The complete chloroplast genome of Semiaquilegia danxiashanensis (Ranunculaceae), a rare species endemic to Danxia landform in Guangdong, China.

Semiaquilegia danxiashanensis is currently known only from the type locality, Danxia Mountain, characterized by its spectacular red sandstone cliffscape. In this study, we assembled the complete chloroplast genome sequence of S. danxiashanensis and inferred its phylogenetic relationships. Total length of the chloroplast genome was 160,548 bp, with an overall GC content of 39%. The chloroplast genome had typical quadripartite structure and contained one LSC region (89,882 bp) and one SSC region (17,386 bp), which were separated by two IRs regions (26,640 bp, respectively). It comprised 133 genes, including 84 protein coding genes, 41 tRNA genes and eight rRNA genes. The maximum likelihood phylogenetic analysis indicated that S. danxiashanensis was sister to S. adoxoides; meanwhile, Semiaquilegia was closely related to both Urophysa and Aquilegia in Ranunculaceae. This study sheds light on the evolutionary history of Semiaquilegia and provides preliminary data for future comparative analysis of chloroplast genomes.

Dysregulated arginine metabolism in precursor B-cell acute lymphoblastic leukemia in children: a metabolomic study.

BACKGROUND: Precursor B-cell acute lymphoblastic leukemia (B-ALL) is the most common cancers in children. Failure of induction chemotherapy is a major factor leading to relapse and death in children with B-ALL. Given the importance of altered metabolites in the carcinogenesis of pediatric B-ALL, studying the metabolic profile of children with B-ALL during induction chemotherapy and in different minimal residual disease (MRD) status may contribute to the management of pediatric B-ALL. METHODS: We collected paired peripheral blood plasma samples from children with B-ALL at pre- and post-induction chemotherapy and analyzed the metabolomic profiling of these samples by ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS). Healthy children were included as controls. We selected metabolites that were depleted in pediatric B-ALL and analyzed the concentrations in pediatric B-ALL samples. In vitro, we study the effects of the selected metabolites on the viability of ALL cell lines and the sensitivity to conventional chemotherapeutic agents in ALL cell lines. RESULTS: Forty-four metabolites were identified with different levels between groups. KEGG pathway enrichment analyses revealed that dysregulated linoleic acid (LA) metabolism and arginine (Arg) biosynthesis were closely associated with pediatric B-ALL. We confirmed that LA and Arg were decreased in pediatric B-ALL samples. The treatment of LA and Arg inhibited the viability of NALM-6 and RS4;11 cells in a dose-dependent manner, respectively. Moreover, Arg increased the sensitivity of B-ALL cells to L-asparaginase and daunorubicin. CONCLUSION: Arginine increases the sensitivity of B-ALL cells to the conventional chemotherapeutic drugs L-asparaginase and daunorubicin. This may represent a promising therapeutic approach.

An Accelerated Adaptive Gain Design in Stochastic Learning Control.

This study investigates the trajectory tracking problem for stochastic systems and proposes a novel adaptive gain design to enhance the transient convergence performance of the learning control scheme. Differing from the existing results that mainly focused on gain's transition from constant to decreasing ones to suppress noise influence, this study leverages the adaptive mechanisms based on noisy signals to achieve an acceleration capability by addressing diverse performance at different time instants throughout the operation interval. Specifically, an additional gain matrix is introduced into the adaptive gain design to further enhance transient convergence performance. An iterative learning control approach with such a gain design is proposed to realize high precision tracking and it is proven that the input error generated by the newly proposed learning control scheme converges almost surely to zero. The effectiveness of the proposed scheme and its improvement on the transient performance of the learning process are numerically validated.

FOXM1 affects oxidative stress, mitochondrial function, and the DNA damage response by regulating p21 in aging oocytes.

Fertilization capacity and embryo survival rate are decreased in postovulatory aging oocytes, which results in a reduced reproductive rate in female animals. However, the key regulatory genes and related regulatory mechanisms involved in the process of postovulatory aging in oocytes remain unclear. In this study, RNA-Seq revealed that 3237 genes were differentially expressed in porcine oocytes between the MII and aging stages (MII + 24 h). The expression level of FOXM1 was increased at the aging stage, and FOXM1 was also observed to be enriched in many key biological processes, such as cell senescence, response to oxidative stress, and transcription, during porcine oocyte aging. Previous studies have shown that FOXM1 is involved in the regulation of various biological processes, such as oxidative stress, DNA damage repair, mitochondrial function, and cellular senescence, which suggests that FOXM1 may play a crucial role in the process of postovulatory aging. Therefore, in this study, we investigated the effects and mechanisms of FOXM1 on oxidative stress, mitochondrial function, DNA damage, and apoptosis during oocyte aging. Our study revealed that aging oocytes exhibited significantly increased ROS levels and significantly decreased GSH, SOD, T-AOC, and CAT levels than did oocytes at the MII stage and that FOXM1 inhibition exacerbated the changes in these levels in aging oocytes. In addition, FOXM1 inhibition increased the levels of DNA damage, apoptosis, and cell senescence in aging oocytes. A p21 inhibitor alleviated the effects of FOXM1 inhibition on oxidative stress, mitochondrial function, and DNA damage and thus alleviated the degree of senescence in aging oocytes. These results indicate that FOXM1 plays a crucial role in porcine oocyte aging. This study contributes to the understanding of the function and mechanism of FOXM1 during porcine oocyte aging and provides a theoretical basis for preventing oocyte aging and optimizing conditions for the in vitro culture of oocytes.

Parent-adolescent family resilience and psychological adjustment in chronically ill adolescents: An actor-partner interdependence mediation model.

AIMS: This study explored the relationships between family resilience, dyadic coping and psychological adjustment among adolescents with chronic illnesses and their parents. The actor-partner interdependence mediation model was used to validate the mediating role of dyadic coping in the relationship between family resilience and psychological adjustment. DESIGN: This is a cross-sectional study. METHODS: A total of 318 parent-adolescents dyads were recruited from three paediatric hospitals in Wenzhou, Hangzhou, Shanghai city, China, between June 2022 and August 2023. The parents had a mean age of 41.62 years, and the adolescents had a mean age of 12.66 years. Participants independently completed a self-report questionnaire assessed family resilience, dyadic coping and psychological adjustment. Data analysis was conducted using the actor-partner interdependence mediation model. RESULTS: The findings suggest that in the actor effects, family resilience directly influenced psychological adjustment, and family resilience is related to psychological adjustment through positive dyadic coping. In the partner effect, parents' family resilience influenced adolescents' psychological adjustment through the parents' positive dyadic coping. Similarly, adolescents' family resilience influenced parents' psychological adjustment through both parents' positive dyadic coping and adolescents' negative dyadic coping. Additionally, there was a partner effect between parents' family resilience and adolescents' psychological adjustment. CONCLUSION: This study demonstrated the importance of developing effective dyadic interventions based on family resilience or positive dyadic coping strategies to improve the mental health of adolescents with chronic illnesses and their parents. IMPACT: The mediating role of dyadic coping in the relationship between family resilience and psychological adjustment among adolescents with chronic illnesses and their parents was demonstrated. Future psychosocial interventions should focus on increasing parents' positive dyadic coping strategies and improving adolescents' negative dyadic coping strategies. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Histone deacetylase inhibitor and PD­1 blockade synergistically inhibit B­cell lymphoma progression in mice model by promoting T­cell infiltration and apoptosis.

B­cell lymphoma is difficult to cure because of its biological and clinical heterogeneity, and due to native chemoresistance. Immunotherapies that overcome cancer­induced immune evasion have been the center of recent developments in oncology. This is emphasized by the accomplishment of various agents that disrupt programmed cell death protein 1 (PD­1)­mediated immune suppression in diverse tumors. However, while PD­1 blockade has been effective in numerous malignancies, a significant proportion of cancers, including B­cell lymphoma, show certain rates of primary resistance to these therapeutic strategies. Histone deacetylase inhibitors (HDACis) have exhibited anticancer activity though suppressing cell proliferation, inducing differentiation and triggering apoptosis. The present study aimed to explore a therapeutic strategy combining a HDACi (romidepsin) and PD­1 blockade (BMS­1) in B­cell lymphoma, utilizing a constructed mouse model of B­cell lymphoma. The IC50 of the two inhibitors was confirmed by MTT assay, and their inhibitory effects were revealed to be dose­ and time­dependent. The data demonstrated that the combined treatment of romidepsin and BMS­1 synergistically inhibited the growth of B­cell lymphoma. Furthermore, it was revealed that romidepsin and BMS­1 synergistically triggered apoptosis in mouse B­cell lymphoma. The synergistic effect of these agents was capable of activating tumor­infiltrating lymphocytes, particularly CD3+CD4+ and CD3+CD8+ T cells. The results of the present study underscore the potential of HDAC inhibition in conjunction with PD­1 blockade as a novel therapeutic approach for B­cell lymphoma, highlighting the synergistic effects of these two mechanisms in enhancing antitumor immunity.

Hybrid chain reaction nanoscaffold-based functional nucleic acid nanomaterial cascaded with rolling circle amplification for signal enhanced miRNA let-7a detection.

A novel functional nucleic acid (FNA) nanomaterial based on hybrid chain reaction (HCR) nanoscaffolds is proposed to solve the problem of time superposition and repeated primer design in sensitive miRND detection using cascade amplification technique. Rolling circle amplification (RCA) was cascaded with the prepared FNA nanomaterials for miRNA let-7a (as a model target) sensitive detection by lateral flow assay (LFA). Under the optimal conditions, the proposed RCA-FNA-LFA assay demonstrated the specificity and accuracy for miRNA let-7a detection with a detection limit of 1.07 pM, which increased sensitivity by nearly 20 times compared with that of RCA -LFA assay. It is worth noting that the non-target-dependent self-assembly process of HCR nanoscaffolds does not take up the whole detection time, thus, less time is taken than that of the conventional cascaded method. Moreover, the proposed assay does not need to consider the system compatibility between two kinds of isothermal amplification techniques. As for detection of different miRNAs, only the homologous arm of the padlock probe of RCA needs to be changed, while the FNA nanomaterial does not need any change, which greatly simplifies the primer design of the cascaded amplification techniques. With further development, the proposed RCA-FNA-LFA assay might achieve more sensitive and faster results to better satisfy the requirements of clinical diagnosis combing with more sensitive labels or small strip reader.

Semantic memory-based dynamic neural network using memristive ternary CIM and CAM for 2D and 3D vision.

The brain is dynamic, associative, and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are static, unable to associate inputs with past experiences, and run on digital computers with physically separated memory and processing. We propose a hardware-software co-design, a semantic memory-based dynamic neural network using a memristor. The network associates incoming data with the past experience stored as semantic vectors. The network and the semantic memory are physically implemented on noise-robust ternary memristor-based computing-in-memory (CIM) and content-addressable memory (CAM) circuits, respectively. We validate our co-designs, using a 40-nm memristor macro, on ResNet and PointNet++ for classifying images and three-dimensional points from the MNIST and ModelNet datasets, which achieves not only accuracy on par with software but also a 48.1 and 15.9% reduction in computational budget. Moreover, it delivers a 77.6 and 93.3% reduction in energy consumption.

Photocatalytic deuterocarboxylation of alkynes with oxalate.

Herein, a catalytic photoredox-neutral strategy for alkyne deuterocarboxylation with tetrabutylammonium oxalate as the carbonyl source and D2O as the deuteration agent was described. For the first time, the oxalic salt acted as both the reductant and carbonyl source through single electron transfer and subsequential homolysis of the C-C bond. The strongly reductive CO2 radical anion species in situ generated from oxalate played significant roles in realizing the global deuterocarboxylation of terminal and internal alkynes to access various tetra- and tri-deuterated aryl propionic acids with high yields and deuteration ratios.

Chemical synthesis and immunological evaluation of cancer vaccines based on ganglioside antigens and α-galactosylceramide.

iNKT cells - often referred as the "Swiss Army knife" of the immune system - have emerged as central players in cancer vaccine therapies. Glycolipids activating iNKT cells, such as α-galactosylceramide (αGalCer), can enhance the immune response against co-delivered cancer antigens and have been applied in the design of self-adjuvanting anti-tumor vaccines. In this context, this work focuses on the chemical synthesis of ganglioside tumor-associated carbohydrate antigens (TACAs), namely GM3 and (Neu5Gc)GM3 antigens, their conjugation to αGalCer, and their formulation into liposomes as an efficient platform for their in vivo delivery. Liposomes containing GM3-αGalCer, (Neu5Gc)GM3-αGalCer, and equimolar amounts of the two conjugates have been fully characterized and their ability to activate iNKT cell has been confirmed ex vivo in mouse and human cell assays. The candidates were tested in in vivo immunization studies, demonstrating an ability to induce both TH1 and TH2 cytokines leading to the production of all subclasses of IgG antibodies. Notably, the study also demonstrated that serum antibodies raised against the two TACAs, alone and in combination, were cross-reactive. This finding has consequences for future vaccine designs - even if a highly tumor-selective antigen is chosen, the resulting antibody response may be broader than anticipated.

Recycling Spent Ternary Cathodes to Oxygen Evolution Catalysts for Pure Water Anion-Exchange Membrane Electrolysis.

Recycling spent lithium-ion batteries (LIBs) to efficient water-splitting electrocatalysts is a promising and sustainable technology route for green hydrogen production by renewables. In this work, a fluorinated ternary metal oxide (F-TMO) derived from spent LIBs was successfully converted to a robust water oxidation catalyst for pure water electrolysis by utilizing an anion-exchange membrane. The optimized catalyst delivered a high current density of 3.0 A cm-2 at only 2.56 V and a durability of >300 h at 0.5 A cm-2, surpassing the noble-metal IrO2 catalyst. Such excellent performance benefits from an artificially endowed interface layer on the F-TMO, which renders the exposure of active metal (oxy)hydroxide sites with a stabilized configuration during pure water operation. Compared to other metal oxides (i.e., NiO, Co3O4, MnO2), F-TMO possesses a higher stability number of 2.4 × 106, indicating its strong potential for industrial applications. This work provides a feasible way of recycling waste LIBs to valuable electrocatalysts.

Atomic-Thin WS2 Kirigami for Bidirectional Polarization Detection.

The assembly and patterning engineering in two-dimensional (2D) materials hold importance for chip-level designs incorporating multifunctional detectors. At present, the patterning and stacking methods of 2D materials inevitably introduce impurity instability and functional limitations. Here, the space-confined chemical vapor deposition method is employed to achieve state-of-the-art kirigami structures of self-assembled WS2, featuring various layer combinations and stacking configurations. With this technique as a foundation, the WS2 nano-kirigami is integrated with metasurface design, achieving a photodetector with bidirectional polarization-sensitive detection capability in the infrared spectrum. Nano-kirigami can eliminate some of the uncontrollable factors in the processing of 2D material devices, providing a freely designed platform for chip-level multifunctional detection across multiple modules.

Comparing Quality, Costs, and Outcomes of VA and Community Primary Care for Patients with Diabetes.

BACKGROUND: The Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act expanded access to independent community providers outside the Veterans Health Administration (VA). Little is known how quality, costs, and outcomes of primary care received in the community compare to that of the VA. OBJECTIVE: To compare quality, costs, and outcomes of community and VA-provided primary care for patients with diabetes over a 12-month episode. DESIGN: A cross-sectional study using VA administrative data and community care claims. Adjusted analyses were conducted using inverse probability weighted regression adjustment to balance patient characteristics. PARTICIPANTS: Veterans with diabetes receiving primary care in the VA or community. MAIN MEASURES: Quality measures included receipt of hemoglobin A1C tests, eye exams, microalbumin urine tests, and flu shots. Outcomes were measured by hospitalizations for an ambulatory care sensitive condition (ACSC). Costs were measured for VA and community outpatient care, inpatient care, and prescription drugs. KEY RESULTS: There were 652,648 VA patients and 3650 community care patients. VA patients were less likely to be White, had shorter mean drive time to VA primary care, and were less likely to be rural than community care patients. In adjusted analyses, community care patients had significantly lower probability of receiving a hemoglobin A1C test, eye exam, microalbumin urine test, and flu shot compared to the VA group. There was no difference in probability of an ACSC hospitalization. Community care patients had higher mean total costs ($1741 [95% CI, $431, $3052]), driven by higher inpatient and prescription drug costs but lower emergency care costs than VA patients. CONCLUSION: Patients receiving community primary care had worse diabetes quality and higher costs than patients receiving VA primary care. There was no difference in health outcomes. Care provided by an integrated delivery system may have advantages in quality and value.

Scientific misconduct responsibility attribution: An empirical study on byline position and team identity in Chinese medical papers.

OBJECTIVE: The primary objective of this inquiry was to explore the nexus between authorship attribution in medical literature and accountability for scientific impropriety while assessing the influence of authorial multiplicity on the severity of sanctions imposed. METHODS: Probit regression models were employed to scrutinize the impact of authorship on assuming accountability for scientific misconduct, and unordered multinomial logistic regression models were used to examine the influence of authorship and the number of bylines on the severity of punitive measures. RESULTS: First authors and corresponding authors were significantly more likely to be liable for scientific misconduct than other authors and were more likely to be penalized particularly severely. Furthermore, a negative correlation was observed between the number of authors' affiliations and the severity of punitive measures. CONCLUSION: Authorship exerts a pronounced influence on the attribution of accountability in scientific research misconduct, particularly evident in the heightened risk of severe penalties confronting first and corresponding authors owing to their principal roles. Hence, scientific research institutions and journals must delineate authorship specifications meticulously, ascertain authors' contributions judiciously, bolster initiatives aimed at fostering scientific research integrity, and uphold an environment conducive for robust scientific inquiry.

AGML: Adaptive Graph-based Multi-label Learning for Prediction of RBP and AS Event Associations During EMT.

Increasing evidence has indicated that RNA-binding proteins (RBPs) play an essential role in mediating alternative splicing (AS) events during epithelial-mesenchymal transition (EMT). However, due to the substantial cost and complexity of biological experiments, how AS events are regulated and influenced remains largely unknown. Thus, it is important to construct effective models for inferring hidden RBP-AS event associations during EMT process. In this paper, a novel and efficient model was developed to identify AS event-related candidate RBPs based on Adaptive Graph-based Multi-Label learning (AGML). In particular, we propose to adaptively learn a new affinity graph to capture the intrinsic structure of data for both RBPs and AS events. Multi-view similarity matrices are employed for maintaining the intrinsic structure and guiding the adaptive graph learning. We then simultaneously update the RBP and AS event associations that are predicted from both spaces by applying multi-label learning. The experimental results have shown that our AGML achieved AUC values of 0.9521 and 0.9873 by 5-fold and leave-one-out cross-validations, respectively, indicating the superiority and effectiveness of our proposed model. Furthermore, AGML can serve as an efficient and reliable tool for uncovering novel AS events-associated RBPs and is applicable for predicting the associations between other biological entities. The source code of AGML is available at https://github.com/yushanqiu/AGML.

Reaction Mechanism for CO Reduction by Mo-Nitrogenase Studied by QM/MM.

We have studied the conversion of two molecules of carbon monoxide to ethylene catalyzed by nitrogenase. We start from a recent crystal structure showing the binding of two carbon monoxide molecules to nitrogenase and employ the combined quantum mechanics and molecular mechanics approach. Our results indicate that the reaction is possible only if S2B dissociates as H2S (i.e., the charge of the FeMo cluster remains the same as in the E0 state, indicating that the Fe ions are formally reduced two steps when CO binds). Eight electrons and protons are needed for the reaction, and our mechanism suggests that the first four bind alternatively to the two carbon atoms. The C-C bond formation takes place already after the first protonation (in the E3 state). The next two protons bind to the same O atom, which then dissociates as water. In the same state (E8), the second C-O bond is cleaved, forming the ethylene product. The last two electrons and protons are used to form a water molecule that can be exchanged by S2B or by two CO molecules to start a new reaction cycle.

Identification and validation of inflammatory subtypes in intrahepatic cholangiocellular carcinoma.

BACKGROUND: Inflammation plays a critical role in tumor development. Inflammatory cell infiltration and inflammatory mediator synthesis cause changes in the tumor microenvironment (TME) in several cancers, especially in intrahepatic cholangiocellular carcinoma (ICC). However, methods to ascertain the inflammatory state of patients using reliable biomarkers are still being explored. METHOD: We retrieved the RNA sequencing and somatic mutation analyses results and the clinical characteristics of 244 patients with ICC from published studies. We performed consensus clustering to identify the molecular subtypes associated with inflammation. We compared the prognostic patterns, clinical characteristics, somatic mutation profiles, and immune cell infiltration patterns across inflammatory subtypes. We performed quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) to confirm gene expression. We performed logistic regression analyses to construct a nomogram predicting the inflammatory status of patients with ICC. RESULTS: Our results confirmed that ICC can be categorized into an inflammation-high subtype (IHS) and an inflammation-low subtype (ILS). Patients from each group had distinct prognosis, clinical characteristics, and TME composition. Patients with ICC in the IHS group showed poorer prognosis owing to the immunosuppressive microenvironment and high frequency of KRAS and TP53 mutations. Cancer-associated fibroblast (CAF)-derived COLEC11 reduced myeloid inflammatory cell infiltration and attenuated inflammatory responses. The results of qRT-PCR and IHC experiments confirmed that COLEC11 expression levels were significantly reduced in tumor tissues compared to those in paracancerous tissues. Patients with ICC in the IHS group were more likely to respond to treatment with immune checkpoint inhibitors (ICIs) owing to their higher tumor mutational burden (TMB) scores, tumor neoantigen burden (TNB) scores, neoantigen counts, and immune checkpoint expression levels. Finally, we developed a nomogram to effectively predict the inflammatory status of patients with ICC based on their clinical characteristics and inflammatory gene expression levels. We evaluated the calibration, discrimination potential, and clinical utility of the nomogram. CONCLUSION: The inflammatory response in IHS is primarily induced by myeloid cells. COLEC11 can reduce the infiltration level of this group of cells, and myeloid inflammatory cells may be a novel target for ICC treatment. We developed a novel nomogram that could effectively predict the inflammatory state of patients with ICC, which will be useful for guiding individualized treatment plans.

Enhanced quantification of chlorophyll a and its degradation products in olive oil using time-resolved laser-induced fluorescence fingerprint analysis.

Potential errors in the fluorescence analysis of chlorophylls and their degradation products, primarily due to spectral overlap and inner filter, are widely acknowledged. This study aimed to devise a sensitivity-enhanced technique for the concurrent quantification of chlorophyll a and its degradation products while minimizing effects from type-B chlorophylls. Initially, a time-resolved laser-induced fluorescence spectroscopic system was designed and tested on stardard chlorophyll samples. The origins, implications, and mitigation strategies of spectral overlap and the inner filter effect on the measured fluorescence intensity were thoroughly examined. Then, this methodology was proved to be efficacious within complex liquid matrices derived from olive oil. The experimental outcomes not only shed additional light on the mechanisms of chlorophyll fluorescence overlap and the inner filter effect, but also establish a general framework for developing spectrally and timely resolved fluorescence fingerprint analysis for the simultaneous quantification of chlorophylls and their degradation products at high concentrations.

A Convolutional Neural Network with Multifrequency and Structural Similarity Loss Functions for Electromagnetic Imaging.

In this paper, artificial intelligence (AI) technology is applied to the electromagnetic imaging of anisotropic objects. Advances in magnetic anomaly sensing systems and electromagnetic imaging use electromagnetic principles to detect and characterize subsurface or hidden objects. We use measured multifrequency scattered fields to calculate the initial dielectric constant distribution of anisotropic objects through the backpropagation scheme (BPS). Later, the estimated multifrequency permittivity distribution is input to a convolutional neural network (CNN) for the adaptive moment estimation (ADAM) method to reconstruct a more accurate image. In the meantime, we also improve the definition of loss function in the CNN. Numerical results show that the improved loss function unifying the structural similarity index measure (SSIM) and root mean square error (RMSE) can effectively enhance image quality. In our simulation environment, noise interference is considered for both TE (transverse electric) and TM (transverse magnetic) waves to reconstruct anisotropic scatterers. Lastly, we conclude that multifrequency reconstructions are more stable and precise than single-frequency reconstructions.