Biogeochemical Fe(II) generators as a new strategy for limiting Cd uptake by rice and its implication for agricultural sustainability.

This work has developed a new strategy of biogeochemical Fe(II) generators for activating microbial Fe(II) generation to immobilize Cd in soils through protons scavenging and coprecipitation. A new biochar modified magnetite (FeBC15) has been fabricated through a top-down method, with which microbial respiration can be stimulated in paddy soil. The FeBC15 exhibits a higher adsorption capacity for Cd than pristine magnetite (1.7 times). The results show that the available Cd can be reduced by 14.4% after adding FeBC15 compared to the control. More importantly, FeBC15 particles promote the conversion of MgCl2 - Cd to stable crystalline Fe/Al bound Cd under the incubation period. The enhanced pH and Fe(II) leads to a comparably lower Cd availability in soils than in pristine soils, which are supported by the enhanced relative abundance of Geobacter and Clostridium with the FeBC15 treatment (i.e. up to 7.44-7.68 × 109 copies/g soil). The Diffusive Gradients in Thin-films (DGT) study indicates that FeBC15 can lower the replenish capacity of soils (i.e. KdL values of 0.2-3.6 mL/g) to soil pore waters and limit root absorption. Pot experiments demonstrate that this strategy can alleviate the rice Cd content by 38.4% (< 0.2 mg/kg). This work paves a new pathway for reducing Cd uptake in rice, enabling sustainable remediation of paddy soil.

Phase Compatible NiFe2O4 Coating Tunes Oxygen Redox in Li-Rich Layered Oxide.

Li-rich layered oxides have attracted intense attention for lithium-ion batteries, as provide substantial capacity from transition metal cation redox simultaneous with reversible oxygen-anion redox. However, unregulated irreversible oxygen-anion redox leads to critical issues such as voltage fade and oxygen release. Here, we report a feasible NiFe2O4 (NFO) surface-coating strategy to turn the nonbonding coordination of surface oxygen into metal-oxygen decoordination. In particular, the surface simplex M-O (M = Ni, Co, Mn from MO6 octahedra) and N-O (N = Ni, Fe from NO6 octahedra) bonds are reconstructed in the form of M-O-N bonds. By applying both in operando and ex situ technologies, we found this heterostructural interface traps surface lattice oxygen, as well as restrains cation migration in Li-rich layered oxide during electrochemical cycling. Therefore, surface lattice oxygen behavior is significantly sustained. More interestingly, we directly observe the surface oxygen redox decouple with cation migration. In addition, the NFO-coating blocks HF produced from electrolyte decomposition, resulting in reducing the dissolution of Mn. With this strategy, higher cycle stability (91.8% at 1 C after 200 cycles) and higher rate capability (109.4 mA g-1 at 1 C) were achieved in this work, compared with pristine Li-rich layered oxide. Our work offers potential for designing electrode materials utilizing oxygen redox chemistry.

Exploring Natural Allelic Variations of the ß-Triketone Herbicide Resistance Gene HIS1 for Application in indica Rice and Particularly in Two-Line Hybrid Rice.

BACKGROUND: Benzobicyclon (BBC) is a ß-triketone herbicide (bTH) used in rice paddy fields. It has the advantages of high efficiency, low toxicity, high crop safety, and good environmental compatibility, and shows efficacy against paddy weeds resistant to other types of herbicides. However, as some important indica rice varieties are susceptible to BBC, BBC is currently only registered and applied in japonica rice cultivation areas. RESULTS: By analyzing haplotypes of the bTHs broad-spectrum resistance gene HIS1 and phenotypes for BBC in 493 major indica rice accessions in China, we identified a novel non-functional allelic variant of HIS1 in addition to the previously reported 28-bp deletion. Through detection with markers specific to the two non-functional mutations, it was clear that 25.4% of indica conventional varieties, 59.9% of fertility restorers, and 15.9% of sterile lines were susceptible to BBC. In addition, due to natural allelic variations of the HIS1 gene in the sterile and restorer lines, some two-line hybrid sterile lines were sensitive to bTHs, and the corresponding restorers were resistant. We showed the potential effectiveness of using bTHs to address the issue of two-line hybrid rice seed purity stemming from the self-crossing of sterile lines during hybrid rice seed production. Finally, allelic variations of the HIS1 gene may also play an important role in the mechanized seed production of hybrid rice. CONCLUSIONS: Our findings offer guidance for the application of BBC in indica rice areas and provide a non-transgenic approach to address the seed purity issue of two-line hybrid rice.

Control of the interface graphitized/amorphous carbon of biomass-derived carbon microspheres for symmetric supercapacitors.

Rational interface control of porous carbon electrode materials is of significance for achieving efficient supercapacitors. Herein, biomass-derived carbon microspheres with a highly graphitized porous surface and amorphous subsurface were well constructed via a flexible coupled catalysis-activation process. The unique structure not only endows the carbon microspheres with rapid electron transfer but also an ultra-high specific surface area. Owing to the optimized graphitized/amorphous structure, the obtained graphitized and activated starch-derived carbon microspheres display obviously impressive energy storage capability among the reported starch-derived carbon materials, even though they were evaluated in a narrow voltage window. The assembled symmetrical supercapacitor based on the optimized carbon microspheres exhibits a high capacitance of 198 F g-1 at 1 A g-1, a high energy density of 14.67 W h kg-1 at a power density of 4142.80 W kg-1, robust cycle performance, and good rate performance in alkaline aqueous electrolyte. This work provides a strategy for flexible construction of biomass-derived carbon electrode materials, with an optimized graphitized/amorphous and porous structure, for boosted energy storage in supercapacitor applications.

A Multi-Period Curve Fitting Model for Short-Term Prediction of the COVID-19 Spread in the U.S. Metropolitans.

The COVID-19 has wreaked havoc upon the world with over 248 million confirmed cases and a death toll of over 5 million. It is alarming that the United States contributes over 18% of these confirmed cases and 14% of the deaths. Researchers have proposed many forecasting models to predict the spread of COVID-19 at the national, state, and county levels. However, due to the large variety in the mitigation policies adopted by various state and local governments; and unpredictable social events during the pandemic, it is incredibly challenging to develop models that can provide accurate long-term forecasting for disease spread. In this paper, to address such a challenge, we introduce a new multi-period curve fitting model to give a short-term prediction of the COVID-19 spread in Metropolitan Statistical Areas (MSA) within the United States. Since most counties/cities within a single MSA usually adopt similar mitigation strategies, this allows us to substantially diminish the variety in adopted mitigation strategies within an MSA. At the same time, the multi-period framework enables us to incorporate the impact of significant social events and mitigation strategies in the model. We also propose a simple heuristic to estimate the COVID-19 fatality based on our spread prediction. Numerical experiments show that the proposed multi-period curve model achieves reasonably high accuracy in the prediction of the confirmed cases and fatality.

Q-MKL: Matrix-induced Regularization in Multi-Kernel Learning with Applications to Neuroimaging.

Multiple Kernel Learning (MKL) generalizes SVMs to the setting where one simultaneously trains a linear classifier and chooses an optimal combination of given base kernels. Model complexity is typically controlled using various norm regularizations on the base kernel mixing coefficients. Existing methods neither regularize nor exploit potentially useful information pertaining to how kernels in the input set 'interact'; that is, higher order kernel-pair relationships that can be easily obtained via unsupervised (similarity, geodesics), supervised (correlation in errors), or domain knowledge driven mechanisms (which features were used to construct the kernel?). We show that by substituting the norm penalty with an arbitrary quadratic function Q 0, one can impose a desired covariance structure on mixing weights, and use this as an inductive bias when learning the concept. This formulation significantly generalizes the widely used 1- and 2-norm MKL objectives. We explore the model's utility via experiments on a challenging Neuroimaging problem, where the goal is to predict a subject's conversion to Alzheimer's Disease (AD) by exploiting aggregate information from many distinct imaging modalities. Here, our new model outperforms the state of the art (p-values ⪡ 10-3). We briefly discuss ramifications in terms of learning bounds (Rademacher complexity).

Scale Invariant cosegmentation for image groups.

Our primary interest is in generalizing the problem of Cosegmentation to a large group of images, that is, concurrent segmentation of common foreground region(s) from multiple images. We further wish for our algorithm to offer scale invariance (foregrounds may have arbitrary sizes in different images) and the running time to increase (no more than) near linearly in the number of images in the set. What makes this setting particularly challenging is that even if we ignore the scale invariance desiderata, the Cosegmentation problem, as formalized in many recent papers (except [1]), is already hard to solve optimally in the two image case. A straightforward extension of such models to multiple images leads to loose relaxations; and unless we impose a distributional assumption on the appearance model, existing mechanisms for image-pair-wise measurement of foreground appearance variations lead to significantly large problem sizes (even for moderate number of images). This paper presents a surprisingly easy to implement algorithm which performs well, and satisfies all requirements listed above (scale invariance, low computational requirements, and viability for the multiple image setting). We present qualitative and technical analysis of the properties of this framework.

Role adaptation of family caregivers for ventilator-dependent patients: transition from respiratory care ward to home.

AIM AND OBJECTIVES: To explore the underlying theoretical framework for the role adaptation of family caregivers for ventilator-dependent patients after transferring from respiratory care ward to home. BACKGROUND: The number of ventilator-dependent patients has been increasing worldwide. Under Taiwan's National Health Insurance policy, if ventilator-dependent patients are stable, they should be transferred from an acute care hospital to a subacute unit or home. DESIGN: A qualitative design based on grounded theory was adopted for this study. METHODS: One-on-one, in-depth interviews were conducted with a purposive sample of 15 family caregivers who were caretaking ventilator-dependent patients at their home two months after hospital discharge. Theoretical sampling was used until concepts emerging in data analysis were saturated. Analysis of audio-taped interview transcripts generated a process of role adaptation for family caregivers of a ventilator-dependent patient. RESULTS: The caregiver's transition to the care-giving role is a dynamic process with consequences that are impacted by level of support from the family, affective rewards from the patient, patient's health condition and a balanced life schedule for the caregiver. CONCLUSIONS: The results of this study can provide respiratory care professionals with skills to assess the needs of caregivers for ventilator-dependent patients and individualise interventions to caregivers' specific needs. RELEVANCE TO CLINICAL PRACTICE: The findings of this study contribute to nurses' understanding and promotion of role adaptation for family caregivers among ventilator-dependent patients.

Ensemble Clustering using Semidefinite Programming with Applications.

In this paper, we study the ensemble clustering problem, where the input is in the form of multiple clustering solutions. The goal of ensemble clustering algorithms is to aggregate the solutions into one solution that maximizes the agreement in the input ensemble. We obtain several new results for this problem. Specifically, we show that the notion of agreement under such circumstances can be better captured using a 2D string encoding rather than a voting strategy, which is common among existing approaches. Our optimization proceeds by first constructing a non-linear objective function which is then transformed into a 0-1 Semidefinite program (SDP) using novel convexification techniques. This model can be subsequently relaxed to a polynomial time solvable SDP. In addition to the theoretical contributions, our experimental results on standard machine learning and synthetic datasets show that this approach leads to improvements not only in terms of the proposed agreement measure but also the existing agreement measures based on voting strategies. In addition, we identify several new application scenarios for this problem. These include combining multiple image segmentations and generating tissue maps from multiple-channel Diffusion Tensor brain images to identify the underlying structure of the brain.

Learning Kernels for variants of Normalized Cuts: Convex Relaxations and Applications.

We propose a new algorithm for learning kernels for variants of the Normalized Cuts (NCuts) objective - i.e., given a set of training examples with known partitions, how should a basis set of similarity functions be combined to induce NCuts favorable distributions. Such a procedure facilitates design of good affinity matrices. It also helps assess the importance of different feature types for discrimination. Rather than formulating the learning problem in terms of the spectral relaxation, the alternative we pursue here is to work in the original discrete setting (i.e., the relaxation occurs much later). We show that this strategy is useful - while the initial specification seems rather difficult to optimize efficiently, a set of manipulations reveal a related model which permits a nice SDP relaxation. A salient feature of our model is that the eventual problem size is only a function of the number of input kernels and not the training set size. This relaxation also allows strong optimality guarantees, if certain conditions are satisfied. We show that the sub-kernel weights obtained provide a complementary approach for MKL based methods. Our experiments on Caltech101 and ADNI (a brain imaging dataset) show that the quality of solutions is competitive with the state-of-the-art.

Ensemble Clustering using Semidefinite Programming.

We consider the ensemble clustering problem where the task is to 'aggregate' multiple clustering solutions into a single consolidated clustering that maximizes the shared information among given clustering solutions. We obtain several new results for this problem. First, we note that the notion of agreement under such circumstances can be better captured using an agreement measure based on a 2D string encoding rather than voting strategy based methods proposed in literature. Using this generalization, we first derive a nonlinear optimization model to maximize the new agreement measure. We then show that our optimization problem can be transformed into a strict 0-1 Semidefinite Program (SDP) via novel convexification techniques which can subsequently be relaxed to a polynomial time solvable SDP. Our experiments indicate improvements not only in terms of the proposed agreement measure but also the existing agreement measures based on voting strategies. We discuss evaluations on clustering and image segmentation databases.