Serum folate and MTHFR C677T and MTR A2576G gene polymorphisms were the determinants for tHcy levels. The interaction between low levels of serum Cbl and MTHFR (C677T or A1298C) or MTRR A66G gene polymorphisms was associated with increased tHcy.

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When compared to tracking airborne targets, tracking ground targets on urban terrains brings a new set of challenges. Target mobility is constrained by road networks, and the quality of measurements is affected by dense clutter, multipath, and limited line-of-sight. We investigate the integration of detection, signal processing, tracking, and scheduling by exploiting distinct levels of diversity: (1) spatial diversity through the use of coordinated multistatic radars; (2) waveform diversity by adaptively scheduling the transmitted radar waveform according to the scene conditions; and (3) motion model diversity by using a bank of parallel filters, each one matched to a different maneuvering model. Specifically, at each scan, the waveform that yields the minimum one-step-ahead error covariance matrix determinant is transmitted; the received signal is then matched-filtered, and quadratic curve fitting is applied to extract range and azimuth measurements that are input to the LMIPDA-VSIMM algorithm for data association and filtering. Monte Carlo simulations are used to demonstrate the effectiveness of the proposed system on a realistic urban scenario. A more traditional open-loop system, in which waveforms are scheduled on a round-robin fashion and with no other modes of diversity available, is used as a baseline for comparison. Simulation results show that our closed-loop system significantly outperforms the baseline system, presenting both a reduction on the number of lost tracks, and a reduction on the volume of the estimation uncertainty ellipse. The interdisciplinary nature of this work highlights the challenges involved in designing a closed-loop active sensing platform for next-generation urban tracking systems.

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Multisensor applications rely on effectively managing sensor resources. In particular, next-generation multifunctional agile radars demand innovative resource management techniques to achieve a common sensing goal while satisfying resource constraints. We consider an active sensing platform where multiple waveform-agile radars scan a hostile surveillance area for targets. A central controller adaptively selects which transmitters should be active and which waveforms should be transmitted. The controller's goal is to choose the sequence of (transmitter, waveform) pairs that yields the most accurate tracking estimate. We formulate this problem as a partially observable Markov decision process (POMDP), and propose a novel "two-level" scheduling scheme that uses two distinct schedulers: (1) at the lower level, a myopic waveform scheduler; and (2) at the upper level, a non-myopic transmitter scheduler. Scheduling decisions at these two levels are carried out differently. While waveforms are updated at every radar scan, a new set of transmitters only becomes active if the overall tracking accuracy falls below a given threshold, or if the "detection risk" is exceeded, given by a limit on the number of consecutive scans during which a set of transmitters is active. By simultaneously exploiting myopic and non-myopic scheduling schemes, we benefit from trading off short-term for long-term performance, while maintaining low computational costs. Moreover, in certain situations, the myopic scheduling of waveforms at each radar scan improves on non-myopic actions taken in the past. Monte Carlo simulations are used to evaluate the performance of the proposed adaptive sensing scheme in a multitarget tracking setting.

We investigate the challenging problem of integrating detection, signal processing, target tracking, and adaptive waveform scheduling with lookahead in urban terrain. We propose a closed-loop active sensing system to address this problem by exploiting three distinct levels of diversity: (1) spatial diversity through the use of coordinated multistatic radars; (2) waveform diversity by adaptively scheduling the transmitted waveform; and (3) motion model diversity by using a bank of parallel filters matched to different motion models. Specifically, at every radar scan, the waveform that yields the minimum trace of the one-step-ahead error covariance matrix is transmitted; the received signal goes through a matched-filter, and curve fitting is used to extract range and range-rate measurements that feed the LMIPDA-VSIMM algorithm for data association and filtering. Monte Carlo simulations demonstrate the effectiveness of the proposed system in an urban scenario contaminated by dense and uneven clutter, strong multipath, and limited line-of-sight.

Methionine synthase (MS) is a cobalamin dependent enzyme that catalyses the remethylation of homocysteine to methionine. The methionine synthase reductase (MSR) maintains adequate levels of methylcob(III)alamin, the activated cofactor for MS. The aim of this study was to investigate the effect of MS A2756G and MSR A66G polymorphisms on total homocysteine (tHcy), S-adenosylmethionine (SAM), S-adenosylhomocysteine (SAH) levels in 390 pregnant women and their 292 newborns, from Sorocaba city, Brazil. Genotypes of two polymorphisms were determined by PCR-RFLP. Pregnant women with MS 2756AA genotype have higher tHcy and lower Cbl levels than those with 2756G alleles. The MMA values were increased in neonates with MS 2756AA genotypes (Table 1). There are no difference between the maternal values of Cbl, serum folate, tHcy, MMA and SAM according to MSR A66G genotypes.The values of SAM were lower in neonates with MSR 66G alleles than those with AA genotypes (Table 2). We conclude that MS 2756AA genotypes are associated with higher tHcy levels in pregnant women and higher MMA levels in neonates. The MSR 66GG genotypes is associated with lower SAM levels in neonates. Table 1- Distribution of geometric means and confidence intervals 95% (CI 95%) and numbers of subjects for maternal and neonatal values of cobalamin (Cbl), serum folate, total homocysteine (tHcy), methymalonic acid (MMA) and S- adenosylmethionine (SAM) according to genotypes for the polymorphism MS A2756G. Variables Genotypes for MS A2756G Student t Test AA AG + GG Pregnant Women Cbl (pmol/L) 139 (133 – 144) 235 156 (146 – 166) 129 P= 0.001 SF (nmol/L) 14.3 (13.6 – 15.0) 234 14.5 (13.6 – 15.5) 129 P= 0.667 tHcy( μmol/L) 6.8 (6.5 – 7.1) 235 6.2 (5.9 – 6.6) 128 P= 0.036 MMA(nmol/L) 234 (219 – 245) 194 241 (219 – 265) 106 P= 0.610 SAM(nmol/L) 81.8 (77.9 – 86,0) 229 83.1 (79.1 – 87.4) 124 P= 0.663 Neonates Cbl (pmol/L) 227 (212 – 244) 188 234 (213 – 257) 101 P= 0.646 SF (nmol/L) 32.0 (31.0 – 33.0) 186 32.0 (30.8 – 33.2) 99 P= 0.967 tHcy μmol/L) 5.8 (5.5 – 6.1) 185 5.5 (5.1 – 5.9) 100 P= 0.229 MMA(nmol/L) 383 (364 – 402) 183 342 (317 – 369) 100 P= 0.011 SAM(nmol/L) 188 (181 – 196) 178 182 (168 – 197) 98 P= 0.491 Table 2 - Distribution of geometric means and confidence intervals 95% (CI (%%) and number of subjects for neonatal values of cobalamin (Cbl), serum folate, total homocysteine (tHcy), methylmalonic acid (MMA) and S- adenosylmethionine (SAM) according to genotypes for the polymorphism MSR A66G. Variables Genotypes MSR A66G Student t Test AA AG GG Groups not sharing a common superscript letter are significantly different at P<0.05 based on Tukey’s test Neonates Cbl (pmol/L) 225 (202 – 249) 86 229 (214 – 246) 166 247 (205 – 298) 35 P= 0.602 SF (nmol/L) 33.0 (31.6 – 34.4) 84 31.7 (30.7 – 32.7) 166 31.8 (29.7 – 34.2) 33 P= 0.352 tHcy μmol/L) ( 5.7 (5.3 – 6.2) 84 5.6 (5.3 – 5.9) 165 5.9 (5.3 – 6.6) 34 P= 0.618 MMA (nmol/L) 360 (334 – 389) 84 378 (357 – 400) 163 339 (299 – 384) 34 P= 0.230 SAM (nmol/L) 200 (186 – 215)a 82 184 (176 – 192)a 159 170 (149 – 195)b 33 P= 0.032