Graphical evolving transformation system machine

For years, scientists have challenged the machine intelligence problem. Learning classes of objects followed by the classification of objects into their classes is a common task in machine intelligence. For this task, two objects representation schemes are often used: a vector-based representation, and a graph-based representation. While the vector representation has sound mathematical background and optimization tools, it lacks the ability to encode relations between the patterns and their parts, thus lacking the complexity of human perception. On the other hand, the graph-based representation naturally captures the intrinsic structural properties, but available algorithms usually have exponential complexity. In this work, we build an inductive learning algorithm that relies on graph-based representation of objects and their classes, and test the framework on a competitive dataset of human actions in static images. The method incorporates three primary measures of class representation: likelihood probability, family resemblance typicality, and minimum description length. Empirical benchmarking shows that the method is robust to the noisy input, scales well to real-world datasets, and achieves comparable performance to current learning techniques. Moreover, our method has the advantage of intuitive representation regarding both patterns and class representation. While applied to a specific problem of human pose recognition, our framework, named graphical Evolving Transformation System (gETS), can have a wide range of applications and can be used in other machine learning tasks

Research of multi-response optimization of milling process of hardened S50C steel using minimum quantity lubrication of Vietnamese peanut oil

This study aims to build a regression model when surveying the milling process on S50C steel using Minimum Quantity Lubrication (MQL) of Vietnamese peanut oil-based on Response Surface Methodology. The paper analyses and evaluates the effect of cutting parameters, flow rates, and pressures in minimum quantity lubrication system on cutting force and surface roughness in the milling process of S50C carbon steel materials after heat treatment (reaching a hardness of 52 HRC). The Taguchi method, one of the most effective experimental planning methods nowadays, is used in this study. The statistical analysis software, namely Minitab 19, is utilized to build a regression model between parameters of the cutting process, flow rates and pressures of the minimum quantity lubrication system and the cutting force, surface roughness of the part when machining on a 5-axis CNC milling machine. Thereby analyzing and predicting the effect of cutting parameters and minimum quantity lubrication conditions on the surface roughness and cutting force during machining to determine the influence level them. In this work, the regression models of Ra and F were achieved by using the optimizer tool in Minitab 19. Moreover, the multi-response optimization problem was solved. The optimum cutting parameters and lubricating conditions are as follows: Cutting velocity Vc=190.909 m/min, feed rate fz=0.02 mm/tooth, axial depth of cut ap=0.1 and nozzle pressure P=5.596 MPa, flow rate Q=108.887 ml/h. The output parameters obtained from the above parameters are Ra=0.0586  and F=162.035 N, respectively. This result not only provides the foundation for future research but also contributes reference data for the machining proces

BIM-based Competitive Advantages and Competitive Strategies for Construction Consultancy SMEs: A Case Study in Vietnam

Building Information Modelling (BIM) has been proven as an innovative approach to bring values to construction projects as well as stakeholders, including construction consultancy firms. BIM adoption could assist construction consultancy Small and Medium-sized Enterprise (SMEs) in enhancing their competitive capability. Using a case study with a pioneer BIM service providers  which is an SME in Vietnam (the Consultant), the paper explores the core competences for delivering BIM services in relation with potential competitive advantages. Four typical BIM market segments have been discovered, which include: i) BIM strategic services, (ii) BIM services, (iii) BIM-enabled services, and (iv) BIM tools development. Exploring six BIM cases, the realized core competences of the Consultant which are reported in the paper include the BIM-related skillful human resources (both in-house and from external), BIM know-hows, reputation, and also the benefits from a BIM network that the Consultant established as an outcome of a granted BIM research project. Focusing on only the first three market segments, the Consultant has taken advantage of their core competences to deliver differentiation and focus strategies to compete and generate competitive advantages. Cost leadership strategies were not very successful in the case study due to that the economies of scale could not be met; however, they can be considered with the provision of BIM-enabled services, when BIM services are delivered together with other consultancy and/or construction services

Software comparison for clinical Named Entity Recognition (NER): A phase-1 study for developing a computer assisted medical claims billing and coding system

Claims billing and coding is non-trivial for health care providers. Accurate coding can help medical providers get reimbursements that they deserve for their professional services. Meanwhile, incorrect coding (e.g. up-coding) is considered by authorities to be one of the most important frauds with severe penalties. Therefore, accurate coding is of great importance to medical professionals. However, claims coding is challenging. Besides the knowledge of the E/M coding system, accurate coding requires an adequate depiction of patient health conditions and treatments, part of which are contained in unstructured clinical notes, e.g. discharge summaries and physician notes. We aim to develop a coding decision support system by leveraging state-of-the-art natural language processing (NLP) techniques and algorithms. The expected result of the project is to build an effective system that can extract essential information for claims coding from real clinical narratives. This phase-1 study compared five popular existing NLP software in named entity recognition based on 108 public available transcribed medical discharge summary notes from MTsamples.com. Qualitative comparison finds that CLAMP, Amazon Comprehend Medical, and cTAKES are more powerful. Quantitative analysis shows that CLAMP is more accurate and efficient than Amazon Comprehend Medical. Future work includes integrating a section segmentation tool before NER recognition as well as testing and implementation of the system in a clinical scenario.Health Systems InnovationComputer Scienc

Comprehensive study of mobility related function in clinical notes

Use of free text in Electronic Health Records (EHRs) for clinical, administrative, and research purposes has proliferated in recent years. Using the Mobility domain of the ICF as a framework, we comprehensively analyze the structure and characteristics of mobility related concepts found in physical therapy notes from the National Institutes of Health’s Clinical Center. The result is a mobility entity framework comprised of 5 entities types, 3 relations, 8 attributes, and 33 attribute values. Two domain experts manually curated a gold standard corpus of 14,281 nested entity mentions from 400 clinical notes. Inter-annotator agreement (IAA) of exact matching averaged 92.3% F1-score on mention text spans, and 96.6% Cohen’s kappa on attributes assignments. A novel ensemble machine learning model for named entity recognition was trained and evaluated using the gold standard corpus. Average F1-score on exact entity matching of our ensemble method (83.31%) outperformed both baseline methods: a probabilistic graphical model (80.4%), and an artificial neural network (81.82%). Overcoming the irregularities and challenges in capturing functioning concepts, this work pioneers a representational framework, an annotated gold standard corpus, and a cutting-edge machine learning model that identify concepts in the Mobility domain of the ICF.Computer Scienc

Gallbladder carriage generates genetic variation and genome degradation in Salmonella Typhi

Despite recent advances in typhoid fever control, asymptomatic carriage of Salmonella Typhi in the gallbladder remains poorly understood. Aiming to understand if S. Typhi becomes genetically adapted for long-term colonisation in the gallbladder, we performed whole genome sequencing on a collection of S. Typhi isolated from the gallbladders of typhoid carriers. These sequences were compared to contemporaneously sampled sequences from organisms isolated from the blood of acute patients within the same population. We found that S. Typhi carriage was not restricted to any particular genotype or conformation of antimicrobial resistance genes, but was largely reflective of S. Typhi circulating in the general population. However, gallbladder isolates showed a higher genetic variability than acute isolates, with median pairwise SNP distances of 21 and 13 SNPs (p = 2.8x10-9), respectively. Within gallbladder isolates of the predominant H58 genotype, variation was associated with a higher prevalence of nonsense mutations. Notably, gallbladder isolates displayed a higher frequency of non-synonymous mutations in genes encoding hypothetical proteins, membrane lipoproteins, transport/binding proteins, surface antigens, and carbohydrate degradation. Specifically, we identified several gallbladder-specific non-synonymous mutations involved in LPS synthesis and modification, with some isolates lacking the Vi capsular polysaccharide vaccine target due to the 134Kb deletion of SPI-7. S. Typhi is under strong selective pressure in the human gallbladder, which may be reflected phylogenetically by long terminal branches that may distinguish organisms from chronic and acute infections. Our work shows that selective pressures asserted by the hostile environment of the human gallbladder generate new antigenic variants and raises questions regarding the role of carriage in the epidemiology of typhoid fever

Temporal fluctuation of multidrug resistant salmonella typhi haplotypes in the mekong river delta region of Vietnam.

BACKGROUND: typhoid fever remains a public health problem in Vietnam, with a significant burden in the Mekong River delta region. Typhoid fever is caused by the bacterial pathogen Salmonella enterica serovar Typhi (S. Typhi), which is frequently multidrug resistant with reduced susceptibility to fluoroquinolone-based drugs, the first choice for the treatment of typhoid fever. We used a GoldenGate (Illumina) assay to type 1,500 single nucleotide polymorphisms (SNPs) and analyse the genetic variation of S. Typhi isolated from 267 typhoid fever patients in the Mekong delta region participating in a randomized trial conducted between 2004 and 2005. PRINCIPAL FINDINGS: the population of S. Typhi circulating during the study was highly clonal, with 91% of isolates belonging to a single clonal complex of the S. Typhi H58 haplogroup. The patterns of disease were consistent with the presence of an endemic haplotype H58-C and a localised outbreak of S. Typhi haplotype H58-E2 in 2004. H58-E2-associated typhoid fever cases exhibited evidence of significant geo-spatial clustering along the Sông H u branch of the Mekong River. Multidrug resistance was common in the established clone H58-C but not in the outbreak clone H58-E2, however all H58 S. Typhi were nalidixic acid resistant and carried a Ser83Phe amino acid substitution in the gyrA gene. SIGNIFICANCE: the H58 haplogroup dominates S. Typhi populations in other endemic areas, but the population described here was more homogeneous than previously examined populations, and the dominant clonal complex (H58-C, -E1, -E2) observed in this study has not been detected outside Vietnam. IncHI1 plasmid-bearing S. Typhi H58-C was endemic during the study period whilst H58-E2, which rarely carried the plasmid, was only transient, suggesting a selective advantage for the plasmid. These data add insight into the outbreak dynamics and local molecular epidemiology of S. Typhi in southern Vietnam

The burden and characteristics of enteric fever at a healthcare facility in a densely populated area of Kathmandu

Enteric fever, caused by Salmonella enterica serovars Typhi and Paratyphi A (S. Typhi and S. Paratyphi A) remains a major public health problem in many settings. The disease is limited to locations with poor sanitation which facilitates the transmission of the infecting organisms. Efficacious and inexpensive vaccines are available for S. Typhi, yet are not commonly deployed to control the disease. Lack of vaccination is due partly to uncertainty of the disease burden arising from a paucity of epidemiological information in key locations. We have collected and analyzed data from 3,898 cases of blood culture-confirmed enteric fever from Patan Hospital in Lalitpur Sub-Metropolitan City (LSMC), between June 2005 and May 2009. Demographic data was available for a subset of these patients (n = 527) that were resident in LSMC and who were enrolled in trials. We show a considerable burden of enteric fever caused by S. Typhi (2,672; 68.5%) and S. Paratyphi A (1,226; 31.5%) at this Hospital over a four year period, which correlate with seasonal fluctuations in rainfall. We found that local population density was not related to incidence and we identified a focus of infections in the east of LSMC. With data from patients resident in LSMC we found that the median age of those with S. Typhi (16 years) was significantly less than S. Paratyphi A (20 years) and that males aged 15 to 25 were disproportionately infected. Our findings provide a snapshot into the epidemiological patterns of enteric fever in Kathmandu. The uneven distribution of enteric fever patients within the population suggests local variation in risk factors, such as contaminated drinking water. These findings are important for initiating a vaccination scheme and improvements in sanitation. We suggest any such intervention should be implemented throughout the LSMC area.This work was supported by The Wellcome Trust, Euston Road, London, United Kingdom. MFB is supported by the Medical Research Council (grant G0600718). SB is supported by an OAK foundation fellowship through Oxford University