Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibition of PIP 4 K ameliorates the pathological effects of mutant huntingtin protein 1

23 The discovery of the causative gene for Huntington’s disease (HD) has promoted numerous 24 efforts to uncover cellular pathways that lower levels of mutant huntingtin protein (mHtt) and 25 potentially forestall the appearance of HD-related neurological defects. Using a cell-based 26 model of pathogenic huntingtin expression, we identified a class of compounds that protect cells 27 through selective inhibition of a lipid kinase, PIP4K. Pharmacological inhibition or knock-down 28 of PIP4K modulates the equilibrium between phosphatidylinositide (PI) species within the cell 29 and increases basal autophagy, reducing the total amount of mHtt protein in human patient 30 fibroblasts and aggregates in neurons. In two Drosophila models of Huntington’s disease, 31 genetic knockdown of PIP4K ameliorated neuronal dysfunction and degeneration as assessed 32 using motor performance and retinal degeneration assays respectively. Together, these results 33 suggest that PIP4K is a druggable target whose inhibition enhances productive autophagy and 34 mHtt proteolysis, revealing a useful pharmacological point of intervention for the treatment of 35 Huntington’s disease, and potentially for other neurodegenerative disorders. 36
Closed-Loop Hybrid Gaze Brain-Machine Interface Based Robotic Arm Control with Augmented Reality Feedback

Brain-machine interface (BMI) can be used to control the robotic arm to assist paralysis people for performing activities of daily living. However, it is still a complex task for the BMI users to control the process of objects grasping and lifting with the robotic arm. It is hard to achieve high efficiency and accuracy even after extensive trainings. One important reason is lacking of sufficient feedback information for the user to perform the closed-loop control. In this study, we proposed a method of augmented reality (AR) guiding assistance to provide the enhanced visual feedback to the user for a closed-loop control with a hybrid Gaze-BMI, which combines the electroencephalography (EEG) signals based BMI and the eye tracking for an intuitive and effective control of the robotic arm. Experiments for the objects manipulation tasks while avoiding the obstacle in the workspace are designed to evaluate the performance of our method for controlling the robotic arm. According to the experimental results obtained from eight subjects, the advantages of the proposed closed-loop system (with AR feedback) over the open-loop system (with visual inspection only) have been verified. The number of trigger commands used for controlling the robotic arm to grasp and lift the objects with AR feedback has reduced significantly and the height gaps of the gripper in the lifting process have decreased more than 50% compared to those trials with normal visual inspection only. The results reveal that the hybrid Gaze-BMI user can benefit from the information provided by the AR interface, improving the efficiency and reducing the cognitive load during the grasping and lifting processes.
Pulsed Electrodeposition of Gold Nanoparticles on Fluorine-Doped Tin Oxide Glass and Absorption-Based Surface Plasmon Resonance Evaluation

Synthesis and immobilization of Au nanoparticles (AuNPs) was performed on transparent fluorine-doped tin oxide (FTO) substrate by pulse electrodeposition method. The method was cost effective, simple and capable of producing nanoparticles strongly attached to the substrate. Effects of several influencing factors such as duty cycle, pulse frequency, current density, solution concentration, deposition period and annealing procedure on the optical properties of AuNPs-FTO electrode were investigated. AuNPs-FTO electrodes were characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), atomic force microscopy (AFM) and UV-Vis absorption analysis. Controllability of the plasmon absorption of the electrodeposited film by tuning of the electrodeposition conditions and thermal annealing procedure was important achievements helpful to the progress of the AuNP film applications in the tunable localized surface plasmon resonance spectroscopy (LSPR) manufacturing industry.
Is the Optic Nerve Head Structure Impacted by a Diagnostic Lumbar Puncture in Humans?

Munoz-Negrete FJ, et al. The use of Xalacom and deterioration in cases of vitiligo. Is there a causal relationship? Arch Soc Esp Oftalmol. 2004;79:315–316. 3. Mishima HK, Masuda K, Kitazawa Y, et al. A comparison of latanoprost and timolol in primary open-angle glaucoma and ocular hypertension. A 12-week study. Arch Ophthalmol. 1996;114:929–932. 4. Ziai N, Dolan JW, Kacere RD, et al. The effects on aqueous dynamics of PhXA41, a new prostaglandin F2 alpha analogue, after topical application in normal and ocular hypertensive human eyes. Arch Ophthalmol (Chicago, Ill 1960). 1993;111:1351–1358. 5. Sjöquist B, Stjernschantz J. Ocular and systemic pharmacokinetics of latanoprost in humans. Surv Ophthalmol. 2002;47 (suppl 1): S6–S12. 6. Peak AS, Sutton BM. Systemic adverse effects associated with topically applied latanoprost. Ann Pharmacother. 1998;32: 504–505.
Dose reduction of risperidone and olanzapine can improve cognitive function and negative symptoms in stable schizophrenic patients: A single-blinded, 52-week, randomized controlled study

Background: The long-term effects of dose reduction of atypical antipsychotics on cognitive function and symptomatology in stable patients with schizophrenia remain unclear. We sought to determine the change in cognitive function and symptomatology after reducing risperidone or olanzapine dosage in stable schizophrenic patients. Methods: Seventy-five stabilized schizophrenic patients prescribed risperidone (≥4 mg/day) or olanzapine (≥10 mg/day) were randomly divided into a dose-reduction group (n=37) and a maintenance group (n=38). For the dose-reduction group, the dose of antipsychotics was reduced by 50%; for the maintenance group, the dose remained unchanged throughout the whole study. The Positive and Negative Syndrome Scale, Negative Symptom Assessment-16, Rating Scale for Extrapyramidal Side Effects, and Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery were measured at baseline, 12, 28, and 52 weeks. Linear mixed models were performed to compare the Positive and Negative Syndrome Scale, Negative Symptom Assessment-16, Rating Scale for Extrapyramidal Side Effects and MATRICS Consensus Cognitive Battery scores between groups. Results: The linear mixed model showed significant time by group interactions on the Positive and Negative Syndrome Scale negative symptoms, Negative Symptom Assessment-16, Rating Scale for Extrapyramidal Side Effects, speed of processing, attention/vigilance, working memory and total score of MATRICS Consensus Cognitive Battery (all p<0.05). Post hoc analyses showed significant improvement in Positive and Negative Syndrome Scale negative subscale, Negative Symptom Assessment-16, Rating Scale for Extrapyramidal Side Effects, speed of processing, working memory and total score of MATRICS Consensus Cognitive Battery for the dose reduction group compared with those for the maintenance group (all p<0.05). Conclusions: This study indicated that a risperidone or olanzapine dose reduction of 50% may not lead to more severe symptomatology but can improve speed of processing, working memory and negative symptoms in patients with stabilized schizophrenia.
Measuring Spark on AWS: A Case Study on Mining Scientific Publications with Annotation Query

Annotation Query (AQ) is a program that provides the ability to query many different types of NLP annotations on a text, as well as the original content and structure of the text. The query results may provide new annotations, or they may select subsets of the content and annotations for deeper processing. Like GATE's Mimir, AQ is based on region algebras. Our AQ is implemented to run on a Spark cluster. In this paper we look at how AQ's runtimes are affected by the size of the collection, the number of nodes in the cluster, the type of node, and the characteristics of the queries. Cluster size, of course, makes a large difference in performance so long as skew can be avoided. We find that there is minimal difference in performance when persisting annotations serialized to local SSD drives as opposed to deserialized into local memory. We also find that if the number of nodes is kept constant, then AWS' storage-optimized instance performs the best. But if we factor in total cost, the compute-optimized nodes provides the best performance relative to cost.
Incorporation of different types of n-3 fatty acids into tissue lipids of poultry.

The objective of the present study was to compare the incorporation of different types of n-3 polyunsaturated fatty acids into thigh muscle lipids of poultry. Nine groups of broiler chicks were fed diets supplemented with three levels (1.0, 2.5, and 5.0%) of either corn, linseed, or menhaden oil. Birds supplemented with linseed oil, rich in linolenic acid (C18:3n-3), had significantly higher levels of n-3 fatty acids and higher n-3:n-6 ratios than those supplemented with the same level of menhaden oil, primarily due to an accumulation of C18:3n-3. Levels of eicosapentaenoic acid (C20:5n-3) were increased (P less than .05), compared with the controls fed the same level of corn oil, in the groups fed the two higher levels of linseed oil, and in all the groups fed menhaden oil. Linolenic acid is less susceptible to auto-oxidation, and is less likely to impart an off-flavor to the muscle. Thus, if it is desirable to increase the n-3:n-6 ratio in poultry, seed meals or oils with a high content of C18:3n-3 could be used in poultry feeds.
Formulation and evaluation of buccoadhesive quetiapine fumarate tablets

The aim of present study was to develop and evaluate buccoadhesive Quetiapine Fumarate (QF) tablets, which is extensively metabolised by liver. Buccoadhesive tablets of QF were prepared using HPMC K4M, HPMC K15M and combination of carbopol and HPC as mucoadhesive polymers by direct compression method. Sodium deoxycholate was added to formulation to improve the permeation of drug. The formulations were tested for bioadhesion strength, ex vivo residence time, swelling time and in vitro dissolution studies and ex vivo permeation studies. Optimized formulation (F3) showed 92% in vitro release in 8 h and 67% permeation of drug through porcine buccal mucosa and followed fickian release mechanism with zero order kinetics. FTIR studies of optimized formulation showed no evidence of interaction between the drug and polymers. In vivo mucoadhesive behaviour of optimized formulation was performed and subjective parameters were evaluated.
Real-time determination of power system frequency

The main frequency is an important parameter of an electrical power system. The frequency can change over a small range due to generation-load mismatches. Some power system protection and control applications require accurate and fast estimates of the frequency. Most digital algorithms for measuring frequency have acceptable accuracy if voltage waveforms are not distorted. However, due to non-linear devices the voltage waveforms can include higher harmonics. The paper presents a new method based on digital filtering and Prony's model. Computers simulation results confirm, that the method is more accurate than others.
Efficient rank aggregation using partial data

The need to rank items based on user input arises in many practical applications such as elections, group decision making and recommendation systems. The primary challenge in such scenarios is to decide on a global ranking based on partial preferences provided by users. The standard approach to address this challenge is to ask users to provide explicit numerical ratings (cardinal information) of a subset of the items. The main appeal of such an approach is the ease of aggregation. However, the rating scale as well as the individual ratings are often arbitrary and may not be consistent from one user to another. A more natural alternative to numerical ratings requires users to compare pairs of items (ordinal information). On the one hand, such comparisons provide an "absolute" indicator of the user's preference. On the other hand, it is often hard to combine or aggregate these comparisons to obtain a consistent global ranking. In this work, we provide a tractable framework for utilizing comparison data as well as first-order marginal information (see Section 2) for the purpose of ranking. We treat the available information as partial samples from an unknown distribution over permutations. We then reduce ranking problems of interest to performing inference on this distribution. Specifically, we consider the problems of (a) finding an aggregate ranking of n items, (b) learning the mode of the distribution, and (c) identifying the top k items. For many of these problems, we provide efficient algorithms to infer the ranking directly from the data without the need to estimate the underlying distribution. In other cases, we use the Principle of Maximum Entropy to devise a concise parameterization of a distribution consistent with observations using only O(n2) parameters, where n is the number of items in question. We propose a distributed, iterative algorithm for estimating the parameters of the distribution. We establish the correctness of the algorithm and identify its rate of convergence explicitly.
Neural Dynamic Programming for Musical Self Similarity

We present a neural sequence model designed specifically for symbolic music. The model is based on a learned edit distance mechanism which generalises a classic recursion from computer sci- ence, leading to a neural dynamic program. Re- peated motifs are detected by learning the transfor- mations between them. We represent the arising computational dependencies using a novel data structure, the edit tree; this perspective suggests natural approximations which afford the scaling up of our otherwise cubic time algorithm. We demonstrate our model on real and synthetic data; in all cases it out-performs a strong stacked long short-term memory benchmark.
LIPG signaling promotes tumor initiation and metastasis of human basal-like triple-negative breast cancer

Current understanding of aggressive human basal-like triple-negative breast cancer (TNBC) remains incomplete. In this study, we show endothelial lipase (LIPG) is aberrantly overexpressed in basal-like TNBCs. We demonstrate that LIPG is required for in vivo tumorigenicity and metastasis of TNBC cells. LIPG possesses a lipase-dependent function that supports cancer cell proliferation and a lipase-independent function that promotes invasiveness, stemness and basal/epithelial-mesenchymal transition features of TNBC. Mechanistically, LIPG executes its oncogenic function through its involvement in interferon-related DTX3L-ISG15 signaling, which regulates protein function and stability by ISGylation. We show that DTX3L, an E3-ubiquitin ligase, is required for maintaining LIPG protein levels in TNBC cells by inhibiting proteasome-mediated LIPG degradation. Inactivation of LIPG impairs DTX3L-ISG15 signaling, indicating the existence of DTX3L-LIPG-ISG15 signaling. We further reveal LIPG-ISG15 signaling is lipase-independent. We demonstrate that DTX3L-LIPG-ISG15 signaling is essential for malignancies of TNBC cells. Targeting this pathway provides a novel strategy for basal-like TNBC therapy.
National Diet and Nutrition Survey: UK food consumption and nutrient intakes from the first year of the rolling programme and comparisons with previous surveys

The National Diet and Nutrition Survey (NDNS) is a cross-sectional survey designed to gather data representative of the UK population on food consumption, nutrient intakes and nutritional status. The objectives of the present paper were to identify and describe food consumption and nutrient intakes in the UK from the first year of the NDNS rolling programme (2008–09) and compare these with the 2000–01 NDNS of adults aged 19–64 years and the 1997 NDNS of young people aged 4–18 years. Differences in median daily food consumption and nutrient intakes between the surveys were compared by sex and age group (4–10 years, 11–18 years and 19–64 years). There were no changes in energy, total fat or carbohydrate intakes between the surveys. Children aged 4–10 years had significantly lower consumption of soft drinks (not low calorie), crisps and savoury snacks and chocolate confectionery in 2008–09 than in 1997 (all P < 0·0001). The percentage contribution of non-milk extrinsic sugars to food energy was also significantly lower than in 1997 in children aged 4–10 years (P < 0·0001), contributing 13·7–14·6 % in 2008–09 compared with 16·8 % in 1997. These changes were not as marked in older children and there were no changes in these foods and nutrients in adults. There was still a substantial proportion (46 %) of girls aged 11–18 years and women aged 19–64 years (21 %) with mean daily Fe intakes below the lower reference nutrient intake. Since previous surveys there have been some positive changes in intakes especially in younger children. However, further attention is required in other groups, in particular adolescent girls.
A Latent Group Model for Group Recommendation

Increasingly, thousands of mobile services are provided by mobile Internet service portals. In order to push information that users may fond of, recommender system is needed. In some circumstances, recommendation to groups is necessary, e.g., Recommending movies to a group of friends. In reality, users are in some hidden social network, which can be viewed as groups. So group recommendation is proposed. Time efficiency is a key problem in mobile group recommendation. Research on group recommendation have concentrated on two approaches: aggregating members' ratings into a group profile and aggregating users' recommendations into a group recommendation list. This paper proposes a latent group model LGM, based on the assumption that users are influenced implicitly by some latent factors. LGM presents a novel route to detect groups by taking latent factors into account and makes users' profiles exist in latent factor format. Then users' latent factor profiles are aggregated into a group profile and multiplying method is used for group recommendation. This paper compares LGM with two approaches proposed before in efficiency and accuracy. It achieves better efficiency and accuracy for group recommendation on Movie Lens dataset.
Sialic acid-to-urea ratio as a measure of airway surface hydration.

Although airway mucus dehydration is key to pathophysiology of cystic fibrosis (CF) and other airways diseases, measuring mucus hydration is challenging. We explored a robust method to estimate mucus hydration using sialic acid as a marker for mucin content. Terminal sialic acid residues from mucins were cleaved by acid hydrolysis from airway samples, and concentrations of sialic acid, urea, and other biomarkers were analyzed by mass spectrometry. In mucins purified from human airway epithelial (HAE), sialic acid concentrations after acid hydrolysis correlated with mucin concentrations (r2 = 0.92). Sialic acid-to-urea ratios measured from filters applied to the apical surface of cultured HAE correlated to percent solids and were elevated in samples from CF HAEs relative to controls (2.2 ± 1.1 vs. 0.93 ± 1.8, P < 0.01). Sialic acid-to-urea ratios were elevated in bronchoalveolar lavage fluid (BALF) from β-epithelial sodium channel (ENaC) transgenic mice, known to have reduced mucus hydration, and mice sensitized to house dust mite allergen. In a translational application, elevated sialic acid-to-urea ratios were measured in BALF from young children with CF who had airway infection relative to those who did not (5.5 ± 3.7 vs. 1.9 ± 1.4, P < 0.02) and could be assessed simultaneously with established biomarkers of inflammation. The sialic acid-to-urea ratio performed similarly to percent solids, the gold standard measure of mucus hydration. The method proved robust and has potential to serve as flexible techniques to assess mucin hydration, particularly in samples like BALF in which established methods such as percent solids cannot be utilized.
Hybrid Decision Making: When Interpretable Models Collaborate With Black-Box Models

Interpretable machine learning models have received increasing interest in recent years, especially in domains where humans are involved in the decision-making process. However, the possible loss of the task performance for gaining interpretability is often inevitable. This performance downgrade puts practitioners in a dilemma of choosing between a top-performing black-box model with no explanations and an interpretable model with unsatisfying task performance. In this work, we propose a novel framework for building a Hybrid Decision Model that integrates an interpretable model with any black-box model to introduce explanations in the decision making process while preserving or possibly improving the predictive accuracy. We propose a novel metric, explainability, to measure the percentage of data that are sent to the interpretable model for decision. We also design a principled objective function that considers predictive accuracy, model interpretability, and data explainability. Under this framework, we develop Collaborative Black-box and RUle Set Hybrid (CoBRUSH) model that combines logic rules and any black-box model into a joint decision model. An input instance is first sent to the rules for decision. If a rule is satisfied, a decision will be directly generated. Otherwise, the black-box model is activated to decide on the instance. To train a hybrid model, we design an efficient search algorithm that exploits theoretically grounded strategies to reduce computation. Experiments show that CoBRUSH models are able to achieve same or better accuracy than their black-box collaborator working alone while gaining explainability. They also have smaller model complexity than interpretable baselines.
It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data

Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.