Air-Cooling and Heating Systemfor Tiger in Zoo using Earth Tube Heat Exchanger

A specially designed air-cooling (and heating) system using Earth Tube Heat Exchanger (ETHE) was installed in the dwelling of a 15-year old white tiger (Panthera tigris) named Mahesh at Kamala Nehru Zoological Garden, Ahmedabad (India) in October 2000. This was done to alleviate the stresses experienced by Mahesh in summer, which is long and hot; and in winter nights, which can be quite cold. Summer temperatures in Ahmedabad remain around 40oC for a long time and can reach as high as 45oC. Night temperatures in winter can drop to 10oC or below. The system does both--provide cooling in summer and warming in winter. In winter the system warms up the ambient (cold) air by as much as 10oC at night. In summer the system cools the ambient (hot) air also by as much as 8 - 10oC during the day.

An integer programming framework for inferring disease complexes from network data

MOTIVATION: Unraveling the molecular mechanisms that underlie disease calls for methods that go beyond the identification of single causal genes to inferring larger protein assemblies that take part in the disease process. RESULTS: Here, we develop an exact, integer-programming-based method for associating protein complexes with disease. Our approach scores proteins based on their proximity in a protein-protein interaction network to a prior set that is known to be relevant for the studied disease. These scores are combined with interaction information to infer densely interacting protein complexes that are potentially disease-associated. We show that our method outperforms previous ones and leads to predictions that are well supported by current experimental data and literature knowledge. AVAILABILITY AND IMPLEMENTATION: The datasets we used, the executables and the results are available at www.cs.tau.ac.il/roded/disease_complexes.zip. CONTACT: [email protected]

Comparative study between interlock nailing and dynamic compression plating in humerus diaphyseal fractures in its functional and surgical outcome

Background: The aim of the study was to analyse compare study between interlock nailing and dynamic compression plating in humerus diaphyseal fractures in its functional and surgical outcome. Methods: The 2019 to 2022, patients were randomly divided into two groups with the help of computer-generated coded envelopes, group A (humerus diaphyseal fractures treated with dynamic compression plating) and group B (humerus diaphyseal fractures treated with interlock nailing) with 20 patients in each group. Outcomes were evaluated based on operative time, blood loss, neurovascular deficit, surgical site infection, union, shoulder stiffness, constant Murley score, Mayo elbow performance index at 1 year of follow up. Results: On radiology as non-union and union, most common study participants show union, on follow up of 1 year constant Murley score and Mayo elbow performance index was calculated and constant Murley score was more in patients treated with dynamic compression plating, shoulder stiffness was more in patients treated with interlock nailing. However blood loss was more in patients treated with dynamic compression plating. Conclusions: The result of our study shows that interlock nailing is associated with less blood loss but it is associated with decreased shoulder function postoperatively and marked shoulder stiffness which is more than patients treated with dynamic compression plating. Hence dynamic compression plating should be considered gold standard for operative treatment of humerus shaft fracture

A 4D Light-Field Dataset and CNN Architectures for Material Recognition

We introduce a new light-field dataset of materials, and take advantage of the recent success of deep learning to perform material recognition on the 4D light-field. Our dataset contains 12 material categories, each with 100 images taken with a Lytro Illum, from which we extract about 30,000 patches in total. To the best of our knowledge, this is the first mid-size dataset for light-field images. Our main goal is to investigate whether the additional information in a light-field (such as multiple sub-aperture views and view-dependent reflectance effects) can aid material recognition. Since recognition networks have not been trained on 4D images before, we propose and compare several novel CNN architectures to train on light-field images. In our experiments, the best performing CNN architecture achieves a 7% boost compared with 2D image classification (70% to 77%). These results constitute important baselines that can spur further research in the use of CNNs for light-field applications. Upon publication, our dataset also enables other novel applications of light-fields, including object detection, image segmentation and view interpolation.Comment: European Conference on Computer Vision (ECCV) 201

Antimicrobial susceptibility patterns of uropathogenic Escherichia coli and their prevalence among people in and around Dhanbad, Jharkhand

Background: Urinary tract infection (UTI) is one of the most common infections, which causes high morbidity and mortality among human population. The purpose of this study was to evaluate the prevalence and their antibiogram profile of uropathogenic Escherichia coli (UPEC) in and around Dhanbad. Methods: A total of 641 urine samples were collected from the suspected patients of UTI. The samples were cultured on MacConkey agar for isolation and identification. Antibiotic susceptibility test was done by disc diffusion method. Both male and female patients of different age groups were included for this study. Results: 45.70% urinary isolates were identified as E. coli. 43.56% UPEC isolates were sensitive to nitrofurantoin and piperacillin/tazobactum. 22.77% isolates were susceptible to levofloxacin and amikacin followed by cefotaxime (21.78%). These isolates were mostly resistant to ampicillin and trimethoprim/sulfamethoxazole, their susceptibility pattern was found to be 11.88% and 5.94% respectively. Conclusion: Prevalence of E. coli among urinary isolates was high in our study. Antibiogram profile of these isolates varies to different antibiotics in terms of their susceptibility pattern. Continuous surveillance of antibiogram of UPEC isolate is mandatory because it vary significantly in different geographical area. Thus empirical selection of antimicrobials should be based on the knowledge of local prevalence and individual sensitivity rather than on universal guideline

Hemorrhage into an Occult Spinal Ependymoma after Epidural Anesthesia

Summary of Background Data: Five cases of hemorrhage into a spinal neoplasm after spinal or epidural anesthesia are reported in the literature. Presentation ranges from severe low back pain to acute cauda equina syndrome. Methods: A case study of a patient who hemorrhaged into an intradural, extramedullary spinal cord mass was performed. A detailed literature review is also provided. Results: A 27 year old female underwent epidural anesthesia for Cesarean section delivery. She presented with a 3 week history of increasing low back pain with bilateral radiculopathy. Imaging studies revealed a large hemorrhagic intradural mass compressing the lower conus medullaris and cauda equina, which operatively was confirmed to be a myxopapillary ependymoma. Conclusions: We report a case of hemorrhage into a previously unrecognized ependymoma after epidural anesthesia. Underlying tumors may rarely complicate regional anesthesia in the lumbar spine

Second-order Democratic Aggregation

Aggregated second-order features extracted from deep convolutional networks have been shown to be effective for texture generation, fine-grained recognition, material classification, and scene understanding. In this paper, we study a class of orderless aggregation functions designed to minimize interference or equalize contributions in the context of second-order features and we show that they can be computed just as efficiently as their first-order counterparts and they have favorable properties over aggregation by summation. Another line of work has shown that matrix power normalization after aggregation can significantly improve the generalization of second-order representations. We show that matrix power normalization implicitly equalizes contributions during aggregation thus establishing a connection between matrix normalization techniques and prior work on minimizing interference. Based on the analysis we present {\gamma}-democratic aggregators that interpolate between sum ({\gamma}=1) and democratic pooling ({\gamma}=0) outperforming both on several classification tasks. Moreover, unlike power normalization, the {\gamma}-democratic aggregations can be computed in a low dimensional space by sketching that allows the use of very high-dimensional second-order features. This results in a state-of-the-art performance on several datasets

Case Report: Hemorrhage into an Occult Spinal Ependymoma after Epidural Anesthesia

Epidural anesthesia is a procedure which is well tolerated and has a low incidence of adverse events. In performing caesarean sections, regional anesthesia (spinal or epidural) is the preferred modality for anesthetic delivery. Although rare with continuous epidural anesthesia, epidural hematomas have been reported to occur with an incidence between 1:150,000 and 1:190,00010. An underlying bleeding diathesis has been implicated as a causative factor. We present the sixth reported case of hemorrhage into an occult intradural neoplasm after spinal or epidural anesthesia. Similar lesions have not been reported in the recent spine literature

Associating Genes and Protein Complexes with Disease via Network Propagation

A fundamental challenge in human health is the identification of disease-causing genes. Recently, several studies have tackled this challenge via a network-based approach, motivated by the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. However, most of these approaches use only local network information in the inference process and are restricted to inferring single gene associations. Here, we provide a global, network-based method for prioritizing disease genes and inferring protein complex associations, which we call PRINCE. The method is based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. We exploit this function to predict not only genes but also protein complex associations with a disease of interest. We test our method on gene-disease association data, evaluating both the prioritization achieved and the protein complexes inferred. We show that our method outperforms extant approaches in both tasks. Using data on 1,369 diseases from the OMIM knowledgebase, our method is able (in a cross validation setting) to rank the true causal gene first for 34% of the diseases, and infer 139 disease-related complexes that are highly coherent in terms of the function, expression and conservation of their member proteins. Importantly, we apply our method to study three multi-factorial diseases for which some causal genes have been found already: prostate cancer, alzheimer and type 2 diabetes mellitus. PRINCE's predictions for these diseases highly match the known literature, suggesting several novel causal genes and protein complexes for further investigation