Miracle-dz/UniFAR-FTUs huggingface dataset:
Comprehensive Response of Rhodosporidium kratochvilovae to Glucose Starvation: A Transcriptomics-Based Analysis

Microorganisms adopt diverse mechanisms to adapt to fluctuations of nutrients. Glucose is the preferred carbon and energy source for yeast. Yeast cells have developed many strategies to protect themselves from the negative impact of glucose starvation. Studies have indicated a significant increase of carotenoids in red yeast under glucose starvation. However, their regulatory mechanism is still unclear. In this study, we investigated the regulatory mechanism of carotenoid biosynthesis in Rhodosporidium kratochvilovae YM25235 under glucose starvation. More intracellular reactive oxygen species (ROS) was produced when glucose was exhausted. Enzymatic and non-enzymatic (mainly carotenoids) antioxidant systems in YM25235 were induced to protect cells from ROS-related damage. Transcriptome analysis revealed massive gene expression rearrangement in YM25235 under glucose starvation, leading to alterations in alternative carbon metabolic pathways. Some potential pathways for acetyl-CoA and then carotenoid biosynthesis, including fatty acid β-oxidation, amino acid metabolism, and pyruvate metabolism, were significantly enriched in KEGG analysis. Overexpression of the fatty acyl-CoA oxidase gene (RkACOX2), the first key rate-limiting enzyme of peroxisomal fatty acid β-oxidation, demonstrated that fatty acid β-oxidation could increase the acetyl-CoA and carotenoid concentration in YM25235. These findings contribute to a better understanding of the overall response of red yeast to glucose starvation and the regulatory mechanisms governing carotenoid biosynthesis under glucose starvation.
Prediction of Cardiovascular Events and All-Cause Mortality With Arterial Stiffness

Results The pooled relative risk (RR) of clinical events increased in a stepwise, linear-like fashion from the first to the third tertile of aortic PWV. The pooled RRs of total CV events, CV mortality, and all-cause mortality were 2.26 (95% confidence interval: 1.89 to 2.70, 14 studies), 2.02 (95% confidence interval: 1.68 to 2.42, 10 studies), and 1.90 (95% confidence interval: 1.61 to 2.24, 11 studies), respectively, for high versus low aortic PWV subjects. For total CV events and CV mortality, the RR was significantly higher in high baseline risk groups (coronary artery disease, renal disease, hypertension) compared with low-risk subjects (general population). An increase in aortic PWV by 1 m/s corresponded to an age-, sex-, and risk factor–adjusted risk increase of 14%, 15%, and 15% in total CV events, CV mortality, and all-cause mortality, respectively. An increase in aortic PWV by 1 SD was associated with respective increases of 47%, 47%, and 42%.
Pulmonary nodule detection based on 3D multi-scale and semantic context heterogeneity

Automated lung nodule detection methods are challenged by the fact that pulmonary nodules often present multiple morphologies and scales and by the low differentiation of nodules from surrounding tissues. To address the above issues, we propose a pulmonary nodule detection method based on 3D multiscale and semantic context heterogeneity. The method uses dilated convolutions with different dilation rates to construct a Context Fusion Module (CFM) to extract pulmonary nodule multiscale fused features, thus enhancing the detection ability of non-significant and small nodules. The Channel Semantic Context Heterogeneity Model (CSHM) is proposed to address the problem of noise and redundant information in multi-scale fusion features, to suppress noise and redundant information from feature channel-level contextual relevance and redundancy, to enhance potential discriminative features, and to reduce the interference of useless information, so as to enhance the ability of nodule detection and localization. Experiments were conducted on a publicly available dataset (Luna Nodule Analysis 16, LUNA16) with a high sensitivity and an average sensitivity of 83.6% over eight false alarm numbers, which is better than the other method, validating the superiority of the proposed method.
On the Performance of LDPC Codes over Radio Stripes System

In the scope of beyond 5G and the forthcoming 6G, Radio Stripes (RS) have been proposed as a potential technique to enhance communications quality for new users. In this paper, we examine the functionality of an RS-aided wireless communication systems, where the transmitted signal is encoded with low-density parity check (LDPC) codes to improve the signal quality. In order to provide high-power, low-latency in 5G communications, LDPC codes, which permit encoding-decoding, have been used. Inter-channel interferences and performances are aimed to be improved with LDPC codes by achieving over 2.5 dB gain, in terms of Bit Error Rate. Using Monte Carlo simulations for various numbers of RS-reflecting elements, we show the validity of the beneficial effect of this type of codes.
Numerical Modeling of Physical Cell Trapping in Microfluidic Chips

Microfluidic methods have proven to be effective in separation and isolation of cells for a wide range of biomedical applications. Among these methods, physical trapping is a label-free isolation approach that relies on cell size as the selective phenotype to retain target cells on-chip for follow-up analysis and imaging. In silico models have been used to optimize the design of such hydrodynamic traps and to investigate cancer cell transmigration through narrow constrictions. While most studies focus on computational fluid dynamics (CFD) analysis of flow over cells and/or pillar traps, a quantitative analysis of mechanical interaction between cells and trapping units is missing. The existing literature centers on longitudinally extended geometries (e.g., micro-vessels) to understand the biological phenomenon rather than designing an effective cell trap. In this work, we aim to make an experimentally informed prediction of the critical pressure for a cell to pass through a trapping unit as a function of cell morphology and trapping unit geometry. Our findings show that a hyperelastic material model accurately captures the stress-related softening behavior observed in cancer cells passing through micro-constrictions. These findings are used to develop a model capable of predicting and extrapolating critical pressure values. The validity of the model is assessed with experimental data. Regression analysis is used to derive a mathematical framework for critical pressure. Coupled with CFD analysis, one can use this formulation to design efficient microfluidic devices for cell trapping and potentially perform downstream analysis of trapped cells.
Optimizing AI for Mobile Malware Detection by Self-Built-Dataset GAN Oversampling and LGBM

The cyber detection industry focuses on analyzing the behavior of threats in order to develop IOCs and triggers. This process makes the detection always behind the attackers, as there is an analysis time between the attack tool launch and the detection ability. To address the challenges, a dedicated Sandbox environment was built, and thousands of mobile devices' samples were tested, resulted in creation of an up-to-date training dataset that is not based on the attacks analysis. With this dataset, the research focus was directed towards optimizing the AI methodology to achieve the best detection rates for a compromised mobile device. A CupolaGAN was implemented to oversample dataset and to compare results obtained from training LGBM models on both original imbalanced dataset and oversampled dataset. Classification scores on the oversampled data increase by maximum of 0.47+/-0.37%. The performance of the fine-tuned model using Optuna on the balanced data reaches 99.36+/-0.19% accuracy.
Do People Want the ‘New Normal’? A Mixed Method Investigation of Young Person, Parent, and Clinician Experience and Preferences for Eating Disorder Treatment Delivery in the Post-COVID-19 World

Eating disorder treatment was predominantly provided online during the COVID-19 pandemic, which has continued into the post-pandemic world. This mixed method study explored young person, parent/caregiver, and clinician experiences of child and adolescent eating disorder treatment. In total, 90 participants (25 young people, 49 parents/caregivers, and 16 clinicians) completed online surveys about the experience of online working. Data were compared to similar data collected by the same service earlier in the pandemic. The results show that preferences are largely unchanged since 2020; online treatment is considered helpful and acceptable by all groups. Nevertheless, face-to-face assessment sessions (young people: 52.2%; and parents/caregivers: 68.9%) and final sessions (young people: 82.6%; and parents/caregivers: 82.2%) were preferred compared to online. There was also a preference for early treatment sessions to either be always or mostly face-to-face (young people: 65.2%; and parents/caregivers: 73.3%). The middle and latter parts of treatment were a time when preferences shifted slightly to a more hybrid mode of delivery. Participants reported finding engagement with the therapist (young people: 70.6%; and parents/caregivers: 52.5%) easier during face-to-face treatment. Stepping away from the binary of online or face-to-face, the current data suggest that a hybrid and flexible model is a way forward with current findings providing insights into how to structure this.
Knocking Cells: Latency-Based Identification of IPv6 Cellular Addresses on the Internet

IPv6 mobile networks are becoming increasingly important. Many jobs rely on understanding IPv6 mobile networks at the IP level. Previous works on cellular identification suffer from coarse identification granularity, proprietary data, or not working for IPv6. The high latency in mobile networks makes identifying cellular addresses possible using Round-Trip Time (RTT) difference. However, due to the impact of packet loss on the measurement of RTT difference, identifying cellular addresses with less overhead is challenging. In this paper, by triggering the non-zero RTT difference of the cellular /48 subnets with probes, we propose an accurate latency-based method to identify cellular /48 subnets from fixed subnets. Experiments demonstrate that the method can identify cellular subnets with a precision of 93.52% to 99.95% and a recall of 99.96% on a worldwide dataset. The overhead of measuring RTT difference reduces to at least 1/10th compared to the existing methods while robust to packet loss.
Ecologically Sustainable Visitor Use of Australia’s World Heritage Areas

This report is one of a series in the Sustainable Tourism Corporative Research Centre's overall project to develop a framework, guidelines and tools to enhance assessment, evaluation and reporting of visitor use in protected areas. In this report we review the approaches management agencies have taken to research and assess ecologically sustainable visitor use of Australia's World Heritage Areas. We also review how they assess performance in managing visitor use. World Heritage Areas are popular tourism and recreation destinations with internationally significant conservation values. Research, assessment and reporting processes for World Heritage Areas are better resourced than for other protected areas. Therefore, how agencies research, assess and evaluate the pressures and threats associated with visitor use should represent Australian best practices.
Adherence to the Mediterranean Diet during Pregnancy: Associations with Sociodemographic and Anthropometric Parameters, Perinatal Outcomes, and Breastfeeding Practices

Background and Objectives: The Mediterranean diet (MD) has been recognized as a beneficial nutritional pattern that promotes human health, decreasing the risks of a variety of human disorders and pathological states, including adverse pregnancy outcomes. In this aspect, the current survey aimed to assess the potential association of compliance with the MD during gestation with various sociodemographic and anthropometric parameters, perinatal outcomes, and breastfeeding practices. Materials and Methods: This was a cross-sectional study performed on 5688 pregnant women from 10 distinctive Greek areas. Face-to-face interviews with qualified questionnaires and thorough retrievals of medical records were performed to collect data concerning the participants’ sociodemographic and anthropometric parameters, perinatal outcomes, and breastfeeding practices. Results: Elevated compliance with the MD during pregnancy was independently related with older age, higher educational status, and better economic status as well as decreased incidences of pre-pregnancy overweight/obesity and excess gestational weight gain and a lower likelihood of gestational diabetes. Moreover, greater adherence to the MD was independently associated with an increased prevalence of delivering vaginally and a greater prevalence of exclusive breastfeeding for at least 16 weeks postpartum. Conclusions: A higher level of compliance with the MD for the period of gestation was associated with several favorable lifestyle factors that may promote maternal health. Further studies with a prospective design as well as studies exploring the potential effects of maternal compliance with the MD for the period of pregnancy on the health of children should be performed. Future studies should also be extended beyond the MD by assessing the potential beneficial effects of adopting a Mediterranean lifestyle on maternal and child health.
Development of a Simulation Model for Fluidized Bed Mild Gasifier

This Thesis-Restricted is protected by copyright and/or related rights. It has been brought to you by ScholarWorks@UNO with permission from the rights-holder(s). You are free to use this Thesis-Restricted in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/or on the work itself.
Therapeutic strategies and promising vaccine for hepatitis C virus infection

Hepatitis C virus (HCV) infection is still a significant global health problem despite therapeutic advancements. Ribavirin and interferon therapy have been the sole available treatments for HCV infection for a number of years with low efficacy. Thus, currently, a number of therapeutic strategies are being used, including nanoparticles (NPs), micro‐RNAs such as small interfering RNA (siRNA), RNAi‐based gene silencing and antisense oligonucleotide‐based microRNA‐122, microRNA‐155, and short hairpin RNAs (shRNAs), and immunotherapeutic approaches such as anti‐programmed cell death 1(PD‐1), monoclonal antibodies (mAb or moAb), and monocyte‐derived dendritic cells (Mo‐DCs). Furthermore, direct‐acting antivirals (DAAs) and host‐targeting agents (HTA) were also the current therapeutic approaches with great efficacy. In spite of different clinical trials on HCV vaccine developments, nowadays there is no effective HCV vaccine in opposition to virus due to various challenges including genetic diversity, lack of immunocompetent small animal models, shortage of HCV vaccination testing alternatives, lack of an effective tissue culture method for replicating HCV, and inadequate knowledge regarding to immune responses against HCV infection. Nowadays, mRNA vaccine, recombinant viral vector, peptides vaccine, virus‐like particles, DNA vaccine, rational designed vaccine, and recombinant polyantigenic T‐cell‐based vaccine are novel promising candidates for HCV vaccine based on various clinical trials. This review summarizes the different therapeutic approaches and the advancements of vaccine candidates for HCV infection.
Prokaryote Distribution Patterns along a Dissolved Oxygen Gradient Section in the Tropical Pacific Ocean

Oceanic oxygen levels are decreasing significantly in response to global climate change; however, the microbial diversity and ecological functional responses to dissolved oxygen (DO) in the open ocean are largely unknown. Here, we present prokaryotic distribution coupled with physical and biogeochemical variables and DO gradients from the surface to near the bottom of a water column along an approximately 12,000-km transect from 13° N to 18° S in the Tropical Pacific Ocean. Nitrate (11.42%), temperature (10.90%), pH (10.91%), silicate (9.34%), phosphate (4.25%), chlorophyll a (3.66%), DO (3.50%), and salinity (3.48%) significantly explained the microbial community variations in the studied area. A distinct microbial community composition broadly corresponding to the water masses formed vertically. Additionally, distinct ecotypes of Thaumarchaeota and Nitrospinae belonging to diverse phylogenetic clades that coincided with specific vertical niches were observed. Moreover, the correlation analysis revealed large-scale natural feedback in which chlorophyll a (organic matter) promoted Thaumarchaeotal biomass at depths that subsequently coupled with Nitrospina, produced and replenished nitrate for phytoplankton productivity at the surface. Low DO also favored Thaumarchaeota growth and fueled nitrate production.
Vertical Diamond p-n Junction Diode with Step Edge Termination Structure Designed by Simulation

In this paper, diamond-based vertical p-n junction diodes with step edge termination are investigated using a Silvaco simulation (Version 5.0.10.R). Compared with the conventional p-n junction diode without termination, the step edge termination shows weak influences on the forward characteristics and helps to suppress the electric field crowding. However, the breakdown voltage of the diode with simple step edge termination is still lower than that of the ideal parallel-plane one. To further enhance the breakdown voltage, we combine a p-n junction-based junction termination extension on the step edge termination. After optimizing the structure parameters of the device, the depletion regions formed by the junction termination extension overlap with that of the p-n junction on the top mesa, resulting in a more uniform electric field distribution and higher device performance.
The Possibilities of Using Fractal Antennas in Modern Wireless Communication Technologies

This article discusses the possibilities of using fractal antenna systems in wireless communication systems, their features and the main advantages of using them. The presence of the property of self-similarity of fractal structures allows them to be implemented in the creation of broadband and multiband antennas. Therefore, today the capabilities of a fractal antenna are widely studied in wireless communication systems such as 5G, 4G (LTE), Bluetooth, WLAN, WiMAX, GPS and others. The article also presents the main characteristics of fractal antennas and the practical implementation of the antenna model in the Ansys HFSS program. Simulation results, such as S11-parameter, radiation pattern and gain for MIMO patch antenna based on anisotropic fractal, and L, S band patch antenna for CubeSat nanosatellites are presented.
A simplified guide to determination of sample size requirements for estimating the value of intraclass correlation coefficient: a review

Intraclass correlation coefficient (ICC) measures the extent of agreement and consistency among raters for two or more numerical or quantitative variables. This review paper aimed to present several tables that could illustrate the minimum sample sizes required for estimating the desired effect size of ICC, which is a measurement of the magnitude of an agreement. Determination of the minimum sample size under such circumstances is based on the two fundamentally important parameters, namely the actual value of the ICC and the number of observations made by each subject. The sample size calculations are derived from Power Analysis and Sample Size (PASS) software where the alpha and minimum required power is fixed at 0.05 and higher than 0.80 respectively. A discussion on how to use these tables for determining sample sizes required for each of the various scenarios and the limitations associated with their use in each of these scenarios is provided.
Colloidal Manipulation through Plasmonic and Non‐plasmonic Laser‐Assisted Heating

In spite of the long‐term awareness of the conversion of light to heat even in materials with low absorption coefficient via the photothermal effect and consequent usage of the effect to evaluate thermo‐optic properties of the materials, only recently has the thermal field created via photon‐to‐phonon conversion been exploited for manipulation of colloidal objects as well as living cells. As compared to conventional direct photon‐assisted manipulation via optical tweezers, the optothermal manipulation technique employs much lower optical source power and can manipulate particles over a long range. In this review, the working mechanisms, concepts, and applications of a series of recently established optothermal techniques are discussed for the manipulation of diverse species including micro/nanoparticles, biological cells, molecules, and micelles in various fluidic environments. The physical mechanism of the optical manipulation that relies on the coordinated action of thermal convection, Marangoni convection, thermophoresis, thermoelectricity, depletion attraction, and thermo‐osmotic flow is discussed in detail. With their low‐power operation, diverse functionalities, and simple optics employed, optothermal manipulation techniques are increasingly finding a wide range of applications in colloidal science, life sciences, materials science, and nanoscience, as well as in the developments of colloidal functional devices and nanomedicine.
Self-Supervised Deep-Learning Segmentation of Corneal Endothelium Specular Microscopy Images

Computerized medical evaluation of the corneal endothelium is challenging because it requires costly equipment and specialized personnel, not to mention that conventional techniques require manual annotations that are difficult to acquire. This study aims to obtain reliable segmentations without requiring large data sets labeled by expert personnel. To address this problem, we use the Barlow Twins approach to pre-train the encoder of a UNet model in an unsupervised manner. Then, with few labeled data, we train the segmentation. Encouraging results show that it is possible to address the challenge of limited data availability using self-supervised learning. This model achieved a precision of 86%, obtaining a good performance. Using many images to learn good representations and a few labeled images to learn the semantic segmentation task is feasible.
A Survey on Industrial Internet of Things Security: Requirements, Attacks, AI-Based Solutions, and Edge Computing Opportunities

The Industrial Internet of Things (IIoT) paradigm is a key research area derived from the Internet of Things (IoT). The emergence of IIoT has enabled a revolution in manufacturing and production, through the employment of various embedded sensing devices connected by an IoT network, along with a collection of enabling technologies, such as artificial intelligence (AI) and edge/fog computing. One of the unrivaled characteristics of IIoT is the inter-connectivity provided to industries; however, this characteristic might open the door for cyber-criminals to launch various attacks. In fact, one of the major challenges hindering the prevalent adoption of the IIoT paradigm is IoT security. Inevitably, there has been an inevitable increase in research proposals over the last decade to overcome these security concerns. To obtain an overview of this research area, conducting a literature survey of the published research is necessary, eliciting the various security requirements and their considerations. This paper provides a literature survey of IIoT security, focused on the period from 2017 to 2023. We identify IIoT security threats and classify them into three categories, based on the IIoT layer they exploit to launch these attacks. Additionally, we characterize the security requirements that these attacks violate. Finally, we highlight how emerging technologies, such as AI and edge/fog computing, can be adopted to address security concerns and enhance IIoT security.