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Sepsis is a life-threatening systemic syndrome characterized by various biological, biochemical, and physiological abnormalities. Due to its high mortality, identifying sepsis patients with high risk of in-hospital death early and accurately will help doctors make optimal clinical decisions and reduce the mortality of sepsis patients. In this paper, we propose a length insensitive TCN-based model to predict sepsis patient's death risk in the future k hours, which is the first work for sepsis death risk early warning model only based on vital signs time series to our best knowledge. Furthermore, we design residual connections between temporal residual blocks to improve the prediction performance and stability especially on short input sequences. We validate and evaluate our model on two freely-available datasets, i.e., MIMIC-IV and eICU, from which 16,520 and 29,620 patients are selected respectively. The experiment results show that our model outperforms LSTM and other machine learning methods, as it has the highest sensitivity and Youden index in almost all cases. Meanwhile, the Youden index of the TCN-based model only slightly decreases by 0.0233 and 0.0307 when the time range of the input sequence changes from 24 to 4 h for k equal to 6 and 12, respectively.

Platinum (Pt) is considered as the most efficient catalyst for hydrogen evolution reaction (HER) with a nearly zero overpotential, but it is limited by the high cost and poor stability. Herein, we report an efficient electrocatalyst of Pt–Ni alloy nanoparticles (NPs) supported on the La-modified flexible carbon nanocomposite fibers (PtNi@La-CNFs) for HER. The rare earth metal oxide in the catalyst has a structure-effect relationship with the carbon fibers to form a flexible fiber membrane. Experimental results show that the macroscopic and microscopic properties of carbon nanocomposite fibers can be optimized by doping La2O3, and the Pt–Ni NPs can be anchored effectively. The Pt1Ni1@La-CNFs electrocatalyst exhibits a small overpotential of 32 mV to achieve current density of 10 mA cm−2 with a low Tafel slope of 51 mV dec−1 in alkaline medium, outperforming that of Pt@La-CNFs and the commercial Pt/C catalyst. This study reveals that the multiple coupling effect of rare earth compound, precious metal, and transition metal in composite catalyst can tailor its the electronic configuration, and results in an enhanced HER performance. This work opens up a novel approach to design high active and low cost Pt-based HER catalysts.

Pd/H2-PrxOy nanotubes were prepared by electrostatic spinning, high-temperature calcination and modified deposition–precipitation method, and exhibited high catalytic performance for Suzuki coupling reaction under mild conditions.

The heterostructure between two semiconductor materials that had suitable band edge positions can contribute to the separation of photoelectrons and holes. In this paper, the heterostructure MoS2-C-g-C3N4 photocatalysts were in-situ synthesized at one-pot high temperature processing. The obtained 0.4 MoS2-C-g-C3N4 composites displayed the highest photocatalytic hydrogen evolution activity with a corresponding H2 evolution rate of 238 μmol g−1h−1, which was about 4.5 times higher than that of 3% C-g-C3N4, and the photocatalyts exhibited excellent stability which was used for photocatalytic hydrogen evolution reaction for 12 h. The 0.4 MoS2-C-g-C3N4 sample displayed well degradation activities for MO, MB, RhB, MR and Ar18, and the scavenging studies indicated the major involvement of ·O2– radicals in the degradation process. The enhanced photocatalytic activity of MoS2-C-g-C3N4 composite was predominantly attributed to the synergistic effects of type II heterostructure between g-C3N4 and MoS2, which effectively accelerated the transfer and separation of photogenerated charge carriers. Besides, the introduction of noble metal-free MoS2 co-catalyst further improved visible light absorption and provided more active sites for H2 evolution reaction. Such work is promising for designing a novel heterostructure photocatalysts for solar-to-fuel conversion and environmental modification.

The authors have requested that this preprint be removed from Research Square.

crocins, water-soluble apocarotenoids, are a kind of crocetin glucosyl-esters that are due to the action of glycosyltransferases that are added glucose moieties to crocetin. To date, the glycosyltransferases involved in crocin biosynthesis are gradually discovered in the plant kingdom. In this work, a putative crocetin glycosyltransferase UGTCs4was first functionally characterized from cultured crocus cells. The constructed model of UGTCs4 based on the given model of UGT75L2 showed that highly structural similarity with that of UGT75L6 and UGT74AD1, suggesting a common function specializing in glycosylation of the glucose moieties of crocetin glucosides. Function characterization demonstrated that UGTCs4 could glycosylate crocetin yielding crocetin-monoglycoside(crocin 5) and crocetin-diglycoside(crocin 3) which were produced in a time-dependent manner. This work represents, to our knowledge, the first functional identification of a C. sativus crocetin glycosyltransferase involved in the crocin biosynthetic pathway employing the purified recombinant protein.

To develop a grading prediction model of traumatic hemorrhage volume based on deep learning and assist in predicting traumatic hemorrhage volume.A retrospective observational study was conducted based on the experimental data of pig gunshot wounds in the time-effect assessment database for experiments on war-traumatized animals constructed by the General Hospital of the Chinese People's Liberation Army. The hemorrhage volume data of the study population were extracted, and the animals were divided into 0-300 mL, 301-600 mL, and600 mL groups according to the hemorrhage volume. Using vital signs indexes as the predictive variables and hemorrhage volume grading as the outcome variable, trauma hemorrhage volume grading prediction models were developed based on four traditional machine learning and ten deep learning methods. Using laboratory test indexes as predictive variables and hemorrhage volume grading as outcome variables, trauma hemorrhage volume grading prediction models were developed based on the above fourteen methods. The effect of the two groups of models was evaluated by accuracy and area under the receiver operator characteristic curve (AUC), and the optimal models in the two groups were mixed to obtain hybrid model 1. Feature selection was conducted according to the genetic algorithm, and hybrid model 2 was constructed according to the best feature combination. Finally, hybrid model 2 was deployed in the animal experiment database system.Ninety-six traumatic animals in the database were enrolled, including 27 pigs in the 0-300 mL group, 40 in the 301-600 mL group, and 29 in the600 mL group. Among the fourteen models based on vital signs indexes, fully convolutional network (FCN) model was the best [accuracy: 60.0%, AUC and 95% confidence interval (95%CI) was 0.699 (0.671-0.727)]. Among the fourteen models based on laboratory test indexes, recurrent neural network (RNN) model was the best [accuracy: 68.9%, AUC (95%CI) was 0.845 (0.829-0.860)]. After mixing the FCN and RNN models, the hybrid model 1, namely RNN-FCN model was obtained, and the performance of the model was improved [accuracy: 74.2%, AUC (95%CI) was 0.847 (0.833-0.862)]. Feature selection was carried out by genetic algorithm, and the hybrid model 2, namely RNN-FCNBased on deep learning, a grading prediction model of traumatic hemorrhage volume was developed and deployed in the information system to realize the intelligent grading prediction of traumatic animal hemorrhage volume.

Improving resistance to Verticillium wilt is of great significance for achieving high and stable yields of Gossypium hirsutum. To deeply understand the genetic basis of cotton resistance to Verticillium wilt, the G. hirsutum Verticillium wilt-resistant material Lumianyan 28 and four Verticillium wilt-susceptible Acala cotton materials were used to create an NAM population of 469 families through nested hybridization. Phenotypic data collected in five stressful environments were used to screen resistant and sensitive materials and create a mixed pool of extreme phenotypes for BSA-seq. A total of 8 QTLs associated with Verticillium wilt resistance were identified on 4 chromosomes, of which qVW-A12-5 was detected simultaneously in the NAM population and in 1 RIL population that composed the NAM population and was identified for the first time. According to the sequence comparison and transcriptome analysis of candidate genes in the QTL interval between parents and pools, 4 genes were screened in the qVW-A12-5 interval. qRT-PCR of parental and phenotypically extreme materials revealed that Gh_CPR30 was induced by and may be a candidate gene for resistance to Verticillium wilt in G. hirsutum. Furthermore, VIGS technology revealed that the disease severity index (DSI) of a Gh_CPR30-silenced strain was significantly higher than that of the control. These results indicate that the Gh_CPR30 gene plays an important role in the resistance of G. hirsutum to Verticillium wilt, and the study provides a molecular basis for analyzing the molecular mechanism underlying G. hirsutum resistance to Verticillium wilt.