Beyond Universal Transformer: block reusing with adaptor in Transformer for automatic speech recognition

Transformer-based models have recently made significant achievements in the application of end-to-end (E2E) automatic speech recognition (ASR). It is possible to deploy the E2E ASR system on smart devices with the help of Transformer-based models. While these models still have the disadvantage of requiring a large number of model parameters. To overcome the drawback of universal Transformer models for the application of ASR on edge devices, we propose a solution that can reuse the block in Transformer models for the occasion of the small footprint ASR system, which meets the objective of accommodating resource limitations without compromising recognition accuracy. Specifically, we design a novel block-reusing strategy for speech Transformer (BRST) to enhance the effectiveness of parameters and propose an adapter module (ADM) that can produce a compact and adaptable model with only a few additional trainable parameters accompanying each reusing block. We conducted an experiment with the proposed method on the public AISHELL-1 corpus, and the results show that the proposed approach achieves the character error rate (CER) of 9.3%/6.63% with only 7.6M/8.3M parameters without and with the ADM, respectively. In addition, we also make a deeper analysis to show the effect of ADM in the general block-reusing method

An experimental investigation on air-side performances of finned tube heat exchangers for indirect air-cooling tower

A tremendous quantity of water can be saved if the air cooling system is used, comparing with the ordinary water-cooling technology. In this study, two kinds of finned tube heat exchangers in an indirect air-cooling tower are experimentally studied, which are a plain finned oval-tube heat exchanger and a wavy-finned flat-tube heat exchanger in a cross flow of air. Four different air inlet angles (90°, 60 °, 45°, and 30°) are tested separately to obtain the heat transfer and resistance performance. Then the air-side experimental correlations of the Nusselt number and friction factor are acquired. The comprehensive heat transfer performances for two finned tube heat exchangers under four air inlet angles are compared. For the plain finned oval-tube heat exchanger, the vertical angle (90°) has the worst performance while 45° and 30° has the best performance at small ReDc and at large ReDc, respectively. For the wavy-finned flat-tube heat exchanger, the worst performance occurred at 60°, while the best performance occurred at 45° and 90° at small ReDc and at large ReDc, respectively. From the comparative results, it can be found that the air inlet angle has completely different effects on the comprehensive heat transfer performance for the heat exchangers with different structures

Recoverable Rosin-Based Epoxy Vitrimers with Robust Mechanical Properties and High Thermostability

Renewably biobased epoxy vitrimers can reduce the generation of polymer waste and the consumption of fossil resources. However, both mechanical properties and dynamic properties of biobased epoxy vitrimers cannot be improved simultaneously, limiting their applications. In this study, the rosin derivate with rigid hydrophenanthrene ring and three terminal epoxy groups was cured using 2,2′-(1,4-phenylene)­bis­[5-carboxylic acid, 5-methyl-1,3,2-dioxaborinane] (BDCB) as cure agent by the reaction of epoxy–carboxyl. The obtained rigid rosin-based epoxy networks possess high mechanical strength and high thermal stability. Moreover, the good shape memory properties, and excellent thermal-healing and recycling performances were observed, because dynamic β-hydroxy ester bonds, boronic ester bonds, and hydrogen bonding readily allow the topological network rearrangement of the rosin-based epoxy networks. Besides, the rosin-based epoxy networks display outstanding adhesive properties (adhesion strength of ∼54.6 MPa), indicating they are promising candidates for repairable and recyclable adhesives application

HSADab: A Comprehensive Database for Human Serum Albumin

Human Serum Albumin (HSA), the most prevalent protein in human body fluids, is integral to the transportation, absorption, metabolism, distribution, and excretion of drugs. Its influence on a drug\u27s therapeutic efficacy is substantial. Despite the importance of HSA as a drug target, the available data on its interactions with external agents (e.g., drug-like molecules and antibodies) are rather limited, which poses challenges for both molecular modelling investigations and the development of empirical scoring functions or machine learning predictors on this target. Moreover, the reported entries in existing databases often contain major inconsistencies due to varied experiments and conditions, which incurs worries about the data quality. To address these issues, we established a pioneering database through extensively reviewing more than 30000 scientific publications published between 1987 and 2023, encompassing over 5000 affinity data at multiple temperatures and more than 130 crystal structures that involve both the ligand-bound and apo forms. The current HSADab resource (www.hsadab.cn) serves as a reliable foundation for protocol validations of molecular simulations (e.g., traditional virtual screening workflow using docking, end-point and alchemical free energy techniques) as well as the data source for the implementation of machine learning predictors

YCl3 Promotes Neuronal Cell Death by Inducing Apoptotic Pathways in Rats

The pollutants rare earth elements (REEs) have posed great threats to human health. To investigate the cytotoxicity of yttrium (Y), a model that rats have free access to water containing YCl3 for 6 months is utilized. The results showed that YCl3 treatment promoted neuronal cell apoptosis by upregulating the proapoptotic factors Bax, caspase-3, Cyto c, and DAPK and by downregulating the antiapoptotic factors Bcl-2 and XIAP at both mRNA and protein levels. Conclusively, YCl3 exhibited cytotoxicity and promoted neuronal cell death by the induction of apoptotic pathways

A diagnostic model for sepsis-induced acute lung injury using a consensus machine learning approach and its therapeutic implications

Abstract Background A significant proportion of septic patients with acute lung injury (ALI) are recognized late due to the absence of an efficient diagnostic test, leading to the postponed treatments and consequently higher mortality. Identifying diagnostic biomarkers may improve screening to identify septic patients at high risk of ALI earlier and provide the potential effective therapeutic drugs. Machine learning represents a powerful approach for making sense of complex gene expression data to find robust ALI diagnostic biomarkers. Methods The datasets were obtained from GEO and ArrayExpress databases. Following quality control and normalization, the datasets (GSE66890, GSE10474 and GSE32707) were merged as the training set, and four machine learning feature selection methods (Elastic net, SVM, random forest and XGBoost) were applied to construct the diagnostic model. The other datasets were considered as the validation sets. To further evaluate the performance and predictive value of diagnostic model, nomogram, Decision Curve Analysis (DCA) and Clinical Impact Curve (CIC) were constructed. Finally, the potential small molecular compounds interacting with selected features were explored from the CTD database. Results The results of GSEA showed that immune response and metabolism might play an important role in the pathogenesis of sepsis-induced ALI. Then, 52 genes were identified as putative biomarkers by consensus feature selection from all four methods. Among them, 5 genes (ARHGDIB, ALDH1A1, TACR3, TREM1 and PI3) were selected by all methods and used to predict ALI diagnosis with high accuracy. The external datasets (E-MTAB-5273 and E-MTAB-5274) demonstrated that the diagnostic model had great accuracy with AUC value of 0.725 and 0.833, respectively. In addition, the nomogram, DCA and CIC showed that the diagnostic model had great performance and predictive value. Finally, the small molecular compounds (Curcumin, Tretinoin, Acetaminophen, Estradiol and Dexamethasone) were screened as the potential therapeutic agents for sepsis-induced ALI. Conclusion This consensus of multiple machine learning algorithms identified 5 genes that were able to distinguish ALI from septic patients. The diagnostic model could identify septic patients at high risk of ALI, and provide potential therapeutic targets for sepsis-induced ALI

Characteristics of myeloid sarcoma in mice and patients with TET2 deficiency

Myeloid sarcoma (MS) carries a poor prognosis, and information on epigenetic modifications in MS is currently limited. In the present study, 214 ten-eleven translocation-2 ( TET2 ) −/− mice were successfully constructed. In addition, 436 patients with myelodysplastic syndrome (MDS) and 354 with acute myeloid leukemia (AML) patients were recruited. The incidence of MS in mice and patients with TET2 deficiency was examined, and the efficiency of hypomethylating agents (HMAs) was also be evaluated. A total of 93% of the TET2 −/− mice developed myeloid malignancies, 5.5% of which were accompanied by MS (n=11). The survival of these TET2 −/− mice ranged between 3 and 25 months. No significant difference was observed between the survival of MS and non-MS mice with TET2 loss (P>0.05). In addition, MS cells were transplantable, and their recipients exhibited myeloproliferative characteristics, such as increased white blood cell counts, monocytosis, low erythrocyte counts and hepatosplenomegaly. Their median survival duration was 94.8 days. In the clinical setting, 9.7% of MDS and 11.6% of AML patients with TET2 deficiency developed MS, which was higher compared with previous reports (1.5–9.1%). The median age of the MS patients was 44 years old. 5-Aza-2′-deoxycytidine (5-Aza-dC) reduced the incidence of MS in TET2 −/− mice, and decitabine was a suitable treatment strategy for MS patients. These data indicate that TET2 deficiency plays a key role in MS and its prognostic significance requires further investigation. HMAs may be a useful treatment for MS patients with TET2 mutations

High driving pressure ventilation induces pulmonary hypertension in a rabbit model of acute lung injury

Abstract Background Mechanical ventilation may cause pulmonary hypertension in patients with acute lung injury (ALI), but the underlying mechanism remains elucidated. Methods ALI was induced in rabbits by a two-hit injury, i.e., hydrochloric acid aspiration followed by mechanical ventilation for 1 h. Rabbits were then ventilated with driving pressure of 10, 15, 20, or 25 cmH2O for 7 h. Clinicopathological parameters were measured at baseline and different timepoints of ventilation. RNA sequencing was conducted to identify the differentially expressed genes in high driving pressure ventilated lung tissue. Results The two-hit injury induced ALI in rabbits was evidenced by dramatically decreased PaO2/FiO2 in the ALI group compared with that in the control group (144.5 ± 23.8 mmHg vs. 391.6 ± 26.6 mmHg, P < 0.001). High driving pressure ventilation (20 and 25 cmH2O) significantly elevated the parameters of acute pulmonary hypertension at different timepoints compared with low driving pressure (10 and 15 cmH2O), along with significant increases in lung wet/dry ratios, total protein contents in bronchoalveolar lavage fluid, and lung injury scores. The high driving pressure groups showed more pronounced histopathological abnormalities in the lung compared with the low driving pressure groups, accompanied by significant increases in the cross-sectional areas of myocytes, right ventricular weight/body weight value, and Fulton’s index. Furthermore, the expression of the genes related to ferroptosis induction was generally upregulated in high driving pressure groups compared with those in low driving pressure groups. Conclusions A rabbit model of ventilation-induced pulmonary hypertension in ALI was successfully established. Our results open a new research direction investigating the exact role of ferroptosis in ventilation-induced pulmonary hypertension in ALI