Determination of the Digestibility of a Whole-Cell DHA-Rich Algal Product and Its Effect on the Lipid Composition of Rainbow Trout and Atlantic Salmon

A whole-cell DHA-rich algal product (A-DHA, provided by Evonik Industries) that is rich in DHA (125 mg DHA/g dry matter) is a possible replacement for fish oil in salmonid diets. The nutrient digestibilities of the algal product were measured in rainbow trout in freshwater and in Atlantic salmon in saltwater (32-33 ppm). In experiment 1, rainbow trout (initial weight ~ 300g) were randomly assigned to 12 x 120 L tanks (n = 10 per tank). A reference diet containing 1% Celite as an indigestible marker and three test diets with increasing percentage of A-DHA substitution (6.67%, 13.33% and 20%) were fed. Feces were collected using a settling column and feed and feces analyzed for digestible dry matter (DM), gross energy (GE), ash, crude protein (CP), essential amino acids and total lipid. The digestibility of six long-chain fatty acids including 18:1n-9 (OA), 18:2n-6 (LA), 18:3n-3 (ALA), 20:4n-6 (ARA), 20:5n-3 (EPA) and 22:6n-3 (DHA) was measured. In experiment 2, Atlantic salmon (~170g) were randomly distributed to 12 fiberglass tanks (600L) with 106 fish per tank. The fish were assigned to four diets with the same levels of A-DHA inclusion as for rainbow trout and yttrium oxide (Y2O3) was used as an inert marker. Feces were collected by stripping and the digestibilities of DM, CP and lipid as well as OA, LA, ALA, ARA, EPA and DHA were determined. In experiment 1, the apparent digestibility of dietary DM, GE and lipid in rainbow trout declined significantly with increasing inclusion of A-DHA (P 0.05). Furthermore, increased inclusion of A-DHA resulted in significantly lower digestibility of ARA, EPA and DHA (P 0.05). In experiment 2, dietary inclusion of A-DHA had a significantly negative effect on lipid digestibility in Atlantic salmon, at all inclusion rates whereas the significant negative effect on digestibilities of DM and CP was only observed in fish fed 20% A-DHA. The digestibilities of OA, LA, ALA and EPA were greater than 91%. In contrast, the apparent digestibilities of ARA and DHA decreased significantly with increasing substitution of A-DHA (P < 0.01). Significantly negative linear and quadratic regressions were found between nutrient contribution from A-DHA to the diets and apparent digestibility of DM, CP and lipid, so were LA, EPA and DHA. However, there were only significant quadratic regressions for OA, ALA and ARA, but not significant linear effects. Subsequently, a twelve-week feeding trial in rainbow trout was conducted to investigate the impact of replacing fish oil with A-DHA in canola-oil-based diets on the growth performance and fatty acid composition and retention. Four experimental diets containing only canola oil (CO; 13.5%), fish oil (FO; 13.5%), canola oil and fish oil (C+F; 7.4% and 6.1%, respectively) or canola oil and A-DHA (C+A; 15.5% and 6%, respectively) were formulated to contain 386.2 g/kg digestible crude protein and 17.58 MJ/kg digestible energy. In addition, the C+A diet was formulated to have the same DHA concentration as in the C+F diet. Each diet was fed to three tanks of rainbow trout (average initial weight of 70g; n = 17/tank) and the fish were fed to apparent satiation 2 times daily. At the end of the growth trial, all fish approximately tripled their weight. No significant differences were noted between the dietary treatments in growth performance as measured by final weight, average weight gain, feed intake, specific growth rate (SGR) and feed conversion ratio (FCR). Although FO and C+A fed fish tended to accumulate more lipids, final whole body lipid content did not differ significantly between dietary treatments (P = 0.11). The concentrations of EPA, DHA as well as total n-3 fatty acid were significantly higher in fish fed the FO diet than fish fed the other 3 diets. The C+A fed fish had lower EPA and higher DHA concentrations compared with the CO and C+F fed fish; however, the differences were not significant. Apparent retention of total lipid in the trout was not significantly influenced by treatments (P > 0.05). Similarly, dietary treatments had no significant effect on the apparent retention of total saturated fatty acids, total mono-unsaturated fatty acids, n-3 polyunsaturated fatty acids and n-6 polyunsaturated fatty acids. The retention of 18:4n-3 (SDA) was significantly higher (> 100%) in fish fed CO and C+A compared with fish fed FO and C+F (< 51%), indicating greater bioconversion of ALA to SDA in the CO and C+A fed fish than in FO and C+F fed fish. The retention of EPA in the CO and C+A fed fish was over 100%, suggesting a net synthesis of EPA in these treatment groups. In contrast, the EPA retention in the FO and C+F fed fish was 55 and 21%, respectively, which showed a tendency to be significantly lower than that in the other two groups (P = 0.09). The CO fed fish had significantly higher DHA retention than fish fed the other 3 diets. The DHA retention in the FO fed fish (112%) was numerically but not significantly higher than in the C+F (66%) and C+A fed fish (73%). Thus, feeding the C+A to rainbow trout resulted in DHA retention equal to feeding the C+F

Awesome-META+: Meta-Learning Research and Learning Platform

Artificial intelligence technology has already had a profound impact in various fields such as economy, industry, and education, but still limited. Meta-learning, also known as "learning to learn", provides an opportunity for general artificial intelligence, which can break through the current AI bottleneck. However, meta learning started late and there are fewer projects compare with CV, NLP etc. Each deployment requires a lot of experience to configure the environment, debug code or even rewrite, and the frameworks are isolated. Moreover, there are currently few platforms that focus exclusively on meta-learning, or provide learning materials for novices, for which the threshold is relatively high. Based on this, Awesome-META+, a meta-learning framework integration and learning platform is proposed to solve the above problems and provide a complete and reliable meta-learning framework application and learning platform. The project aims to promote the development of meta-learning and the expansion of the community, including but not limited to the following functions: 1) Complete and reliable meta-learning framework, which can adapt to multi-field tasks such as target detection, image classification, and reinforcement learning. 2) Convenient and simple model deployment scheme which provide convenient meta-learning transfer methods and usage methods to lower the threshold of meta-learning and improve efficiency. 3) Comprehensive researches for learning. 4) Objective and credible performance analysis and thinking

System Fingerprint Recognition for Deepfake Audio: An Initial Dataset and Investigation

The malicious use of deep speech synthesis models may pose significant threat to society. Therefore, many studies have emerged to detect the so-called ``deepfake audio". However, these studies focus on the binary detection of real audio and fake audio. For some realistic application scenarios, it is needed to know what tool or model generated the deepfake audio. This raises a question: Can we recognize the system fingerprints of deepfake audio? Therefore, in this paper, we propose a deepfake audio dataset for system fingerprint recognition (SFR) and conduct an initial investigation. We collected the dataset from five speech synthesis systems using the latest state-of-the-art deep learning technologies, including both clean and compressed sets. In addition, to facilitate the further development of system fingerprint recognition methods, we give researchers some benchmarks that can be compared, and research findings. The dataset will be publicly available.Comment: 12 pages, 3 figures. arXiv admin note: text overlap with arXiv:2208.0964

Transcribing Latin Manuscripts in Respect to Linguistics

Current text detection software, although can transcribe modern languages with high accuracy, has flaws detecting texts and transcribing original Latin manuscripts sufficiently. This paper proposes a general approach for transcribing Latin manuscripts in respect to linguistics and develops a system to transcribe Latin manuscripts containing intricate abbreviations, which combines basic object detection algorithms with linguistics. We used methods from image processing and made changes based on the characteristics of Latin.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Exciton-polariton based WS2 polarization modulator controlled by optical Stark beam

The recent era of fast optical manipulation and optical devices owe a lot to exciton-polaritons being lighter in mass, faster in speed and stronger in nonlinearity due to hybrid light-matter characteristics. The room temperature existence of polaritons in two dimensional materials opens up new avenues to the design and analysis of all optical devices and has gained the researchers attention. Here, spin-selective optical Stark effect is introduced to form a waveguide effect in uniform community of polaritons, and is used to realize polarization modulation of polaritons. The proposed device basically takes advantage of the spin-sensitive properties of optical Stark effect of polaritons inside the WS2 microcavity so as to guide different modes and modulate polarization of polaritons. It is shown that polaritonic wavepacket of different mode profiles can be generated by changing intensity of the optical Stark beam and the polarization of polaritons can be controlled and changed periodically along the formed waveguide by introduction birefringence that is sensitive to polarization degree of the optical Stark beam

Language to Rewards for Robotic Skill Synthesis

Large language models (LLMs) have demonstrated exciting progress in acquiring diverse new capabilities through in-context learning, ranging from logical reasoning to code-writing. Robotics researchers have also explored using LLMs to advance the capabilities of robotic control. However, since low-level robot actions are hardware-dependent and underrepresented in LLM training corpora, existing efforts in applying LLMs to robotics have largely treated LLMs as semantic planners or relied on human-engineered control primitives to interface with the robot. On the other hand, reward functions are shown to be flexible representations that can be optimized for control policies to achieve diverse tasks, while their semantic richness makes them suitable to be specified by LLMs. In this work, we introduce a new paradigm that harnesses this realization by utilizing LLMs to define reward parameters that can be optimized and accomplish variety of robotic tasks. Using reward as the intermediate interface generated by LLMs, we can effectively bridge the gap between high-level language instructions or corrections to low-level robot actions. Meanwhile, combining this with a real-time optimizer, MuJoCo MPC, empowers an interactive behavior creation experience where users can immediately observe the results and provide feedback to the system. To systematically evaluate the performance of our proposed method, we designed a total of 17 tasks for a simulated quadruped robot and a dexterous manipulator robot. We demonstrate that our proposed method reliably tackles 90% of the designed tasks, while a baseline using primitive skills as the interface with Code-as-policies achieves 50% of the tasks. We further validated our method on a real robot arm where complex manipulation skills such as non-prehensile pushing emerge through our interactive system.Comment: https://language-to-reward.github.io

Dispersive solid-phase microextraction with graphene oxide based molecularly imprinted polymers for determining bis(2-ethylhexyl) phthalate in environmental water

A novel graphene oxide-molecularly imprinted polymers (GO-MIPs) was prepared and applied for selective extraction and preconcentration of bis(2-ethylhexyl) phthalate (DEHP) in environmental water samples by using the dispersive solid-phase microextraction (DSPME) method. The GO-MIPs was synthesized via precipitation polymerization using GO, DEHP, methacrylic acid, and ethylene dimethacrylate as supporting materials, template molecules, functional monomer, and cross-linker, respectively. The prepared GO-MIPs were characterized by scanning electron microscope and Fourier transform infrared spectroscopy. The GO-MIPs-DSPME conditions including type and volume of elution solvents, adsorbents amount, initial concentration of DEHP, pH and ionic strength of water samples were investigated. Under optimized conditions, the DEHP was selectively and effectively extracted in real water samples and enrichment factors of over 100-fold were achieved. Good linearity was obtained with correlation coefficients (R2) over 0.999 and the detection limit (S/N = 3) was 0.92 ng mL−1. The average recoveries of the spiked samples at three concentration levels of DEHP ranged from 82% to 92% with the relative standard deviations less than 6.7%. The results indicated that the proposed GO-MIPs-DSPME extraction protocol combined with HPLC-UV determination could be applied for selective and sensitive analysis of trace DEHP phthalate in environmental water samples

Combined searches for the production of supersymmetric top quark partners in proton-proton collisions at root s=13 TeV

A combination of searches for top squark pair production using proton-proton collision data at a center-of-mass energy of 13 TeV at the CERN LHC, corresponding to an integrated luminosity of 137 fb(-1) collected by the CMS experiment, is presented. Signatures with at least 2 jets and large missing transverse momentum are categorized into events with 0, 1, or 2 leptons. New results for regions of parameter space where the kinematical properties of top squark pair production and top quark pair production are very similar are presented. Depending on themodel, the combined result excludes a top squarkmass up to 1325 GeV for amassless neutralino, and a neutralinomass up to 700 GeV for a top squarkmass of 1150 GeV. Top squarks with masses from 145 to 295 GeV, for neutralino masses from 0 to 100 GeV, with a mass difference between the top squark and the neutralino in a window of 30 GeV around the mass of the top quark, are excluded for the first time with CMS data. The results of theses searches are also interpreted in an alternative signal model of dark matter production via a spin-0 mediator in association with a top quark pair. Upper limits are set on the cross section for mediator particle masses of up to 420 GeV