Epizootiologija fascioloze u bivola držanih u različitim uvjetima.

Epidemiological studies were undertaken at slaughter houses, livestock farms, veterinary hospitals and on household buffaloes under the different climatic conditions existing in Punjab province. Infection rate was 25.59, 26.16, 13.7 and 10.5 per cent, respectively in slaughtered buffaloes, buffaloes at livestock farms, veterinary hospitals and in household buffaloes. Overall highest (24.0%) seasonal prevalence in all types of buffaloes was recorded during autumn, followed by spring (20.0%), winter (13.0%). While the lowest (9.0%) was recorded during summer. It was noticed that a higher infection rate was recorded in older buffaloes than in youngsters (below 2 years of age) where as sex showed no significant difference. Buffaloes of either sex are equally affected.Epizootiološka istraživanja fascioloze u bivola držanih pod različitim klimatskim uvjetima na području Punjaba provedena su na klaonicama, većim farmama, manjim seoskim gospodarstvima i na veterinarskim klinikama. Učestalost invazije u zaklanih bivola iznosila je 25,59%, u bivola s farmi 26,16%, veterinarskih klinika 13,7% te malih seoskih gospodarstava 10.5%. Najveća učestalost ustanovljena je u jesen (24,0%), zatim u proljeće (20,0%) te zimi (13,0%), dok je najmanji postotak invadiranih životinja (9,0%) utvrđen u ljetnom razdoblju. Istraživanjem je potvrđeno da su češće bile invadirane starije životinje. Nisu utvrđene razlike učestalosti po spolu

Detection of BCR-ABL kinase domain mutations in CD34+ cells from newly diagnosed chronic phase CML patients and their association with imatinib resistance

BCR-ABL kinase domain (KD) mutations, the most common cause of imatinib resistance, are infrequently detected in newly diagnosed chronic-phase chronic myeloid leukemia (CP-CML) patients. Recent studies indicate pre-existing mutations (PEMs) can be detected in a higher percentage of CML patients using CD34+ stem/progenitor cells, and these mutations may correlate with imatinib resistance. We investigated KD mutations in CD34+ stem cells from 100 CP-CML patients by multiplex ASO-PCR and sequencing ASO-PCR products at the time of diagnosis. PEMs were detected in 32/100 patients and included F311L, M351T, and T315I. After a median follow-up of 30 months (range 8-48), all patients with PEMs exhibited imatinib resistance. Of 68 patients without PEMs, 24 developed imatinib resistance. Mutations were detected in 21 of these patients by ASO-PCR and KD sequencing. All 32 patients with PEMs had the same mutations. In imatinib-resistant patients without PEMs, we detected F311L, M351T, Y253F, and T315I mutations. All imatinib-resistant patients without T315I and Y253F mutations responded to imatinib dose escalation. In conclusion, BCR-ABL PEMs can be detected in a substantial number of CP-CML patients when investigated using CD34+ stem/progenitor cells. These mutations are associated with imatinib resistance, and mutation testing using CD34+ cells may facilitate improved, patient-tailored treatment

HawkEye: Advancing Robust Regression with Bounded, Smooth, and Insensitive Loss Function

Support vector regression (SVR) has garnered significant popularity over the past two decades owing to its wide range of applications across various fields. Despite its versatility, SVR encounters challenges when confronted with outliers and noise, primarily due to the use of the $\varepsilon$-insensitive loss function. To address this limitation, SVR with bounded loss functions has emerged as an appealing alternative, offering enhanced generalization performance and robustness. Notably, recent developments focus on designing bounded loss functions with smooth characteristics, facilitating the adoption of gradient-based optimization algorithms. However, it's crucial to highlight that these bounded and smooth loss functions do not possess an insensitive zone. In this paper, we address the aforementioned constraints by introducing a novel symmetric loss function named the HawkEye loss function. It is worth noting that the HawkEye loss function stands out as the first loss function in SVR literature to be bounded, smooth, and simultaneously possess an insensitive zone. Leveraging this breakthrough, we integrate the HawkEye loss function into the least squares framework of SVR and yield a new fast and robust model termed HE-LSSVR. The optimization problem inherent to HE-LSSVR is addressed by harnessing the adaptive moment estimation (Adam) algorithm, known for its adaptive learning rate and efficacy in handling large-scale problems. To our knowledge, this is the first time Adam has been employed to solve an SVR problem. To empirically validate the proposed HE-LSSVR model, we evaluate it on UCI, synthetic, and time series datasets. The experimental outcomes unequivocally reveal the superiority of the HE-LSSVR model both in terms of its remarkable generalization performance and its efficiency in training time

RoBoSS: A Robust, Bounded, Sparse, and Smooth Loss Function for Supervised Learning

In the domain of machine learning algorithms, the significance of the loss function is paramount, especially in supervised learning tasks. It serves as a fundamental pillar that profoundly influences the behavior and efficacy of supervised learning algorithms. Traditional loss functions, while widely used, often struggle to handle noisy and high-dimensional data, impede model interpretability, and lead to slow convergence during training. In this paper, we address the aforementioned constraints by proposing a novel robust, bounded, sparse, and smooth (RoBoSS) loss function for supervised learning. Further, we incorporate the RoBoSS loss function within the framework of support vector machine (SVM) and introduce a new robust algorithm named $\mathcal{L}_{rbss}$-SVM. For the theoretical analysis, the classification-calibrated property and generalization ability are also presented. These investigations are crucial for gaining deeper insights into the performance of the RoBoSS loss function in the classification tasks and its potential to generalize well to unseen data. To empirically demonstrate the effectiveness of the proposed $\mathcal{L}_{rbss}$-SVM, we evaluate it on $88$ real-world UCI and KEEL datasets from diverse domains. Additionally, to exemplify the effectiveness of the proposed $\mathcal{L}_{rbss}$-SVM within the biomedical realm, we evaluated it on two medical datasets: the electroencephalogram (EEG) signal dataset and the breast cancer (BreaKHis) dataset. The numerical results substantiate the superiority of the proposed $\mathcal{L}_{rbss}$-SVM model, both in terms of its remarkable generalization performance and its efficiency in training time

Support matrix machine: A review

Support vector machine (SVM) is one of the most studied paradigms in the realm of machine learning for classification and regression problems. It relies on vectorized input data. However, a significant portion of the real-world data exists in matrix format, which is given as input to SVM by reshaping the matrices into vectors. The process of reshaping disrupts the spatial correlations inherent in the matrix data. Also, converting matrices into vectors results in input data with a high dimensionality, which introduces significant computational complexity. To overcome these issues in classifying matrix input data, support matrix machine (SMM) is proposed. It represents one of the emerging methodologies tailored for handling matrix input data. The SMM method preserves the structural information of the matrix data by using the spectral elastic net property which is a combination of the nuclear norm and Frobenius norm. This article provides the first in-depth analysis of the development of the SMM model, which can be used as a thorough summary by both novices and experts. We discuss numerous SMM variants, such as robust, sparse, class imbalance, and multi-class classification models. We also analyze the applications of the SMM model and conclude the article by outlining potential future research avenues and possibilities that may motivate academics to advance the SMM algorithm

Association of NS1 Antigen, IgM, IgG Antibodies and RT-PCR in the Diagnosis of Dengue Virus Infection

Background: To determine the association of ELISA based serological markersNS1 antigen, IgM, IgG antibodies and RT-PCR in the diagnosis of dengue virus infection Methods: In this descriptive cross sectional study 420 serum samples from patients with suspicion of dengue fever were tested for detection of dengue by NS1 antigen ELISA, IgG, IgM ELISA. RT-PCR for dengue was carried out in all NS1 antigen ELISA positive cases for confirmation of dengue. Results: Out of 420 cases , 249 cases were positive for either one of the three markers NS1, IgM,IgG. Males constituted 71.66%.Two hundred and two (48.09%) were positive for NS1 only,13 (3.09%) were positive for NS1 and IgG, 07 (1.66%) were NS1, IgM and IgG positive,16 (3.80%) were positive for IgG only ,11 (2.61%) were positive for NS1 and IgM whereas 171 (40.17%) samples were reported negative for NS1, IgM and IgG.RT-PCR was conducted on 233 NS1 positive cases out of which 80.06% cases turned out positive. Maximum number of cases belonged to DEN-2 genotype. Conclusion: Early diagnosis helps in improved patient care, suitable treatment, prevents severe complications and helps limit the spread of the disease. RT PCR is  a reliable test for the  diagnosis of acute dengue fever

Safety Evaluation of Oil Samples Collected from Different Food Points of Multan City of Pakistan

Cooking oil has become a part and parcel of modern food system and therefore its safety is of prime significance for health agencies around the globe to ensure good health among the community. Current study was designed to investigate the physicochemical properties including free fatty acids, peroxide value and conjugated dienes; minerals (nickel & cobalt) and heavy metals (lead and cadmium) in oil samples collected from different areas of Multan city of Pakistan. The findings of this study revealed that free fatty acid percentages, conjugated dienes, cobalt and nickel concentrations were in normal ranges while the peroxide values, lead and cadmium concentrations were recorded above the norms. Strict regulatory measures need to be adopted to ensure good quality oil supply and to protect the people from health implications of physicochemical and metallic hazards prevailing in fried oils and fried foods

Analysing the Functions of Lexical Bundles for Teaching Academic Writing to Graduate Students

Academic writing skills contribute greatly in academic performance. One of the key elements of good academic writing is to know the proper use of the strings of the words usually occur together. These words are termed as lexical bundles in corpus linguistics. Some research has been conducted on the use and functions of lexical bundles in spoken and written discourses (Chen & Chen, 2020). However, there is scarce research on the functions of lexical bundles and their use in Pakistani research writing. While drawing on the corpus of 90201 corpora of the 12 Pakistani research articles, we investigated the functions and use of lexical bundles in research papers of Pakistani authors. The list of the lexical bundles by Simpson-Vlach and Ellis (2010) was used as a source to interpret the lexical bundles. We found the referential expressions were the highest lexical bundles in research articles of Pakistani authors followed by the expressions of ability and possibility and hedges in the list. Based on these findings, we argue that Pakistani authors are aware about the various types of the lexical bundles and their functions in academic writing. It is suggested that the high-frequency bundles can help the learners to improve the use of modal words used as hedges in academic writing. As per the findings of this study, it is recommended that a large corpus may be built for better results in the future.