Miracle-dz/UniFAR-FTUs huggingface dataset:
Movable Wireless Access Point for IoT-Based Home Automation

IoT-based home automation system is a rapidly emerging technology and has great potential to improve safety, security and efficient utilization of energy resource. This paper presents a movable wireless access point for home automation, to reach those IoT sensors which are placed at remote corners of a house where the radio signal cannot reach. The proposed system mainly focuses on allowing unreachable IoT Devices / Sensors to communicate using a movable wireless access point. The IoT command is issued by the user and given to the movable wireless access point using an Android mobile device. The command enables the access point to handover the sensor specific command to the respective IoT sensors. The wireless access point is designed using Raspberry Pi and with a proper movement mechanism. The experimental results show the functional mechanism and correctness of the proposed home automation system.
Numerical analysis of Lavrentiev-regularized state constrained elliptic control problems

In the present work, we apply semi-discretization proposed by the rst author in [13] to Lavrentievregularized state constrained elliptic control problems. We extend the results of [17] and prove weak convergence of the adjoint states and multipliers of the regularized problems to their counterparts of the original problem. Further, we prove error estimates for nite element discretizations of the regularized problem and investigate the overall error imposed by the nite element discretization of the regularized problem compared to the continuous solution of the original problem. Finally we present numerical results which con rm our analytical ndings.
The Role of Autophagy and Autophagy Receptor NDP52 in Microbial Infections

Autophagy is a general protective mechanism for maintaining homeostasis in eukaryotic cells, regulating cellular metabolism, and promoting cell survival by degrading and recycling cellular components under stress conditions. The degradation pathway that is mediated by autophagy receptors is called selective autophagy, also named as xenophagy. Autophagy receptor NDP52 acts as a ‘bridge’ between autophagy and the ubiquitin-proteasome system, and it also plays an important role in the process of selective autophagy. Pathogenic microbial infections cause various diseases in both humans and animals, posing a great threat to public health. Increasing evidence has revealed that autophagy and autophagy receptors are involved in the life cycle of pathogenic microbial infections. The interaction between autophagy receptor and pathogenic microorganism not only affects the replication of these microorganisms in the host cell, but it also affects the host’s immune system. This review aims to discuss the effects of autophagy on pathogenic microbial infection and replication, and summarizes the mechanisms by which autophagy receptors interact with microorganisms. While considering the role of autophagy receptors in microbial infection, NDP52 might be a potential target for developing effective therapies to treat pathogenic microbial infections.
Histologic Subtypes Do Not Confer Unique Outcomes in Early-Stage Lymphoma: Long-Term Follow-Up of SWOG 8736.

Analysis of patients with advanced non-Hodgkin’s lymphoma (NHL) suggests unique clinical presentations and outcome according to histologic subtype. Little is known of outcome as it relates to histologic subytpe in patients with limited disease. In a previous report (NEJM1998;339:21–6), 401 patients with limited disease (Stage I or non-bulky II) intermediate or high grade NHL, enrolled in SWOG study 8736, were randomized to chemotherapy or chemotherapy plus radiotherapy. Results at 5 years showed superior survival and less toxicity for those treated with 3 cycles of CHOP chemotherapy plus radiotherapy. These 401 patients, with additional followup and reclassification of histology according to World Health Organization (WHO) criteria, form the basis of the current report. Of the 401 pts, 2 were excluded because of clinical findings and 34 patients were excluded for insufficient pathology material. 365 pts were evaluable. On the basis of hematoxylin-eosin and immunohistochemistry results, pt cases were classified as diffuse large B cell (174pts), follicular, Grade 3 (36), Burkitt-like (27), peripheral T cell, unspecified (9), anaplastic large T cell (4), morphologically consistent with mantle cell (4), morphologically consistent with MALToma (3), morphologically diffuse small cleaved cell (2), unclassifiable (1), suggesting anaplastic large cell CD30+ (1), diffuse large cell without immunohistochemical results (104). At 10 years, there was no significant difference in overall survival (OS) or failure free survival (FFS) between the 2 treatments (OS, p=0.48; FFS, p=0.77) (Blood2001;98:724a). When analyzed by histology, including T lineage, neither OS nor FFS were different for any histologic subytpe (OS, p=0.94; FFS, p=0.99). In addition, the estimated shapes of the survival curves were similar for all histologic subtypes among these 365 evaluable pts. On the basis of these findings it does not appear that either the WHO or the Working Formulation (WF) classification system is able to predict outcome for limited stage disease pts treated with CHOP +/− radiotherapy. Therefore, histologic type should not be a criterion for exclusion for limited stage studies of lymphoma. Further, it does not appear that any histologic subtype presenting as early stage disease needs CNS prophylaxis or a unique chemotherapy treatment strategy. There was only one central nervous system (CNS) relapse (in a patient with testis involvement), and none among the 27 Burkitt-like pts. The lack of an observed survival plateau in the first 10 years of followup for these pts is in contrast to advanced stage pts who achieve a plateau within 6 years. The biology of limited stage lymphoma may be fundamentally different from that of advanced stage disease and more significanct in determining outcome than histologic type.
Granulocyte colony-stimulating factor therapy for facioscapulohumeral dystrophy: a case report

__________________________________________________________________________________________ We examined the safety and effectiveness of a low dose of analog granulocyte-colony stimulating factor in a 15-year-old boy with facioscapulohumeral dystrophy. The onset of disease was noted at 12 years of age. The physical examination noted general muscle atrophy more pronounced at left side of the body. He was able to walk 300 meters within 6 minute walk test. Granulocyte colony-stimulating factor 5 μg/kg was given subcutaneously daily for 5 days/month for 1, 2, 3, 6 and 12 months. Clinical examination, laboratory tests including blood, biochemical tests, and CD34+ cells were performed. A significant increase of muscle strength in the lower and upper limbs between baseline, and after 3 months of treatment, after 6, and after 12 months was found. He was able to walk 480 meters within 6 minutes after 12 months. Electromyography demonstrated increase of amplitude in the examined in upper and lower limbs after six months compared to baseline. Leukocyte levels remained below 25000/μL. CD34+ increased significantly at day 5 of granulocyte colony-stimulating factor admini-stration. It was safe and well tolerated by the patient. A significant increase in muscle strength in this patient with facioscapulohumeral dystrophy after 3 months of treatment, after 6, and after 12 months since the first treatment course was completed may indicate beneficial effects of granulocyte colony-stimulating factor in this disorder.
A Trial of Digital Pen Technology

The Peter MacCallum Cancer Centre in Melbourne undertook a trial of Digital Pen technology to measure its potential for hospital applications. Digital Pens have an embedded camera, which captures the pen's movement across a specialised dot pattern printed on a form. Due to the nature of the dot pattern, when the pen is docked the form's contents are reproduced electronically. Objectives: Through matching clinicians' workflow with appropriate technology, the trial aimed to improve data collection rates, improve data quality and increase the utilisation of existing hospital resources. We also wanted to assess the potential of this technology for other applications within the hospital. The pilot enabled the recruitment of two of the highest volume (outpatient visits) services of Peter Mac into the assessment data collection phase of an internally developed oncology minium dataset database. Previously these services were not reporting data due to resistance by medical staff to enter information. However, medical staff had indicated that they would have the capacity to provide information by completing forms. Method: This innovative technology was trialled for a six month period to facilitate in the collection of new patient assessment details in the consultative clinics associated with two clinical services of the hospital. A major component of the project implementation was the designing of forms to match the clinicians' workflow and increase the rate of data capture via the pen's software. Digital Pens perform best with check boxes and numerical form elements; handwriting recognition has the lowest accuracy and was avoided where critical data items are required but needed to be evaluated. Once the form elements are validated (either through human or software validation), the form elements were imported into the hospital dataset. Results: The trial reviewed and evaluated the technology against the following criteria: 1. Ease of use; 2. Hardware reliability; 3. Accuracy of data; 4. Rate of data capture. Discussion and conclusions: This paper will provide the findings of this trial and discuss the benefits and issues relating to the use of Digital Pen technology in a health care environment.
Transgender people in Swedish healthcare: The experience of being met with ignorance

In meetings with healthcare professionals, transgender people are often met with ignorance. The meetings tend to focus more on the person’s gender identity rather than reasons for the contact and therefore may be perceived as being offensive to that person. The aim of the study was to describe transgender people’s experiences in their meetings with healthcare professionals. Fourteen transgender persons were interviewed in this descriptive qualitative study. The semi-structured interviews were audiotaped. Data were collected during January–February 2015 and analysed using content analysis. Three themes were identified: ‘Experience of treatment by healthcare professionals’, ‘Expectations of meeting with healthcare professionals’ and ‘Consequences of meetings with healthcare professionals’. Findings revealed that healthcare professionals do not always respect the patient’s gender identity. This could lead to their avoiding contact with healthcare services. More education/training of healthcare personnel for the treatment of transgender people seeking care is requested.
TISSUE ENGINEERING AND ITS FUTURE PERSPECTIVE IN THERAPEUTIC MEDICINE- A BRIEF REVIEW

Tissue engineering is the field of functional restoration of tissue structure and physiology for impaired or damaged tissues because of cancer, disease, and trauma. The field has gained importance due to the inadequate supply of organs and tissues for patients requiring organ and tissue replacement. Research in this field continues to advance knowledge about how an organism develops from a single cell and how healthy cells replace damaged cells in adult organisms. Tissue-engineered oral mucosal equivalents have been developed for clinical applications and also for in vitro studies of biocompatibility, mucosal irritation, disease, and other basic oral biology phenomena.This paper reviews different tissue-engineering strategies used for the production of human oral mucosal equivalents, and their relative applications.
Formalising Commitments Using the Event Calculus (full paper)

Smart Contracts enable the automated execution of exchanges on the blockchain. From an ontological perspective, smart contracts create and automate the fulfillment of social commitments between actors. Whereas traditional deontic logic is used to make a legal determination in contractual multi-actor interactions, we focus on the consequences of these actions resulting from that determination, thereby shifting the focus from monitoring to execution. The interactions between actors and the consequences in terms of commitments have not yet been formalized for smart contracts. The perspective of smart contracts is interesting, since they are considered to be autonomous agents, able to generate automated actions. We use the Event Calculus to formalize logic in order to represent and reason about the effects of these automated actions and the resulting commitments. Since the Event Calculus deals with local events and the consideration of time, this approach enables the uniform representation of commitments, including their operations and reasoning rules about them.
A Deep Learning Based Approach on Categorization of Tea Leaf

Tea grading is a very prominent factor of the tea industry. The standard, fragrance and sweetness of tea mostly relies on this grading system. This research is a step to introduce machine learning with the tea industry, where image classification and recognition is deployed to digitize the grading system by eradicating human intervention in it. Three models are used in this system in which two were pre-trained. They are Faster RCNN (Inception-v2), and VGG16. The other one is manually trained, that is Sequential model or CNN. After a successful session of compulsory augmentation and scaling, we gathered 3000 raw images which were used to train and test the model spontaneously. Our productivity has rendered us tremendous satisfaction by supplying astonishing accuracy. So, it will be not wrong saying that this research has amalgamated machine learning technology with the grading system of tea very productively which can escort a great revolution to the tea industry.
A Proposed Model for Bengali Named Entity Recognition Using Maximum Entropy Markov Model Incorporated with Rich Linguistic Feature Set

Named Entity Recognition (NER) is a subpart of Information Extraction task that helps to find named entities and their class in a document. This is one of the fundamental tasks for many natural language processing tasks. NER categorizes the named entities in some predefined class like person, name, organization, date, number etc. In this paper, we have proposed a model to recognize the named entities from Bengali language. Three major approaches are followed to recognize named entities. They are rule-based approach, machine learning based approach and hybrid approach. Most of the existing Bengali named entity recognition model followed machine learning approaches. Existing work done on Conditional Random Forest (CRF), Support Vector Machine (SVM) and Hidden Markov Model (HMM) etc. As. Bengali is a complex language enriched with complex linguistic feature set. , in this paper we have adopted a Maximum Entropy Markov Model (MEMM) based machine learning approach that can deal with complex sequences better than other approaches to classify the named entities from Bengali language. Also, we have described a rich linguistic feature set for training our model.
AB0464 The Rate of and Risk Factors for Frequent Hospitalization in Systemic Lupus Erythematosus: Results from The Korean Lupus Network Registry

Objectives The survival rate of patients with systemic lupus erythematosus (SLE) has improved in the last few decades, but the rate of hospitalization and health care costs for these patients remain higher than in the general population. Thus, we evaluated the rate of hospitalization and associated risk factors in an inception cohort of Korean patients with lupus. Methods Of the 507 patients with SLE enrolled in the KORean lupus NETwork (KORNET registry), we investigated an inception cohort consisting of 196 patients with SLE presenting within 6 months of diagnosis based on the ACR classification criteria. We evaluated the causes of hospitalization, demographic characteristics, and laboratory and clinical data at the time of SLE diagnosis of hospitalized patients and during a follow-up period. We calculated the hospitalization rate as the number of total hospitalizations divided by the disease duration, and defined “frequent hospitalization” as hospitalization more than once per year. Results Of the 196 patients, 117 (59.6%) were admitted to hospital a total of 257 times during the 8-year follow-up period. Moreover, 22 (11.2%) patients were hospitalized frequently. The most common reasons for hospitalization included disease flares, infection, and pregnancy-related morbidity. In the univariate regression analysis, malar rash, arthritis, pericarditis, renal involvement, fever, SLE disease activity index >12, hemoglobin level <10 mg/dl, albumin level <3.5 mg/dl, and anti–Sjögren's syndrome A (SS-A) positivity were associated with frequent hospitalization. Finally, multivariate analysis showed that arthritis, pericarditis, and anti–SS-A antibody positivity at the time of diagnosis were risk factors for frequent hospitalization. Conclusions Our results showed that frequent hospitalization occurred in 11.2% of hospitalized patients and arthritis, pericarditis, and anti–SS-A antibody positivity at the time of diagnosis were risk factors for frequent hospitalization. Disclosure of Interest None declared
An Improved Class-wise Principal Component Analysis Based Feature Extraction Framework for Hyperspectral Image Classification

It has been a challenging task for researchers to improve the classification accuracy of hyperspectral images in recent years. High dimensionality or the high number of spectral bands with respect to a comparatively less amount of training sample causes higher misclassification rate. The problem is known as 'Hughes Phenomenon'. To overcome this problem many feature selection as well as feature extraction methods are applied to reduce the dimensionality of hyperspectral image. In this paper, we introduce a new framework of class-wise Principal Component Analysis (PCA) operation for the dimensionality reduction of hyperspectral images. Our proposed approach comprises four major steps. The first step involves separating the pixels according to different existing classes in the image. Secondly, we considered the problem of feature extraction with PCA. Thirdly, we took the same number of features, extracted with PCA, from each class considering their maximum average variance. In the final step, we used the SVM classifier to find out the classification results. Experiment results show that our proposed approach turns out to be better than the primary classification results. The primary classification accuracy shown by the SVM classifier is 89.02% and the classification accuracy of our proposed method is 95.44%.
Single-Cell Approaches to Profile the Response to Immune Checkpoint Inhibitors

Novel treatments based upon the use of immune checkpoint inhibitors have an impressive efficacy in different types of cancer. Unfortunately, most patients do not derive benefit or lasting responses, and the reasons for the lack of therapeutic success are not known. Over the past two decades, a pressing need to deeply profile either the tumor microenvironment or cells responsible for the immune response has led investigators to integrate data obtained from traditional approaches with those obtained with new, more sophisticated, single-cell technologies, including high parameter flow cytometry, single-cell sequencing and high resolution imaging. The introduction and use of these technologies had, and still have a prominent impact in the field of cancer immunotherapy, allowing delving deeper into the molecular and cellular crosstalk between cancer and immune system, and fostering the identification of predictive biomarkers of response. In this review, besides the molecular and cellular cancer-immune system interactions, we are discussing how cutting-edge single-cell approaches are helping to point out the heterogeneity of immune cells in the tumor microenvironment and in blood.
The harmonious number of graphs and related parameters

In this paper, we introduce the notion of (strong) harmonious number of a graph, and provide a necessary condition for a graph to have finite harmonious number. We also provide sufficient conditions for a graph to have infinite (strong) harmonious number. In addition, we examine the relations between (strong) harmonious numbers and other parameters that have previously been studied in the area of graph labelings. As applications of these, we determine the formulas for the (strong) harmonious numbers of some 2-regular graphs and all complete bipartite graphs, which lead us to formulas for other parameters of the same classes of graphs.
Discriminant Feature Extraction using Disease Segmentation for Automatic Leaf Disease Diagnosis

Plant leaf disease is a serious issue in agriculture worldwide. Image processing-based leaf disease detection and diagnosis approaches are becoming popular. The distinguishable features of leaf image are important to improve the performance of disease detection and diagnosis model. However, proper segmentation of the diseased part of a leaf helps to extract discriminant features. From this point of view, this paper presents an efficient leaf disease diagnosis model, where discriminant features are extracted from disease segmented leaf images using optimal segmentation algorithms. In this paper, the disease and healthy parts of the leaf are separated using segmentation techniques. After that, different distinguishable features are extracted based on color and intensity value of image pixels of leaf image. Finally, the Multiclass Support vector machine (MC-SVM) with RBF Gaussian kernel is used for detecting and diagnosis the leaf disease. To validate the proposed model, an online benchmarked leaf images dataset named "PlantVillage-Dataset" is used. The performance evaluation of the proposed model shows a satisfactory result of disease detection performance. Moreover, three well-known image segmentation algorithms are used and an optimal segmentation method is investigated to find better accuracy of disease diagnosis. According to the experimental result, the optimal results are observed for the leaf disease diagnosis using the Otsu thresholding-based disease segmentation.