Miracle-dz/UniFAR-FTUs huggingface dataset:
Progress of CRISPR‐based programmable RNA manipulation and detection

Prokaryotic clustered regularly interspaced short palindromic repeats and CRISPR associated (CRISPR‐Cas) systems provide adaptive immunity by using RNA‐guided endonucleases to recognize and eliminate invading foreign nucleic acids. Type II Cas9, type V Cas12, type VI Cas13, and type III Csm/Cmr complexes have been well characterized and developed as programmable platforms for selectively targeting and manipulating RNA molecules of interest in prokaryotic and eukaryotic cells. These Cas effectors exhibit remarkable diversity of ribonucleoprotein (RNP) composition, target recognition and cleavage mechanisms, and self discrimination mechanisms, which are leveraged for various RNA targeting applications. Here, we summarize the current understanding of mechanistic and functional characteristics of these Cas effectors, give an overview on RNA detection and manipulation toolbox established so far including knockdown, editing, imaging, modification, and mapping RNA‐protein interactions, and discuss the future directions for CRISPR‐based RNA targeting tools.
Light yield nonproportionality of CsI(Tl), CsI(Na), and YAP

CsI(Tl), CsI(Na), and YAP light yield nonproportionality has been characterized using the Compton Coincidence Technique. Measured electron responses were used to analytically calculate photon responses of scintillators studied. Results from electron response measurement and photon response calculation were then compared with previously reported results. While the CsI(Na) electron response was observed to have the largest deviation from proportionality (about 40%), YAP was observed to have a nearly proportional response. CsI(Tl) and CsI(Na) calculated photon responses are generally in agreement to within 1% with measured photon responses for photon energies above 60 keV. Below 60 keV, deviations up to 5% were observed in the calculated responses.
Serum proteome profiles in cats with chronic enteropathies

Abstract Background Serum protein biomarkers are used to diagnose, monitor treatment response, and to differentiate various forms of chronic enteropathies (CE) in humans. The utility of liquid biopsy proteomic approaches has not been examined in cats. Hypothesis/Objectives To explore the serum proteome in cats to identify markers differentiating healthy cats from cats with CE. Animals Ten cats with CE with signs of gastrointestinal disease of at least 3 weeks duration, and biopsy‐confirmed diagnoses, with or without treatment and 19 healthy cats were included. Methods Cross‐sectional, multicenter, exploratory study with cases recruited from 3 veterinary hospitals between May 2019 and November 2020. Serum samples were analyzed and evaluated using mass spectrometry‐based proteomic techniques. Results Twenty‐six proteins were significantly (P < .02, ≥5‐fold change in abundance) differentially expressed between cats with CE and controls. Thrombospondin‐1 (THBS1) was identified with >50‐fold increase in abundance in cats with CE (P < 0.001) compared to healthy cats. Conclusions and Clinical Importance Damage to the gut lining released marker proteins of chronic inflammation that were detectable in serum samples of cats. This early‐stage exploratory study strongly supports THBS1 as a candidate biomarker for chronic inflammatory enteropathy in cats.
Vignettes in string theory : using geometry to probe the worldsheet and strongly coupled physics

In this thesis, we construct hybrid linear models in which the chiral anomaly of a gauged linear sigma model is canceled by the classical anomaly of a gauged WZW model. Semi-classically, this corresponds to fibering the WZW model over the naive target space of the sigma model. When the gauge group is abelian, we recover known non-Kahler compactifications; non-abelian models describe novel quasi-geometric flux vacua of the heterotic string. Second, we also investigate sigma models that break worldvolume Lorentz invariance. Specifically, we calculate the one loop beta function for a target space metric whose worldvolume scales space and time differently, with dynamical exponent z 2. We find, as in the isotropic case, the beta function is proportional to the Ricci curvature so that conformal invariance demands Ricci-flatness. We extend this analysis to the case where space and time derivatives come with different target space metrics. We also speculate about coupling the theory to gravity. Finally, we continue the investigation of the recently discovered holographic correspondence between Reissner-Nordstrom black holes in AdS 4 and fermion correlation functions describing Non Fermi Liquids. We numerically study the effects of adding magnetic and electric dipole couplings for the fermions in the bulk. In general, the low energy physics is controlled by an emergent AdS 2 conformal dimension. We find that adding the dipole couplings changes the attainable dimensions. We also find that these couplings can drastically change the locations of fermi surfaces in momentum space. Thesis Supervisor: John McGreevy Title: Assistant Professor
Adaptive Dual Control of Discrete‐Time Lqg Problems with Unknown‐But‐Bounded Parameter

The dual control for a class of discrete‐time linear‐quadratic Gaussian (LQG) problems is studied in this paper, where there exist unknown‐but‐bounded parameter uncertainties both in the state equation and the observation equation. It is assumed that the unknown parameter belongs to a known bounded interval. To achieve the adaptive dual control, a subdivision for the continuous bounded interval is used, thus, the computational tractability is ensured. Based on such subdivision, the adaptive dual controller developed can learn a more accurate interval that contains the true value of the unknown parameter with a learning error given in advance. The performance of the controller is illustrated with two examples.
Dynamic Threshold Finite-Time Prescribed Performance Control for Nonlinear Systems With Dead-Zone Output

This article investigates the tracking control problem for nonlinear systems. An adaptive model is proposed to represent the dead-zone phenomenon and solve its control challenge with a Nussbaum function in conjunction. Drawing inspiration from the existing prescribed performance control schemes, a novel dynamic threshold scheme is developed that fuses a proposed continuous function with a finite-time performance function. A dynamic event-triggered strategy is applied to reduce the redundant transmission. The proposed time-varying threshold control strategy has fewer updates than the traditional fixed threshold and improves the efficiency of resource utilization. A command filter backstepping approach is employed to prevent the complexity explosion faced by the computation. The suggested control strategy ensures that all system signals are bounded. The validity of the simulation results has been verified.
Template‐guided method for protein–ligand complex structure prediction: Application to CASP15 protein–ligand studies

Critical Assessment of Structure Prediction 15 (CASP15) added a new category of ligand prediction to promote the development of protein/RNA‐ligand modeling methods, which have become important tools in modern drug discovery. A total of 22 targets were released, including 18 protein–ligand targets and 4 RNA‐ligand targets. We applied our recently developed template‐guided method to the protein–ligand complex structure predictions. The method combined a physicochemical, molecular docking method, and a bioinformatics‐based ligand similarity method. The Protein Data Bank was scanned for template structures containing the target protein, homologous proteins, or proteins sharing a similar fold with the target protein. The binding modes of the co‐bound ligands in the template structures were used to guide the complex structure prediction for the target. The CASP assessment results show that the overall performance of our method was ranked second when the top predicted model was considered for each target. Here, we analyzed our predictions in detail, and discussed the challenges including protein conformational changes, large and flexible ligands, and multiple diverse ligands in a binding pocket.
Moving beyond nanotechnology to uncover a glimmer of hope in diabetes medicine: Effective nanoparticle‐based therapeutic strategies for the management and treatment of diabetic foot ulcers

Hyperglycemia, a distinguishing feature of diabetes mellitus that might cause a diabetic foot ulcer (DFU), is an endocrine disorder that affects an extremely high percentage of people. Having a comprehensive understanding of the molecular mechanisms underlying the pathophysiology of diabetic wound healing can help researchers and developers design effective therapeutic strategies to treat the wound healing process in diabetes patients. Using nanoscaffolds and nanotherapeutics with dimensions ranging from 1 to 100 nm represents a state‐of‐the‐art and viable therapeutic strategy for accelerating the wound healing process in diabetic patients, particularly those with DFU. Nanoparticles can interact with biological constituents and infiltrate wound sites owing to their reduced diameter and enhanced surface area. Furthermore, it is noteworthy that they promote the processes of vascularization, cellular proliferation, cell signaling, cell‐to‐cell interactions, and the formation of biomolecules that are essential for effective wound healing. Nanomaterials possess the ability to effectively transport and deliver various pharmacological agents, such as nucleic acids, growth factors, antioxidants, and antibiotics, to specific tissues, where they can be continuously released and affect the wound healing process in DFU. The present article elucidates the ongoing endeavors in the field of nanoparticle‐mediated therapies for the management of DFU.
Dynamic recrystallization in friction stir welded AA2014 aluminium alloy joints to replace riveted joints

Abstract The demand for lightweight materials (aluminium and magnesium alloy) in structural applications is increasing due to strength ratio, corrosion resistance, formability, and recyclability. Fusion welding of aluminium and its alloy is difficult due to the formation of hot cracking, alloy segregation, porosity, etc. Henceforth, fusion welding is not an ideal process for joining aluminium and its alloy. Steel rivets are being used to join similar and dissimilar alloys in different joint configurations. Since the use of steel rivets, aircraft weight has increased drastically. Although, dissimilar metal corrosion has been encountered. These two are the main problems in structural fabrication industries. The solid-state welding friction stir welding process eliminates the issues mentioned earlier. This process can weld materials well below the melting point. Moreover, the formation of weld in the weld line could be achieved by severe plastic deformation and recrystallized grains. This metallurgical joint may replace the rivets.
Comparison of aspartate aminotransferase platelet ratio index score and insulin resistance in type 2 diabetes mellitus with non-alcoholic fatty liver disease

Abstract Objective. Nonalcoholic fatty liver disease (NAFLD) is a spectrum of liver diseases characterized by the presence of ectopic fat in the liver and steatosis, which cannot be explained by alcohol consumption. The association between NAFLD and type 2 diabetes mellitus (T2DM) is well established. As liver fibrosis progresses in a patient with NAFLD, insulin resistance (IR) increases and may worsen diabetes control. The aspartate aminotransferase platelet ratio index (APRI) score is a simple and inexpensive bedside marker that can detect liver fibrosis and cirrhosis. Several studies have shown an association between APRI and NAFLD. However, there is a gap in correlation with IR in patients with diabetes. In this study, we sought to correlate IR and NAFLD in diabetes using the APRI score. Methods. This observational hospital-based cross-sectional study was conducted in the Department of General Medicine, one of the tertiary care hospitals in North India, from February 2019 to July 2020. A total of 70 patients were taken for the study. Patients with T2DM, aged >30 years, who had no history of alcohol use and who had or were newly diagnosed with NAFLD were enrolled in the study. Results. Significant differences in mean HbAc1, AST, serum insulin, APRI score and homeo-static model assessment-2 (HOMA2) IR between NAFLD grade 1, grade 2, and grade 3 groups were found. Pearson correlation between APRI score and HOMA2 IR total values revealed a significant positive correlation between them. Conclusions. The data of the present study indicate that the APRI score can be used to assess the IR degree and provide important information for improving glycemic control in T2DM patients with NAFLD.
Machine Learning Applied on Hydraulic Actuator Control

This paper presents a comparison of two different types of neural networks when used in the control of a hydraulic actuator. The advantages of using hydraulic actuators are pondered when facing the nonlinearities present in their model, which difficult their control difficult. The state of the art seeks several solutions, mostly in the use of neural networks. In this way, this paper addressed a study regarding the replacement of traditional sigmoidal networks by the use of wavelet networks in the representation of friction on the walls of hydraulic cylinders and reverse valve dynamics. Different architectures are tested and trained using the quickpropagation algorithm. Finally, the efficiency of the networks is compared regarding generalization for friction and reverse dynamics of the valve, as well as their use in a cascade neural control.
zCeph: Achieving High Performance On Storage System Using Small Zoned ZNS SSD

ZNS SSDs (Zoned Namespace SSD) are block devices that provide stable performance and low price by forcing sequential writes, however their users have to pay the price to guarantee strong write order. In addition, to get the best performance from small zoned ZNS SSDs that give the users control over device-internal parallel elements, the users need to utilize the SSDs in detail. Due to these overheads, Ceph, one of the distributed storage systems, has up to 69% lower performance when using ZNS SSDs compared to using legacy SSD. In this paper, we present zCeph which solves the problems that occur when using small zoned ZNS SSD in storage systems. We implemented zCeph based on legacy Ceph and evaluated it using synthesized and real-world workloads, showing that the performance improved by up to 4.1x and 7x, respectively, compared to the legacy Ceph using ZNS SSD.
Dynamic Load Balancing of RDF Reasoning in Fog-Computing Environments

With the rapid increase in the number of IoT devices, fog computing has attracted attention as a distributed processing environment suitable for various real applications. Fog computing can improves the concentration on the cloud server and communication latency, but their computational power and resources are limited to cover all kinds of computations. In the meantime, RDF has been accepted as a data format for expressing data relationships. However, the existing RDF reasoning methods in fog computing do not take into account the load-balancing among fog nodes, causing performance degradation due to the imbalanced workload. To address this problem, we propose a scheme of load-aware RDF inference. Specifically, we continuously monitor the load status (CPU utilization) of each node, and when a node is detected as overloaded, we balance the load by shipping the reasoning process from the fog node to the cloud server. Experiments show the proposed method is effective in improving performance even if there exists load imbalance among fog nodes.
HSMR-RAID: Enabling a Low Overhead RAID-5 System over a Host-managed Shingled Magnetic Recording Disk Array

Some previous studies have been proposed to alleviate the impact of the frequently-updated parity data on an SMR-based RAID 5 storage system through the integration of extra hardware devices. They always utilize solid-state drives as a buffer to absorb the impact of random write operations on the SMR-based storage system performance. Unlike previous studies, this work proposes an SMR-friendly RAID 5 storage system, namely HSMR-RAID, that is developed on a pure SMR environment without any additional storage drives. The HSMR-RAID is based on a host-managed SMR architecture. The principle of HSMR-RAID is to exploit the benefits brought by data information from the host system side to put data blocks that would be invalidated at the same time in the same stripe. By this principle, we can reduce the amount of data movement and recalculate parity data so as to minimize garbage collection overhead. According to our experiments, the proposed scheme can speed up garbage collection performance by up to 29%.
CEM: an Ontology for Crime Events in Newspaper Articles

The adoption of semantic technologies for the representation of crime events can help law enforcement agencies (LEAs) in crime prevention and investigation. Moreover, online newspapers and social networks are valuable sources for crime intelligence gathering. In this paper, we propose a new lightweight ontology to model crime events as they are usually described in online news articles. The Crime Event Model (CEM) can integrate specific data about crimes, i.e., where and when they occurred, who is involved (author, victim, and other subjects involved), which is the reason for the occurrence, and details about the source of information (e.g., the news article). Extracting structured data from multiple online sources and interconnecting them in a Knowledge Graph using CEM allow events relationships extraction, patterns and trends identification, and event recommendation. The CEM ontology is available at https://w3id.org/CEMontology.
Analysis of AI Models for Student Admissions: A Case Study

This research uses machine learning-based AI models to predict admissions decisions at a large urban research university. Admissions data spanning five years was used to create an AI model to determine whether a given student would be directly admitted into the School of Science under various scenarios. During this time, submission of standardized test scores as part of a student's application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We first developed AI models and analyzed these models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his family to attend college.
Interpretable ML enhanced CNN Performance Analysis of cuBLAS, cuDNN and TensorRT

Deep learning models such as convolutional neural networks (CNNs) have a wide range of perception applications in image classification and object detection. However, despite the same CNN architectures, inference performance is different from implementations of specific libraries such as cuBLAS, cuDNN, and TensorRT. To investigate the performance effects of the state-of-the-art GPU libraries, this paper performs a case study of comparison and performance analysis of cuBLAS, cuDNN, and TensorRT implementations/libraries on YOLOv4-tiny, introducing crucial nvprof metrics for fair comparison and rationales of different performance and proposing interpretable machine learning (ML) model-based analysis. The results of our interpretable ML models show 100% accuracy in the classification and 0.0094 MAPE in the regression tasks respectively.
The not-so-easy task of taking heavy-lift ML models to the edge: a performance-watt perspective

Edge computing is a new development paradigm that brings computational power to the network edge through novel intelligent end-user services. It allows latency-sensitive applications to be placed where the data is created, thus reducing communication overhead and improving security, mobility and power consumption. There is a plethora of applications benefiting from this type of processing. Of particular interest is emerging edge-based image classification at the microscopic level. The scale and magnitude of the objects to segment, detect and classify are very challenging, with data collected using order of magnitude in magnification. The required data processing is intense, and the wish list of end-users in this space includes tools and solutions that fit into a desk-based device. Taking heavy-lift classification models initially built in the cloud to desk-based image analysis devices is a hard job for application developers. This work looks at the performance limitations and energy consumption footprint in embedding deep learning classification models in a representative edge computing device. Particularly, the dataset and heavy-lift models explored in the case study are phytoplankton images to detect Harmful Algae Blooms (HAB) in aquaculture at early stages. The work takes a deep learning model trained for phytoplankton classification and deploys it at the edge. The embedded model, deployed in a base form alongside optimised options, is submitted to a series of system stress experiments. The performance and power consumption profiling help understand system limitations and their impact on the microscopic grade image classification task.
Survey on Trust in Software Engineering for Autonomous Dynamic Ecosystems

Software systems across various application domains are undergoing a major shift, from static systems of systems to dynamic ecosystems characterized by largely autonomous software agents, engaging in mutual coalitions and partnerships to complete complex collaborative tasks. One of the key challenges facing software engineering along with this shift, is our preparedness to leverage the concept of mutual trust building among the dynamic system components, to support safe collaborations with (possibly malicious or misbehaving) components outside the boundaries of our control. To support safe evolution towards dynamic software ecosystems, this paper examines the current progress in the research on trust in software engineering across various application domains. To this end, it presents a survey of existing work in this area, and suggests the directions in which further research is needed. These directions include the research of social metrics supporting trust assessment, fine-grained quantification of trust-assessment results, and opening the discussion on governance mechanisms responsible for trust-score management and propagation across the integrated software ecosystems.
Mapping Strategies for Declarative Queries over Online Heterogeneous Biological Databases for Intelligent Responses

The emergence of Alexa and Siri, and more recently, OpenAI's Chat-GPT, raises the question whether ad hoc biological queries can also be computed without end-users' active involvement in the code writing process. While advances have been made, current querying architectures for biological databases still assume some degree of computational competence and significant structural awareness of the underlying network of databases by biologists, if not active code writing. Given that biological databases are highly distributed and heterogeneous, and most are not FAIR compliant, a significant amount of expertise in data integration is essential for a query to be accurately crafted and meaningfully executed. In this paper, we introduce a flexible and intelligent query reformulation assistant, called Needle, as a back-end query execution engine of a natural language query interface to online biological databases. Needle leverages a data model called BioStar that leverages a meta-knowledgebase, called the schema graph, to map natural language queries to relevant databases and biological concepts. The implementation of Needle using BioStar is the focus of this article.