Association of major depression with blood pressure and vascular complications of type 2 diabetes mellitus

Background: The WHO estimates the diabetic population to increase to 366 million by 2030 worldwide, with maximum 79.4 million Indians. Depression is an undiagnosed co-morbidity leading to significant disability, non-compliance and postulated to cause poorer glycemic control leading to early disease complications. We aimed to detect depression and study its correlation with vascular complications among type 2 diabetes mellitus (T2DM) patients.Methods: In an observational study, 312 randomly selected T2DM patients were evaluated at tertiary care center in Northern India. Socio-demographic, clinical and laboratory data was collected. Montgomery Asberg depression rating scale (MADRS) was used to detect depression. Groups with and without major depression were compared for various diabetes variables. Statistical analysis was carried out using the SPSS version 14.0.Results: One third T2DM patients (32.05%) suffered from major depression. Depression was significantly associated with diabetic patients having cardiac (p 0.01), ophthalmic (p 0.04), nephropathy (p 0.01), cerebrovascular (p 0.001) complications and diabetic foot (p 0.04). However, depression showed no significant association with systolic blood pressure, neuropathic and infectious complications.Conclusions: Identification of depression and its appropriate management may go a long way in delaying diabetic vascular complications by improving treatment adherence and subsequently glycemic control.

Underwater Data Collection Using Robotic Sensor Networks

We examine the problem of utilizing an autonomous underwater vehicle (AUV) to collect data from an underwater sensor network. The sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication. The AUV must plan a path that maximizes the information collected while minimizing travel time or fuel expenditure. We propose AUV path planning methods that extend algorithms for variants of the Traveling Salesperson Problem (TSP). While executing a path, the AUV can improve performance by communicating with multiple nodes in the network at once. Such multi-node communication requires a scheduling protocol that is robust to channel variations and interference. To this end, we examine two multiple access protocols for the underwater data collection scenario, one based on deterministic access and another based on random access. We compare the proposed algorithms to baseline strategies through simulated experiments that utilize models derived from experimental test data. Our results demonstrate that properly designed communication models and scheduling protocols are essential for choosing the appropriate path planning algorithms for data collection.United States. Office of Naval Research (ONR N00014-09-1-0700)United States. Office of Naval Research (ONR N00014-07-1-00738)National Science Foundation (U.S.) (NSF 0831728)National Science Foundation (U.S.) (NSF CCR-0120778)National Science Foundation (U.S.) (NSF CNS-1035866

Communication protocols for underwater data collection using a robotic sensor network

We examine the problem of collecting data from an underwater sensor network using an autonomous underwater vehicle (AUV). The sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication to the AUV. One challenge in this scenario is to plan paths that maximize the information collected and minimize travel time. While executing a path, the AUV can improve performance by communicating with multiple nodes in the network at once. Such multi-node communication requires a scheduling protocol that is robust to channel variations and interference. To solve this problem, we develop and test a multiple access control protocol for the underwater data collection scenario. We perform simulated experiments that utilize a realistic model of acoustic communication taken from experimental test data. These simulations demonstrate that properly designed scheduling protocols are essential for choosing the appropriate path planning algorithms for data collection.United States. Office of Naval Research (Grant N00014-09-1-070)United States. Office of Naval Research (Grant N00014-07-1-00738)National Science Foundation (U.S.) (Grant 0831728)National Science Foundation (U.S.) (Grant CCR-0120778)National Science Foundation (U.S.) (Grant CNS-1035866

Lactate-Mediated Epigenetic Reprogramming Regulates Formation of Human Pancreatic Cancer-Associated Fibroblasts

Even though pancreatic ductal adenocarcinoma (PDAC) is associated with fibrotic stroma, the molecular pathways regulating the formation of cancer associated fibroblasts (CAFs) are not well elucidated. An epigenomic analysis of patient-derived and de-novo generated CAFs demonstrated widespread loss of cytosine methylation that was associated with overexpression of various inflammatory transcripts including CXCR4. Co-culture of neoplastic cells with CAFs led to increased invasiveness that was abrogated by inhibition of CXCR4. Metabolite tracing revealed that lactate produced by neoplastic cells leads to increased production of alpha-ketoglutarate (aKG) within mesenchymal stem cells (MSCs). In turn, aKG mediated activation of the demethylase TET enzyme led to decreased cytosine methylation and increased hydroxymethylation during de novo differentiation of MSCs to CAF. Co-injection of neoplastic cells with TET-deficient MSCs inhibited tumor growth in vivo. Thus, in PDAC, a tumor-mediated lactate flux is associated with widespread epigenomic reprogramming that is seen during CAF formation

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM