Miracle-dz/UniFAR-FTUs huggingface dataset:
Gene Expression of Flavanone 3-Hydroxylase (F3H), Anthocyanidin Synthase (ANS), and p-Coumaroyl Ester 3-Hydroxilase (C3H) in Tzimbalo Fruit

The current investigation emphasises the expression of candidate genes for the future improvement of fruit quality; the objectives were to describe morphological variation on Solanum caripense Dunal (tzimbalo) ecotypes; identify gene expression of F3H and ANS; and analyse gene expression of C3H. Ecuadorian (BIO) and Peruvian (IBT) samples were utilized. Morphological descriptors for Solanum muricatum Aiton were used; RNA was isolated for identification of F3H and ANS transcripts in BIO-Ltg1 and BIO-Cyb1 through reverse transcription followed by semiquantitative PCR (RT-PCR); C3H relative expression was analysed in IBT-Lib1 for zero, five and fourteen days under influence of controlled conditions (10 ± 2 °C; 16 h day/8 h night) through reverse transcription followed by quantitative PCR (RT-qPCR). The cophenetic correlation (0.88) of conglomerate analysis (CA) pointed out good similarity for Ecuadorian ecotypes and two subgroups for Peruvian ecotypes. The first three principal components (PC) explained qualitatively 71.39% and quantitatively 81.34% of total variation; Fr-Flavour, Se-Diameter, Fl-CorollaColour, Fr-stripes, Fr-Length, Fr-PlacentLength and Fr-PlacentBreadth were characters that contributed more to the variability. The expression of F3H was identified in BIO-Ltg1. The expression of ANS was similar (BIO-Ltg1→48.20 ng/µL; BIO-Cyb1→36.19 ng/µL). The value of the mean fold change in C3H expression was 3.32, 4.52 and 6.24 for zero, five and fourteen days; C3H transcripts level was significantly different and increased 2.92 units after fourteen days. These results demonstrate the expression of F3H and ANS in BIO-Ltg1 and BIO-Cyb1, differential expression of C3H in IBT-Lib1, and focus the nutritional value of tzimbalo fruit.
Assessment of INPP4B Expression Level in Acute Myeloid Leukemia Patients and Its Prognostic Significance. An Egyptian Study

Background: Acute myeloid leukemia (AML) is a complex and heterogeneous hematopoietic tissue neoplasm caused by gene mutations, chromosomal rearrangement and deregulation of gene expression. Inositol polyphosphate 4-phosphatase type II (INPP4B) is a clinically relevant factor in the phosphoinositide 3 kinase (PI3K) pathwayassociated cancers, has been found to have a bad prognostic role in AML. However, the exact mechanism is still unclear. Objective: We aimed in this study to investigate the prognostic role of INPP4B overexpression in Egyptian AML patients. Patients and methods: A total of 80 patients with newly diagnosed AML were included in the study. In addition, 40 apparently healthy, age and sex matched subjects served as control group. Immunophenotyping, cytogenetic analysis and quantitative assessment of INPP4B gene transcript were performed using real time PCR. Results: INPP4B overexpression was detected in 27.5 % of newly diagnosed AML patients. There was a statistically significant decrease in the probability of achieving complete remission (CR) with shorter overall survival (OS), event free survival (EFS) and disease-free survival (DFS) in the INPP4B high expression group compared with the low expression group (p=0.003, 0.03, 0.02 and <0.001; respectively). Conclusion: We can conclude that INPP4B overexpression is associated with poor response to therapy with poor outcome, and add prognostic value in patients with AML. INPP4B overexpression could be a valuable tool for making therapeutic decisions.
Calculation of Field Dependent Mobility in MoS2 and WS2 with Multi-Valley Monte Carlo Method

Since the two dimensional (2D) material are found to have potential for transistor application due to their suppression in short channel effect, they have attracted significant attention from worldwide researchers, and gradually demonstrate the potential for the next-generation transistors. In all of 2D materials, transition metal dichalcogenide (TMD) monolayers are the most popular ones because they have wider bandgap to provide better on/off ratio, and also have good mobility in theoretical prediction [1] . In recent years, there are some groups using TMD monolayers to make transistors and getting on/off ratio larger than 10 7 [2] . However, due to difficulties to grow perfect 2D monolayer, mobility reported by experiments is much lower than the theoretical prediction. The real intrinsic transport properties of TMD materials are still unclear. As a result, it is needed to be further discussed. There are several ways to calculate mobility. One common way is Boltzmann transport equation. Another way is Monte Carlo approach, which can simulate the movement of carriers in materials. It can calculate the velocity under different electric fields and also saturation velocity. As transistors get smaller, besides low-field mobility, high-field velocity becomes more critical. As a result, in this work, Monte Carlo method is adopted to simulate transport of electrons in monolayer MoS 2 and WS2 2 . We will discuss the electron velocity under different electric fields and also discuss the influence of satellite Q valleys on electron transport in both TMD materials.
Optical Wireless Performance Monitoring Using Asynchronous Amplitude Histograms

Optical performance monitoring (OPM) aims to estimate the amount of distortion in optical networks. Its importance relies on building robust and efficient networks with self and dynamic diagnosis. In this work, channel impairment monitoring is investigated for optical wireless communication. The monitoring is achieved using a support vector machine (SVM) regressor. A cost-effective and straightforward acquisition system is used to build the training features, which are asynchronous amplitude histograms. Three different parameters related to three common channel impairments are monitored using these features:optical signal-to-noise ratio (OSNR), visibility range, and $\xi$ parameter related to pointing error. First, each parameter is monitored when there is only one isolated channel impairment. Then, each parameter is monitored when two and three jointly channel impairments occur. The results showed that using this low complex machine learning (ML) technique, the achieved prediction accuracy was very high ($>$0.98) for most channel conditions except for the monitoring of the OSNR parameter, where the prediction accuracy dropped to 0.86 under harsh channel conditions. Moreover, the superiority of ML-based techniques is compared with non-ML-based techniques for the OSNR parameter monitoring. The results indicated that the ML-based technique achieved high prediction accuracy than the non-ML-based technique, especially for harsh channel conditions.
Resilient Impulsive Control for Second-Order Consensus Under Malicious Nodes

In this brief, we investigate the problem of resilient consensus in continuous-time second-order networks, where some nodes might be faulty or adversarial. Despite the existence of such malicious and unexpected behaviors, the normal agents still aim to reach an agreement. Towards this end, a resilient impulsive algorithm is proposed, in which the signal transmission among agents occurs with aperiodic intervals. At each sampling time, the normal agent removes the most extreme values in the neighborhood and then derives its control signal with the remaining ones. We show the influence of the malicious nodes on the normal ones would be limited by this manner. Sufficient conditions related to the network topology and tolerable number of misbehaving nodes are finally established to achieve the resilient consensus.
High Performance Balanced Bandpass Filters With Wideband Common Mode Suppression

Two compact and high selectivity balanced bandpass filters with excellent common mode rejection are designed in this brief. Compared with the half-wavelength microstrip transmission line, 180° phase shift is realized by two small swap structures, which are composed by double-sided parallel-strip line (DSPSL), and this way can easily realize a wideband common mode rejection with compact circuit size. Two out-of-band transmission zeros are introduced by the dual-mode ring resonator in the first proposed structure; thus, high selectivity can be achieved. In the second filter, shorted/open coupled microstrip lines are added between the dual-mode ring resonator and 180° swap structure, two additional transmission zeros can be achieved. Finally, two prototypes center at 2.2 GHz are fabricated, and the measured 3-dB bandwidths for the differential mode are 23.6% and 11.8%, respectively.
Accurate and Fast Subdomain Model for Electromagnetic Design Purpose of Wound-Field Linear Resolver

Linear resolvers are position sensors that are being used in a wide range of applications for linear motion systems. The electromagnetic design of a linear resolver, similar to any other electromagnetic device, demands an accurate, functional, and fast tool. While the finite element method (FEM) is considered the most accurate tool for electromagnetic analysis, it is not a good candidate for an optimization-based design which is an iterative process. In this article, first off, an analytical tool based on the subdomain model is briefly presented. This model is very accurate and much faster than FEM. However, it has high computational time for iterative design and optimization processes. Therefore, in the second step, simplification assumptions are utilized to make the model even faster without compromising the accuracy of the model. In this regard, model numbers 1–4 are developed and then compared to each other in terms of computational time and accuracy. Time stepping FEM (TSFEM) is employed as the reference for comparison between different models. A prototype of the linear resolver is then fabricated to show the validity of the study. Close agreement between simulations and experimental results demonstrates the competency of the study.
Effect of Pressure and Temperature on Dielectric Response of Silicone Composites

Use of lightweight polymeric materials in aircraft insulation, leads to reduction in overall weight as well as aids in improved performance. However, for successful implementation of these polymeric materials, specific studies in terms of, improvement in processability, performance evaluation due to variation in pressure and temperature, are required. In this study, silicone rubber filled with silica of different particle size; micrometer and nanometer are considered, and the fillers are loaded to the maximum possible weight percent. An in-house built electrostatic disperser is employed to homogeneously disperse the fillers within the silicone matrix. The thermal and dielectric properties are compared and analyzed with changes in temperature and pressure.
Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach

Global navigation satellite system-reflectometry (GNSS-R) can retrieve Earth's surface parameters, such as soil moisture (SM) using the reflected signals from GNSS constellations with advantages of noncontact, all-weather, real-time, and continuity, particularly the space-borne cyclone GNSS (CYGNSS) mission. However, the accuracy and efficiency of SM estimation from CYGNSS still need to improve. In this article, the global SM is estimated using machine learning (ML) regression aided by a preclassification strategy. The total observations are classified by land types and corresponding subsets are built for constructing ML regression submodels. Ten-fold cross-validation technique is adopted. The overall performance of SM estimation with/without preclassification is compared, and the results show that the SM estimations using different ML algorithms all have substantial improvement with the preclassification strategy. Then, the optimal XGBoost predicted model with root-mean-square error (RMSE) of 0.052 cm3/cm3 is adopted. In addition, the satisfactory daily and seasonal SM prediction outcomes with an overall correlation coefficient value of 0.86 and an RMSE value of 0.056 cm3/cm3 are achieved at a global scale, respectively. Furthermore, the extensive temporal and spatial variations of CYGNSS SM predictions are evaluated. It shows that the reflectivity plays a main role among the predictors in SM estimation, and the next is vegetation. In some extremely dry places, the roughness may become more important. The value of SM is positively correlated with RMSE and also another limit condition that will constrain the variation of predictors, thus affecting correlation coefficient R and RMSE. Also, we compare both SMAP and CYGNSS SM predictions against in situ SM measurements from 301 stations. Similar low-median unbiased RMSEs are obtained, and the daily averaged CYGNSS-based SM against the in situ networks is 0.049 cm3/cm3. The presented approach succeeds in providing SM estimation at a global scale with employing the least ancillary data with superior results and this article reveals the spatio-temporal heterogeneity for SM estimation using CYGNSS data.
Meta-Learning For Few-Shot Time Series Classification

State-of-the-art deep learning methods require large quantities of labeled data pairs for high performance. While satellite data is now available in abundance, ground truth labels remain scarce. Moreover, ground truth labels are distributed unevenly around the globe; high-resource regions (e.g. US, Europe) have many more labels than low-resource regions (e.g. Africa, parts of Asia). We hypothesize that a great deal of information can be shared across tasks involving satellite imagery, due to shared image acquisition and similarities in landscapes and objects worldwide. Meta-learning is a machine learning discipline in which models are explicitly trained to adapt to new tasks with few labeled data pairs per task. In this work, we use model agnostic meta-learning (MAML) to train a neural network on tasks from high-resource regions to improve land cover classification accuracies in low-resource regions. We observe that a meta-learned neural network outperforms both the same network trained from scratch and the network pretrained on high-resource data and finetuned on low-resource data.
Simulating the role of molecular binding to mineral surfaces : from biomineral growth to cell attachment

The interactions between organic molecules and minerals is fundamental to the under- standing of processes such as biomineralisation, the attachment of bacteria to surfaces and the design of synthetic materials within biomimetics. This thesis shows how molec- ular dynamics simulations can be employed to study the organic - inorganic interactions and give new insights into the molecular binding at mineral surfaces that play a role in these processes. The incorporation of amino acids within calcium carbonate crystals was simulated and show a high energy associated with the incorporation of these molecules. The amino acids get incorporated in-between the lattice planes of the crystal, causing small anisotropic distortions to the crystal. The inclusion of these molecules occurs via a goodness-of-fit principle, where disruptions to the crystal lattice should be kept to a minimum. These simulations show good agreement with experimental X-ray data. Simulations of multiple tripeptides show a different conformational behaviour of the peptides in solution than on the surface of calcium carbonate. Whereas the pep- tides exhibit a flexible behaviour in solution, binding to the mineral surface induces a disorder-to-order transition and the peptides become rigid. These changes in con- formational behaviour offer insight into the structure and behaviour of intrinsically disordered proteins. The polymer poly acrylic acid was simulated to analyse its conformational behaviour. In the presence of counter ions the polymer exhibits a flexible, extended conformation, whereas a coiled conformation is found in the absence of counter ions. The simulations in this work agree well with experimental spectroscopy studies. The binding of the polymer to a mineral surface is not only governed by the number of functional groups, but also the flexibility of the polymer. These results give an insight in how such molecules can aid the attachment of bacteria to surfaces.
Polarity reversal in HVDC joints - the effect of the axial thermal conduction

It has been shown that the establishment of a thermal gradient over the radius of HVDC cables involves the accumulation of space charge within the dielectric layer. High thermal gradients over the insulation thickness of loaded cables can lead to the inversion of the radial electric field pattern. In this scenarios, transient overvoltages and polarity reversal can lead to local and transitory peaks of electric field. Since the temperature distribution plays an important role in reaching critical values of the electric field, it has been considered interesting to have a more in-depth view of the thermal behavior of HVDC systems close the discontinuities of the geometry along the cable axis. The main goal of this research is to investigate the effect of the axial thermal conduction on space charge phenomena occurring on joints between two HVDC cable segments. In particular, the values of the electric field have been calculated in several probe points in a joint area during a polarity reversal event. The calculations are carried out by means of Comsol Multiphysics® on a 2D axial symmetric geometric model. The results demonstrate that the role of the axial heat transfer in the behavior of the electric field during a voltage transient strongly depends on the thermal conductivity of the joint insulation.
A Review Construction-based Approach to Collaborative Filtering

User reviews, which contain rich user preferences and item characteristics, can help to better predict user ratings in collaborative filtering (CF). However, one major issue is that reviews are usually posted after users have interacted with items, which limits the ways to use reviews and the ability to improve the performance of existing CF methods. To address this issue, this paper proposes a review construction-based CF method including: 1) a review construction network (RCN), which constructs vector representations of reviews before users interact with items; 2) a rating prediction network (RPN), which shares the intermediate layers with RCN to improve rating prediction performance; 3) several learning tricks, e.g., gradient reversal, to further boost the performance. Moreover, other CF methods, e.g., matrix factorization (MF), can be easily integrated into the proposed method to further boost the performance. Extensive studies on real-world datasets demonstrate that the proposed method integrated with MF significantly outperforms the state-of-the-art CF methods in recommendation accuracy.
Persistent Daytime Sleepiness after Successful Maxillary-Mandibular Advancement Surgery: A Clinical Case Report

Background: Continuous positive airway pressure is a cost-effective treatment of obstructive sleep apnea (OSA), but in case of craniofacial anomalies, which have a recognized role in OSA development, maxillary-mandibular advancement surgery is a recommended therapeutic option. Clinical Presentation: We describe a patient with severe OSA (apnea-hypopnea index = 42) treated by maxillary-mandibular advancement (MMA) surgery. Effective results were confirmed by polysomnography at 6 months post-surgery with normalization of the polysomnographic parameters (apnea-hypopnea index = 17) but due to persistent sleepiness, the patient was unable to resume his work. Conclusion: Maxillary-mandibular surgery can be a valid option for treating severe OSA associated with craniofacial anomalies. However, our results emphasize the need of critically assessing surgical approaches and the need to redefine success for OSA management.
The Post-tenure Apex: Unrewarding, Unproductive, Unhappy. Is Continuing Learning a Remedy for Mid-Career Misery?

Academics who are in midto late-stages of their career are often overlooked as participants when leaders of higher education are planning continuing learning opportunities. The reasons are varied but typically originate from a lack of understanding about this long and important phase in an academic’s career. Prior research has reported that a crisis can happen at this career apex, illustrating a need for continuing learning. Many academics who move into midcareer encounter issues such as plateauing (e.g., no longer finding new research results), career disappointments (e.g., no longer able to attain research funding), and changing perspectives about their priorities (e.g., publication outputs are no longer a priority). The purpose of this study was to extend our understanding of the value of continuing learning for midto late-career faculty. We conducted a study on the perceived value and impact of continuing learning for midto late-career academics. Our findings indicate that when development centres are planning activities for midto late-career faculty, it will have the greatest value when (a) based on careerstage appropriate needs (e.g., high priority areas identified); and (b) activities are directed to midto late-career academics. While prior research has shown that interaction and collaboration are important for midto late-career academics, findings from this study indicate technical and practical knowledge are a higher priority. Heather Kanuka *
The social context of retrospective-prospective changes in pregnancy desire during the transition to adulthood: The role of fathers and intimate relationships

BACKGROUND Researchers have questioned the accuracy of retrospective measures of unintended pregnancy, which ask women whether they wanted a pregnancy before it was conceived. OBJECTIVE We investigated whether pregnant women’s retrospective recollections of their preconception desires for pregnancy were shaped by intimate relationships, their own reactions, and their perceptions of their partners’ reactions to their pregnancies. METHODS We used the Relationship Dynamics and Social Life (RDSL) study, which included weekly survey interviews with 971 young women, of whom 175 experienced 203 pregnancies during the 2.5-year study period. We estimated logistic regression models of whether women’s retrospective recollections of their pre-conception desires were stable, shifted positive, or shifted negative compared to their prospectively reported desires, along with formal mediation tests of potential mechanisms. RESULTS Women were more likely to remember their undesired pregnancies as desired before conception if they themselves reacted happily to the pregnancy, they were married or engaged, or they perceived their partner as reacting positively. The association with perceiving her partner as positive was mediated by her own happiness about the pregnancy.