Miracle-dz/UniFAR-FTUs huggingface dataset:
OAS_A_355808 31..37

Background: Differentiating benign from malignant thyroid nodules is not possible clinically. The aim of this study was to assess the prevalence and risk of malignancy postoperatively among patients who had a cytologic diagnosis of follicular neoplasia preoperatively. Methods: All patients who were operated on with histopathologic diagnosis of follicular neoplasia from January 2015 to December 2019, at St Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia were studied retrospectively. Operation theatre log books and individual patient charts were reviewed for the study variables. Both patient and nodular characteristics were analyzed for risk of malignancy and an odds ratio was used to identify the association of factors for follicular carcinoma. Results: A total of 110 patients’ charts were retrieved for analysis. Two-thirds of patients (78; 70.9%) had benign findings postoperatively and only 32 (29%) patients had malignancy consisting of 21 (19.1%) follicular carcinoma and 11 (10%) papillary carcinoma. The mean age of patients with malignant and benign histopathology is 48.3 (42.9–53.7) and 41.7 (38.7–44.7) respectively (p <0.05). Only tumor size, consistency of nodules, and presence of associated symptoms of malignancy were identified as important predictors upon stepwise logistic regression. The odds of cancer for nodules greater than 4 cm is 4 times higher than the odds for smaller nodules. Conclusion: In our finding, the prevalence of follicular carcinoma in patients with a cytologic diagnosis of follicular neoplasia is comparable to those reported in other pieces of literature. Clinical characteristics such as size of tumor, consistency of the nodule, and presence of associated symptoms of malignancy are still important predictors of malignancy.
Performance Evaluation for the Sustainable Supply Chain Management

Supply chain SC activities transform natural resources, raw materials, and components into various finished products that are delivered to end customers. A high efficient SC would bring great benefits to an enterprise such as integrated resources, reduced logistics costs, improved logistics efficiency, and high quality of overall level of services. In contrast, an inefficient SC will bring additional transaction costs, information management costs, and resource waste, reduce the production capacity of all enterprises on the chain, and unsatisfactory customer relationships. So the evaluation of a SC is important for an enterprise to survive in a competitive market in a globalized business environment. Therefore, it is important to research the various methods, performance indicator systems, and technology for evaluating, monitoring, predicting, and optimizing the performance of a SC. A typical procedure of the performance evaluation (PE) of a SC is to use the established evaluation performance indicators, employ an analytical method, follow a given procedure, to carry out quantitatively or qualitatively comparative analysis to provide the objective and accurate evaluation of a SC performance in a selected operation period. Various research works have been carried out in proposing the performance indicator systems and methods for SC performance evaluations. But there are no widely accepted indicator systems that can be applied in practical SC performance evaluations due to the fact that the indicators in different systems have been defined without a common understanding of the meanings and the relationships between them, and they are nonlinear and very complicated. In this chapter, the conception of SC performance is first presented, then relevant theories about supply chain performance evaluation are discussed and several common methods of supply chain performance evaluation are discussed. Finally, an evaluation model is presented for SC performance evaluation using 5 dimensional balanced scorecard (5DBSC) and Levenberg–Marquardt Back‐propagation (LM‐BP) neural network algorithm.
Neurogenic Interventions for Fear Memory via Modulation of the Hippocampal Function and Neural Circuits

Fear memory helps animals and humans avoid harm from certain stimuli and coordinate adaptive behavior. However, excessive consolidation of fear memory, caused by the dysfunction of cellular mechanisms and neural circuits in the brain, is responsible for post-traumatic stress disorder and anxiety-related disorders. Dysregulation of specific brain regions and neural circuits, particularly the hippocampus, amygdala, and medial prefrontal cortex, have been demonstrated in patients with these disorders. These regions are involved in learning, memory, consolidation, and extinction. These are also the brain regions where new neurons are generated and are crucial for memory formation and integration. Therefore, these three brain regions and neural circuits have contributed greatly to studies on neural plasticity and structural remodeling in patients with psychiatric disorders. In this review, we provide an understanding of fear memory and its underlying cellular mechanisms and describe how neural circuits are involved in fear memory. Additionally, we discuss therapeutic interventions for these disorders based on their proneurogenic efficacy and the neural circuits involved in fear memory.
Strategies for Enzymatic Inactivation of the Veterinary Antibiotic Florfenicol

Large quantities of the antibiotic florfenicol are used in animal farming and aquaculture, contaminating the ecosystem with antibiotic residues and promoting antimicrobial resistance, ultimately leading to untreatable multidrug-resistant pathogens. Florfenicol-resistant bacteria often activate export mechanisms that result in resistance to various structurally unrelated antibiotics. We devised novel strategies for the enzymatic inactivation of florfenicol in different media, such as saltwater or milk. Using a combinatorial approach and selection, we optimized a hydrolase (EstDL136) for florfenicol cleavage. Reaction kinetics were followed by time-resolved NMR spectroscopy. Importantly, the hydrolase remained active in different media, such as saltwater or cow milk. Various environmentally-friendly application strategies for florfenicol inactivation were developed using the optimized hydrolase. As a potential filter device for cost-effective treatment of waste milk or aquacultural wastewater, the hydrolase was immobilized on Ni-NTA agarose or silica as carrier materials. In two further application examples, the hydrolase was used as cell extract or encapsulated with a semi-permeable membrane. This facilitated, for example, florfenicol inactivation in whole milk, which can help to treat waste milk from medicated cows, to be fed to calves without the risk of inducing antibiotic resistance. Enzymatic inactivation of antibiotics, in general, enables therapeutic intervention without promoting antibiotic resistance.
Hybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data Classification

The hypercube optimization search (HOS) approach is a new efficient and robust metaheuristic algorithm that simulates the dove's movement in quest of new food sites in nature, utilizing hypercubes to depict the search zones. In medical informatics, the classification of medical data is one of the most challenging tasks because of the uncertainty and nature of healthcare data. This paper proposes the use of the HOS algorithm for training multilayer perceptrons (MLP), one of the most extensively used neural networks (NNs), to enhance its efficacy as a decision support tool for medical data classification. The proposed HOS-MLP model is tested on four significant medical datasets: orthopedic patients, diabetes, coronary heart disease, and breast cancer, to assess HOS's success in training MLP. For verification, the results are compared with eleven different classifiers and eight well-regarded MLP trainer metaheuristic algorithms: particle swarm optimization (PSO), biogeography-based optimizer (BBO), the firefly algorithm (FFA), artificial bee colony (ABC), genetic algorithm (GA), bat algorithm (BAT), monarch butterfly optimizer (MBO), and the flower pollination algorithm (FPA). The experimental results demonstrate that the MLP trained by HOS outperforms the other comparative models regarding mean square error (MSE), classification accuracy, and convergence rate. The findings also reveal that the HOS help the MLP to produce more accurate results than other classification algorithms for the prediction of diseases.
Characterization of the complete chloroplast genome of Achnatherum pekinense (Poaceae), a widespread weed

Abstract Achnatherum pekinense belongs to Poaceae. The complete chloroplast genome of A. pekinense was reported in this study. The chloroplast genome was 137,837 bp in size with a canonical quadripartite structure, including two inverted repeat regions (IR) of 21,635 bp for each, a large single-copy (LSC) region of 81,787 bp in length, and a small single-copy (SSC) region of 12,780 bp in length. The overall guanine-cytosine (GC) content of this chloroplast genome was 38.8%, and the corresponding values of the LSC, SSC, and IR regions were 36.9%, 33.1%, and 44.1%, respectively. A total of 113 unique genes were annotated in this chloroplast genome, including four rRNA genes, 31 tRNA genes, and 78 protein-coding genes. The phylogenetic analysis showed that A. pekinense was clustered with A. inebrians.
The role of sensory marketing and brand experience in building emotional attachment and brand loyalty in luxury retail stores

Prior studies on the in ‐ store experience focus on the impact of store atmosphere. Sensory marketing and brand experience, on the other hand, have been found to be significant in providing a better consumer experience in recent studies. Thus, the goal of this paper is to broaden the scope of this study by examining the causal effect of sensory marketing cues and brand experience on emotional attachment, and subsequent brand loyalty in a luxury retail store setting. We also studied the moderating role of store image in the relationships. To this end, the three separate but related studies (Study 1, N = 409, Study 2, N = 294, Study 3, N = 139) were conducted. Study 1 shows that sensory marketing cues positively contribute to enhancing the luxury retail brand experiences. Both sensory marketing and brand experiences appeared promising in increasing emotional attachment and subsequent brand loyalty. Study 2 findings further suggest that sensory marketing cues and brand experience interact significantly with store image to improve consumers' emotional attachment with luxury brands. Study 3 replicated the pattern observed in Studies 1 and 2 and further suggested that consumers in the luxury store setting expressed stronger emotional attachment and brand loyalty than consumers in the nonluxury store environment.
Characterizing discourses about COVID-19 vaccines on Twitter: a topic modeling and sentiment analysis approach

ABSTRACT Background Evidence-based health communication is crucial for facilitating vaccine-related knowledge and addressing vaccine hesitancy. To that end, it is important to understand the discourses about COVID-19 vaccination and attend to the publics’ emotions underlying those discourses. Methods We collect tweets related to COVID-19 vaccines from March 2020 to March 2021. In total, 304,292 tweets from 134,015 users are collected. We conduct a Latent Dirichlet Allocation (LDA) modeling analysis and a sentiment analysis to analyze the discourse themes and sentiments. Results This study identifies seven themes of COVID-19 vaccine-related discourses. Vaccine advocacy (24.82%) is the most widely discussed topic about COVID-19 vaccines, followed by vaccine hesitancy (22.29%), vaccine rollout (12.99%), vaccine facts (12.61%), recognition for healthcare workers (12.47%), vaccine side effects (10.07%), and vaccine policies (4.75%). Trust is the most salient emotion associated with COVID-19 vaccine discourses, followed by anticipation, fear, joy, sadness, anger, surprise, and disgust. Among the seven topics, vaccine advocacy tweets are most likely to receive likes and comments, and vaccine fact tweets are most likely to receive retweets. Conclusions When talking about vaccines, publics’ emotions are dominated by trust and anticipation, yet mixed with fear and sadness. Although tweets about vaccine hesitancy are prevalent on Twitter, those messages receive fewer likes and comments than vaccine advocacy messages. Over time, tweets about vaccine advocacy and vaccine facts become more dominant whereas tweets about vaccine hesitancy become less dominant among COVID-19 vaccine discourses, suggesting that publics become more confident about COVID-19 vaccines as they obtain more information.
Lycopene improves testicular damage and sperm quality in experimentally induced varicocele: Relationship with apoptosis, hypoxia, and hyperthermia

Abstract Varicocele is considered the main reason for male infertility. Antioxidants are common drugs used to reduce the complications of varicocele in these patients. So, we investigated the effects of lycopene on sperm quality, testicular histology, and the expression of some genes in experimentally induced varicocele. Fifty adult male Wistar rats were divided into three groups: control (n = 12), sham (n = 5), and varicocele (n = 33) groups. After 2 months of induced varicocele, five rats were randomly sacrificed and induced varicocele was investigated in each group. Finally, 35 rats were divided into five groups: the control, varicocele, varicocele reserving solvent, and varicocele reserving lycopene (4 and 10 mg/kg) for 2 months. At the end of the experiment, sperm viability, membrane integrity, the expression of Bax, Bcl2, hypoxia (hypoxia‐inducible factor 1α [HIF1‐α]), heat‐shock protein (heat‐shock protein A2 [HSPA2]) genes, and the histology of testes were measured. The results showed a significant decrease in the sperm viability, membrane integrity, Johnson's score, and the expression of the Bcl2 gene in the varicocele group compared to the control group. Also, there was a significant increase in Bax, HSPA2, and HIF1‐α expressions in the varicocele group compared to the control group. Although the administration of lycopene (10 mg/kg) in rats with varicocele improved sperm viability and membrane integrity, Johnson's score, and Bax expression compared to the varicocele group. Our findings indicated that the administration of lycopene in the varicocele group improved sperm quality and testicular injury induced by varicocele via decreasing apoptosis.
Morphological Computation as Morphogenesis: From Leibniz and Goethe to René Thom and Beyond

: The paper is intended as an exploration of the potential methodology for hypercomputing using the long intellectual experience of the study of morphology and morphogenesis. The ﬁrst part of the paper shows that the missing element of the process of computation in the studies searching for the generalization of computation is encoding. Turing Machines can work only on information encoded in a very speciﬁc way. When we look for ways to generalize computation, the analysis of encoding is the most likely source of ideas for the extension of this concept. The second part of the paper is an exploration of the history of morphogenesis from the perspective of the search for a method of unconventional encoding information.
Assessing the Transmissibility of the New SARS-CoV-2 Variants: From Delta to Omicron

Omicron, the latest SARS-CoV-2 Variant of Concern (VOC), first appeared in Africa in November 2021. At present, the question of whether a new VOC will out-compete the currently predominant variant is important for governments seeking to determine if current surveillance strategies and responses are appropriate and reasonable. Based on both virus genomes and daily-confirmed cases, we compare the additive differences in growth rates and reproductive numbers (R0) between VOCs and their predominant variants through a Bayesian framework and phylo-dynamics analysis. Faced with different variants, we evaluate the effects of current policies and vaccinations against VOCs and predominant variants. The model also predicts the date on which a VOC may become dominant based on simulation and real data in the early stage. The results suggest that the overall additive difference in growth rates of B.1. 617.2 and predominant variants was 0.44 (95% confidence interval, 95% CI: −0.38, 1.25) in February 2021, and that the VOC had a relatively high R0. The additive difference in the growth rate of BA.1 in the United Kingdom was 6.82 times the difference between Delta and Alpha, and the model successfully predicted the dominating process of Alpha, Delta and Omicron. Current vaccination strategies remain similarly effective against Delta compared to the previous variants. Our model proposes a reliable Bayesian framework to predict the spread trends of VOCs based on early-stage data, and evaluates the effects of public health policies, which may help us better prepare for the upcoming Omicron variant, which is now spreading at an unprecedented speed.
Expression of Irisin/FNDC5 in Breast Cancer

Irisin is a myokine formed from fibronectin type III domain-containing protein 5 (FNDC5), which can be found in various cancer tissues. FNDC5 and irisin levels have been poorly studied in the tumor tissues of breast cancer (BC). The aim of this study was to determine the levels of irisin expression in BC tissues and compare them to clinicopathological factors and Ki-67 and PGC-1α expression levels. Tissue microarrays (TMAs) with 541 BC tissues and 61 samples of non-malignant breast disease (NMBD; control) were used to perform immunohistochemical reactions. FNDC5 gene expression was measured in 40 BC tissue samples, 40 samples from the cancer margin, and 16 NMBD samples. RT-PCR was performed for the detection of FNDC5 gene expression. Higher irisin expression was found in BC patients compared to normal breast tissue. FNDC5/irisin expression was higher in patients without lymph node metastases. Longer overall survival was observed in patients with higher irisin expression levels. FNDC5/irisin expression was increased in BC tissues and its high level was a good prognostic factor for survival in BC patients.
Clinical and Laboratory Characteristics of Normal Weight and Obese Individuals with Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) has had, over the past few decades, a progressively growing prevalence among the general population all over the world, in parallel with metabolic conditions such as type 2 diabetes mellitus (T2DM), dyslipidemia, and obesity. However, NAFLD is also detected in 10–13% of subjects with a body mass index (BMI) ≤ 25 kg/m² (lean-NAFLD), whose major risk factors remain unknown. In this study, we aimed to characterize the clinical features and associated risk factors of lean-NAFLD in comparison with obese-NAFLD patients. Consecutive patients diagnosed with NAFLD by vibration-controlled transient elastography and controlled attenuation parameter were prospectively enrolled. Biological and clinical data obtained from the participants were stratified according to their BMI in two groups: lean-NAFLD and obese-NAFLD. In total, 331 patients (56.8% males) were included in the final analysis. Most of the subjects were obese-NAFLD (n = 258, 77.9%) and had a higher prevalence of T2DM, dyslipidemia, and components of the metabolic syndrome, together with abnormal biological parameters. Regarding liver stiffness measurements, the proportion of subjects with at least significant fibrosis (≥F2) was approximately twofold higher among obese-NAFLD (43.81%) in comparison with lean-NAFLD patients (23.29%). Moreover, obese individuals had a higher risk for liver fibrosis (OR = 2.6, 95%, CI 1.5–4.42, p < 0.001) than lean individuals. Although associated metabolic conditions and at least significant liver fibrosis were present in approximately one-quarter of the patients, these were more frequent among obese-NAFLD patients. Therefore, individualized screening strategies for NAFLD should be established according to BMI.
New downscaling prediction models for spring drought in China

Accurate prediction of spring drought in China is helpful toward reducing associated agricultural losses. In this study, spring drought is defined as the three‐month Standardized Precipitation Evapotranspiration Index (SPEI) ending in May. Based on the year‐to‐year increment and downscaling method, three single‐predictor prediction models (P1 model, P2 model, and P3 model) and two multi‐predictor models for spring drought at 677 stations in China are developed for the period 1983–2020. As physical processes affecting spring drought in the China region, the tropical Pacific–Indian Ocean sea surface temperature (SST) in winter, the Davis Strait–Barents sea‐ice concentration (SIC) in winter, and the 500‐hPa spring vertical velocity, predicted by the Climate Forecast System, version 2 (CFSv2), are considered in the prediction models. The prediction skill of the downscaling models for the spring SPEI is measured by cross‐validation for the period 1983–2020. The CFSv2 model only shows convincing prediction skill for the spring SPEI at 35 of 677 stations. However, among the 677 stations, the temporal correlation coefficient between the observed and predicted spring SPEI at 621 stations for P1 model, 598 stations for P2 model, 545 stations for P3 model, 674 stations for the statistical downscaling model (SD model), and 675 stations for the hybrid downscaling dynamical–statistical prediction model (HD model) exceeds the 95% confidence level. Therefore, compared to the CFSv2 model, the prediction skill for spring drought is improved by the single‐predictor and multi‐predictor models. The prediction skill of HD model for spring drought, which combines preceding observational predictors and the simultaneous predictor of the CFSv2 model, is higher than that of SD model. In addition, the severe drought that occurred in Northeast and North China in spring 2017 can be successfully predicted by HD model.
A Two-Branch Convolutional Neural Network Based on Multi-Spectral Entropy Rate Superpixel Segmentation for Hyperspectral Image Classification

Convolutional neural networks (CNNs) can extract advanced features of joint spectral–spatial information, which are useful for hyperspectral image (HSI) classification. However, the patch-based neighborhoods of samples with fixed sizes are usually used as the input of the CNNs, which cannot dig out the homogeneousness between the pixels within and outside of the patch. In addition, the spatial features are quite different in different spectral bands, which are not fully utilized by the existing methods. In this paper, a two-branch convolutional neural network based on multi-spectral entropy rate superpixel segmentation (TBN-MERS) is designed for HSI classification. Firstly, entropy rate superpixel (ERS) segmentation is performed on the image of each spectral band in an HSI, respectively. The segmented images obtained are stacked band by band, called multi-spectral entropy rate superpixel segmentation image (MERSI), and then preprocessed to serve as the input of one branch in TBN-MERS. The preprocessed HSI is used as the input of the other branch in TBN-MERS. TBN-MERS extracts features from both the HSI and the MERSI and then utilizes the fused spectral–spatial features for the classification of HSIs. TBN-MERS makes full use of the joint spectral–spatial information of HSIs at the scale of superpixels and the scale of neighborhood. Therefore, it achieves excellent performance in the classification of HSIs. Experimental results on four datasets demonstrate that the proposed TBN-MERS can effectively extract features from HSIs and significantly outperforms some state-of-the-art methods with a few training samples.