Evaluation of liver regeneration diet supplemented with omega-3 fatty acids: experimental study in rats

Objective: to evaluate liver regeneration in rats after partial hepatectomy of 60% with and without action diet supplemented with fatty acids through the study of the regenerated liver weight, laboratory parameters of liver function and histological study. Methods: thirty-six Wistar rats, males, adults were used, weighing between 195 and 330 g assigned to control and groups. The supplementation group received the diet by gavage and were killed after 24h, 72h and seven days. Evaluation of regeneration occurred through analysis of weight gain liver, serum aspartate aminotransferase, alanine aminotransferase, gamma-glutamyltranspeptidase, and mitosis of the liver stained with H&E. Results: the diet supplemented group showed no statistical difference (p>0.05) on the evolution of weights. Administration of fatty acids post-hepatectomy had significant reduction in gamma glutamyltransferase levels and may reflect liver regeneration. Referring to mitotic index, it did not differ between period of times among the groups. Conclusion: supplementation with fatty acids in rats undergoing 60% hepatic resection showed no significant interference related to liver regeneration.

Evaluation of an enzyme-linked immunosorbent assay to detect antibodies against Anaplasma marginale

A rapid indirect enzyme-linked immunosorbent assay (ELISA) was developed for measuring antibodies against Anaplasma marginale using a partially soluble antigen prepared from semi-purified initial bodies from erythrocytes with 80.0% of rickettsiaemia. This technique utilized alkaline phosphatase and p-nitrophenyl phosphate as reaction indicators. The high sensitivity (100.0%) was confirmed with sera from 100 calves experimentally-infected with A. marginale. All of these animals showed seroconversion before or at the same time of the first rickettsiaemia or even when it was not detected. Also the elevated specificity (94.0%) was confirmed by the low percentage of cross-reactions with sera from animals experimentally-infected with Babesia bigemina and Babesia bovis (1.4 and 6.6%, respectively). Performances of ELISA and indirect fluorescent antibody test (IFAT) with 324 sera from enzootically stable area did not show statistical difference (P>0.05), since the former showed 96.9% and the latter 97.2% of positive reactions. The advantage of this ELISA is a shorter execution time than others developed until now, allowing more samples to be analyzed.

Immunohistochemical evaluation of e-cadherin, Ki-67 and PCNA in canine mammary neoplasias: correlation of prognostic factors and clinical outcome

E-cadherin is a cell-cell adhesion molecule and low e-cadherin expression is related to invasiveness and may indicate a bad prognosis in mammary neoplasms. The expression of cell proliferation markers PCNA and especially Ki-67, has also proved to have a strong prognostic value in this tumor class. The expression of these markers was related to the clinical-pathological characteristics of 73 surgically removed mammary tumors in female dogs by immunohistochemistry. There was no statistical correlation between these markers and death by neoplasm, survival time and disease-free interval. However, the loss of e-cadherin expression and marked Ki-67 expression (p=0.016) were considered statistically significant for the diagnosis (p=0.032). When evaluated as independent factors, there was evidence of the relationship between the loss of e-cadherin expression and high PCNA expression with changes in the body status (divided into obese, normal and cachectic) of female dogs (p=0.030); there was also evidence of the relationship between pseudopregnancy and e-cadherin alone (p=0.021) and for ulceration and PCNA alone (p=0.035). The significant correlation between the markers expression and these well known prognostic factors used individually or in combination suggests their prognostic value in canine mammary tumors.

Morphological and morphometric evaluation of prepubertal gilt ovaries, uterine tubes and uterus at different oestrus cycle stages

Studies are performed in developing techniques/procedures that provide greater reproductive performance in farm animals, including pigs. In this sense, the study of gilts reproductive organs at different oestrus cycle stages for assessing the presence of abnormalities and/or other parameters that may affect the future animal fertility is important. In order to evaluate the morphological, morphometric and histomorphometric features of ovaries, uterus and uterine tubes (UTs) characteristics of prepubertal gilts at different oestrus cycle stages, reproductive tracts from 48 animals immediately after slaughter were obtained. After, the structures were dissected and removed, and the ovaries were used for classification of oestrus cycle stage of each gilt in follicular phase (FP) and luteal phase (FL). Then, morphometric evaluations of ovaries, UTs, uterine horns and uterine body were performed. Besides that, medial segments of UTs and uterus were fixed in Bouin solution, processed and included in paraffin, when histological sections of 5.0 micrometers (µm) were obtained and stained with Hematoxylin and Eosin. Histomorphometric analyzes using image capture system and specific software were performed. Afterwards, data were submitted to Student's t test for assessment the statistical differences (P<0.05) between the two different oestrus cycle stages (FP × LP) and between the placement of reproductive structures (right × left antimer). Among the gilts evaluated, 35 were in the FP and 13 in LP. There was no difference (P>0.05) between morphometric parameters of ovaries, UTs and uterus of gilts in FP and LP. Likewise, in respect to the placement of reproductive structures, both in the oestrus cycle stages, as in the general average, there was no difference (P>0.05). Regarding the histomorphometric variables, gilts classified in FP presented a higher (P<0.05) height of glandular and UT epithelium compared to animals in LP. On the other hand, the diameter of endometrial glands was higher (P<0.05) in gilts at LP compared to FP. Furthermore, gilts in LP presented a higher (P<0.05) proportion of endometrium occupied by glands, whereas animals in FP had a higher (P<0.05) proportion of connective tissue and blood vessels. In conclusion, in prepubertal gilts, the histomorphometric parameters as endometrial glands diameter, the height of glandular epithelium and of UT epithelium and the proportion of endometrium occupied by connective tissue, besides the glands and blood vessels varies through the oestrus cycle, possibly under the influence of ovarian steroids.

Asleep or not asleep? Evaluation of the Quality of Patients’ Sleep in Critical Care Nursing

Sleep is important for the recovery of a critically ill patient, as lack of sleep is known to influence negatively a person’s cardiovascular system, mood, orientation, and metabolic and immune function and thus, it may prolong patients’ intensive care unit (ICU) and hospital stay. Intubated and mechanically ventilated patients suffer from fragmented and light sleep. However, it is not known well how non-intubated patients sleep. The evaluation of the patients’ sleep may be compromised by their fatigue and still position with no indication if they are asleep or not. The purpose of this study was to evaluate ICU patients’ sleep evaluation methods, the quality of non-intubated patients’ sleep, and the sleep evaluations performed by ICU nurses. The aims were to develop recommendations of patients’ sleep evaluation for ICU nurses and to provide a description of the quality of non-intubated patients’ sleep. The literature review of ICU patients’ sleep evaluation methods was extended to the end of 2014. The evaluation of the quality of patients’ sleep was conducted with four data: A) the nurses’ narrative documentations of the quality of patients’ sleep (n=114), B) the nurses’ sleep evaluations (n=21) with a structured observation instrument C) the patients’ self-evaluations (n=114) with the Richards-Campbell Sleep Questionnaire, and D) polysomnographic evaluations of the quality of patients’ sleep (n=21). The correspondence of data A with data C (collected 4–8/2011), and data B with data D (collected 5–8/2009) were analysed. Content analysis was used for the nurses’ documentations and statistical analyses for all the other data. The quality of non-intubated patients’ sleep varied between individuals. In many patients, sleep was light, awakenings were frequent, and the amount of sleep was insufficient as compared to sleep in healthy people. However, some patients were able to sleep well. The patients evaluated the quality of their sleep on average neither high nor low. Sleep depth was evaluated to be the worst and the speed of falling asleep the best aspect of sleep, on a scale 0 (poor sleep) to 100 (good sleep). Nursing care was mostly performed while the patients were awake, and thus the disturbing effect was low. The instruments available for nurses to evaluate the quality of patients’ sleep were limited and measured mainly the quantity of sleep. Nurses’ structured observatory evaluations of the quality of patients’ sleep were correct for approximately two thirds of the cases, and only regarding total sleep time. Nurses’ narrative documentations of the patients’ sleep corresponded with patients’ self-evaluations in just over half of the cases. However, nurses documented several dimensions of sleep that are not included in the present sleep evaluation instruments. They could be classified according to the components of the nursing process: needs assessment, sleep assessment, intervention, and effect of intervention. Valid, more comprehensive sleep evaluation methods for nurses are needed to evaluate, document, improve and study patients’ quality of sleep.

Hygienic-sanitary quality of vegetables and evaluation of treatments for the elimination of indigenous E. coli and E. coli O157:H7 from the surface of leaves of lettuce (Lactuca sativa L.)

The purpose of this study is to evaluate the hygienic-sanitary quality of vegetables and irrigation water and assess the effectiveness of lemon juice and vinegar in reducing E. coli strains inoculated on lettuce. One hundred and forty samples of vegetables and 45 samples of irrigation water were investigated for thermotolerant coliforms and Salmonella spp. In order to verify the effectiveness of natural household sanitizers in reducing E. coli in inoculated lettuce, four treatment solutions were tested: fresh lemon juice, alcohol vinegar, lemon juice-vinegar mixture, and lemon juice-vinegar-water mixture. The microbiological analysis revealed high rates of contamination by thermotolerant coliforms and identified the presence of E. coli in 32% of the tested vegetable samples and 56% of the water samples. While no significant statistical difference (p < 0, 05) was identified in the tested solutions, the treatment with a combination of lemon juice and vinegar resulted in the highest Decimal Reductions (DR) of E. coli O157: H7 while the treatment with vinegar alone was the most effective against the indigenous E. coli strain

Evaluation of the PetrifilmTM and TEMPO® systems and the conventional method for counting microorganisms in pasteurized milk

New microbiological methods have been developed and commercialized, but their performance must be guaranteed. The aim of the present study was to evaluate the PetrifilmTM and TEMPO® systems compared to the conventional method for counting microorganisms in pasteurized milk. A total of 141 samples of pasteurized milk were analyzed by counting mesophilic aerobic, Coliforms at 35 ºC, Coliforms at 45 ºC, and Escherichia coli microorganisms. High correlation was found between the methods for counting Coliforms at 35 ºC, but low correlation was found for counting mesophilic aerobic, Coliforms at 45 ºC, and Escherichia coli. No significant statistical difference was found among the three methods for counting Coliforms at 35 ºC; however, the mean counts of mesophilic aerobic, Coliforms at 45 ºC, and Escherichia coli showed significant statistical difference. PetrifilmTM and TEMPO® systems had satisfactory results for Coliforms at 35 ºC in pasteurized milk but low performance for mesophilic aerobic, Coliforms at 45 ºC and Escherichia coli.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Evaluation of a stage II screening protocol for prostate cancer

In 2003, prostate cancer (PCa) is estimated to be the most commonly diagnosed cancer and third leading cause of cancer death in Canada. During PCa population screening, approximately 25% of patients with a normal digital rectal examination (DRE) and intermediate serum prostate specific antigen (PSA) level have PCa. Since all patients typically undergo biopsy, it is expected that approximately 75% of these procedures are unnecessary. The purpose of this study was to compare the degree of efficacy of clinical tests and algorithms in stage II screening for PCa while preventing unnecessary biopsies from occurring. The sample consisted of 201 consecutive men who were suspected of PCa based on the results of a DRE and serum PSA. These men were referred for venipuncture and transrectal ultrasound (TRUS). Clinical tests included TRUS, agespecific reference range PSA (Age-PSA), prostate specific antigen density (PSAD), and free-to-total prostate specific antigen ratio (%fPSA). Clinical results were evaluated individually and within algorithms. Cutoffs of 0.12 and 0.15 ng/ml/cc were employed for PSAD. Cutoffs that would provide a minimum sensitivity of 0.90 and 0.95, respectively were utilized for %fPSA. Statistical analysis included ROC curve analysis, calculated sensitivity (Sens), specificity (Spec), and positive likelihood ratio (LR), with corresponding confidence intervals (Cl). The %fPSA, at a 23% cutoff ({ Sens=0.92; CI, 0.06}, {Spec=0.4l; CI, 0.09}, {LR=1.56; CI, O.ll}), proved to be the most efficacious independent clinical test. The combination of PSAD (cutoff 0.15 ng/ml/cc) and %fPSA (cutoff 23%) ({Sens=0.93; CI, 0.06}, {Spec=0.38; CI, 0.08}, {LR=1.50; CI, 0.10}) was the most efficacious clinical algorithm. This study advocates the use of %fPSA at a cutoff of 23% when screening patients with an intermediate serum PSA and benign DRE.

Statistical Inference for Computable General Equilibrium Models with Application to a Model of the Moroccan Economy

We study the problem of measuring the uncertainty of CGE (or RBC)-type model simulations associated with parameter uncertainty. We describe two approaches for building confidence sets on model endogenous variables. The first one uses a standard Wald-type statistic. The second approach assumes that a confidence set (sampling or Bayesian) is available for the free parameters, from which confidence sets are derived by a projection technique. The latter has two advantages: first, confidence set validity is not affected by model nonlinearities; second, we can easily build simultaneous confidence intervals for an unlimited number of variables. We study conditions under which these confidence sets take the form of intervals and show they can be implemented using standard methods for solving CGE models. We present an application to a CGE model of the Moroccan economy to study the effects of policy-induced increases of transfers from Moroccan expatriates.

Evaluation of Reducibility of Trunk Asymmetry in Lateral Bending

The value of the lateral bending test is important in the assessment of spinal curve mobility and prediction of surgical outcome in the treatment of adolescent idiopathic scoliosis (AIS). However, radiographic bending tests are unable to assess the reducibility of trunk asymmetry. This study aims to exploit surface topography measurement in order to evaluate the changes in shape of the trunk (a) between bending and neutral standing positions, and (b) between standing pre- and post-operative visits, in a cohort of adolescents with AIS having undergone surgical correction; and to correlate the differences measured in cases (a) and (b). Our cohort includes 13 patients with right thoracic AIS. Each patient had their 3D trunk surface digitized with a multi-head InSpeck system in standing posture (at the pre-op and post-op visits) and in maximum voluntary right and left bending (at the pre-op visit). We developed a novel trunk shape analysis method which produces a set of inclined trunk cross-sections allowing comparison between different postures. Two asymmetry indices, trunk rotation (TR) and back surface rotation (BSR), were computed in all cases and a statistical analysis was performed. Our correlation study (Pearson test) showed fair correlations in most cases between the changes in side-bending and those post-surgery, with the strongest relationship (p-value < 0.01) when combining the TR measurements from both bendings. These results provide evidence that the bending test can be used to assess trunk asymmetry reducibility. The proposed approach could provide a non-invasive trunk asymmetry reducibility test for routine clinical use in AIS surgery planning.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

Chaotic, memory, and cooling rate effects in spin glasses: Evaluation of the Edwards-Anderson model

We investigate chaotic, memory, and cooling rate effects in the three-dimensional Edwards-Anderson model by doing thermoremanent (TRM) and ac susceptibility numerical experiments and making a detailed comparison with laboratory experiments on spin glasses. In contrast to the experiments, the Edwards-Anderson model does not show any trace of reinitialization processes in temperature change experiments (TRM or ac). A detailed comparison with ac relaxation experiments in the presence of dc magnetic field or coupling distribution perturbations reveals that the absence of chaotic effects in the Edwards-Anderson model is a consequence of the presence of strong cooling rate effects. We discuss possible solutions to this discrepancy, in particular the smallness of the time scales reached in numerical experiments, but we also question the validity of the Edwards-Anderson model to reproduce the experimental results.

A Framework of Statistical Machine Translator from English to Malayalam

In this paper we describe the methodology and the structural design of a system that translates English into Malayalam using statistical models. A monolingual Malayalam corpus and a bilingual English/Malayalam corpus are the main resource in building this Statistical Machine Translator. Training strategy adopted has been enhanced by PoS tagging which helps to get rid of the insignificant alignments. Moreover, incorporating units like suffix separator and the stop word eliminator has proven to be effective in bringing about better training results. In the decoder, order conversion rules are applied to reduce the structural difference between the language pair. The quality of statistical outcome of the decoder is further improved by applying mending rules. Experiments conducted on a sample corpus have generated reasonably good Malayalam translations and the results are verified with F measure, BLEU and WER evaluation metrics

English to Malayalam Translation: A Statistical Approach

This paper underlines a methodology for translating text from English into the Dravidian language, Malayalam using statistical models. By using a monolingual Malayalam corpus and a bilingual English/Malayalam corpus in the training phase, the machine automatically generates Malayalam translations of English sentences. This paper also discusses a technique to improve the alignment model by incorporating the parts of speech information into the bilingual corpus. Removing the insignificant alignments from the sentence pairs by this approach has ensured better training results. Pre-processing techniques like suffix separation from the Malayalam corpus and stop word elimination from the bilingual corpus also proved to be effective in training. Various handcrafted rules designed for the suffix separation process which can be used as a guideline in implementing suffix separation in Malayalam language are also presented in this paper. The structural difference between the English Malayalam pair is resolved in the decoder by applying the order conversion rules. Experiments conducted on a sample corpus have generated reasonably good Malayalam translations and the results are verified with F measure, BLEU and WER evaluation metrics