Arthrobacter nitrophenolicus sp. nov. a new 2-chloro-4-nitrophenol degrading bacterium isolated from contaminated soil

Strain SJCon(T), a 2-chloro-4-nitrophenol (2C4NP) degrading bacterium, was isolated from soil collected from a pesticide-contaminated site in Punjab, India. The strain, which stained Gram positive, displayed a rod-coccus life cycle, and possessed a type A3(α) peptidoglycan (l-Lys–l-Ala(3)), MK-9(H2) as the major menaquinone, anteiso-C15 and iso-C15:0 as the major cellular fatty acids, and diphosphatidylglycerol, phosphatidylglycerol, phosphatidylinositol and a glycolipid as the major polar lipids, showed morphological and chemotaxonomic properties consistent with those reported for members of the genus Arthrobacter. Phylogenetic analysis of the 16S rRNA gene sequence of strain SJCon(T) confirmed that it was a member of this genus with Arthrobacter globiformis DSM 20124(T) being the closest relative (sequence similarity of 97 %). The DNA G + C content of strain SJCon(T) was 69 ± 1 mol% and DNA homology with A. globiformis DSM 20124(T) was 45 %, suggesting that strain SJCon(T) represented a novel species of the genus Arthrobacter, which we have named Arthrobacter nitrophenolicus sp. nov The type strain is SJCon(T) (=MTCC 10104(T) =DSM 23165(T))

Development of an Expert System as a Screening Tool to Minimize Groundwater Contamination from Pesticides�

Environmental Engineerin

ENHANCING SECURITY AND PRIVACY IN THE CLOUD COMPUTING

ABSTRACT Cloud computing is current buzzword in the market. Security to this Service is an important issue. PKI (Public key Infrastructure), as a Service and its focus is to evaluate the possibility to deploy a Public Key Infrastructure as a Cloud service. This is interesting since more and more organizations are moving their services and infrastructure to the cloud to benefit from the possibilities and advantages of cloud services and avoid problems with having own infrastructures. It is also interesting since the cloud could utilize the distributed architecture of PKI and in this way increase the reliability and availability and decrease the response times for validation of certificates

Chromium Oxides and Lithiated Chromium Oxides. Promising Cathode Materials for Secondary Lithium Batteries

Chromium oxides and lithiated chromium oxides were synthesized by thermal decomposition of chromium trioxide (CrO3) at high temperatures and oxygen pressures. Synthesis temperature and pressure markedly affect the performance of these cathode materials. Higher pressures lead to a higher O/Cr ratio and fewer impurities in the final product. These materials are stable intercalation hosts for lithium, and exhibit a higher capacity than any of the prominent positive electrodes used in secondary lithium batteries. m-CrOx has a capacity of 255 mAh/g, while m-LiCrOx has a capacity of 210 mAh/g, during the first discharge. The average voltage of these cells is 3.0 V vs. Li/Li+ that gives an average energy density of approximately 650 Wh/Kg

Neglected fracture shaft femur presenting with pseudoaneurysm: a case report

A pseudoaneurysm is a collection of blood leaking from a damaged arterial wall. Development of the false aneurysm is due to either initial injury of the vessel or is a complication of internal fixation of the femoral fracture. Femoral artery pseudoaneurysms (FAPs) may close spontaneously if the tear is small enough to allow for clotting and sealing. On the other hand, rupture of the aneurysm can trigger thrombosis, distal embolization and compression of adjacent structures. We present a case of left superficial femoral arterial pseudoaneurysm in a 36-year-old male with fracture of left femoral shaft. A 36-year-old male with history of road traffic accident presented to our institute with pain and swelling in left thigh. Patient was investigated and diagnosed with fracture left femoral shaft with a pseudoaneurysm of the left superficial femoral artery (SFA). Stenting was done for SFA followed by open reduction and internal fixation of the femoral shaft fracture. Such cases require multidisciplinary approach and a proper planning with involvement of different medical specialities to achieve optimal results and to minimise any intraoperative and post operative complications

Gall Bladder Cancer Detection from US Images with Only Image Level Labels

Automated detection of Gallbladder Cancer (GBC) from Ultrasound (US) images is an important problem, which has drawn increased interest from researchers. However, most of these works use difficult-to-acquire information such as bounding box annotations or additional US videos. In this paper, we focus on GBC detection using only image-level labels. Such annotation is usually available based on the diagnostic report of a patient, and do not require additional annotation effort from the physicians. However, our analysis reveals that it is difficult to train a standard image classification model for GBC detection. This is due to the low inter-class variance (a malignant region usually occupies only a small portion of a US image), high intra-class variance (due to the US sensor capturing a 2D slice of a 3D object leading to large viewpoint variations), and low training data availability. We posit that even when we have only the image level label, still formulating the problem as object detection (with bounding box output) helps a deep neural network (DNN) model focus on the relevant region of interest. Since no bounding box annotations is available for training, we pose the problem as weakly supervised object detection (WSOD). Motivated by the recent success of transformer models in object detection, we train one such model, DETR, using multi-instance-learning (MIL) with self-supervised instance selection to suit the WSOD task. Our proposed method demonstrates an improvement of AP and detection sensitivity over the SOTA transformer-based and CNN-based WSOD methods. Project page is at https://gbc-iitd.github.io/wsod-gbcComment: Accepted at MICCAI 202

RadFormer: Transformers with Global-Local Attention for Interpretable and Accurate Gallbladder Cancer Detection

We propose a novel deep neural network architecture to learn interpretable representation for medical image analysis. Our architecture generates a global attention for region of interest, and then learns bag of words style deep feature embeddings with local attention. The global, and local feature maps are combined using a contemporary transformer architecture for highly accurate Gallbladder Cancer (GBC) detection from Ultrasound (USG) images. Our experiments indicate that the detection accuracy of our model beats even human radiologists, and advocates its use as the second reader for GBC diagnosis. Bag of words embeddings allow our model to be probed for generating interpretable explanations for GBC detection consistent with the ones reported in medical literature. We show that the proposed model not only helps understand decisions of neural network models but also aids in discovery of new visual features relevant to the diagnosis of GBC. Source-code and model will be available at https://github.com/sbasu276/RadFormerComment: To Appear in Elsevier Medical Image Analysi