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Opioid overdose suppresses brainstem respiratory circuits, causes apnoea and may result in death. Epidural electrical stimulation (EES) at the cervical spinal cord facilitated motor activity in rodents and humans, and we hypothesized that EES of the cervical spinal cord could antagonize opioid‐induced respiratory depression in humans. Eighteen patients requiring surgical access to the dorsal surface of the spinal cord between C2 and C7 received EES or sham stimulation for up to 90 s at 5 or 30 Hz during complete (OFF‐State) or partial suppression (ON‐State) of respiration induced by remifentanil. During the ON‐State, 30 Hz EES at C4 and 5 Hz EES at C3/4 increased tidal volume and decreased the end‐tidal carbon dioxide level compared to pre‐stimulation control levels. EES of 5 Hz at C5 and C7 increased respiratory frequency compared to pre‐stimulation control levels. In the OFF‐State, 30 Hz cervical EES at C3/4 terminated apnoea and induced rhythmic breathing. In cadaveric tissue obtained from a brain bank, more neurons expressed both the neurokinin 1 receptor (NK1R) and somatostatin (SST) in the cervical spinal levels responsive to EES (C3/4, C6 and C7) compared to a region non‐responsive to EES (C2). Thus, the capacity of cervical EES to oppose opioid depression of respiration may be mediated by NK1R+/SST+ neurons in the dorsal cervical spinal cord. This study provides proof of principle that cervical EES may provide a novel therapeutic approach to augment respiratory activity when the neural function of the central respiratory circuits is compromised by opioids or other pathological conditions. Key points: Epidural electrical stimulation (EES) using an implanted spinal cord stimulator (SCS) is an FDA‐approved method to manage chronic pain.We tested the hypothesis that cervical EES facilitates respiration during administration of opioids in 18 human subjects who were treated with low‐dose remifentanil that suppressed respiration (ON‐State) or high‐dose remifentanil that completely inhibited breathing (OFF‐State) during the course of cervical surgery.Dorsal cervical EES of the spinal cord augmented the respiratory tidal volume or increased the respiratory frequency, and the response to EES varied as a function of the stimulation frequency (5 or 30 Hz) and the cervical level stimulated (C2–C7).Short, continuous cervical EES restored a cyclic breathing pattern (eupnoea) in the OFF‐State, suggesting that cervical EES reversed the opioid‐induced respiratory depression.These findings add to our understanding of respiratory pattern modulation and suggest a novel mechanism to oppose the respiratory depression caused by opioids. [ABSTRACT FROM AUTHOR]

In recent years, deep learning methods, especially convolutional neural network (CNN), have received extensive attentions and applications in fault diagnosis. However, recent studies have shown that the shift-invariance of CNN is not good enough, resulting in fragile feature extraction and sharp reduction in model performance when the shift occurs in the input. To improve the shift-invariance of CNN, considering the periodic characteristics of vibration signals, a multiscale CNN with feature alignment (MSCNN-FA) is proposed for bearing fault diagnosis under different working conditions. First, by analyzing the operating principles of the convolutional layer and pooling layer, a feature alignment module including single-stride convolution layer, adaptive max-pooling layer, and global average pooling layer is designed to obtain aligned features. Next, to extract shift-invariant robust features from vibration signals, a multiscale convolution strategy is utilized, and a feature-aligned multiscale feature extractor is constructed. Finally, a classifier composed of fully connected (FC) layers is constructed for bearing fault diagnosis. The effectiveness of the method is verified by a rolling bearing experiment, which outperforms other related existing CNN-based methods in terms of diagnosis accuracy and feature robustness. [ABSTRACT FROM AUTHOR]

Motor deficits are observed in Alzheimer's disease (AD) prior to the appearance of cognitive symptoms. To investigate the role of amyloid proteins in gait disturbances, we characterized locomotion in APP-overexpressing transgenic J20 mice. We used three-dimensional motion capture to characterize quadrupedal locomotion on a treadmill in J20 and wild-type mice. Sixteen J20 mice and fifteen wild-type mice were studied at two ages (4- and 13-month). A random forest (RF) classification algorithm discriminated between the genotypes within each age group using a leave-one-out cross-validation. The balanced accuracy of the RF classification was 92.3 ± 5.2% and 93.3 ± 4.5% as well as False Negative Rate (FNR) of 0.0 ± 0.0% and 0.0 ± 0.0% for the 4-month and 13-month groups, respectively. Feature ranking algorithms identified kinematic features that when considered simultaneously, achieved high genotype classification accuracy. The identified features demonstrated an age-specific kinematic profile of the impact of APP-overexpression. Trunk tilt and unstable hip movement patterns were important in classifying the 4-month J20 mice, whereas patterns of shoulder and iliac crest movement were critical for classifying 13-month J20 mice. Examining multiple kinematic features of gait simultaneously could also be developed to classify motor disorders in humans. [ABSTRACT FROM AUTHOR]

• A novel digital twin-driven fault diagnosis method is proposed. • A high-fidelity digital twin model is built for the gearbox. • A physical-virtual data fusion is designed to improve the virtual data quality. • One case study for a truck transmission is conducted. The acknowledged challenge of intelligent fault diagnosis methods is that constructing a reliable diagnosis model requires numerous labeled datasets as training data, which is difficult to collect such high-quality labeled data in the practical industry. The digital twin methodology provides a brand-new and potentially powerful solution to mitigate this challenge. However, during the practical application of digital twin-driven fault diagnosis methods, an information gap can exist between the virtual and physical spaces and poses a hurdle in adopting these methods. Therefore, this paper proposes a novel digital twin-driven approach based on physical-virtual data fusion for gearbox fault diagnosis to enhance the diagnosis performance with insufficient collected fault data. One case study on a truck transmission is carried out. First, a digital twin model of the transmission is established, which can effectively mirror the vibration characteristics and generate vibration data with different health states. Second, a physical-virtual data fusion method based on the Wasserstein generative adversarial networks with gradient penalty is designed to improve the quality of the virtual fault data further. Finally, the virtual fault data through the physical-virtual fusion are used to train a fault diagnosis model. The experimental results indicate that the proposed method significantly enhances the diagnostic performance when few measured fault data from the physical space are available. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has become a promising tool for rotary machinery fault diagnosis, but it works well only when testing samples and training samples are independent and identically distributed. In practice, rotary machinery usually works under variable speed. The change of speed leads to the variation of samples’ distribution, which can significantly decrease the performance of the deep learning model. Scholars try to utilize transfer learning techniques for solving this problem. However, most exiting methods can just work well under target speed instead of all speed, while the target samples are always required in model training. In this article, a deep semisupervised domain generalization network (DSDGN) is proposed for rotary machinery fault diagnosis under variable speed, which can generalize the model to the fault diagnosis task under unseen speed. Under the setting of semisupervised domain generalization, only one fully labeled source (LS) domain data set and one totally unlabeled source (US) domain data set are available during training. To make full use of these data, the proposed method simultaneously utilizes Wasserstein generative adversarial network with gradient penalty (WGAN-GP)-based adversarial learning and pseudolabel-based semisupervised learning for training. The transmission and bearing fault diagnosis cases are utilized for evaluation. The comparative experiments indicate that the proposed method has a better performance than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the manufacturing industry stepping into the emerging new era of big data and intelligence, the amount of data collected from perception and monitoring systems with multiple smart sensors has increased tremendously. Such huge amount of multisensory data may not only power many aspects of fault diagnosis, but also bring great opportunities and challenges in modern manufacturing industry. In addition, with respect to intelligent fault diagnosis for machinery, few researches have been focused on the compound fault diagnosis under big-data circumstance. Therefore, a novel, intelligent, compound, fault decoupling method based on deep capsule network (CN) and ensemble learning is developed for compound fault decoupling and diagnosis using multisensory data. First, a decoupling CN (DCN) is constructed as the basic model. Second, taking the full advantage of multisensory data, the DCN model can be pretrained with multiple sensor data, which can obtain various pretrained DCN models. Finally, combining with ensemble learning skill, the pretrained DCN models are integrated by a combination strategy to obtain the deep ensemble CN (DECN) model for intelligent compound fault decoupling and diagnosis. The performance of the DECN model is validated on an automobile transmission (AT) data set with two compound faults, and the experimental results illustrate that the DECN model obtains higher diagnosis accuracy and decouples the compound fault correctly. [ABSTRACT FROM AUTHOR]

Accurate remaining useful life (RUL) prediction for rolling bearings encounters many challenges such as complex degradation processes, varying working conditions, and insufficient run-to-failure data. Transfer learning (TL), one paradigm of artificial intelligence technology, has demonstrated its powerful performance and great effectiveness for such challenges. As a result, many TL-based solutions have been widely developed and extensively studied for rolling bearing RUL prediction. Admittedly, several review articles have been published on RUL prediction. Nevertheless, the majority of these articles only concentrated on deep learning-based RUL prediction methods, and a review article that systematically overviews the status of TL-based RUL prediction has not been published. Therefore, it is urgent and significant to thoroughly summarize the academic publications and industrial applications related to TL-based RUL prediction, and present its potential challenges and future research directions. With such goals, the problem definitions of TL-based RUL prediction, the general procedure of RUL prediction, and typical TL-based RUL prediction algorithms are first introduced to help researchers quickly overview the state-of-the-art approaches and recent developments. Thereafter, relevant TL-based RUL prediction solutions are comprehensively discussed from the perspectives of three industrial scenarios, providing suggestions to researchers and engineers for selecting appropriate solutions in practical industrial applications. Finally, the key challenges and future trends in RUL prediction are presented to conclude this paper. We hope that this review of TL-based RUL prediction for rolling bearings can contribute to a better understanding of intelligent prognostic technology and will inspire researchers to extend their work on RUL prediction. [ABSTRACT FROM AUTHOR]

Catastrophic forgetting of learned knowledges and distribution discrepancy of different data are two key problems within fault diagnosis fields of rotating machinery. However, existing intelligent fault diagnosis methods generally tackle either the catastrophic forgetting problem or the domain adaptation problem. In complex industrial environments, both the catastrophic forgetting problem and the domain adaptation problem will occur simultaneously, which is termed as continual transfer problem. Therefore, it is necessary to investigate a more practical and challenging task where the number of fault categories are constantly increasing with industrial streaming data under varying operation conditions. To address the continual transfer problem, a novel framework named deep continual transfer learning network with dynamic weight aggregation (DCTLN-DWA) is proposed in this study. The DWA module is used to retain the diagnostic knowledge learned from previous phases and learn new knowledge from the new samples. The adversarial training strategy is applied to eliminate the data distribution discrepancy between source and target domains. The effectiveness of the proposed framework is investigated on an automobile transmission dataset. The experimental results demonstrate that the proposed framework can effectively handle the industrial streaming data under different working conditions and can be utilized as a promising tool for solving actual industrial problem. [ABSTRACT FROM AUTHOR]

Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can not only leverage the advantages of Deep Learning (DL) in feature representation, but also benefit from the superiority of Transfer Learning (TL) in knowledge transfer. As a result, DTL techniques can make DL-based fault diagnosis methods more reliable, robust and applicable, and they have been widely developed and investigated in the field of Intelligent Fault Diagnosis (IFD). Although several systematic and valuable review articles have been published on the topic of IFD, they summarized relevant research only from an algorithm perspective and overlooked practical applications in industry scenarios. Furthermore, a comprehensive review on DTL-based IFD methods is still lacking. From this insight, it is particularly important and more necessary to comprehensively survey the relevant publications of DTL-based IFD, which will help readers to conveniently understand the current state-of-the-art techniques and to quickly design an effective solution for solving IFD problems in practice. First, theoretical backgrounds of DTL are briefly introduced to present how the transfer learning techniques can be integrated with deep learning models. Then, major applications of DTL and their recent developments in the field of IFD are detailed and discussed. More importantly, suggestions on how to select DTL algorithms in practical applications, and some future challenges are shared. Finally, conclusions of this survey are given. At last, we have reason to believe that the works done in this article can provide convenience and inspiration for the researchers who want to devote their efforts in the progress and advance of IFD. [ABSTRACT FROM AUTHOR]

Simple Summary: A new species of odd-scaled snake in the genus Achalinus is described from Dabie Mountains Luan City, Anhui Province, China, based on one male and two female specimens. Bayesian inference and maximum likelihood analyses based on a mitochondrial DNA fragment (CO1) indicated the new taxon is different from its congeners (p–distance ≥ 9.4%). Morphologically, the new species can be diagnosed from the other species by a combination of 12 characters. The recognition of the new species brings the number of described Achalinus species to 22. A new species of Xenodermid snake, Achalinus dabieshanensis sp. nov., was described based on three specimens (two female and one male) collected from the Dabie Mountains of western Anhui Province. It can be distinguished from known congeners by a significant genetic divergence in the mitochondrial gene fragment COI (p-distance ≥ 9.4%) and the following combination of characteristics: (1) length of the suture between the internasals being distinctly shorter than between the prefrontals; (2) a single loreal; (3) dorsal scales strongly keeled, in 23 rows throughout the body; (4) two pairs of prefrontals; (5) six supralabials; (6) five infralabials; (7) temporals 2 + 2 + 3 (or 2 + 2 + 4); (8) 141–155 ventrals; (9) 45–55 subcaudals, unpaired; (10) anal entire; (11) weakly iridescent tinged, uniform, brown to black dorsum with vertebral scales and about three adjacent dorsal scales dark brown forming a longitudinal vertebral line from posterior margin of parietals to tail tip; (12) light brown venter, ventral shields wide, visible on both sides, light brown flanks, giving the appearance of a black subcaudal streak. The recognition of the new species increases the number of described Achalinus species to 22. [ABSTRACT FROM AUTHOR]

L5469 --> Motor deficits are observed in Alzheimer's disease (AD) prior to the appearance of cognitive symptoms. To investigate the role of amyloid proteins in gait disturbances, we characterized locomotion in APP‐overexpressing transgenic J20 mice. We used three‐dimensional motion capture to characterize quadrupedal locomotion on a treadmill in J20 and wild‐type mice. Sixteen J20 mice and fifteen wild‐type mice were studied at two ages (4‐ and 13‐month). A random forest (RF) classification algorithm discriminated between the genotypes within each age group using a leave‐one‐out cross‐validation. The balanced accuracy of the RF classification was 92.3 ± 5.2 % and 93.3 ± 4.5 % as well as False Negative Rate (FNR) of 0.0 ± 0.0% and 0.0 ± 0.0% for the 4‐month and 13‐month groups, respectively. Feature ranking algorithms identified kinematic features, which when considered simultaneously, achieved high genotype classification accuracy. The identified features demonstrated an age‐specific kinematic profile of the impact of APP‐overexpression. Trunk tilt and unstable hip movement patterns were important in classifying the 4‐month J20 mice, whereas patterns of the shoulder and iliac crest movement were critical for classifying 13‐month J20 mice. Examining multiple kinematic features of gait simultaneously could also be developed to classify motor disorders in humans. [ABSTRACT FROM AUTHOR]

Straw used for biogas fermentation raw materials is an important way to reduce the environmental pollution and utilize straw resource. Owing to waxy layer on the straw particles surface during the fermentation process, biogas production and pollutant removal rate is not high enough, restricting the further promotion. In order to achieve high-efficient fermentation and biogas production, new type biogas fermentation material liquid circulation fluidization scheme is proposed in this study. Based on hydraulic mixing, the raw straw surface loose flocculating organization is broken, the dense part is exposed, the surface waxy layer formed in the long time of fermentation is teared, so as to enhance surface contact effect between solid straw and liquid material, increase organic matter and bacteria in the slurry of biochemical reaction, then promote efficiency of anaerobic co-digestion. Through the comparison experiment of three 635.5 L anaerobic digesters, it's found that the high centralized pressure outlet scheme and the high distributed pressure outlet scheme increase the average daily gas production rate by 176% and 253%, the removal rate of total solid (TS) increase by 41% and 69%, and the removal rate of volatile solid (VS) increases by 51% and 71%, respectively, compared with the non-mixing scheme. • New type biogas fermentation circulation fluidization scheme is proposed. • Hydraulic mixing shear stress can remove straw particles surface spore structure. • Hydraulic mixing shear stress can tear surface wax layer. • Hydraulic mixing shear stress can improve anaerobic co-digestion efficiency. [ABSTRACT FROM AUTHOR]

Ganglioside GM3 is well known as a tumor-associated carbohydrate antigen on several types of tumors. Many studies have demonstrated that GM3 plays roles in cells proliferation, adhesion, motility and differentiation, which is involved in the process of cancer development. In the present study, we developed methods to synthesize GM3 analogues conveniently. By enzymatic hydrolysis and chemical procedures, two novel analogues and two known analogues were synthesized, containing lactose and glucosamine. Then anti-proliferation and anti-migration activities were evaluated by cytotoxicity assays and wound healing tests, and the data demonstrated that these analogues exhibited anticancer activities. Based on our previous studies, the structure-activity relationships were discussed. This study could provide valuable sight to find new antitumor agents for cancer therapy. Results exhibited that these analogues can inhibit cancer cell growth and migration. Image 1 • Two novel GM3 analogues containing lactose and glucosamine were synthesized. • GM3 analogues showed antitumor activities by cytotoxicity assays and wound healing tests. • This work provided valuable information to study carbohydrate derivatives as potential agents for cancer therapy. [ABSTRACT FROM AUTHOR]

Rituximab resistance is one of the great challenges in the treatment of diffuse large B-cell lymphoma (DLBCL), but relevant biomarkers and signalling pathways remain to be identified. Coptis chinensis and its active ingredients have antitumour effects; thus, the potential bioactive compounds and mechanisms through which Coptis chinensis acts against rituximab-resistant DLBCL are worth exploring. To elucidate the core genes involved in rituximab-resistant DLBCL and the potential therapeutic targets of candidate monomers of Coptis chinensis. Using the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), the Similarity Ensemble Approach and Swiss Target Prediction, the main ingredients and pharmacological targets of Coptis chinensis were identified through database searches. Through the overlap between the pharmacological targets of Coptis chinensis and the core targets of rituximab-resistant DLBCL, we identified the targets of Coptis chinensis against rituximab-resistant DLBCL and constructed an active compound-target interaction network. The targets and their corresponding active ingredients of Coptis chinensis against rituximab-resistant DLBCL were molecularly docked. Berberine, quercetin, epiberberine and palmatine, the active components of Coptis chinensis, have great potential for improving rituximab-resistant DLBCL via PIK3CG. This study revealed biomarkers and Coptis chinensis-associated molecular functions for rituximab-resistant DLBCL. [ABSTRACT FROM AUTHOR]

Anti-PD-1 immunotherapy has been widely applied in patients with some types of lymphoma. Classical Hodgkin's lymphoma (cHL) is highly sensitive to immunotherapy, but non-Hodgkin's lymphoma (NHL) does not show a good response. Studies have indicated that haematopoietic progenitor kinase 1 (HPK1) suppresses T cells and reduces antitumour immunity. Therefore, HPK1 inhibitors may restore and elicit antitumour immune responses and are promising candidate drug targets for cancer immunotherapy. We first explored the Gene Expression Profile Interactive Analysis (GEPIA) database and predicted that HPK1 expression was increased in diffuse large B-cell lymphoma (DLBCL) and associated with Nod-like receptor protein 3 (NLRP3) expression. We investigated whether an HPK1 inhibitor could enhance the tumour response to anti-PD-1 immunotherapy in NHL and the association between HPK1 and NLRP3 expression. Employing shHPK1 and an inhibitor, we demonstrated that the HPK1 inhibitor increased anti-PD-1-mediated T-cell cytotoxicity in BJAB and WSU-DLCL2 cells cocultured with peripheral blood mononuclear cells (PBMCs). HPK1 inhibitor treatment increased PD-1, PD-L1, Bax, p53 and NK-kB expression but decreased NLRP3 expression, indicating that the HPK1 inhibitor promoted apoptosis and blocked the NLRP3 inflammasome pathway to affect anti-PD-1-mediated T-cell cytotoxicity. Moreover, the HPK1 inhibitor enhanced the efficiency of anti-PD-1 immunotherapy in vivo in a zebrafish xenograft model of NHL. In summary, this study provides evidence that an HPK1 inhibitor enhanced the tumour response to anti-PD-1 immunotherapy in NHL by promoting apoptosis and blocking the NLRP3 pathway. These findings provide a potential therapeutic option for NHL combining HPK1 inhibitor treatment and anti-PD-1 immunotherapy. [ABSTRACT FROM AUTHOR]

• A self-adaptive dynamic clustering approach is proposed for automatically selecting and fusing multimodal data. • A cluster-ensemble transfer regression method is developed for RUL prediction under cross working conditions. • A multi-level feature learning strategy is provided to learn the domain-invariant temporal degradation knowledge. • The method outperforms other SOAT RUL prediction methods on N CMAPSS dataset released in 2021. Remaining useful life (RUL) prediction based on multimodal sensing data is indispensable for predictive maintenance of aero-engine under cross-working conditions. Although data-driven methods have emerged as a powerful tool in RUL prediction, it is still limited in industrial applications because the majority of existing methods manually select or fuse multisensory data and ignore the inconsistency of the sensing data collected from different engines. Therefore, an intelligent RUL prediction approach is proposed for aero-engine by integrating multimodal data fusion methodology and ensemble transfer learning technology to dynamically select sensing data and make a robust RUL prediction under cross-working conditions. Specifically, a self-adaptive dynamic clustering approach is developed to select useful multimodal data into different clusters, each of which has a consistent degradation tendency. Furthermore, a cluster-ensemble transfer regression network is constructed by building multiple regressors for different clusters to predict the RUL values of aero-engine under cross-working conditions, where a multi-level feature learning strategy is provided to learn the domain-invariant temporal degradation knowledge. Comparative experiments are conducted on the N CMAPSS dataset released in 2021. The results show that the proposed method outperforms other state-of-the-art RUL prediction methods. [ABSTRACT FROM AUTHOR]

Aim: To provide quality care to older adults, healthcare professionals should be aware that osteoporotic vertebral compression fractures (OVCFs) might occur sequentially in the same patient, involving different vertebral bodies, each separated by short intervals. This situation is called chronologically clustered OVCFs (CCOVCF). Methods: A total of 40 patients with CCOVCFs (index cohort) were retrospectively analyzed, and compared with 40 patients having only one OVCF (comparison cohort). All fractures were treated with percutaneous balloon kyphoplasty. Results: In the index cohort, the number of patients having the second, third, fourth and fifth OVCF events within 3 months were 40, 15, five and two, respectively. Recurring pain or seemingly non‐stop pain were the major reasons why new OCVFs were found. The average interval between pain relief provided by percutaneous balloon kyphoplasty and radiographic diagnosis of new OVCFs was significantly longer than that between pain relief and a new episode of disabling pain (26.7 ± 16.8 vs 16.4 ± 15.8 days, P < 0.0001), reflecting how shortly new OCVFs occurred after successful surgery, and how often they were neglected. The mean T‐score of the index cohort was significantly lower than that of the comparison cohort (−3.66 ± 0.79 vs −3.17 ± 0.80, P = 0.01). Conclusions: CCOVCFs make a patient seem constantly in pain, despite repeated admissions and operations. Recurrent symptoms after an effective procedure should be taken as a warning that a new OCVF might have occurred, even if only a few days apart. Advanced osteoporosis is a significant risk factor for CCOVCFs. Geriatr Gerontol Int 2023; 23: 44–49. [ABSTRACT FROM AUTHOR]

Background: Overseeing proper insulin initiation and titration remains a challenging task in diabetes care. Recent advances in mobile technology have enabled new models of collaborative care between patients and healthcare providers (HCPs). We hypothesized that the adoption of such technology could help individuals starting basal insulin achieve better glycemic control compared with standard clinical practice.Materials and Methods: This was a 12 ± 2-week randomized controlled study with 40 individuals with type 2 diabetes who were starting basal insulin due to poor glycemic control. The control group (n = 20) received standard face-to-face care and phone follow-up as needed in a tertiary center, whereas the intervention group (n = 20) received care through the cloud-based diabetes management program where regular communications about glycemic control and insulin doses were conducted via patient self-tracking tools, shared decision-making interfaces, secure text messages, and virtual visits (audio, video, and shared screen control) instead of office visits.Results: By intention-to-treat analysis, the intervention group achieved a greater hemoglobin A1c decline compared with the control group (3.2 ± 1.5% vs. 2.0% ± 2.0%; P = 0.048). The Diabetes Treatment Satisfaction Questionnaire showed a significant improvement in the intervention group compared with the control group (an increase of 10.1 ± 11.7 vs. 2.1 ± 6.5 points; P = 0.01). HCPs spent less time with patients in the intervention group compared with those in the control group (65.9 min per subject vs. 81.6 min per subject). However, the intervention group required additional training time to use the mobile device.Conclusions: Mobile health technology could be an effective tool in sharing data, enhancing communication, and improving glycemic control while enabling collaborative decision making in diabetes care. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI)-driven fault diagnosis methods are crucial for ensuring rotating machinery's safety and effective operation. The success of most current methods relies on the assumption that sufficient high-quality labeled datasets can be obtained for model training. However, in real-world industrial scenarios, obtaining such datasets is difficult or nearly impossible, thereby hindering the practical implementation of these methods. The integration of virtual modeling and transfer learning offers a powerful approach to meet the above challenge. Abundant virtual data of different fault categories can be acquired in the virtual space with highly flexible and at a low cost, and transfer learning can enhance the practical utility of these virtual data for contributing to the construction of diagnosis models. Therefore, this paper proposes a digital twin-driven partial domain fault diagnosis method based on unlabeled physical data and labeled virtual data. First, a virtual model of rotating machinery is built to generate labeled virtual fault data with enough fault types. Then, an adversarial transfer learning network is developed to leverage the effective knowledge from the virtual and physical data. Meanwhile, a weighting learning module is introduced to reduce the negative effect caused by the redundant fault categories in the virtual space. Finally, the proposed digital twin-driven transfer learning network is trained with the labeled virtual data and unlabeled physical data. Experiments on a light truck transmission system demonstrate that the proposed method achieves satisfactory diagnostic performance even without labeled physical fault data, contributing to the advancement of AI engineering applications. [ABSTRACT FROM AUTHOR]

Current literatures suggest a growing body of evidence highlighting the pivotal role of Immunogenic Cell Death (ICD) in multiple tumor types. Nevertheless, the potential and mechanisms of ICD in diffuse large B-cell lymphoma (DLBCL) remain inadequately studied. To address this gap, our current study aims to examine the impact of ICD on DLBCL and identify a corresponding gene signature in DLBC. Using the expression profiles of ICD-associated genes, the gene expression omnibus (GEO) samples were segregated into ICD-high and ICD-low subtypes utilizing non-negative matrix factorization clustering. Next, univariate and LASSO Cox regression analyses were employed to establish the ICD-related gene signature. Subsequently, the CIBERSORT tool, ssGSEA, and ESTIMATE algorithm were utilized to examine the association between the signature and tumor immune microenvironment of DLBC. Finally, the oncoPredict algorithm was implemented to evaluate the drug sensitivity prediction of DLBCL patients. These findings suggest that the immune microenvironment of the ICD-high group with a poor prognosis was significantly suppressed. An 8-gene ICD-related signature was identified and validated to prognosticate and evaluate the tumor immune microenvironment in DLBCL. Similarly, the high-risk group exhibited a worse prognosis compared to the low-risk group, and the immune function was considerably suppressed. Moreover, the results of oncoPredict algorithm indicated that patients in the high-risk group exhibited higher sensitivity to Cisplatin, Cytarabine, Epirubicin, Oxaliplatin, and Vincristine with low IC50. In conclusion, the present study provides novel insights into the role of ICD in DLBCL by identifying a new biomarker for the disease and may have implications for the development of immune-targeted therapies for the tumor. [ABSTRACT FROM AUTHOR]

Decentralized biogas facilities have become an important component of agricultural sectors in Chinese rural area. This study is the first attempt to investigate how transition in Chinese rural areas affects the revolution of decentralized biogas facilities and its sustainability. The characteristics of the household biogas digesters (HBD), simple biogas plants (SBP), and well equipped biogas plants (WEBP) with higher level of technologies were clarified, and their practical feasibility was correspondingly investigated through a comprehensive survey of 15 selected villages. More effort was devoted to the adoption of feedstock availability, energy supply stability, and digestate distribution optimization for each facility. The results showed that feedstock collection and insufficient biogas production were the main restraining factors for HBD, while SBP found to be ineffective facilities for competitive energy production due to the un-guaranteed biogas supply. The survey results demonstrated that the average biogas expenditure of dwellers, who were connected to WEBP, accounted for 39.61% commercial energy cost. According to the one-year operation data collected from a 52-household club revealed that WEBP would effectively maintain stability and continuous biogas supply with the average feeding interval of 6.2 d. Accordingly, WEBP groups were found as the best option for regional development respected with the professional management unit. Overall, commercial biogas is the orientation of decentralized biogas facility construction. However, to form a robust biogas-centric industrial chain, future research should focus on how to establish relative policies and regulations supporting the comprehensive operation framework of a multi-participant collaboration leadership system. • Decentralized biogas facilities in Chinese rural area have been scrutinized. • Household digesters, simple, and equipped plants are prevalent biogas facilities. • Feedstock collection and low gas production were restraining factors in HBD. • WEBPs were found as the best option for regional development. [ABSTRACT FROM AUTHOR]

• A generalized open-set fault diagnosis (OSFD) scenario is defined. • A multiple metric weighting learning network is constructed to address two OSFD issues simultaneously. • Enhanced domain similarity measurement is designed to leverage class label information. • Ensemble uncertainty measurement is developed to reduce misclassification interference. • Twenty-seven OSFD diagnosis tasks are used to evaluate the proposed approach. The problem of practical open-set domain adaptation diagnosis has gained great attention considering unobserved fault categories in target domain. However, existing studies assume that the label space of source domain is a subset of target domain, ignoring that source domain may also contain private fault categories. This generalized open-set diagnosis issue is more challenging, making existing techniques less effective. To tackle this problem, a novel approach is proposed that focuses on addressing two open-set diagnosis issues simultaneously. A multiple metric weighting learning strategy with the integration of an enhanced domain similarity measurement and an ensemble uncertainty measurement is constructed to adaptively weight the importance of samples across domains. Then, weighted adversarial training with multiple metric weight functions is implemented to learn domain-invariant features by performing alignment across different distributions. As such, both unknown and known fault categories can be simultaneously and effectively recognized. Experiments on three bearing datasets are carried out. Results demonstrate the proposed approach can effectively deal with generalized open-set diagnosis tasks, outperforming existing diagnosis approaches. [ABSTRACT FROM AUTHOR]

Abstract Fermentation material fluid mixing is an indispensable subsidiary technology for modern biogas engineering, which can increase fermentation efficiency substantially. Taking advantage of computational fluid dynamics (CFD), the flowing progress of biogas fermentation material fluid in the anaerobic digesters can be reappeared, the distribution curve of flow pattern can be obtained, the visualization of flow pattern can be achieved, the features and defect of flow configuration can be differentiated on the basis of the flow pattern visualization. Scholars quantified and evaluated advantages and disadvantages of fermentation material fluid mixing schemes by using CFD and setting the parameters of "poor mixing zone", "dead zone" and so on, which became the important basis of modern biogas engineering design. Meanwhile, the mixing form and running parameters can be excellently designed and thus no longer added at random. On the basis of the classic k-ε model, researchers have developed a variety of improved models, which are more suitable for complex and diverse conditions of biogas fermentation material fluid. In this present study, the applicable algorithm models of various working conditions are proposed after subdivision research. During the first decade of the 21st century, one research upsurge of simulating biogas fermentation material mixing by using the CFD simulation is growing up in the biogas field, which makes a great progress on the aspect of optimal design of digesters shape, mixing form, mixing running parameters and methods of fluid, promotes the research levels of biogas science and becomes one of the most important stage of the biogas science phylogeny. All of these represent the development direction of future, accordingly, the summary of progress related with above research aspects is given in this present study. Graphical abstract fx1 Highlights • The measured results of many flow field parameters can be fitted with simulation. • The methods of setting the mixing parameters by CFD are listed. • Successful examples of optimization of biogas engineering design by CFD are presented. • The applicable algorithm models of various working conditions are proposed. • The role of CFD in promoting the upgrading of biogas project is discussed. [ABSTRACT FROM AUTHOR]

Marital status have been found as an independent prognostic factor for survival and spousal support could provide a survival advantage in various cancer types. However, the specific effect of marital status on survival in hepatocellular carcinoma (HCC) has not been explored in detail. In this study, we used the Surveillance, Epidemiology and End Results program to identify iagnosed with HCC between 1988 and 2007. Kaplan-Meier methods and multivariable Cox regression models were used to analyze long-term cancer-specific survival (CSS) outcomes and risk factors stratified by marital status. There were significant differences among these different marital status subgroups with regard to 5-year CSS rates (P < 0.001). Married HCC patients had a better 5 year CSS rate than those unmarried patients, and widowed patients were more likely to die of their cancer. A stratified analysis showed that widowed patients always had the lowest CSS rate across different cancer stage, age and gender subgroups. Even after adjusting for known confounders, unmarried patients were at greater risk of cancer-specific mortality. Social support aimed at this population could improve the likelihood of achieving cure. [ABSTRACT FROM AUTHOR]

The peripheral nervous system may play an important role in normal bone maintenance and remodeling. Substance P (SP) is a neuropeptide associated with bone loss and formation that may mediate the effects of the nervous system. The purpose of this study is to determine if treadmill running combined with electro-acupuncture at Jiaji acupoints (Jiaji-EA) affects tibial bone mass and SP expression in rabbits with sciatic nerve injury. Twenty-four juvenile male New Zealand white rabbits were randomly assigned to one of 4 groups: sham injury control (sham), sciatic never crush control (SNCr), treadmill running (treadmill), and Jiaji-EA combined with treadmill running (ET group). The SNCr, treadmill, and ET groups all had an induced sciatic never crush injury of approximately 2mm. Control groups received no intervention; the treadmill and ET groups were trained by treadmill; the ET group also received Jiaji-EA. After the 4 weeks of treatment, toe-spreading index (TSI), BMD, bone strength, and SP expression in the tibia were significantly lower in the nerve injury groups (SNCr, treadmill, and ET) compared to the sham groups (p<0.05). Treatment (treadmill and ET groups) increased all measures compared to the SNCr group (p<0.05). Further, TSI, BMD, bone strength, and SP expression in the ET group were higher than the treadmill group (p<0.05). Our results indicate that treadmill therapy combined with electro-acupuncture at Jiaji acupoints prevents bone loss in rabbit tibias after sciatic nerve injury. This may occur in two ways: indirectly in association with axon regeneration and directly via loading on the bone mediated through increased SP expression. This study provides important evidence for the clinical treatment of bone loss after peripheral nerve injury. [ABSTRACT FROM AUTHOR]

Six positional isomers of triptolide–glucose conjugates (TG1α, TG1β, TG2, TG3, TG4 and TG6) were designed and synthesized. These conjugates exhibited better water solubility, and had selective cytotoxicity between tumor cells with high expression of glucose transport-1 (Glut-1) and non-tumor cells with low expression of Glut-1, in which TG2 formed by triptolide (TPL) and d -glucose C2–OH had the strongest cytotoxicity to tumor cells and lowest toxicity in non-tumor cells, therefore the highest relative therapeutic index, which was 5.7 times that of triptolide and consequent the most powerful selective antitumor activity in vitro. The cytotoxicity of TG2 was highly correlated with Glut-1 function. As a prodrug of triptolide, TG2 could promote RNA Pol II degradation and induce apoptosis as TPL does. TG2 had a stronger dose-dependent antitumor effect in vivo than TPL and no adverse reaction occurred when its tumor inhibition was higher than 90%, which was associated with its selective distribution in tumor tissues. TG2 could be used as a promising drug candidate for the treatment of solid tumors with high expression of Glut-1, which is worthy of further study. [Display omitted] TG2 conjugated by triptolide and d -glucose C2–OH has a stronger selective antitumor activity than triptolide due to selective transport of TG2 into tumor cells via glucose transporters. • ●Triptolide-Glucose Conjugate TG2 formed by triptolide C14–OH and d -glucose C2–OH. • ●The cytotoxicity of TG2 is highly dependent on Glut-1 function. • ●TG2 distributes in tumor selectively and has a highly selective antitumor activity. [ABSTRACT FROM AUTHOR]

Chronic inflammation plays a central role in hepatocellular carcinoma (HCC), but the contribution of hepatocytes to tumor-associated inflammation is not clear. Here, we report that the zinc finger transcription factor Miz1 restricted hepatocyte-driven inflammation to suppress HCC, independently of its transcriptional activity. Miz1 was downregulated in HCC mouse models and a substantial fraction of HCC patients. Hepatocyte-specific Miz1 deletion in mice generated a distinct sub-group of hepatocytes that produced pro-inflammatory cytokines and chemokines, which skewed the polarization of the tumor-infiltrating macrophages toward pro-inflammatory phenotypes to promote HCC. Mechanistically, Miz1 sequestrated the oncoprotein metadherin (MTDH), preventing MTDH from promoting transcription factor nuclear factor κB (NF-κB) activation. A distinct sub-group of pro-inflammatory cytokine-producing hepatocytes was also seen in a subset of HCC patients. In addition, Miz1 expression inversely correated with disease recurrence and poor prognosis in HCC patients. Our findings identify Miz1 as a tumor suppressor that prevents hepatocytes from driving inflammation in HCC. [Display omitted] • Miz1 suppresses liver cancer independently of its transcriptional activity • Miz1 restricts the ability of hepatocytes to drive macrophage-dependent inflammation • Miz1 prevents oncoprotein MTDH from promoting hepatocyte NF-κB activity • Miz1 expression inversely correlates with recurrence and poor prognosis in HCC patients Chronic inflammation plays a crucial role in hepatocellular carcinoma (HCC), but the contribution of tumor hepatocytes to tumor-associated inflammation remains unclear. Zhang et al. find that loss of the transcription factor Miz1 in hepatocytes promotes NF-κB activation, producing a distinct sub-cluster of tumor hepatocytes that skew tumor-infiltrating macrophages toward a pro-inflammatory phenotype and drive inflammation in HCC. [ABSTRACT FROM AUTHOR]