To Duckweeds (\u3cem\u3eLandoltia punctata\u3c/em\u3e), Nanoparticulate Copper Oxide is More Inhibitory than the Soluble Copper in the Bulk Solution

CuO nanoparticles (CuO-NP) were synthesized in a hydrogen diffusion flame. Particle size and morphology were characterized using scanning mobility particle sizing, Brunauer–Emmett–Teller analysis, dynamic light scattering, and transmission electron microscopy. The solubility of CuO-NP varied with both pH and presence of other ions. CuO-NP and comparable doses of soluble Cu were applied to duckweeds, Landoltia punctata. Growth was inhibited 50% by either 0.6 mg L−1 soluble copper or by 1.0 mg L−1 CuO-NP that released only 0.16 mg L−1 soluble Cu into growth medium. A significant decrease of chlorophyll was observed in plants stressed by 1.0 mg L−1 CuO-NP, but not in the comparable 0.2 mg L−1 soluble Cu treatment. The Cu content of fronds exposed to CuO-NP is four times higher than in fronds exposed to an equivalent dose of soluble copper, and this is enough to explain the inhibitory effects on growth and chlorophyll content

Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems

Modern deep learning-based recommendation systems exploit hundreds to thousands of different categorical features, each with millions of different categories ranging from clicks to posts. To respect the natural diversity within the categorical data, embeddings map each category to a unique dense representation within an embedded space. Since each categorical feature could take on as many as tens of millions of different possible categories, the embedding tables form the primary memory bottleneck during both training and inference. We propose a novel approach for reducing the embedding size in an end-to-end fashion by exploiting complementary partitions of the category set to produce a unique embedding vector for each category without explicit definition. By storing multiple smaller embedding tables based on each complementary partition and combining embeddings from each table, we define a unique embedding for each category at smaller memory cost. This approach may be interpreted as using a specific fixed codebook to ensure uniqueness of each category's representation. Our experimental results demonstrate the effectiveness of our approach over the hashing trick for reducing the size of the embedding tables in terms of model loss and accuracy, while retaining a similar reduction in the number of parameters.Comment: 11 pages, 7 figures, 1 tabl

De novo SNP discovery and genetic linkage mapping in poplar using restriction site associated DNA and whole-genome sequencing technologies

Detailed information on genetic distance and linkage phase between adjacent SNP markers on the genetic linkage map of the female P. deltoides ‘I-69’. The corresponding identical SNPs identified based on the P. trichocarpa reference genome are also included. (XLS 452 kb

A Correlation Attack on Full SNOW-V and SNOW-Vi

In this paper, a method for searching correlations between the binary stream of Linear Feedback Shift Register (LFSR) and the keystream of SNOW-V and SNOW-Vi is presented based on the technique of approximation to composite functions. With the aid of the linear relationship between the four taps of LFSR input into Finite State Machine (FSM) at three consecutive clocks, we present an automatic search model based on the SAT/SMT technique and search out a series of linear approximation trails with high correlation. By exhausting the intermediate masks, we find a binary linear approximation with a correlation $-2^{-47.76}$. Using such approximation, we propose a correlation attack on SNOW-V with an expected time complexity $2^{246.53}$, a memory complexity $2^{238.77}$ and $2^{237.5}$ keystream words generated by the same key and Initial Vector (IV). For SNOW-Vi, we provide a binary linear approximation with the same correlation and mount a correlation attack with the same complexity as that of SNOW-V. To the best of our knowledge, this is the first known attack on full SNOW-V and SNOW-Vi, which is better than the exhaustive key search with respect to time complexity. The results indicate that neither SNOW-V nor SNOW-Vi can guarantee the 256-bit security level if we ignore the design constraint that the maximum length of keystream for a single pair of key and IV is less than $2^{64}$

Synergy of carboxymethyl cellulose stabilized nanoscale zero-valent iron and Penicillium oxalicum SL2 to remediate Cr(VI) contaminated site soil

Nano zero-valent iron (nZVI) acting as a high-cost disposable material in soil Cr(VI) remediation faces significant challenges due to its easily oxidizable nature and biological toxicity. In addressing this issue, the present study undertook the synthesis of a series of modified nZVI and combined the selected material with Cr(VI)-resistant filamentous fungus Penicillium oxalicum SL2 for real-site chromium pollution remediation. Adsorption experiments demonstrated that the inclusion of carboxymethyl cellulose (CMC) significantly enhanced the adsorption capacity of nZVI for Cr(VI) by 19.3% (from 73.25 to 87.4 mg/L), surpassing both biochar (37.42 mg/L) and bentonite modified nZVI (48.03 mg/L). Characterization results validated the successful synthesis of the nano composite material. Besides, oxidative stress analysis explained the unique detoxification effects of CMC on SL2, acting as a free radical scavenger and isolating layer. In real-sites soil remediation experiments, a low dosage (0.4% w/w) of nZVI/CMC@SL2 (CMC modified nZVI combined with SL2) exhibited an impressive reduction of over 99.5% in TCLP-Cr(VI) and completely transformed 18% of unstable Cr to stable forms. Notably, nZVI/CMC demonstrated its capability to facilitate SL2 colonization in highly contaminated soil and modulate the microbial community structure, enriching chromium-removing microorganisms. In summary, the synergistic system of nZVI/CMC@SL2 merges as a cost-effective and efficient approach for Cr(VI) reduction, providing meaningful insights for its application in the remediating contaminated site soils

The Stem Cell Niche in Leaf Axils Is Established by Auxin and Cytokinin in Arabidopsis

Plants differ from most animals in their ability to initiate new cycles of growth and development, which relies on the establishment and activity of branch meristems harboring new stem cell niches. In seed plants, this is achieved by axillary meristems, which are established in the axil of each leaf base and develop into lateral branches. Here, we describe the initial processes of Arabidopsis thaliana axillary meristem initiation. Using reporter gene expression analysis, we find that axillary meristems initiate from leaf axil cells with low auxin through stereotypical stages. Consistent with this, ectopic overproduction of auxin in the leaf axil efficiently inhibits axillary meristem initiation. Furthermore, our results demonstrate that auxin efflux is required for the leaf axil auxin minimum and axillary meristem initiation. After lowering of auxin levels, a subsequent cytokinin signaling pulse is observed prior to axillary meristem initiation. Genetic analysis suggests that cytokinin perception and signaling are both required for axillary meristem initiation. Finally, we show that cytokinin overproduction in the leaf axil partially rescue axillary meristem initiation-deficient mutants. These results define a mechanistic framework for understanding axillary meristem initiation

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not functions of a single molecular structure, but rather determined from the equilibrium distribution of structures. Traditional methods for obtaining these distributions, such as molecular dynamics simulation, are computationally expensive and often intractable. In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. Inspired by the annealing process in thermodynamics, DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system, such as a chemical graph or a protein sequence. This framework enables efficient generation of diverse conformations and provides estimations of state densities. We demonstrate the performance of DiG on several molecular tasks, including protein conformation sampling, ligand structure sampling, catalyst-adsorbate sampling, and property-guided structure generation. DiG presents a significant advancement in methodology for statistically understanding molecular systems, opening up new research opportunities in molecular science.Comment: 80 pages, 11 figure

HemoglobinA1c Is a Risk Factor for Changes of Bone Mineral Density: A Mendelian Randomization Study

BackgroundAs a valuable blood glucose measurement, HemoglobinA1c (HbA1c) is of great clinical value for diabetes. However, in previous observational studies, studies on its effect on bone mineral density (BMD) have different results. This study aimed to use Mendelian randomization (MR) to assess the effect of HbA1c on bone mineral density and fracture risk, and try to further explore whether this association was achieved through glycemic or non-glycemic factors.MethodsTake HbA1c measurement as exposure, and BMD estimated from quantitative heel ultrasounds (eBMD) and bone fractures as outcomes. Two-Sample MR Analysis was conducted to assess the causal effect of HbA1C on heel BMD and risk fracture. Then, we performed the analysis using two subsets of these variants, one related to glycemic measurement and the other to erythrocyte indices.ResultsGenetically increased HbA1C was associated with the lower heel eBMD [odds ratio (OR) 0.91 (95% CI 0.87, 0.96) per %-unit, P = 3 × 10−4(IVW)]. Higher HbA1C was associated with lower heel eBMD when using only erythrocytic variants [OR 0.87 (0.82, 0.93), P=2× 10−5(IVW)]; However, when using only glycemic variants, this casual association does not hold. In further MR analysis, we test the association of erythrocytic traits with heel eBMD.ConclusionOur study revealed the significant causal effect of HbA1c on eBMD, and this causal link might achieve through non-glycemic pathways (erythrocytic indices)