Miracle-dz/UniFAR-FTUs huggingface dataset:
Hexagonal‐Close‐Packed Colloidal Crystals in Glenea celestis Beetles

Colloidal crystals reflecting interference‐based structural colors have been reported in many organisms, but many are yet to be accurately described. Herein, the bright, iridescent scales of green and blue Glenea celestis beetles are investigated using optical and ultrastructural techniques. The ultrastructural studies based on focused ion‐beam scanning electron microscopy and synchrotron small‐angle X‐ray scattering data reveal the origin of the colors to stem from previously undocumented hexagonal‐close‐packed colloidal crystals within their scales. The optical properties of various colloidal structures are investigated using full‐wave optical simulations. The results highlight the need for further research regarding the development of structural color in beetles.
Small RNA-Seq to Unveil the miRNA Expression Patterns and Identify the Target Genes in Panax ginseng

Panax ginseng, renowned for its medicinal properties, relies on adventitious roots and hairy roots as crucial sources for the production of ginsenosides. Despite the widespread utilization of ginseng, investigations into its miRNAs have remained scarce. To address this gap, two samples of ginseng adventitious roots and ginseng hairy roots were collected, and subsequent construction and sequencing of small RNA libraries of ginseng adventitious roots and hairy roots were performed using the Illumina HiSeq X Ten platform. The analysis of the sequencing data unveiled total miRNAs 2432. The miR166 and miR396 were the most highly expressed miRNA families in ginseng. The miRNA expression analysis results were used to validate the qRT-PCR. Target genes of miRNA were predicted and GO function annotation and KEGG pathway analysis were performed on target genes. It was found that miRNAs are mainly involved in synthetic pathways and biological processes in plants, which include metabolic and bioregulatory processes. The plant miRNAs enriched KEGG pathways are associated with some metabolism, especially amino acid metabolism and carbohydrate metabolism. These results provide valuable insights miRNAs and their roles in metabolic processes in ginseng.
Spatial patterns of bird-parasite interactions along an urbanisation gradient

14 ACKNOWLEDGMENTS . 16 PROLOGUE . 18 RESEARCH AIMS . 21 OVERVIEW OF THIS THESIS . 22 CHAPTER ONE: PARASITE-BIRD INTERACTIONS IN URBAN AREAS: CURRENT EVIDENCE AND EMERGING QUESTIONS . 27 1.
Early prediction and analysis of pancreatitis laboratory data based on neural network

Pancreatitis is a serious pancreatic disease. Due to its inconspicuous early symptoms, it is easy to be ignored or misdiagnosed, causing many patients to miss the best time for treatment, and even life-threatening. According to statistics, the prevalence of pancreatitis is increasing year by year, and it has become one of the diseases that endanger human health. Therefore, early warning has become an important link in the treatment of pancreatitis, and laboratory test data is a reliable predictor. In this study, an early warning system for laboratory data was constructed based on neural networks to predict the occurrence of pancreatitis. The neural network model uses a 3-layer feed-forward neural network and uses the backpropagation algorithm for training. The experimental data comes from the laboratory test results of 90 patients with pancreatitis. Predictive indicators included 24 blood cell analysis data. Through the model verification experiment, this study achieved good prediction results, proved the feasibility and prediction accuracy of the model, and provided ideas and basis for the practice of computer-aided diagnosis of late pancreatitis. In view of the limited sample data, the experiment only The two-category research is limited. In order to better expand, the sample data will be expanded later to cover common diagnostic types and improve the generalization ability of the model.
Fused Deposition Modelling 3D-Printed Gastro-Retentive Floating Device for Propranolol Hcl Tablets

Three-dimensional printing has revolutionized drug manufacturing and has provided a solution to the limitations associated with the conventional manufacturing method by designing complex drug delivery systems with customized drug release profiles for personalized therapies. The present investigation aims to design a gastric floating tablet with prolonged gastric floating time and sustained drug release profile. In the present study, a gastro retentive floating device (GRFD) was designed and fabricated using a fused deposition modelling (FDM)-based 3D printing technique. This device acts as a multifunctional dosage form exhibiting prolonged gastric retention time and sustained drug release profile with improved oral bioavailability in the upper gastrointestinal tract. Commercial polyvinyl alcohol (PVA) and polylactic acid (PLA) filaments were used to design GRFD, which was comprised of dual compartments. The outer sealed compartment acts as an air-filled chamber that imparts buoyancy to the device and the inner compartment is filled with a commercial propranolol hydrochloride immediate-release tablet. The device is designed as a round-shaped shell with a central opening of varying size (1 mm, 2 mm, 3 mm, and 4 mm), which acts as a drug release window. Scanning electron microscope (SEM) images were used to determine morphological characterization. The in vitro buoyancy and drug release were evaluated using the USP type II dissolution apparatus. All the designed GRFDs exhibit good floating ability and sustained drug release profiles. GRFDs fabricated using PLA filament show maximum buoyancy (>24 h) and sustained drug release for up to 10 h. The floating ability and drug release from the developed devices were governed by the drug release window opening size and the filament material affinity towards the gastric fluid. The designed GRFDs show great prospects in modifying the drug release characteristics and could be applied to any conventional immediate-release product.
Threat Modeling with Mitre ATT&CK Framework Mapping for SD-IOT Security Assessment and Mitigations

The integration of software-defined network (SDN) and Internet of Things (IoT) networks offers solutions to IoT network issues. However, this integration also introduces new security challenges and increases the attack surface of IoT networks. Existing studies on the security of SD-IoT networks lack structure and real-world descriptions of attack vectors. To address these limitations, our paper proposes a formal methodology for evaluating SD-IoT framework security through threat modeling. Our approach classifies attack vectors using the STRIDE model, describes their Tactics, Techniques, and Procedures (TTPs) using the Mitre ATT&CK framework, and proposes countermeasures. We demonstrate the potential of our methodology by applying it to a use case of modeling security for Software-Defined Vehicle Networks (SDVNs) in intelligent transport systems (ITS). Our preliminary findings are very promising towards a formal methodology for evaluating SDIoT framework security, which could serve as a foundation for developing a new SD-IoT security standard.
A patient with neuropathy and ataxia: what do I have to consider?

Purpose of review An increasing number of peripheral neuro(no)pathies are identified as involving other components of the neurological system, particularly those that further impair balance. Here we aim to outline an evidence-based approach to the diagnosis of patients who present with a somatosensory disorder which also involves at least one other area of neurological impairment such as the vestibular, auditory, or cerebellar systems. Recent findings Detailed objective investigation of patients who present with sensory impairment, particularly where the degree of imbalance is greater than would be expected, aids the accurate diagnosis of genetic, autoimmune, metabolic, and toxic neurological disease. Summary Diagnosis and management of complex somatosensory disorders benefit from investigation which extends beyond the presenting sensory impairment.
A comparison of methodologies for the real-time identification of hospitalized patients with acute exacerbations of COPD

Background: COPD is a lung disease characterized by chronic, irreversible airway obstruction that can precipitate into acute exacerbations of COPD (AECOPD) often requiring hospitalization. Improving these outcomes will require proactive innovations in care delivery to at-risk populations. Data-driven models to identify patients with AECOPD on admission to the hospital are needed, but do not exist. Objective: This study aimed to compare the performance of several models designed to identify patients with AECOPD within 24 hours of hospital admission. Methods: Clinical factors associated with admissions for AECOPD that are available within 24 hours of an encounter were combined into six different models and then tested retrospectively to evaluate each model’s performance in predicting AECOPD. The data set incorporated billing and clinical data from patients who were older than 40 years of age with an inpatient or observation encounter in 2016 at one of the nine hospitals within a large integrated healthcare system. Results: Of the 116,329 encounters, 6,383 had a billing diagnosis for AECOPD. The models showed a wide range of sensitivity (0.473 vs 0.963) and positive predictive value (0.190 vs 0.827). Conclusion: It is possible to leverage clinical and administrative data to identify patients admitted with AECOPD in real-time for quality improvement or research purposes. Because models relied on clinical data, local variation in care delivery also likely contributed to performance variation across hospitals. These findings emphasize the importance of testing model performance on local data and choosing the model that best aligns with the specific goals of the targeted initiative.
Multiple Covers with Balls

We describe arrangements of three-dimensional spheres from a geometrical and topological point of view. Real data (fitting this setup) often consist of soft spheres which show certain degree of deformation while strongly packing against each other. In this context, we answer the following questions: If we model a soft packing of spheres by hard spheres that are allowed to overlap, can we measure the volume in the overlapped areas? Can we be more specific about the overlap volume, i.e. quantify how much volume is there covered exactly twice, three times, or $k$ times? What would be a good optimization criteria that rule the arrangement of soft spheres while making a \emph{good} use of the available space? Fixing a particular criterion, what would be the optimal sphere configuration? The first result of this thesis are short formulas for the computation of volumes covered by at least $k$ of the balls. The formulas exploit information contained in the order-$k$ Voronoi diagrams and its closely related Level-$k$ complex. The used complexes lead to a natural generalization into \emph{poset diagrams}, a theoretical formalism that contains the order-$k$ and degree-$k$ diagrams as special cases. In parallel, we define different criteria to determine what could be considered an optimal arrangement from a geometrical point of view. Fixing a criterion, we find optimal soft packing configurations in 2D and 3D where the ball centers lie on a lattice. As a last step, we use tools from computational topology on real physical data, to show the potentials of higher-order diagrams in the description of melting crystals. The results of the experiments leaves us with an open window to apply the theories developed in this thesis in real applications.
Effects of Commercial Natural Compounds on Postharvest Decay of Strawberry Fruit

Gray mold and Rhizopus rot, which is caused by Botrytis cinerea and Rhizopus stolonifer, respectively, are the most destructive forms of postharvest decay of the strawberry fruit. In this work, we tested the effectiveness of the control on the postharvest decay of the strawberry fruit (Fragaria × ananassa Duch cv. ‘Monterey’) following postharvest applications of six commercial natural compounds: chitosan-based coating compound (1% of ‘ChitP’, ‘ChitS’, ‘ChitK’, ‘ChitO’), commercial essential oil (EOs) products based on grapefruit seed extract (0.5% of ‘GraFr’), sweet orange (0.5% of ‘SwOr’), a product that included eugenol, geraniol, and thymol EO, (0.4% of ‘EuGeTh’), an organic compound as humic acid (0.5% w/v of ‘HuAc’), and, lastly, methyl jasmonate plant growth regulator (1% v/v ‘MeJA’). Strawberries were dipped in solution for 30 s and incubated at room temperature (20 ± 0.5 °C) or at cold storage conditions (4 ± 0.5 °C) following 4 days of shelf life at 20 °C. The treatments with ‘ChitP’, ‘ChitS’, and ‘ChitO’ provided ~30%–40% reduction of gray mold in cold storage conditions, while the ‘MeJA’, ‘SwOr’, and ‘GraFr’ with high activities of volatile substances were more effective at controlling gray mold at room temperature. ‘HuAc’, ‘ChitK’, and ‘ChitO’ were more effective at controlling Rhizopus rot in both cold storage (~50%) and room temperature conditions.
Performance of CoAP in the Limited Resource Environment: An Empirical Study

Several low bandwidth services have become very popular in the recent years. Wireless Sensor Networks (WSN) and the Internet of things (IoT) are two of them which need fewer resources than the typical modern communication systems. In order to handle the challenges associated with the limited resources, there are appropriate protocols designed for these low resource consuming systems. In case of IoT, where the resources are very limited these protocols play the crucial roles to provide the services without interruption. Constrained Application Protocol (CoAP) is one of them. It handles the resource limited scenarios very efficiently. In this study, features and characteristics of CoAP are analyzed. This protocol has the potential to maintain the long term sustainability of the low bandwidth IoT and WSN.
DTRadar: Accelerating Search Process of Decision Trees in Packet Classification

Packet classification is an essential part of computer networks. Existing algorithms propose a partition process to address the memory explosion problem of the decision tree algorithm caused by the huge number of rules with multiple fields. However, the search process requires traversing multiple trees generated by the partition, which reduces the search efficiency. The existing algorithms take simple approaches to optimize the search process, which is low efficiency or high hardware overhead. In this paper, we propose DTRadar, a framework for expediting the decision tree packet lookup process. Its key idea is building an abstract One-Big-Tree(OBT) for multiple decision trees by establishing the middle data structure. DTRadar considers each decision tree as a splittable tree and organizes these subtrees by intermediate data structures. Extensive experiments show that DTRadar benefits existing decision tree-based solutions in classification time by 61.60%, and the memory footprint only increased by 4.21% on average.
Pleistocene fossil snake traces on South Africa’s Cape south coast

Abstract Snakes form a large, familiar, and distinctive component of the world’s reptile fauna, with a rich body fossil record stretching back to the Jurassic. The sparse, minimal, and questionable evidence of snake traces in the ichnological record is therefore surprising. Extant snakes in southern Africa employ three types of locomotion—rectilinear, sidewinding, and undulatory, all of which result in distinctive, recognizable traces. A site exhibiting convincing evidence of rectilinear motion, probably made by a puff adder, has been identified in Pleistocene aeolianites on South Africa’s Cape south coast. A new ichnogenus and ichnospecies, Anguinichnus linearis, have been erected to describe this trace. A new suite of optically stimulated luminescence (OSL) ages from aeolianites from the De Kelders Cave locality, 1.4 km to the south, suggests that the site dates to ∼93–83 ka. Trace fossil evidence of sidewinding and undulatory motion is more equivocal and open to alternative interpretations.
Peer mentoring: one institution's approach to mentoring academic librarians

Purpose – Aims to describe a peer‐mentoring program in one academic library and to provide suggestions and recommendations for setting up a similar program in other libraries.Design/methodology/approach – Formal one‐on‐one mentoring for tenure‐track librarians is a standard component of many professional development programs. This qualitative article describes the development of a newer method, the peer‐mentoring group.Findings – Information about several peer‐mentoring programs and the importance of mentoring as a component of professional development in libraries is provided.Originality/value – A list of recent articles on peer‐mentoring programs is provided along with selected articles about mentoring in libraries and higher education. The focus is for academic libraries, but public libraries may still be interested in this newer model of mentoring.
Updating the evidence on the effectiveness of the alcohol reduction app, Drink Less: using Bayes factors to analyse trial datasets supplemented with extended recruitment [version 2; peer review: 2 approved]

Background : A factorial experiment evaluating the Drink Less app found no clear evidence for main effects of enhanced versus minimal versions of five components but some evidence for an interaction effect. Bayes factors (BFs) showed the data to be insensitive. This study examined the use of BFs to update the evidence with further recruitment. Methods : A between-subject factorial experiment evaluated the main and two-way interaction effects of enhanced versus minimal version of five components of Drink Less. Participants were excessive drinkers, aged 18+, and living in the UK. After the required sample size was reached (n=672), additional data were collected for five months. Outcome measures were change in past week alcohol consumption and Alcohol Use Disorders Identification Test (AUDIT) score at one-month follow-up, amongst responders only (those who completed the questionnaire). BFs (with a half-normal distribution) were calculated (BF<0.33 indicate evidence for null hypothesis; 0.33 Results : Of the sample of 2586, 342 (13.2%) responded to follow-up. Data were mainly insensitive but tended to support there being no large main effects of the enhanced version of individual components on consumption (0.22 Conclusions : Data from extended recruitment in a factorial experiment evaluating components of Drink Less remained insensitive but tended towards individual and pairs of components not having a large effect. In an exploratory analysis, there was weak, anecdotal
Optimization of False Target Type Jamming Against CA-CFAR Utilizing Radars

In this paper, the multiple false target jamming technique applied against radars using the Cell Averaging Constant False Alarm Rate (CA-CFAR) algorithm has been analyzed. The aim of the multiple false target technique is to ensure the detection of the highest number of false targets in the threat radar. Parameters related to the CA-CFAR algorithm directly affect the success rate of real target detection for the radar, and also play a decisive role in detection performance under jamming. In the jamming technique aiming to produce multiple false target, the pulse to pulse interval (gap) and pulse width (PW) are design parameters. In this study, through simulation, the jamming parameters that provide the maximum number of false targets that can be created using the multiple false target technique in radar receivers with different CA-CFAR parameters have been obtained. Based on the simulation results, using linear regression, a linear model representing the relationship between jamming parameters (PW and gap) and CA-CFAR parameters are obtained and presented as a rule of thumb for jamming parameter selection.
Beneficial Effects of Dietary Flaxseed Oil through Inflammation Pathways and Gut Microbiota in Streptozotocin-Induced Diabetic Mice

Flaxseed oil (FO) has displayed potential anti-diabetes properties by providing a high content of α-linolenic acid. However, the effects and mechanisms of FO on type 1 diabetes are still unclear. The present study aims to explore the effects of different doses of FO feeding on hepatic inflammation and gut microbiota in streptozotocin-induced diabetic mice. Forty-eight six-week-old C57BL/6J male mice were divided into a control group (CON), a diabetic group (MOD), a diabetes with 7.0% w/w FO feeding group (FO-L), and a diabetes with 10.5% w/w FO feeding group (FO-H) for six weeks. The 7.0% w/w and 10.5% w/w FO feeding groups exhibited potential recovery of the number and size of pancreas tissues. The fasting blood glucose level was significantly decreased only after 4 weeks of feeding with 10.5% w/w FO in diabetic mice. The 10.5% w/w FO feeding group significantly decreased the postprandial blood glucose level of mice in the OGTT test. Hepatic glycogen levels were dramatically upregulated in the mice fed with both 7.0% w/w and 10.5% w/w FO. FO feeding significantly attenuated hepatic LPS, TNF-α, and IL-1β levels. In addition, we observed that 7.0% w/w and 10.5% w/w FO feedings notably downregulated hepatic gene and protein expressions of TLR4, MyD88, and P65. Furthermore, only 10.5% FO regulated fecal microbiota by increasing the relative abundance of the Bacteroidetes phylum, Lactococcus family, and Muribaculaceae and Streptococcaceae family and genus in streptozotocin-induced diabetic mice. Therefore, we conclude that FO feeding plays a role in anti-inflammation via the regulation of hepatic LPS/TLR4/MyD88 pathways and gut microbiota. In addition, different doses of FO supplementation may exhibit varying mechanisms in streptozotocin-induced mice.
Sensor Control Using Vehicle Dynamics

Sensor control provides a framework for the autonomous functioning of mobile sensors. The parameters of a target is estimated using a cost function. By optimizing the cost function, the actions to be taken by the vehicle is determined. In the literature, the vehicles are assumed as point robots so far and vehicle dynamics are ignored. In this work, the vehicle dynamics are involved in the equations for the first time. Performance of the systems with and without vehicle dynamics are analyzed in V-REP simulation environment and the results are presented.
Development of AnkleReHabit - A Low-Cost Robot-Aided Ankle Rehabilitation Platform

The ankle joint plays a critical part for human body weight support and daily movements. Any ankle joint injury can strongly affect the patient's daily life, so ankle joint rehabilitation is essential to ensure the functional recovery of the ankle after ankle dysfunction. The AnkleReHabit system, a full system including both hardware and software for ankle rehabilitation training, was developed in this paper. To test the operation of the proposed system, one healthy subject has been enrolled. The AnkleReHabit system works well during the test with both Mobile App and serious game. The feedback from the enrolled subject shows that the system can be used with comfortable, the training process can be more exciting with the serious game developed on the computer. The AnkeReHabit platform was proven for feasibility in ankle rehabilitation training. Further work should include testing the effectiveness and the reliability of the AnkleReHabit system with the patients.