Ukupna kinetika redukcije niskokvalitetnog piroluzita smjesom hemiceluloze i lignina kao redukcijskog sredstva

Manganese is widely used in many fields. Many efforts have been made to recover manganese from low-grade pyrolusite due to the depletion of high-grade manganese ore. Thus, it is of practical significance to develop a clean, energy-saving and environmentally friendly technical route to reduce the low-grade pyrolusite. The reported results show that biomass wastes from crops, crop waste, wood and wood waste are environmentally friendly, energy-saving, and low-cost reducing agents for roasting reduction of low-grade pyrolusite. Kinetics of the reduction reactions is necessary for an efficient design of biomass reduction of pyrolusite. Therefore, it is important to look for a general kinetics equation to describe the reduction of pyrolusite by different kinds of biomass, because there is a wide variety of biomass wastes, meaning that it is impossible to investigate the kinetics for each biomass waste. In this paper, thermal gravimetric analysis and differential thermal analysis were applied to study the overall reduction kinetics of pyrolusite using a mixture of hemicellulose and lignin, two major components of biomass. Overall reduction process is the overlap of the respective reduction processes. A new empirical equation based on the Johnson–Mehl–Avrami equation can be used to describe the respective reduction kinetics using hemicellulose and lignin as reductants, and the corresponding apparent activation energy is 30.14 kJ mol−1 and 38.91 kJ mol−1, respectively. The overall kinetic model for the reduction of pyrolusite by the mixture of hemicellulose and lignin can be simulated by the summation of the respective kinetics by considering their mass-loss fractions, while a unit step function was used to avoid the invalid conversion data. The obtained results in this work are necessary to understand the biomass reduction of pyrolusite and provide valuable assistance in the development of a general kinetics equation.Ukupna kinetika redukcije piroluzita istraživana je termogravimetrijom i diferencijalnom termogravimetrijom. Kao redukcijsko sredstvo upotrijebljeni su hemiceluloza i lignin, glavni sastojci poljoprivrednog biljnog biootpada, drva i drvnog otpada. Ukupnu redukciju čine isprepleteni pojedinačni redukcijski procesi. Kinetika redukcije piroluzita smjesom hemiceluloze i lignina može se opisati novom empirijskom jednadžbom temeljenoj na jednadžbi Johnson–Mehl–Avrami, a odgovarajuća prividna energija aktivacije iznosi 30.14 kJ mol−1, odnosno 38.91 kJ mol−1. Sveobuhvatna kinetika može se modelirati kao zbroj pojedinačnih udjela uzimajući u obzir masene udjele sastojaka smjese te uz primjenu jedinične odskočne funkcije kako bi se izbjegli nevaljani podaci

Multilevel leapfrogging initialization for quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) is a prospective hybrid quantum-classical algorithm widely used to solve combinatorial optimization problems. However, the external parameter optimization required in QAOA tends to consume extensive resources to find the optimal parameters of the parameterized quantum circuit, which may be the bottleneck of QAOA. To meet this challenge, we first propose multilevel leapfrogging learning (M-Leap) that can be extended to quantum reinforcement learning, quantum circuit design, and other domains. M-Leap incrementally increases the circuit depth during optimization and predicts the initial parameters at level $p+r$ ($r>1$) based on the optimized parameters at level $p$, cutting down the optimization rounds. Then, we propose a multilevel leapfrogging-interpolation strategy (MLI) for initializing optimizations by combining M-Leap with the interpolation technique. We benchmark its performance on the Maxcut problem. Compared with the Interpolation-based strategy (INTERP), MLI cuts down at least half the number of rounds of optimization for the classical outer learning loop. Remarkably, the simulation results demonstrate that the running time of MLI is 1/3 of INTERP when MLI gets quasi-optimal solutions. In addition, we present the greedy-MLI strategy by introducing multi-start, which is an extension of MLI. The simulation results show that greedy-MLI can get a higher average performance than the remaining two methods. With their efficiency to find the quasi-optima in a fraction of costs, our methods may shed light in other quantum algorithms

HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning

In recent times, a plethora of hardware accelerators have been put forth for graph learning applications such as vertex classification and graph classification. However, previous works have paid little attention to Knowledge Graph Completion (KGC), a task that is well-known for its significantly higher algorithm complexity. The state-of-the-art KGC solutions based on graph convolution neural network (GCN) involve extensive vertex/relation embedding updates and complicated score functions, which are inherently cumbersome for acceleration. As a result, existing accelerator designs are no longer optimal, and a novel algorithm-hardware co-design for KG reasoning is needed. Recently, brain-inspired HyperDimensional Computing (HDC) has been introduced as a promising solution for lightweight machine learning, particularly for graph learning applications. In this paper, we leverage HDC for an intrinsically more efficient and acceleration-friendly KGC algorithm. We also co-design an acceleration framework named HDReason targeting FPGA platforms. On the algorithm level, HDReason achieves a balance between high reasoning accuracy, strong model interpretability, and less computation complexity. In terms of architecture, HDReason offers reconfigurability, high training throughput, and low energy consumption. When compared with NVIDIA RTX 4090 GPU, the proposed accelerator achieves an average 10.6x speedup and 65x energy efficiency improvement. When conducting cross-models and cross-platforms comparison, HDReason yields an average 4.2x higher performance and 3.4x better energy efficiency with similar accuracy versus the state-of-the-art FPGA-based GCN training platform

Fluorescent protein tagged hepatitis B virus capsid protein with long glycine-serine linker that supports nucleocapsid formation

Fusion core proteins of Hepatitis B virus can be used to study core protein functions or capsid trafficking. A problem in constructing fusion core proteins is functional impairment of the individual domains in these fusion proteins, might due to structural interference. We reported a method to construct fusion proteins of Hepatitis B virus core protein (HBc) in which the functions of fused domains were partially kept. This method follows two principles: (1) fuse heterogeneous proteins at the N terminus of HBc; (2) use long Glycine-serine linkers between the two domains. Using EGFP and RFP as examples, we showed that long flexible G4S linkers can effectively separate the two domains in function. Among these fusion proteins constructed, GFP-G4S186-HBc and RFP-G4S47-HBc showed the best efficiency in rescuing the replication of an HBV replicon deficient in the core protein expression, though both of the two fusion proteins failed to support the formation of the relaxed circular DNA. These fluorescent protein-tagged HBcs might help study related to HBc or capsids tracking in cells

The Protective Effect of Magnolol in Osteoarthritis: In vitro and in vivo Studies

Osteoarthritis (OA), defined as a long-term progressive joint disease, is characterized by cartilage impairment and erosion. In recent decades, magnolol, as a type of lignin extracted from Magnolia officinalis, has been proved to play a potent anti-inflammatory role in various diseases. The current research sought to examine the latent mechanism of magnolol and its protective role in alleviating the progress of OA in vivo as well as in vitro experimentations. In vitro, the over-production of Nitric oxide (NO), prostaglandin E2 (PGE2), cyclooxygenase-2 (COX-2), inducible nitric oxide synthase (iNOS), tumor necrosis factor alpha (TNF-α), and interleukin-6 (IL-6), induced by interleukin-1 beta (IL-1β), were all inhibited notably by magnolol in a concentration-dependent manner. Moreover, magnolol could also downregulate the expression of metalloproteinase 13 (MMP13) and thrombospondin motifs 5 (ADAMTS5). All these changes ultimately led to the deterioration of the extracellular matrix (ECM) induced by IL-1β. Mechanistically, magnolol suppressed the activation of PI3K/Akt/NF-κB pathway. Furthermore, a powerful binding capacity between magnolol and PI3K was also revealed in our molecular docking research. In addition, magnolol-induced protective effects in OA development were also detected in a mouse model. In summary, this research suggested that magnolol possessed a new therapeutic potential for the development of OA

The Concentration of Non-structural Carbohydrates, N, and P in Quercus variabilis Does Not Decline Toward Its Northernmost Distribution Range Along a 1500 km Transect in China

Understanding the mechanisms that determine plant distribution range is crucial for predicting climate-driven range shifts. Compared to altitudinal gradients, less attention has been paid to the mechanisms that determine latitudinal range limit. To test whether intrinsic resource limitation contributes to latitudinal range limits of woody species, we investigated the latitudinal variation in non-structural carbohydrates (NSC; i.e., total soluble sugar plus starch) and nutrients (nitrogen and phosphorus) in mature and juvenile Chinese cork oak (Quercus variabilis Blume) along a 1500 km north-south transect in China. During the growing season and dormant season, leaves, branches, and fine roots were collected from both mature and juvenile oaks in seven sites along the transect. Tissue concentration of NSCs, N, and P did not decrease with increasing latitude irrespective of sampling season and ontogenetic stage. Furthermore, higher levels of NSCs and N in tissues of juveniles relative to mature trees were found during the dormant season. Partial correlation analysis also revealed that during the dormant season, soluble sugar, NSC, the ratio of soluble sugar to starch, and tissue nitrogen concentration were correlated positively with latitude but negatively with precipitation and mean temperature of dormant season. Our results suggest that carbon or nutrient availability may not be the driving factors of the latitudinal range limit of the studied species. Further studies should be carried out at the community or ecosystem level with multiple species to additionally test the roles of factors such as regeneration, competition, and disturbance in determining a species’ northern distribution limit

Quantum metric nonlinear Hall effect in a topological antiferromagnetic heterostructure

Quantum geometry - the geometry of electron Bloch wavefunctions - is central to modern condensed matter physics. Due to the quantum nature, quantum geometry has two parts, the real part quantum metric and the imaginary part Berry curvature. The studies of Berry curvature have led to countless breakthroughs, ranging from the quantum Hall effect in 2DEGs to the anomalous Hall effect (AHE) in ferromagnets. However, in contrast to Berry curvature, the quantum metric has rarely been explored. Here, we report a new nonlinear Hall effect induced by quantum metric by interfacing even-layered MnBi2Te4 (a PT-symmetric antiferromagnet (AFM)) with black phosphorus. This novel nonlinear Hall effect switches direction upon reversing the AFM spins and exhibits distinct scaling that suggests a non-dissipative nature. Like the AHE brought Berry curvature under the spotlight, our results open the door to discovering quantum metric responses. Moreover, we demonstrate that the AFM can harvest wireless electromagnetic energy via the new nonlinear Hall effect, therefore enabling intriguing applications that bridges nonlinear electronics with AFM spintronics.Comment: 19 pages, 4 figures and a Supplementary Materials with 66 pages, 4 figures and 3 tables. Originally submitted to Science on Oct. 5, 202

Multi-omics analysis reveals a molecular landscape of the early recurrence and early metastasis in pan-cancer

Cancer remains a formidable challenge in medicine due to its propensity for recurrence and metastasis, which can result in unfavorable treatment outcomes. This challenge is particularly acute for early-stage patients, who may experience recurrence and metastasis without timely detection. Here, we first analyzed the differences in clinical characteristics among the primary tumor, recurrent tumor, and metastatic tumor in different stages of cancer, which may be caused by the molecular level. Moreover, the importance of predicting early cancer recurrence and metastasis is emphasized by survival analyses. Next, we used a multi-omics approach to identify key molecular changes associated with early cancer recurrence and metastasis and discovered that early metastasis in cancer demonstrated a high degree of genomic and cellular heterogeneity. We performed statistical comparisons for each level of omics data including gene expression, mutation, copy number variation, immune cell infiltration, and cell status. Then, various analytical techniques, such as proportional hazard model and Fisher’s exact test, were used to identify specific genes or immune characteristics associated with early cancer recurrence and metastasis. For example, we observed that the overexpression of BPIFB1 and high initial B-cell infiltration levels are linked to early cancer recurrence, while the overexpression or amplification of ANKRD22 and LIPM, mutation of IGHA1 and MUC16, high fibroblast infiltration level, M1 polarization of macrophages, cellular status of DNA repair are all linked to early cancer metastasis. These findings have led us to construct classifiers, and the average area under the curve (AUC) of these classifiers was greater than 0.75 in The Cancer Genome Atlas (TCGA) cancer patients, confirming that the features we identified could be biomarkers for predicting recurrence and metastasis of early cancer. Finally, we identified specific early sensitive targets for targeted therapy and immune checkpoint inhibitor therapy. Once the biomarkers we identified changed, treatment-sensitive targets can be treated accordingly. Our study has comprehensively characterized the multi-omics characteristics and identified a panel of biomarkers of early cancer recurrence and metastasis. Overall, it provides a valuable resource for cancer recurrence and metastasis research and improves our understanding of the underlying mechanisms driving early cancer recurrence and metastasis