Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning control for a class of discrete-time, nonlinear systems

Over the last few decades, control theory has developed to the level where reliable methods exist to achieve satisfactory performance on even the largest and most complex of dynamical systems. The application of these control methods, though, often require extensive modelling and design effort. Recent techniques to alleviate the strain on modellers use various schemes which allow a particular system to learn about itself by measuring and storing a large, arbitrary collection of data in compact structures such as neural networks, and then using the data to augment a controller. Although many such techniques have demostrated their capabilites in simulation, performance guarantees are rare. This thesis proposes an alternate learning technique, where a controller, based on minimal initial knowledge of system dynamics, acquires a prescribed data set on which a new controller, with guaranteed performance improvements, is based. Thesis Supervisor: Anuradha Annaswamy Title: Associate Professor Technical Supervisor: Rami Mangoubi Title: Senior Member of Technical Staff, Draper Laboratory ACKNOWLEDGMENT June 30, 1999 This thesis was prepared at The Charles Stark Draper Laboratory, Inc., under IR&D project number 18587. Publication of this thesis does not constitute approval by Draper of the findings or conclusions contained herein. It is published for the exchange and stimulation of ideas. Permission is hereby granted by the Author to the Massachusetts Institute of Technology to reproduce any or all of this thesis.
Metabolomic Impact of Lidocaine on a Triple Negative Breast Cancer Cell Line

Background: Metabolomics and onco-anesthesia are two emerging research fields in oncology. Metabolomics (metabolites analysis) is a new diagnostic and prognostic tool that can also be used for predicting the therapeutic or toxic responses to anticancer treatments. Onco-anesthesia studies assess the impact of anesthesia on disease-free and overall survival after cancer surgery. It has been shown that local anesthetics (LA), particularly lidocaine (LIDO), exert antitumor properties both in vitro and in vivo and may alter the biologic fingerprints of cancer cells. As LA are known to impair mitochondrial bioenergetics and byproducts, the aim of the present study was to assess the impact of LIDO on metabolomic profile of a breast cancer cell line. Methods: Breast cancer MDA-MB-231 cells were exposed for 4 h to 0.5 mM LIDO or vehicle (n = 4). The metabolomic fingerprint was characterized by high resolution magic angle spinning NMR spectroscopy (HRMAS). The multivariate technique using the Algorithm to Determine Expected Metabolite Level Alteration (ADEMA) (Cicek et al., PLoS Comput. Biol., 2013, 9, e1002859), based on mutual information to identify expected metabolite level changes with respect to a specific condition, was used to determine the metabolites variations caused by LIDO. Results: LIDO modulates cell metabolites levels. Several pathways, including glutaminolysis, choline, phosphocholine and total choline syntheses were significantly downregulated in the LIDO group. Discussion: This is the first study assessing the impact of LIDO on metabolomic fingerprint of breast cancer cells. Among pathways downregulated by LIDO, many metabolites are reported to be associated with adverse prognosis when present at a high titer in breast cancer patients. These results fit with the antitumor properties of LIDO and suggest its impact on metabolomics profile of cancer cells. These effects of LIDO are of clinical significance because it is widely used for local anesthesia with cutaneous infiltration during percutaneous tumor biopsy. Future in vitro and preclinical studies are necessary to assess whether metabolomics analysis requires modification of local anesthetic techniques during tumor biopsy.
A Quantitative Approach to Unravel the Role of Host Genetics in IgG-FcγR Complex Formation After Vaccination

Fc-mediated immune functions have been correlated with protection in the RV144 HIV vaccine trial and are important for immunity to a range of pathogens. IgG antibodies (Abs) that form complexes with Fc receptors (FcRs) on innate immune cells can activate Fc-mediated immune functions. Genetic variation in both IgGs and FcRs have the capacity to alter IgG-FcR complex formation via changes in binding affinity and concentration. A growing challenge lies in unraveling the importance of multiple variations, especially in the context of vaccine trials that are conducted in homogenous genetic populations. Here we use an ordinary differential equation model to quantitatively assess how IgG1 allotypes and FcγR polymorphisms influence IgG-FcγRIIIa complex formation in vaccine-relevant settings. Using data from the RV144 HIV vaccine trial, we map the landscape of IgG-FcγRIIIa complex formation predicted post-vaccination for three different IgG1 allotypes and two different FcγRIIIa polymorphisms. Overall, the model illustrates how specific vaccine interventions could be applied to maximize IgG-FcγRIIIa complex formation in different genetic backgrounds. Individuals with the G1m1,17 and G1m1,3 allotypes were predicted to be more responsive to vaccine adjuvant strategies that increase antibody FcγRIIIa affinity (e.g. glycosylation modifications), compared to the G1m-1,3 allotype which was predicted to be more responsive to vaccine boosting regimens that increase IgG1 antibody titers (concentration). Finally, simulations in mixed-allotype populations suggest that the benefit of boosting IgG1 concentration versus IgG1 affinity may be dependent upon the presence of the G1m-1,3 allotype. Overall this work provides a quantitative tool for rationally improving Fc-mediated functions after vaccination that may be important for assessing vaccine trial results in the context of under-represented genetic populations.
Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

Wearable robotic upper limb orthoses (ULO) are promising tools to assist or enhance the upper-limb function of their users. While the functionality of these devices has continuously increased, the robust and reliable detection of the user's intention to control the available degrees of freedom remains a major challenge and a barrier for acceptance. As the information interface between device and user, the intention detection strategy (IDS) has a crucial impact on the usability of the overall device. Yet, this aspect and the impact it has on the device usability is only rarely evaluated with respect to the context of use of ULO. A scoping literature review was conducted to identify non-invasive IDS applied to ULO that have been evaluated with human participants, with a specific focus on evaluation methods and findings related to functionality and usability and their appropriateness for specific contexts of use in daily life. A total of 93 studies were identified, describing 29 different IDS that are summarized and classified according to a four-level classification scheme. The predominant user input signal associated with the described IDS was electromyography (35.6%), followed by manual triggers such as buttons, touchscreens or joysticks (16.7%), as well as isometric force generated by residual movement in upper-limb segments (15.1%). We identify and discuss the strengths and weaknesses of IDS with respect to specific contexts of use and highlight a trade-off between performance and complexity in selecting an optimal IDS. Investigating evaluation practices to study the usability of IDS, the included studies revealed that, primarily, objective and quantitative usability attributes related to effectiveness or efficiency were assessed. Further, it underlined the lack of a systematic way to determine whether the usability of an IDS is sufficiently high to be appropriate for use in daily life applications. This work highlights the importance of a user- and application-specific selection and evaluation of non-invasive IDS for ULO. For technology developers in the field, it further provides recommendations on the selection process of IDS as well as to the design of corresponding evaluation protocols.
Acromegaly: Clinical Care in Central and Eastern Europe, Israel, and Kazakhstan

Acromegaly is a rare condition typically caused by benign pituitary adenomas, resulting in excessive production of growth hormone. Clinical manifestations of acromegaly are diverse, varying from the overgrowth of body tissue to cardiovascular, metabolic, and osteoarticular disorders. Symptoms may emerge slowly, overlapping with other diseases and often involve many different healthcare specialists. In the last decade, efforts to provide an accurate and timely diagnosis of acromegaly have improved disease management and clinical experience. Despite this progress, marked differences in the diagnosis, treatment, and management of acromegaly exist from country-to-country. To address these inconsistencies in the region comprising Central and Eastern Europe, Israel, and Kazakhstan, a panel of acromegaly experts from 13 of these countries was convened. Acromegaly experts from each country provided available information on the approaches from their country, including regional treatment centers and multidisciplinary teams, treatment access, reimbursement and availability, and physician education, disease awareness, and patient advocacy. Across several areas of acromegaly management, divergent approaches were identified and discussed, including the provision of multidisciplinary care, approved and available treatments, and disease awareness programs. These were recognized as areas of potential improvement in the management of acromegaly, in addition to participation in national and regional acromegaly registries. Further experience exchange will facilitate the identification of specific strategies that can be adapted in each country, and widespread participation in acromegaly registries will enable their evaluation. It is anticipated that this approach will support the optimization of acromegaly patient care across this region.
Effects of Plate Velocity Slowdown on Altering Continental Collision Patterns and Crustal-Lithospheric Deformation During the Collision Process

Continental collision zones are widely distributed across the earth’s surface with diverse types of tectonic processes. Even the same collision zone shows significant lateral tectonic variations along its strike. In this study, we systematically investigated how plate velocity slowdown after the closure of the ocean influences the continental collision evolution, as well as the effects of kinematic characteristics and continental rheology on varying the continental collision modes in a plate velocity slowdown model. From the comparison between the constant plate velocity system (CVS) and the plate velocity-dropping system (VDS), we can conclude the following: Plate velocity dropping promotes the extension inside the slab by decreasing the movement of the surface plate, whereas slab pull increases as subduction continues. The timing of the subducting slab break-off and the polarity alteration was initiated earlier in the plate velocity drop models than in the constant plate velocity models, and fast convergence may have triggered multiple episodes of slab break-off and caused strong deformation adjacent to the collision zone. Parametric tests of the initial subducting angle, plate convergence velocity, and continental crustal rheological strength in VDS indicated the following: (1) Three end members of the continental lithospheric mantle deformation modes were identified from the VDS; (2) models with a low subducting angle, fast continental convergence velocity, and medium-strength overriding crust were more likely to evolve into a polarity reversed mode, whereas steep-subducting-angle, slow-plate-velocity, weak-overriding-crust models tended toward a two-sided mode; (3) a strong overriding continent is more liable to develop a stable mode; and (4) overriding crustal rheological strength plays a significant role in controlling changes in continental collision modes.
Construction and Validation of a Ferroptosis-Related lncRNA Signature as a Novel Biomarker for Prognosis, Immunotherapy and Targeted Therapy in Hepatocellular Carcinoma

Recently, immunotherapy combined with targeted therapy has significantly prolonged the survival time and improved the quality of life of patients with hepatocellular carcinoma (HCC). However, HCC treatment remains challenging due to the high heterogeneity of this malignancy. Sorafenib, the first-line drug for the treatment of HCC, can inhibit the progression of HCC by inducing ferroptosis. Ferroptosis is associated with the formation of an immunosuppressive microenvironment in tumours. Moreover, long non-coding RNAs (lncRNAs) are strongly associated with ferroptosis and the progression of HCC. Discovery of ferroptosis-related lncRNAs (FR-lncRNAs) is critical for predicting prognosis and the effectiveness of immunotherapy and targeted therapies to improve the quality and duration of survival of HCC patients. Herein, all cases from The Cancer Genome Atlas (TCGA) database were divided into training and testing groups at a 6:4 ratio to construct and validate the lncRNA signatures. Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression analyses were used to screen the six FR-lncRNAs (including MKLN1-AS, LINC01224, LNCSRLR, LINC01063, PRRT3-AS1, and POLH-AS1). Kaplan–Meier (K–M) and receiver operating characteristic (ROC) curve analyses demonstrated the optimal predictive prognostic ability of the signature. Furthermore, a nomogram indicated favourable discrimination and consistency. For further validation, we used real-time quantitative polymerase chain reaction (qRT-PCR) to analyse the expression of LNCSRLR, LINC01063, PRRT3-AS1, and POLH-AS1 in HCC tissues. Moreover, we determined the ability of the signature to predict the effects of immunotherapy and targeted therapy in patients with HCC. Gene set enrichment analysis (GSEA) and somatic mutation analysis showed that ferroptosis-related pathways, immune-related pathways, and TP53 mutations may be strongly associated with the overall survival (OS) outcomes of HCC patients. Overall, our study suggests that a new risk model of six FR-lncRNAs has a significant prognostic value for HCC and that it could contribute to precise and individualised HCC treatment.
Protection Duration of COVID-19 Vaccines: Waning Effectiveness and Future Perspective

The coronavirus disease 2019 (COVID-19) vaccines have very successfully decreased the disease risk as we know; some key information remains unknown due to the short development history and the lack of long-term follow-up studies in vaccinated populations. One of the unanswered issues is the protection duration conferred after COVID-19 vaccination, which appears to play a pivotal role in the future impact of pathogens and is critical to inform the public health response and policy decisions. Here, we review current information on the long-term effectiveness of different COVID-19 vaccines, persistence of immunogenicity, and gaps in knowledge. Meanwhile, we also discuss the influencing factors and future study prospects on this topic.
Establishing a Prognostic Model Based on Ulceration and Immune Related Genes in Melanoma Patients and Identification of EIF3B as a Therapeutic Target

Ulceration and immune status are independent prognostic factors for survival in melanoma patients. Herein univariate Cox regression analysis revealed 53 ulcer-immunity-related DEGs. We performed consensus clustering to divide The Cancer Genome Atlas (TCGA) cohort (n = 467) into three subtypes with different prognosis and biological functions, followed by validation in three merged Gene Expression Omnibus (GEO) cohorts (n = 399). Multiomics approach was used to assess differences among the subtypes. Cluster 3 showed relatively lesser amplification and expression of immune checkpoint genes. Moreover, Cluster 3 lacked immune-related pathways and immune cell infiltration, and had higher proportion of non-responders to immunotherapy. We also constructed a prognostic model based on ulceration and immune related genes in melanoma. EIF3B was a hub gene in the intersection between genes specific to Cluster 3 and those pivotal for melanoma growth (DepMap, https://depmap.org/portal/download/). High EIF3B expression in TCGA and GEO datasets was related to worst prognosis. In vitro models revealed that EIF3B knockdown inhibited melanoma cell migration and invasion, and decreased TGF-β1 level in supernatant compared with si-NC cells. EIF3B expression was negatively correlated with immune-related signaling pathways, immune cell gene signatures, and immune checkpoint gene expression. Moreover, its low expression could predict partial response to anti-PD-1 immunotherapy. To summarize, we established a prognostic model for melanoma and identified the role of EIF3B in melanoma progression and immunotherapy resistance development.
Edge Computing Application, Architecture, and Challenges in Ubiquitous Power Internet of Things

The Ubiquitous Power Internet of Things (UPIoT) is a concrete manifestation of the Internet of things (IoT) in the power industry, which is a deep integration of the interconnected power network and communication network, realizing full perception of the system status and full business penetration in all links of power production, transmission, and consumption. The introduction of edge computing in UPIoT fully meets the requirements of rapid response, real-time perception, and to some extent, privacy protection. However, there is currently no comprehensive investigation on the application of edge computing technology in UPIoT. First, this paper introduces the development background and construction of UPIoT and its technical architecture. Then the challenges faced by UPIoT in the process of construction are analyzed. Furthermore, the paper elaborates on the functions and features of edge computing, proposes that the support of edge computing technology can solve the challenges of efficient, fast, and secure processing of massive edge data faced by the traditional cloud-based centralized big data processing technology of UPIoT, and analyzes the architecture of the edge computing-assisted UPIoT. For the three typical scenarios of UPIoT, namely power monitoring system, smart energy system and power metering system, the edge computing architecture of the three scenarios are analyzed, and the specific application methods and roles played by edge computing in the three scenarios are also elaborated. Finally, we discuss the challenges of edge computing in UPIoT, in terms of policy challenges, market challenges, and technical challenges, as well as outline the outlooks of the technical challenges.
Development and Validation of a Competitive Risk Model in Elderly Patients With Chromophobe Cell Renal Carcinoma: A Population-Based Study

Background Renal cell carcinoma (RCC) is the most common renal malignancy in adults, and chromophobe renal cell carcinoma (chRCC) is the third most common subtype of RCC. We aimed to construct a competitive risk model to predict cancer-specific survival (CSS) in elderly patients with chRCC. Methods The clinicopathological information of the patients was downloaded from the SEER database, and the patients were randomly divided into the training and validation cohorts. Patients' risk factors for cancer-specific death (CSM) were analyzed using proportional subdistribution hazard (SH). We constructed a competitive risk model to predict the CSS of elderly chRCC patients. Consistency index (C-index), the area under receiver operating curve (AUC), and a calibration curve were used to validate the model's accuracy. Decision curve analysis (DCA) was used to test the clinical value of the model. Results A total of 3,522 elderly patients with chRCC were included in the analysis. Patients were randomly assigned to either the training cohort (N = 2,474) or the validation cohort (N = 1,048). SH analysis found that age, race, T, N, and M stage, tumor size, and surgery were risk factors for CSM. We constructed a competitive risk model to predict patients' CSS. In the training set, the model predicted patients' 1-, 3-, and 5-year CSS with C-indices of 82.2, 80.8, and 78.2, respectively. The model predicted patient 1-, 3-, and 5-year CSS in the validation cohort with C-indices of 84.7, 83.4, and 76.9, respectively. The calibration curve showed that the model's predicted value is almost consistent with the observed value, which indicated that the model has good accuracy. The AUC of the training set and validation queue also suggested that the model has good discrimination. The clinical utility of the DCA model in predicting patients' CSS is higher than that of traditional TNM staging. Conclusions We constructed a competitive risk model to predict CSS in elderly patients with chRCC. The model has good accuracy and reliability, which can help doctors and patients to make clinical decisions and follow-up strategies.
Insights Into the Species-Specific Microbiota of Greenideinae (Hemiptera: Aphididae) With Evidence of Phylosymbiosis

Aphids and their symbionts represent an outstanding model for studies of insect–symbiont interactions. The aphid microbiota can be shaped by aphid species, geography and host plants. However, the relative importance of phylogenetic and ecological factors in shaping microbial community structures is not well understood. Using Illumina sequencing of the V3–V4 hypervariable region of the 16S rRNA gene, we characterized the microbial compositions of 215 aphid colonies representing 53 species of the aphid subfamily Greenideinae from different regions and plants in China, Nepal, and Vietnam. The primary endosymbiont Buchnera aphidicola and secondary symbiont Serratia symbiotica dominated the microbiota of Greenideinae. We simultaneously explored the relative contribution of host identity (i.e., aphid genus and aphid species), geography and host plant to the structures of bacterial, symbiont and secondary symbiont communities. Ordination analyses and statistical tests highlighted the strongest impact of aphid species on the microbial flora in Greenideinae. Furthermore, we found a phylosymbiosis pattern in natural Greenideinae populations, in which the aphid phylogeny was positively correlated with microbial community dissimilarities. These findings will advance our knowledge of host-associated microbiota assembly across both host phylogenetic and ecological contexts.