Combination of Decitabine and a Modified Regimen of Cisplatin, Cytarabine and Dexamethasone: A Potential Salvage Regimen for Relapsed or Refractory Diffuse Large B-Cell Lymphoma After Second-Line Treatment Failure

ObjectiveThe prognosis for patients with relapsed or refractory diffuse large B-cell lymphoma (R/R-DLBCL) after second-line treatment failure is extremely poor. This study prospectively observed the efficacy and safety of decitabine with a modified cisplatin, cytarabine, and dexamethasone (DHAP) regimen in R/R-DLBCL patients who failed second-line treatment.MethodsTwenty-one R/R-DLBCL patients were enrolled and treated with decitabine and a modified DHAP regimen. The primary endpoints were overall response rate (ORR) and safety. The secondary endpoints were progression-free survival (PFS) and overall survival (OS).ResultsORR reached 50% (complete response rate, 35%), five patients (25%) had stable disease (SD) with disease control rate (DCR) of 75%. Subgroup analysis revealed patients over fifty years old had a higher complete response rate compared to younger patients (P = 0.005), and relapsed patients had a better complete response rate than refractory patients (P = 0.031). Median PFS was 7 months (95% confidence interval, 5.1-8.9 months). Median OS was not achieved. One-year OS was 59.0% (95% CI, 35.5%-82.5%), and two-year OS was 51.6% (95% confidence interval, 26.9%-76.3%). The main adverse events (AEs) were grade 3/4 hematologic toxicities such as neutropenia (90%), anemia (50%), and thrombocytopenia (70%). Other main non-hematologic AEs were grade 1/2 nausea/vomiting (40%) and infection (50%). No renal toxicity or treatment-related death occurred.ConclusionDecitabine with a modified DHAP regimen can improve the treatment response and prognosis of R/R-DLBCL patients with good tolerance to AEs, suggesting this regimen has potential as a possible new treatment option for R/R-DLBCL patients after second-line treatment failure.Clinical Trial RegistrationClinicalTrials.gov, identifier: NCT03579082

The oyster genome reveals stress adaptation and complexity of shell formation

The Pacific oyster Crassostrea gigas belongs to one of the most species-rich but genomically poorly explored phyla, the Mollusca. Here we report the sequencing and assembly of the oyster genome using short reads and a fosmid-pooling strategy, along with transcriptomes of development and stress response and the proteome of the shell. The oyster genome is highly polymorphic and rich in repetitive sequences, with some transposable elements still actively shaping variation. Transcriptome studies reveal an extensive set of genes responding to environmental stress. The expansion of genes coding for heat shock protein 70 and inhibitors of apoptosis is probably central to the oyster's adaptation to sessile life in the highly stressful intertidal zone. Our analyses also show that shell formation in molluscs is more complex than currently understood and involves extensive participation of cells and their exosomes. The oyster genome sequence fills a void in our understanding of the Lophotrochozoa. © 2012 Macmillan Publishers Limited. All rights reserved

Impacts of Climate Change on Wildfires in Central Asia

This study analyzed fire weather and fire regimes in Central Asia from 2001–2015 and projected the impacts of climate change on fire weather in the 2030s (2021–2050) and 2080s (2071–2099), which would be helpful for improving wildfire management and adapting to future climate change in the region. The study area included five countries: Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan. The study area could be divided into four subregions based on vegetation type: shrub (R1), grassland (R2), mountain forest (R3), and rare vegetation area (R4). We used the modified Nesterov index (MNI) to indicate the fire weather of the region. The fire season for each vegetation zone was determined with the daily MNI and burned areas. We used the HadGEM2-ES global climate model with four scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) to project the future weather and fire weather of Central Asia. The results showed that the fire season for shrub areas (R1) was from 1 April to 30 November, for grassland (R2) was from 1 March to 30 November, and for mountain forest (R3) was from 1 April to 30 October. The daily burned areas of R1 and R2 mainly occurred in the period from June–August, while that of R3 mainly occurred in the April–June and August–October periods. Compared with the baseline (1971–2000), the mean daily maximum temperature and precipitation, in the fire seasons of study area, will increase by 14%–23% and 7%–15% in the 2030s, and 21%–37% and 11%–21% in the 2080s, respectively. The mean MNI will increase by 33%–68% in the 2030s and 63%–146% in the 2080s. The potential burned areas of will increase by 2%–8% in the 2030s and 3%–13% in the 2080s. Wildfire management needs to improve to adapt to increasing fire danger in the future

Effects of Early Experiences On Behavioral Development: An Experimental Study Based on an "Human-Rat Interaction Paradigm""

Cultivating the next generation of sound emotional, cognitive and socio-behavioral development is fundamental to human civilization, and the impact of early experiences cannot be ignored from the point of view of probabilistic epigenesis. This study aims to investigate the causal relationship between early experiences and later behavioral development based on a novel experimental model termed the “human-rat interaction paradigm” (HRIP). Thirty-six one-month-old male Sprague-Dawley rats were selected as subjects. Based on the HRIP, three groups (Positive early experiences (PEE) / Negative early experiences (NEE) / Control) were intervened for 3 weeks, and the effects of the manipulation of early experiences on behavioral development were tested through a battery of behavioral paradigms. The results showed that: 1) During the emotional behavior tests, compared with the other two groups, the PEE group was more active in the open arm of the O-maze, more active in the center area of the open field, ate faster in the new and familiar environment, and had less hesitation to adapt to and utilize the new learning device. 2) During the learning behavior tests, the PEE group showed most rule-breaking exploratory behavior in the integrated-learning maze; while the majority of the NEE group learned to open the gate during the early stage of procedural learning, the firmness of their long-term memory was the lowest during the new object recognition task; the control group was overall passive during the whole series of learning behavior tests. 3) During the social behavior tests, the PEE group showed the most interests towards the toy rat, while the NEE group showed the most aversion towards the toy rat. At the same time, while all groups preferred a real rat to a toy rat, only the intervention groups (both PEE and NEE) showed clear preference in interacting with a real stranger rat to a real familiar rat. Moreover, during the empathy and pro-social behavioral tests, when there were no food rewards, all three groups of rats generally would open the gate to rescue the entrapped rat, and after multiple trials their latency to rescue became shorter and shorter; however, when there were food rewards to be shared with the entrapped rat, both the PEE and NEE groups were less likely to open the gate, and after multiple trials, their latency to rescue became longer and longer. When the entrapped rat was unable to reach the food reward without the subject’s active sharing, the NEE group showed much more frequent behavior of feeding interruption and vigilant sniffing, possibly for fear of losing the food to the entrapped rat. 4) During the social competition tests, when there were no food rewards in the tube test, the control group had the highest success rate; when there were food rewards to be competed for, the PEE group had the highest success rate. At the same time, the degree of social rank differentiation was smallest in the control group and largest in the PEE group. The NEE group showed clear differentiation between the high-rank individual and the middle/low-ranked individual. The success rate of the NEE group was overall the lowest during the inter-group social competition tests. We arrived at the following conclusions: 1) On the long run, the early experience intervention based on the HRIP will have sustained and stable effects on the behavioral development. 2) Rich early experiences can improve the sensitivity to learning and social rules. Lack of early experiences can passivate learning and social behavior. 3) Positive early experience can promote the individual to have more interests in exploring "objects", produce more rule-breaking exploratory behavior and maintain the stability of goal behavior; in contrast, negative early experience can cause excessive arousal of negative emotions, inhibit exploration and interfere with the maintenance of goal behavior

Characterizing the Phase-Structure and Rheological Response-Behavior of Multi-Walled Carbon Nanotubes Modified Asphalt-Binder

In this study, the phase-structure and rheological response-behavior of multi-walled carbon nanotube (MWCNTs) modified asphalt-binder (MWCNTs-MA) were measured and quantified in the laboratory. The changes in the molecular dynamics due to MWCNTs modification were simulated and quantified based on the intermolecular interaction energy computations, electrostatic potential surface analyses and phase-structure modeling of the asphalt-binder matrix. The rheological properties such as the asphalt-binder viscosity and complex modulus, of both the base and modified asphalt-binders, were determined using the standard Brookfield viscometer (BV) and dynamic shear rheology (DSR) test devices, respectively. In comparison to the base asphalt-binder, the corresponding BV-DSR test results exhibited higher viscosity and complex modulus for the MWCNTs modified asphalt-binder, with reduced sensitivity and susceptibility to temperature variations. From the study results, it was observed that MWCNTs significantly improved the rheological properties and high-temperature performance of the asphalt-binder. Overall, the study has demonstrated that MWCNT modified asphalt-binder has great promising potential for application and usage as a road-pavement material, particularly with respect to mitigating the high temperature related distresses such as rutting

Volterra-Aided Neural Network Equalization for Channel Impairment Compensation in Visible Light Communication System

This paper addresses the channel impairment to enhance the system performance of visible light communication (VLC). Inspired by the model-solving procedure in the conventional equalizer, the channel impairment compensation is formulated as a spatial memory pattern prediction problem, then we propose efficient deep-learning (DL)-based nonlinear post-equalization, combining the Volterra-aided convolutional neural network (CNN) and long-short term memory (LSTM) neural network, to mitigate the system nonlinearity and then recover the original transmitted signal from the distorted one at the receiver end. The Volterra structure is employed to construct a spatial pattern that can be easily interpreted by the proposed scheme. Then, we take advantage of the CNN to extract the implicit feature of channel impairments and utilize the LSTM to predict the memory sequence. Results demonstrate that the proposed scheme can provide a fairly fast convergence during the training stage and can effectively mitigate the overall nonlinearity of the system at testing. Furthermore, it can recover the original signal accurately and exhibits an excellent bit error rate performance as compared with the conventional equalizer, demonstrating the prospect and validity of this methodology for channel impairment compensation

Volterra-Aided Neural Network Equalization for Channel Impairment Compensation in Visible Light Communication System

This paper addresses the channel impairment to enhance the system performance of visible light communication (VLC). Inspired by the model-solving procedure in the conventional equalizer, the channel impairment compensation is formulated as a spatial memory pattern prediction problem, then we propose efficient deep-learning (DL)-based nonlinear post-equalization, combining the Volterra-aided convolutional neural network (CNN) and long-short term memory (LSTM) neural network, to mitigate the system nonlinearity and then recover the original transmitted signal from the distorted one at the receiver end. The Volterra structure is employed to construct a spatial pattern that can be easily interpreted by the proposed scheme. Then, we take advantage of the CNN to extract the implicit feature of channel impairments and utilize the LSTM to predict the memory sequence. Results demonstrate that the proposed scheme can provide a fairly fast convergence during the training stage and can effectively mitigate the overall nonlinearity of the system at testing. Furthermore, it can recover the original signal accurately and exhibits an excellent bit error rate performance as compared with the conventional equalizer, demonstrating the prospect and validity of this methodology for channel impairment compensation