Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Pool-Based Active Learning for Linear Regression

In many real-world machine learning applications, unlabeled data can be easily obtained, but it is very time-consuming and/or expensive to label them. So, it is desirable to be able to select the optimal samples to label, so that a good machine learning model can be trained from a minimum amount of labeled data. Active learning (AL) has been widely used for this purpose. However, most existing AL approaches are supervised: they train an initial model from a small amount of labeled samples, query new samples based on the model, and then update the model iteratively. Few of them have considered the completely unsupervised AL problem, i.e., starting from zero, how to optimally select the very first few samples to label, without knowing any label information at all. This problem is very challenging, as no label information can be utilized. This paper studies unsupervised pool-based AL for linear regression problems. We propose a novel AL approach that considers simultaneously the informativeness, representativeness, and diversity, three essential criteria in AL. Extensive experiments on 14 datasets from various application domains, using three different linear regression models (ridge regression, LASSO, and linear support vector regression), demonstrated the effectiveness of our proposed approach.
Schema2QA: Answering Complex Queries on the Structured Web with a Neural Model

Virtual assistants have proprietary third-party skill platforms; they train and own the voice interface to websites based on their submitted skill information. This paper proposes Schema2QA, an open-source toolkit that leverages the Schema.org markup found in many websites to automatically build skills. Schema2QA has several advantages: (1) Schema2QA is more accurate than commercial assistants in answering compositional queries involving multiple properties; (2) it has a low-cost training data acquisition methodology that requires only writing a small number of annotations per domain and paraphrasing a small number of sentences. Schema2QA uses a novel neural model that combines the BERT pretrained model with an LSTM; the model can generalize a training set containing only synthesized and paraphrase data to understand real-world crowdsourced questions. We apply Schema2QA to two different domains, showing that the skills we built can answer useful queries with little manual effort. Our skills achieve an overall accuracy between 73% and 81%. With transfer learning, we show that a new domain can achieve a 59% accuracy without manual effort. The open-source Schema2QA lets each website create and own its linguistic interface.
PINOID regulates floral organ development by modulating auxin transport and interacts with MADS16 in rice

Summary In rice (Oryza sativa L.), floral organ development is an important trait. Although a role for PINOID in regulating floral organ development was reported recently, the underlying molecular mechanism remains unclear. Here, we isolated and characterized an abnormal floral organ mutant and mapped the causative gene through an improved MutMap method. Molecular study revealed that the observed phenotype is caused by a point mutation in OsPINOID (OsPID) gene; therefore, we named the mutation as ospid‐4. Our data demonstrate that OsPID interacts with OsPIN1a and OsPIN1b to regulate polar auxin transport as shown previously. Additionally, OsPID also interacts with OsMADS16 to regulate transcription during floral organ development in rice. Together, we propose a model that OsPID regulates floral organ development by modulating auxin polar transport and interaction with OsMADS16 and/or LAX1 in rice. These results provide a novel insight into the role of OsPID in regulating floral organ development of rice, especially in stigma development, which would be useful for genetic improvement of high‐yield breeding of rice.
Overexpression of RACK1 Predicts Poor Prognosis in Melanoma

Melanoma is a highly malignant skin cancer with limited treatment options, the mechanism of the occurrence and development of melanoma is still unclear till now. Receptor for activated C kinase 1 (RACK1) is a scaffolding protein that mediates multiple signaling pathways; it interconnects distinct signaling pathways to control essential cellular processes. RACK1 was reported as an oncogene in human tumorigenesis, but little is known about its role in melanoma. This study aimed to investigate the expression of RACK1 in patients with melanoma and to reveal its possible functions in melanoma cells. The expression profiles of RACK1 detected in tumor tissues from melanoma patients showed that RACK1 was higher in tumor tissues, and its expression level was well associated with the clinical progression of melanoma (TNM stage, P=0.009). Furthermore, RNA interfering (RNAi) knockdown of RACK1 could efficiently suppress the proliferation, migration and invasion of A375 and A875 cells and promote their apoptosis. Taken together, these results suggest that RACK1 may be a poor prognostic factor in human melanoma, and it may be a new therapeutic target for melanoma treatment.
Molecular Characterization and Expression Profile of PaCOL1, a CONSTANS-like Gene in Phalaenopsis Orchid

CONSTANS (CO) and CONSTANS-like (COL) genes play important roles in coalescing signals from photoperiod and temperature pathways. However, the mechanism of CO and COLs involved in regulating the developmental stage transition and photoperiod/temperature senescing remains unclear. In this study, we identified a COL ortholog gene from the Taiwan native orchid Phalaenopsis aphrodite. The Phalaenopsis aphrodite CONSTANS-like 1 (PaCOL1) belongs to the B-box protein family and functions in the nucleus and cytosol. Expression profile analysis of Phalaenopsis aphrodite revealed that PaCOL1 was significantly expressed in leaves, but its accumulation was repressed during environmental temperature shifts. We found a differential profile for PaCOL1 accumulation, with peak accumulation at late afternoon and at the middle of the night. Arabidopsis with PaCOL1 overexpression showed earlier flowering under short-day (SD) conditions (8 h/23 °C light and 16 h/23 °C dark) but similar flowering time under long-day (LD) conditions (16 h/23 °C light and 8 h/23 °C dark). Transcriptome sequencing revealed several genes upregulated in PaCOL1-overexpressing Arabidopsis plants that were previously involved in flowering regulation of the photoperiod pathway. Yeast two-hybrid (Y2H) analysis and bimolecular fluorescence complementation (BiFC) analysis revealed that PaCOL1 could interact with a crucial clock-associated regulator, AtCCA1, and a flowering repressor, AtFLC. Furthermore, expressing PaCOL1 in cca1.lhy partially reversed the mutant flowering time under photoperiod treatment, which confirms the role of PaCOL1 function in the rhythmic associated factors for modulating flowering.
A Support Detection and Root Finding Approach for Learning High-dimensional Generalized Linear Models

Feature selection is important for modeling high-dimensional data, where the number of variables can be much larger than the sample size. In this paper, we develop a support detection and root finding procedure to learn the high dimensional sparse generalized linear models and denote this method by GSDAR. Based on the KKT condition for $\ell_0$-penalized maximum likelihood estimations, GSDAR generates a sequence of estimators iteratively. Under some restricted invertibility conditions on the maximum likelihood function and sparsity assumption on the target coefficients, the errors of the proposed estimate decays exponentially to the optimal order. Moreover, the oracle estimator can be recovered if the target signal is stronger than the detectable level. We conduct simulations and real data analysis to illustrate the advantages of our proposed method over several existing methods, including Lasso and MCP.
Structural dynamics and allostery of Rab proteins: strategies for drug discovery and design

Rab proteins represent the largest family of the Rab superfamily guanosine triphosphatase (GTPase). Aberrant human Rab proteins are associated with multiple diseases, including cancers and neurological disorders. Rab subfamily members display subtle conformational variations that render specificity in their physiological functions and can be targeted for subfamily-specific drug design. However, drug discovery efforts have not focused much on targeting Rab allosteric non-nucleotide binding sites which are subjected to less evolutionary pressures to be conserved, hence are likely to offer subfamily specificity and may be less prone to undesirable off-target interactions and side effects. To discover druggable allosteric binding sites, Rab structural dynamics need to be first incorporated using multiple experimentally and computationally obtained structures. The high-dimensional structural data may necessitate feature extraction methods to identify manageable representative structures for subsequent analyses. We have detailed state-of-the-art computational methods to (i) identify binding sites using data on sequence, shape, energy, etc., (ii) determine the allosteric nature of these binding sites based on structural ensembles, residue networks and correlated motions and (iii) identify small molecule binders through structure- and ligand-based virtual screening. To benefit future studies for targeting Rab allosteric sites, we herein detail a refined workflow comprising multiple available computational methods, which have been successfully used alone or in combinations. This workflow is also applicable for drug discovery efforts targeting other medically important proteins. Depending on the structural dynamics of proteins of interest, researchers can select suitable strategies for allosteric drug discovery and design, from the resources of computational methods and tools enlisted in the workflow.
Midterm Fire Danger Prediction Using Satellite Imagery and Auxiliary Thematic Layers

Wildfires constitute a significant environmental pressure in Europe, particularly in the Mediterranean countries. The prediction of fire danger is essential for sustainable forest fire management since it provides critical information for designing effective prevention measures and for facilitating response planning to potential fire events. This study presents a new midterm fire danger index (MFDI) using satellite and auxiliary geographic data. The proposed methodology is based on estimations of a dry fuel connectivity measure calculated from the Moderate Imaging Spectrometer (MODIS) time-series data, which are combined with biophysical and topological variables to obtain accurate fire ignition danger predictions for the following eight days. The index’s accuracy was assessed using historical fire data from four large wildfires in Greece. The results showcase that the index predicted high fire danger (≥3 on a scale within [ 1 , 4 ] ) within the identified fire ignition areas, proving its strong potential for deriving reliable estimations of fire danger, despite the fact that no meteorological measurements or forecasts are used for its calculation.
Progress in quality evaluation and clinic application of orally disintegrating tablets

In recent years many new analysis methods were applied to evaluate the quality of o- rally disintegrating tablets,many kinds of drugs in the form of orally disintegrating tablet were applied in the clinics and had good effects.Orally disintegrating tablets will have a good application future.This pa- per introduces the progress in quality evaluation and clinic application of orally disintegrating tablets.
Draft Genome Sequence of Rhodococcus aetherivorans JCM 14343T, a Bacterium Capable of Degrading Recalcitrant Noncyclic and Cyclic Ethers

Here, we report the draft genome sequence of Rhodococcus aetherivorans JCM 14343T, which possesses the versatile ability to degrade recalcitrant noncyclic and cyclic ether compounds. The 4.2-Mbp genome of this bacterium contains alkane hydroxylase and propane monooxygenase genes involved in the degradation of noncyclic and cyclic ethers, respectively. ABSTRACT Here, we report the draft genome sequence of Rhodococcus aetherivorans JCM 14343T, which possesses the versatile ability to degrade recalcitrant noncyclic and cyclic ether compounds. The 4.2-Mbp genome of this bacterium contains alkane hydroxylase and propane monooxygenase genes involved in the degradation of noncyclic and cyclic ethers, respectively.
Influence of Ternary Emulsifier Mixtures on Oxidative Stability of Nanoemulsions Based on Avocado Oil

The aim of this work was to study the effect of two emulsifiers (M1: SL-soy lecithin, Tw80-Tween 80 and CasCa-calcium caseinate and M2: SL-soy lecithin, Tw80-Tween 80 and SE-sucrose esters) on the oxidative stability of avocado oil-based nanoemulsions. Oil-in-water nanoemulsions were prepared using 3.6% w/w of two emulsifier mixtures, which were optimized by mixture experimental design in order to minimize particle size (PS) and polydispersity index (PdI). Then, the oxidative stability of nanoemulsions was evaluated through both an induction period and a quantification of hydroperoxides and thiobarbituric acid reactive species (TBARs) under accelerated storage conditions. The simplex-centroid mixture design showed that PS and PdI varied when proportions of different emulsifiers were modified, obtaining an optimized concentration for each mixture of: 85% SL, 10% Tw80 and 5%CasCa (M1) and 85% SL, 7.4% Tw80 and 7.6% SE (M2) that produced nanoemulsions with PS ~116 nm and PdI < 0.2. Nanoemulsions elaborated with M1 and M2 presented similar particle characteristics and physical stability to the control sample with Tw80. However, M1 nanoemulsions were more stable against lipid oxidation, since they showed the highest induction period and lower formation of hydroperoxides and TBARs during storage.
Hippocampal Neurogenesis Is Enhanced in Adult Tau Deficient Mice

Tau dysfunction is common in several neurodegenerative diseases including Alzheimer’s disease (AD) and frontotemporal dementia (FTD). Affective symptoms have often been associated with aberrant tau pathology and are commonly comorbid in patients with tauopathies, indicating a connection between tau functioning and mechanisms of depression. The current study investigated depression-like behavior in Mapt−/− mice, which contain a targeted deletion of the gene coding for tau. We show that 6-month Mapt−/− mice are resistant to depressive behaviors, as evidenced by decreased immobility time in the forced swim and tail suspension tests, as well as increased escape behavior in a learned helplessness task. Since depression has also been linked to deficient adult neurogenesis, we measured neurogenesis in the hippocampal dentate gyrus and subventricular zone using 5-bromo-2-deoxyuridine (BrdU) labeling. We found that neurogenesis is increased in the dentate gyrus of 14-month-old Mapt−/− brains compared to wild type, providing a potential mechanism for their behavioral phenotypes. In addition to the hippocampus, an upregulation of proteins involved in neurogenesis was observed in the frontal cortex and amygdala of the Mapt−/− mice using proteomic mass spectrometry. All together, these findings suggest that tau may have a role in the depressive symptoms observed in many neurodegenerative diseases and identify tau as a potential molecular target for treating depression.
A Generic Approach toward Indoor Navigation and Pathfinding with Robust Marker Tracking

Indoor navigation and localization has gained a key attention of the researchers in the recent decades. Various technologies such as WiFi, Bluetooth, Ultra Wideband (UWB), and Radio-frequency identification (RFID) have been used for indoor navigation and localization. However, most of these existing methods often fail in providing a reasonable solution to the key challenges such as implementation cost, accuracy and extendibility. In this paper, we proposed a low-cost, and extendable framework for indoor navigation. We used simple markers printed on the paper, and placed on ceilings of the building. These markers are detected by a smartphone’s camera, and the audio and visual information associated with these markers are used as a user guidance. The system finds shortest path between any two arbitrary nodes for user navigation. In addition, it is extendable having the capability to cover new sections by installing new nodes at any place in the building. The system can be used for guidance of the blind people, tourists and new visitors in an indoor environment. The evaluation results reveal that the proposed system can guide users toward their destination in an efficient and accurate manner.
Comparative analysis of diverse toxins from a new pharmaceutical centipede, Scolopendra mojiangica

As the oldest venomous animals, centipedes use their venom as a weapon to attack prey and for protection. Centipede venom, which contains many bioactive and pharmacologically active compounds, has been used for centuries in Chinese medicine, as shown by ancient records. Based on comparative analysis, we revealed the diversity of and differences in centipede toxin-like molecules between Scolopendra mojiangica, a substitute pharmaceutical material used in China, and S. subspinipes mutilans. More than 6 000 peptides isolated from the venom were identified by electrospray ionization-tandem mass spectrometry (ESI-MS/MS) and inferred from the transcriptome. As a result, in the proteome of S. mojiangica, 246 unique proteins were identified: one in five were toxin-like proteins or putative toxins with unknown function, accounting for a lower percentage of total proteins than that in S. mutilans. Transcriptome mining identified approximately 10 times more toxin-like proteins, which can characterize the precursor structures of mature toxin-like peptides. However, the constitution and quantity of the toxin transcripts in these two centipedes were similar. In toxicity assays, the crude venom showed strong insecticidal and hemolytic activity. These findings highlight the extensive diversity of toxin-like proteins in S. mojiangica and provide a new foundation for the medical-pharmaceutical use of centipede toxin-like proteins.
miR-218 Inhibits Mitochondrial Clearance by Targeting PRKN E3 Ubiquitin Ligase

The selective elimination of dysfunctional mitochondria through mitophagy is crucial for preserving mitochondrial quality and cellular homeostasis. The most described mitophagy pathway is regulated by a positive ubiquitylation feedback loop in which the PINK1 (PTEN induced kinase 1) kinase phosphorylates both ubiquitin and the E3 ubiquitin ligase PRKN (Parkin RBR E3 ubiquitin ligase), also known as PARKIN. This event recruits PRKN to the mitochondria, thus amplifying ubiquitylation signal. Here we report that miR-218 targets PRKN and negatively regulates PINK1/PRKN-mediated mitophagy. Overexpression of miR-218 reduces PRKN mRNA levels, thus also reducing protein content and deregulating the E3 ubiquitin ligase action. In fact, following miR-218 overexpression, mitochondria result less ubiquitylated and the autophagy machinery fails to proceed with correct mitochondrial clearance. Since mitophagy defects are associated with various human diseases, these results qualify miR-218 as a promising therapeutic target for human diseases.
Associations between the proportion of fat-free mass loss during weight loss, changes in appetite, and subsequent weight change: results from a randomized 2-stage dietary intervention trial.

BACKGROUND Dynamic changes in body composition which occur during weight loss may have an influential role on subsequent energy balance behaviors and weight. OBJECTIVES The aim of this article is to consider the effect of proportionate changes in body composition during weight loss on subsequent changes in appetite and weight outcomes at 26 wk in individuals engaged in a weight loss maintenance intervention. METHODS A subgroup of the Diet, Obesity, and Genes (DiOGenes) study (n = 209) was recruited from 3 European countries. Participants underwent an 8-wk low-calorie diet (LCD) resulting in ≥8% body weight loss, during which changes in body composition (by DXA) and appetite (by visual analog scale appetite perceptions in response to a fixed test meal) were measured. Participants were randomly assigned into 5 weight loss maintenance diets based on protein and glycemic index content and followed up for 26 wk. We investigated associations between proportionate fat-free mass (FFM) loss (%FFML) during weight loss and 1) weight outcomes at 26 wk and 2) changes in appetite perceptions. RESULTS During the LCD, participants lost a mean ± SD of 11.2 ± 3.5 kg, of which 30.4% was FFM. After adjustment, there was a tendency for %FFML to predict weight regain in the whole group (β: 0.041; 95% CI: -0.001, 0.08; P = 0.055), which was significant in men (β: 0.09; 95% CI: 0.02, 0.15; P = 0.009) but not women (β: 0.01; 95% CI: -0.04, 0.07; P = 0.69). Associations between %FFML and change in appetite perceptions during weight loss were inconsistent. The strongest observations were in men for hunger (r = 0.69, P = 0.002) and desire to eat (r = 0.61, P = 0.009), with some tendencies in the whole group and no associations in women. CONCLUSIONS Our results suggest that composition of weight loss may have functional importance for energy balance regulation, with greater losses of FFM potentially being associated with increased weight regain and appetite. This trial was registered at clinicaltrials.gov as NCT00390637.
Embedding of FRPN in CNN architecture

This paper extends the fully recursive perceptron network (FRPN) model for vectorial inputs to include deep convolutional neural networks (CNNs) which can accept multi-dimensional inputs. A FRPN consists of a recursive layer, which, given a fixed input, iteratively computes an equilibrium state. The unfolding realized with this kind of iterative mechanism allows to simulate a deep neural network with any number of layers. The extension of the FRPN to CNN results in an architecture, which we call convolutional-FRPN (C-FRPN), where the convolutional layers are recursive. The method is evaluated on several image classification benchmarks. It is shown that the C-FRPN consistently outperforms standard CNNs having the same number of parameters. The gap in performance is particularly large for small networks, showing that the C-FRPN is a very powerful architecture, since it allows to obtain equivalent performance with fewer parameters when compared with deep CNNs.
Frequency of somatic mutations in head and neck melanoma: A single‐institution experience

Cutaneous head and neck melanoma (HNM) is a separate subgroup of cutaneous melanoma that has a worse prognosis than other primary sites. The aim of this article is to examine the status of somatic mutations (BRAF, NRAS, and KIT) of primary lesion in head and neck regions in our institution experience. Mutational status was correlated to the clinicopathological features and the disease progression. We retrospectively analyzed 76 cases of primary HNM diagnosed from January 2005 to June 2017, from the database of the Melanoma Unit, University of Bologna. The study included 19 cases with molecular assays. BRAF mutations in HNM was positive in five patients (26.3%); NRAS p.Q61L was detected in a NM of the scalp, when the other cases were wild type for the investigated gene mutations. Interestingly, we found that HNM BRAF mutations were the most representative in our cases, although BRAF mutations in the Literature are less frequently found in melanomas occurring at sites of chronic sun damage such as the head and neck. The aim of this study is to stimulate future research.