Miracle-dz/UniFAR-FTUs huggingface dataset:
Anterior decompression and fusion versus laminoplasty for cervical myelopathy due to ossification of posterior longitudinal ligament

Background: Both anterior decompression and fusion (ADF) and laminoplasty (LAMP) are frequently used for the treatment of cervical myelopathy due to ossification of the posterior longitudinal ligament (OPLL). However, some controversies still remained in surgical options. We investigated whether ADF had better neurological outcome than LAMP in the treatment of cervical myelopathy due to OPLL. Secondary outcomes included operation time, blood loss, rate of complication and reoperation. Methods: PubMed, EMBASE and the Cochrane Register of Controlled Trials database were searched to identify potential clinical studies compared ADF with LAMP for treatment of cervical myelopathy owing to OPLL. We also manually searched the reference lists of articles and reviews for possible relevant studies. Quality assessment was performed according to Cochrane Handbook and meta-analysis was conducted using Stata 12.0 software. Results: Nine studies involving 712 patients were finally included in this analysis. Compared with LAMP, ADF was associated with an increase of the Japanese Orthopaedic Association (JOA) score (WMD = 1.86, 95% CI 0.43 to 3.29, P = .011) and recovery JOA score at final follow-up (WMD = 30.94, 95% CI 20.56 to 41.33, P = .000). And, ADF was associated with a decrease of the late neurologic deterioration than LAMP group (RR = 0.34, 95% CI 0.12 to 0.92, P = .003). However, ADF was associated with an increase of the postoperative cervical lordosis (WMD = 4.47, 95% CI 1.58 to 7.36, P = .002) than LAMP. There was no significant difference between the complication, reoperation rate (P > .05). What's more, ADF was associated with an increase of the operation time than LAMP (P < .05). Conclusions: ADF yields better neurological improvement, but higher cervical lordosis and longer operation time compared with LAMP for cervical myelopathy caused by OPLL. No significant difference was found in the complication and re-operation rate.
Experimental polycystic ovarian syndrome is associated with reduced expression and function of P2Y2 receptors in rat theca cells

Extracellular purines through specific receptors have been recognized as new regulators of ovarian function. It is known that P2Y2 receptor activity induces theca cell proliferation, we hypothesized that purinergic signaling controls the changes related to hyperthecosis in polycystic ovarian syndrome (PCOS). The aim of this study was to analyze the expression of UTP‐sensitive P2Y receptors and their role in theca cells (TC) proliferation in experimentally‐induced PCOS (EI‐PCOS). In primary cultures of TC from intact rats, all the transcripts of P2Y receptors were detected by polymerase chain reaction; in these cells, UTP (10 μM) induced extracellular signal‐regulated kinases (ERK) phosphorylation. Rats with EI‐PCOS showed a reduced expression of P2Y2R in TC whereas P2Y4R did not change. By analyzing ERK phosphorylation, it was determined that P2Y2R is the most relevant receptor in TC. UTP promoted cell proliferation in TC from control but not from EI‐PCOS rats. The in silico analysis of P2yr2 promoter indicated the presence of androgen response elements; the stimulation of TC primary cultures with testosterone promoted a significant reduction in the expression of the P2yr2 transcript. We concluded that P2Y2R participates in controlling the proliferative rate of TCs from healthy ovaries, but this regulation is lost during EI‐PCOS.
Determination of the Environmental Pollution Potential of Some Herbicides by the Assessment of Cytotoxic and Genotoxic Effects on Allium cepa

The present study aims to evaluate the potential for the pollution of the environment by two herbicides (quizalofop-p-ethyl and cycloxydim), using the Allium test. The species in question is Allium cepa (onion, 2n = 16), one of the most common plant indicators of environmental pollution. The working method consisted of obtaining the meristematic roots of Allium cepa and their treatment with herbicides at three different concentrations (0.5%, 1%, and 1.5%) for each herbicide for 24 h, for comparison with an untreated control. The results obtained from the cytological study indicated a strong cytotoxic and genotoxic effect for both herbicides, but especially for quizalofop-p-ethyl, where the mitotic index decreased from 30.2% (control) to 9.6% for the variant treated with 1.5% herbicide. In this case, a strong mitodepressive effect was shown by a highly significant percentage (35.4%) of chromosomal aberrations and nuclear alterations: stickiness, fragments, C-mitosis, lobulated nucleus, micronuclei, and nuclear erosion. The mitodepressive effect as well as the percentage of chromosomal aberrations increased with a higher herbicide concentration. The obtained results suggest the strong potential for pollution of the two herbicides, particularly at concentrations higher than 0.5%; therefore, we recommend caution in their use to avoid undesirable effects on the environment.
Change in the Distribution of Phosphorus Fractions in Aggregates under Different Land Uses: A Case in Sanjiang Plain, Northeast China

Phosphorus in agro-ecosystems has attracted much attention due to its impact on the nutrient supply of plants and the risk of loss of non-point source pollution. This study investigated the fraction distribution and release of phosphorus from soil aggregates structure under different land uses (rice, maize and soybean). The soil aggregates were characterized as large macro-aggregates (L-mac, >1 mm), small macro-aggregates (S-mac, 0.25–1 mm), micro-aggregates (MIC, 0.053–0.25 mm) and silt clay (SC, <0.053 mm) with the wet-sieving method. A sequential chemical extraction scheme was used to separate phosphorus into labile inorganic phosphorus (L-Pi), labile organic phosphorus (L-Po), moderately labile organic phosphorus (Ml-Po), iron-aluminum bound phosphorus (Fe.Al-P), calcium-magnesium bound phosphorus (Ca.Mg-P), humic phosphorus (Hu-P) and residual phosphorus (Re-P). Experimental results indicated that soil aggregates were mainly S-mac and MIC, followed by L-mac and SC, and they accounted for 52.16%, 25.20%, 14.23% and 8.49% in rice fields, 44.21%, 34.61%, 12.88% and 8.30% in maize fields, and 28.87%, 47.63%, 3.52% and 19.99% in soybean fields, respectively. Total nitrogen (TN), soil organic matter (SOM), Fe and Mn in soil aggregate fractions decreased with the reduction in soil aggregate grain-sizes. For phosphorus fractions (P-fractions), Fe.Al-P and Re-P tended to condense in L-mac and S-mac. MIC and SC were the primary carriers of Ca.Mg-P. Adsorption isotherm simulation results demonstrated that L-mac and S-mac have a strong capacity to retain phosphorus. In rice fields, phosphorus bioavailability and utilization rate were high. However, the P-fractions there were easily changed under aerobic-anaerobic conditions. Therefore, the risk of phosphorus loss during drainage should be given considerable attention.
miR-3664-5P suppresses the proliferation and metastasis of gastric cancer by attenuating the NF-κB signaling pathway through targeting MTDH

Gastric cancer (GC) is one of the most common and fatal types of cancers worldwide and the specific mechanism has not been completely elucidated. microRNA (miR)‑3664‑5P has rarely been studied and the aim of the present study was to assess an association between miR‑3664‑5P and GC. Differences in miR‑3664‑5P expression in 100 GC (0.1846±0.08276) and paired normal tissues (0.4382±0.1595) were detected using reverse transcription‑quantitative polymerase chain reaction assays (P<0.001). 5‑Ethynyl‑2‑deoxyuridine, Cell Counting Kit‑8, transwell and flow cytometry assays were performed in vitro and the results were further verified using a mouse xenotransplantation and a lung metastasis model in vivo. miR‑3664‑5P was significantly downregulated in GC tissues when compared with normal tissues and positively associated with the prognosis of patients with GC (P<0.001). Overexpression of miR‑3664‑5P suppressed and miR‑3664‑5P knockdown promoted the proliferation and metastasis of GC cells in vitro and in vivo. Following the application of bioinformatic algorithms and luciferase reporter assays, metadherin (MTDH) was confirmed as the target gene of miR‑3664‑5P. miR‑3664‑5P reduced MTDH expression and downregulated the nuclear factor (NF)‑κB signaling pathway. Rescue experiments demonstrated that suppression of MTDH restored the effect of miR‑3664‑5P inhibitors on GC cell lines. The results suggested that miR‑3664‑5P suppressed the proliferation and metastasis of GC cells by attenuating the NF‑κB signaling pathway via MTDH targeting. Consequently, miR‑3664‑5P may have potential to be an independent prognostic factor and biomarker in GC.
Health Status of Female and Male Vegetarian and Vegan Endurance Runners Compared to Omnivores—Results from the NURMI Study (Step 2)

Health effects of vegetarian and vegan diets are well known. However, data is sparse in terms of their appropriateness for the special nutritional demands of endurance runners. Therefore, the aim of this study was to investigate the health status of vegetarian (VER) and vegan endurance runners (VGR) and compare it to omnivorous endurance runners (OR). A total of 245 female and male recreational runners completed an online survey. Health status was assessed by measuring health-related indicators (body weight, mental health, chronic diseases, and hypersensitivity reactions, medication intake) and health-related behavior (smoking habits, supplement intake, food choice, healthcare utilization). Data analysis was performed by using non-parametric ANOVA and MANOVA. There were 109 OR, 45 VER and 91 VGR. Significant differences (p < 0.05) were determined for the following findings: (i) body weight for VER and VGR was less than for OR, (ii) VGR had highest food choice scores, and (iii) VGR reported the lowest prevalences of allergies. There was no association (p > 0.05) between diet and mental health, medication intake, smoking habits, supplement intake, and healthcare utilization. These findings support the notion that adhering to vegetarian kinds of diet, in particular to a vegan diet, is associated with a good health status and, thus, at least an equal alternative to an omnivorous diet for endurance runners.
Endoscopic submucosal dissection of papillary adenocarcinoma of stomach; protocol for a systematic review and meta-analysis

Background: Endoscopic submucosal dissection (ESD) is a primary treatment for the early gastric cancer (EGC) who has a negligible risk of lymph node metastasis. Papillary adenocarcinoma (PAC) of stomach is a rare histologic variant of gastric cancer and categorized into EGC with differentiated-histology. However, aggressive features such as higher rate of lymphovascular invasion (LVI) or submucosal invasion have been reported, whereas comparable lymph node metastasis (LNM) rate to the lesions meeting the current ESD criteria also has been reported. This study aimed to evaluate the feasibility of ESD for EGC with PAC. Methods: We will search the core databases (MEDLINE (through PubMed), the Cochrane Library, and Embase) from their inception to December 2018 by 2 independent evaluators. The P.I. C.O. is as follows; Patients: who have EGC with PAC, Intervention: ESD or surgery, Comparison: none, Outcome: at least one among the rate of complete resection, curative resection, en bloc resection, recurrence, procedure-related adverse event, LVI or LNM that enabled an evaluation of the feasibility of ESD. All types of study design with full text will be sought and included. The risk of bias will be assessed using the ROBINS-I tool. Descriptive data synthesis is planned, and quantitative synthesis will be used if the included studies are sufficiently homogenous. Publication bias will be assessed with quantitative analyses if more than 10 articles are enrolled. Results: The results will provide evidence for efficacy and safety of ESD for EGC with PAC. Conclusion: This study will provide evidence of ESD for EGC with PAC.
Bispectral index monitoring to assess the level of consciousness in patients with brain injury.

BACKGROUND It is important to assess the level of consciousness in patients with brain injuries to determine modes of treatment and prognosis. We evaluated the Bispectral Index (BIS) to determine if it could be used as an objective tool for evaluation of the level of consciousness in brain-injured patients. We also compared the BIS values to clinical sedation scales such as the Glasgow Coma Scale (GCS), Richmond Agitation-Sedation Scale (RASS), and the Reaction Level Scale (RLS). METHODS Thirty eight patients with brain injuries that were admitted to the neurosurgery intensive care unit (NSICU) were enrolled in this study. An investigator evaluated the clinical sedation scales (GCS, RASS, RLS), while a blind observer noted the BIS in the same patient. The BIS score was obtained three times at an interval of 5 hours. The BISs were measured for 1 minute at 5 min prior to the nursing assessment, during the nursing assessment, and at 5 min after the nursing assessment. The BISs used in the data analysis were the maximal, minimal, and mean values obtained during 1 min, which were defined as BISmax, BISmin, and BISmean. A Spearman's rank correlation coefficient was used to determine if the clinical sedation scales were correlated with the BIS scores. RESULTS In 38 patients, the BISmax, BISmin, and BISmean were found to be significantly correlated with the GCS, RASS, and RLS. The BISmean had the highest correlation with GCS (r = 0.445, P < 0.01), while the BIS min had the lowest correlation with RLS (r = -0.278, P < 0.01). CONCLUSIONS The results of BIS monitoring were found to be significantly correlated with sedation scales in patients with brain injuries. These findings suggest that BIS can be used as an objective and continuous method for assessment of the level of consciousness in patients with brain injury.
Impaired olfaction is associated with cognitive decline and neurodegeneration in the brain

Objective We aimed to examine whether impaired olfaction is associated with cognitive decline and indicators of neurodegeneration in the brain of dementia-free older adults. Methods Within the Rush Memory and Aging Project, 380 dementia-free participants (mean age = 78 years) were followed for up to 15 years, and underwent MRI scans. Olfactory function was assessed using the Brief Smell Identification Test (B-SIT) at baseline, and categorized as anosmia (B-SIT <6), hyposmia (B-SIT 6–10 in men and 6–10.25 in women), and normal (B-SIT 10.25–12 in men and 10.5–12 in women). Cognitive function was annually assessed with a battery of 21 tests, from which composite scores were derived. Structural total and regional brain volumes were estimated. Data were analyzed using linear regression and mixed-effects models. Results At study entry, 138 (36.3%) had normal olfactory function, 213 (56.1%) had hyposmia, and 29 (7.6%) had anosmia. In multiadjusted mixed-effects models, hyposmia (β = −0.03, 95% confidence interval [CI] −0.05 to −0.02) and anosmia (β = −0.13, 95% CI −0.16 to −0.09) were associated with faster rate of cognitive decline compared to normal olfaction. On MRI, impaired olfaction (hyposmia or anosmia) was related to smaller volumes of the hippocampus (β = −0.19, 95% CI −0.33 to −0.05), and in the entorhinal (β = −0.16, 95% CI −0.24 to −0.08), fusiform (β = −0.45, 95% CI −0.78 to −0.14), and middle temporal (β = −0.38, 95% CI −0.72 to −0.01) cortices. Conclusion Impaired olfaction predicts faster cognitive decline and might indicate neurodegeneration in the brain among dementia-free older adults.
Teeth Classification Based on Extreme Learning Machine

Teeth are important organs in vertebrates. The accurate classification of teeth is beneficial for dentists to diagnose. In this manuscript, we put forward a novel teeth classification algorithm combined principle component analysis and extreme learning machine. Principle component analysis was used to extract features from the teeth images and extreme learning machine served as the classifier. Experimental results suggested that our method is effective in identification of four categories of teeth.
Measurement of Location and Attitude for Bookstore Management Robot in Narrow Path

This paper deals with the location problem for the bookstore management mobile robot in the narrow path. The objective is to study measuring approach of the point location and the attitude angle for the bookstore management mobile robot based on radio frequency identification (RFID) technology. First, the installation position of the RFID reader and writer is designed. Then, the data encoding rules of the RFID label are formulated. Finally, the measurement algorithm of the point location and the attitude angle is proposed. This location approach provides the state feedback information for the path control system of the mobile robot without increasing the hardware cost of the system, only in the robot controller to increase the location algorithm in the control software. It is especially suitable for the fixed-point target control of the library management mobile robot in the narrow path.
Open Platform Systems Under Scrutiny: A Cybersecurity Analysis of the Device Tree

Embedded systems are deployed in systems ranging from personal devices to systems deployed in critical infrastructure. Due to the advent of Industry 4.0 and the rising need for platform transparency and uniformity, developers opt for open-source modular system software that replace the conventional monolithic firmware approach. A common part of the modern embedded system is the Device Tree, a hardware description structure leveraged by the OS kernel. This modular approach and its malleability, however, can be exposed as a vulnerability and enable an array of attacks that would disrupt and destabilize the system. In this paper, we consider the Device Tree from a cybersecurity perspective. We identify scenarios that would enable covert attacks on a target system as well as propose countermeasures from the state-of-the-art.
Cubic SVM Classifier Based Feature Extraction and Emotion Detection from Speech Signals

The detection of emotions from the speech is one of the most stirring and intriguing research areas in the field of artificial intelligence. In this paper, the emotion identification from Hindi language speech which is a popular language of India is carried out in a noisy environment after which multifarious emotions are classified into 4 main groups of emotional states namely happiness, sadness, anger and neutral. The proposed technique involves extraction of prosodic and spectral features of an acoustic signal like pitch, energy, formant, Mel-frequency Cepstrum Coefficients (MFCC) and Linear Prediction Cepstral Coefficient (LPCC) along with their classification using a cubic spine Support Vector Machine (SVM) classifier model. The system gave an overall accuracy of, 98.75% in male actor utterances and 95% in female actors. Experimental results manifest that the proposed technique garners better accuracy by correctly identifying the emotions and these results were moreover compared to the other existing methods of speech emotion detection. Furthermore, the extracted features along with, different classifier models were contrasted in this paper for better evaluation.
Survey: Deep Learning Concepts and Techniques for Electronic Health Record

There is a huge amount of information that have been stored in electronic health records (EHR). EHR designed to store patient medical information and administrative tasks. While a lot of researchers employed these records in clinical informatics tasks. Recently, researcher's community discovered the advances of deep learning, that greatly applied to EHR. In this paper, we review deep learning concepts, technical aspects that applied on EHR. As well as, some of the recent deep learning projects in EHR with challenges.
DTCS: An Integrated Strategy for Enhancing Data Trustworthiness in Mobile Crowdsourcing

Mobile crowdsourcing systems (MCSs) are important sources of information for the positioning services in Internet-of-Things such as gathering location information through employing citizens to participate in data collection. Although MCSs have attracted significant research and development efforts, there are salient open issues and challenges in security and privacy for MCS, which is an essential factor for its success. This paper proposes an integrated strategy named data trustworthiness enhanced crowdsourcing strategy (DTCS) to enhance data trustworthiness and defend against the internal threats for mobile crowdsourcing. The DTCS integrates effective methods including an evaluation scheme for the attribute relevancy and familiarity of participants, a trust relationship establishment method, a group division strategy based on attributes and metagraph, and a core-selecting-based incentive mechanism. The simulation results show that the DTCS improves the performance of the crowdsourcing strategy compared to the state-of-the-art including the TSCM and PPPCM. The DTCS can effectively defend against internal conflicting behavior attacks and collusion attacks to enhance data trustworthiness for mobile crowdsourcing.
Coupling Matrix Synthesis and Design of a Chained-Function Waveguide Filter

This paper presents, for the first time, the coupling matrix synthesis for the application of a chained-function to waveguide filters. The realization of this filter is carried out by successively adding one resonator at a time by comparing the simulated responses of each stage with those obtained using the coupling matrix synthesized from the chained-function. A sixth-order chained-function waveguide bandpass filter centered at 28 GHz with a fractional bandwidth of 2% is demonstrated. The simulated S-parameter responses and the sensitivity analysis pertaining to the manufacturing errors is included. The chained-function waveguide has the lowest percentage changes in terms of the return loss performance given the same amount of distortion as compared with those of the Chebyshev response filters. This results in a lower sensitivity rate with respect to the manufacturing errors, leading to achieving a high-performance filter implementation with a minimum tuning effort.
Sentiment Analysis for Arabic Text using Ensemble Learning

In this paper, an ensemble of machine learning classifiers approach is used to classify the sentiment polarity of Arabic text. This approach is based on the majority voting algorithm in conjunction with four classifiers, namely Naive Bayes, Support Vector Machines, Decision Trees and K-Nearest Neighbor algorithms. Four combinations of these classifiers are formed and three classifiers are chosen for each voting combination. The performance of each classifier is evaluated and compared to ensemble voting combination performance. Different experiments have been performed to evaluate unigram and bigram features. Three datasets with different sizes are used in our experiments. The first dataset contains 500 movie reviews, the second one contains 2000 Arabic tweets and the third one contains 16448 of Arabic book reviews. The experimental results show that the ensemble of the classifiers comparatively gives better results than individual classifiers. They also reveal that the support vector machine classifier outperforms the other individual classifiers. Moreover, the results of the bigram feature are better than the results of the unigram feature.