Early-life maternal deprivation in rats increases sensitivity to the subsequent stressors: a pilot study.

Early life experiences, particularly maternal deprivation (MD), have long-lasting implications on emotional and cognitive development. Using Wistar rats as a model, this study explores the impact of MD followed by predator stress exposure (PSS) to simulate aspects of post-traumatic stress disorder (PTSD). A cohort of 41 male rat pups underwent MD from postnatal days 2 to 14, followed by PSS at day 90. Female rat pups were not included in the experiment. Behavior was subsequently assessed using the Elevated Plus Maze test 14 days post-PSS. While MD led to subtle changes such as decreased activity and increased anxiety-like behavior, PSS induced pronounced anxiogenic effects. Notably, PSS after MD resulted in decreased basal corticosterone levels, mirroring conditions observed in PTSD. The findings suggest that although MD itself does not induce significant behavioral changes, it predisposes individuals to heightened sensitivity to subsequent stressors. This study underscores the utility of a two-stage PSS model for more accurately reflecting the complexities inherent in stress-related disorders, including PTSD.

Russian forest sequesters substantially more carbon than previously reported.

Since the collapse of the Soviet Union and transition to a new forest inventory system, Russia has reported almost no change in growing stock (+ 1.8%) and biomass (+ 0.6%). Yet remote sensing products indicate increased vegetation productivity, tree cover and above-ground biomass. Here, we challenge these statistics with a combination of recent National Forest Inventory and remote sensing data to provide an alternative estimate of the growing stock of Russian forests and to assess the relative changes in post-Soviet Russia. Our estimate for the year 2014 is 111 ± 1.3 × 109 m3, or 39% higher than the value in the State Forest Register. Using the last Soviet Union report as a reference, Russian forests have accumulated 1163 × 106 m3 yr-1 of growing stock between 1988-2014, which balances the net forest stock losses in tropical countries. Our estimate of the growing stock of managed forests is 94.2 × 109 m3, which corresponds to sequestration of 354 Tg C yr-1 in live biomass over 1988-2014, or 47% higher than reported in the National Greenhouse Gases Inventory.

Hexobarbital Sleep Test for Predicting the Susceptibility or Resistance to Experimental Posttraumatic Stress Disorder.

Hexobarbital sleep test (HST) was performed in male Wistar rats (hexobarbital 60 mg/kg, i.p.) 30 days prior to stress exposure. Based on the duration of hexobarbital-induced sleep, rats were divided into two groups, animals with high intensity (fast metabolizers (FM), sleep duration <15 min) or low intensity of hexobarbital metabolism (slow metabolizers (SM), sleep duration ≥15 min). The SM and FM groups were then divided into two subgroups: unstressed and stressed groups. The stressed subgroups were exposed to predator scent stress for 10 days followed by 15 days of rest. SM and FM rats from the unstressed group exhibited different behavioral and endocrinological patterns. SM showed greater anxiety and higher corticosterone levels. In stressed animals, anxiety-like posttraumatic stress disorder (PTSD) behavior was aggravated only in SM. Corticosterone levels in the stressed FM, PTSD-resistant rats, were lower than in unstressed SM. Thus, HST was able to predict the susceptibility or resistance to experimental PTSD, which was consistent with the changes in glucocorticoid metabolism.

Efficient Kernel-Based Subsequence Search for Enabling Health Monitoring Services in IoT-Based Home Setting.

This paper presents an efficient approach for subsequence search in data streams. The problem consists of identifying coherent repetitions of a given reference time-series, also in the multivariate case, within a longer data stream. The most widely adopted metric to address this problem is Dynamic Time Warping (DTW), but its computational complexity is a well-known issue. In this paper, we present an approach aimed at learning a kernel approximating DTW for efficiently analyzing streaming data collected from wearable sensors, while reducing the burden of DTW computation. Contrary to kernel, DTW allows for comparing two time-series with different length. To enable the use of kernel for comparing two time-series with different length, a feature embedding is required in order to obtain a fixed length vector representation. Each vector component is the DTW between the given time-series and a set of "basis" series, randomly chosen. The approach has been validated on two benchmark datasets and on a real-life application for supporting self-rehabilitation in elderly subjects has been addressed. A comparison with traditional DTW implementations and other state-of-the-art algorithms is provided: results show a slight decrease in accuracy, which is counterbalanced by a significant reduction in computational costs.

Exciton-like electromagnetic excitations in non-ideal microcavity supercrystals.

We study localized photonic excitations in a quasi-two-dimensional non-ideal binary microcavity lattice with use of the virtual crystal approximation. The effect of point defects (vacancies) on the excitation spectrum is investigated by numerical modelling. We obtain the dispersion and the energy gap of the electromagnetic excitations which may be considered as Frenkel exciton-like quasiparticles and analyze the dependence of their density of states on the defect concentrations in a microcavity supercrystal.