Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of PCL PolyHIPE Substrates for 3D Breast Cancer Cell Culture

Cancer is a becoming a huge social and economic burden on society, becoming one of the most significant barriers to life expectancy in the 21st century. In particular, breast cancer is one of the leading causes of death for women. One of the most significant difficulties to finding efficient therapies for specific cancers, such as breast cancer, is the efficiency and ease of drug development and testing. Tissue-engineered (TE) in vitro models are rapidly developing as an alternative to animal testing for pharmaceuticals. Additionally, porosity included within these structures overcomes the diffusional mass transfer limit whilst enabling cell infiltration and integration with surrounding tissue. Within this study, we investigated the use of high-molecular-weight polycaprolactone methacrylate (PCL–M) polymerised high-internal-phase emulsions (polyHIPEs) as a scaffold to support 3D breast cancer (MDA-MB-231) cell culture. We assessed the porosity, interconnectivity, and morphology of the polyHIPEs when varying mixing speed during formation of the emulsion, successfully demonstrating the tunability of these polyHIPEs. An ex ovo chick chorioallantoic membrane assay identified the scaffolds as bioinert, with biocompatible properties within a vascularised tissue. Furthermore, in vitro assessment of cell attachment and proliferation showed promising potential for the use of PCL polyHIPEs to support cell growth. Our results demonstrate that PCL polyHIPEs are a promising material to support cancer cell growth with tuneable porosity and interconnectivity for the fabrication of perfusable 3D cancer models.
ANALYTICAL APPROXIMATE SOLUTION TO THE TIME-SPACE NONLINEAR PARTIAL FRACTIONAL DIFFERENTIAL EQUATIONS

The fractional derivatives in the sense of Caputo, and the Homotopy analysis method HAM are used to construct approximate solutions for some nonlinear partial fractional differential equations via the fractional cubic nonlinear Schrodinger equation. The numerical results show that the approaches are easy to implement and accurate when applied to space-time fractional cubic nonlinear fractional equation. The methods introduce a promising tool for solving many space-time fractional partial differential equations. This method is efficient and powerful in solving wide classes of nonlinear evolution fractional order equation. AMS Subject Classification: 14F35, 26A33, 34A08
Machine learning-based prediction of sand and dust storm sources in arid Central Asia

ABSTRACT With the emergence of multisource data and the development of cloud computing platforms, accurate prediction of event-scale dust source regions based on machine learning (ML) methods should be considered, especially accounting for the temporal variability in sample and predictor variables. Arid Central Asia (ACA) is recognized as one of the world’s primary potential sand and dust storm (SDS) sources. In this study, based on the Google Earth Engine (GEE) platform, four ML methods were used for SDS source prediction in ACA. Fourteen meteorological and terrestrial factors were selected as influencing factors controlling SDS source susceptibility and applied in the modeling process. Generally, the results revealed that the random forest (RF) algorithm performed best, followed by the gradient boosting tree (GBT), maximum entropy (MaxEnt) model and support vector machine (SVM). The Gini impurity index results of the RF model indicated that the wind speed played the most important role in SDS source prediction, followed by the normalized difference vegetation index (NDVI). This study could facilitate the development of programs to reduce SDS risks in arid and semiarid regions, particularly in ACA.
Effect of storage on physicochemical attributes of ice cream enriched with microencapsulated anthocyanins from black carrot

Abstract The present study was conducted to investigate the effect of storage on quality attributes of microencapsulated black carrot anthocyanins‐enriched ice cream. Purposely, black carrot anthocyanins were obtained using ethanolic extraction. Later on, extracts were acidified and microencapsulated with gum arabic and maltodextrin (1:1). Results showed that anthocyanin contents for T3 (9% microencapsulated anthocyanins powder‐enriched ice cream) had highest anthocyanin contents in the range of 143.21 ± 1.14 mg/100 g. However, during the storage, it was revealed that there was a slight decline in constituents concentration reasoned to oxygen exposure and interaction with other food ingredients. Similarly, for total phenolic content, the highest amount was found in T3 as 545.38 ± 4.34 mg GAE/100 g. The quality attributes of prepared ice cream treatments were also found acceptable till the end of the study (60 days). Conclusively, the addition of microencapsulated anthocyanins powder in ice cream proved to stabilize black carrot anthocyanins and contributed positively to the sensory characteristics of ice cream.
Parainfluenza Virus Type 3 Co-infection with Other Respiratory Pathogens Among Hospitalized Children with Acute Respiratory Infections in Wuhan, China

Background: Bacterial and viral co-infections are increasingly recognized as the cause of Acute Respiratory Infection (ARI). The role of co-infection in ARI patients with Parainfluenza Virus type 3 (PIV3) infection is unclear. Objectives: This study aimed to determine the prevalence of PIV3 co-infections in hospitalized children and assess the co-infections' role in ARI patients with PIV3 infections. Methods: Between January 2018 and December 2021, children were confirmed to have a PIV3 infection via throat swabs or nasopharyngeal aspirates. Some digital clinical data were analyzed, including demographic, epidemiological, diagnostic, and laboratory data. Results: During the study period from 2018 to 2021, 2,539 patients were hospitalized with ARI caused by PIV3. Of them, 34.0% had co-infection with other pathogens, and 2.4% had co-infection with more than two pathogens. Mycoplasma pneumoniae was the most common co-infecting pathogen (71.3%), followed by other bacteria (13.3%) and viruses (8.2%). A significantly higher proportion of patients with M. pneumoniae co-infection was found in girls (χ2 = 19.233, P < 0.001). Co-infections with M. pneumoniae were observed principally in patients aged 1 – 2 years (χ2 = 202.130, P < 0.001). In contrast, viral (56.3%) and bacterial (66.1%) co-infections occurred mainly in children younger than one year. The diagnosis of PIV3 as a single infection included pneumonia (41.2%), bronchitis (39.9%), upper respiratory tract infections (15.0%), and laryngitis (3.9%), which were distinguished from those with bacterial co-infections (χ2 = 16.424, P = 0.001) and co-infections with more than two pathogens (χ2 = 11.687, P = 0.010). Co-infections of PIV3 with any pathogen were not associated with admissions to intensive care units or ventilator support. However, the mean hospitalization was significantly higher in M. pneumoniae co-infections (t = 2.367, P = 0.018), bacterial co-infections (t = 2.402, P = 0.016), and co-infections with more than two pathogens (t = 2.827, P = 0.006) than in single PIV3 infection. Conclusions: Parainfluenza virus type 3 frequently occurs with other pathogens. The epidemiological and clinical characteristics of co-infections with different pathogens differed. Mycoplasma pneumoniae co-infections, bacterial co-infections, and co-infections with more than two pathogens lengthened the hospitalization. Bacterial co-infections and co-infections with more than two pathogens increased the severity of ARI and worsened the symptoms.
Amplitude variation with angle inversion using the exact Zoeppritz equations — Theory and methodology

To overcome the weaknesses of conventional prestack amplitude variation with angle inversion based on various linear or quasi-linear approximations, we have conducted a nonlinear inversion method using the exact Zoeppritz matrix (EZAI). However, the inversion using the exact Zoeppritz matrix was highly nonlinear and often unstable, if not properly treated. To tackle these issues, we have used an iteratively regularizing Levenberg-Marquardt scheme (IRLM), which regularizes the inversion problem within an algorithm that minimizes the misfit between the observed and the modeled data at the same time by incorporating the Tikhonov regularization method. As a result, the new EZAI method solved using the IRLM scheme is feasible for seismic data sets with large incidence angles, even up to or beyond the critical angle as well as strong parameter contrasts. Single and multilayered synthetic examples were used to test these features. These tests also showed that EZAI is robust on noisy gathers for parameter extraction and has weak dependence on the initial model. For the influence of inaccurate amplitudes, dominant frequencies, and phase angles, we found that EZAI is less sensitive to the variation in amplitude and phase shifts than to the dominant frequencies. Specifically, the inversion results of EZAI for P- and S-wave velocities and density were reliable if the inaccurate range for the amplitude was within 20% or the angle of the phase shift was no more than 20°. The superiority of EZAI makes it a very promising method for the estimation of subsurface elastic parameters.
Household secondary attack rate in mild COVID-19

Background: The coronavirus disease 2019 (COVID-19) pandemic has reached a staggering number of almost 280 million cases worldwide, with over 5.4 million deaths as of 29 December 2021. A further understanding of the factors related to the household spread of the infection might help to bring about specific protocols to curb such transmission. Objective: This study aims to find the secondary attack rate (SAR) and factors affecting SAR among the households of mild COVID-19 cases. Methods: An observational study was designed where data of patients admitted at All India Institute of Medical Sciences, New Delhi due to mild COVID-19 were collected, and outcome was noted after the discharge of the patient. Index cases who were the first in the household to have a positive infection only were included. Based on these data, the overall household SAR, factors related to the index case and contacts that affected transmissibility were noted. Results: A total of 60 index cases having contacts with 184 household members were included in the present study. The household SAR was measured to be 41.85%. At least one positive case was present in 51.67% households. Children below 18 years old had lower odds of getting a secondary infection compared to adults and elderly [odds ratio (OR) = 0.46, 95%CI = 0.22–0.94, p = 0.0383). An exposure period of more than a week was significantly associated with a higher risk of infection (p = 0.029). The rate of transmissibility drastically declined with effective quarantine measures adopted by the index case (OR = 0.13, 95%CI = 0.06–0.26, p < 0.00001). Symptomatic index cases contributed more to the SAR than asymptomatic primaries (OR = 4.74, 95%CI = 1.03–21.82, P = 0.045). Healthcare worker index cases had lower rates of spread (OR = 0.29, 95%CI = 0.15–0.58, P = 0.0003). Conclusion: The high SAR shows the household is a potential high-risk unit for transmissibility of COVID-19. Proper quarantine measures of all those exposed to the index case can mitigate such spread and lead to reduction of risk of COVID-19 within a household.
Retrospective analysis of patients with surgically proven ovarian torsion, our experience

Aim: To study the patients who were admitted to our hospital with surgically proven ovarian torsion and were operated for the same and to study for whom detorsion was done. Materials and Methods: A retrospective analysis of the medical records and surgical notes of 150 patients with surgically proven ovarian torsion over a 10-year period between January 2011 and January 2021 was carried out. Surgical notes included details like mode of the surgery (laparotomy or laparoscopy), type of surgery (oophorectomy, detorsion, detorsion with cystectomy), whether fixation was done or not, size of mass/ovary, laterality, appearance of the torted ovary, color of the ovary, and number of twists. Histopathologic reports of the patients who underwent oophorectomy or detorsion with cystectomy were also recorded. Results: During the 10-year study period, 88 (58.7%) patients had undergone laparotomy and 62 (41.2%) patients had undergone laparoscopy. Detorsion with cystectomy was done in 96 (64%) cases, detorsion alone in 14 (9.3%) cases, and oophorectomy was done in 40 (26.6%) cases. There was no significant difference in terms of increase in postoperative complications. Conclusion: Laparoscopic detorsion with cystectomy is the most common surgical procedure used for ovarian torsion at King Hussein Medical Center.
SVDnet: Singular Value Control and Distance Alignment Network for 3D Object Detection

The SOTA methods proposed voxelization or pillarization to regularize unordered point clouds, improving computing efficiency for LiDAR-based 3D object detection. However, they usually trade partial accuracy for speed. Thus, we bring up a new problem setting: “Is it possible to keep high detection accuracy while point-cloud quantization is applied? ”. To this end, we found that the inconsistent sparsity of the point cloud over the depth distance, which is still an open question, might be the main reason. To address the inconsistency effect, we first proposed a new pillar-based vehicle detection model, named SVDnet, in which novel plug-ins are introduced in its backbone and neck. Specifically, a novel low-rank objective is designed to force the backbone to extract distance/sparsity-aware features and suppress the other feature variations among vehicle samples. Next, we alleviated the remaining feature inconsistency resulting from distance/sparsity in the neck by dynamic feature selection and adaptive feature fusion. Here, feature selection is realized by a position attention network, while feature fusion is achieved by a Distance Alignment Ratio-generation Network (DARN). Later, the selected and fused features, less sensitive to sparsity, are concatenated and fed to an SSD-like detection head. Besides, we also integrate the proposed plug-ins with multiple pillar/voxel-based methods for performance boosting. Our evaluation shows that SVDnet improves the average precision of the distant cases by 8.11% with only 0.23 milliseconds speed drop compared with PointPillars. Furthermore, the extensional results validate that our plug-ins can help SOTA pillar/voxel-based methods to gain noticeable improvement, especially for far-range objects.
Enhance the Accuracy of Landslide Detection in UAV Images Using an Improved Mask R-CNN Model: A Case Study of Sanming, China

Extracting high-accuracy landslide areas using deep learning methods from high spatial resolution remote sensing images is a hot topic in current research. However, the existing deep learning algorithms are affected by background noise and landslide scale effects during the extraction process, leading to poor feature extraction effects. To address this issue, this paper proposes an improved mask regions-based convolutional neural network (Mask R-CNN) model to identify the landslide distribution in unmanned aerial vehicles (UAV) images. The improvement of the model mainly includes three aspects: (1) an attention mechanism of the convolutional block attention module (CBAM) is added to the backbone residual neural network (ResNet). (2) A bottom-up channel is added to the feature pyramidal network (FPN) module. (3) The region proposal network (RPN) is replaced by guided anchoring (GA-RPN). Sanming City, China was selected as the study area for the experiments. The experimental results show that the improved model has a recall of 91.4% and an accuracy of 92.6%, which is 12.9% and 10.9% higher than the original Mask R-CNN model, respectively, indicating that the improved model is more effective in landslide extraction.
fMRI-Based Brain Disease Diagnosis: A Graph Network Approach

As a non-invasive brain imaging technology, functional magnetic resonance imaging (fMRI) provides a basic tool for brain functional network modeling and brain disease diagnosis. Problems, such as large number of parameters, low training efficiency, and poor interpretability, are encountered in mainstream models because of the high complexity of fMRI and brain networks. To solve these problems, a novel structure feature combined graph neural network (SFC-GNN) with a low number of parameters is proposed. In particular, SFC-GNN is composed of 1) the graph convolution layer of the brain region perception and 2) the node pooling layer of the graph structure feature (GSF). It also receives the sparse brain graph modeled by each subject’s fMRI as input. Especially, the GSF layer can select brain regions that are important for classification, thereby localizing all active regions related to brain disease. Moreover, a group network is constructed according to the correlation among subjects, and SFC-GNN can be extended further to a node classification model to achieve better diagnosis performance. The proposed method has been validated on the ABIDE and ADNI datasets, thereby showing the effectiveness of our proposed method in various experiments.
Neuromedin B excites central lateral amygdala neurons and reduces cardiovascular output and fear‐potentiated startle

Neuromedin B (NMB) and gastrin‐releasing peptide (GRP) are the two mammalian analogs in the bombesin peptide family that exert a variety of actions including emotional processing, appetitive behaviors, cognition, and tumor growth. The bombesin‐like peptides interact with three receptors: the NMB‐preferring bombesin 1 (BB1) receptors, the GRP‐preferring bombesin 2 (BB2) receptors and the orphan bombesin 3 (BB3) receptors. Whereas, injection of bombesin into the central amygdala reduces satiety and modulates blood pressure, the underlying cellular and molecular mechanisms have not been determined. As administration of bombesin induces the expression of Fos in the lateral nucleus of the central amygdala (CeL) which expresses BB1 receptors, we probed the effects of NMB on CeL neurons using in vitro and in vivo approaches. We showed that activation of the BB1 receptors increased action potential firing frequency recorded from CeL neurons via inhibition of the inwardly rectifying K+ (Kir) channels. Activities of phospholipase Cβ and protein kinase C were required, whereas intracellular Ca2+ release was unnecessary for BB1 receptor‐elicited potentiation of neuronal excitability. Application of NMB directly into the CeA reduced blood pressure and heart rate and significantly reduced fear‐potentiated startle. We may provide a cellular and molecular mechanism whereby bombesin‐like peptides modulate anxiety and fear responses in the amygdala.
Countries most exposed to individual and concurrent extremes and near-permanent extreme conditions at different global warming levels

Abstract. It is now certain that human-induced climate change is increasing the incidence of extreme temperature, precipitation and drought events globally. A critical aspect of these extremes is their potential concurrency that can result in substantial impacts on society and environmental systems. Therefore, quantifying concurrent extremes in current and projected climate is necessary to take measures and adapt to future challenges associated with such conditions. Here we investigate changes in individual and concurrent extremes in multi-model simulations of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) for different global warming levels (GWLs). We focus on the individual and simultaneous occurrence of the extreme events, encompassing heatwaves, droughts, maximum 1 d precipitation (Rx1day), and extreme wind (wind), as well as the compound events heatwave–drought and Rx1day–wind in the pre-industrial period (1850–1900; reference period), for approximately present conditions (+1 ∘C of global warming), and at three higher global warming levels (GWLs of +1.5, +2 and +3 ∘C). We focus our analysis on 139 countries and three climatic macro-regions: northern mid- and high-latitude countries (MHC), subtropical countries (STC), and tropical countries (TRC). We find that, on a global scale, most individual extremes become more frequent and affect more land area for higher GWLs. Changes in frequency of individual heatwaves, droughts, Rx1day and extreme wind with higher GWLs cause shifts in timing and disproportionate increases in frequency of concurrent events across different months and different regions. As a result, concurrent occurrences of the investigated extremes become 2.0 to 9.6 times more frequent at +3 ∘C of global warming compared to the pre-industrial period. At +3 ∘C the most dramatic increase is identified for concurrent heatwave–drought events, with a 9.6-times increase for MHC, an 8.4-times increase for STC and a 6.8-times increase for TRC compared to the pre-industrial period. By contrast, Rx1day–wind events increased the most in TRC (5.3 times), followed by STC (2.3 times) and MHC (2.0 times) at +3 ∘C with respect to the pre-industrial period. Based on the 2015 population, these frequency changes imply an increase in the number of concurrent heatwave–drought (Rx1day–wind) events per capita for 82 % (41 %) of countries. Our results also suggest that there are almost no time periods (on average 0 or only 1 month per year) without heatwaves, droughts, Rx1day and extreme wind for 21 countries at +1.5 ∘C of global warming, 37 countries at +2 ∘C and 85 countries at +3 ∘C, compared to 2 countries at +1 ∘C of global warming. This shows that a large number of countries will shift to near-permanent extreme conditions even at global warming levels consistent with the limits of the Paris Agreement. Given the projected disproportionate frequency increases and decreasing non-event months across GWLs, our results strongly emphasize the risks of uncurbed greenhouse gas emissions.
TCP Transcription Factors in Plant Reproductive Development: Juggling Multiple Roles

TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTOR (TCP) transcription factors (TFs) are plant-specific transcriptional regulators exerting multiple functions in plant growth and development. Ever since one of the founding members of the family was described, encoded by the CYCLOIDEA (CYC) gene from Antirrhinum majus and involved in the regulation of floral symmetry, the role of these TFs in reproductive development was established. Subsequent studies indicated that members of the CYC clade of TCP TFs were important for the evolutionary diversification of flower form in a multitude of species. In addition, more detailed studies of the function of TCPs from other clades revealed roles in different processes related to plant reproductive development, such as the regulation of flowering time, the growth of the inflorescence stem, and the correct growth and development of flower organs. In this review, we summarize the different roles of members of the TCP family during plant reproductive development as well as the molecular networks involved in their action.