The purpose of this study was to review the experience with lung cancer as a second primary malignancy (LCSPM) and to determine whether a history of previous malignancy was an independent prognostic factor in resected non small cell lung cancer (NSCLC). We included 414 patients undergoing pulmonary resection with a curative intention between January 1986 and January 2007. The criteria for classifying a lesion as a lung cancer as a second primary malignancy (LCSPM) were a different histological type or a tumor of identical histological type occurring more than 3 years after treatment of the primary tumor, or separated from the initial primary tumor by more than 2 cm of clinically normal epithelium. Thirty-four patients (8.2%) had a history of a previous malignancy. LCSPM were more frequent during the last decade (11.4% vs. 3.91%, P = .0009). Breast, uroepithelial, and lung cancer were the most prevalent initial primary tumors. In 64.7% of patients the time interval between the last malignancy and the current lung cancer was 2 years or shorter. Patients with NSCLC and a previous malignancy did not have a significantly different survival rate than patients with the current NSCLC as the first malignancy (5-year survival 65.3% vs. 58.6%, log-rank P = .416). The effectiveness of the surgical resection and the subsequent survival of these patients appear to be well demonstrated, and operative mortality and complications are no higher than for other lung cancer patients.

Until now, the buccinid genus Chauvetia was considered of European or West African origin, and is still endemic to these areas today. This paper describes the oldest representative of the genus, Chauvetia inopinata nov. sp., from the upper Burdigalian-lower Langhian transition Cantaure Formation of Venezuela. This surprising record suggests a New World tropical origin to the genus and subsequent immigration to the Old World before the earliest known Old World record, which is upper Tortonian. We postulate that this pre-late Tortonian (pre-8.12-7.42 Ma) dispersal of the tropical Gatunian west-Atlantic Chauvetia into the tropical East Atlantic European-West African Province most probably happened during the 10.71-9.36 Ma interval (early-mid Tortonian) during which the Circum-Tropical Current weakened, and the northward Intra-Caribbean Current had started, enhancing the Gulf Stream and the North Atlantic Current. This new data constitutes compelling evidence of a pre-Pliocene eastward dispersal of New World shallow marine organisms across the Atlantic.

LiDAR data are being increasingly used to provide a detailed characterization of the vertical profile of forests. This characterization enables the generation of new insights on the influence of environmental drivers and anthropogenic disturbances on forest structure as well as on how forest structure influences important ecosystem functions and services. Unfortunately, extracting information from LiDAR data in a way that enables the spatial visualization of forest structure, as well as its temporal changes, is challenging due to the high dimensionality of these data. We show how the Latent Dirichlet Allocation model applied to LiDAR data (LidarLDA) can be used to identify forest structural types and how the relative abundance of these forest types changes throughout the landscape. The code to fit this model is made available through the open-source r package LidarLDA in github. We illustrate the use of LidarLDA both with simulated data and data from a large-scale fire experiment in the Brazilian Amazon region. Using simulated data, we demonstrate that LidarLDA accurately identifies the number of forest types as well as their spatial distribution and absorptance probabilities. For the empirical data, we found that LidarLDA detects both landscape-level patterns in forest structure as well as the strong interacting effect of fire and forest fragmentation on forest structure based on the experimental fire plots. More specifically, LidarLDA reveals that proximity to forest edge exacerbates the impact of fires, and that burned forests remain structurally different from unburned areas for at least 7 years, even when burned only once. Importantly, LidarLDA generates insights on the 3D structure of forest that cannot be obtained using more standard approaches that just focus on top-of-the-canopy information (e.g. canopy height models based on LiDAR data). By enabling the mapping of forest structure and its temporal changes, we believe that LidarLDA will be of broad utility to the ecological research community.

The Brazilian Amazon has been a focus of land development with large swaths of forests converted to agriculture. Forest degradation by selective logging and fires has accompanied the agricultural frontier and has resulted in significant impacts on Amazonian ecosystems. Changes in forest structure resulting from forest disturbances have large impacts on the surface energy balance, including on land surface temperature (LST) and evapotranspiration (ET). This study's objective is to assess the effects of forest disturbances on water fluxes and forest structure in a transitional forest site in the Southern Amazon. We used ET and LST products from MODIS and Landsat 8 and GEDI-derived forest structure data to address our research questions. We found that disturbances induced seasonal water stress, more pronounced in croplands/pastures than in forests (differences up to 20% in the dry season), and more pronounced in second-growth and recently burned areas than in logged and intact forests (differences up to 12% in the dry season). Moreover, ET and LST were negatively related, with more consistent relationships across disturbance classes in the dry season (R2: 0.41–0.87) than in the wet season (R2: 0.18–0.49). Forest and cropland or pasture classes showed contrasting relationships in the dry season. Finally, we found that forest structure exhibited stronger relationships with ET and LST in the most disturbed forests (R2: 0.01–0.43) than in the least disturbed forests (R2 < 0.05). Our findings help to elucidate degraded forests functioning under a changing climate and to improve estimates of water and energy fluxes in Amazonian degraded forests.

Demand-side management initiatives such as voluntary demand response provide significant energy savings in the residential sector, which is a major peak demand contributor. The potential of such savings remains unexplored in Ghanaian households due to insufficient electricity consumption data, lack of end-user behavior information and knowledge about the cost-effectiveness of such programs. This research combines 80 household survey information and energy use monitoring data of household appliances, to assess the residential demand response potential of Ghana. A bottom-up approach based on modified end-use model is used to develop aggregate hourly load curve. The estimated electricity consumption is categorized based on their degree of control to determine peak demand reduction potential for the period 2018-2050. The average daily peak load reduction ranged between 65-406 MW representing 2-14% for the considered scenarios by 2050. The results show appreciable economic viability for investment in demand response with net present value varying between 28-645 million US$. We find that price, energy security and environment signals influence end-users’ electricity use behavior. Authors observe that for energy and cost savings to be realized, utility providers and consumers need effective cooperation on information delivery and feedbacks, and consumers should be incentivized to balance the benefits.

Deep learning techniques have been applied to electroencephalogram (EEG) signals, with promising applications in the field of psychiatry. Schizophrenia is one of the most disabling neuropsychiatric disorders, often characterized by the presence of auditory hallucinations. Auditory processing impairments have been studied using EEG-derived event-related potentials and have been associated with clinical symptoms and cognitive dysfunction in schizophrenia. Due to consistent changes in the amplitude of ERP components, such as the auditory N100, some have been proposed as biomarkers of schizophrenia. In this paper, we examine altered patterns in electrical brain activity during auditory processing and their potential to discriminate schizophrenia and healthy subjects. Using deep convolutional neural networks, we propose an architecture to perform the classification based on multi-channels auditory-related EEG single-trials, recorded during a passive listening task. We analyzed the effect of the number of electrodes used, as well as the laterality and distribution of the electrical activity over the scalp. Results show that the proposed model is able to classify schizophrenia and healthy subjects with an average accuracy of 78% using only 5 midline channels (Fz, FCz, Cz, CPz, and Pz). The present study shows the potential of deep learning methods in the study of impaired auditory processing in schizophrenia with implications for diagnosis. The proposed design can provide a base model for future developments in schizophrenia research.

Background

Accurate estimation of aboveground forest biomass (AGB) and its dynamics is of paramount importance in understanding the role of forest in the carbon cycle and the effective implementation of climate change mitigation policies. LiDAR is currently the most accurate technology for AGB estimation. LiDAR metrics can be derived from the 3D point cloud (echo-based) or from the canopy height model (CHM). Different sensors and survey configurations can affect the metrics derived from the LiDAR data. We evaluate the ability of the metrics derived from the echo-based and CHM data models to estimate AGB in three different biomes, as well as the impact of point density on the metrics derived from them.

Results

Our results show that differences among metrics derived at different point densities were significantly different from zero, with a larger impact on CHM-based than echo-based metrics, particularly when the point density was reduced to 1 point m^-2. Both data models-echo-based and CHM-performed similarly well in estimating AGB at the three study sites. For the temperate forest in the Sierra Nevada Mountains, California, USA, R² ranged from 0.79 to 0.8 and RMSE (relRMSE) from 69.69 (35.59%) to 70.71 (36.12%) Mg ha^-1 for the echo-based model and from 0.76 to 0.78 and 73.84 (37.72%) to 128.20 (65.49%) Mg ha^-1 for the CHM-based model. For the moist tropical forest on Barro Colorado Island, Panama, the models gave R² ranging between 0.70 and 0.71 and RMSE between 30.08 (12.36%) and 30.32 (12.46) Mg ha^-1 [between 0.69-0.70 and 30.42 (12.50%) and 61.30 (25.19%) Mg ha^-1] for the echo-based [CHM-based] models. Finally, for the Atlantic forest in the Sierra do Mar, Brazil, R² was between 0.58-0.69 and RMSE between 37.73 (8.67%) and 39.77 (9.14%) Mg ha^-1 for the echo-based model, whereas for the CHM R² was between 0.37-0.45 and RMSE between 45.43 (10.44%) and 67.23 (15.45%) Mg ha^-1.

Conclusions

Metrics derived from the CHM show a higher dependence on point density than metrics derived from the echo-based data model. Despite the median of the differences between metrics derived at different point densities differing significantly from zero, the mean change was close to zero and smaller than the standard deviation except for very low point densities (1 point m^-2). The application of calibrated models to estimate AGB on metrics derived from thinned datasets resulted in less than 5% error when metrics were derived from the echo-based model. For CHM-based metrics, the same level of error was obtained for point densities higher than 5 points m^-2. The fact that reducing point density does not introduce significant errors in AGB estimates is important for biomass monitoring and for an effective implementation of climate change mitigation policies such as REDD + due to its implications for the costs of data acquisition. Both data models showed similar capability to estimate AGB when point density was greater than or equal to 5 point m^-2.

Despite intensive effort and resulting gains in understanding the mechanisms underlying neuropathic pain, limited success in therapeutic approaches have been attained. A recently identified, nonchannel, nonneurotransmitter therapeutic target for pain is the enzyme soluble epoxide hydrolase (sEH). The sEH degrades natural analgesic lipid mediators, epoxy fatty acids (EpFAs), therefore its inhibition stabilizes these bioactive mediators. Here we demonstrate the effects of EpFAs on diabetes induced neuropathic pain and define a previously unknown mechanism of pain, regulated by endoplasmic reticulum (ER) stress. The activation of ER stress is first quantified in the peripheral nervous system of type I diabetic rats. We demonstrate that both pain and markers of ER stress are reversed by a chemical chaperone. Next, we identify the EpFAs as upstream modulators of ER stress pathways. Chemical inducers of ER stress invariably lead to pain behavior that is reversed by a chemical chaperone and an inhibitor of sEH. The rapid occurrence of pain behavior with inducers, equally rapid reversal by blockers and natural incidence of ER stress in diabetic peripheral nervous system (PNS) argue for a major role of the ER stress pathways in regulating the excitability of the nociceptive system. Understanding the role of ER stress in generation and maintenance of pain opens routes to exploit this system for therapeutic purposes.

Epoxyeicosatrienoic acids (EETs) are potent endogenous analgesic metabolites produced from arachidonic acid by cytochrome P450s (P450s). Metabolism of EETs by soluble epoxide hydrolase (sEH) reduces their activity, while their stabilization by sEH inhibition decreases both inflammatory and neuropathic pain. Here, we tested the complementary hypothesis that increasing the level of EETs through induction of P450s by omeprazole (OME), can influence pain related signaling by itself, and potentiate the anti-hyperalgesic effect of sEH inhibitor. Rats were treated with OME (100mg/kg/day, p.o., 7 days), sEH inhibitor TPPU (3mg/kg/day, p.o.) and OME (100mg/kg/day, p.o., 7 days)+TPPU (3mg/kg/day, p.o., last 3 days of OME dose) dissolved in vehicle PEG400, and their effect on hyperalgesia (increased sensitivity to pain) induced by PGE2 was monitored. While OME treatment by itself exhibited variable effects on PGE2 induced hyperalgesia, it strongly potentiated the effect of TPPU in the same assay. The significant decrease in pain with OME+TPPU treatment correlated with the increased levels of EETs in plasma and increased activities of P450 1A1 and P450 1A2 in liver microsomes. The results show that reducing catabolism of EETs with a sEH inhibitor yielded a stronger analgesic effect than increasing generation of EETs by OME, and combination of both yielded the strongest pain reducing effect under the condition of this study.