Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring functions of long non-coding RNAs in HIV-associated neurocognitive disorder through weighted gene co-expression network analysis

// Jun Wang 1, 2, 3, * , Yongmei Yin 1, * , Jinhua Qiu 2 , Xiaoyan Wu 2 , Huaqian Dong 2 , Renfang Chen 4 , Huiqin Xing 2 and Lihua Huang 1, 3 1 Center of Clinical Laboratory, The Fifth People’s Hospital of Wuxi, Affiliated to Jiangnan University, Wuxi, Jiangsu 214005, China 2 Institute of Neuroscience, College of Medicine, Xiamen University, Xiamen, Fujian 361102, China 3 The international Joint Laboratory of Infection and Immunity (Chinese-Germany), Jiangnan University, Wuxi, Jiangsu 214005, China 4 Department of Infectious Diseases, The Fifth People’s Hospital of Wuxi, Affiliated to Jiangnan University, Wuxi, Jiangsu 214005, China * These authors have contributed equally to this work Correspondence to: Lihua Huang, email: huanglihua1964@sina.com Huiqin Xing, email: xhqw63@hotmail.com Keywords: HAND; lncRNA; WGCNA Received: September 04, 2017 Accepted: January 02, 2018 Published: January 09, 2018 ABSTRACT HIV-associated neurocognitive disorder (HAND) remains an unresolved issue of cognitive impairment. Due to treatment with combination antiretroviral therapy for nearly two decades, the severe form of HAND is rare, and most cases of HAND are the milder form. However, the proportion of HIV+ individuals with neurocognitive impairment has remained unchanged. Increasing evidence indicates that long non-coding RNAs (lncRNAs) play important roles in the pathogenesis of several types of neurodegenerative disorders. However, there are few relevant reports about the role of lncRNAs in promoting or delaying pathogenesis of HAND. Our study is the first to use the weighted gene co-expression network analysis approach with microarray data from the Gene Expression Omnibus to investigate the functions of lncRNAs that differ significantly between HIV+ patients without neurocognitive impairment and HIV+ patients with neurocognitive disorders. A total of 28 differentially expressed lncRNAs (DE-lncRNAs) were identified including 13 DE-lncRNAs in frontal white matter, 13 DE-lncRNAs in basal ganglia, and 2 DE-lncRNAs in frontal neocortex. Most of these DE-lncRNAs had not been previously reported to be related to HAND. Our co-expression network and function enrichment analysis indicate that DE-lncRNAs should play fundamental roles in HAND development and neurodegeneration associated with synaptic plasticity and synaptogenesis, neuronal inflammation and apoptosis, neurotransmitter transport and metabolism. Although the biological significance of DE-lncRNAs requires further evaluation, our study proposes a simple and efficient strategy to identify important lncRNAs associated with HAND and predict their potential functional roles, which may guide subsequent experimental studies.
Retrospective Analyses of 200 Patients Over 65 Years Receiving Albumin Treatment in Intensive Care Unit

The aim of this study is to evaluate the effectiveness of albumin infusion in critically ill geriatric patients and discuss the prognostic impact on mortality. This study is a retrospective analysis of patients with hypoalbuminemia (≤2.5 g/dl) over 65 years and older followed-up in a 38-bed-mixed medical-surgical tertiary intensive care unit (ICU) of a 650-bed-academic hospital affiliated to University of Health Sciences in Istanbul during 1-year-period. We collected the data including sex, cause of admission, length of stay, duration of mechanical ventilation and APACHE II scores from patients’ medical records. The amount of albumin infused per patient and laboratory evaluation concerning albumin levels was processed by hospital electronic database. Overall 200 patients were included study. Among the patients, 90 (45%) were female, and 110 (53.5%) were male with overall mean age of 78.62±7.63 years. The cause of admission was due to non-surgical reasons in 119(59.5%) patients. Mean length of stay and mechanical ventilation time was significantly higher in non-survivors. The amount of albumin used per patient was similar in both study groups (p=0.276). Pretreatment level of serum album was 2.15±0.28 g/dl and 2.10±0.31 g/dl in both groups respectively (p=0.364) having no significant increase after treatment (p=0.352). In the non-survivors group, APACHE II score was significantly high. Long-term mortality ratio was significantly high which indicated that albumin had no beneficial effect on long-term mortality. The albumin transfusion in critically ill patients over 65 years old has no considerable effect on the treatment of hypoalbuminemia and ICU mortality.
Information-theoretic locality properties of natural language

I present theoretical arguments and new empirical evidence for an information-theoretic principle of word order: information locality, the idea that words that strongly predict each other should be close to each other in linear order. I show that information locality can be derived under the assumption that natural language is a code that enables efﬁcient communication while minimizing information-processing costs involved in online language comprehension, using recent psycholinguistic theories to characterize those processing costs information-theoretically. I argue that information locality subsumes and extends the previously-proposed principle of dependency length minimization (DLM), which has shown great explanatory power for predicting word order in many languages. Finally, I show corpus evidence that information locality has improved explanatory power over DLM in two domains: in predicting which dependencies will have shorter and longer lengths across 50 languages, and in predicting the preferred order of adjectives in English.
A Study on Antioxidant Properties and Antimicrobial Activity of Various Extracts of Carduus adpressus

Carduus cinsi, Anadolu halk tibbinda pek cok rahatsizligin giderilmesinde geleneksel olarak kullanilir. Bu calismada, Carduus adpressus ’un farkli bitki ekstrelerinin potansiyel antioksidan ozellikleri ve antimikrobiyal aktivitesi incelendi. Toplam polifenoller, toplam fenolik ve toplam flavonoid icerikleri sirasiyla fosfomolibdat, Folin-Chiocalteu reaktifi ve AlCl 3 ile yapildi. Antioksidan aktivite, ABTS (2,2´-azino-bis, 3-etilbenzotiazolin-6-sulfonik asit) ve DPPH (2,2-difenil-1-pikrilhidrazil) metodlari kullanilarak tespit edildi. Antimikrobiyal aktivite ise, disk difuzyon ve mikrodilusyon yontemleri kullanilarak ortaya cikarildi. Etanol ekstresi en yuksek toplam antioksidan (377.9 mg GAE g-1 ekstre), flavonoid (60,06 mg QE g -1 ekstre) ve fenolik (291,03 mg GAE g -1 ekstre) icerige sahipti. Ayrica, en guclu radikal temizleme etkinligi, etanol ekstresinde (ABTS: 78,26 mg TE g -1 ekstre ve DPPH: 74,45 mg TE g -1 ekstre) tespit edildi. Tum ekstreler en az bir mikroorganizmaya karsi yuksek bir antimikrobiyal aktivite sergiledi. Sonuc olarak, C. adpressus bitkisinin dogal bir antioksidan ve antimikrobiyal ajan olarak kullanilma potansiyeli oldugu dusunulmektedir.
Chikungunya Virus Uveitis during French Polynesia Outbreak, 2014-2015

Chikungunya virus (CHIKV) is an emerging arthropod-borne virus (arbovirus) of the genus Alphavirus in the family Togaviridae. CHIKV is transmitted by the bite of infected mosquitoes; materno-foetal transmission and transmission via corneal graft have been reported. During the past decade, the status of chikungunya has changed, from a relatively uncommon and poorly documented disease, to an emerging disease, and now to a global public health concern. CHIKV now circulates in all inhabited continents. From 2011, CHIKV emerged in the Pacific region and was responsible for a massive outbreak in French Polynesia in 2014-2015 affecting about 25% of the population in a context of co-circulation with dengue virus (DENV). CHIKV mainly causes acute fever and severe and persistent polyarthralgia. Ocular involvement has been described during chikungunya fever but few were well-documented. We herein report a laboratory-confirmed case of CHIKV-associated uveitis during the French Polynesia outbreak.
Deep learning for face recognition on mobile devices

Mobility implies a great variability of capturing conditions, which is not easy to control and directly affects to face detection and the extraction of facial features. Deep learning solutions seem to be the most interesting choice for automatic face recognition, but they are highly dependent on the model generated during the training stage. In addition, the size of the models makes it difficult for their integration into applications oriented to mobile devices, particularly when the model must be embedded. In this work, a small-size deep-learning model was trained for face recognition on low capacity devices and evaluated in terms of accuracy, size and timings to provide quantitative data. This evaluation is aimed to cover as many scenarios as possible, so different databases were employed, including public and private datasets specifically oriented to recreate the complexity of mobile scenarios. Also, publicly available models and traditional approaches were included in the evaluation to carry out a fair comparison. Moreover, given the relevance of template matching and face detection stages, the assessment is complemented with different classifiers and detectors. Finally, a JAVA-Android implementation of the system was developed and evaluated to obtain performance data of the whole system integrated on a mobile phone.
The effect of Sharia bank knowledge, promotion, and facilities on savings decisions at Sharia banks with savings interest as mediation variables

Purpose - The purpose of this study is to explain the effect of Sharia bank knowledge, promotion, and facility on saving decisions in Shariah bank both directly and through saving interest.Method - The population in this study is the students of Sharia banking department at Islamic Business and Economics Faculty of IAIN Purwokerto Batch 2016/2017 of 106 students. All populations in this study were used as the sample. The technique of data collection in this study used questionnaires. Techniques of data analysis used descriptive statistic analysis and path analysis. Result - The results showed that Sharia bank knowledge, promotion, and facility give a positive and significant effect on saving decisions in Sharia Bank. Saving interest can mediate Sharia bank knowledge, promotion, and facility on saving decisions in Sharia Bank.Implication - Suggestion from this study are expected that Sharia Bank can fix the facility and improve promotions so that the customer candidate can know what kind of product offered so that saving interest and saving decision in Sharia Bank can be improved.Originality - This study adds Islamic bank knowledge and saving interest variable as the intervening variable.
100% automatic metrology with UAV photogrammetry and embedded GPS and its application in dike monitoring

The development of unmanned aerial vehicle (UAV) platform makes it nowadays avaluable source of data for inspection, surveillance, mapping and 3D modeling issues. UAV photogrammetry opens various new applications in close-range aerial domain and introduces a low-cost alternative to classical manned vehicle photogrammetry. Corridor mapping, one of the fields with substantial importance for UAV photogrammetry, is largely demanded in the surveillance and management of infrastructure assets – highways, railways, waterways and pipelines. Due to the special geometry of the scene, corridor mapping is one case that needs taking precautions. Errors accumulate easily on the longer dimension of the scene and a bowl effect is often observed; the flatness of the scene introduces strong correlations between parameters to be determi-ned, these two factors together, make it more difficult to obtain high accuracy results in corridor configuration than in block one. This thesis is a research project initiated by a river concessionary, which has the responsibility of maintenance and surveillance of its hydraulic facility – the dikes. The objective is to apply UAV photogrammetry, which is faster, cheaper and offers a continuous geometric surveillance, for the monitoring of dikes. While maintaining a high surveying accuracy, a reduced field work, i.e. the number of ground control points (GCPs) is expected.In the first place, simulations are carried out to gain an insight of several problems of corridor mapping that interest us. Then, thorough investigations are conducted on three aspects that interest us the most. The first study focuses on the aerial acquisition geometry, discussions are given on the influence of oblique images, nadir images of different flight heights as well as the possibility of an in-flight camera calibration and its application on unfavourable acquisition geometry. The second study aims to improve the acquisition performance for high-end metric cameras. The image deformation introduced by camera temperature variation is investigated and modelled. A method for the correction of this thermal effect is proposed, its performance is evaluated on both terrestrial and aerial datasets of corridor configuration. The last study interests in the rolling shutter effect for consumer-grade cameras with rolling shutter, which is commonly seen in UAV platforms on the market. Two methods are presented to calibrate the camera readout time, a property that is often not given by camera manufacturers. A two-step method is proposed for the correction of rolling shutter effect, its performance is evaluated in both block and corridor configurations
Nothing Ventured, Nothing Gained: Parasite Infection is Associated with Entrepreneurial Initiation, Engagement, and Performance

There is growing evidence that human biology and behavior are influenced by infectious microorganisms. One such microorganism is the protozoan Toxoplasma gondii (TG). Using longitudinal data covering the female population of Denmark, we extend research on the relationship between TG infection and entrepreneurial activity and outcomes. Results indicate that TG infection is associated with a subsequent increase in the probability of becoming an entrepreneur, and is linked to other outcomes including venture performance. With parasite behavioral manipulation antithetical to rational judgment, we join a growing conversation on biology and alternative drivers of business venturing.
Self-Gated Respiratory Motion Rejection for Optoacoustic Tomography

Respiratory motion in living organisms is known to result in image blurring and loss of resolution, chiefly due to the lengthy acquisition times of the corresponding image acquisition methods. Optoacoustic tomography can effectively eliminate in vivo motion artifacts due to its inherent capacity for collecting image data from the entire imaged region following a single nanoseconds-duration laser pulse. However, multi-frame image analysis is often essential in applications relying on spectroscopic data acquisition or for scanning-based systems. Thereby, efficient methods to correct for image distortions due to motion are imperative. Herein, we demonstrate that efficient motion rejection in optoacoustic tomography can readily be accomplished by frame clustering during image acquisition, thus averting excessive data acquisition and post-processing. The algorithm’s efficiency for two- and three-dimensional imaging was validated with experimental whole-body mouse data acquired by spiral volumetric optoacoustic tomography (SVOT) and full-ring cross-sectional imaging scanners.
Surface-enhanced Raman spectroscopy with partial least squares regression for rapid and accurate detection of malachite green in aquaculture water using large-size gold nanoparticles

Abstract Malachite green is not used in aquaculture owing to its severe toxic effects on humans and other living organisms. The presence of this compound in fishery water should be quantified and reported in all water quality reports. In this paper, a method based on surface-enhanced Raman scattering combined with partial least squares regression for rapid, accurate, reliable, and sensitive detection of trace malachite green in fishery water is described. A prepared sol of 100-nm-size gold particles was used as an active substrate for Raman spectral data acquisition. Three parameters that affect detection, namely, the volume of gold nanoparticles (400 µL), the volume of sodium chloride (300 µL), and the adsorption time (8 min) were optimized in single-factor experiments. Under the optimal condition, the proposed method demonstrated a wide linear range of detectable concentrations (0.01–5.0 µmol/L) and the limit of detection was 2.7 × 10−9 M. The proposed method successfully detected spiked malachite green in fishery water samples, with satisfactory recoveries and relative standard deviations. Thus, this study suggests a rapid, simple, accurate, and sensitive approach for detecting malachite green residues in fishery water samples, which is promising for wide applications in environmental monitoring and food safety.
Chameleon: Bringing Interactivity to Static Digital Documents

Documents such as presentations, instruction manuals, and research papers are disseminated using various file formats, many of which barely support the incorporation of interactive content. To address this lack of interactivity, we present Chameleon, a system-wide tool that combines computer vision algorithms used for image identification with an open database format to allow for the layering of dynamic content. Using Chameleon, static documents can be easily upgraded by layering user-generated interactive content on top of static images, all while preserving the original static document format and without modifying existing applications. We describe the development of Chameleon, including the design and evaluation of vision-based image replacement algorithms, the new document-creation pipeline as well as a user study evaluating Chameleon.
Comparative study of two control strategies proportional integral and fuzzy logic for the control of a doubly fed induction generator dedicated to a wind application

This paper presents a study analysis of a complete wind energy conversion system, the system based on a doubly fed induction generator (DFIG); a vector control with stator flux orientation of the DFIG is also used to control independently the active and reactive powers. A comparative study have been performed between the conventional PI controller and fuzzy logic control to investigate its dynamic and static performances. This research work involves the study of a phase in advance, to provide effective assistance, to all those who have to make decisions regarding the planning and implementation of wind energy projects. The main objective is to model the wind chain and the use of two types of strategies for the control of this generator to ensure a good regulation we started with the modeling of the wind chain then the modeling of the DFIG and then the use of the two strategies for the regulation of the latter . The complete system is modeled and simulated in the MATLAB/ Simulink. The performance and robustness are analyzed and compared by Matlab / Simulink . Simulation results prove the excellent performance of fuzzy control unit as improving power quality and stability of wind turbine.
A screening of the MMV Pathogen Box® reveals new potential antifungal drugs against the etiologic agents of chromoblastomycosis

Chromoblastomycosis (CBM) is a chronic subcutaneous mycosis caused by traumatic implantation of many species of black fungi. Due to the refractoriness of some cases and common recurrence of CBM, a more effective and less time-consuming treatment is mandatory. The aim of this study was to identify compounds with in vitro antifungal activity in the Pathogen Box® compound collection against different CBM agents. Synergism of these compounds with drugs currently used to treat CBM was also assessed. An initial screening of the drugs present in this collection at 1μM was performed with a Fonsecaea pedrosoi clinical strain according to the EUCAST protocol. The compounds with activity against this fungus were also tested against other seven etiologic agents of CBM (Cladophialophora carrionii, Phialophora verrucosa, Exophiala jeanselmei, Exophiala dermatitidis, Fonsecaea monophora, Fonsecaea nubica, and Rhinocladiella similis) at concentrations ranging from 0.039 to 10 μM. The analysis of potential synergism of these compounds with itraconazole and terbinafine was performed by the checkerboard method. Eight compounds inhibited more than 60% of the F. pedrosoi growth: difenoconazole, bitertanol, iodoquinol, azoxytrobin, MMV688179, MMV021013, trifloxystrobin, and auranofin. Iodoquinol produced the lowest MIC values (1.25 - 2.5μM) and MMV688179 showed MICs that were higher than all compounds tested (5 - >10μM). When auranofin and itraconazole were tested in combination, a synergistic interaction (FICI = 0.37) was observed against the C. carrionii isolate. In summary, auranofin, iodoquinol, and MMV021013 were identified as promising compounds to be tested in CBM models of infection.
Statistical Process Control with Intelligence Based on the Deep Learning Model

Statistical process control (SPC) is an important tool of enterprise quality management. It can scientifically distinguish the abnormal fluctuations of product quality. Therefore, intelligent and efficient SPC is of great significance to the manufacturing industry, especially in the context of industry 4.0. The intelligence of SPC is embodied in the realization of histogram pattern recognition (HPR) and control chart pattern recognition (CCPR). In view of the lack of HPR research and the complexity and low efficiency of the manual feature of control chart pattern, an intelligent SPC method based on feature learning is proposed. This method uses multilayer bidirectional long short-term memory network (Bi-LSTM) to learn the best features from the raw data, and it is universal to HPR and CCPR. Firstly, the training and test data sets are generated by Monte Carlo simulation algorithm. There are seven histogram patterns (HPs) and nine control chart patterns (CCPs). Then, the network structure parameters and training parameters are optimized to obtain the best training effect. Finally, the proposed method is compared with traditional methods and other deep learning methods. The results show that the quality of extracted features by multilayer Bi-LSTM is the highest. It has obvious advantages over other methods in recognition accuracy, despite the HPR or CCPR. In addition, the abnormal patterns of data in actual production can be effectively identified.