Miracle-dz/UniFAR-FTUs huggingface dataset:
Poly(ionic liquid)s as phase-transporter for graphene oxide liquid crystals from aqueous to non-polar organic phase via noncovalent functionalization

ABSTRACT We synthesise a novel poly(ionic liquid) (PIL) and develop a procedure that allows the phase transfer of graphene oxides (GO) from water to organic solvent, retaining graphene oxide liquid crystal (GOLC) phase in various organic solvents, especially non-polar organic solvents. PIL ([PEP-MIM]DBS) is exploited in this procedure as a noncovalent functional material that is capable of transporting GO from aqueous to non-polar organic phase. PILs can decorate GO noncovalently and keep GO nano-sheets exfoliated in solid-state PILs/GO hybrids, which can well redisperse in organic solvents without any agglomeration. This expands the number of known solvents which can support GOLC phase to dimethyl formamide, dimethyl sulfoxide, acetonitrile, tetrahydrofuran, and a number of other non-polar organic solvents, many of which were not known to afford GOLC phase prior to this report, such as dichloromethane, tetrachloromethane, dichloroethane and tetrachloroethane. Graphical Abstract
CK1BP Reduces α-Synuclein Oligomerization and Aggregation Independent of Serine 129 Phosphorylation

The pathological accumulation of α-Synuclein (α-Syn) is the hallmark of neurodegenerative α-synucleinopathies, including Parkinsons’s disease (PD). In contrast to the mostly non-phosphorylated soluble α-Syn, aggregated α-Syn is usually phosphorylated at serine 129 (S129). Therefore, S129-phosphorylation is suspected to interfere with α-Syn aggregation. Among other kinases, protein kinase CK1 (CK1) is known to phosphorylate α-Syn at S129. We overexpressed CK1 binding protein (CK1BP) to inhibit CK1 kinase activity. Using Bimolecular Fluorescence Complementation (BiFC) in combination with biochemical methods, we monitored the S129 phosphorylation and oligomerization of α-Syn in HEK293T cells. We found that CK1BP reduced the overall protein levels of α-Syn. Moreover, CK1BP concomitantly reduced S129 phosphorylation, oligomerization and the amount of insoluble α-Syn. Analyzing different α-Syn variants including S129 mutations, we show that the effects of CK1BP on α-Syn accumulation were independent of S129 phosphorylation. Further analysis of an aggregating polyglutamine (polyQ) protein confirmed a phosphorylation-independent decrease in aggregation. Our results imply that the inhibition of CK1 activity by CK1BP might exert beneficial effects on NDDs in general. Accordingly, CK1BP represents a promising target for the rational design of therapeutic approaches to cease or at least delay the progression of α-synucleinopathies.
Prognostic assessment

Prognostic assessment in patients with acute venous thromboembolism is essential to drive clinical management in terms of disposition for hospitalization or home treatment, and revascularization strategies or anticoagulation alone. In these patients, single predictors, combinations of predictors, or prognostic models can be used for prognostic assessment. Overall, the risk for short-term death is about tenfold higher after acute pulmonary embolism than after acute deep vein thrombosis. Localization of thrombosis (proximal or distal) and comorbidities are the main predictors of prognosis in patients with deep vein thrombosis. In patients with pulmonary embolism, prognostic assessment includes the evaluation of clinical features, findings at imaging (computed tomography angiography, echocardiography, and/or ultrasonography of the lower limbs), or biomarkers of right ventricular dysfunction or injury. The presence of shock or sustained hypotension is the main criterion for the identification of high-risk patients. Clinical models with high negative predictive value have been validated to identify patients at low risk for short-term death. Whether the assessment of right ventricular dysfunction in these patients can further improve the negative predictive value of clinical models remains to be defined. Right ventricular dysfunction as assessed by echocardiography or computed tomography angiography as well as right ventricular damage by troponin levels are associated with an about twofold increase in the risk for short-term death in intermediate risk patients. However, prognostic assessment in these patients requires further improvement before it can be used to drive decisions on treatment.
Elevated Level of Nerve Growth Factor (NGF) in Serum-Derived Exosomes Predicts Poor Survival in Patients with Breast Cancer Undergoing Neoadjuvant Chemotherapy

Simple Summary Exosomes and cytokines play crucial roles in the process of tumor progression. Recent studies have reported that cytokines can be packaged into exosomes, leading to drug resistance. The aim of this study is to evaluate the potential value of cytokines in both serum and exosomes as prognostic biomarkers of long-term outcomes in patients with breast cancer treated with neoadjuvant chemotherapy. We observed significant differences in expression patterns between serum cytokines and exosomal cytokines. Elevated levels of serum IP-10, serum MMP-1, and exosomal NGF were associated with poor overall survival. In multivariate analysis, exosomal NGF was an independent prognostic factor for overall survival. These findings suggest that exosomal NGF is useful for identifying patients with poor survival outcomes. Abstract Neoadjuvant chemotherapy (NAC) is a standard treatment strategy for patients with locally advanced breast cancer (LABC). However, there are no established predictors of chemosensitivity and survival in LABC patients who undergo NAC. Many studies have demonstrated that exosomes and cytokines are important players in intercellular communication between tumors and their environments, and are involved in chemotherapy resistance. Recently, it was reported that cytokines can be packaged into exosomes, but whether exosomal cytokines serve as biomarkers in breast cancer patients is still unclear. In this study, we examined the roles of cytokines in both serum and exosomes as prognostic biomarkers for long-term outcomes in patients with breast cancer who undergo NAC. We isolated exosomes from the blood of 129 patients with early breast cancer who were receiving neoadjuvant chemotherapy between 2008 and 2011 at Samsung Medical Center. The levels of cytokines and growth factors in serum and exosomes were measured with ProcartaPlex immune-related panels. We investigated correlations between clinic-pathologic variables and patient survival, and Cox proportional hazards regression analysis was performed for prognostic evaluation. We detected significant differences in expression patterns between serum cytokines and exosomal cytokines. In both serum and exosomes, many cytokines were positively correlated with age. In univariate analysis, patients with high serum IP-10, serum MMP-1, and exosomal NGF had shorter overall survival. Exosomal NGF showed significantly poorer overall survival in multivariate analysis. These findings suggest that exosomal NGF is useful for identifying patients with poor survival outcomes.
Contribution to the vascular flora of the archaeological park of Selinunte and Cave of Cusa (South-Western Sicily, Italy): preliminary results

Scafidi, F. & Raimondo, F. M. : Contribution to the vascular flora of the archaeological park of Selinunte and Cave of Cusa (South-Western Sicily, Italy): preliminary results. — Fl. Medit. 28: 225-232. 2018. — ISSN: 1120-4052 printed, 2240-4538 online. This study aims to present a complete inventory of the vascular flora of the Archaeological Park of Selinunte. A total of 443 specific and infraspecific taxa are here reported. Fabaceae was the most collected family with 47 species. Asteraceae and Poaceae were the next largest families with 45 and 35 species, respectively. Euphorbia was the largest genus, represented by 9 species. The analysis of the biological spectrum of the vascular flora indicate the predominance of therophytes (42%) and hemicryptophytes (22%) while, from a chorological point of view, most of the species show a Mediterranean distribution. The presence of Cynara cardunculus subsp. zingaroensis (taxon endemic to W-Sicily) is reported for the first time for the investigated area.
Study of the Prevalence of Burnout in University Professors in the Period 2005–2020

The purpose of this research is to carry out a systematic review of the existing scientific literature on the prevalence of Burnout in university professors in the time period 2005–2020. For that purpose, an exploratory review through the Web of Science (WoS) and Scopus related to this psychosocial syndrome under the PRISMA methodology has been made. After the application of the inclusion and exclusion criteria, a final group of 12 studies were obtained. The results show the presence of high levels of Burnout in a sample of 2,841 university professors in the period studied, which makes it necessary to implement psychosocial intervention programs to prevent this syndrome and promote the personal and professional accomplishment of teachers.
Long-term stability and risk assessment of copper and cadmium in a smelter-impacted soil treated by four amendments

ABSTRACT In an effort to address public concerns of the long-term stability and ecological risk reduction of Cu and Cd in a farmland located at the Guixi, Jiangxi Province, China, containing ∼ 800 mg kg−1 Cu and 0.8 mg kg−1 Cd soil, were treated in situ by attapulgite, apatite, montmorillonite and lime at the rate: 10, 10, 10 and 4 g kg−1 soil, respectively. Field experiment consisted of 2 × 3-m plots arranged in a randomised complete block design with each treatment. Soil and plant samples were collected in sixth years post-treatments and analysed for Cu and Cd bioaccessibility, chemical fraction and Cu, Cd concentration in plant tissue. The results indicated that the apatite and lime treatments significantly reduced bioaccessible and exchangeable fractions Cu and Cd in the soil at sixth years post the treatments. Cu and Cd concentration in plant tissue was positively related to the bioaccessibility of Cu and Cd. The treatment used 10 g apatite kg−1 soil appeared to be most effective for overall risk reduction. The Cu and Cd stabilisation and risk reduction by the apatite treatments were accomplished by the induced transformation of labile Cu and Cu species to relatively insoluble forms. This study illustrated that in situ Cu and Cd stabilisation by apatite would be long-term and ecologically safe, which could safeguard human health and ecosystem from Cu and Cd contamination in mining areas.
Pd Segregation on the Surface of Bimetallic PdAu Nanoparticles Induced by Low Coverage of Adsorbed CO

For bimetallic Pd–Au/HOPG model catalysts, reversible enrichment of the surface by Pd under CO oxidation conditions was found using a near-ambient pressure X-ray photoelectron spectroscopy technique. Density functional calculations combined with calculations using the topological energy expression method were applied to reveal the mechanism of this phenomenon and to quantify the stability of different arrangements of metal atoms in bimetallic PdAu nanoparticles in the presence of CO adsorbate. According to results of this computational approach, adsorption of CO already at a rather moderate coverage is sufficient to make energetically feasible segregation of Pd at terraces of PdAu nanoparticles similar in size with experimentally studied ones.
Semi-supervised physics guided deep learning framework for predicting the I-V characteristics of GAN HEMT

This letter proposes a novel deep learning framework (DLF) that addresses two major hurdles in the adoption of deep learning techniques for solving physics-based problems: 1) requirement of the large dataset for training the DL model, 2) consistency of the DL model with the physics of the phenomenon. The framework is generic in nature and can be applied to model a phenomenon from other fields of research too as long as its behaviour is known. To demonstrate the technique, a semi-supervised physics guided neural network (SPGNN) has been developed that predicts I-V characteristics of a gallium nitride-based high electron mobility transistor (GaN HEMT). A two-stage training method is proposed, where in the first stage, the DL model is trained via the unsupervised learning method using the I-V equations of a field-effect transistor as a loss function of the model that incorporates physical behaviors in the DL model and in the second stage, the DL model has been fine-tuned with a very small set of experimental data. The SPGNN significantly reduces the requirement of the training data by more than 80% for achieving similar or better performance than a traditional neural network (TNN) even for unseen conditions. The SPGNN predicts 32.4% of the unseen test data with less than 1% of error and only 0.4% of the unseen test data with more than 10% of error.
SymbioLCD: Ensemble-Based Loop Closure Detection using CNN-Extracted Objects and Visual Bag-of-Words

Loop closure detection is an essential tool of Simultaneous Localization and Mapping (SLAM) to minimize drift in its localization. Many state-of-the-art loop closure detection (LCD) algorithms use visual Bag-of-Words (vBoW), which is robust against partial occlusions in a scene but cannot perceive the semantics or spatial relationships between feature points. CNN object extraction can address those issues, by providing semantic labels and spatial relationships between objects in a scene. Previous work has mainly focused on replacing vBoW with CNN derived features. In this paper we propose SymbioLCD, a novel ensemble-based LCD that utilizes both CNN-extracted objects and vBoW features for LCD candidate prediction. When used in tandem, the added elements of object semantics and spatial-awareness creates a more robust and symbiotic loop closure detection system. The proposed SymbioLCD uses scale-invariant spatial and semantic matching, Hausdorff distance with temporal constraints, and a Random Forest that utilizes combined information from both CNN-extracted objects and vBoW features for predicting accurate loop closure candidates. Evaluation of the proposed method shows it outperforms other Machine Learning (ML) algorithms - such as SVM, Decision Tree and Neural Network, and demonstrates that there is a strong symbiosis between CNN-extracted object information and vBoW features which assists accurate LCD candidate prediction. Furthermore, it is able to perceive loop closure candidates earlier than state-of-the-art SLAM algorithms, utilizing added spatial and semantic information from CNN-extracted objects.
High Resolution Asynchronous Digital Event Timer

Many scientific and astronomical instruments need precise time measurement with high resolution between two or more events which is very challenging since decades. Presently a fast response of high resolution 17.1ps Digital Event Timer (DET) has been implemented using FPGA. The floor plan of FPGA is purposely arranged to get the higher resolution for timing measurement. As compared to various event timers (time to digit converters), the present design has better accuracy and higher resolution. The present DET is using the clock frequency (GHz) capable of measuring time in fraction microseconds and a unique asynchronous vernier approach to measure the time fraction of clock cycle which increases its resolution compared to Digital Event Timers. Fast response and higher resolution make it a better choice for multi KHz satellite laser ranging and other LIDAR applications.
Focusing on Difficult Directions for Learning HMC Trajectory Lengths

Hamiltonian Monte Carlo (HMC) is a pre-mier Markov Chain Monte Carlo (MCMC) algorithm for continuous target distributions. Its full potential can only be unleashed when its problem-dependent hyperparameters are tuned well. The adaptation of one such hyperparameter, trajectory length ( τ ), has been closely examined by many research programs with the No-U-Turn Sampler (NUTS) com-ing out as the preferred method in 2011. A decade later, the evolving hardware pro-ﬁle has lead to the proliferation of personal and cloud based SIMD hardware in the form of Graphics and Tensor Processing Units (GPUs, TPUs) which are hostile to certain algorithmic details of NUTS. This has opened up a hole in the MCMC toolkit for an algorithm that can learn τ while maintaining good hardware utilization. In this work we build on recent advances along this direction and introduce SNAPER-HMC, a SIMD-accelerator-friendly adaptive-MCMC scheme for learning τ . The algorithm maximizes an upper bound on per-gradient eﬀective sample size along an estimated principal component. We empirically show that SNAPER-HMC is stable when combined with mass-matrix adaptation, and is tolerant of certain pathological target distribution covariance spectra while providing excellent long and short run sampling eﬃciency. We provide a complete implementation for continuous multi-chain adaptive HMC combining trajectory learning with standard step-size and mass-matrix adaptation in one turnkey inference package.
Automatic Detection of Injection and Press Mold Parts on 2D Drawing Using Deep Neural Network

This paper proposes a deep learning-based method to automatically detect the injection mold parts (i.e., hook or boss) and press mold parts (i.e., DPS or Embo) in 3D CAD models of commercial TV. We first converted the 3D CAD models into 2D drawings and cropped them into a smaller image patch for the training efficiency of a deep neural network. Then, we found the position and type of mold parts using Cascade R-CNN and estimated the orientation of the detected mold parts using ResNet-50. Finally, we converted the 2D position of the mold parts to the 3D position of the original image. We obtained detection accuracy of injection mold parts with an average precision (AP) of 84.1% and an average recall (AR) of 91.2% and detection accuracy of press mold parts with an AP of 72.0% and an AR of 87.0%, as well as an orientation accuracy of injection and press mold parts with 94.4% and 92.0%, respectively, which can facilitate faster industrial product design.
The Optimal Pilot Power Allocation Strategy for Multi-IRS Assisted Communication Systems

Intelligent reflecting surface (IRS) is a promising technology that enables the precise control of the electromagnetic environment in future wireless communication networks. To leverage the IRS effectively, the acquisition of channel state information (CSI) is crucial in IRS-assisted communication systems, which, however, is challenging. In this paper, we propose the optimal pilot power allocation strategy for the channel estimation of IRS-assisted communication systems, which is capable of further improving the achievable rate performance with imperfect CSI. More specifically, first of all, we introduce a multi-IRS-assisted communication system in the face of practical channel estimation errors. Furthermore, the ergodic capacity with imperfect CSI is derived in an explicit closed-form expression under the single-input single-output (SISO) consideration. Secondly, we formulate the optimization problem of maximizing the ergodic capacity with imperfect CSI, subject to the constraint of the average uplink pilot power. Thirdly, the method of Lagrange multipliers is invoked to solve the ergodic rate maximizing problem and thus to obtain the optimal pilot power allocation strategy. The resultant pilot power allocation solution suggests allocating more amount of power to the pilots for estimating the weak reflection channels. Besides, we also elaborate on the expense of the proposed pilot power allocation strategy upon analyzing the peak-to-average-power ratio (PAPR) increase quantitatively. Finally, the extensive simulation results verify our analysis and reveal some interesting results. For example, for the user in the vicinity of a large IRS, it is suggested to switch off other IRSs and only switch on the IRS nearest the user; For the user near a small IRS, it is better to switch on all IRSs and perform the optimal pilot power allocation for enhancing the achievable rate performance.
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes

In Bayesian Optimization (BO) we study black-box function optimization with noisy point evaluations and Bayesian priors. Convergence of BO can be greatly sped up by batching, where multiple evaluations of the black-box function are performed in a single round. The main difficulty in this setting is to propose at the same time diverse and informative batches of evaluation points. In this work, we introduce DPP-Batch Bayesian Optimization (DPP-BBO), a universal framework for inducing batch diversity in sampling based BO by leveraging the repulsive properties of Determinantal Point Processes (DPP) to naturally diversify the batch sampling procedure. We illustrate this framework by formulating DPP-Thompson Sampling (DPP-TS) as a variant of the popular Thompson Sampling (TS) algorithm and introducing a Markov Chain Monte Carlo procedure to sample from it. We then prove novel Bayesian simple regret bounds for both classical batched TS as well as our counterpart DPP-TS, with the latter bound being tighter. Our real-world, as well as synthetic, experiments demonstrate improved performance of DPP-BBO over classical batching methods with Gaussian process and Cox process models.
Clustering of Bank Customers using LSTM-based encoder-decoder and Dynamic Time Warping

Clustering is an unsupervised data mining technique that can be employed to segment customers. The efficient clustering of customers enables banks to design and make offers based on the features of the target customers. The present study uses a real-world financial dataset (Berka, 2000) to cluster bank customers by an encoder-decoder network and the dynamic time warping (DTW) method. The customer features required for clustering are obtained in four ways: Dynamic Time Warping (DTW), Recency Frequency and Monetary (RFM), LSTM encoder-decoder network, and our proposed hybrid method. Once the LSTM model was trained by customer transaction data, a feature vector of each customer was automatically extracted by the encoder.Moreover, the distance between pairs of sequences of transaction amounts was obtained using DTW. Another vector feature was calculated for customers by RFM scoring. In the hybrid method, the feature vectors are combined from the encoder-decoder output, the DTW distance, and the demographic data (e.g., age and gender). Finally, feature vectors were introduced as input to the k-means clustering algorithm, and we compared clustering results with Silhouette and Davies-Bouldin index. As a result, the clusters obtained from the hybrid approach are more accurate and meaningful than those derived from individual clustering techniques. In addition, the type of neural network layers had a substantial effect on the clusters, and high network error does not necessarily worsen clustering performance.
Nonlinear Interactions in Spherically Polarized Alfv\'{e}nic Turbulence

Trevor A. Bowen, ∗ Samuel T. Badman, Stuart D. Bale, 2 Thierry Dudok de Wit, Timothy S. Horbury, Kristopher G. Klein, Davin Larson, Alfred Mallet, Lorenzo Matteini, Michael D. McManus, 2 and Jonathan Squire Space Sciences Laboratory, University of California, Berkeley, CA 94720-7450, USA Physics Department, University of California, Berkeley, CA 94720-7300, USA LPC2E, CNRS, CNES, and University of Orléans, Orléans, France The Blackett Laboratory, Imperial College London, London, SW7 2AZ, UK Department of Planetary Sciences & Lunar and Planetary Laboratory, University of Arizona, Tucson, AZ 85721 Department of Physics, University of Otago, 730 Cumberland St., Dunedin 9016, New Zealand