Miracle-dz/UniFAR-FTUs huggingface dataset:
Fully automated radiomic screening pipeline for osteoporosis and abnormal bone density with a deep learning-based segmentation using a short lumbar mDixon sequence.

Background Although lumbar bone marrow fat fraction (BMFF) has been demonstrated to be predictive of osteoporosis, its utility is limited by the requirement of manual segmentation. Additionally, quantitative features beyond simple BMFF average remain to be explored. In this study, we developed a fully automated radiomic pipeline using deep learning-based segmentation to detect osteoporosis and abnormal bone density (ABD) using a <20 s modified Dixon (mDixon) sequence. Methods In total, 222 subjects underwent quantitative computed tomography (QCT) and lower back magnetic resonance imaging (MRI). Bone mineral density (BMD) were extracted from L1-L3 using QCT as the reference standard; 206 subjects (48.8±14.9 years old, 140 females) were included in the final analysis, and were divided temporally into the training/validation set (142/64 subjects). A deep-learning network was developed to perform automated segmentation. Radiomic models were built using the same training set to predict ABD and osteoporosis using the mDixon maps. The performance was evaluated using the temporal validation set comprised of 64 subjects, along with the automated segmentation. Additional 25 subjects (56.1±8.8 years, 14 females) from another site and a different scanner vendor was included as independent validation to evaluate the performance of the pipeline. Results The automated segmentation achieved an outstanding mean dice coefficient of 0.912±0.062 compared to manual in the temporal validation. Task-based evaluation was performed in the temporal validation set, for predicting ABD and osteoporosis, the area under the curve, sensitivity, specificity, and accuracy were 0.925/0.899, 0.923/0.667, 0.789/0.873, 0.844/0.844, respectively. These values were comparable to that of manual segmentation. External validation (cross-vendor) was also performed; the area under the curve, sensitivity, specificity, and accuracy were 0.688/0.913, 0.786/0.857, 0.545/0.944, 0.680/0.920 for ABD and osteoporosis prediction, respectively. Conclusions Our work is the first attempt using radiomics to predict osteoporosis with BMFF map, and the deep-learning based segmentation will further facilitate the clinical utility of the pipeline as a screening tool for early detection of ABD.
Genome-wide characterization of Mariner-like transposons and their derived MITEs in the Whitefly Bemisia tabaci (Hemiptera: Aleyrodidae)

Abstract The whitefly, Bemisia tabaci is a hemipteran pest of vegetable crops vectoring a broad category of viruses. Currently, this insect pest showed a high adaptability and resistance to almost all the chemical compounds commonly used for its control. In many cases, transposable elements (TEs) contributed to the evolution of host genomic plasticity. This study focuses on the annotation of Mariner-like elements (MLEs) and their derived Miniature Inverted repeat Transposable Elements (MITEs) in the genome of B. tabaci. Two full-length MLEs belonging to mauritiana and irritans subfamilies were detected and named Btmar1.1 and Btmar2.1, respectively. Additionally, 548 defective MLE sequences clustering mainly into 19 different Mariner lineages of mauritiana and irritans subfamilies were identified. Each subfamily showed a significant variation in MLE copy number and size. Furthermore, 71 MITEs were identified as MLEs derivatives that could be mobilized via the potentially active transposases encoded by Btmar 1.1 and Btmar2.1. The vast majority of sequences detected in the whitefly genome present unusual terminal inverted repeats (TIRs) of up to 400 bp in length. However, some exceptions are sequences without TIRs. This feature of the MLEs and their derived MITEs in B. tabaci genome that distinguishes them from all the other MLEs so far described in insects, which have TIRs size ranging from 20 to 40 bp. Overall, our study provides an overview of MLEs, especially those with large TIRs, and their related MITEs, as well as diversity of their families, which will provide a better understanding of the evolution and adaptation of the whitefly genome.
The usefulness of graphic illustrations in online dictionaries

Abstract The aim of the paper is to investigate the usefulness of different illustration formats in online English learners’ dictionaries for the accuracy and speed of meaning comprehension as well as immediate and delayed retention. In a controlled online experiment, the meaning of selected English words and phrases had to be explained with the help of purpose-built monolingual dictionary entries. Four experimental conditions were created, which reflected the presence and format of illustrations in the entries: color pictures, greyscale pictures, line drawings, no illustrations. Meaning retention was checked immediately after exposure and two weeks later. The results show that it is worthwhile to include illustrations in online learners’ dictionaries and suggest the most beneficial illustration formats. Line drawings prove the most recommendable; they considerably improve meaning comprehension, reduce reception time, and stimulate the best immediate and delayed retention. Color pictures emerge as the second best. They produce results comparable with those for line drawings, except they do not help so much to remember meaning in the long run. Entries with greyscale pictures are the least recommendable. They do not contribute more to meaning comprehension and delayed retention. Yet, they even shorten reception time and help users to remember more words immediately after exposure.
Predictive Value of Decreased Serum Estradiol Levels Following Human Chorionic Gonadotropin Trigger For Unexpected Ovulation During In Vitro Fertilization Cycle of Patients With Poor Ovarian Response

Background Unexpected spontaneous ovulation typically occurs in patients with poor ovarian response (POR); however, reliable predictors are lacking. We analyzed explore the predictive value of decreased serum estradiol (E2) levels on the day after human chorionic gonadotropin (hCG) trigger during in vitro fertilization (IVF) cycles. Methods We retrospectively analyzed clinical data (n = 978) in IVF cycles between 2017 and 2020. According to decreased ratios of E2 levels (△E2%) between day 1 and day 0 of hCG trigger, patients were divided into: ≤10% (group A), 10–20% (group B), and ≥ 20% (group C). Basic characteristics and laboratory data were compared using analysis of variance or Mann–Whitney U-test. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to assess the relationship between △E2% and spontaneous unexpected ovulation. Fresh embryo transfer cycles in groups A, B, and C were subgrouped as A1, B1, and C1, respectively; clinical outcomes were compared. Results Mean E2 and luteinizing hormone (LH) levels, ratio of premature LH peak and high progesterone levels, time interval between hCG trigger and oocyte retrieval, number of follicles ≥ 11 mm in diameter, and dose regimen of gonadotropin among the three groups showed significant differences (P < 0.05). After adjusting for relevant confounders, likelihood ratios of unexpected ovulation in groups B and C were 2.96 (95 % confidence interval [CI]: 0.64–13.70), and 11.74 (95 % CI: 3.09–44.62), respectively, compared with that in group A. The test for trend was significant before and after correction (P for trend < 0.05). On combining premature LH peak (> 10 mIU/mL) or high progesterone level (> 1.0 ng/mL) on the day of hCG, area under ROC curve (AUC) was 0.849 (95 % CI, 0.764–0.934, P < 0.05). However, when we only considered △E2%, AUC was 0.738 (95 % CI. 0.562–0.914, P < 0.05). Rates of embryo implantation, clinical pregnancy, live birth in fresh embryo transfer cycle, and early abortion among the three subgroups did not differ significantly (P > 0.05). Conclusions E2 levels on the day after hCG trigger are valuable for predicting unexpected ovulation. However, their decreased levels do not directly affect clinical pregnancy outcomes.
Community-Centers in Israel during the COVID-19 Pandemic—A Collective Impact

The COVID-19 epidemic is a natural disaster crisis on a global scale. Despite the extensive experience in emergency operations that the Israeli community-centers have accumulated during times of war, community centers were required to develop original methods in order to make their services accessible to the communities, especially in these times of social distance and quarantines. This study examined the implementation of three collaboration models that enabled joint action whether multi or single sector, using the unique assets of each sector or organization. Three models of collaborations aimed at producing a common social impact were examined: 1) The wisdom of the crowd; 2) Strategic cross-sectoral partnerships; and 3) Collective impact. Qualitative data was collected from community centers in five local authorities, in an attempt to determine which model was the most influential in the community centers’ performance throughout the crisis. Interviews were conducted with ten senior managers in the community, as the performance of community centers throughout the pandemic was highly dynamic and could best be captured using this method. Results showed that the model of Collective Impact provided the most desired outcomes among the investigated models. Another important finding highlights the importance of the managers’ experience in the community centers’ performance. Based on these findings, conclusions and recommendations for future operations of third-sector organizations were drawn.
Faculty in the Applied and Pure Sciences May Have Limited Experience with E-books

A Review of: Bierman, J., Ortega, L. , & Rupp-Serrano, K. (2010). E-book usage in pure and applied sciences. Science & technology libraries, 29(1-2), 69-91. https://doi.org/10.1080/01942620903579393 Abstract Objective – To determine the usage of and attitudes toward e-books among faculty in the applied and pure sciences. Design – Online survey and in-person interviews. Setting – A large public university in the United States. Subjects – 11 faculty members. Methods – Participants completed an 11-item survey covering demographic data and questions about electronic book experience and preferences. This was followed up by an in-person interview with the researchers. The interviews were structured into three sections: opening questions about e-book usage, an interactive demonstration and discussion of two preselected e-books, and final follow-up questions. Interviews followed a general script of prepared questions, but also encouraged open discussion and dialogue. Main Results – Most participants in the study reported limited experience with e-books and only 3 of the 11 participants reported using library-purchased e-books in their research and instruction. Participants noted ease of access and searchability as key advantages of e-books. Concerns included the belief that reading and learning is more difficult on a desktop computer, as well as concerns about the stability and reliability of e-book access. Participants also felt negatively about the necessity to create a new login profile and password to access e-books. The study found no difference in the way faculty in pure and applied sciences approached e-books. Conclusion – The authors determine that e-books will likely become more commonly used in academia. Users want e-books that are easy to use and customizable. In addition, the authors conclude that librarians need to understand their patrons’ needs as e-book users and proactively promote and market their e-book collections.
Segmentação de Imagens Infravermelhas Para Detecção do Câncer de Mama Utilizando U-NET CNN

O câncer de mama é o principal tipo de câncer entre as mulheres. De acordo com o World Cancer Research Fund, em 2018, mais de 2 milhões de novos casos foram detectados em todo o mundo. Apesar de sua alta ocorrência, a detecção precoce proporciona um melhor prognóstico e auxilia no aumento da sobrevida do paciente oncológico. Avanços signiﬁcativos nas técnicas de rastreamento, como as imagens infravermelhas, forneceram uma maneira barata e menos invasiva forma de detectar a doença. Além disso, ferramentas computacionais podem ser utilizadas para auxiliar os médicos a fornecerem um melhor diagnóstico. Assim, este artigo apresenta um método de segmentação baseado em Redes Neurais Convolucionais U-Net. Em contraste com o estado da arte, as abordagens de aprendizado de máquina têm se mostrado eﬁcientes para a segmentação da região de interesse deste trabalho, atingindo uma acurácia de 98,24% e uma Intersecção-Sobre-União de 94,38%. O uso deste método de segmentação pode ser muito útil para tarefas de classiﬁcação, uma vez que a região de interesse é bem delimitada para extração de características.
Adaptive Transfer Learning Based on a Two-Stream Densely Connected Residual Shrinkage Network for Transformer Fault Diagnosis over Vibration Signals

Vibration signal analysis is an efficient online transformer fault diagnosis method for improving the stability and safety of power systems. Operation in harsh interference environments and the lack of fault samples are the most challenging aspects of transformer fault diagnosis. High-precision performance is difficult to achieve when using conventional fault diagnosis methods. Thus, this study proposes a transformer fault diagnosis method based on the adaptive transfer learning of a two-stream densely connected residual shrinkage network over vibration signals. First, novel time-frequency analysis methods (i.e., Synchrosqueezed Wavelet Transform and Synchrosqueezed Generalized S-transform) are proposed to convert vibration signals into different images, effectively expanding the samples and extracting effective features of signals. Second, a Two-stream Densely Connected Residual Shrinkage (TSDen2NetRS) network is presented to achieve a high accuracy fault diagnosis under different working conditions. Furthermore, the Residual Shrinkage layer (RS layer) is applied as a nonlinear transformation layer to the deep learning framework to remove unimportant features and enhance anti-interference performance. Lastly, an adaptive transfer learning algorithm that can automatically select the source data set by using the domain measurement method is proposed. This algorithm accelerates the training of the deep learning network and improves accuracy when the number of samples is small. Vibration experiments of transformers are conducted under different operating conditions, and their results show the effectiveness and robustness of the proposed method.
Community, Competition, and Citizen Science: Voluntary Distributed Computing in a Globalized World

Dysphagia and resulting pulmonary sequelae are frequently observed in children with spastic cerebral palsy (SCP). However, physiological evidence regarding airway protective behaviors (specifically swallowing and cough function) in these children is sparse. The aim of this investigation was to quantify specific feeding, swallowing and cough impairments in children with SCP compared to controls. Eleven children with SCP (mean age: 7 + 2 years; GMFCS: I-V; MACS: I-V) and 10 age-matched controls participated. Clinical feeding and swallowing performance was evaluated with the Dysphagia Disorder Survey (DDS) using standardized liquid, puree, and chewable solid consistencies. Suprahyoid muscle activity and respiratory-swallow patterns were assessed with simultaneous surface electromyography and respiratory inductance plethysmography as children swallowed the various consistencies. Voluntary cough airflow measures were also obtained. Nonparametric tests were used for group comparisons and correlational analyses. Compared to controls, children with SCP demonstrated more signs of clinical feeding and swallowing impairment ( p < .0001, η 2 = .771), heightened suprahyoid muscle activity for puree ( p = .014, η 2 = .305) and chewable solids ( p = .001, η 2 = .528), more frequent post-swallow inhalation across liquid ( p = .005, η 2 = .401), puree ( p = .014, η 2 = .304) and chewable solids ( p = .035, η 2 = .223), and lower cough volume acceleration ( p = .019, η 2 = .289). Post-swallow inhalation for chewable solids was correlated with the DDS Part 1 ( r s = .734, p = .010), DDS Part 2 ( r s = .610, p = .046) and the DDS Total scores ( r s = .673, p = .023). This study is the first to provide evidence of specific physiological deficits of both swallowing and voluntary cough in children with SCP. Potential hypotheses explaining these deficits and implications for physiologically driven management are explored.
The Protective Role of Garlic Aqueous Extract (Allium sativum) against 950MHz Electromagnetic Field Induced Rats Brain Damage

DS P ysico-Chemical and Organolyptical Cha eristics Of Cake Fortified By Irradiated Broccoli (Brassica Oleracea L.Var Italica) Powder Khalaf, H.H. A.1; El Saadani, R.M.A.1; Mervat, M. Anwar2 and Hamed Aly2 Received: 10/01/2018 Accepted: 13/02/2018 E.mail:hamedaly86@gmail.com Broccoli Heads Powder, Broccoli Leaves Powder, Low Caloric Cake, Replacement, Total Phenolic Compounds, Antioxidant, Physical Properties, Sensory Properties. J. Nucl. Tech. Appl. Sci, Vol. 6, No. 1, PP. 15 : 32 (2018) Journal of NUCLEAR Technology in Applied Science ISSN 2314-8209 e-ISSN 2314-8217 F d Tech. Dept., Fac. of Agric. , Benha Un v. , Egypt. 2. Plant Research Department, Nuclear Research Center, Atomic Energy Authority, Inshas, 13759, Egypt. The present study aims to improve the antioxidant and antimicrobial properties of cake and produce low calorie cake through substitution of wheat flour (WF) by irradiated broccoli (Brassica oleracea L.var italica) powder. In this study broccoli heads powder and broccoli leaves powder were gamma irradiated at dose levels of 0, 3, 5 and 7 kGy. Results showed that ethanolic (70%) extract of irradiated broccoli heads powder (IBHP) and irradiated broccoli leaves powder (IBLP) at a dose level of 5 kGy had higher total phenolic compounds (TPC) and antioxidant activity (AOA) compared to control and other doses. Thus, IBHP and IBLP at dose level of 5 kGy were selected for fortification of cake. IBHP was used to substitute (0, 1.5, 3, and 4.5 %) of WF in making cake, as well, replacement of WF (0, 1, 2 and 3%) by IBLP. The results showed that the cake processed from IBHP and IBLP had pronounced improvement (%) in its chemical composition (protein, lipids, ash and fiber content) while, the energy value and carbohydrate content decreased with increasing the replacement level. Also, the results showed that the TPC content, AOA, volume and specific volume were increased by increasing substitution level of IBHP and IBLP compared to control samples. On the other hand, total intensity, L*and a* values of the crust and crumb were decreased, whereas Chroma and b* values were increased for crumb and decreased for crust for all cake treatments by the addition of IBHP and IBLP compared to control sample. For microbiological properties, the results J. Nucl. Tech. Appl. Sci., Vol. 7, PP. 9 : 21 ( 019) ( 10 ) Shoman, H.M. et al. J. Nucl. Tech. Appl. Sci., Vol. 7, 2019
Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles

In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC.
Molecular dynamics simulations of the hydrogen embrittlement base case: atomic hydrogen in a defect free single crystal

ABSTRACT While significant research has been conducted on the various mechanisms of hydrogen embrittlement, there remains a lack of quantitative understanding on the effect of atomic hydrogen concentration on mechanical properties. Previous work suggests that an increased hydrogen concentration will degrade both the elastic modulus and yield stress. However, experimental samples often contain other atomistic defects that make it difficult to determine the role hydrogen alone plays on material behaviour. Further, experimental studies are often unable to directly quantify the effect of hydrogen concentration on modulus. The purpose of this study was to use molecular dynamics simulations to quantify the effect of interstitial hydrogen on the mechanical properties of iron. The potential type used was shown to significantly affected predicted results. Atomic hydrogen was shown to linearly degrade the elastic modulus and stress to initiate dislocations at all temperatures considered. Increasing hydrogen concentration was shown to promote the formation of dislocations at a lower stress, resulting in a higher density of dislocations and shorter slip distances. This study provides a foundation for better understanding of the role of hydrogen on the degradation of mechanical properties during loading.
Actions and Therapeutic Potential of Madecassoside and Other Major Constituents of Centella asiatica: A Review

Centella asiatica is a popular herb well-known for its wide range of therapeutic effects and its use as a folk medicine for many years. Its therapeutic properties have been well correlated with the presence of asiaticoside, madecassoside, asiatic and madecassic acids, the pentacyclic triterpenes. The herb has been extensively known to treat skin conditions; nevertheless, several pre-clinical and clinical studies have scientifically demonstrated its effectiveness in other disorders. Among the active constituents that have been identified in Centella asiatica, madecassoside has been the subject of only a relatively small number of scientific reports. Therefore, this review, while including other major constituents of this plant, focuses on the therapeutic potential, pharmacokinetics and toxicity of madecassoside.