Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Object Classification Method Based on Saliency Detection

In recent years, image classification has become a research hot area in computer vision, Most of the methods directly train data sets to obtain classifiers. However, if the images in the data set contain massive backgrounds, causing the image itself to have a lot of noise will degrade the classification results. This paper presents a new object classification method based on salient object detection. Firstly, a salient object detection method based on CNN is applied to the dataset image to locate the object in the image. Then use the Bag of Feature model to vectorize the salient region features. Finally, use the multi-class linear SVM to classify the feature vectors. For marveldataset2016, the classification accuracy of the pre-processed classification method is 10.8% higher than that of the non-preprocessing classification method, and the classifier-training is 44% less time-consuming.
Toward Artificial Synesthesia : Linking Images and Sounds via Words

We tackle a new challenge of modeling a perceptual experience in which a stimulus in one modality gives rise to an experience in a different sensory modality, termed synesthesia. In this thesis, we focus on a common synesthesia between visual modality and auditory modality. Our goal is to associate images with proper sounds and vice versa. The former task is termed image composition, and the latter is termed sound illustration. Unfortunately, the correspondence information between images and sounds is scarce and expensive. Thus, traditional method for learning correspondence information may not be applicable in this task. To meet the challenge, we propose a probabilistic framework based on graphical models that enables to implicitly link visual modalities and auditory modalities via natural language text. The proposed framework can accomplish image composition and sound illustration elegantly. Moreover, an online prototype system is developed for allowing human judgement to evaluate the model’s performance. Experimental results indicate effectiveness and applicability of the framework.
Low concentration of morphine protects against cell death, oxidative stress and calcium accumulation by nicotine in PC12 cells.

OBJECTIVE Nicotine causes cell death in many cell lines. Morphine at low concentrations has protective effects against cell death. We investigated the effects of low concentration of morphine on nicotine-induced cell death in PC12 cells. MATERIALS AND METHODS PC12 are cells that grow in DMEM culture medium. Cell viability was detected by MTT test and cells cytotoxicity was measured by LDH test. The activity of caspase-3 was diagnosed by the caspase activity colorimetric assay kit, and detection of mitochondrial membrane potential was confirmed by rhodamine 123 and TUNEL test was performed for DNA fragmentation detection. The fura-2 AM and also rhod 2-AM was used for measurement of intracellular calcium (Ca2+) ic and mitochondrial calcium (Ca2+) m and finally, measurement of antioxidant enzyme activities was assessed. RESULTS The low concentration of morphine increased cell viability and suppressed cell cytotoxicity, cell death and the formation of mitochondrial membrane potential compared to nicotine treated cells. It also reduced the intracellular calcium (Ca2+) ic and mitochondrial calcium (Ca2+)m concentration, respectively. CONCLUSION Morphine as a pain reducer drug, in low concentrations, can protect PC12 cells from nicotine-induced cell death (Fig. 7, Ref. 59).
“My mama told me it would happen”: menarche and menstruation experiences across generations

ABSTRACT The purpose of this study was to understand women’s menarche and menstruation-related knowledge, attitudes, and behaviors across generations. Women ages 18 years and older (Mean = 49.13 ± 14.76 years; Range = 19–78 years) living in South Carolina were recruited to participate in semi-structured interviews during May-November 2016. A total of 70 interviews were conducted, which were audio-recorded and transcribed verbatim. Thematic analysis using open and axial coding techniques from grounded theory provided the framework for data interpretation. Women described vivid menarche memories; however, most women had not received proper education or preparation for what to expect prior to onset. Participants discussed their experiences with menstrual products, oral contraceptives, and medical procedures to suppress, manipulate, or manage menstruation. Findings provide practical recommendations for health professionals to develop further effective and timely messaging related to menarche and menstruation across the lifespan. Messaging should inform women about what to expect with menarche, menstruation management, and health-related impacts, as well as methods to reduce menses-related myths and stigmas.
Interaction of Arsenic Species with Organic Ligands: Competitive Removal from Water by Coagulation-Flocculation-Sedimentation (C/F/S)

The co-occurrence of arsenic (As) and organic ligands in water bodies has raised environmental concerns due to their toxicity and adverse effects on human health. The present study aims to elucidate the influences of hydrophobic/hydrophilic organic ligands, such as humic acid (HA) and salicylic acid (SA), on the interactive behavior of As species in water. Moreover, the competitive removal behaviors of As(III, V) species and total organic carbon (TOC) were systematically investigated by coagulation-flocculation-sedimentation (C/F/S) under various aqueous matrices. The results showed the stronger binding affinity of As(V) than As(III) species, with a higher complexation ability of hydrophobic ligands than hydrophilic. The media containing hydrophilic ligands require smaller ferric chloride (FC) doses to achieve the higher As(III, V) removal, while the optimum FC dose required for As(III) removal was found to be higher than that for As(V). Moreover, hydrophobic ligands showed higher TOC removal than hydrophilic ligands. The pronounced adverse effect of a higher concentration of hydrophobic ligands on the removal efficiencies of As(V) and TOC was observed. The adsorption of As(V) on Fe precipitates was better fitted with the Langmuir model but the Freundlich isotherm was more suitable for As(III) in the presence of hydrophilic SA. Moreover, TOC removal was substantially decreased in the As(V) system as compared to the As(III) system due to the dissolution of Fe precipitates at higher As(V) concentrations. The results of FC composite flocs demonstrated that the combined effect of oxidation, charge neutralization and adsorption played an important role in the removal of both toxicants during the C/F/S process. In summary, the findings of the present study provide insights into the fate, mobility and competitive removal behavior of As(III, V) species and organic ligands in the water treatment process.
Factors Correlating to the Development of Hepatitis C Virus Infection in Hemodialysis Patients—Findings Mainly from Asiatic Populations: A Systematic Review and Meta-Analysis

Hemodialysis is an effective replacement therapy for chronic renal failure patients. In recent decades, the number of hemodialysis patients has grown rapidly and some measures for preventing blood-borne diseases have been implemented, but hepatitis C virus (HCV) infection remains a significant problem. The meta-analysis published in 2009 on HCV infection-related factors was based on localized study objects, and some additional studies have been published since 2009; however, the contribution of these factors remains under dispute. Our study pooled the odds ratios (ORs) or mean standard deviations (MDs) with 95% confidence intervals (CIs) and analyzed sensitivity using Review Manager 5.1 software (5.1 version Copenhagen: The Nordic Cochrane Centre; 2011) by searching data in the PubMed, Elsevier, Springer, Wiley, and EBSCO databases. Spearman correlation analysis was performed using the SPSS package. In our meta-analysis, 1715 HCV-infected hemodialysis patients and 7093 non-HCV-infected hemodialysis patients from 44 studies were analyzed. The pooled ORs with 95% CIs were: histories of blood transfusion, 4.30 (3.11, 5.96); weekly hemodialysis times > 2, 6.00 (3.25, 11.06); kidney transplantation, 5.80 (3.95, 8.52); hemodialysis units > 2, 6.90 (2.42, 19.68); shared hemodialysis devices, 5.00 (2.35, 10.65); and drug addiction, 4.73 (1.54, 14.47). The pooled MDs with 95% CIs were duration of hemodialysis (months) 27.48 (21.67, 33.30). There was a positive correlation between duration of hemodialysis and the HCV infection rate (p < 0.01). Hemodialysis patients, especially from Asia, with shared hemodialysis devices, hemodialysis units > 2, blood transfusion, kidney transplantation, and drug addiction were at increased risk of HCV infection. The HCV infection rate increased with the duration of hemodialysis. High-risk hemodialysis patients should be monitored and receive timely screening.
Identification of Tomato Plant Diseases by Leaf Image Using Squeezenet Model

One of the problems in the field of agriculture is regarding plant diseases. Plant diseases can cause a decrease in agricultural production. Therefore, early detection and diagnosis of plant diseases is very important. Plant diseases often appear on the leaves, and the characteristics of the affected leaves can be varied and difficult to distinguish. This makes it difficult to identify the disease automatically. Increased smartphone usage and advances in the field of computer vision through deep learning have made it possible to connect smartphones as a tool in diagnosing diseases. Plants used as case studies in this study were tomato plants. The method used is Convolutional Neural Network (CNN). This CNN method has many types based on the architecture it builds, one of which is squeezenet architecture. Squeezenet architecture can produce a model with a relatively small size, so that the model can be implemented on smartphone devices, server computing, and microcontroller devices. This research will focus on building a model based on squeezenet architecture to classify seven types of tomato plant diseases on the leaves including healthy leaves. The model is built using the help of Keras deep learning frameworks. The data used in this study is the image of tomato plant leaves obtained from the Vegetable Crops Research Institute (Balitsa) in Lembang, West Java. With 200 classes for each class. This study has successfully detected tomato plant disease through its leaf image automatically with an average accuracy of identification of 86.92%.
Association of BRCA1- and BRCA2-deficiency with mutation burden, expression of PD-L1/PD-1, immune infiltrates, and T cell-inflamed signature in breast cancer

Immune checkpoint inhibitors have demonstrated effective anti-tumour response in cancer types with high mutation burden (e.g. melanoma) and in subset of cancers with features of genomic instability (e.g. mismatch-repair deficiency). One possible explanation for this effect is the increased expression of immune checkpoint molecules and pre-existing adaptive immune response in these cancers. Given that BRCA1 and BRCA2 are integral in maintaining genomic integrity, we hypothesise that the inactivation of these genes may give rise to breast cancers with such immunogenic phenotype. Therefore, using two large series of publicly available breast cancer datasets, namely that from The Cancer Genome Atlas and Wellcome Trust Institute, we sought to investigate the association between BRCA1- and BRCA2-deficiency with features of genomic instability, expression of PD-L1 and PD-1, landscape of inferred tumour-infiltrating immune cells, and T-cell inflamed signature in breast cancers. Here, we report that BRCA1 and BRCA2-deficient breast cancers were associated with features of genomic instability including increased mutation burden. Interestingly, BRCA1-, but not BRCA2-, deficient breast cancers were associated with increased expression of PD-L1 and PD-1, higher abundance of tumour-infiltrating immune cells, and enrichment of T cell-inflamed signature. The differences in immunophenotype between BRCA1- and BRCA2-deficient breast cancers can be attributed, in part, to PTEN gene mutation. Therefore, features of genomic instability such as that mediated by BRCA1- and BRCA2- deficiency in breast cancer were necessary, but not always sufficient, for yielding T cell-inflamed tumour microenvironment, and by extension, predicting clinical benefit from immunotherapy.
NetPyNE , a tool for data-driven 1 multiscale modeling of brain circuits 2

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly 17 growing and disparate experimental datasets at multiple scales. The NetPyNE tool 18 (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven 19 multiscale network models in NEURON. NetPyNE clearly separates model parameters from 20 implementation code. Users provide specifications at a high level via a standardized declarative 21 language, e.g., connectivity rules, to create millions of cell-to-cell connections. NetPyNE then 22 enables users to generate the NEURON network, run efficiently parallelized simulations, optimize 23 and explore network parameters through automated batch runs, and use built-in functions for 24 visualization and analysis – connectivity matrices, voltage traces, spike raster plots, local field 25 potentials, and information theoretic measures. NetPyNE also facilitates model sharing by 26 exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being 27 used to teach computational neuroscience students and by modelers to investigate brain regions 28 and phenomena. 29
Automatic Sketch Colorization with Tandem Conditional Adversarial Networks

Similar to stacked generative adversarial networks (GANs) whose each GAN undertakes one decomposition task, such as a Gaussian pyramid module or a palette module. In this paper, we use the Unet network in a tandem format to deal with the sketch-to-image problem. The first conditional GAN generates a grayscale image and the second cGAN automatically generates color comics based on the grayscale image. We use the SED model using structured random forests to obtain the original sketch. Compared with DeepColor, PaintsChainer and PaintsTransfer, it shows that the algorithm proposed in this paper can achieve higher quality automatic manga colorization which is visually more closed to the real comics.
Strain and Shear Wave Elastography in Diagnosis of Retrobulbar Neuritis.

BACKGROUND In the early stages of retrobulbar neuritis (RN), the optic disc appears normal on ophthalmoscopy. Therefore, the diagnosis of RN is usually made clinically. However, imaging tools are needed for precise diagnosis of RN, and usually MRI is used. In this study, we investigated the diagnostic performance of strain elastography (SE) and shear wave elastography (SWE) in patients with RN. Elastography is a new sonographic technique and a noninvasive ultrasound method for evaluating the elastic properties of tissues based on static compression: the elastographic techniques of SE compress the tissues axially, and SWE uses waves that are generated by transducers and interact with the tissue. METHODS The study included 40 eyes of 20 patients with sudden visual loss, who were diagnosed with RN. The eyes of the patients were divided into 2 groups: the eye with a diagnosis of RN was the group of RN eyes, and the healthy second eye was the group of control eyes. Ophthalmologic examination, orbital and brain MRI, SE, and SWE were performed. SE color mapping was divided into 3 types: blue-hardest tissue (Type 1), blue/green-hard tissue (Type 2), and green-intermediate tissue (Type 3). All patients were treated with high-dose corticosteroids. The measurements of SE and SWE were made immediately after diagnosis and 1 month after treatment. RESULTS The mean age of 11 male and 9 female patients was 38.3 ± 12.2 years. At the time of diagnosis, the mean shear wave values for the control eyes were 18.47 ± 7.26 kPa (kilopascals), and the mean shear wave values for the RN eyes were 37.21 ± 8.24 kPa. There was a statistically significant difference between the control and RN eyes at the time of diagnosis (P < 0.001). The mean shear wave value was 19.92 ± 4.77 kPa in the RN eyes after treatment. There was a statistically significant difference in values at the time of diagnosis and after treatment (P < 0.001). Strain types found in the RN eyes before treatment were Type 1 in 60% of eyes, Type 2 in 25%, and Type 3 in 15%; at the end of the treatment, Type 2 was observed in 25% of eyes and Type 3 in 75%, while Type 1 was not observed. CONCLUSIONS SE and SWE may be important alternative diagnostic tools in the diagnosis of RN.
lncRNA GAS5/miR‐223/NAMPT axis modulates the cell proliferation and senescence of endothelial progenitor cells through PI3K/AKT signaling

Endothelial progenitor cells (EPCs) have been reported to replace the damaged endothelial cells to repair the injured or dead endothelium. However, EPC senescence might lead to the failure in EPC function. Thus, developing an in‐depth understanding of the mechanism of EPC senescence might provide novel strategies for related vascular disorders’ treatments. Herein, nicotinamide phosphoribosyltransferase (NAMPT) overexpression could increase cell proliferation and suppress cell senescence in EPCs. miR‐223 directly bound to the 3′‐untranslated region of NAMPT to inhibit its expression, therefore modulating EPC proliferation and senescence through NAMPT and phosphoinositide 3‐kinase (PI3K)/protein kinase B (AKT) signaling. Long noncoding RNA (lncRNA) GAS5 sponges miR‐223, consequently downregulating miR‐223 expression. GAS5 knockdown inhibited EPC proliferation and promoted senescence. GAS5 might serve as a competing endogenous RNA for miR‐223 to counteract miR‐223‐mediated suppression on NAMPT, thus regulating EPC proliferation and senescence via the PI3K/AKT signaling pathway. In summary, our findings provide a solid experimental basis for understanding the role and mechanism of lncRNA GAS5/miR‐223/NAMPT axis in EPC proliferation and senescence.
Using Probabilistic Machine Learning Methods to Improve Beef Cattle Price Modeling and Promote Beef Production Efficiency and Sustainability in Canada

Accurate agricultural commodity price models enable efficient allocation of limited natural resources, leading to improved sustainability in agriculture. Because of climate change, price volatility and uncertainty in the sector are expected to increase in the future, increasing the need for improved price modeling. With the emergence of machine learning (ML) algorithms, novel tools are now available to enhance the modeling of agricultural commodity prices. This research explores both univariate and multivariate ML techniques to perform probabilistic price prediction modeling for the Canadian beef industry, taking into account beef production, commodity markets, and international trade features to enhance accuracy. We model Alberta fed steer prices using three multivariate ML algorithms (support vector regression (SVR), random forest (RF), and Adaboost (AB)) and three univariate ML algorithms (autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and the seasonal autoregressive integrated moving average with exogenous factors (SARIMAX)). We apply these models to monthly fed steer price data between January 2005 and September 2023 and compare predicted prices with observed prices using several validation metrics. The outcomes indicate that both random forest (RF) and Adaboost (AB) show superior overall performance in accurately predicting Alberta fed steer prices in comparison to other algorithms. To better account for the variance of the best model performance, we subsequently adopted a probabilistic approach by considering uncertainty in our best-selected ML model. The beef industry can use these improved price models to minimize resource waste and inefficiency in the sector and improve the long-term sustainability prospects for beef producers in Canada.
Draft Genome Sequences of Two Fusobacterium necrophorum Strains Isolated from the Uterus of Dairy Cows with Metritis

A commensal in the gastrointestinal tract, Fusobacterium necrophorum is involved in the pathogenicity of abscesses, foot rot, and metritis in cattle. Here, we present the draft genomes of two Fusobacterium necrophorum isolates from the uterus of dairy cows with metritis to allow for future comparative genome studies. ABSTRACT A commensal in the gastrointestinal tract, Fusobacterium necrophorum is involved in the pathogenicity of abscesses, foot rot, and metritis in cattle. Here, we present the draft genomes of two Fusobacterium necrophorum isolates from the uterus of dairy cows with metritis to allow for future comparative genome studies.
A Review of Breast Cancer Detection in Medical Images

Breast cancer is a malignant tumor that occurs in the glandular epithelium of the breast. It is considered to be one of the most common cancers affecting women in the world. However, there is not an effective way to cure breast cancer yet, the key to reducing the risk of death is the early detection and diagnosis of breast cancer. Accurate diagnosis of breast cancer normally requires analysis of medical images of different modalities. There is a great need of automated system that could analyze these images accurately and rapidly. In this paper, we introduce some commonly used medical imaging methods for diagnosis of breast cancer, and based on them we investigate some recently proposed approaches for breast cancer detection with computer vision and machine learning techniques. Finally, we compare and analyze the detection performance of different methods on histological images and mammograph images respectively.
Skin Lesion Semantic Segmentation Using Convolutional Encoder Decoder Architecture

Computerized skin lesion analysis system often used segmentation technique which is always advantageous due skin lesion unequal size, shape and border. In this research paper deep convolutional encoder decoder neural network is proposed for pixel-wise semantic segmentation of dermascopic image of skin lesion. Proposed segmentation network consist sequence of encoder block and subsequent decoder block and final output is fed to pixel-wise classification layer. The proposed segmentation network is trained and tested on publicly available dermatology images obtained from challenge host by International Skin Imaging Collaboration (ISIC) in the beginning 2016 on “Skin Lesion Analysis towards Melanoma Detection”. This challenge consists of 900 training sample of dermascopic skin images and 379 for evaluation. Experimental results of proposed segmentation network are very encouraging compare to state of the art result which achieves jaccard index value of 0.928.