Non-target site resistance (NTSR) to herbicides is an increasing concern for weed control. Metabolic herbicide resistance is an important mechanism for NTSR. However, little is known about metabolic resistance at the genetic level. In this study, we have identified three fenoxaprop-P-ethyl-resistant American sloughgrass (Beckmannia syzigachne Steud.) populations, in which the molecular basis for NTSR remains unclear. To reveal the mechanisms of metabolic resistance, the genes likely to be involved in herbicide metabolism (e.g. for cytochrome P450s, esterases, hydrolases, oxidases, peroxidases, glutathione S-transferases, glycosyltransferases, and transporter proteins) were isolated using transcriptome sequencing, in combination with RT-PCR (reverse transcription-PCR) and RACE (rapid amplification of cDNA ends). Consequently, we established a herbicide-metabolizing enzyme library containing at least 332 genes, and each of these genes was cloned and the sequence and the expression level compared between the fenoxaprop-P-ethyl-resistant and susceptible populations. Fifteen metabolic enzyme genes were found to be possibly involved in fenoxaprop-P-ethyl resistance. In addition, we found five metabolizing enzyme genes that have a different gene sequence in plants of susceptible versus resistant B. syzigachne populations. These genes may be major candidates for herbicide metabolic resistance. This established metabolic enzyme library represents an important step forward towards a better understanding of herbicide metabolism and metabolic resistance in this and possibly other closely related weed species. This new information may help to understand weed metabolic resistance and to develop novel strategies of weed management.

BackgroundThe prognosis of Chinese patients with eyelid sebaceous carcinoma (SC) has not been updated for >3 decades. The prognostic predictors are multifactorial, and there is no validated prognostic model for eyelid SC.MethodsThis study included 238 consecutive patients with eyelid SC. All eligible patients were followed up for metastasis and mortality. The predictors of tumor-related survival were explored by Cox analyses. A prognostic nomogram was developed and validated using bootstrap resampling. The predictive accuracy and discriminative ability were compared between the nomogram and the Tumor, Node, Metastasis (TNM) staging system.FindingsAfter a median follow-up period of 55.5 months, 27 (11.3%) patients died of metastatic SC, with a median survival time of 48.0 months. The 5-year and 10-year tumor-related survival rates were 88.1% and 77.9%, respectively. Orbital involvement (HR: 3.11, p = .022), the greatest tumor basal diameter (HR: 1.06, p = .003), the presence of pagetoid spread (HR: 2.90, p = .017), and having lymph node metastasis at initial diagnosis (HR: 13.66, p < .001) were independent risk factors for tumor-related death. A nomogram integrating these 4 factors was developed with a C-index of 0.887, which is significantly better than that of the TNM staging system (p = .002). The risk groups stratified by nomogram scores (p < .001 (low vs intermediate risk); p = .001 (intermediate vs high risk)) displayed better discrimination ability than TNM staging (T1 vs T2: p = .358; T2 vs T3: p = .171; T3 vs T4: p < .001) in patients at an early stage.InterpretationThe prognosis of Chinese patients with eyelid SC has improved over the last 3 decades, and it is comparable to that of patients from other countries. This nomogram provides more accurate individualized estimates of survival for eyelid SC patients and may guide clinicians in their therapeutic decisions.

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Many factors influence the connection states between nodes of wireless sensor networks, such as physical distance, and the network load, making the network's edge length dynamic in abundant scenarios. This dynamic property makes the network essentially form a graph with stochastic edge lengths. In this paper, we study the stochastic shortest path problem on a directional graph with stochastic edge lengths, using reinforcement learning algorithms. we regard each edge length as a random variable following unknown probability distribution and aim to find the stochastic shortest path on this stochastic graph. We evaluate the performance of path-finding algorithms using regret, which represents the cumulative reward difference between the practical path-finding algorithm and the optimal strategy that chooses the global stochastic shortest path every time. We model the path-finding procedure as a Markov decision process and propose two online path-finding algorithms: Q SSP algorithm and SARSA SSP algorithm, both combined with specifically-devised average reward mechanism. We justify the convergence property and correctness of the proposed algorithms theoretically. Experiments conducted on two benchmark graphs illustrate the superior performance of the proposed Q SSP algorithm which outperforms the SARSA SSP algorithm and other competitive algorithms about the regret metric.

Osteolysis is a principal reason for arthroplasty failure like aseptic loosening induced by Titanium (Ti) particle. It is a challenge for orthopedic surgeons. Recent researches show that 20(S)-protopanaxadiol can inhibit inflammatory cytokine release in vitro. This study aims to assess the effect of 20(S)-protopanaxadiol on Ti particle-induced osteolysis and RANKL-mediated osteoclastogenesis. Micro-CT and histological analysis in vivo indicated the inhibitory effects of 20(S)-protopanaxadiol on osteoclastogenesis and the excretion of inflammatory cytokines. Next, we demonstrated that 20(S)-protopanaxadiol inhibited osteoclast differentiation, bone resorption area, and F-actin ring formation in a dose-dependent manner. Moreover, mechanistic studies suggested that the suppression of MAPK and NF-κB signaling pathways were found to mediate the inhibitory effects of 20(S)-protopanaxadiol. In conclusion, 20(S)-protopanaxadiol may suppress osteoclastogenesis in a dose- dependent manner and it could be a potential treatment of Ti particle-induced osteolysis.

Saussurea involucrata grows in high mountain areas covered by snow throughout the year. The temperature of this habitat can change drastically in one day. To gain a better understanding of the cold response signaling pathways and molecular metabolic reactions involved in cold stress tolerance, genome-wide transcriptional analyses were performed using RNA-Seq technologies. A total of 199,758 transcripts were assembled, producing 138,540 unigenes with 46.8 Gb clean data. Overall, 184,416 (92.32%) transcripts were successfully annotated. The 365 transcription factors identified (292 unigenes) belonged to 49 transcription factor families associated with cold stress responses. A total of 343 transcripts on the signal transduction (132 upregulated and 212 downregulated in at least any one of the conditions) were strongly affected by cold temperature, such as the CBL-interacting serine/threonine-protein kinase (CIPKs), receptor-like protein kinases, and protein kinases. The circadian rhythm pathway was activated by cold adaptation, which was necessary to endure the severe temperature changes within a day. There were 346 differentially expressed genes (DEGs) related to transport, of which 138 were upregulated and 22 were downregulated in at least any one of the conditions. Under cold stress conditions, transcriptional regulation, molecular transport, and signal transduction were involved in the adaptation to low temperature in S. involucrata. These findings contribute to our understanding of the adaptation of plants to harsh environments and the survival traits of S. involucrata. In addition, the present study provides insight into the molecular mechanisms of chilling and freezing tolerance.