Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional and Nutritional Quality of Protein and Lipid Recovered from Fish Processing by-Products and Underutilized Aquatic Species Using Isoelectric Solubilization / Precipitation

The isoelectric behavior of food proteins has been well characterized in the food science literature. The isoelectric point (pI) of a protein is a pH at which the protein maintains a zero net electrostatic charge. In this state, protein-protein hydrophobic interactions overcome protein-water electrostatic interactions and the minimum solubility of proteins results. Consequently, several food science laboratories have begun active research on the application of the pI to recover functional muscle proteins, particularly fish myofibrillar proteins. Fish stocks are declining and several fisheries are currently over-exploited and may collapse by mid century. Fish processing by-products are considerable and include heads, frames, viscera, and etc. By-products are land-filled, ground-and-discarded or otherwise diverted from human consumption. By-products retain ample muscle proteins and oil. The oil contains omega-3 fatty acids. Due to the lack of commercially available technology to recover proteins and lipids from fish processing by-products or underutilized aquatic species, this tremendous resource is currently unavailable for human consumption. Fish proteins and oil from otherwise low-value by-products can be recovered using isoelectric solubilization/precipitation with recovery yields of approximately 90%. Recovered proteins and oil retain functionality and nutritional value for human food products. This article reviews the fundamental biochemical principles of food proteins and lipids as well as their structure and interaction with water in relation to the isoelectric behavior. Additionally, the most recent developments regarding application of isoelectric solubilization/precipitation to recover functional and nutritious proteins and oil from fish processing byproducts and underutilized aquatic species are addressed.
Video Panoptic Segmentation

Panoptic segmentation has become a new standard of visual recognition task by unifying previous semantic segmentation and instance segmentation tasks in concert. In this paper, we propose and explore a new video extension of this task, called video panoptic segmentation. The task requires generating consistent panoptic segmentation as well as an association of instance ids across video frames. To invigorate research on this new task, we present two types of video panoptic datasets. The first is a re-organization of the synthetic VIPER dataset into the video panoptic format to exploit its large-scale pixel annotations. The second is a temporal extension on the Cityscapes val. set, by providing new video panoptic annotations (Cityscapes-VPS). Moreover, we propose a novel video panoptic segmentation network (VPSNet) which jointly predicts object classes, bounding boxes, masks, instance id tracking, and semantic segmentation in video frames. To provide appropriate metrics for this task, we propose a video panoptic quality (VPQ) metric and evaluate our method and several other baselines. Experimental results demonstrate the effectiveness of the presented two datasets. We achieve state-of-the-art results in image PQ on Cityscapes and also in VPQ on Cityscapes-VPS and VIPER datasets.
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization

We consider the task of decentralized minimization of the sum of smooth strongly convex functions stored across the nodes a network. For this problem, lower bounds on the number of gradient computations and the number of communication rounds required to achieve $\varepsilon$ accuracy have recently been proven. We propose two new algorithms for this decentralized optimization problem and equip them with complexity guarantees. We show that our first method is optimal both in terms of the number of communication rounds and in terms of the number of gradient computations. Unlike existing optimal algorithms, our algorithm does not rely on the expensive evaluation of dual gradients. Our second algorithm is optimal in terms of the number of communication rounds, without a logarithmic factor. Our approach relies on viewing the two proposed algorithms as accelerated variants of the Forward Backward algorithm to solve monotone inclusions associated with the decentralized optimization problem. We also verify the efficacy of our methods against state-of-the-art algorithms through numerical experiments.
Bidirectionally self-normalizing neural networks

The problem of vanishing and exploding gradients has been a long-standing obstacle that hinders the effective training of neural networks. Despite various tricks and techniques that have been employed to alleviate the problem in practice, there still lacks satisfactory theories or provable solutions. In this paper, we address the problem from the perspective of high-dimensional probability theory. We provide a rigorous result that shows, under mild conditions, how the vanishing/exploding gradients problem disappears with high probability if the neural networks have sufficient width. Our main idea is to constrain both forward and backward signal propagation in a nonlinear neural network through a new class of activation functions, namely Gaussian-Poincaré normalized functions, and orthogonal weight matrices. Experiments on both synthetic and real-world data validate our theory and confirm its effectiveness on very deep neural networks when applied in practice.
Building Multiple Access Channels with a Single Particle

A multiple access channel describes a situation in which multiple senders are trying to forward messages to a single receiver using some physical medium. In this paper we consider scenarios in which this medium consists of just a single classical or quantum particle. In the quantum case, the particle can be prepared in a superposition state thereby allowing for a richer family of encoding strategies. To make the comparison between quantum and classical channels precise, we introduce an operational framework in which all possible encoding strategies consume no more than a single particle. We apply this framework to an N-port interferometer experiment in which each party controls a path the particle can traverse. When used for the purpose of communication, this setup embodies a multiple access channel (MAC) built with a single particle.We provide a full characterization of the N-party classical MACs that can be built from a single particle, and we show that every non-classical particle can generate a MAC outside the classical set. To further distinguish the capabilities of a single classical and quantum particle, we relax the locality constraint and allow for joint encodings by subsets of 1<K≤N parties. This generates a richer family of classical MACs whose polytope dimension we compute. We identify a "generalized fingerprinting inequality'' as a valid facet for this polytope, and we verify that a quantum particle distributed among N separated parties can violate this inequality even when K=N−1. Connections are drawn between the single-particle framework and multi-level coherence theory. We show that every pure state with K-level coherence can be detected in a semi-device independent manner, with the only assumption being conservation of particle number.
Characterization of Selected Common Lambsquarters (Chenopodium album) Biotypes with Tolerance to Glyphosate

Abstract Biotypes of common lambsquarters with tolerance to glyphosate have been identified in a number of states, but little is known about their fitness characteristics. Field and greenhouse studies were conducted to characterize the response of selected glyphosate-tolerant common lambsquarters biotypes to glyphosate, and also their biological and reproductive characteristics. In a greenhouse dose-response study, GR50 and GR90 values for four tolerant biotypes ranged from 1.48 to 3.22 and 8.73 to 18.7 kg ae ha−1, respectively, compared to 0.57 and 2.39 kg ae ha−1, respectively, for a glyphosate-sensitive biotype. In a field dose-response study, the GR50 and GR90 values were 0.06 and 0.48 kg ae ha−1, respectively, for a tolerant biotype, compared to 0.036 and 0.19 kg ae ha−1, respectively, for the sensitive biotype. The growth rate, time until flowering, and seed production of eight tolerant and two sensitive biotypes was evaluated in a field study. The tolerant biotypes grew taller, amassed more leaf area and dry weight, and advanced through growth stages more rapidly than sensitive biotypes during the early portion of the growing season. The tolerant biotypes were taller than sensitive biotypes at 6 and 10 wk after transplanting, but had lower dry weight at maturity. Tolerant biotypes initiated flower primordia approximately 6 to 8 wk after transplanting, whereas sensitive biotypes required 12 wk. However, no apparent fitness penalties were observed in glyphosate-tolerant biotypes based on seed-production estimates. Nomenclature: Glyphosate; common lambsquarters, Chenopodium album L.
Modeling glioma invasion with anisotropy- and hypoxia-triggered motility enhancement: from subcellular dynamics to macroscopic PDEs with multiple taxis

We deduce a model for glioma invasion that accounts for the dynamics of brain tissue being actively degraded by tumor cells via excessive acidity production, but also according to the local orientation of tissue fibers. Our approach has a multiscale character: we start with a microscopic description of single cell dynamics including biochemical and/or biophysical effects of the tumor microenvironment, translated on the one hand into cell stress and corresponding forces and on the other hand into receptor binding dynamics. These lead on the mesoscopic level to kinetic equations involving transport terms with respect to all considered kinetic variables and eventually, by appropriate upscaling, to a macroscopic reaction–diffusion equation for glioma density with multiple taxis, coupled to (integro-)differential equations characterizing the evolution of acidity and macro- and mesoscopic tissue. Our approach also allows for a switch between fast and slower moving regimes, according to the local tissue anisotropy. We perform numerical simulations to assess the importance of each tactic term and investigate the influence of two models for tissue dynamics on the tumor shape. We also suggest how the model can be used to perform a numerical necrosis-based tumor grading or support radiotherapy planning by dose painting. Finally, we discuss alternative ways of including cell level environmental influences in such a multiscale modeling approach, ultimately leading in the macroscopic limit to (multiple) taxis.
Bedding Plant Growth in Greenhouse Waste and Biosolid Compost

●ADDITIONAL INDEX WORDS. begonia, impatiens, sewage sludge, biosolids, yard trimmings, recycling, used greenhouse growing substrate, Begonia ×semperflorens-cultorum, Impatiens wallerana SUMMARY. Growth of ‘Oasis Scarlet’ begonia (Begonia ×semperflorenscultorum Hort.) and ‘Super Elfin Violet’ impatiens (Impatiens wallerana Hook. f.) was compared in substrates containing compost made from used greenhouse substrates and yard trimmings (GHC) and in compost made from biosolids and yard trimmings (SYT). Treatments consisted of 100% compost (GHC or SYT) or compost combined with control substrate components at 60%, 30%, or 0%. Substrates containing SYT compost produced significantly larger begonia and impatiens plants than substrates containing GHC compost. Higher initial substrate nutrient concentrations in substrates containing SYT probably prompted increased begonia and impatiens growth because substrates containing SYT compost had significantly higher initial soluble salt, nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg)
Ecological Simplification: Human Influences on Riverscape Complexity

The rationale of most restoration strategies is that with reconstruction of natural habitats comes biodiversity, and ecosystem functioning and services will follow suit. Uncertainty and frequent failure in restoration outcomes, however, are recurrent and likely related to the complexity of ecosystem properties. Here, we propose ecological simplification as the general mechanism by which human impacts have modified cross-scale relationships among landscape complexity, integrity, and niche diversity in ecosystems. To manage and reverse the negative effects of ecological simplification, the interplay between research and management must quantify the large-scale complexity of reference to restore simplified systems and to link these measures to niche diversity quantified at finer scales. Because of their historical interaction with human societies, we use riverine floodplains as model ecosystems to review the causes and consequences of simplification and to discuss how contemporary restoration can minimize the effects of simplification on biodiversity, functioning, and services of riverine floodplains.
Galois-theoretic features for 1-smooth pro-p groups

Abstract Let p be a prime. A pro-p group G is said to be 1-smooth if it can be endowed with a continuous representation $\theta \colon G\to \mathrm {GL}_1(\mathbb {Z}_p)$ such that every open subgroup H of G, together with the restriction $\theta \vert _H$ , satisfies a formal version of Hilbert 90. We prove that every 1-smooth pro-p group contains a unique maximal closed abelian normal subgroup, in analogy with a result by Engler and Koenigsmann on maximal pro-p Galois groups of fields, and that if a 1-smooth pro-p group is solvable, then it is locally uniformly powerful, in analogy with a result by Ware on maximal pro-p Galois groups of fields. Finally, we ask whether 1-smooth pro-p groups satisfy a “Tits’ alternative. ”
Preference of Seeds and Seed Particles by Messor arenarius (Hymenoptera: Formicidae) During Food Choice Experiments

Abstract This study deals with preference of different seed particles of a novel type of foods by ants of the species Messor arenarius (Fabricius). Given choices between different wheat seed particles at the same distances from the nest, the first time wheat was offered to M. arenarius they reacted as follows: They preferred halves of wheat seeds cut longitudinally most significantly over whole-wheat seeds. Three-quartered of wheat seeds were preferred significantly over one-quartered wheat seeds. There was no preference in the choice between whole-wheat seeds and halves of wheat seeds cut transversely. From the second day and up to the fourth day when these choices were offered, the ants showed no preference in choice between whole-wheat seeds and halves of wheat seeds cut longitudinally. The preferences in the other choices were about the same as the first time that wheat was offered to the ants. The average returning time of ants to the nest with a whole-wheat seed did not differ significantly from that with a half seed cut longitudinally. From these findings, it seems that the first time wheat is offered to the ants, both the odor and the relative length of the food item are preference criteria used by the ants in their food choice.
Superheating fields of semi-infinite superconductors and layered superconductors in the diffusive limit: structural optimization based on the microscopic theory

The superheating field Hsh of the Meissner state is thought to determine the theoretical field-limit of superconducting accelerator cavities. We investigate Hsh of semi-infinite superconductors and layered structures in the diffusive limit using the well-established quasiclassical Green’s function formalism of the BCS theory. The coupled Maxwell–Usadel equations are self-consistently solved to obtain the spatial distributions of the magnetic field, screening current density, penetration depth, pair potential, and Hsh . For a semi-infinite superconductor in the diffusive limit, we obtain Hsh=0.795Hc0 at the temperature T→0 . Here, Hc0 is the thermodynamic critical-field at the zero temperature. By laminating a superconducting film (S) with the thickness d on a semi-infinite superconductor (Σ), we can engineer Hsh(d) of the layered structure. When d is the optimum thickness dm , Hsh can be larger than that of the simple semi-infinite superconductors made from the S and Σ materials: Hsh(dm) >max{Hsh(S),Hsh(Σ)} . The present study addresses the calculation of Hsh of the dirty heterostructure using the microscopic theory from beginning to end for the first time, which contributes to the microscopic understanding of the surface engineering for pushing up the accelerating gradient of superconducting cavities for particle accelerators.
Antibacterial activity of garlic and lime on isolates of extracted carious teeth.

Inhibitory activity of garlic (Allium sativum Linn.) and lime (Citrus aurantifolia Linn.) on seven bacterial species (Streptococcus mutans, Lactobacillus acidophilus, Norcadia asteroides, Pseudomonas aeruginosa, Actinomyces viscosus, Staphylococcus aureus and Veillonella alcaligens) isolated from 240 extracted, carious teeth were investigated using standard techniques. The mean zones of inhibition for garlic, lime and a mixture of both, ranged from (15.0 ± 0.7) - (27.0 ± 1.0); (21.0 ± 1.0) - (27.0 ± 0.7) and (21.0 ± 0.7) - (30.0 ± 0.5) respectively, as compared with that for Gentamycin (18.0 ± 0.0) - (23.0 ± 0.5), indicating antibacterial activity. MICs for garlic, lime and a mixture of both, were 31.25 - 62.50 mg mL-1; 1:2 v/v and 15.63 - 31.25 mg mL-1 respectively, which coincided with the respective MBCs. The result of this investigation suggests that a paste made by blending garlic and lime could be used as a mouth wash in the treatment of dental caries, mouth-sore, sore-throat and also, be incorporated into toothpaste to prevent dental caries.
Generating Annotated High-Fidelity Images Containing Multiple Coherent Objects

Recent developments related to generative models have enabled the generation of diverse and high-fidelity images. In particular, layout-to-image generation models have gained significant attention due to their capability to generate realistic and complex images containing distinct objects. These models are generally conditioned on either semantic layouts or textual descriptions. However, unlike natural images, providing auxiliary information can be extremely hard in domains such as biomedical imaging and remote sensing. In this work, we propose a multi-object generation framework1 that can synthesize images with multiple objects without explicitly requiring their contextual information during the generation process. Based on a vector-quantized variational autoencoder (VQ-VAE) backbone, our model learns to preserve spatial coherency within an image as well as semantic coherency through the use of powerful autoregressive priors. An advantage of our approach is that the generated samples are accompanied by object-level annotations. The efficacy of our approach is demonstrated through application on medical imaging datasets, where we show that augmenting the training set with the samples generated by our approach improves the performance of existing models.
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training inefficiency as the action space of the high-level, i.e., the goal space, is often large. Searching in a large goal space poses difficulties for both high-level subgoal generation and low-level policy learning. In this paper, we show that this problem can be effectively alleviated by restricting the high-level action space from the whole goal space to a $k$-step adjacency region centered by the current state using an adjacency constraint. We theoretically prove that the proposed adjacency constraint preserves the optimal hierarchical policy, and show that this constraint can be practically implemented by training an adjacency network that can discriminate between adjacent and non-adjacent subgoals. Experimental results on discrete and continuous control tasks show that our method outperforms the state-of-the-art HRL approaches.
Development and utilization of novel SSRs in foxtail millet [Setaria italica (L.) P. Beauv.].

Although the foxtail millet [Setaria italica (L.) P. Beauv. ] is recently regarded as a model crop for studying functional genomics of biofuel grasses, its genetic improvement to some extent was limited due to the non-availability of molecular markers, particularly the microsatellite markers and the saturated genetic linkage map. Considering this, we attempted to generate a significant number of microsatellite markers in cultivar ‘Prasad’. Two hundred and fifty-six clones were sequenced to generate 41.82-kb high-quality sequences retrieved from genomic library enriched with dinucleotide repeat motifs. Microsatellites were identified in 194 (76%) of the 256 positive clones, and 64 primer pairs (pp) were successfully designed from 95 (49%) unique SSR-containing clones. The 67.4% primer designing ability, 100% PCR amplification efficiency and 45.3% polymorphic potential in the parents of F2 mapping population established the efficacy of genomic microsatellites. All the 64 microsatellite markers displayed high level of cross-species amplification (~67%) in 10 millets and non-millets species. These experimental findings suggest the utility and efficacy of SSRs in diverse genotyping applications, resolving QTLs, phylogenetic relationships and transferability in several important grass species.
In vitro N degradability and N digestibility of raw, roasted or extruded canola, linseed and soybean

The N degradability and N digestibility of raw, roasted or extruded oilseeds were studied using an in vitro enzyme method. The N degradability and N digestibility of canola, linseed and soybean were calculated based on the proportional difference in N remaining after incubation and the initial N content. Heat treatments increased the undegradable N fraction of linseed and soybean, whereas that of canola was decreased by extrusion. Heat treatments did not decrease the N digestibility of the oilseeds compared to raw samples. The high N digestibility and lower acid detergent insoluble N values of heat treated oilseeds indicated no indigestible complexes were formed. In conclusion, roasting or extrusion can be used to increase the undegradable N fraction of linseed and soybean to increase the dietary protein availability for digestion in ruminants, but was less effective for canola. The present heat treatments did not damage the protein or affect the N digestibility of the oilseeds. ;
Isolation, identification, and purification of cinnamaldehyde from Cinnamomum zeylanicum bark oil. An antibacterial study

The present study was conducted to isolate the most important bioactive compound from Cinnamomum zeylanicum L. (Lauraceae) bark oil. The plant essential oil was extracted via steam distillation. Cinnamaldehyde was separated using a separating funnel and identified according to Tollen’s test followed by detection on TLC plates in comparison with standard cinnamaldehyde that served as positive control. Moreover, FTIR spectrometry and HPLC analysis were used to confirm the purity and identity of cinnamaldehyde. The isolated material was investigated for its antibacterial activity against six selected pathogenic bacteria. The Gram-positive bacteria were Staphylococcus aureus and Bacillus cereus; Gram-negative bacteria included Escherichia coli, Proteus mirabilis, Klebsiella pneumonia, and Pseudomonas aeruginosa. Cinnamaldehyde at different concentrations (1:1, 1:5, 1:10 and 1:20) was active against all tested bacteria and the highest inhibitory effect was observed against B. cereus (zone of inhibition: 25.3 mm) using the disk diffusion method. The minimal inhibitory concentration (MIC) of cinnamaldehyde was determined using a broth microdilution method in 96-well microtiter plates. MIC values ranged from 31.2 to 125.0 μg/mL. The most promising result was observed against B. cereus, while S. aureus, E. coli, and K. pneumonia ranked next (MIC: 62.5 μg/mL) followed by P. mirabilis and P. aeruginosa with a MIC of 125.0 μg/mL.