A novel fluorescent "turn-on" chemosensor for nanomolar detection of Fe(III) from aqueous solution and its application in living cells imaging

An electronically active and spectral sensitive fluorescent “turn-on” chemosensor (BTP-1) based on the benzo-thiazolo-pyrimidine unit was designed and synthesized for the highly selective and sensitive detection of Fe³⁺ from aqueous medium. With Fe³⁺, the sensor BTP-1 showed a remarkable fluorescence enhancement at 554 nm (λex=314 nm) due to the inhibition of photo-induced electron transfer. The sensor formed a host-guest complex in 1:1 stoichiometry with the detection limit down to 0.74 nM. Further, the sensor was successfully utilized for the qualitative and quantitative intracellular detection of Fe³⁺ in two liver cell lines i.e., HepG2 cells (human hepatocellular liver carcinoma cell line) and HL-7701 cells (human normal liver cell line) by a confocal imaging technique

Cornual Abscess Rupture: A Rare Etiology of Acute Abdomen

Ruptured cornual abscess or pyometra can resemble other more common causes of acute abdomen, including appendicitis, diverticulitis, tubo-ovarian abscess, and perforated viscus. Despite its rarity, the diagnosis of ruptured pyometra should always be considered in females presenting with acute abdominal pain, particularly in the setting of a retained intrauterine device

Modeling Of Depressurization And Thermal Reservoir Simulation To Predict Gas Production From Methane -Hydrate Formations

Thesis (Ph.D.) University of Alaska Fairbanks, 2007Gas hydrates represent a huge potential future resource of natural gas. However, significant technical issues need to be resolved before this enormous resource can be considered to be an economically producible reserve. Developments in numerical reservoir simulations give useful information in predicting the technical and economic analysis of the hydrate-dissociation process. For this reason, a commercial reservoir simulator, CMG (Computer Modeling Group) STARS (Steam, Thermal, and Advanced Processes Reservoir Simulator) has been adapted in this study to model gas hydrate dissociation caused by several production mechanisms (depressurization, hot water injection and steam injection). Even though CMG is a commercially available simulator capable of handling thermal oil recovery processes, the novel approach of this work is the way by which the simulator was modified by formulating a kinetic and thermodynamic model to describe the hydrate decomposition. The simulator can calculate gas and water production rates from a well, and the profiles of pressure, temperature and saturation distributions in the formation for various operating conditions. Results indicate that a significant amount of gas can be produced from a hypothetical hydrate formation overlying a free gas accumulation by several different production scenarios. However, steam injection remarkably improves gas production over depressurization and hot water injection. A revised axisymmetric model for simulating gas production from hydrate decomposition in porous media by a depressurization method is also presented. Self-similar solutions are obtained for constant well pressure and fixed natural gas output. A comparison of these two boundary conditions at the well showed that a higher gas flow rate can be achieved in the long run in the case of constant well pressure over that of fixed gas output in spite of slower movement of the dissociation front. For different reservoir temperatures and various well boundary conditions, distributions of temperature and pressure profiles, as well as the gas flow rate in the hydrate zone and the gas zone, are evaluated

Endophytic Mycoflora of Indian Medicinal Plant, Terminalia arjuna and their Biological Activities

Studies were undertaken to isolate, identify and evaluate the biological activities of endophytic mycoflora of Indian medicinal plant, Terminalia arjuna. A total of 20 isolates of endophytic fungi were obtained from the leaves, twigs and bark tissues of the Terminalia arjuna. Out of 20, six isolates exhibited promising antibacterial, antifungal and anti-inflammatory activities when cultivated at shake flask level. The selected isolates were identified on the basis of morphology and ITS gene sequencing. Three isolates, designated as TA BA 1, TA L1 and TA L2 were identified as Aspergillus flavus whereas; the remaining three endophytic fungi were identified as Diaporthe arengae (TA TW2), Alternaria Sp. (TA TW1) and Lasiodiplodia theobromae (TA BA2). Aspergillus flavus was found as the predominant endophyte in leaves and bark tissues of the plant. The crude extract of the test isolates showed considerable antimicrobial activity against common human bacterial (Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Proteus vulgaris, Salmonella abony and Bacillus subtilis) and fungal (Candida albicans, Aspergillus niger and Penicilium sp.) pathogens. The extract of Diaporthe arengae (TA TW2) significantly reduced the concentration of DPPH free radical as percent DPPH scavenging activity was found to be highest (69.56%) in comparison with other isolates. The % inhibition of hemolysis of RBCs was found to be highest (82.85%) with Diaporthe arengae (TA TW2) in comparison (83.26%) with standard drug (Ibuprofen). Among all, the extract of the Diaporthe arengae (TA TW2) showed excellent biological activities and hence was subjected to further characterization. The phytochemical investigation of the extract revealed the presence of terpenoids as the major phytoconstituents which was supported by TLC and UV spectroscopic studies. The results indicate that the isolated endophytes could be the valuable source of these bioactive molecules with diverse biological activities. The bioactivities may be attributed to the terpenoids present in the endophytic extract

Food insecurity in rural Tanzania is associated with maternal anxiety and depression

Food insecurity is a major health problem that has pervasive effects on many human biological outcomes. In particular, there are compelling theoretical and empirical reasons to expect that the relationship between food insecurity may be directly related to mental health morbidities, and may be quantifiable in developing country settings. This preliminary study examined whether caretaker reports of food insecurity were associated with anxiety and depression among four ethnic groups in two communities of rural Tanzania. In-home interviews were conducted in June–August of 2005 among female caretakers (n = 449). In addition to collecting household and demographic data, modified versions of the USDA's food security module and Hopkins Symptom Checklist (HSCL) were used to measure food insecurity and anxiety and depression. Consistent with predictions, the results showed a strong positive correlation between a caretaker's score on the food insecurity instrument and her summed response on the HSCL ( P < 0.0001). This association was maintained in all four ethnic groups, even when controlling for individual-level covariates such as caretaker's age and marital status. Issues of causality and hypotheses that might explain this robust finding are discussed, as are methodological and theoretical implications. Am. J. Hum. Biol. 18:359–368, 2006. © 2006 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/50663/1/20505_ftp.pd

Classification of traffic over collaborative IoT and Cloud platforms using deep learning recurrent LSTM

Internet of Things (IoT) and cloud based collaborative platforms are emerging as new infrastructures during recent decades. The classification of network traffic in terms of benign and malevolent traffic is indispensable for IoT-cloud based collaborative platforms to utilize the channel capacity optimally for transmitting the benign traffic and to block the malicious traffic. The traffic classification mechanism should be dynamic and capable enough to classify the network traffic in a quick manner, so that the malevolent traffic can be identified in earlier stages and benign traffic can be channelized to the destined nodes speedily. In this paper, we are presenting deep learning recurrent LSTM based technique to classify the traffic over IoT-cloud platforms. Machine learning techniques (MLTs) have also been employed for comparison of the performance of these techniques with the proposed LSTM RNet classification method. In the proposed research work, network traffic is classified into three classes namely Tor-Normal, NonTor-Normal and NonTor-Malicious traffic. The research outcome shows that the proposed LSTM RNet classify the traffic accurately and also helps in reducing the network latency and in enhancing the data transmission rate as well as network throughput