Synthesis of Unusual Trimers in 1,3-Indandione

Dimethyl Sulphoxide-acetic anhydride reagent converts 1,3-indandione to the ylide (2) and a much awaited dimer (3) at room temperature. However , when 1,3-indandione was interacted with pre- heated  DMSO-acetic anhydride  at  waterbath temperature it affords an unusual trimer (4) and a novel compound (5), methine-tris-1,3-indandione  along with the ylide (2) Key words: DMSO, Acetic anhydride, Indandione,Ylide Trimer

Post-Partum Pituitary Insufficiency and Livedo Reticularis Presenting a Diagnostic Challenge in a Resource Limited Setting in Tanzania: A Case Report, Clinical Discussion and Brief Review of Existing Literature.

Pituitary disorders following pregnancy are an important yet under reported clinical entity in the developing world. Conversely, post partum panhypopituitarism has a more devastating impact on women in such settings due to high fertility rates, poor obstetric care and scarcity of diagnostic and therapeutic resources available. A 37 year old African female presented ten years post partum with features of multiple endocrine deficiencies including hypothyroidism, hypoadrenalism, lactation failure and secondary amenorrhea. In addition she had clinical features of an underlying autoimmune condition. These included a history of post-partum thyroiditis, alopecia areata, livedo reticularis and deranged coagulation indices. A remarkable clinical response followed appropriate hormone replacement therapy including steroids. This constellation has never been reported before; we therefore present an interesting clinical discussion including a brief review of existing literature. Post partum pituitary insufficiency is an under-reported condition of immense clinical importance especially in the developing world. A high clinical index of suspicion is vital to ensure an early and correct diagnosis which will have a direct bearing on management and patient outcome

Advances in Nematode Identification: A Journey from Fundamentals to Evolutionary Aspects

Nematodes are non-segmented roundworms evenly distributed with various habitats ranging to approximately every ecological extremity. These are the least studied organisms despite being the most diversified group. Nematodes are the most critical equilibrium-maintaining factors, having implications on the yield and health of plants as well as well-being of animals. However, taxonomic knowledge about nematodes is scarce. As a result of the lack of precise taxonomic features, nematode taxonomy remains uncertain. Morphology-based identification has proved inefficacious in identifying and exploring the diversity of nematodes, as there are insufficient morphological variations. Different molecular and new evolving methodologies have been employed to augment morphology-based approaches and bypass these difficulties with varying effectiveness. These identification techniques vary from molecular-based targeting DNA or protein-based targeting amino acid sequences to methods for image processing. High-throughput approaches such as next-generation sequencing have also been added to this league. These alternative approaches have helped to classify nematodes and enhanced the base for increased diversity and phylogeny of nematodes, thus helping to formulate increasingly more nematode bases for use as model organisms to study different hot topics about human well-being. Here, we discuss all the methods of nematode identification as an essential shift from classical morphometric studies to the most important modern-day and molecular approaches for their identification. Classification varies from DNA/protein-based methods to the use of new emerging methods. However, the priority of the method relies on the quality, quantity, and availability of nematode resources and down-streaming applications. This paper reviews all currently offered methods for the detection of nematodes and known/unknown and cryptic or sibling species, emphasizing modern-day methods and budding molecular techniques

The burden of diabetes mellitus during pregnancy in low- and middle-income countries : a systematic review

Peer reviewedPublisher PD

Operative technique and early experience for robotic-assisted laparoscopic nephroureterectomy (RALNU) using da Vinci Xi

Purpose: Robotic-assisted laparoscopic nephroureterectomy (RALNU) has been previously utilized for management of upper tract urothelial carcinoma. The da Vinci Xi surgical system was released in April of 2014. We describe our operative technique and early experience for RALNU using the da Vinci Xi system highlighting unique features of this surgical platform. Materials and methods: A total of 10 patients with a diagnosis of upper tract urothelial carcinoma underwent RALNU using the da Vinci Xi system between April and November of 2014. A novel, oblique “in line ” robotic trocar configuration was utilized to access the upper abdomen (nephrectomy portion) and pelvis (bladder cuff excision) without undocking. The port hopping feature of da Vinci Xi was utilized to facilitate optimal, multi-quadrant visualiza-tion during RALNU. Results: Robotic-assisted laparoscopic nephroureterectomy was successfully completed without open conversion in all 10 patients. Mean operative time was 184 min (range 140–300 min), mean estimated blood loss was 121 cc (range 60–300 cc), and mean hospital stay was 2.4 days. Final pathology demonstrated high grade urothelial carcinoma in all patients. Surgical margins were negative in all patients. No intra-operative complications were encountered. One patient developed a pulmonary embolus after being discharged. No patients required a blood transfusion. Mean patient follow-up was 130 days (range 15–210 days). Conclusion: The use of da Vinci Xi with a novel, oblique “in line ” port configuration and camera port hopping tech-nique allows for an efficient and reproducible method for RALNU without the need for repositioning the patient or the robot during surgery

Data Stream Clustering for Real-Time Anomaly Detection: An Application to Insider Threats

Insider threat detection is an emergent concern for academia, industries, and governments due to the growing number of insider incidents in recent years. The continuous streaming of unbounded data coming from various sources in an organisation, typically in a high velocity, leads to a typical Big Data computational problem. The malicious insider threat refers to anomalous behaviour(s) (outliers) that deviate from the normal baseline of a data stream. The absence of previously logged activities executed by users shapes the insider threat detection mechanism into an unsupervised anomaly detection approach over a data stream. A common shortcoming in the existing data mining approaches to detect insider threats is the high number of false alarms/positives (FPs). To handle the Big Data issue and to address the shortcoming, we propose a streaming anomaly detection approach, namely Ensemble of Random subspace Anomaly detectors In Data Streams (E-RAIDS), for insider threat detection. E-RAIDS learns an ensemble of p established outlier detection techniques [Micro-cluster-based Continuous Outlier Detection (MCOD) or Anytime Outlier Detection (AnyOut)] which employ clustering over continuous data streams. Each model of the p models learns from a random feature subspace to detect local outliers, which might not be detected over the whole feature space. E-RAIDS introduces an aggregate component that combines the results from the p feature subspaces, in order to confirm whether to generate an alarm at each window iteration. The merit of E-RAIDS is that it defines a survival factor and a vote factor to address the shortcoming of high number of FPs. Experiments on E-RAIDS-MCOD and E-RAIDS-AnyOut are carried out, on synthetic data sets including malicious insider threat scenarios generated at Carnegie Mellon University, to test the effectiveness of voting feature subspaces, and the capability to detect (more than one)-behaviour-all-threat in real-time. The results show that E-RAIDS-MCOD reports the highest F1 measure and less number of false alarm = 0 compared to E-RAIDS-AnyOut, as well as it attains to detect approximately all the insider threats in real-time

Influence of reaction time and synthesis temperature on the physical properties of ZnO nanoparticles synthesized by the hydrothermal method

Influence of synthesis temperature and reaction time on the structural and optical properties of ZnO nanoparticles synthesized by the hydrothermal method was investigated using X-ray diffraction (XRD), high resolution transmission electron microscopy (HR-TEM), energy-dispersive X-ray, Fourier transform infra-red spectroscopy, and UV–visible and fluorescence spectroscopy. The XRD pattern and HR-TEM images confirmed the presence of crystalline hexagonal wurtzite ZnO nanoparticles with average crystallite size in the range 30–40 nm. Their energy gap determined by fluorescence was found to depend on the synthesis temperature and reaction time with values in the range 2.90–3.78 eV. Thermal analysis, thermogravimetric and the differential scanning calorimetry were used to study the thermal reactions and weight loss with heat of the prepared ZnO nanoparticles