A bilobed Gallbladder (Vesica Fellea Divisa) in Cattle Slaughtered at Jimma Municipal Abattoir, West Oromiya, Ethiopia

Gallbladder abnormalities occur rarely. The recognized abnormalities recorded so far comprised duplication, septation, abnormal position and total absence of the gallbladder. The bilobed gallbladder of the cross bred oxen slaughtered at Jimma municipality abattoir constituted two lobes separated by a deep cleft. However, the two lobes were joined at the neck and drained by one duct. Both the lobes were of equal size and filled with bile.Key words: Bilobed, Cattle, gallbladder, Jimma, Muncipal abattoi

Waste-waste treatment technology and environmental management using sawdust bio-mixture

AbstractThe industrial wastewater (WW) of potato-chips factory is characterized by its high biological oxygen demand (BOD) and chemical oxygen demand (COD), in addition to a medium content of oil & grease (O&G), total dissolved slats (TDS) and total suspended solids (TSS). A new technique for wastewater treatment has been applied using bio-mixture of selected strains of Aspergillus terreus or Rhizopus sexualis in addition to the natural flora of sawdust (SD-BIOMIX) in the form of mobile micro-carrier in activated sludge system. Different kinds of composted sawdust were used as a microbial carrier, support and source of nutrients and enzymes to enhance the wastewater treatment process; in order to improve the quality of treated wastewater and resulting sludge. The parameters of treated wastewater in terms of BOD, COD, O&G, TDS and TSS were greatly improved by 85.0, 79.0, 82.7, 74.6 and 87.7% respectively, in relation to the retention time and kind of tested materials. The 14 days microbial–treated (composted) sawdust by A. terreus, or R. sexualis as (SD-BIOMIX) exhibited the highest enzymes contents and was the most efficient materials for the wastewater treatment process in comparison with commercial biomixture products e.g. C157 and EM solution. Furthermore, the retention time of the treatment process could be reduced to 4 hr only. Finally, the resulting sludge(s) of (SD-BIOMIX) was easy to separate (in 5–10 min.) from wastewater. The sludge, according the chemical analysis, can be safely used in agriculture as an organic fertilizer and soil conditioner. In addition, different kinds of resulting sludge have been tested as biosorbents and exhibited high ability to remove chromium (89.1 – 99.3%), nickel (84.3 – 98.0%) and zinc (85.6 – 97.7%) from the heavy industrial wastewater. Data indicated the possibility of magnifying the introduced (SD-BIOMIX) as a new technique for the treatment of wastewater and as new trend for wastes management and pollution prevention and could be applied in Kingdom of Saudi Arabia as one of advanced biotechnology to solve many of environmental problems in KSA

CWI at TREC 2012, KBA track and Session Track

We participated in two tracks: Knowledge Base Acceleration (KBA) Track and Session Track. In the KBA track, we focused on experi- menting with different approaches as it is the first time the track is launched. We experimented with supervised and unsupervised re- trieval models. Our supervised approach models include language models and a string-learning system. Our unsupervised approaches include using: 1)DBpedia labels and 2) Google-Cross-Lingual Dic- tionary (GCLD). While the approach that uses GCLD targets the central and relvant bins, all the rest target the central bin. The GCLD and the string-learning system have outperformed the oth- ers in their respective targeted bins. The goal of the Session track submission is to evaluate whether and how a logic framework for representing user interactions with an IR system can be used for improving the approximation of the relevant term distribution that another system that is supposed to have access to the session infor- mation will then calculate. the documents in the stream corpora. Three out of the seven runs used a Hadoop cluster provide by Sara.nl to process the stream cor- pora. The other 4 runs used a federated access to the same corpora distributed among 7 workstations

Document Filtering for Long-tail Entities

Filtering relevant documents with respect to entities is an essential task in the context of knowledge base construction and maintenance. It entails processing a time-ordered stream of documents that might be relevant to an entity in order to select only those that contain vital information. State-of-the-art approaches to document filtering for popular entities are entity-dependent: they rely on and are also trained on the specifics of differentiating features for each specific entity. Moreover, these approaches tend to use so-called extrinsic information such as Wikipedia page views and related entities which is typically only available only for popular head entities. Entity-dependent approaches based on such signals are therefore ill-suited as filtering methods for long-tail entities. In this paper we propose a document filtering method for long-tail entities that is entity-independent and thus also generalizes to unseen or rarely seen entities. It is based on intrinsic features, i.e., features that are derived from the documents in which the entities are mentioned. We propose a set of features that capture informativeness, entity-saliency, and timeliness. In particular, we introduce features based on entity aspect similarities, relation patterns, and temporal expressions and combine these with standard features for document filtering. Experiments following the TREC KBA 2014 setup on a publicly available dataset show that our model is able to improve the filtering performance for long-tail entities over several baselines. Results of applying the model to unseen entities are promising, indicating that the model is able to learn the general characteristics of a vital document. The overall performance across all entities---i.e., not just long-tail entities---improves upon the state-of-the-art without depending on any entity-specific training data.Comment: CIKM2016, Proceedings of the 25th ACM International Conference on Information and Knowledge Management. 201

A monoclonal antibody to Siglec-8 suppresses non-allergic airway inflammation and inhibits IgE-independent mast cell activation.

In addition to their well characterized role in mediating IgE-dependent allergic diseases, aberrant accumulation and activation of mast cells (MCs) is associated with many non-allergic inflammatory diseases, whereby their activation is likely triggered by non-IgE stimuli (e.g., IL-33). Siglec-8 is an inhibitory receptor expressed on MCs and eosinophils that has been shown to inhibit IgE-mediated MC responses and reduce allergic inflammation upon ligation with a monoclonal antibody (mAb). Herein, we evaluated the effects of an anti-Siglec-8 mAb (anti-S8) in non-allergic disease models of experimental cigarette-smoke-induced chronic obstructive pulmonary disease and bleomycin-induced lung injury in Siglec-8 transgenic mice. Therapeutic treatment with anti-S8 inhibited MC activation and reduced recruitment of immune cells, airway inflammation, and lung fibrosis. Similarly, using a model of MC-dependent, IL-33-induced inflammation, anti-S8 treatment suppressed neutrophil influx, and cytokine production through MC inhibition. Transcriptomic profiling of MCs further demonstrated anti-S8-mediated downregulation of MC signaling pathways induced by IL-33, including TNF signaling via NF-κB. Collectively, these findings demonstrate that ligating Siglec-8 with an antibody reduces non-allergic inflammation and inhibits IgE-independent MC activation, supporting the evaluation of an anti-Siglec-8 mAb as a therapeutic approach in both allergic and non-allergic inflammatory diseases in which MCs play a role