requires between 128 and 256 MiB of memory. Unlike some other spaceefficient implementations, TinyTriia allows adding and removing keys at runtime.
An GMM Method in IoT Approach to Improve Energy Efficiency in Smart Building

The rapid growth of commercial building energy use has created a need for innovative methods for reducing and optimizing building energy use. Building Energy Management Systems have recently become popular as a result ofincreasinginterest in buildingenergyefficiency and savings. Smart buildings and the Internet of Things are the waves of the future. The combination of various structures related to improving various facets of the smart buildings (for instance, electronic health, environmental sources, fuel, liquid, and power tracking, or extreme emergency recognition, and many others) could be used to create a sustainable and secure building. It can transform data to identify the fastest possible and quickest routes and save effort and time. Development of sustainable energy The IoT devices for power management are usedto optimize the renewable sources of energy providers. The Internet of Things refers to an internet backbone connected to everyday objects, most of which are perceptive; it is just a framework for workgroups in which it communicates electronically and shares specific data and information with both the world all over each other, incorporating traditional and cyber elements to adopt new technologies. A review of the substation’s power management components, mechanization protocols, home appliances, and technical specifications. Implementation of Internet of Things completely of transit systems has supplemented an institutional remedy to such difficulties even while providing marginal commuting time data, laying the groundwork for better, extra briefed transit to traffic management systems. Once data processing, a large issue is efficiently and successfully obtaining and assessing the huge amount of sensor-generated information. In this article, clustering-based Gaussian Mixture Models are used to find the exact energy consumption on a weekly and monthly basis for consumers’ requirements. To propose a framework for IoT sensorinformation thatuses a machine Intelligence-based GMM derived from actual data acquisition.
Robot-Based Uniform-Coverage and High-Resolution LIDAR Mapping for Physically-Grounded Metaverse Applications

Connecting the tangible world bidirectionally with its digital counterpart is fundamental for Physically-grounded Metaverse Applications (PMAs). For instance, in visually-guided human assistance, space-time analytics, eXtended Reality (XR) or automation use cases, the ability to capture, model, and exploit dynamic environments is essential. PMAs unify tangible and digital premises through intelligent capabilities built on detailed, gap-free and up-to-date 3D models of buildings delivering dependable functionality with utility value. To create and democratize such Metaverse benefits, it is fundamental to enable non-experts to easily capture, annotate and exploit full-fledged 3D models of buildings. Therefore, open IoT-XR platforms, algorithms, tools, and compelling user experiences are needed. Key factors to address such production-level 3D mapping technologies are time, cost, instrumentation- and operational-complexity for generating high-resolution maps with uniform-coverage. This work introduces an 3D surveying method via a semi-automatic mobile robot-mapping system. The robotic platform and novel algorithms improve the mapping process by ensuring sampling regularity, increasing spatial coverage, rejecting outlier points, and reducing total mapping time. This article focuses on real-world challenges and lessons learned from a novel closed-loop volumetric scanning-and-analysis method. Extensive experiments support claims in large maps.
A High-Speed MCML Logic Gate and Multiplexer Design in 45 nm CMOS Technology

Current Mode Logic (CML) is generally utilized for high-speed circuits. This is a differential digital logic used to design inverters, buffers and gates, as well as the board-level signaling of digital data. Among the various types of CML circuits, in various digital circuit designs, MOS Current Mode Logic (MCML) is generally implemented. This logic can also be used to design registers and memory units. In this paper, a novel MOS CML circuit is designed using a flipped voltage follower-based tri-state circuit for Inverter/Buffer and AND/NAND logic and also for the high-speed multiplexer. The proposed MCML logic gate is analyzed using the Cadence virtuoso tool in 45 nm technology at 1V power supply and a temperature of 27°C. The results are verified with Monte Carlo simulations using a histogram plot. The process variations for different corners are simulated and compared for the conventional, existing and proposed structures under 45 nm CMOS technology. The result proves that the proposed circuit provides high performance and operates in a high-speed environment.