Validation of Reference Genes for RT-qPCR Studies of Gene Expression in Preharvest and Postharvest Longan Fruits under Different Experimental Conditions

Reverse transcription quantitative PCR (RT-qPCR), a sensitive technique for quantifying gene expression, relies on stable reference gene(s) for data normalization. Although a few studies have been conducted on reference gene validation in fruit trees, none have been done on preharvest and postharvest longan fruits. In this study, 12 candidate reference genes, namely, CYP, RPL, GAPDH, TUA, TUB, Fe-SOD, Mn-SOD, Cu/Zn-SOD, 18SrRNA, Actin, Histone H3 and EF-1a, were selected. Expression stability of these genes in 150 longan samples was evaluated and analyzed using geNorm and NormFinder algorithms. Preharvest samples consisted of seven experimental sets, including different developmental stages, organs, hormone stimuli (NAA, 2,4-D and ethephon) and abiotic stresses (bagging and girdling with defoliation). Postharvest samples consisted of different temperature treatments (4 and 22 °C) and varieties. Our findings indicate that appropriate reference gene(s) should be picked for each experimental condition. Our data further showed that the commonly used reference gene Actin does not exhibit stable expression across experimental conditions in longan. Expression levels of the DlACO gene, which is a key gene involved in regulating fruit abscission under girdling with defoliation treatment, was evaluated to validate our findings. In conclusion, our data provide a useful framework for choice of suitable reference genes across different experimental conditions for RT-qPCR analysis of preharvest and postharvest longan fruits

GUDN: A novel guide network with label reinforcement strategy for extreme multi-label text classification

In natural language processing, extreme multi-label text classification is an emerging but essential task. The problem of extreme multi-label text classification (XMTC) is to recall some of the most relevant labels for a text from an extremely large label set. Large-scale pre-trained models have brought a new trend to this problem. Though the large-scale pre-trained models have made significant achievements on this problem, the valuable fine-tuned methods have yet to be studied. Though label semantics have been introduced in XMTC, the vast semantic gap between texts and labels has yet to gain enough attention. This paper builds a new guide network (GUDN) to help fine-tune the pre-trained model to instruct classification later. Furthermore, GUDN uses raw label semantics combined with a helpful label reinforcement strategy to effectively explore the latent space between texts and labels, narrowing the semantic gap, which can further improve predicted accuracy. Experimental results demonstrate that GUDN outperforms state-of-the-art methods on Eurlex-4k and has competitive results on other popular datasets. In an additional experiment, we investigated the input lengths' influence on the Transformer-based model's accuracy. Our source code is released at https://t.hk.uy/aFSH.Comment: 12 pages, 6 figure

From Static to Dynamic Structures: Improving Binding Affinity Prediction with a Graph-Based Deep Learning Model

Accurate prediction of the protein-ligand binding affinities is an essential challenge in the structure-based drug design. Despite recent advance in data-driven methods in affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally depicted by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, we curated an MD dataset containing 3,218 different protein-ligand complexes, and further developed Dynaformer, which is a graph-based deep learning model. Dynaformer was able to accurately predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that our model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, we performed a virtual screening on the heat shock protein 90 (HSP90) using Dynaformer that identified 20 candidates and further experimentally validated their binding affinities. We demonstrated that our approach is more efficient, which can identify 12 hit compounds (two were in the submicromolar range), including several newly discovered scaffolds. We anticipate this new synergy between large-scale MD datasets and deep learning models will provide a new route toward accelerating the early drug discovery process.Comment: totally reorganize the texts and figure

Correlation between inflammatory markers over time and disease severity in status epilepticus: a preliminary study

ObjectivesConvulsive status epilepticus (CSE) is a major subtype of status epilepticus that is known to be closely associated with systemic inflammation. Some important inflammatory biomarkers of this disorder include the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune inflammation index (SII), and pan-immune inflammation value (PIV). This study aimed to determine the NLR, PLR, MLR, SII, and PIV levels before and after treatment in adult patients with CSE and investigated the relationship of these parameters with disease severity.MethodsThis retrospective study analyzed data from 103 adult patients with CSE and 103 healthy controls. The neutrophil, monocyte, platelet, and lymphocyte counts, as well as the NLR, PLR, MLR, SII, and PIV, were compared in adult patients with CSE during acute seizures (within 2 h of admission) and after treatment relief (1–2 weeks of complete seizure control). Furthermore, multivariate linear regression analysis investigated the relationship between NLR, PLR, MLR, SII, and PIV with the Status Epilepticus Severity Score (STESS).ResultsThe data revealed significant differences (p < 0.05) in neutrophils, monocytes, lymphocytes, NLR, PLR, MLR, SII, and PIV between adult patients with CSE during acute seizures and after treatment relief. The average neutrophil count was high during acute seizures in the patient group and decreased after remission. In contrast, the average lymphocyte count was lower after remission (p < 0.05). Furthermore, significant differences (p < 0.05) were observed in monocytes, lymphocytes, platelets, NLR, PLR, MLR, and PIV levels between adult patients with CSE after remission and the healthy control group. Multivariate linear regression analysis showed no significant correlation between NLR, PLR, MLR, SII, and PIV with STESS.ConclusionThe results of this study indicated that adult patients with CSE experienced a transient systemic inflammatory response during acute seizures, which gradually returned to baseline levels after remission. However, there was a lack of robust clinical evidence correlating the severity of adult CSE and systemic inflammatory response

Transient dynamics of terrestrial carbon storage : mathematical foundation and its applications

Terrestrial ecosystems have absorbed roughly 30 % of anthropogenic CO2 emissions over the past decades, but it is unclear whether this carbon (C) sink will endure into the future. Despite extensive modeling and experimental and observational studies, what fundamentally determines transient dynamics of terrestrial C storage under global change is still not very clear. Here we develop a new framework for understanding transient dynamics of terrestrial C storage through mathematical analysis and numerical experiments. Our analysis indicates that the ultimate force driving ecosystem C storage change is the C storage capacity, which is jointly determined by ecosystem C input (e.g., net primary production, NPP) and residence time. Since both C input and residence time vary with time, the C storage capacity is time-dependent and acts as a moving attractor that actual C storage chases. The rate of change in C storage is proportional to the C storage potential, which is the difference between the current storage and the storage capacity. The C storage capacity represents instantaneous responses of the land C cycle to external forcing, whereas the C storage potential represents the internal capability of the land C cycle to influence the C change trajectory in the next time step. The influence happens through redistribution of net C pool changes in a network of pools with different residence times. Moreover, this and our other studies have demonstrated that one matrix equation can replicate simulations of most land C cycle models (i.e., physical emulators). As a result, simulation outputs of those models can be placed into a three-dimensional (3-D) parameter space to measure their differences. The latter can be decomposed into traceable components to track the origins of model uncertainty. In addition, the physical emulators make data assimilation computationally feasible so that both C flux- and pool-related datasets can be used to better constrain model predictions of land C sequestration. Overall, this new mathematical framework offers new approaches to understanding, evaluating, diagnosing, and improving land C cycle models.This work was partially done through the working group, Nonautonomous Systems and Terrestrial Carbon Cycle, at the National Institute for Mathematical and Biological Synthesis, an institute sponsored by the National Science Foundation, the US Departmernt of Homeland Security, and the US Department of Agriculture through NSF award no. EF-0832858, with additional support from the University of Tennessee, Knoxville, Research in Yiqi Luo EcoLab was financially supported by US Department of Energy grants DE-SC0008270, DE-SC0014085, and US National Science Foundation (NSF) grants EF 1137293 and OIA-1301789.Ye

Investigation on overlapping interference on VLC networks consisting of multiple LEDs

Visible light communication (VLC) has become an alternative candidate in next-generation indoor wireless local area network. However, it is a great challenge for a receiver to obtain the signals from multiple LED sources that transmit different information. In this paper, we explore the BER degradation due to the overlapping of multiple LED sources in the visible light communication network. We experimentally demonstrate a Multiple Input Signal Output (MISO) VLC system utilizing space–time block coding (STBC) to overcome the signal interference due to multiple inputs. A throughput of 1.6 Gbit/s is successfully achieved, which reveals great improvement in the robustness and compatibility of LED based network system

Reversed Three-Dimensional Visible Light Indoor Positioning Utilizing Annular Receivers with Multi-Photodiodes

Exploiting the increasingly wide use of light emitting diodes (LEDs) lighting, in this paper we propose a reversed indoor positioning system (IPS) based on LED visible light communication (VLC) in order to improve indoor positioning accuracy. Unlike other VLC positioning systems, we employ two annular receivers with multi-photodiodes installed on the ceiling to locate the persons who carry LEDs. The basic idea is using multi-photodiodes to calculate the angle while using the received signal strength (RSS) method to calculate the distance. The experiment results show that the effective positioning range of the proposed system is 1.8 m when the distance between two receivers is 1.2 m. Moreover, a positioning error less than 0.2 m can be achieved under the condition that the radius of the PIN circle is between 0.16 m and 0.2 m, and the distance of the transmitter-receiver plane is less than 1.8 m, which will be effective in practice