Miracle-dz/UniFAR-FTUs huggingface dataset:
Laboratory measurements of the sedimentation velocity of hexagonal planar ice crystals

New experimental measurements of the falling velocity, size and orientation of individual hexagonal plate‐like ice crystals are reported. The measurements were conducted at three different temperatures: −13, −16 and −20 °C. The diameter of the ice crystals measured in the experiments were between 50 μm and 250 μm, a size range which is in agreement with the size found in natural clouds. In this range, ice crystals show a random orientation during free fall and a falling velocity which increases with size. Results show that the fall velocity is insensitive to the temperature at which the ice crystals grow for the temperatures used in this study. An empirical power‐law between the Best and Reynolds numbers is presented using the capacitance as characteristic length and an estimation of the ice crystal mass. Despite the dispersion of the experimental data, the Best–Reynolds relationship found seems to be similar to the relationship for falling spheres in Stokes flow using the capacitance as the hydrodynamic radius. The fall velocities of hexagonal and columnar ice crystals were compared. The columnar ice crystals show a velocity larger than that of hexagonal ice crystals with the same value of capacitance. However, both crystalline habits show a unique empirical Be–Re relationship.
Slicing a white neutron bunch via time-of-flight measurement for fast neutron resonance radiography and nucleus identification

The under-construction white neutron beam line at the China Spallation Neutron Source will be used mainly for nuclear data measurements. It will be characterized by its high flux and broad energy spectra. To exploit the beam line as a neutron imaging source, we propose a liquid scintillator fiber array for fast neutron resonance radiography (FNRR). The fiber detector unit has an extremely small exposed area, which will limit the event counts and separate the events in time, thus satisfying the requirements for single-event time-of-flight (SEToF) measurement. The current study addresses the physical design criteria for ToF measurement, including flux estimation and detector response. Future development and potential application of the technology are also discussed.
Polynucleotides Suppress Inflammation and Stimulate Matrix Synthesis in an In Vitro Cell-Based Osteoarthritis Model

Osteoarthritis (OA) is characterized by degeneration of the joint cartilage, inflammation, and a change in the chondrocyte phenotype. Inflammation also promotes cell hypertrophy in human articular chondrocytes (HC-a) by activating the NF-κB pathway. Chondrocyte hypertrophy and inflammation promote extracellular matrix degradation (ECM). Chondrocytes depend on Smad signaling to control and regulate cell hypertrophy as well as to maintain the ECM. The involvement of these two pathways is crucial for preserving the homeostasis of articular cartilage. In recent years, Polynucleotides Highly Purified Technology (PN-HPT) has emerged as a promising area of research for the treatment of OA. PN-HPT involves the use of polynucleotide-based agents with controlled natural origins and high purification levels. In this study, we focused on evaluating the efficacy of a specific polynucleotide sodium agent, known as CONJURAN, which is derived from fish sperm. Polynucleotides (PN), which are physiologically present in the matrix and function as water-soluble nucleic acids with a gel-like property, have been used to treat patients with OA. However, the specific mechanisms underlying the effect remain unclear. Therefore, we investigated the effect of PN in an OA cell model in which HC-a cells were stimulated with interleukin−1β (IL−1β) with or without PN treatment. The CCK-8 assay was used to assess the cytotoxic effects of PN. Furthermore, the enzyme-linked immunosorbent assay was utilized to detect MMP13 levels, and the nitric oxide assay was utilized to determine the effect of PN on inflammation. The anti-inflammatory effects of PN and related mechanisms were investigated using quantitative PCR, Western blot analysis, and immunofluorescence to examine and analyze relative markers. PN inhibited IL−1β induced destruction of genes and proteins by downregulating the expression of MMP3, MMP13, iNOS, and COX-2 while increasing the expression of aggrecan (ACAN) and collagen II (COL2A1). This study demonstrates, for the first time, that PN exerted anti-inflammatory effects by partially inhibiting the NF-κB pathway and increasing the Smad2/3 pathway. Based on our findings, PN can potentially serve as a treatment for OA.
The DEAD-Box RNA Helicase Ded1 Is Associated with Translating Ribosomes

DEAD-box RNA helicases are ATP-dependent RNA binding proteins and RNA-dependent ATPases that possess weak, nonprocessive unwinding activity in vitro, but they can form long-lived complexes on RNAs when the ATPase activity is inhibited. Ded1 is a yeast DEAD-box protein, the functional ortholog of mammalian DDX3, that is considered important for the scanning efficiency of the 48S pre-initiation complex ribosomes to the AUG start codon. We used a modified PAR-CLIP technique, which we call quicktime PAR-CLIP (qtPAR-CLIP), to crosslink Ded1 to 4-thiouridine-incorporated RNAs in vivo using UV light centered at 365 nm. The irradiation conditions are largely benign to the yeast cells and to Ded1, and we are able to obtain a high efficiency of crosslinking under physiological conditions. We find that Ded1 forms crosslinks on the open reading frames of many different mRNAs, but it forms the most extensive interactions on relatively few mRNAs, and particularly on mRNAs encoding certain ribosomal proteins and translation factors. Under glucose-depletion conditions, the crosslinking pattern shifts to mRNAs encoding metabolic and stress-related proteins, which reflects the altered translation. These data are consistent with Ded1 functioning in the regulation of translation elongation, perhaps by pausing or stabilizing the ribosomes through its ATP-dependent binding.
Interaction of Cloud Dynamics and Microphysics During the Rapid Intensification of Super‐Typhoon Nanmadol (2022) Based on Multi‐Satellite Observations

Using multi‐satellite observations, the cloud dynamic and microphysical characteristics were revealed during the rapid intensification (RI) of super‐typhoon Nanmadol (2022). As the storm intensifies, the eyewall contracts, the upper‐level divergence strengthens, and the cirrus cloud increases, leading to stronger upper‐level radial outflow and the vertical updraft. Meanwhile, it is found that there exists a dynamically attractive area in the outer rainbands, where particles grow effectively and form “a small amount of large particles” around 300 km from the eye. A theory of cloud dynamics‐microphysics interaction, called “tunnel theory,” is further proposed to explain the generation, accumulation, and concentrated downflow of large particles in the outer rainbands during RI. Results suggest the unique feature of particle distribution in the outer rainbands could be a potential indicator for RI.
Enhancing the Performance of Generative Adversarial Networks with Identity Blocks and Revised Loss Function to Improve Training Stability

Generative adversarial networks (GANs) are a powerful deep learning model for synthesizing realistic images; however, they can be difficult to train and are prone to instability and mode collapse. This paper presents a modified deep learning model called Identity Generative Adversarial Network (IGAN) to address the challenges of training and instability faced by generative adversarial models in synthesizing realistic images. The IGAN model includes three modifications to improve the performance of DCGAN: a non-linear identity block to ease complex data fitting and reduce training time; a modified loss function with label smoothing to smooth the standard GAN loss function; and minibatch training to use other examples from the same minibatch as side information for better quality and variety of generated images. The effectiveness of IGAN was evaluated and compared with other state-of-the-art generative models using the inception score (IS) and Fréchet inception distance (FID) on CelebA and stacked MNIST datasets. The experiments demonstrated that IGAN outperformed the other models in terms of convergence speed, stability, and diversity of results. Specifically, in 200 epochs, IGAN achieved an IS of 13.6 and an FID of 46.2. Furthermore, the IGAN collapsed modes were compared with other generative models using a stacked MNIST dataset, showing the superiority of IGAN in producing all the modes while the other models failed to do so. These results demonstrate that the modifications implemented in IGAN can significantly enhance the performance of GANs in synthesizing realistic images, providing a more stable, high-quality, and diverse output.
Risk factors for SARS-CoV-2 infection during the early stages of the COVID-19 pandemic: a systematic literature review

Introduction Identifying SARS-CoV-2 infection risk factors allows targeted public health and social measures (PHSM). As new, more transmissible variants of concern (VoC) emerge, vaccination rates increase and PHSM are eased, it is important to understand any potential change to infection risk factors. The aim of this systematic literature review is to describe the risk factors for SARS-CoV-2 infection by VoC. Methods A literature search was performed in MEDLINE, PubMed and Embase databases on 5 May 2022. Eligibility included: observational studies published in English after 1 January 2020; any age group; the outcome of SARS-CoV-2 infection; and any potential risk factors investigated in the study. Results were synthesized into a narrative summary with respect to measures of association, by VoC. ROBINS-E tool was utilized for risk of bias assessment. Results Of 6,197 studies retrieved, 43 studies were included after screening. Common risk factors included older age, minority ethnic group, low socioeconomic status, male gender, increased household size, occupation/lower income level, inability to work from home, public transport use, and lower education level. Most studies were undertaken when the ancestral strain was predominant. Many studies had some selection bias due to testing criteria and limited laboratory capacity. Conclusion Understanding who is at risk enables the development of strategies that target priority groups at each of the different stages of a pandemic and helps inform vaccination strategies and other interventions which may also inform public health responses to future respiratory infection outbreaks. While it was not possible to determine changes to infection risk by recent VoC in this review, the risk factors identified will add to the overall understanding of the groups who are at greatest risk of infection in the early stages of a respiratory virus outbreak. Systematic review registration https://www.crd. york.ac. uk/prospero/display_record. php? ID=CRD42022330706, PROSPERO [CRD42022330706].
Manganese Removal Using Functionalised Thiosalicylate-Based Ionic Liquid: Water Filtration System Application

Aiming at the generation of new functionalised thiosalicylate-based ionic liquids, a polymeric hydrogel consisting of 1-hexylimidazole propionitrile thiosalicylate [HIMP][TS], with a solid biomaterial support based on polyvinyl alcohol (PVA)–alginate beads, was produced. This study aimed to develop a treatment method for removing manganese (Mn) heavy metal from industrial wastewater, which is known to be toxic and harmful towards the environment and human health. The method utilised an adsorption-based approach with an alginate adsorbent that incorporated a functionalised thiosalicylate-based ionic liquid. The synthesised smooth round beads of PVA–alginate–[HIMP][TS] adsorbent were structurally characterised using Fourier transform infrared spectroscopy (FTIR) and field emission scanning electron microscopy (FESEM). The Mn concentration and removal efficiency were evaluated using atomic absorption spectroscopy (AAS). Three important parameters were evaluated: pH, adsorbent dosage, and contact time. During optimisation using the interactive factor design of experiments through the Box–Behnken model, the results showed that the system achieved a maximum Mn removal efficiency of 98.91% at an initial pH of 7.15, with a contact time of 60 min, using a bead dosage of 38.26 g/L. The beads were also tested in an available water filtration prototype system to illustrate their industrial application, and the performance showed a removal efficiency of 99.14% with 0 NTU total suspended solid (TSS) and 0.13 mg/L turbidity analysis. The recyclability of PVA–alginate–[HIMP][TS] beads using 0.5 M HCl resulted in four cycles with constant 99% Mn removal. The adsorption capacity of Mn was also determined in optimum conditions with 56 mg/g. Therefore, the alginate–thiosalicylate-based ionic liquid system is considered an effective and environmentally friendly method for removing Mn heavy metal due to the high removal efficiency achieved.
Spatial resolution properties of krypton-based mixtures using a 100 μm thick Gas Electron Multiplier

A gaseous imaging detector based on a cascade of two non-standard gas electron multipliers 100 μm thick GEM) was operated in five different krypton-based mixtures and the achievable position resolution was determined. The micropattern gaseous detector (MPGD) has an active area of 10 × 10cm2. The GEM foils are twice as thick as a standard GEM, being more robust to electric discharges and less prone to sparking-caused damage. Krypton, being the noble gas with the lowest value of intrinsic position resolution in the range 14– 34 keV, is a good candidate for imaging applications based on gaseous detectors. Our measurements have confirmed that krypton-CO2 mixtures showed an improvement in position resolution above 18 keV and up to 30 keV relatively to Ar-CO2 mixtures. The MTF value at 10% in the energy range 22–24 keV was evaluated, being 0.5876(342)lp/cm for Ar-CO2 (70:30) and a maximum of 3.2702(15)lp/cm for the Kr-CO2 (90:10) mixture.
The Deficits of Insulin Signal in Alzheimer’s Disease and the Mechanisms of Vanadium Compounds in Curing AD

Vanadium is a well-known essential trace element, which usually exists in oxidation states in the form of a vanadate cation intracellularly. The pharmacological study of vanadium began with the discovery of its unexpected inhibitory effect on ATPase. Thereafter, its protective effects on β cells and its ability in glucose metabolism regulation were observed from the vanadium compound, leading to the application of vanadium compounds in clinical trials for curing diabetes. Alzheimer’s disease (AD) is the most common dementia disease in elderly people. However, there are still no efficient agents for treating AD safely to date. This is mainly because of the complexity of the pathology, which is characterized by senile plaques composed of the amyloid-beta (Aβ) protein in the parenchyma of the brain and the neurofibrillary tangles (NFTs), which are derived from the hyperphosphorylated tau protein in the neurocyte, along with mitochondrial damage, and eventually the central nervous system (CNS) atrophy. AD was also illustrated as type-3 diabetes because of the observations of insulin deficiency and the high level of glucose in cerebrospinal fluid (CSF), as well as the impaired insulin signaling in the brain. In this review, we summarize the advances in applicating the vanadium compound to AD treatment in experimental research and point out the limitations of the current study using vanadium compounds in AD treatment. We hope this will help future studies in this field.
UFCN: a fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle

Abstract. Road extraction in imagery acquired by low altitude remote sensing (LARS) carried out using an unmanned aerial vehicle (UAV) is presented. LARS is carried out using a fixed wing UAV with a high spatial resolution vision spectrum (RGB) camera as the payload. Deep learning techniques, particularly fully convolutional network (FCN), are adopted to extract roads by dense semantic segmentation. The proposed model, UFCN (U-shaped FCN) is an FCN architecture, which is comprised of a stack of convolutions followed by corresponding stack of mirrored deconvolutions with the usage of skip connections in between for preserving the local information. The limited dataset (76 images and their ground truths) is subjected to real-time data augmentation during training phase to increase the size effectively. Classification performance is evaluated using precision, recall, accuracy, F1 score, and brier score parameters. The performance is compared with support vector machine (SVM) classifier, a one-dimensional convolutional neural network (1D-CNN) model, and a standard two-dimensional CNN (2D-CNN). The UFCN model outperforms the SVM, 1D-CNN, and 2D-CNN models across all the performance parameters. Further, the prediction time of the proposed UFCN model is comparable with SVM, 1D-CNN, and 2D-CNN models.
Disruption avoidance through the prevention of NTM destabilization in TCV

Destabilization of a stationary neoclassical tearing mode due impurity influx can lead to a potentially destructive disruption and is of significant concern for current and future tokamaks. A representative scenario was developed on TCV to experiment with applicable disruption avoidance techniques and produce a real time control system capable of handling such an event. Soft x-ray (SXR) radiation intensity and magnetic diagnostics analyses available in real time were used to provide plasma state information to the control system. Electron cyclotron current drive (ECCD) was employed to prevent NTM destabilization. Deposition of ECCD near the calculated q = 2 surface was able to prevent destabilization of the NTM if a large increase in SXR radiation intensity was used as the trigger. A delay in avoidance resulted in the plasma entering a disruptive state which required over 100 ms of continuous ECCD around the q = 2 surface to stabilize. Ramp down scenarios were studied to complete the design of a closed loop system. This system was then successfully tested using increasingly disruptive scenarios, through increased gas quantities, and the system was able to extend the discharge for a prescribed amount of time and safely ramp down the plasma current to the minimum controllable level. The system demonstrated in this work is presently limited to this specific type of disruption but this approach could be applied to other disruptive situations on the path to building a global disruption handling system.
Prevalence of Poor Sleep Quality and Its Determinants Among Men Suffering from Erectile Dysfunction

Objective: Recent findings implicate a higher prevalence of erectile dysfunction (ED) among men with sleep disorders. We investigated sleep quality among men with ED and reveal possible interrelations in a reverse manner. Materials and Methods: A prospective study was conducted at 12 different andrology outpatient clinics among men with complaints of insufficient erections. The subjective quality of sleep was measured using the Pittsburgh Sleep Quality Inventory. Erectile function and its dimensions were measured using the International Index of Erectile Function (IIEF) questionnaire. The age-adjusted Charlson comorbidity index and Beck Depression Inventory were used to evaluate interrelations with comorbid disease severity and depression of the subjects consecutively in the study. Results: Among 431 eligible men, the median age of the participants and their erectile function scores in the IIEF were 51 [interquartile range (IQR)=43-59] and 16 (IQR=11-19), respectively. Poor sleep was observed in 192 of 431 (44.5%) subjects. Multivariable analysis revealed that younger age [odds ratio (OR)=0.95, p=0.01], lower body mass (OR=0.86, p=0.01), lower serum high-density lipoprotein cholesterol (OR=0.95, p=0.02), and higher severity of depression (OR=2.1, p=0.0001) were predictors of poor sleep quality among men with ED. Conclusion: Nearly half of the men with ED also suffer from poor sleep. Younger age, lean body mass, and severe depression were the main predictors of poor sleep quality among ED sufferers.
Investigation of static transmission expansion planning using the symbiotic organisms search algorithm

ABSTRACT Transmission expansion planning (TEP) has become a complex problem in restructured electricity markets. This article presents the symbiotic organisms search (SOS) algorithm, a novel metaheuristic optimization technique for solving TEP problems in power systems. The SOS algorithm is inspired by the interactions among organisms in an ecosystem. The TEP problem is formulated here as an optimization problem to determine the cost-effective expansion planning of electrical power systems. Several constraints, such as power flow of the lines, right-of-way validity and maximum line addition, are taken into consideration. First, the SOS algorithm is tested with several benchmark functions. Then, it is applied on three standard power system networks (IEEE 24-bus system, Brazilian 46-bus system and Brazilian 87-bus system) in a TEP study to demonstrate the optimization capability of the proposed SOS algorithm. The results are compared with those produced by other state-of-the-art algorithms.
CRISPR/Cas9: A Powerful Strategy to Improve CAR-T Cell Persistence

As an emerging treatment strategy for malignant tumors, chimeric antigen receptor T (CAR-T) cell therapy has been widely used in clinical practice, and its efficacy has been markedly improved in the past decade. However, the clinical effect of CAR-T therapy is not so satisfying, especially in solid tumors. Even in hematologic malignancies, a proportion of patients eventually relapse after receiving CAR-T cell infusions, owing to the poor expansion and persistence of CAR-T cells. Recently, CRISPR/Cas9 technology has provided an effective approach to promoting the proliferation and persistence of CAR-T cells in the body. This technology has been utilized in CAR-T cells to generate a memory phenotype, reduce exhaustion, and screen new targets to improve the anti-tumor potential. In this review, we aim to describe the major causes limiting the persistence of CAR-T cells in patients and discuss the application of CRISPR/Cas9 in promoting CAR-T cell persistence and its anti-tumor function. Finally, we investigate clinical trials for CRISPR/Cas9-engineered CAR-T cells for the treatment of cancer.
Structure–property relationships in lath-shaped triads based on multialkynylbenzene

ABSTRACT This report elaborates the synthesis of symmetrical triads based on multialkynylbenzene linked via flexible alkyl spacers. Four mesogens were synthesised in which multialkynylbenzene units were connected to each other in a side-by-side fashion with varying flexible alkyl spacers. The compound with longest alkyl spacer, i.e. n = 7, exhibited ND phase which has been characterised by polarised optical microscopy and detailed X-ray scattering studies (small/wide-angle X-ray scattering). Surprisingly, this triad shows ND phase at high temperature as compared to our previous reports on room-temperature ND phases. GRAPHICAL ABSTRACT