LLukas22/scidocs huggingface dataset:
Proteomics to investigate fertility in bulls

In dairy cattle breeding, herd reproductive management is the primary focus, affecting a large part of the general costs. A negative association was observed between the level of milk production and fertility. Some studies have shown that a significant percentage of reproductive failure is attributable to semen quality; therefore, if reproduction management is based on artificial insemination, then it is important to assess the fertility level of the sires. In this study, proteomic analysis was used to compare the protein expression profiles from sperm of high- and low-fertility bulls. Comparative proteomic analysis showed that expression of several proteins [nine different two-dimensional electrophoresis (2-DE) spots] is related to fertility level (p ≤ 0.05). These proteins are involved in sperm-egg interactions and cell cycle regulation. Differences in protein expression levels might explain reductions in fertility due to mistakes in sperm-oocyte communication or in cell cycle regulation. Proteomics of sperm can be a valuable tool to identify protein expression changes related to fertility; in particular, 2-DE-based proteome analysis is very useful for the characterization of spermatozoa protein expression related to high- and low-fertility rates. Furthermore, analysis of expression profiles could be critical to the identification of protein biomarkers of bull fertility.
Improving the capture of fall events in hospitals: combining a service for evaluating inpatient falls with an incident report system.

OBJECTIVES
To determine the utility of a fall evaluation service to improve the ascertainment of falls in acute care.


DESIGN
Six-month observational study.


SETTING
Sixteen adult nursing units (349 beds) in an urban, academically affiliated, community hospital.


PARTICIPANTS
Patients admitted to the study units during the study period.


INTERVENTION
Nursing staff identifying falls were instructed to notify, using a pager, a trained nurse "fall evaluator." Fall evaluators provided 24-hour-per-day 7-day-per-week coverage throughout the study. Data on patient falls gathered by fall evaluators were compared with falls data obtained through the hospital's incident reporting system.


RESULTS
During 51,180 patient-days of observation, 191 falls were identified according to incident reports (3.73 falls/1,000 patient-days), whereas the evaluation service identified 228 falls (4.45 falls/1,000 patient-days). Combining falls reported from both data sources yielded 266 falls (5.20 falls/1,000 patient-days), a 39% relative rate increase compared with incident reports alone (P<.001). For falls with injury, combining data from both sources yielded 79 falls (1.54 injurious falls/1,000 patient-days), compared with 57 falls (1.11 injurious falls/1,000 patient-days) filed in incident reports--a 28% increase (P=.06). In the 16 nursing units, the relative percentage increase of captured fall events using the combined data sources versus the incident reporting system alone ranged from 13% to 125%.


CONCLUSION
Incident reports significantly underestimate both injurious and noninjurious falls in acute care settings and should not be used as the sole source of data for research or quality improvement initiatives.
Finline Ortho-Mode Transducer for Millimeter Waves

We evaluate the possibility of using a finline orthomode transducer (OMT) at millimeter wavelengths. A finline OMT has low loss, low cross-polarization, and good return loss over a full waveguide band. We propose a novel finline OMT structure for millimeter-wavelengths and present results at X-band.
Effect of probiotics and synbiotics on blood glucose: a systematic review and meta-analysis of controlled trials.

PURPOSE
High fasting blood glucose (FBG) can lead to chronic diseases such as diabetes mellitus, cardiovascular and kidney diseases. Consuming probiotics or synbiotics may improve FBG. A systematic review and meta-analysis of controlled trials was conducted to clarify the effect of probiotic and synbiotic consumption on FBG levels.


METHODS
PubMed, Scopus, Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature databases were searched for relevant studies based on eligibility criteria. Randomized or non-randomized controlled trials which investigated the efficacy of probiotics or synbiotics on the FBG of adults were included. Studies were excluded if they were review articles and study protocols, or if the supplement dosage was not clearly mentioned.


RESULTS
A total of fourteen studies (eighteen trials) were included in the analysis. Random-effects meta-analyses were conducted for the mean difference in FBG. Overall reduction in FBG observed from consumption of probiotics and synbiotics was borderline statistically significant (-0.18 mmol/L 95 % CI -0.37, 0.00; p = 0.05). Neither probiotic nor synbiotic subgroup analysis revealed a significant reduction in FBG. The result of subgroup analysis for baseline FBG level ≥7 mmol/L showed a reduction in FBG of 0.68 mmol/L (-1.07, -0.29; ρ < 0.01), while trials with multiple species of probiotics showed a more pronounced reduction of 0.31 mmol/L (-0.58, -0.03; ρ = 0.03) compared to single species trials.


CONCLUSION
This meta-analysis suggests that probiotic and synbiotic supplementation may be beneficial in lowering FBG in adults with high baseline FBG (≥7 mmol/L) and that multispecies probiotics may have more impact on FBG than single species.
Developing a Reliable Core Stability Assessment Battery for Patients with Nonspecific Low Back Pain.

STUDY DESIGN
Test-retest design.


OBJECTIVE
The objective was to examine the intrarater (test-retest) reliability of the core stability related tests and to develop a reliable core stability assessment battery.


SUMMARY OF BACKGROUND DATA
Studies suggest that core stability exercises may improve function and decrease pain in patients with nonspecific low back pain (LBP). Reliable clinical tests are required to implement adequate rehabilitation and to evaluate results of these interventions.


METHODS
The study had a test-retest design. Thirty-three different tests that might relate to core stability were identified with their mostly used protocols. Five different components of core stability including endurance, flexibility, strength, functional performance, and motor control were assessed in 38 patients with nonspecific LBP. The same testing procedure was performed again after 48 to 72 hours. Intraclass correlation coefficients (ICCs), standard error of measurement, and minimal detectable change were calculated to assess the intrarater reliability.


RESULTS
The intrarater reliability of the tests ranged from little to very high (ICC = 0.08-0.98). Partial curl-up (ICC = 0.90), lateral bridge (ICC = 0.95-0.96), trunk flexor endurance (ICC = 0.97), sit and reach (ICC = 0.98), single-legged hop (ICC = 0.98-0.97), lateral step-down (ICC = 0.93-0.92), eyes open right and left leg unilateral stance (ICC = 0.97 and 0.91) tests had the highest intrarater reliability for each core stability component.


CONCLUSION
The results indicated that the partial curl-up test (strength), side bridge and trunk flexor tests (endurance), sit-and-reach test (flexibility), single-legged hop, and lateral step-down (functional), unilateral stance test with eyes open (motor control) had very high intrarater reliability. A core stability assessment battery involving these tests can be used in patients with nonspecific LBP to assess all components of core stability.


LEVEL OF EVIDENCE
3.
Global Models for the Orientation Field of Fingerprints: An Approach Based on Quadratic Differentials

Quadratic differentials naturally define analytic orientation fields on planar surfaces. We propose to model orientation fields of fingerprints by specifying quadratic differentials. Models for all fingerprint classes such as arches, loops and whorls are laid out. These models are parametrised by few, geometrically interpretable parameters which are invariant under Euclidean motions. We demonstrate their ability in adapting to given, observed orientation fields, and we compare them to existing models using the fingerprint images of the NIST Special Database 4. We also illustrate that these model allow for extrapolation into unobserved regions. This goes beyond the scope of earlier models for the orientation field as those are restricted to the observed planar fingerprint region. Within the framework of quadratic differentials we are able to verify analytically Penrose's formula for the singularities on a palm [L. S. Penrose, "Dermatoglyphics"' Scientific American, vol. 221, no.~6, pp. 73--84, 1969]. Potential applications of these models are the use of their parameters as indices of large fingerprint databases, as well as the definition of intrinsic coordinates for single fingerprint images.
Joint entity and relation extraction based on a hybrid neural network

Entity and relation extraction is a task that combines detecting entity mentions and recognizing entities’ semantic relationships from unstructured text. We propose a hybrid neural network model to extract entities and their relationships without any handcrafted features. The hybrid neural network contains a novel bidirectional encoder-decoder L STM module (BiL STM-ED) for entity extraction and a CNN module for relation classification. The contextual information of entities obtained in BiLSTM-ED further pass though to CNN module to improve the relation classification. We conduct experiments on the public dataset ACE05 (Automatic Content Extraction program) to verify the effectiveness of our method. The method we proposed achieves the state-of-the-art results on entity and relation extraction task. © 2017 Elsevier B.V. All rights reserved.
Maximum likelihood method for the analysis of time-resolved fluorescence decay curves

The usefulness of fluorescence techniques for the study of macromolecular structure and dynamics depends on the accuracy and sensitivity of the methods used for data analysis. Many methods for data analysis have been proposed and used, but little attention has been paid to the maximum likelihood method, generally known as the most powerful statistical method for parameter estimation. In this paper we study the properties and behavior of maximum likelihood estimates by using simulated fluorescence intensity decay data. We show that the maximum likelihood method provides generally more accurate estimates of lifetimes and fractions than does the standard least-squares approach especially when the lifetime ratios between individual components are small. Three novelties to the field of fluorescence decay analysis are also introduced and studied in this paper: a) discretization of the convolution integral based on the generalized integral mean value theorem: b) the likelihood ratio test as a tool to determine the number of exponential decay components in a given decay profile; and c) separability and detectability indices which provide measures on how accurately, a particular decay component can be detected. Based on the experience gained from this and from our previous study of the Padé-Laplace method, we make some recommendations on how the complex problem of deconvolution and parameter estimation of multiexponential functions might be approached in an experimental setting.
A 2D Finite Element Thermal Model of a Three-Phase-Inverter Heat Sink

This paper presents the analysis and simulation of a three-phase voltage-source inverter. The inverter is driven using space vector modulation and it has a dead time of 2 ?s. During this simulation, power losses on the power electronic switches are calculated and used on a thermal model. The thermal conduction equation was solved to predict the temperature distribution. The finite element numerical method was used to solve the thermal conduction equation. A C code finite element thermal model was programmed.
A precision agriculture management system based on Internet of Things and WebGIS

This thesis is based on the application of Internet of Things (IoT) and WebGIS in precision agriculture. Through analyzing the current development of precision agriculture in China and considering its advantages and shortcomings, we choose an ecology farm as an example to conduct a new precision agriculture management system (PAMS) based on the above two techniques. We designed the four architectures of PAMS: the spatial information infrastructure platform, the IoT infrastructure platform, the agriculture management platform and the mobile client. Users can monitor and manage the agriculture production by PAMS. What's more, module integration method and open source software can help us to reduce the development cost and to improve the system efficiency.
A systematic review of the clinical survival of zirconia implants

The aim of this review was to evaluate the clinical success and survival rates of zirconia ceramic implants after at least 1 year of function and to assess if there is sufficient evidence to justify using them as alternatives to titanium implants. An electronic search in MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Clinical Trials (CENTRAL) databases was performed in April 2015 by two independent examiners to retrieve clinical studies focusing on the survival rate of zirconia implants after at least 1 year of function. Implant survival was estimated using the overall proportion reported in the studies with a Clopper-Pearson 95 % confidence interval (random effect model with a Der-Simonian Laird estimate). Fourteen articles were selected out of the 1519 titles initially screened. The overall survival rate of zirconia one- and two-piece implants was calculated at 92 % (95 % CI 87–95) after 1 year of function. The survival of implants at 1 year for the selected studies revealed considerable heterogeneity. In spite of the unavailability of sufficient long-term evidence to justify using zirconia oral implants, zirconia ceramics could potentially be the alternative to titanium for a non-metallic implant solution. However, further clinical studies are required to establish long-term results, and to determine the risk of technical and biological complications. Additional randomized controlled clinical trials examining two-piece zirconia implant systems are also required to assess their survival and success rates in comparison with titanium as well as one-piece zirconia implants. Zirconia implants provide a potential alternative to titanium ones. However, clinicians must be aware of the lack of knowledge regarding long-term outcomes and specific reasons for failure.
A phase I/II study of XELIRI plus bevacizumab as second-line chemotherapy for Japanese patients with metastatic colorectal cancer (BIX study).

BACKGROUND
Capecitabine is used mainly with oxaliplatin to treat metastatic colorectal cancer (mCRC). Results from capecitabine plus irinotecan (XELIRI) with or without bevacizumab (BV) have been reported in Europe but not in Japan. Consequently, the safety and efficacy of XELIRI plus BV in Japanese patients with mCRC were assessed in a single-arm phase II study.


METHODS
Eligible patients had had prior chemotherapy containing BV for mCRC and wild-type or heterozygous UGT1A1. Therapy in each 21-day treatment cycle consisted of capecitabine (800 mg/m(2) twice daily on days 1-15), irinotecan (200 mg/m(2) on day 1), and BV (7.5 mg/kg on day 1). The primary endpoint was dose-limiting toxicity in phase I and progression-free survival (PFS) in phase II.


RESULTS
A total of 34 patients (6 in phase I, 28 in phase II) were enrolled from May 2010 to June 2011. Baseline characteristics included a median age of 60 years (range: 22-74 years) for 24 men and 10 women. No dose-limiting toxicities appeared in phase I. Median PFS was 240 days (95% confidence interval: 179-311 days). Overall response rate was 18.1%, and the disease-control rate was 90.9%. The incidence of adverse events frequently associated with irinotecan and capecitabine were neutropenia (any grade, 55.9%; grade 3 or 4, 11.8%), diarrhea (any grade, 50%; grade 3 or 4, 5.9%), and hand-foot syndrome (any grade, 61.8%; grade 3 or 4, 5.9%).


CONCLUSION
Our results suggest that XELIRI plus BV is well tolerated and effective as a second-line treatment for mCRC in Japanese patients. This regimen could be especially appropriate for patients resistant to oxaliplatin-based regimens.
The MRI-detected osteophyte score is a predictor for undergoing joint replacement in patients with end-stage knee osteoarthritis.

OBJECTIVES
The aim of this prospective cohort study was to examine whether MRI-detected osteoarthritis (OA)-structural changes at baseline could predict knee OA patients who would undergo total knee arthroplasty (TKA).


METHODS
In total, 128 end-stage medial-type knee OA patients were enrolled and followed up for 6 months. MRI using the whole-organ MRI scoring (WORMS) method, radiographic findings, visual analog scale (VAS) for pain and a patient-oriented outcome measure, and the Japanese Knee Osteoarthritis Measure (JKOM) were recorded at baseline. The area under the curve (AUC) was estimated to determine the discriminative value of the prediction models.


RESULTS
While 74 patients (57.8%) did not undergo TKA, the remaining 54 patients (42.2%) underwent TKA during this period. The AUCs of the receiver operating characteristic (ROC) curve for the activities of daily living (ADL) score evaluated by the JKOM ADL score [0.70 (95% CI: 0.60-0.79)] and osteophyte score [0.72 (0.64-0.81)] were 0.70 or greater. The JKOM ADL score (17/40) and the osteophyte score (30/98) showed relative risks (RR) of 2.61 (1.32-5.15) and 3.01 (1.39-6.52) for undergoing TKA, respectively.


CONCLUSION
The osteophyte score detected by MRI, in addition to ADL score, was found to be an important factor in determining whether the patient should undergo TKA.
Methods for Plant Data-Based Process Modeling in Soft-Sensor Development

There has been an increased use of soft-sensors in process industr y in recent years. These soft-sensors are computer programs that are used as a relatively cheap alternative to ha rdware sensors. Since process variables, which are concerned with final product quality, cannot always be mea sur d by hardware sensors, designing the appropriate soft-sensor can be an interesting solution. Additionally, a sof t-sensor can be used as a backup sensor, when the hardware sensor is in fault or removed due to maintenance or r eplacement. Soft-sensor is based on the mathematical model of the process. Since industrial processes are ge nerally quite complex, a theoretical modeling approach is often impractical, expensive or sometimes even impossible. Therefore, process model building is based on measured data. This approach significantly gets complicated if only plan t data, taken from the process database, are available. In this paper the most popular methods for plant data-bas ed modeling that appeared in the last two decades are summarized and briefly explained. Apart from giving a s hort urvey of the most important papers, tips about choosing the appropriate methodology for process model buildin g are also provided.
POCO-framework for Pareto-optimal resilient controller placement in SDN-based core networks

Recently, Software Defined Networking (SDN) has gained a lot of attention, even for the use in core communication networks. When deploying SDN in large core networks, the number and location of controllers must be carefully planned. A first study on this topic by Heller et al. [3] was followed by more detailed publications that included resilience or dynamic controller provisioning. In our previous work [2], we provide an overview over related work and include different resilience issues in the controller placement process. We argued that it is not sufficient to look only at node-to-controller latencies but a controller placement should also fulfill certain resilience constraints especially for the control plane.
LEARNING FROM DELAYED REWARDS USING INFLUENCE VALUES APPLIED TO COORDINATION IN MULTI-AGENT SYSTEMS

In this work we propose a new paradigm for learning coordination in multi-agent systems. This approach is based on social interaction of people, specially in the fact that people communicate to each other what they think about their actions and this opinion has some influence in the behavior of each other. We propose a model in which multi-agents learn to coordinate their actions giving opinions about the actions of other agents and also being influenced with opinions of other agents about their actions. We use the proposed paradigm to develop a modified version of the Q-learning algorithm. The new algorithm is tested and compared with independent learning (IL) and joint action learning (JAL) in a grid problem with two agents learning to coordinate. Our approach shows to have more probability to converge to an optimal equilibrium than IL and JAL Q-learning algorithms, specially when exploration increases. Also, a nice property of our algorithm is that it does not need to make an entire model of all joint actions like JAL algorithms. Keywords— Influence Value, Reinforcement Learning, Multi-agent coordination.
Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection

Due to deregulation of electricity industry, accurate load forecasting and predicting the future electricity demand play an important role in the regional and national power system strategy management. Electricity load forecasting is a challenging task because electric load has complex and nonlinear relationships with several factors. In this paper, two hybrid models are developed for short-term load forecasting (STLF). These models use “ant colony optimization (ACO)” and “combination of genetic algorithm (GA) and ACO (GA-ACO)” for feature selection and multi-layer perceptron (MLP) for hourly load prediction. Weather and climatic conditions, month, season, day of the week, and time of the day are considered as load-influencing factors in this study. Using load time-series of a regional power system, the performance of ACO + MLP and GA-ACO + MLP hybrid models is compared with principal component analysis (PCA) + MLP hybrid model and also with the case of no-feature selection (NFS) when using MLP and radial basis function (RBF) neural models. Experimental results and the performance comparison with similar recent researches in this field show that the proposed GA-ACO + MLP hybrid model performs better in load prediction of 24-h ahead in terms of mean absolute percentage error (MAPE).