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Background: The structure modification of steroids is commonly used to change the biological activity of steroids in medicinal chemistry. In recent years, it has been found that some derivatives derived from the structural modification of cholesterol display good inhibitory activity against tumor cell proliferation in vitro. Methods: Using cholesterol as the starting material, different types of B-norcholesterol-6-amide derivatives were synthesized by the reaction of 6-carboxyl-B-norcholesterol with different alkyl amines or 6-amino-B-norcholesterol with different acyl chlorides. The inhibitory activity of compounds on the proliferation of tumor cell lines was investigated by the MTT method. Results: The results showed that the B-norcholesterol-6-amide compounds displayed distinct cytotoxicity against Sk-Ov-3 cells but caused no obvious damage against HEK-293T cells. Additionally, the steroidal amide derivatives formed from 6-amino-B-norcholesterol showed stronger cytotoxicity than those produced from 6-carboxyl-B-norcholesterol. Specially, compounds with chloroalkyl structure displayed significant inhibitory activity against all tumor cells tested. Among them, compounds 19-21 showed cytotoxicity like 2-methoxyestradiol as a positive control, and the IC50 value of compound 20 on HeLa cells was 3.9 μM. Conclusion: After introducing chloroalkyl acyl groups into 6-position of 6-amino-B-norcholesterol, the cytotoxicity of resulting B-norcholesterol-6-amide compounds can be greatly enhanced.

Background: The effect of imaging selection paradigms on endovascular thrombectomy outcomes in patients with acute ischemic stroke with large vessel occlusion remains uncertain. The study aimed to assess the effect of basic imaging (noncontrast computed tomography with or without computed tomographic angiography) versus advanced imaging (magnetic resonance imaging or computed tomography perfusion) on clinical outcomes following thrombectomy in patients with stroke with large vessel occlusion in the early and extended windows using a pooled analysis of patient-level data from 2 pivotal randomized clinical trials done in China. Methods: This post hoc analysis used data from 1182 patients included in 2 multicenter, randomized controlled trials in China that evaluated adjunct therapies to endovascular treatment for acute ischemic stroke (Direct Endovascular Treatment for Large Artery Anterior Circulation Stroke performed from May 20, 2018, through May 2, 2020, and Intravenous Tirofiban Before Endovascular Treatment in Stroke from October 10, 2018, through October 31, 2021). Patients with occlusion of the intracranial internal carotid artery or proximal middle cerebral artery (M1/M2 segments) were categorized according to baseline imaging modality (basic versus advanced) as well as treatment time window (early, 0–6 hours versus extended, 6–24 hours from last known well to puncture). The primary outcome was the proportion of patients with functional independence (modified Rankin Scale score of 0–2) at 90 days. Multivariable Poisson regression analysis was performed to determine the association between imaging selection modality and outcomes after endovascular treatment at each time windows. Results: A total of 1182 patients were included in this cohort analysis, with 648 in the early (471 with basic imaging versus 177 advanced imaging) and 534 in the extended (222 basic imaging versus 312 advanced imaging) time window. There were no differences in 90-day functional independence between the advanced and basic imaging groups in either time windows (early window: adjusted relative risk, 0.99 [95% CI, 0.84–1.16]; P =0.91; extended window: adjusted relative risk, 1.00 [95% CI, 0.84–1.20]; P =0.97). Conclusions: In this post hoc analysis of 2 randomized clinical trial pooled data involving patients with large vessel occlusion stroke, an association between imaging selection modality and clinical or safety outcomes for patients undergoing thrombectomy in either the early or extended windows was not detected. Our study adds to the growing body of literature on simpler imaging paradigms to assess thrombectomy eligibility across both the early and extended time windows. Registration: URL: http://www.chictr.org.cn ; Unique identifiers: ChiCTR-IOR-17013568 and ChiCTR-INR-17014167.

We propose a Vision-Language Transformer (VLT) framework for referring segmentation to facilitate deep interactions among multi-modal information and enhance the holistic understanding to vision-language features. There are different ways to understand the dynamic emphasis of a language expression, especially when interacting with the image. However, the learned queries in existing transformer works are fixed after training, which cannot cope with the randomness and huge diversity of the language expressions. To address this issue, we propose a Query Generation Module, which dynamically produces multiple sets of input-specific queries to represent the diverse comprehensions of language expression. To find the best among these diverse comprehensions, so as to generate a better mask, we propose a Query Balance Module to selectively fuse the corresponding responses of the set of queries. Furthermore, to enhance the model's ability in dealing with diverse language expressions, we consider inter-sample learning to explicitly endow the model with knowledge of understanding different language expressions to the same object. We introduce masked contrastive learning to narrow down the features of different expressions for the same target object while distinguishing the features of different objects. The proposed approach is lightweight and achieves new state-of-the-art referring segmentation results consistently on five datasets., TPAMI

Background: Depression is one of the most common symptoms in patients with dementia. Objective: This meta-analysis aimed to evaluate the effect of light therapy on depression associated with dementia by using a single scale. Methods: Published studies based on the terms including “Dementia”, “depression”, and “Phototherapy” were searched. Web of Science, PubMed, Embase, CiNii, CNKI, Wanfang Database, and China Biology Medicine disc were adopted to collect randomized controlled studies or cross-controlled studies using the Cornell Dementia Depression Scale (CSDD) until February 2022. GRADE and Review Manager Version 5.4.1 were employed to assess the risk of bias. A meta-analysis was conducted by R 4.0.2 software based on the changes in CSDD scores. Results: A total of 1,055 studies were retrieved from the databases, and six studies were included after screening. Some 406 people with dementia were included with an average age of over 80 years. Forest plot results showed that light intervention improved depression scores of dementia patients (MD = –2.59, 95% CI: –4.46 to –0.71), and light intensity less than 1000 lux improved depression symptoms of dementia patients (MD = –2.76, 95% CI: –4.55 to –0.97). An intervention that lasted 8 to 12 weeks was the most effective (MD = –3.77, 95% CI: –6.93 to –0.60), and non-stable interventions such as ceiling LED lights exerted more positive effects (MD = –2.12, 95% CI: –3.38 to –0.85). Conclusion: The overall results of the meta-analysis suggested that light intervention can improve the depressive symptoms of older patients with dementia.

The “3 + 2” segmented training between higher vocational colleges and applied undergraduate courses has opened up the rising channel of vocational education from junior college level to undergraduate level, and promoted the organic connection between higher vocational colleges and Universities of Applied Sciences. It is one of the important ways to establish a modern vocational education system. Exploring the monitoring mechanism of talent training quality is an important measure to ensure the achievement of the segmented training goal, and it is a necessary condition to successfully train high-quality skilled applied talents. The talent training quality evaluation of segmented education is viewed as multiple attribute decision-making (MADM) issue. In this paper, an extended probabilistic simplified neutrosophic number GRA (PSNN-GRA) method is established for talent training quality evaluation of segmented education. The PSNN-GRA method integrated with CRITIC method in probabilistic simplified neutrosophic sets (PSNSs) circumstance is applied to rank the optional alternatives and a numerical example for talent training quality evaluation of segmented education is used to proof the newly proposed method’s practicability along with the comparison with other methods. The results display that the approach is uncomplicated, valid and simple to compute.

To relieve the stress of sewage sludge (SS) disposal and effectively increase the use of renewable energy, a novel renewable-energy microgrid system (REMS) was developed, specifically designed to integrate a wind power plant (WPP) with energy storage and the SS pyrolysis process for heat and power generation. Based on a lab-scale pyrolysis experiment and 7-day numerical analysis, we studied the energy-recycling potential of SS and simulated the operational behaviours of REMS. According to the results, the calorific values of the pyrolytic gaseous and liquid products were better than those of the raw material, at 16.19 and 33.53 MJ/kg, respectively. The proposed REMS performed well in power supply and energy utilization with a design performance index of 99.23 when the WPP capacity was 200 MWe and the initial wind-energy curtailment rate was 30%. It indicates that by converting SS into flammable gas, condensable liquid and carbon-rich solid residue, curtailed wind energy could be saved and the synergy between wind power and the SS pyrolysis process enabled the proposed microgrid system to effectively utilize renewable energy and provide reliable on-demand power service. The REMS installed with a 155-MWe WPP achieved the optimal design in system performance, environmental benefit and construction cost under the initial wind-curtailment rate of 34.12%. The design scheme makes REMS capable of satisfying the 15.10-GWh power demand of end users and the 1700-t/day SS disposal need, and the curtailed wind energy could be reduced to zero.

For the whole environmental settings in this research, the conventional affective brain-computer interactions can not build a good performance on energy-efficient resource of network’s forwarding ports and routing paths due to its poor allocation function of cognitive radio networks, based on the novel interactive networking architecture, the model of non-linear iterative prediction scheme in interaction was successively proposed. This research proposes a modified LSTM algorithm with a structure of non-linear iterative in complexity prediction, joins the multiple k modes selection and multi-agent systems, maximizes EERA of forwarding and routing while maintaining the communication quality. Firstly, considering whether this affective brain-computer interactions need the networking communication in system. Secondly, adjusting the forwarding and routing factors of energy-efficient resource allocation by selecting the best optimal energy-efficient resource for the links through the non-linear iterative prediction in a multi-modal perception. The simulation results show that compared with the other models and algorithms, the proposed scheme for affective brain-computer interactions, which has a nice performance on a higher EERA and channel utilization of a networking architecture of brain-computer interactions. &nbsp

Tyrosine kinase inhibitor (TKI) is a standard treatment for patients with NSCLC harboring constitutively active epidermal growth factor receptor (EGFR) mutations. However, most rare EGFR mutations lack treatment regimens except for the well-studied ones. We constructed two EGFR variant libraries containing substitutions, deletions, or insertions using the saturation mutagenesis method. All the variants were located in the EGFR mutation hotspot (exons 18-21). The sensitivity of these variants to afatinib, erlotinib, gefitinib, icotinib, and osimertinib was systematically studied by determining their enrichment in massively parallel cytotoxicity assays using an endogenous EGFR-depleted cell line. A total of 3914 and 70,475 variants were detected in the constructed EGFR Substitution-Deletion (Sub-Del) and exon 20 Insertion (Ins) libraries. Of the 3914 Sub-Del variants, 221 proliferated fast in the control assay and were sensitive to EGFR-TKIs. For the 70,475 Ins variants, insertions at amino acid positions 770-774 were highly enriched in all 5 TKI cytotoxicity assays. Moreover, the top 5% of the enriched insertion variants included a glycine or serine insertion at high frequency. We present a comprehensive reference for the sensitivity of EGFR variants to five commonly used TKIs. The approach used here should be applicable to other genes and targeted drugs. BACKGROUND: Tyrosine kinase inhibitors (TKIs) therapy is a standard treatment for patients with advanced non-small-cell lung carcinoma (NSCLC) when activating epidermal growth factor receptor (EGFR) mutations are detected. However, except for the well-studied EGFR mutations, most EGFR mutations lack treatment regimens. TRANSLATIONAL SIGNIFICANCE: The results demonstrated that patients with rare EGFR mutations were most likely to benefit from osimertinib therapy compared to afatinib, erlotinib, gefitinib, or icotinib therapy. This study provides a case of deep mutational scanning that simultaneously assayed substitution, deletion, and insertion variants. This approach is applicable for other oncogenes and targeted drugs.

Lakes act as one of the reservoirs and dispersal routes of antibiotic resistance genes (ARGs) and pathogenic resistant bacteria in aquatic environments. Previous studies reported the occurrence and distribution of ARGs in lakes worldwide; however, few investigated the biogeography and diversity patterns of antibiotic resistome in the environment. To fill this gap, a large-scale data set of sediment metagenomes was collected from globally distributed lakes and characterized comprehensively using metagenomic assembly-based analysis, aiming to shed light on the biogeography and diversity patterns of ARGs in lake ecosystems from a global perspective. Our analyses showed that abundant and diverse ARGs were found in the global lake sediments, including a set of emerging ARGs such as mcr-type and carbapenem-resistant Enterobacteriaceae related genes. Most of the identified ARGs were generally associated with the commonly used antibiotics, suggesting the role of increasing antibiotic consumptions on the resistome prevalence. Spatially, the composition and diversity of ARGs varied across geographical distances and exhibited a scale-dependent distance-decay relationship. Notably, the composition of ARGs was largely shaped by bacterial community structure, and their diversities were co-governed by stochastic process (∼48%) and deterministic process (∼52%). Findings provide a valuable insight to better understand ecological mechanisms of ARGs in lake ecosystems and have important implication for the prevention and control of resistome risk.

Proteolytic targeting chimera is a class of heterobifunctional molecules that selectively degrades target proteins through the ubiquitin-proteasome system.Protein degradation targeting chimeras can remove some mutated and overexpressed pathogenic proteins.Compared with traditional antitumor drugs, it has good targeting, drug resistance and high degradability, which provides a new idea for the treatment of malignant tumors and has a wide range of application prospects and development space.In recent years, Proteolytic targeting chimera technology has gradually matured in the research and development of anti-tumor drugs, and two Proteolytic targeting chimera small molecule drugs have passed the phase I clinical trial.Based on the existing literature, this paper briefly describes the progress of Proteolytic targeting chimera in anti-tumor research and development, as well as the opportunities and challenges facing it.

Contemporarily, digital art has swiftly integrated into human beings’ daily life. While digital art is becoming increasingly popular and significantly influences conventional art, currently it is needed to be more online communities where art enthusiasts and artists may interact and offer a self-sustaining means of payment. Independent art programs based on ideas like reciprocity are what E-Gallery Art aspires to. Its goal is to encourage teamwork and co-creation in the visual arts. This research discusses the way to create decentralized Dapps based on blockchain that are mutually advantageous for platforms, artists, and users. The Dapp follows a process where users reward artists by donating, and the best-case scenario is that the artist is motivated and contributes more to the community. Users can create galleries and display their work for free, which is an important starting point for E-Gallery Art Dapp to start a self-sustainable cycle. These results shed light on guiding further exploration of trading security design based on blockchain techniques.

The article presents Deep Coherent Point Drift (DeepCPD), a neural-network approach for estimating the six-degrees-of-freedom pose of noncooperative spacecraft during autonomous rendezvous and docking through point cloud data. The method registers unorganized scan point clouds with their reference model point clouds. DeepCPD replaces the Expectation-Maximization procedure in the Gaussian Mixture Model registration algorithm with a neural network that learns point-to-component correspondence, achieving better estimation performance and acceleration of the registration process. The proposed method is also robust to perturbation, corruption, occlusion, and distance, as validated by simulated experimental results. Our code will be available at https://github.com/Zhang-CV/DeepCPD .