A strategy for predicting waste production and planning recycling paths in e-logistics based on improved EMD-LSTM

With the rapid development of e-commerce, express delivery has been chosen and accepted by consumers, and a large number of express packages have resulted in serious waste of resources and environmental pollution. Because of the irregularity of online goods purchases by users in real life, logistics parks are unable to accurately judge the recycling needs of various regions. In order to solve this problem, we propose an improved empirical mode decomposition (IEMD) algorithm combined with a long-short-term memory (LSTM) network to deal with the addresses and categories in logistics data, analyze the distribution of recyclable logistics waste in the logistics park service area and in the express recycling station within the logistics park, judge the value of recyclable logistics waste, optimize the best path for recycling vehicles and improve the success rate of logistics waste recycling. In order to better research and verify the IEMD-LSTM prediction model, we model and simulate the algorithm behavior of the express waste packaging recycling prediction model system, and compare it with other classification methods through specific logistics data experiments. The prediction accuracy, stability and advantages of the four algorithms are analyzed and compared, and the application reliability of the algorithm proposed in this paper to the logistics waste recycling process is verified. The application in the actual express logistics packaging recycling case shows the feasibility and effectiveness of the waste recycling scheme proposed in this paper

Government environmental information disclosure and corporate carbon performance

Environmental problem is the key to the healthy development of China’s eco-economy, and the environmental responsibility of micro-enterprises under the vision of “Dual Carbon” has attracted more attention. Under the effect of formal environmental regulation, firms will improve their environmental performance by improving technology and resource utilization. As an informal environmental system, can government environmental information disclosure (GEID) guide firms to actively carry out green innovation, ultimately improve the carbon emission problem of firms, have a positive impact on the carbon performance of enterprises, and provide strong support to protect ecological environment? To address this question, this study used the Pollution Information Transparency Index (PITI) to measure GEID, and empirically tested the impact of GEID on corporate carbon performance using a sample of listed companies involved in China’s mining and manufacturing industries from 2013 to 2018. The study found that the higher the degree of GEID, the better was the corporate carbon performance. However, the improved public participation weakened the effect of GEID on corporate carbon performance. GEID reduced the carbon emission intensity of firms and improved their carbon performance via green innovation. Further research indicated that the enhanced GEID in state-owned enterprises significantly improved carbon performance of firms. This study provides empirical evidence for GEID to improve corporate carbon performance, and also proposes a policy strategy for the government to guide firms to undertake green innovation and promote firms to improve efficient carbon use

Tuning the \u3cem\u3eJ\u3c/em\u3e\u3csub\u3eeff\u3c/sub\u3e = 1/2 Insulating State Via Electron Doping and Pressure in the Double-Layered Iridate Sr\u3csub\u3e3\u3c/sub\u3eIr\u3csub\u3e2\u3c/sub\u3eO\u3csub\u3e7\u3c/sub\u3e

Sr3Ir2O7 exhibits a novel Jeff = 1/2 insulating state that features a splitting between Jeff = 1/2 and 3/2 bands due to spin-orbit interaction. We report a metal-insulator transition in Sr3Ir2O7 via either dilute electron doping (La3+ for Sr2+) or application of high pressure up to 35 GPa. Our study of single-crystal Sr3Ir2O7 and (Sr1−xLax)3Ir2O7 reveals that application of high hydrostatic pressure P leads to a drastic reduction in the electrical resistivity by as much as six orders of magnitude at a critical pressure PC = 13.2 GPa, manifesting a closing of the gap; but further increasing P up to 35 GPa produces no fully metallic state at low temperatures, possibly as a consequence of localization due to a narrow distribution of bonding angles θ. In contrast, slight doping of La3+ ions for Sr2+ ions in Sr3Ir2O7 readily induces a robust metallic state in the resistivity at low temperatures; the magnetic ordering temperature is significantly suppressed but remains finite for (Sr0.95La0.05)3Ir2O7 where the metallic state occurs. The results are discussed along with comparisons drawn with Sr2IrO4, a prototype of the Jeff = 1/2 insulator

Phase diffusion of Bose-Einstein Condensation close to zero temperature

The correlation function of the quantum fluctuations due to collective excitations is calculated and used to investigate the phase diffusion of a Bose-Einstein condensate close to zero temperature. It is shown that the phase diffusion time of the condensate is much longer than the result obtained by assuming that the correlation time of the quantum fluctuations is infinity.Comment: RevTex, 5 pages, [email protected]; This is the final version of the paper which has been accepted for publication in Physics Letters

7-(4-Chloro­benzyl­idene)-3-[(4-chloro­phen­oxy)meth­yl]-6-(4-nitro­thio­phen-2-yl)-7H-1,2,4-triazolo[3,4-b][1,3,4]thia­diazine

In the title compound, C22H13Cl2N5O3S2, the thia­diazine ring adopts a half-chair conformation. The benzene rings of the chloro­phen­oxy and chloro­benzyl groups and the thio­phene ring form dihedral angles of 35.6 (1), 80.7 (1) and 14.2 (1)°, respectively, with the triazole ring. In the crystal, mol­ecules are connected into sheets parallel to (11) by inter­molecular C—H⋯N and C—H⋯Cl hydrogen bonds. In addition, π–π stacking inter­actions are observed between thio­phene and triazole rings, and between inversion-related triazole rings [centroid–centroid distances = 3.5975 (11) and 3.4324 (11) Å]

Anaerobic copper toxicity and iron-sulfur cluster biogenesis in Escherichia coli

© 2017 American Society for Microbiology. While copper is an essential trace element in biology, pollution of groundwater from copper has become a threat to all living organisms. Cellular mechanisms underlying copper toxicity, however, are still not fully understood. Previous studies have shown that iron-sulfur proteins are among the primary targets of copper toxicity in Escherichia coli under aerobic conditions. Here, we report that, under anaerobic conditions, iron-sulfur proteins in E. coli cells are even more susceptible to copper in medium. Whereas addition of 0.2 mM copper(II) chloride to LB (Luria-Bertani) medium has very little or no effect on iron-sulfur proteins in wild-type E. coli cells under aerobic conditions, the same copper treatment largely inactivates iron-sulfur proteins by blocking iron-sulfur cluster biogenesis in the cells under anaerobic conditions. Importantly, proteins that do not have iron-sulfur clusters (e.g., fumarase C and cysteine desulfurase) in E. coli cells are not significantly affected by copper treatment under aerobic or anaerobic conditions, indicating that copper may specifically target iron-sulfur proteins in cells. Additional studies revealed that E. coli cells accumulate more intracellular copper under anaerobic conditions than under aerobic conditions and that the elevated copper content binds to the iron-sulfur cluster assembly proteins IscU and IscA, which effectively inhibits iron-sulfur cluster biogenesis. The results suggest that the copper-mediated inhibition of iron-sulfur proteins does not require oxygen and that iron-sulfur cluster biogenesis is the primary target of anaerobic copper toxicity in cells

Computational genomics in the era of precision medicine: Applications to variant analysis and gene therapy

Rapid methodological advances in statistical and computational genomics have enabled researchers to better identify and interpret both rare and common variants responsible for complex human diseases. As we continue to see an expansion of these advances in the field, it is now imperative for researchers to understand the resources and methodologies available for various data types and study designs. In this review, we provide an overview of recent methods for identifying rare and common variants and understanding their roles in disease etiology. Additionally, we discuss the strategy, challenge, and promise of gene therapy. As computational and statistical approaches continue to improve, we will have an opportunity to translate human genetic findings into personalized health care

Multiomic analyses implicate a neurodevelopmental program in the pathogenesis of cerebral arachnoid cysts

Cerebral arachnoid cysts (ACs) are one of the most common and poorly understood types of developmental brain lesion. To begin to elucidate AC pathogenesis, we performed an integrated analysis of 617 patient-parent (trio) exomes, 152,898 human brain and mouse meningeal single-cell RNA sequencing transcriptomes and natural language processing data of patient medical records. We found that damaging de novo variants (DNVs) were highly enriched in patients with ACs compared with healthy individuals (P = 1.57 × 10-33). Seven genes harbored an exome-wide significant DNV burden. AC-associated genes were enriched for chromatin modifiers and converged in midgestational transcription networks essential for neural and meningeal development. Unsupervised clustering of patient phenotypes identified four AC subtypes and clinical severity correlated with the presence of a damaging DNV. These data provide insights into the coordinated regulation of brain and meningeal development and implicate epigenomic dysregulation due to DNVs in AC pathogenesis. Our results provide a preliminary indication that, in the appropriate clinical context, ACs may be considered radiographic harbingers of neurodevelopmental pathology warranting genetic testing and neurobehavioral follow-up. These data highlight the utility of a systems-level, multiomics approach to elucidate sporadic structural brain disease

Identification and validation of a novel cuproptosis-related gene signature in multiple myeloma

Background: Cuproptosis is a newly identified unique copper-triggered modality of mitochondrial cell death, distinct from known death mechanisms such as necroptosis, pyroptosis, and ferroptosis. Multiple myeloma (MM) is a hematologic neoplasm characterized by the malignant proliferation of plasma cells. In the development of MM, almost all patients undergo a relatively benign course from monoclonal gammopathy of undetermined significance (MGUS) to smoldering myeloma (SMM), which further progresses to active myeloma. However, the prognostic value of cuproptosis in MM remains unknown.Method: In this study, we systematically investigated the genetic variants, expression patterns, and prognostic value of cuproptosis-related genes (CRGs) in MM. CRG scores derived from the prognostic model were used to perform the risk stratification of MM patients. We then explored their differences in clinical characteristics and immune patterns and assessed their value in prognosis prediction and treatment response. Nomograms were also developed to improve predictive accuracy and clinical applicability. Finally, we collected MM cell lines and patient samples to validate marker gene expression by quantitative real-time PCR (qRT-PCR).Results: The evolution from MGUS and SMM to MM was also accompanied by differences in the CRG expression profile. Then, a well-performing cuproptosis-related risk model was developed to predict prognosis in MM and was validated in two external cohorts. The high-risk group exhibited higher clinical risk indicators. Cox regression analyses showed that the model was an independent prognostic predictor in MM. Patients in the high-risk group had significantly lower survival rates than those in the low-risk group (p < 0.001). Meanwhile, CRG scores were significantly correlated with immune infiltration, stemness index and immunotherapy sensitivity. We further revealed the close association between CRG scores and mitochondrial metabolism. Subsequently, the prediction nomogram showed good predictive power and calibration. Finally, the prognostic CRGs were further validated by qRT-PCR in vitro.Conclusion: CRGs were closely related to the immune pattern and self-renewal biology of cancer cells in MM. This prognostic model provided a new perspective for the risk stratification and treatment response prediction of MM patients

A novel glycolysis-related gene signature for predicting the prognosis of multiple myeloma

Background: Metabolic reprogramming is an important hallmark of cancer. Glycolysis provides the conditions on which multiple myeloma (MM) thrives. Due to MM’s great heterogeneity and incurability, risk assessment and treatment choices are still difficult.Method: We constructed a glycolysis-related prognostic model by Least absolute shrinkage and selection operator (LASSO) Cox regression analysis. It was validated in two independent external cohorts, cell lines, and our clinical specimens. The model was also explored for its biological properties, immune microenvironment, and therapeutic response including immunotherapy. Finally, multiple metrics were combined to construct a nomogram to assist in personalized prediction of survival outcomes.Results: A wide range of variants and heterogeneous expression profiles of glycolysis-related genes were observed in MM. The prognostic model behaved well in differentiating between populations with various prognoses and proved to be an independent prognostic factor. This prognostic signature closely coordinated with multiple malignant features such as high-risk clinical features, immune dysfunction, stem cell-like features, cancer-related pathways, which was associated with the survival outcomes of MM. In terms of treatment, the high-risk group showed resistance to conventional drugs such as bortezomib, doxorubicin and immunotherapy. The joint scores generated by the nomogram showed higher clinical benefit than other clinical indicators. The in vitro experiments with cell lines and clinical subjects further provided convincing evidence for our study.Conclusion: We developed and validated the utility of the MM glycolysis-related prognostic model, which provides a new direction for prognosis assessment, treatment options for MM patients