Miracle-dz/UniFAR-FTUs huggingface dataset:
Invited review: A systematic review of the effects of early separation on dairy cow and calf health

ABSTRACT Concern from the public is growing regarding early cow-calf separation, yet proponents of this practice maintain that artificial rearing is critical for cow and calf health. Early separation is assumed to reduce the risk of transfer of pathogens from dam to neonatal calf, but a wide range of health benefits associated with extended cow-calf contact has also been documented. The aim of this systematic review was to report and synthesize conclusions from the literature on dairy cow and calf health in conventional rearing versus cow-calf contact systems. Peer-reviewed, published manuscripts, written in English, directly comparing dairy cow or calf health in artificial versus suckling systems, were eligible for inclusion. We conducted 7 targeted searches using Web of Science to identify key literature on important health conditions. The resulting manuscripts underwent a 4-step appraisal process, and further manuscripts were sourced from reference lists. This process resulted in a final sample of 70 articles that addressed cow and calf health. Sufficient literature was available to assess mastitis in cows, and scours, cryptosporidiosis, Johne's disease, pneumonia, immunity, and mortality in calves. The results for cryptosporidiosis, pneumonia, immunity, and mortality were mixed, with some differences between studies likely attributable to flawed comparisons between cohorts. Overall, the articles addressing calf scours and mastitis pointed to beneficial or no effects of suckling. The studies addressing Johne's disease did not find cow-calf contact to be a significant risk factor. In conclusion, the scientific peer-reviewed literature on cow and calf health provides no consistent evidence in support of early separation.
Neuromarketing: A Review of Research and Implications for Marketing

In this research, we reviewed existing studies which used neuromarketing techniques in various fields of research. The results revealed that most attempts in neuromarketing have been made for business research. This research provides important results on the use of neuromarketing techniques, their limitations and implications for marketing research. We hope that this research will provide useful information about the neuromarketing techniques, their applications and help the researchers in conducting the research on neuromarketing with insight into the state-of-the-art of development methods.
The comment was uploaded in the form of a supplement:

Received and published: 3 October 2016 The comment was uploaded in the form of a supplement as follow: General comments: This manuscript presents very profound analyses with regard to the aerosol properties in Nanjing, China, using two years of surface-level aerosol observations in combination of coincident meteorological measurements. In particular, the scattering and absorbing properties of aerosols are investigated by linking with RH and visibility, on both diurnal and seasonal scales. The results obtained here gain great insight into how aerosol varies with meteorology, and how scattering aerosol can be differentiated from absorbing aerosols in the highly polluted but populous region of YRD. In this revision, the authors have put more efforts and make thorough changes per the referee's comments, both grammatically and scientifically, which increases its readability. Therefore, I recommend this work be published in ACP after the following concerns have been adequately addressed. To Referee #3: Dear reviewer, thank you very much for reviewing the manuscript and providing us the constructive comments and suggestions on our study. We have learned a lot from your advices. With respect to your comments, necessary revisions of the paper have been made. We will response to your comments carefully point by point; details of the revisions can be referred to the revised version of the manuscript. Relevant changes of the revised manuscript are listed in the last page.
The Effect of Mindfulness-Based Stress Reduction on Anxiety and Stress in Patients with Coronary Heart Disease after Coronary Artery Bypass Graft Surgery: A Single-Subject Study

*Correspondence: Rasoul Heshmati, Department of Psychology, Faculty of Educational Sciences and Psychology, Tabriz University, Tabriz, Iran psy.heshmati@gmail. com Abstract Background and objectives: Coronary artery disease (CAD) is associated with negative emotions of anger and stress, which are one of the nonclinical risk factors of the disease. Therefore, evaluation of the effective therapeutic models alleviating the patients’ negative emotions is of paramount importance. This study aimed to examine the effect of mindfulness-based stress reduction (MBSR) on anger and stress in patients with coronary heart disease after coronary artery bypass graft (CABG). Methods: This research had an A-B single-subject design. The research population encompassed all patients with coronary heart disease referring to Shahid Madani Hospital of Tabriz, Iran during the second half of 2016 for rehabilitation measures. Four patients were selected using convenience sampling. Before the interventions, the participants completed state-trait anger expression inventory (STAXI-2, Spielberger) and depression anxiety stress scale (DASS) throughout two consecutive weeks, which led to establishing two baselines. Afterwards, they received mindfulness-based stress reduction training during seven consecutive weeks and completed the questionnaires after each intervention. Percentage of recovery, percentage of non-overlapping data (PND), standard mean difference (SMD), and effect size were used to analyze the data, and eye diagram analysis was adopted to interpret the data. Results: The findings indicated that the mean recovery rate for all participants was 0.84 and its effect size index was 2.7. Moreover, the mean percentage of recovery and the effect size index of mindfulness intervention on the patients’ anger were 0.18 and 1.38, respectively. Conclusion: According to the results of the study, it could be concluded that mindfulness-based stress reduction is an effective intervention to regulate stress in patients suffering from coronary heart disease.
Challenges and new potential in COPD diagnosis and pulmonary function testing

Chronic obstructive pulmonary disease (COPD) is a chronic lung disease characterized by airflow limitation. All patients with relevant respiratory symptoms who have been exposed to known risk factors for COPD should be considered for diagnostic evaluation. A diagnosis of COPD is based on spirometry. A post-bronchodilator forced expiratory volume in 1 second/forced vital capacity (FEV1/FVC) < 0.7 confirms the presence of COPD. It is essential that COPD is diagnosed correctly so that appropriate treatment can be initiated. However, COPD remains highly undiagnosed and misdiagnosed. Spirometry is the key pulmonary function test in COPD diagnosis and monitoring even though spirometry proves problematic. The problems related to spirometry underline the need for alternative approaches in COPD pulmonary function testing.
A Novel Key Scheduling Algorithm for Lightweight Cryptographic Applications

Advancement in the area of wireless communication has increased the demands for lightweight security schemes as the sensor nodes and the devices used in these networks are resource constrained devices. One such area where the lightweight cryptographic system can be applied is the medical Internet of things. Since most of the devices in medical IOT is resource constrained it poses a challenge to traditional cryptographic algorithms which tend to lower the network performance as it requires high computational complexities, power consumption, and delays and hence the need for lightweight security in these devices. To prevent any unauthorized access or interruption it is crucial to ensure that data secrecy is maintained throughout the medical treatment. The most crucial thing in securing these resource constrained devices is the key used in encryption process since the entire security of the data is dependent on the key used. Even if the attacker has little knowledge of the key used in the encryption process, the entire security of the system can be lost. In this paper a lightweight key scheduling algorithm is proposed which can be used to generate the keys to be applied for encryption process in IOT devices used in medical care to improve the security of data transmitted in the healthcare environment.
Endoscopy lists Distribution between Medical and Surgical Gastroenterology: What’s The Impact on Work Force?

Background: Minimum number of endoscopic procedures must be done to maintain accreditation as an endoscopist.Aims and Objectives: This study aimed to look at the general distribution of endoscopic procedures amongst physician gastroenterologists and the surgeons in a sample district hospital of United Kingdom. It would then infer what impact this could have on the work force.Methods: Prospective study recruited all endoscopy done in a district general hospital of United Kingdom for 90 days.Results: Physicians had 30% less lists while Surgeons had 47.5% more lists according to the eligibility criterion of two lists per consultant per week.Conclusion: Predominance of surgeons doing procedures in endoscopy has a potential of having a direct impact on training of medical gastroenterologists, service delivery and recruitment. Authorities must find correct balance of medical and surgical lists in endoscopy for a cost effective and clinically effective service.
The Effect of three months aerobic exercise with moderate intensity on serum BDNF and TNF-Î± in women with metabolic syndrome

Introduction: The aim of this research was to investigate the effect of twelve weeks aerobic exercise with moderate intensity on BDNF, and TNF-I± in 50-65 years old women with metabolic syndrome. Methods: A total of24 women with metabolic syndromeÂ (Mets) took part voluntarily and divided in to two groups Mets exerciseÂ (ME), Mets controlÂ (MC). ME group participated in an aerobic exercise trainingÂ (AT) programÂ (12 weeks) three session per week each session containing tree performing parts and two rest parts. Also, blood samples were conducted before and after training for evaluating level of BDNF, and TNF-I±. Data were analyzed using Pearson coefficient, paired-samples T-Test, and independent samples T-Test. Results: BDNF after three months significantly was increased, TNF-I± after three month exercise was significantly decreased (Pâ�¤0.05). Also there was not significantly different in BDNF and TNF-I± Pre and post test on control group (Pâ�¥0.05). Conclusion:These findings indicated that twelve week aerobic exercise with moderate intensity ameliorate metabolic risk factors thatâ��s the reason of decreasing pre inflammatory factor, and increasing BDNFs level.
Mind Your Dependencies for Semantic Query Optimization

Semantic query optimization uses dependencies between attributes to formulate query transformations and revise the number of processed rows, with direct impact on performance. Commercial databases present facilities to define dependencies as not enforced constraints. The goal is to help the query optimizer in cases where the database is denormalized or simply lost dependencies in the design. However, feeding these facilities is a manual task which is tedious and error-prone. An attractive alternative is the automatic discovery of dependencies, but the cost of finding dependencies increases with the number of rows and attributes in the dataset. In this paper, we stick to the automatic discovery approach, but to reduce the cost we focus on dependencies matching the current queries in the pipe (ie., workload). Initially, we rely on a large set of functional dependencies computed in batch with state of the art algorithms in the literature. Over time our focused dependency selector (FDSel) chooses exemplars to feed the query optimizer. Therewith we eliminate further manual interactions. The automatically selected exemplars exhibit statistical properties that resemble those of the initial dependency set. This demonstrates the effectiveness of our proposed approach. In the best case scenario, by applying the FDSel for join elimination on a real-world database, we reduce query response time by more than one order of magnitude.
Case series of cultivated oral mucosa epithelium transplantation for limbal stem cell failure

Purpose: To report a case series of seven eyes (six patients) with severe limbal stem cell failure who underwent cultivated oral mucosa epithelium transplantation (COMET) in preparation for corneal transplant surgery and assess the change in visual acuity postoperatively. Design: Case series. Methods: A retrospective analysis of an interventional case series of six patients with severe limbal stem cell deficiency who underwent COMET between 2012 and 2015 was performed. Six eyes had COMET followed by corneal transplant, and one eye had corneal transplant preceding COMET. The change in the visual acuity after operation was evaluated. Results: A total of six patients with seven eyes were studied. All of them had severe ocular surface disease with limbal stem cell deficiency (LSCD) secondary to chemical injury (five eyes), SJS (one eye), and severe ocular rosacea (one eye). Four eyes had improvements in visual acuity, two of which achieved best corrected visual acuity (BCVA) of 6/12. Conclusion: The use of COMET helps to optimize the ocular surface in severe LSCD for further corneal transplant surgeries. It has helped to improve the graft uptake and subsequent visual improvement, especially in chronic chemical burn cases.
Public Traffic Congestion Estimation Using an Artificial Neural Network

Alleviating public traffic congestion is an efficient and effective way to improve the travel time reliability and quality of public transport services. The existing public network optimization models usually ignored the essential impact of public traffic congestion on the performance of public transport service. To address this problem, this study proposes a data-based methodology to estimate the traffic congestion of road segments between bus stops (RSBs). The proposed methodology involves two steps: (1) Extracting three traffic indicators of the RSBs from smart card data and bus trajectory data; (2) The self-organizing map (SOM) is used to cluster and effectively recognize traffic patterns embedded in the RSBs. Furthermore, a congestion index for ranking the SOM clusters is developed to determine the congested RSBs. A case study using real-world datasets from a public transport system validates the proposed methodology. Based on the congested RSBs, an exploratory example of public transport network optimization is discussed and evaluated using a genetic algorithm. The clustering results showed that the SOM could suitably reflect the traffic characteristics and estimate traffic congestion of the RSBs. The results obtained in this study are expected to demonstrate the usefulness of the proposed methodology in sustainable public transport improvements.
Current Utility and Future Applications of ctDNA in Colorectal Cancer

Circulating tumour DNA (ctDNA) shows promise as a minimally invasive biomarker with a myriad of emerging applications including early detection and diagnosis, monitoring of disease and treatment efficacy, and identification of actionable alterations to guide treatment. The potential utility of ctDNA in colorectal carcinoma (CRC) is of particular interest given the limitations of current radiographic imaging and blood-based tumour markers in detecting disease and evaluating therapeutic benefit. While ctDNA has yet to demonstrate clinical utility in CRC, a growing body of research highlights the potential of these novel biomarkers. This chapter provides an overview of the current evidence for employ-ing ctDNA in CRC as well as previewing the future directions that these exciting technologies may take.
Datamining in data van de Intensive Care: Wel of geen bloedtransfusie?

Op de Intensive Care overlijden steeds minder vaak mensen door de technologische verbeteringen. Hierbij wordt echter nog geen gebruik gemaakt van datamining technieken. Door middel van datamining technieken hopen we een betere voorspelling te doen of een patiënt een bloedtransfusie nodig heeft of niet. Eerst hebben we er gekeken naar onderzoeken van andere mensen, om ideeën op te doen voor dit onderzoek. Vervolgens is er aan de hand van het testen van verschillende datamining algoritmen en diverse technieken om met missing values om te gaan, dat het RandomForest-algoritme als beste naar voren komt bij deze toepassing. Dat dit algoritme als beste naar voren is gekomen is gebaseerd op accuracy, precision en recall scores. Met een maximale precision van 95,23% bij een recall van 100% is dit algoritme op deze dataset een goede voorspeller. Echter is dit algoritme op het gebied van interpretatie en visualisatie ondermaats. Deze reden hiervoor is dat het algoritme een model bouwt op basis van meerdere decision trees met elk een grote hoeveelheid nodes.
Improving the Quality of Web Spam Filtering by Using Seed Refinement

Web spam has a significant influence on the ranking quality of web search results because it promotes unimportant web pages. Therefore, web search engines need to filter web spam. web spam filtering is a concept that identifies spam pages - web pages contributing to web spam. TrustRank, Anti-TrustRank, Spam Mass, and Link Farm Spam are well-known web spam filtering algorithms in the research literature. The output of these algorithms depends upon the input seed. Thus, refinement in the input seed may lead to improvement in the quality of web spam filtering. In this paper, we propose seed refinement techniques for the four well-known spam filtering algorithms. Then, we modify algorithms, which we call modified spam filtering algorithms, by applying these techniques to the original ones. In addition, we propose a strategy to achieve better quality for web spam filtering. In this strategy, we consider the possibility that the modified algorithms may support one another if placed in appropriate succession. In the experiments we show the effect of seed refinement. For this goal, we first show that our modified algorithms outperform the respective original algorithms in terms of the quality of web spam filtering. Then, we show that the best succession significantly outperforms the best known original and the best modified algorithms by up to 1.38 times within typical value ranges of parameters in terms of recall while preserving precision.
Efficient video retrieval using complex sketches and exploration based on semantic descriptors

This thesis focuses on novel video retrieval scenarios. More particularly, we aim at the Known-item Search scenario wherein users search for a short video segment known either visually or by a textual description. The scenario assumes that there is no ideal query example available. Our former known-item search tool relying on color feature signatures is extended with major enhancements. Namely, we introduce a multi-modal sketching tool, the exploration of video content with semantic descriptors derived from deep convolutional networks, new browsing/visualization methods and two orthogonal approaches for textual search. The proposed approaches are embodied in our video retrieval tool Enhanced Sketch-based Video Browser (ESBVB). To evaluate ESBVB performance, we participated in international competitions comparing our tool with the state-of-the-art approaches. Repeatedly, our tool outperformed the other methods. Furthermore, we show in our user study that even novice users are able to effectively employ ESBVB capabilities to search and browse known video clips.
ASSOCIATION OF THE ADRB2 GENE POLYMORPHIC VARIANT C79G (rs1072714) WITH THE COURSE OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE IN OBESE AND NON-OBESE PATIENTS

Introduction: Attention has been paid to studying the role of the sympathetic nervous system and polymorphism of the β2-adrenoceptor genes in the development of chronic obstructive pulmonary disease (COPD) in recent years, but the findings of the studies contradict each other.Objective: To establish an association between the ADRB2 gene polymorphic variant C79G (rs1072714) in obese and non-obese COPD patients.Methods: The study involved 48 obese COPD patients and 52 nonobese COPD patients. The ADRB2 gene polymorphic variant C79G (rs1072714) was genotyped and assessed by the CAT test and the BODE index.Results: It has been established that in obese COPD patients the CAT test was significantly higher in the genotype GG (24.25 ± 4.25) compared to those with obesity and the genotype CC and CG (p <0.05). The BODE index was significantly (p <0.05) lower in obese COPD patients. A strong positive correlation between BMI and FEV1 (r = 0.742, p = 0.014) in nonobese COPD patients with GG genotype has been found.Conclusion: According to the CAT test, the quality of life in obese COPD patients with GG genotype is worse. The BODE index in obese COPD patients with CC and CG genotypes was significantly lower than in those without obesity. A strong positive correlation between BMI and FEV1 in COPD patients without obesity with GG genotype has been established.
TOUR TIME MINIMIZATION FOR MULTIPLE UAV IN DEADLINE BASED WSN APPLICATIONS

Today, with scientific and technological advances in the field of robotics, artificial intelligence, control and computers, land, air and sea vehicles, they have been considered. Unmanned Aerial Vehicles (UAVs) have also significantly improved and are very useful for many important applications in the business, urban and military environment. One of the important uses of UAVs in Wireless Sensor Networks (WSNs) is that these devices may not be able to communicate in large areas due to their energy constraints. In this case, a UAV can play mobile collectors for WSN networks. In this paper, we survey the work done towards all of practical applications of UAVs as mobile collectors for wireless sensor networks. We first examine the proposed UAV applications and compared their weaknesses with each other. We also examine the technical challenges we have about the applications of UAVs in the Wireless Sensor Network in detail. Then, in this paper, we provide an energy efficient data gathering with a deadline for wireless sensor networks using the UAV and a series of virtual grid points, named virtual grid energy efficient deadline based data gathering (VGEEDDG), to determine the optimal virtual grid points, optimal sojourn time for deploying multiple UAVs with the minimum time required in a predetermined deadline time to collect buffer data from cluster heads. In fact, in many applications, especially in practical applications, the deadline is limited to the critical level of application, and as a result, this deadline time for collecting data is not enough, and single UAV cannot collect data from cluster head with minimal energy. In this situation, this paper presents seven strategies for solving the problem of inadequate deadline time is provided by multiple UAVs for deadline based WSN applications. The results obtained in the simulation section show that the proposed framework is able to provide efficient data collection with satisfactory energy constraints and a deadline when dependent on the critical level of the application.