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Background: Lethal multiple pterygium syndrome (LMPS) is a rare autosomal recessive inherited disorder typically characterized by intrauterine growth retardation, multiple pterygia, and flexion contractures.Case presentation: We herein report a Chinese case with a history of three adverse pregnancies demonstrating the same ultrasonic phenotypes, including increased nuchal translucency, edema, fetal neck cystoma, reduced movement, joint contractures, and other congenital features. Whole-exome sequencing (WES) revealed novel compound heterozygous variants in the CHRNA1 gene NM_000079.4: c.[1128delG (p.Pro377LeufsTer10)]; [505T>C (p.Trp169Arg)] in the recruited individual, and subsequent familial segregation showed that both parents transmitted their respective mutation.Conclusion: For the first time, we identified an association between the CHRNA1 gene and the recurrent lethal multiple pterygium syndrome (LMPS) in a Chinese family. This finding may also enrich the mutation spectrum of the CHRNA1 gene and promote the applications of WES technology in etiologic diagnosis of ultrasound anomalies in prenatal examination.

IntroductionChildhood systemic lupus erythematosus (cSLE) is a complex multisystem autoimmune disease. In 2019, belimumab was approved for the clinical treatment for cSLE, making it the only biological agent approved for cSLE children aged 5 and older in 60 years.ObjectiveTo review emerging evidence on belimumab in cSLE published up to April 2022, so as to provide information for clinical decision-making.MethodA comprehensive search of relevant publications up to the date of April 2022 in PUBMED, EMBASE, WOS, COCHRANE, ClinicalTrials.gov, CBM, CNKI and WANFANG was performed using the following criteria: (a) English and Chinese language studies; (b) RCT studies, cohort studies, or case-control studies; (c) patients with age ResultsFive publications met the inclusion/exclusion criteria for cSLE: one randomized controlled trial, one retrospective cohort study, and three case series. There was a high degree of heterogeneity among several studies, and the availability of baseline and outcome data provided was uneven.ConclusionAt present, there is a lack of high-quality clinical trials of belimumab in the treatment of cSLE. Based on the current research, it is believed that the use of belimumab can inhibit cSLE activity, reduce the dose of corticosteroids and immunosuppressants, and delay kidney damage. Also it shows clinical benefit in alleviating symptoms of monogenic cSLE refractory to standard therapy. More studies are urgently needed to validate the clinical efficacy of belimumab in cSLE and to evaluate its long-term safety in pediatric populations to promote evidence-based practice.

Background The CIPKs are a group of plant-specific Ser/Thr protein kinases acting in response to calcium signaling, which plays an important role in the physiological and developmental adaptation of plants to adverse environments. However, the functions of halophyte-derived CIPKs are still poorly understood, that limits a potential application of CIPKs from halophytes for improving the tolerance of glycophytes to abiotic stresses. Results In this study, we characterized the NtCIPK11 gene from the halophyte Nitraria tangutorum and subsequently analyzed its role in salt and drought stress tolerance, using Arabidopsis as a transgenic model system. NtCIPK11 expression was upregulated in N. tangutorum root, stem and blade tissues after salt or drought treatment. Overexpressing NtCIPK11 in Arabidopsis improved seed germination on medium containing different levels of NaCl. Moreover, the transgenic plants grew more vigorously under salt stress and developed longer roots under salt or drought conditions than the WT plants. Furthermore, NtCIPK11 overexpression altered the transcription of genes encoding key enzymes involved in proline metabolism in Arabidopsis exposed to salinity, however, which genes showed a relatively weak expression in the transgenic Arabidopsis undergoing mannitol treatment, a situation that mimics drought stress. Besides, the proline significantly accumulated in NtCIPK11-overexpressing plants compared with WT under NaCl treatment, but that was not observed in the transgenic plants under drought stress caused by mannitol application. Conclusions We conclude that NtCIPK11 promotes plant growth and mitigates damage associated with salt stress by regulating the expression of genes controlling proline accumulation. These results extend our understanding on the function of halophyte-derived CIPK genes and suggest that NtCIPK11 can serve as a candidate gene for improving the salt and drought tolerance of glycophytes through genetic engineering.

In this study, it is shown that with the increase of the number of layers of ultrathin MoS2 nanomaterials, the spacing of Raman shift increased and the photoluminescence intensity decreased. The mo...

With increasing deployment of optimization and Artificial Intelligence (AI) to assist high-stake real life decisions, fairness has become an essential factor of consideration for both the designers and users of these tools. This dissertation studies new approaches for formulating, attaining and eliciting fairness. Chapter one begins with a brief introduction of the background on fairness and a selection of common fairness measures. Chapter two studies balancing fairness and efficiency through optimization models. We propose new social welfare functions (SWFs) as combined measures of two well-known criteria, Rawlsian leximax fairness and utilitarianism. We then design a procedure to sequentially maximize these SWFs with mixed integer/linear programming models to find socially optimal solutions. This approach has practical potentials on a wide range of resource allocation applications, and is demonstrated on realistic size applications in healthcare provision and shelter assignment for disaster preparation. Chapter three considers an optimization task motivated by fair machine learning (ML). When developing fair ML algorithms, it is useful to understand the computational costs of fairness in comparison to the standard non-fair setting. For fair ML methods that utilize optimization models for training, specialized optimization algorithms have potentials to offer better computational performances than generic solvers. In this chapter, I explore this question for support vector machines (SVMs), and design block coordinate descent type algorithms to train SVMs containing linear fairness constraints. Numerical experiments highlight that the new specialized algorithms are more efficient than an off-the-shelf solver for training fair SVMs. Chapter four examines social welfare optimization as a general paradigm for formalizing welfare-based fairness in AI systems. Contrary to commonly used statistical bias metrics in fair AI, optimizing a social welfare objective supports broader perspective on fairness motivated by distributive justice considerations. We propose in-processing and post-processing integration schemes between social welfare optimization and AI, in particular, ML and rule-based AI. We implement and evaluate the integration schemes on a simulated loan processing instance. The empirical results demonstrate the advantages of the proposed integration strategies. We conclude this chapter by highlighting research directions to pursue for a holistic view of welfare-based fairness. The next two chapters explore the human-centric perspective to elicit people’s moral values through preference learning. Chapter five studies a general preference learning framework based on online learning (OL) from revealed preferences: a learner learns an agent’s private utility function through interactions in a changing environment. Through designing a new convex loss function, wedesign a flexible OL framework that enables a unified treatment of usual loss functions from literature and supports a variety of online convex optimization algorithms. This framework has advantages in regret performance and solution time over other OL algorithms from the literature. Lastly, chapter six explores a moral decision-making inspired task. This chapter considers the modelling and elicitation of people’s dynamic ethical judgments in the sequential allocation of resources. We utilize a Markov Decision Process model to represent a sequential allocation task, where the state rewards capture people’s moral preferences, thus people’s ethical judgments are reflected via policy rewards. We design a preference inference model which relies on active preference-based reward learning to infer the unknown reward function. The learning framework is applied in human-subject experiments on Amazon Mechanical Turk to understand people’s moral reasoning in a hypothetical scenario of allocating scarce medical resources.