Retail yield and fabrication times for veal as influenced by purchasing options and merchandising styles

Twenty-nine selected styles of subprimals or sections of veal were obtained from a commercial facility to assist in the development of a support program for retailers. They were fabricated into bone-in or boneless retail cuts and associated components by trained meat cutters. Each style selected (n = 6) was used to generate mean retail yields and labor requirements, which were calculated from wholesale and retail weights and processing times. Means and standard errors for veal ribs consisting of five different styles (n = 30) concluded that style #2, 7-rib 4 (10 cm) x 4 (10 cm), had the lowest percentage of total retail yield (P < .05) owing to the greatest percentage of bone. Furthermore, rib style #2 required the longest total processing time (P < .05). Rib styles #3, 7-rib chop-ready, and #5, 6-rib chop ready, yielded the greatest percentage of total retail yield and also had the shortest total processing time (P < .05). Within veal loins, style #2, 4 (10 cm) x 4 (10 cm) loin kidney fat in, had the greatest percentage fat (P < .05). Loin styles #2 and #3, 4 (10 cm) x 4 (10 cm) loin special trimmed, generated more lean and fat trimmings and bone, resulting in lower percentage of total retail yields than loin style #1, 0 (0 cm) x 1 (2.5 cm) loin special trimmed (P < .05). Results indicated that bone-in subprimals and sections required more processing time if fabricated into a boneless end point. In addition, as the number of different retail cuts increased, processing times also increased.     

Differential expression and distribution of focal adhesion and cell adhesion molecules in rat hepatocyte differentiation

The present study has examined the distribution of axons of differing sizes in the optic pathway of the ground squirrel. Axon diameters were measured from electron micrographs at various locations across sections of the optic nerve and tract, and total distributions and numbers were estimated. In both the nerve and tract, roughly 1.2 million optic axons were present. The population of optic axons had a unimodal size distribution, peaking at 0.9 microm in diameter and having an extended tail toward larger diameters. Local axon diameter distributions in the optic tract indicated distinct (though partially overlapping) axon diameter classes, including one of fine sizes peaking at 0.8-0.9 microm, a second of medium sizes peaking around 1.7-1.8 microm, and a third composed of the larger fibers with diameters up to 4.8 microm. The fine-caliber axons were found at all locations in the tract, and were the only axons present immediately adjacent to the pia, while the medium- and coarse-caliber axons were found at deeper locations. Curiously, the larger axons were found primarily in the medial parts of the tract, where axons from the dorsal retina normally course. A similarly restricted distribution of the larger axons was observed in the dorsotemporal parts of the optic nerve, suggesting that this difference in the tract may relate to an asymmetric distribution of ganglion cells on the retina giving rise to these axons. Measurements of axonal size taken within the optic fiber layer in dorsal and ventral parts of the retina confirmed this asymmetry, consistent with previous demonstrations of soma size differences in the dorsal versus ventral retina. The partial segregation of axons by size in the optic tract of the ground squirrel then reflects both the asymmetric distribution of retinal ganglion cell classes and the chronotopic reordering of optic axons that occurs within the chiasmatic region.     

A Stream X-Machine Tool for Modelling and Generating Test Cases for Chronic Diseases Based on State-Counting Approach

Programming and Computer Software - In the biomedical domain, diagrammatical models have been extensively used to describe and understand the behaviour of biological organisms (biological agents)...     

Mood sensing from social media texts and its applications

We present a large-scale mood analysis in social media texts. We organise the paper in three parts: (1) addressing the problem of feature selection and classification of mood in blogosphere, (2) we extract global mood patterns at different level of aggregation from a large-scale data set of approximately 18 millions documents (3) and finally, we extract mood trajectory for an egocentric user and study how it can be used to detect subtle emotion signals in a user-centric manner, supporting discovery of hyper-groups of communities based on sentiment information. For mood classification, two feature sets proposed in psychology are used, showing that these features are efficient, do not require a training phase and yield classification results comparable to state of the art, supervised feature selection schemes; on mood patterns, empirical results for mood organisation in the blogosphere are provided, analogous to the structure of human emotion proposed independently in the psychology literature; and on community structure discovery, sentiment-based approach can yield useful insights into community formation.     

Social reader: towards browsing the social web

We describe Social Reader, a feed-reader-plus-social-network aggregator that mines comments from social media in order to display a user’s relational neighborhood as a navigable social network. Social Reader’s network visualization enhances mutual awareness of blogger communities, facilitates their exploration and growth with a fully dragn- drop interface, and provides novel ways to filter and summarize people, groups, blogs and comments. We discuss the architecture behind the reader, highlight tasks it adds to the workflow of a typical reader, and assess their cost. We also explore the potential of mood-based features in social media applications. Mood is particularly relevant to social media, reflecting the personal nature of the medium. We explore two prototype mood-based features: colour coding the mood of recent posts according to a valence/arousal map, and a mood-based abstract of recent activity using image media. A six week study of the software involving 20 users confirmed the usefulness of the novel visual display, via a quantitative analysis of use logs, and an exit survey.     

Unsupervised context detection using wireless signals

The sensing context plays an important role in many pervasive and mobile computing applications. Continuing from previous work [D. Phung, B. Adams, S. Venkatesh, Computable social patterns from sparse sensor data, in: Proceedings of First International Workshop on Location Web, World Wide Web Conference (WWW), New York, NY, USA, 2008, ACM 69–72.], we present an unsupervised framework for extracting user context in indoor environments with existing wireless infrastructures. Our novel approach casts context detection into an incremental, unsupervised clustering setting. Using WiFi observations consisting of access point identification and signal strengths freely available in office or public spaces, we adapt a density-based clustering technique to recover basic forms of user contexts that include user motion state and significant places the user visits from time to time. High-level user context, termed rhythms, comprising sequences of significant places are derived from the above low-level context by employing probabilistic clustering techniques, latent Dirichlet allocation and its n-gram temporal extension. These user contexts can enable a wide range of context-ware application services. Experimental results with real data in comparison with existing methods are presented to validate the proposed approach. Our motion classification algorithm operates in real-time, and achieves a 10% improvement over an existing method; significant locations are detected with over 90% accuracy and near perfect cluster purity. Richer indoor context and meaningful rhythms, such as typical daily routines or meeting patterns, are also inferred automatically from collected raw WiFi signals.     

Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data

Clustering is a crucial method for deciphering data structure and producing new information. Due to its significance in revealing fundamental connections between the human brain and events, it is essential to utilize clustering for cognitive research. Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties. Noisy data can lead to incorrect object recognition and inference. This research aims to innovate a novel clustering approach, named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering (PNTS3FCM), to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set (PFS) and Neutrosophic Set (NS). Our contribution is to propose a new optimization model with four essential components: clustering, outlier removal, safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data. The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods, standard Picture fuzzy clustering (FC-PFS) and Confidence-weighted safe semi-supervised clustering (CS3FCM) on benchmark UCI datasets. The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.     

Meta-analysis of linkage data under worst-case conditions: a demonstration using the human OB region

To date, few methods have been developed explicitly for meta-analysis of linkage analyses. Moreover, the methods that have been developed or suggested generally depend on certain ideal situations and have not been widely applied. In this article, we apply standard statistical theory and meta-analytic techniques in novel ways to five published papers discussing the evidence of linkage of body mass index (BMI) to the region of the human genome containing the OB gene. These methods are "inference based," meaning that they allow one to make statements about the statistical significance of the entire body of evidence. As currently developed, they do not allow specific statements to be made about the amount of variance explained by any putative locus or allow precise confidence intervals to be placed around the putative location of a linked locus. By applying these techniques to the literature on linkage in the human OB gene region, we are able to show that the evidence for linkage somewhere in the region is extremely strong (P = 1.5 x 10[-5]).     

Retardation of human drug-resistant HL-60 cell in G1 phase and induction of sensitive cell to apoptosis by cyclosporine A

To further study the relationship between resistance to apoptosis and drug resistance in harringtonine-resistant HL-60 cells (HR20), cyclosporine A (CsA) 20, 10 micrograms.ml-1 was shown to induce the sensitive HL-60 cells to apoptosis, showing a typical DNA "ladder" band. But the same concentrations of CsA retarded the HR20 cells in G1 phase and could not induce the cells to apoptosis. The cellular daunorubicin accumulation increased when HR20 cells were treated with low concentration of CsA and the reversal of drug resistance by CsA was unrelated to the retardation of cell cycle progression. High phosphorylation of about 50 kDa protein occured when HR20 cells were treated with CsA 10 micrograms.ml-1. The results domonstrate that cyclosporine A retarded the harringtonine-resistant HL-60 cells in G1 phase but induced HL-60 cells to apoptosis, and the retardation was unrelated to drug resistance.     

Efficient duration and hierarchical modeling for human activity recognition

A challenge in building pervasive and smart spaces is to learn and recognize human activities of daily living (ADLs). In this paper, we address this problem and argue that in dealing with ADLs, it is beneficial to exploit both their typical duration patterns and inherent hierarchical structures. We exploit efficient duration modeling using the novel Coxian distribution to form the Coxian hidden semi-Markov model (CxHSMM) and apply it to the problem of learning and recognizing ADLs with complex temporal dependencies. The Coxian duration model has several advantages over existing duration parameterization using multinomial or exponential family distributions, including its denseness in the space of nonnegative distributions, low number of parameters, computational efficiency and the existence of closed-form estimation solutions. Further we combine both hierarchical and duration extensions of the hidden Markov model (HMM) to form the novel switching hidden semi-Markov model (SHSMM), and empirically compare its performance with existing models. The model can learn what an occupant normally does during the day from unsegmented training data and then perform online activity classification, segmentation and abnormality detection. Experimental results show that Coxian modeling outperforms a range of baseline models for the task of activity segmentation. We also achieve a recognition accuracy competitive to the current state-of-the-art multinomial duration model, while gaining a significant reduction in computation. Furthermore, cross-validation model selection on the number of phases K in the Coxian indicates that only a small K is required to achieve the optimal performance. Finally, our models are further tested in a more challenging setting in which the tracking is often lost and the activities considerably overlap. With a small amount of labels supplied during training in a partially supervised learning mode, our models are again able to deliver reliable performance, again with a small number of phases, making our proposed framework an attractive choice for activity modeling.