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The Kondo lattice is one of the classic examples of strongly correlated electronic systems. We conduct a controlled study of the Kondo lattice in one dimension, highlighting the role of excitations created by the composite fermion operator. Using time-dependent matrix product state methods, we compute various correlation functions and contrast them with both large-N mean-field theory and the strong-coupling expansion. We show that the composite fermion operator creates long-lived, charge-e and spin-1/2 excitations, which cover the low-lying single-particle excitation spectrum of the system. Furthermore, spin excitations can be thought to be composed of such fractionalized quasiparticles with a residual interaction which tend to disappear at weak Kondo coupling. Published by the American Physical Society2024 

The phase transitions of a series of Co-doped Heusler alloys, Ni2Mn1−xCoxGa (0⩽x⩽0.2), were investigated experimentally using the magnetization measurements, x-ray diffraction, and calorimetric measurements up to their respective melting points. With increasing Co concentration, the structural transition temperatures, Curie temperatures, and melting points, were observed to increase, while the order–disorder transition temperatures decreased. Temperature-dependent x-ray diffraction experiments revealed two different crystal structures in the low-temperature martensite phase for different Co concentrations. However, above their respective structural transitions, both low-temperature crystal structures transformed into the L21 cubic structure. These findings enabled the construction of a complete magnetic and structural phase diagram for Ni2Mn1−xCoxGa, spanning from cryogenic temperatures to the melting points. The temperature-dependent XRD results revealed the abrupt changes in interatomic Mn–Mn distances, which validates the crucial role of Mn–Mn interatomic distance and the effect of the magnetic coupling competition in the structural stability between the martensite phase and austenite phase. 

Balancing relative expression of pathway genes to minimize flux bottlenecks and metabolic burden is one of the key challenges in metabolic engineering. This is especially relevant for iterative pathways, such as reverse β-oxidation (rBOX) pathway, which require control of flux partition at multiple nodes to achieve efficient syn thesis of target products. Here, we develop a plasmid-based inducible system for orthogonal control of gene expression (referred to as the TriO system) and demonstrate its utility in the rBOX pathway. Leveraging effortless construction of TriO vectors in a plug-and-play manner, we simultaneously explored the solution space for enzyme choice and relative expression levels. Remarkably, varying individual expression levels led to substantial change in product specificity ranging from no production to optimal performance of about 90% of the theoretical yield of the desired products. We obtained titers of 6.3 g/L butyrate, 2.2 g/L butanol and 4.0 g/L hexanoate from glycerol in E. coli, which exceed the best titers previously reported using equivalent enzyme combinations. Since a similar system behavior was observed with alternative termination routes and higher-order iterations, we envision our approach to be broadly applicable to other iterative pathways besides the rBOX. Considering that high throughput, automated strain construction using combinatorial promoter and RBS libraries remain out of reach for many researchers, especially in academia, tools like the TriO system could democratize the testing and evaluation of pathway designs by reducing cost, time and infrastructure requirements. 

1-Decanol has great value in the pharmaceutical and fragrance industries and plays an important role in the chemical industry. In this study, we engineered Escherichia coli to selectively synthesize 1-decanol by using enzymes of the core reverse β-oxidation (rBOX) pathway and termination module with overlapping chain-length specificity. Through screening for acyl-CoA reductase termination enzymes and proper regulation of rBOX pathway expression, a 1-decanol titer of 1.4 g/L was achieved. Further improvements were realized by engineering pyruvate dissimilation to ensure the generation of NADH through pyruvate dehydrogenase (PDH) and reducing byproduct synthesis via a tailored YigI thioesterase knockout, increasing 1-decanol titer to 1.9 g/L. The engineered strain produced about 4.4 g/L 1-decanol with a yield of 0.21 g/g in 36 h in a bi-phasic fermentation that used a dodecane overlay to increase 1-decanol transport and reduce its toxicity. Adjustment of pathway expression (varying inducer concentration) and cell growth (oxygen availability) enabled 1-decanol production at 6.1 g/L (0.26 g/g yield) and 10.05 g/L (0.2 g/g yield) using rich medium in shake flasks and bioreactor, respectively. Remarkably, the use of minimal medium resulted in 1-decanol production with 100% specificity at 2.8 g/L (0.14 g/g yield) and a per cell mass yield higher than rich medium. These 1-decanol titers, yields and purity are at least 10-fold higher than others reported to date and the engineered strain shows great potential for industrial production. Taken together, our findings suggest that using rBOX pathway and termination enzymes of proper chain-length specificity in combination with optimal chassis engineering should be an effective approach for the selective production of alcohols. 

Monitoring and forecasting hospitalization rates are of essential significance to public health systems in understanding and managing overall healthcare deliveries and strategizing long-term sustainability. Early-stage prediction of hospitalization rates is crucial to meet the medical needs of numerous patients during emerging epidemic diseases such as COVID-19. Nevertheless, this is a challenging task due to insufficient data and experience. In addition, relevant existing work neglects or fails to exploit the extensive contribution of external factors such as news, policies, and geolocations. In this paper, we demonstrate the significant relationship between hospitalization rates and COVID-19 infection cases. We then adapt a transfer learning architecture with dynamic location-aware sentiment and semantic analysis (TLSS) to a new application scenario: hospitalization rate prediction during COVID-19. This architecture learns and transfers general transmission patterns of existing epidemic diseases to predict hospitalization rates during COVID-19. We combine the learned knowledge with time series features and news sentiment and semantic features in a dynamic propagation process. We conduct extensive experiments to compare the proposed approach with several state-of-the-art machine learning methods with different lead times of ground truth. Our results show that TLSS exhibits outstanding predictive performance for hospitalization rates. Thus, it provides advanced artificial intelligence (AI) techniques for supporting decision-making in healthcare sustainability. 

In this study, phase transitions (structural and magnetic) and associated magnetocaloric properties of stoichiometric MnCoGe have been investigated as a function of annealing pressure. Metastable phases were generated by annealing at 800 °C followed by rapid cooling under pressures up to 6.0 GPa. The x-ray diffraction results reveal that the crystal cell volume of the metastable phases continuously decreases with increasing thermal processing pressure, leading to a decrease in the structural transition temperature. The magnetic and structural transitions merge and form a first-order magnetostructural transition between the ferromagnetic orthorhombic and paramagnetic hexagonal phases over a broad temperature range (>80 K) spanning room temperature, yielding considerable magnetic entropy changes. These findings demonstrate the utility of thermal processing under high pressure, i.e., high-pressure annealing, to control the magnetostructural transitions and associated magnetocaloric properties of MnCoGe without altering its chemical composition. 

Recently, solar-induced chlorophyll fluorescence (SIF) is a promising tool to estimate gross primary production (GPP). Photosynthesis gradually saturates with the increasing light, but fluorescence tends to keep increasing, leading to a nonlinear SIF-GPP relationship. This nonlinearity occurs for sunlit leaves but not for shaded leaves for which photosynthesis is light-limited. However, the separation of sunlit and shaded SIF has not been systematically investigated when estimating GPP from SIF. Therefore, it is promising to develop a model for GPP estimation considering such differences. This study proposed an approach to separate the total canopy SIF emission (SIFtotal) from TROPOspheric Monitoring Instrument (TROPOMI) SIF into their sunlit and shaded components (SIFsun and SIFshade). The nonlinearity and linearity in SIF-GPP relationships for sunlit and shaded leaves were incorporated into a two-leaf hybrid model, which was fitted using flux tower data and then evaluated using leave-one-site-out crossing validation. We also elucidated the distinct SIF-GPP relationships between sunlit and shaded leaves using the Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model simulation. Compared to previously used linear (R2 = 0.68, RMSE = 2.13 gC⋅m^-2*d^-1) or hyperbolic (R2 = 0.72, RMSE = 2.01 gC⋅m^-2⋅d^-1) model based on the big-leaf assumption, our proposed two-leaf hybrid model has the best performance on GPP estimation (R2 = 0.77, RMSE = 1.79 gC⋅m^-2⋅d^-1). We also applied this two-leaf hybrid model to estimate the global GPP during the main growing season in Northern Hemisphere, which were highly correlated with several existing GPP products, with R2 ranging from 0.79 to 0.88. These results will improve our understanding of the relationship between SIF and GPP for sunlit and shaded leaves and will advance application of satellite SIF data to GPP estimation. 

Objective We investigated secondary–task–based countermeasures to the vigilance decrement during a simulated partially automated driving (PAD) task, with the goal of understanding the underlying mechanism of the vigilance decrement and maintaining driver vigilance in PAD. Background Partial driving automation requires a human driver to monitor the roadway, but humans are notoriously bad at monitoring tasks over long periods of time, demonstrating the vigilance decrement in such tasks. The overload explanations of the vigilance decrement predict the decrement to be worse with added secondary tasks due to increased task demands and depleted attentional resources, whereas the underload explanations predict the vigilance decrement to be alleviated with secondary tasks due to increased task engagement. Method Participants watched a driving video simulating PAD and were required to identify hazardous vehicles throughout the 45-min drive. A total of 117 participants were assigned to three different vigilance-intervention conditions including a driving-related secondary task (DR) condition, a non-driving-related secondary task (NDR) condition, and a control condition with no secondary tasks. Results Overall, the vigilance decrement was shown over time, reflected in increased response times, reduced hazard detection rates, reduced response sensitivity, shifted response criterion, and subjective reports on task-induced stress. Compared to the DR and the control conditions, the NDR displayed a mitigated vigilance decrement. Conclusion This study provided convergent evidence for both resource depletion and disengagement as sources of the vigilance decrement. Application The practical implication is that infrequent and intermittent breaks using a non-driving related task may help alleviate the vigilance decrement in PAD systems. 

Abstract The phase transitions in MnNiGe compounds were explored by manipulating the heat treatment conditions and through hydrostatic pressure application. As the quenching temperature increased, both the first-order martensitic structural transition temperatures and magnetic transition temperatures decreased relative to those in the slowly-cooled samples. When the samples were quenched from 1200 ^∘C, the first-order martensitic structural transition temperature lowered by more than 200 K. The structural transitions also shifted to lower temperature with the application of hydrostatic pressure during measurement. Temperature-dependent x-ray diffraction results reveal that the changes of the cell parameters resulting from the structural transitions are nearly identical for all samples regardless of the extensive variation in their structural transition temperatures. In addition, neutron scattering measurements confirm the magnetic structure transition between simple and cycloidal spiral magnetic structures.