Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Mining for Biomedicine and Healthcare

Artificial intelligence (AI), as a computational method, attempts tomimic, in a very simplistic way, the human cognition capability so as to solve problems that have defined solution using conventional computational techniques. Medicine is unquestionably the main area in which AI tools and techniques have been applied. Medicine plays an essential role in human life to assess and solve problems with guarantees of success. Medicine can be considered a multidisciplinary science by itself, laying on the advances and discoveries of other sciences (biology, physics, statistics, etc.) and using such results as tools to solve diagnosis or prognosis problems. For this reason, AI in medicine has become a great issue. In this regard, developing and applying novel AI techniques and tools, due to their particular strengths, to solve medical problems and provide valuable information might be of interest to healthcare professionals in their decision making [1, 2]. AI-based medical applications can be classified into either knowledge intensive or data driven. The knowledge intensive approaches intend to obtain computational models, extracted from the clinical literature and experts’ experience, to represent concepts, their relations, and the mechanisms to enable automatic reasoning to support medical decisions. Unlike knowledge intensive, data-driven approaches, at the center of the vision of learning health systems, extract such knowledge directly from the collected clinical data and this knowledge is traditionally used to explain a patient’s current symptoms/vital signs and to predict future disease progression of the patient. While an increasing amount of data is being produced by various biomedical and healthcare systems, they have not yet fully capitalized on the transformative opportunities that these data provide. Applying data-driven techniques to big health data can be of great benefit in the biomedical and healthcare domain, allowing identification and extraction of relevant information and reducing the time spent by biomedical and healthcare professionals and researchers who are trying to find meaningful patterns and new threads of knowledge. The major goal of this special issue is to bring together the researchers in healthcare and data mining to illustrate pressing needs, demonstrate challenging research issues, and showcase the state-of-the-art research and development. The selected papers underwent a rigorous extra refereeing and revision process. It is glad to see that the selected papers presented novel methods that were empirically evaluated on medical datasets. In addition, most of the papers in this special issue include healthcare experts as coauthors, which is beneficial to open new approaches to medical experts in this multidisciplinary field. Moreover, the relevance of this special issue is strengthened by the fact that data-driven medicine is also an object of research in different communities. These communities include AI, medicine, medical informatics, decision support, and healthcare management.
Perishable Items in Multi-Level Inventory Systems

This master thesis studies a two-echelon distribution system for perishable items with two non identical retailers. Each location is managed following a standard continuous (R, Q) ordering policy. The demand occurs solely at the retailers and follows independent Poisson processes. Customers are backordered when the retailer is out of stock. The items are considered as fixed lifetime perishables. Whenever an item perished, it is discarded from the stock. The model includes fix transportation time and the allocation policy at the central warehouse is a First-Come-First-Serve one. This kind of system is very complicated and therefore hard to study. In this master thesis, we focus on a simulation study of 48 different problems with both a FIFO and a LIFO issuing policy at the retailers. The goal of this study is therefore to optimize the values of R in (R, Q) ordering policies considering that the items are perishables. To do so, we try to optimize the values of the reorder points at every location. We also try to find some general behaviour of the system and we compare the FIFO and the LIFO best found solution. More than 1000 hours of computer-time were used for this study. For every problem, we conducted an optimization process to find better values of the reorder points at every location. For the FIFO case, an average cost reduction of more than 20% was found. It exists a good opportunity in term of cost savings while taking into account the perishable characteristic of the items. Another finding of our study is that the LIFO case has good performance comparing to what expected. On average, the costs increase is only 7% while considering a LIFO issuing policy instead of a FIFO one. Moreover, the values of the reorder points for the FIFO best found solution are still the same than the LIFO best found solution in 70% of the problems studied.
Hallucinogen-Tranquilizer Interaction: Its Nature

Study of the competition between hallucinogens and tranquilizers at cerebral synapses and on behavior in various species of animals indicates a continuum of effects from protection to dominance of tranquilizer toxicity as the dose of tranquilizer increases. Data on cat and monkey behavior, supplementing that on the rat, show that it is possible to arrive at a tranquilizer dose that can aggravate instead of protect, in accord with the competitive inhibitory nature of the interaction of hallucinogen and tranquilizer.
Sustainable Agricultural Intensification for Livelihood and Food Security in Nepal

With increased market access and road links to urban centres, settled agriculture in Nepal is becoming transformed into intensified cropping, especially in peri- and semi-urban areas. On a global scale, major driving factors for intensification are: population growth, lack of alternate employment, profit motive, market access, road links, availability of agricultural inputs and organizational cooperation. However, in Nepal the main driving factor is necessity due to lack of other income opportunities. The outcomes of agricultural intensification, namely, improved economic condition of farmers with higher production and good market price are intended to address the developmental challenges of high population growth, food deficit, agricultural trade modalities, fragile ecology, and national policies. In spite of soil fertility loss, erosion, workload and pollution, agricultural intensification is found to be a viable option for better livelihood in developing countries. This review paper discusses the global driving factors of intensification in the local context highlighting their positive and negative impacts.
Complete Sequence (20 Kilobases) of the Polyprotein-Encoding Gene 1 of Transmissible Gastroenteritis Virus

Abstract The entire nucleotide sequence of cloned cDNAs containing the 5′-untranslated region and gene 1 of Purdue-115 strain of transmissible gastroenteritis virus (TGEV) was determined. This completes the sequence of the TGEV genome, which is 28,579 nucleotides long. The gene 1 is composed of two large open reading frames, ORF1a and ORF1b, which contain 4017 and 2698 codons, respectively (stop excluded). A brief, three-codon-long ORF is present upstream of ORF1a. ORF1b overlaps ORF1a by 43 bases in the (-1) reading frame. In vitro experiments indicated that translation of the ORF1a/b polyprotein involves an efficient ribosomal frameshifting activity, as previously shown for other coronaviruses. Analysis of the predicted ORF1a and ORF1b translation products revealed that the putative functional domains identified in infectious bronchitis virus (IBV), mouse hepatitis virus (MHV) and human coronavirus 229E (HCV 229E) are all present in TGEV. The amino-terminal half of the ORFla product exhibits greater divergence then the carboxy-terminal half, including within the TGEV/HCV229E pair. The ORF1b protein is overall highly conserved among the above four coronaviruses, except a divergent region situated near the carboxy terminus.
[Relationship between the increase of hepatic D-bifunctional protein activity and bile acid biosynthesis in rats].

OBJECTIVE To determine the physiological role of D-bifunctional protein (DBP) in bile acid biosynthesis through investigating the effect of increasing activity of DBP on bile acid biosynthesis. METHODS Twenty male Wistar rats were divided into two groups: diethylhexyl phthalate (DEHP) group (n = 10) and control group (n = 10). Serum triglyceride, total cholesterol, hepatic DBP activity, and fecal bile acids were assayed. The mRNA levels of hepatic peroxisome proliferator-activated receptor alpha (PPARalpha), DBP, and cholesterol 7alpha-hydroxylase (CYP7A1) were detected by RT-PCR. RESULTS Compared with control group, serum triglyceride level was decreased significantly and PPARalphamRNA level was increased significantly in DEHP group (P < 0.01). Together with a sharp induction of DBP mRNA expression and DBP activity in DEHP group (P < 0.01), the levels of CYP7A1 mRNA and fecal bile acids were significantly increased by 1.9 times and 1.6 times respectively compared to control group (P < 0.01). There was a significantly positive correlation between DBP mRNA level or DBP activity and CYP7A1 mRNA level (r = 0.89, P < 0.01; r = 0.95, P < 0.01). CONCLUSION The up-regulation of DBP mRNA and activity in liver can result in the increase in CYP7A1 mRNA expression and bile acid biosynthesis, suggesting that DBP may be involved in bile acid biosynthesis together with CYP7A1.
A distributed programming platform using mobile agents

Current distributed computing is based on rigid paradigms such as client server computing, that lack run-time adaptive behaviour. The designer of such systems has to consider all possibilities during the design stage and the services that are offered remain constant until a new re-design is done. Although there have been attempts to provide different programming languages that consider programming both distributed and non distributed applications the same, the two remain however different. New paradigms are needed that consider factors such as dynamic adaptability, interaction, ability to monitor and react to the environment, mobility, network latency, partial failure and concurrency. We show that the agent based programming paradigm, agent-ware, is closer to fulfilling some of these requirements and describe the proposed Agentware Virtual Machine (AVM), a generic distributed agent-based computing environment. With the emergence of intelligent agent-based technology, a number of distributed applications are being re-engineered taking advantage of this new design methodology.
The effect of strategic orientations on business performance in SMEs

Purpose – The present study sheds light on the role of strategic orientations (SOs) in explaining business growth. The purpose of this paper is to examine how different SOs, namely learning orientation, entrepreneurial orientation, market orientation and brand orientation simultaneously affect business performance measured with brand performance, market performance and business growth in SME context and whether these effects vary across countries. Design/methodology/approach – An extensive data set of 1,120 effective responses is collected from two European countries, namely Hungary, representing a post socialist rapidly growing market, and Finland with a stable, highly developed and competitive economy. A multigroup moderation analysis is conducted. Confirmatory factor analysis is used in testing measurement invariance, subsequently followed by structural equation modeling procedure used in testing research hypotheses developed on the basis of a literature review. Findings – The results show that entrepr.
Direction of Arrival Estimation for Off-Grid Signals Based on Sparse Bayesian Learning

The inherent limitation of the predefined spatial discrete grids greatly restricts the precision and feasibility of many sparse signal representation (SSR)-based direction-of-arrival (DOA) estimators. In this paper, we first propose a perturbed SSR-based model to alleviate this limitation by incorporating a bias parameter into the DOA estimation framework. Using this model, a perturbed sparse Bayesian learning-based algorithm, named PSBL, is developed to solve the DOA estimation problem, followed by a theoretical analysis of PSBL. We then present two algorithms based on the covariance matrix of the array output, named perturbed covariance matrix (PCM) and improved PCM (IPCM), respectively, to improve the convergence speed of PSBL. Extensive experiments show that the PSBL enjoys a high estimation accuracy in the cases of limited snapshots, low signal-to-noise-ratio, correlated, and spatially adjacent signals. In particular, PCM not only keeps the merits of PSBL, but also exhibits superiority over PSBL in terms of computational efficiency. IPCM has a better computational efficiency, but with a small sacrifice of its performance in a correlated signal scenario.
Registration of Sheep Brain MR Images for Cell Tracking Using Ferrite-Composite Micro-Beads as Markers

In the recent molecular imaging studies of the magnetic resonance imaging field, in-vivo cell tracking is becoming an issue for the observation of cell therapy and disease behavior. In order to perform cell tracking, high resolution images and long-term studies are required, because those images make better performance than low resolution images in the recognition of cells and cells are traced at least two weeks. Image registration is an essential image processing technique for long-term imaging. In this study, we proposed the image registration technique using ferrite-composite micro- beads which could be used as substitutes for the cells labeling MR contrast agent such as superparamagnetic iron oxide (SPIO) nanoparticles. Registration of sheep brain images tagging micro-beads was performed with the sufficient accuracy for cell tracking.
The Effect of Lexipafant on Bacterial Translocation in Acute Necrotizing Pancreatitis in Rats

Bacterial translocation (BT) from the gastrointestinal tract to mesenteric lymph nodes (MLNs) and other extra intestinal organs is an important source of infection in acute pancreatitis (AP). Lexipafant (BB-882) is a potent platelet-activating factor receptor antagonist that has an anti-inflammatory effect. To examine whether BB-882 could affect BT in acute necrotizing pancreatitis, 48 male Sprague Dawley rats (250–350 g) were studied. AP was induced in Group I and Group II by pressure injection of 3% taurocholate and trypsin into the common biliopancreatic duct (1 mL/kg of body weight). Group I rats received BB-882 (10 mg/kg, ip qd) and Group II rats received a similar volume of normal saline as a placebo postoperatively for 2 days. Group III and Group IV received BB-882 and placebo, respectively, after an exploratory laparotomy. At 48 hours postoperatively, blood was drawn for culture, serum amylase, and tumor necrosis factor (TNF)-α determinations. Specimens from MLNs, spleen, liver, pancreas, and cecum were harvested for culture of Gram-positive, Gram-negative, and anaerobic bacteria. Quantitative cecal cultures of Gram-positive, Gram-negative, and anaerobic bacteria were obtained. A point scoring system for five histological features that include interstitial edema, inflammatory cellular infiltration, fat necrosis, parenchymal necrosis, and hemorrhage was used to evaluate the severity of pancreatitis. There was no difference in serum amylase levels (2415 ± 127 IU/L versus 2476 ± 170 IU/L), serum TNF-α levels (7820 ± 1396 pg/mL versus 7318 ± 681 pg/mL), and the mean pancreatic histology score (5.9 ± 1.2 versus 6.5 ± 1.1) between Group I and Group II, respectively (P gt 0.05). Seven of 12 Group I rats had BT to MLNs, compared with 11 of 12 rats in Group II (P > 0.05). Five of 12 Group I rats had BT to distant sites such as pancreas, spleen, liver, and/or blood, compared with 11 of 12 rats in Group II (P < 0.05). BB-882 treatment decreases bacterial spread to distant sites, but does not reduce serum amylase levels and serum TNF-α levels or ameliorate pancreatic damage in rats with AP.
Healthcare robot systems for a hospital environment: CareBot and ReceptionBot

This paper presents a robot system for healthcare facility environments. Current healthcare robot systems do not address healthcare workflows well and our goal is to provide distributed, heterogeneous multiple robot systems that are capable of integrating with healthcare workflows and are easy to modify when workflow requirements change. The proposed system consists of three subsystems: a receptionist robot system, a nurse assistant robot system, and a medical server. The roles of the receptionist robot and the nurse assistant robot are to do tasks to help the human receptionist and nurse. The healthcare robots upload and download patient information through the medical server and provide data summaries to human care givers via a web interface. We developed these healthcare robot systems based on our new robotic software framework, which is designed to easily integrate different programming frameworks, and minimize the impact of framework differences and new versions. We test the functionalities of each healthcare robot system, evaluate the robot-robot collaboration, and present a case study.
Exercising Professional Judgement in an Era of Sector Neutrality: A Study of Choices Made by New Zealand Reporting Entities

Since 1992, New Zealand has engaged in a sector-neutral approach to accounting standard setting – where a transaction is accounted for based on its underlying economic nature rather than on the type of entity undertaking it. Underpinned by the premise that GAAP can be generalised across the public/private divide, we examine whether systematic differences exist between accounting policies adopted by reporting entities with different ownership and/or operating objectives. We find evidence of sector preference in the choice of useful life for comparable depreciable assets, and differences in policies for the amortisation of goodwill. We also find evidence of sector convergence in the choice of methods for accounting for valuation of non-current assets, deferred taxation and inventory. It is observed that the two accounting policies indicating sector preference have a more direct impact on the Statement of Financial Performance than the other policies examined.
Leveraging Knowledge Management with the Balanced Scorecard

In a rapidly changing environment, innovative firms are increasingly using the Balanced Scorecard (BSC) to identify and communicate key factors that drive future value. Knowledge is described as the only meaningful resource in the Knowledge Economy (KE). Although there is recognition that knowledge is a key business asset, many organizations still do not understand the implications of Knowledge Management (KM). The key is to understand what makes knowledge valuable and, in particular, how knowledge creates wealth in a knowledge-driven economy. KM deals with the production, application, and distribution of knowledge within and between organizations. Such intellectual assets are always constituted through practices and undertakings in an everyday work life setting. This paper seeks to examine how the BSC represents a relevant tool to KM, bridging the gap between strategic objectives set at the senior level within an organization and their operational execution.
Restriction pattern variability of respiratory syncytial virus during three consecutive epidemics in Denmark

A PCR‐based assay was used to distinguish between respiratory syncytial virus (RSV) group A and B in order to analyze their prevalence in Denmark in three consecutive epidemics during the winters of 1992/93, 1993/94 and 1994/95. A total of 96 RSV strains isolated from hospitalized children were examined, showing alternation of group prevalence. Furthermore, the genetic variability of the RSV isolates was illustrated by restriction enzyme analysis of PCR products originating from a part of the F and G proteins that has been reported to be highly variable. We found that, in general, different genome types predominated each year, some types being present in consecutive epidemics, indicating a contribution of strains circulating unattended between outbreak seasons, while others seemed to disappear or became undetectable, being replaced by emerging types. Some of the genome types found seemed related to strains isolated up to more than two decades ago in other parts of the world. This indicates that the temporal fluctuation in predominance of genome types presumably caused by selective pressure exerted by host immunity is due to the favoring of strains from a pool of globally circulating, genetically relatively stable genome types, rather than a molecular evolution in strains induced or directed by immunoselective pressure.
An Efficient Revocable Group Signature Scheme in Vehicular Ad Hoc Networks

Although many revocable group signature schemes has been proposed in vehicular ad hoc networks (VANETs), the existing schemes suffer from long computation delay on revocation that they cannot adapt to the dynamic VANETs. Based on Chinese remainder theorem and Schnorr signature algorithm, this paper proposes an efficient revocable group signature scheme in VANETs. In the proposed scheme, it only need to update the corresponding group public key when a member quits the group, and in the meanwhile the key pairs of unchanged group members are not influenced. Furthermore, this scheme can achieve privacy protection by making use of blind certificates. Before joining to the VANETs, users register at local trusted agencies (LTAs) with their ID cards to obtain blind certificates. The blind certificate will be submitted to road-side units (RSUs) to verify the legality of users. Thus, the real identities of users can be protected. In addition, if there is a dispute, users can combine to submit open applications to RSUs against a disputed member. And LTAs can determine the real identity of the disputed member. Moreover, since the key pairs employed by a user are different in different groups, attackers are not able to track the movement of users with the obtained public keys in a group. Furthermore, performance analysis shows that proposed scheme has less computation cost than existing schemes.