Penalty-Based Imitation Learning With Cross Semantics Generation Sensor Fusion for Autonomous Driving

In recent times, there has been a growing focus on end-to-end autonomous driving technologies. This technology involves the replacement of the entire driving pipeline with a single neural network, which has a simpler structure and faster inference time. However, while this approach reduces the number of components in the driving pipeline, it also presents challenges related to interpretability and safety. For instance, the trained policy may not always comply with traffic rules, and it is difficult to determine the reason for such misbehavior due to the lack of intermediate outputs. Additionally, the successful implementation of autonomous driving technology heavily depends on the reliable and expedient processing of sensory data to accurately perceive the surrounding environment. In this paper, we provide penalty-based imitation learning approach combined with cross semantics generation sensor fusion technologies (P-CSG) to efficiently integrate multiple modalities of information and enable the autonomous agent to effectively adhere to traffic regulations. Our model undergoes evaluation within the Town 05 Long benchmark, where we observe a remarkable increase in the driving score by more than 12% when compared to the state-of-the-art (SOTA) model, InterFuser. Notably, our model achieves this performance enhancement while achieving a 7-fold increase in inference speed and reducing the model size by approximately 30%. For more detailed information, including code-based resources, they can be found at https://hk-zh.github.io/p-csg

What Matters to Enhance Traffic Rule Compliance of Imitation Learning for Automated Driving

More research attention has recently been given to end-to-end autonomous driving technologies where the entire driving pipeline is replaced with a single neural network because of its simpler structure and faster inference time. Despite this appealing approach largely reducing the components in driving pipeline, its simplicity also leads to interpretability problems and safety issues arXiv:2003.06404. The trained policy is not always compliant with the traffic rules and it is also hard to discover the reason for the misbehavior because of the lack of intermediate outputs. Meanwhile, Sensors are also critical to autonomous driving's security and feasibility to perceive the surrounding environment under complex driving scenarios. In this paper, we proposed P-CSG, a novel penalty-based imitation learning approach with cross semantics generation sensor fusion technologies to increase the overall performance of End-to-End Autonomous Driving. We conducted an assessment of our model's performance using the Town 05 Long benchmark, achieving an impressive driving score improvement of over 15%. Furthermore, we conducted robustness evaluations against adversarial attacks like FGSM and Dot attacks, revealing a substantial increase in robustness compared to baseline models.More detailed information, such as code-based resources, ablation studies and videos can be found at https://hk-zh.github.io/p-csg-plus.Comment: 8 pages, 2 figure

Functional Gene Array-Based Ultrasensitive and Quantitative Detection of Microbial Populations in Complex Communities.

While functional gene arrays (FGAs) have greatly expanded our understanding of complex microbial systems, specificity, sensitivity, and quantitation challenges remain. We developed a new generation of FGA, GeoChip 5.0, using the Agilent platform. Two formats were created, a smaller format (GeoChip 5.0S), primarily covering carbon-, nitrogen-, sulfur-, and phosphorus-cycling genes and others providing ecological services, and a larger format (GeoChip 5.0M) containing the functional categories involved in biogeochemical cycling of C, N, S, and P and various metals, stress response, microbial defense, electron transport, plant growth promotion, virulence, gyrB, and fungus-, protozoan-, and virus-specific genes. GeoChip 5.0M contains 161,961 oligonucleotide probes covering >365,000 genes of 1,447 gene families from broad, functionally divergent taxonomic groups, including bacteria (2,721 genera), archaea (101 genera), fungi (297 genera), protists (219 genera), and viruses (167 genera), mainly phages. Computational and experimental evaluation indicated that designed probes were highly specific and could detect as little as 0.05 ng of pure culture DNAs within a background of 1 μg community DNA (equivalent to 0.005% of the population). Additionally, strong quantitative linear relationships were observed between signal intensity and amount of pure genomic (∼99% of probes detected; r > 0.9) or soil (∼97%; r > 0.9) DNAs. Application of the GeoChip to a contaminated groundwater microbial community indicated that environmental contaminants (primarily heavy metals) had significant impacts on the biodiversity of the communities. This is the most comprehensive FGA to date, capable of directly linking microbial genes/populations to ecosystem functions.IMPORTANCE The rapid development of metagenomic technologies, including microarrays, over the past decade has greatly expanded our understanding of complex microbial systems. However, because of the ever-expanding number of novel microbial sequences discovered each year, developing a microarray that is representative of real microbial communities, is specific and sensitive, and provides quantitative information remains a challenge. The newly developed GeoChip 5.0 is the most comprehensive microarray available to date for examining the functional capabilities of microbial communities important to biogeochemistry, ecology, environmental sciences, and human health. The GeoChip 5 is highly specific, sensitive, and quantitative based on both computational and experimental assays. Use of the array on a contaminated groundwater sample provided novel insights on the impacts of environmental contaminants on groundwater microbial communities

Clinical Significance of Potential Unidentified HLA-G Isoforms Without α1 Domain but Containing Intron 4 in Colorectal Cancer Patients

The ectopic HLA-G expression in malignancies has been extensively explored and clinical significance of the molecule was widely acknowledged. Besides previously well-documented seven isoforms (HLA-G1~-G7), other novel isoforms of HLA-G have been reported but their clinical relavenace remians evaluated. In this study, lesion HLA-G expression in 379 case-matched serial section primary colorectal cancers (CRC) were evaluated with mAb 4H84 (recognizing an epitope in HLA-G α1 domain), and mAb 5A6G7 (recognizing an epitope encoded by intron 4), respectively. Data showed that HLA-G positive staining with mAbs 4H84 and 5A6G7 was 70.7 and 60.4%, respectively. When percentage of HLA-G expression detected with mAb 4H84 subtracted that with mAb 5A6G7, the difference (ΔHLA-G) with negative (ΔHLA-Gneg), comparable (ΔHLA-Gcom) and positive (ΔHLA-Gpos) were observed in 64 (16.9%), 159 (42.0%), and 156 (41.2%) cases, respectively. Noteworthy, unexpected immunostaining was observed in 44 (11.6%) lesions that no staining was detected with mAb 4H84 but positive with mAb 5A6G7 (4H84neg5A6G7pos). This staining pattern was unpredictable because all seven known HLA-G isoforms containing the α 1 domain could be recognized by the mAb 4H84. Moreover, patients with ΔHLA-Gneg had obviously better survival than those with ΔHLA-Gcom and ΔHLA-Gpos (p = 0.017), and ΔHLA-G could be an independent prognostic factor for CRC patients (p = 0.008). Our findings provides the first report that potential unidentified HLA-G isoforms is of distinct clinical significance in CRC patients

Climate warming accelerates temporal scaling of grassland soil microbial biodiversity.

Determining the temporal scaling of biodiversity, typically described as species-time relationships (STRs), in the face of global climate change is a central issue in ecology because it is fundamental to biodiversity preservation and ecosystem management. However, whether and how climate change affects microbial STRs remains unclear, mainly due to the scarcity of long-term experimental data. Here, we examine the STRs and phylogenetic-time relationships (PTRs) of soil bacteria and fungi in a long-term multifactorial global change experiment with warming (+3 °C), half precipitation (-50%), double precipitation (+100%) and clipping (annual plant biomass removal). Soil bacteria and fungi all exhibited strong STRs and PTRs across the 12 experimental conditions. Strikingly, warming accelerated the bacterial and fungal STR and PTR exponents (that is, the w values), yielding significantly (P < 0.001) higher temporal scaling rates. While the STRs and PTRs were significantly shifted by altered precipitation, clipping and their combinations, warming played the predominant role. In addition, comparison with the previous literature revealed that soil bacteria and fungi had considerably higher overall temporal scaling rates (w = 0.39-0.64) than those of plants and animals (w = 0.21-0.38). Our results on warming-enhanced temporal scaling of microbial biodiversity suggest that the strategies of soil biodiversity preservation and ecosystem management may need to be adjusted in a warmer world

The Thermoanaerobacter Glycobiome Reveals Mechanisms of Pentose and Hexose Co-Utilization in Bacteria

Author Summary Renewable liquid fuels derived from lignocellulosic biomass could alleviate global energy shortage and climate change. Cellulose and hemicellulose are the main components of lignocellulosic biomass. Therefore, the ability to simultaneously utilize pentose and hexose (i.e., co-utilization) has been a crucial challenge for industrial microbes producing lignocellulosic biofuels. Certain thermoanaerobic bacteria demonstrate this unusual talent, but the genetic foundation and molecular mechanism of this process remain unknown. In this study, we reconstructed the structure and dynamics of the first genome-wide carbon utilization network of thermoanaerobes. This transcriptome-based co-expression network reveals that glucose, xylose, fructose, and cellobiose catabolism are each featured on distinct functional modules. Furthermore, the dynamics of the network suggests a distinct yet collaborative nature between glucose and xylose catabolism. In addition, we experimentally demonstrated that these novel network-derived features can be rationally exploited for product-yield enhancement via optimized timing and balanced loading of the carbon supply in a substrate-specific manner. Thus, the newly discovered modular and precisely regulated network elucidates unique features of thermoanaerobic glycobiomes and reveals novel perturbation strategies and targets for the enhanced thermophilic production of lignocellulosic biofuels.Yeshttp://www.plosgenetics.org/static/editorial#pee

How sulphate-reducing microorganisms cope with stress: lessons from systems biology

Sulphate-reducing microorganisms (SRMs) are a phylogenetically diverse group of anaerobes encompassing distinct physiologies with a broad ecological distribution. As SRMs have important roles in the biogeochemical cycling of carbon, nitrogen, sulphur and various metals, an understanding of how these organisms respond to environmental stresses is of fundamental and practical importance. In this Review, we highlight recent applications of systems biology tools in studying the stress responses of SRMs, particularly Desulfovibrio spp., at the cell, population, community and ecosystem levels. The syntrophic lifestyle of SRMs is also discussed, with a focus on system-level analyses of adaptive mechanisms. Such information is important for understanding the microbiology of the global sulphur cycle and for developing biotechnological applications of SRMs for environmental remediation, energy production, biocorrosion control, wastewater treatment and mineral recovery

Distinctive Oxidative Stress Responses to Hydrogen Peroxide in Sulfate Reducing Bacteria Desulfovibrio vulgaris Hildenborough

Response of Desulfovibrio vulgaris Hildenborough to hydrogen peroxide (H2O2, 1 mM) was investigated with transcriptomic, proteomic and genetic approaches. Microarray data demonstrated that gene expression was extensively affected by H2O2 with the response peaking at 120 min after H2O2 treatment. Genes affected include those involved with energy production, sulfate reduction, ribosomal structure and translation, H2O2 scavenging, posttranslational modification and DNA repair as evidenced by gene coexpression networks generated via a random matrix-theory based approach. Data from this study support the hypothesis that both PerR and Fur play important roles in H2O2-induced oxidative stress response. First, both PerR and Fur regulon genes were significantly up-regulated. Second, predicted PerR regulon genes ahpC and rbr2 were derepressed in Delta PerR and Delta Fur mutants and induction of neither gene was observed in both Delta PerR and Delta Fur when challenged with peroxide, suggesting possible overlap of these regulons. Third, both Delta PerR and Delta Fur appeared to be more tolerant of H2O2 as measured by optical density. Forth, proteomics data suggested de-repression of Fur during the oxidative stress response. In terms of the intracellular enzymatic H2O2 scavenging, gene expression data suggested that Rdl and Rbr2 may play major roles in the detoxification of H2O2. In addition, induction of thioredoxin reductase and thioredoxin appeared to be independent of PerR and Fur. Considering all data together, D. vulgaris employed a distinctive stress resistance mechanism to defend against increased cellular H2O2, and the temporal gene expression changes were consistent with the slowdown of cell growth at the onset of oxidative stress