Miracle-dz/UniFAR-FTUs huggingface dataset:
Submitochondrial particle response to linear alkylbenzene sulfonates, nonylphenol polyethoxylates and their biodegradation derivatives

The effects on mitochondrial respiratory parameters of linear alkylbenzene sulfonates (LAS), nonylphenol polyeth-oxylates (NPEO), and some of their biotransformation products, namely sulfophenyl carboxylates (SPCs), nonylphenol (NP), and nonylphenoxy acetic acid (NP1EC), were recorded by using the in vitro response of submitochondrial particles (SMP) from beef heart. The toxicity of these compounds was estimated by determining their effects on the energy-coupled reverse electron transfer (RET), which is induced by ATP and succinate at the first site level of the respiratory chain and reduces exogenous NAD+ to NADH. The toxicity of the substances, expressed as the toxicant concentration decreasing the reduction rate of NAD+ to an extent of 50% (EC50), ranged from 0.61 mg/L for a commercial LAS mixture to 18,000 mg/L for individual SPCs; from 1.3 mg/L for NPEO, with an average of 10 ethoxy units, to 8.2 and 1.8 mg/L for NP1EC and NP, respectively. These results were related to the molecular structure of each compound class and compared with the toxicity values obtained by a variety of biological systems currently used for toxicity testing. The acute toxicity data have demonstrated that (a) the SMP bioassay is suitable for reproducing the toxicological response of whole organisms, such as fishes and invertebrates, to the tested chemicals; and (b) the hydrophobic moiety of these compounds plays a significant role in eliciting their toxic effects. From a toxicological standpoint, attention must be paid to the occurrence in natural waters of residual LAS, whereas in the case of NPEO both unaltered surfactant and all biotransformation products need to be identified and quantified.
Real-Time Aerosol Density Determination Utilizing a Modified Scanning Mobility Particle Sizer—Aerosol Particle Mass Analyzer System

Real time secondary organic aerosol (SOA) density evolution for m-xylene photo-oxidation and α-pinene ozonolysis was obtained using an Aerosol Particle Mass Analyzer (APM)/Scanning Mobility Particle Spectrometer (SMPS) setup, which has been modified to achieve higher transmission of particles and improved sampling frequency. The aerosol density of SOA generated from α-pinene ozonolysis was found to be 1.24 ± 0.03 g/cm3 while the aerosol generated from m-xylene photo-oxidation was determined to be 1.35 ± 0.03 g/cm3. These results confirm the measurement approach from a combined SMPS and Aerodyne Aerosol Mass Spectrometer (AMS) system and are found to be within good agreement with the effective density measurements.
The Effects of Body Mass Index on Softball Pitchers’ Hip and Shoulder Range of Motion

Abstract Currently it is hypothesized that increased body mass index may contribute to overuse injuries. Thus, if hip or shoulder range of motion is affected by body mass index, pitchers may be placing additional stress on joints as they seek to pitch at maximal velocity. The purpose of this study was to examine if range of motion at the hips and shoulders were related to body mass index classification. A sample of 147 female softball pitchers (17.0±4.2 years; 167.6±11.8 cm; 70.6±17.5 kg; body mass index=24.8±4.7 kg/m2) participated. Bilateral hip and shoulder range of motion were assessed. Multivariate analysis of variance results indicated body mass index [Wilks’ Λ=0.742, F=1.722, p=0.014, η2=0.095] significantly affected range of motion of the shoulder and hip. Post hoc results indicated the underweight group had significantly more range of motion than the obese group in hip internal range of motion on both the throwing side (mean difference=12.39, p=0.005) and glove side (mean difference=11.98, p=0.004). Although body composition is not overly emphasized among softball pitchers, the current study reveals excess weight may inhibit proper mechanics. Coaches, athletic trainers, strength and conditioning personnel, and athletes should acknowledge the role that body composition can play in affecting pitch outcomes.
Sex Difference of Ribosome in Stroke-Induced Peripheral Immunosuppression by Integrated Bioinformatics Analysis

Ischemic stroke (IS) greatly threatens human health resulting in high mortality and substantial loss of function. Recent studies have shown that the outcome of IS has sex specific, but its mechanism is still unclear. This study is aimed at identifying the sexually dimorphic to peripheral immune response in IS progression, predicting potential prognostic biomarkers that can lead to sex-specific outcome, and revealing potential treatment targets. Gene expression dataset GSE37587, including 68 peripheral whole blood samples which were collected within 24 hours from known onset of symptom and again at 24-48 hours after onset (20 women and 14 men), was downloaded from the Gene Expression Omnibus (GEO) datasets. First, using Bioconductor R package, two kinds of differentially expressed genes (DEGs) (nonsex-specific- and sex-specific-DEGs) were screened by follow-up (24-48 hours) vs. baseline (24 hours). 30 nonsex-specific DEGs (1 upregulated and 29 downregulated), 79 female-specific DEGs (25 upregulated and 54 downregulated), and none of male-specific DEGs were obtained finally. Second, bioinformatics analysis of female-specific DEGs was performed. Gene Ontology (GO) functional annotation analysis shows that DEGs were mainly enriched in translational initiation, cytosolic ribosome, and structural constituent of ribosome. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis shows that the top 6 enrichment pathways are ribosome, nuclear factor-kappa B (NF-kappa B) signaling pathway, apoptosis, mineral absorption, nonalcoholic fatty liver disease, and pertussis. Three functional modules were clustered in the protein–protein interaction (PPI) network of DEGs. The top 10 key genes of the PPI network constructed were selected, including RPS14, RPS15A, RPS24, FAU, RPL27, RPL31, RPL34, RPL35A, RSL24D1, and EEF1B2. Sex difference of ribosome in stroke-induced peripheral immunosuppression may be the potential mechanism of sex disparities in outcome after IS, and women are more likely to have stroke-induced immunosuppression. RPS14, RPS15A, RPS24, FAU, RPL27, RPL31, RPL34, RPL35A, RSL24D1, and EEF1B2 may be novel prognostic biomarkers and potential therapeutic targets for IS.
Characterization of sediment microbial communities at two sites with low hydrocarbon pollution in the southeast Gulf of Mexico

Background Coastal ecosystems are prone to hydrocarbon pollution due to human activities, and this issue has a tremendous impact on the environment, socioeconomic consequences, and represents a hazard to humans. Bioremediation relies on the ability of bacteria to metabolize hydrocarbons with the aim of cleaning up polluted sites. Methods The potential of naturally occurring microbial communities as oil degraders was investigated in Sisal and Progreso, two port locations in the southeast Gulf of Mexico, both with a low level of hydrocarbon pollution. To do so, we determined the diversity and composition of bacterial communities in the marine sediment during the dry and rainy seasons using 16S rRNA sequencing. Functional profile analysis (PICRUTSt2) was used to predict metabolic functions associated with hydrocarbon degradation. Results We found a large bacterial taxonomic diversity, including some genera reported as hydrocarbon-degraders. Analyses of the alpha and beta diversity did not detect significant differences between sites or seasons, suggesting that location, season, and the contamination level detected here do not represent determining factors in the structure of the microbial communities. PICRUTSt2 predicted 10 metabolic functions associated with hydrocarbon degradation. Most bacterial genera with potential hydrocarbon bioremediation activity were generalists likely capable of degrading different hydrocarbon compounds. The bacterial composition and diversity reported here represent an initial attempt to characterize sites with low levels of contamination. This information is crucial for understanding the impact of eventual rises in hydrocarbon pollution.
Acupuncture for the Treatment of Liver Cirrhosis: A Meta-analysis

Acupuncture is widely used in the clinical treatment of liver cirrhosis (LC) in China. However, the efficacy of acupuncture on LC has not been fully confirmed by systematic analysis. This current meta-analysis evaluated the impact effect of acupuncture on patients with LC. We conducted a systematic literature search of the China National Knowledge Infrastructure, the Chinese Biomedical Database (SinoMed), VIP medicine information system, Wanfang Data, PubMed, Cochrane Library, Web of Science, and Embase. Further, we used Review Manager 5.3 software for the analysis of the data and Stata 14.0 software for the Egger test to assess publication bias. Fifteen studies involving 1066 patients were included in the meta-analysis. The primary outcome was the efficacy rate of acupuncture therapy. The secondary outcomes were impact on acupuncture on liver function grading assessment and lab tests related to liver functions. The result suggested that acupuncture is an effective treatment option for patients with LC as a complementary therapy. However, the recommendation is weak due to some limitations of the included studies.
Analysis of electrical charging and discharging kinetics of different glasses under electron irradiation in a scanning electron microscope

This paper presents a comparative study of electrical charging and discharging behavior of different glasses submitted to electron beam irradiation in scanning electron microscope. Charge storage and charge spreading in these glasses have been examined with help of a time resolved current method. Our interest concerns more particularly the dynamic behavior and the amount of the space charge build-up during and after electron irradiation under different experimental conditions of primary beam energy and current density. The precise contributions of different possible self regulation processes (leakage current and secondary electron emission) for charge accumulation are analyzed. Moreover, to characterize the ability of glasses to store charges in a stable way we introduce a relevant parameter that expresses quantitatively the variation in the released charge. The primary beam energy and the current density effects on the evolution of secondary electron emission yield during irradiation are also examined. As expected, the charge storage and spreading processes appear to be extremely dependent on the incident beam energy, current density and on the chemical composition of the studied glasses.
Lexically-constrained Text Generation through Commonsense Knowledge Extraction and Injection

Conditional text generation has been a challenging task that is yet to see human-level performance from state-of-the-art models. In this work, we specifically focus on the Commongen benchmark, wherein the aim is to generate a plausible sentence for a given set of input concepts. Despite advances in other tasks, large pre-trained language models that are fine-tuned on this dataset often produce sentences that are syntactically correct but qualitatively deviate from a human understanding of common sense. Furthermore, generated sequences are unable to fulfill such lexical requirements as matching part-of-speech and full concept coverage. In this paper, we explore how commonsense knowledge graphs can enhance model performance, with respect to commonsense reasoning and lexically-constrained decoding. We propose strategies for enhancing the semantic correctness of the generated text, which we accomplish through: extracting commonsense relations from Conceptnet, injecting these relations into the Unified Language Model (UniLM) through attention mechanisms, and enforcing the aforementioned lexical requirements through output constraints. By performing several ablations, we find that commonsense injection enables the generation of sentences that are more aligned with human understanding, while remaining compliant with lexical requirements.
List-decodability with large radius for Reed-Solomon codes

List-decodability of Reed-Solomon codes has re-ceived a lot of attention, but the best-possible dependence between the parameters is still not well-understood. In this work, we focus on the case where the list-decoding radius is of the form $r=1-\varepsilon$ for $\varepsilon$ tending to zero. Our main result states that there exist Reed-Solomon codes with rate $\Omega(\varepsilon)$ which are $(1-\varepsilon, O(1/\varepsilon)$ -list-decodable, meaning that any Hamming ball of radius $1-\varepsilon$ contains at most $O(1/\varepsilon)$ codewords. This trade-off between rate and list-decoding radius is best-possible for any code with list size less than exponential in the block length. By achieving this trade-off between rate and list-decoding radius we improve a recent result of Guo, Li, Shangguan, Tamo, and Wootters, and resolve the main motivating question of their work. Moreover, while their result requires the field to be exponentially large in the block length, we only need the field size to be polynomially large (and in fact, almost-linear suffices). We deduce our main result from a more general theorem, in which we prove good list-decodability properties of random puncturings of any given code with very large distance.
Reinforcement Learning based Multi-Robot Classification via Scalable Communication Structure

In the multi-robot collaboration domain, training with Reinforcement Learning (RL) can become intractable, and performance starts to deteriorate drastically as the number of robots increases. In this work, we proposed a distributed multi-robot learning architecture with a scalable communication structure capable of learning a robust communication policy for time-varying communication topology. We construct the communication structure with Long-Short Term Memory (LSTM) cells and star graphs, in which the computational complexity of the proposed learning algorithm scales linearly with the number of robots and suitable for application with a large number of robots. The proposed methodology is validated with a map classification problem in the simulated environment. It is shown that the proposed architecture achieves a comparable classification accuracy with the centralized methods, maintains high performance with various numbers of robots without additional training cost, and robust to hacking and loss of the robots in the network.
Monte-Carlo Graph Search for AlphaZero

The AlphaZero algorithm has been successfully applied in a range of discrete domains, most notably board games. It utilizes a neural network, that learns a value and policy function to guide the exploration in a Monte-Carlo Tree Search. Although many search improvements have been proposed for Monte-Carlo Tree Search in the past, most of them refer to an older variant of the Upper Confidence bounds for Trees algorithm that does not use a policy for planning. We introduce a new, improved search algorithm for AlphaZero which generalizes the search tree to a directed acyclic graph. This enables information flow across different subtrees and greatly reduces memory consumption. Along with Monte-Carlo Graph Search, we propose a number of further extensions, such as the inclusion of Epsilon-greedy exploration, a revised terminal solver and the integration of domain knowledge as constraints. In our evaluations, we use the CrazyAra engine on chess and crazyhouse as examples to show that these changes bring significant improvements to AlphaZero.
Base Station Coordination Scheme for Multi-Tier Ultra-Dense Networks

In this paper, we consider a relative received link power (RRLP)-based coordinated multi-point (CoMP) joint transmission (JT) in the multi-tier ultra-dense networks (UDN). In this CoMP scheme, we identify the cooperating base stations (BSs) by comparing the average received link power (ARLP) of the neighbouring BSs with respect to the BS having the strongest ARLP (i.e., the main link BS) to a user. To analyze the performance of this CoMP scheme in the downlink multi-tier UDN, we first approximate the received signal power distribution, and derive the coverage probability using stochastic geometry. After revisiting the area spectral efficiency (ASE) to make it more suitable for CoMP transmission in UDN, we also analyze the ASE and the network energy efficiency (NEE). Using simulations, we validate the derived coverage probability, and investigate the CoMP performance in multi-tier UDN. Our simulations show that the RRLP-based CoMP scheme can outperform the fixed number of strongest BS-based CoMP scheme in the high BS density regime. Our study of the NEE performance reveals that not only the RRLP-based CoMP scheme is more efficient than conventional non-CoMP transmission scenario, but also its NEE performance improves with the average number of cooperating BSs.
Sensitivity of Cosmological Parameter Estimation to Nonlinear Prescription from Galaxy Clustering

Next-generation large-scale surveys will probe the nonlinear regime with high resolution. Making viable cosmological inferences based on these observations requires accurate theoretical modeling of the mildly nonlinear regime. In this work we investigate the sensitivity of cosmological parameter measurements from future probes of galaxy clustering to the choice of nonlinear prescription up to . In particular, we calculate the induced parameter bias when the mildly nonlinear regime is modeled by the Halofit fitting scheme. We find significant (∼5σ) bias for some parameters with a future Euclid-like survey. We also explore the contribution of different scales to the parameter estimation for different observational setups and cosmological scenarios, compared for the two nonlinear prescriptions of Halofit and EFTofLSS. We include in the analysis the free parameters of the nonlinear theory and a blind parameterization for the galaxy bias. We find that marginalization over these nuisance parameters significantly boosts the errors of the standard cosmological parameters. This renders the differences in the predictions of the various nonlinear prescriptions less effective when transferred to the parameter space. More accurate modeling of these nuisance parameters would therefore greatly enhance the cosmological gain from the mildly nonlinear regime.
1.3 µm Solid-State Plastic Laser in Dye-Doped Fluorinated-Polyimide Waveguide

1.3 µm solid-state plastic lasers were fabricated using fluorinated-polyimide (FPI) as the host matrix of the active waveguide doped with near-infrared organic dye, IR26. Fabry–Perot type lasing oscillation was observed from the waveguide by using the cleaved waveguide edges under 1.064 µm Nd:YAG laser pulse pumping. The lasing threshold was 4.3 mJ/cm2 and the lasing emission shows transverse electric mode polarization. Photostability of IR26-doped FPI waveguide is shown to be fairly good in the durability test under 1.064 µm laser pumping. This is the first report of plastic lasers in optical communication waveband.
Effectiveness of SCADA System Security Used Within Critical Infrastructure

Since the 1960s Supervisory Control and Data Acquisition (SCADA) systems have been used within industry. Referred to as critical infrastructure (CI), key installations such as power stations, water treatment and energy grids are controlled using SCADA. Existing literature reveals inherent security risks to CI and suggests this stems from the rise of interconnected networks, leading to the hypothesis that the rise of interconnectivity between corporate networks and SCADA system networks pose security risks to CI. The results from studies into previous global attacks involving SCADA and CI, with focus on two highly serious incidents in Iran and Ukraine, reveal that although interconnectivity is a major factor, isolated CIs are still highly vulnerable to attack due to risks within the SCADA controllers and protocols.
5G New Radio Unlicensed: Challenges and Evaluation

To meet the high demand for mobile data, the Third Generation Partnership Project (3GPP) established a set of standards known as 5G New Radio (5G NR). The architecture of 5G NR includes a flexible radio access network and a core network. 3GPP has also been working on a new radio access technology, called 5G NR Unlicensed (5G NR-U), which aims at extending 5G NR to unlicensed bands. In this article, we give an overview of the most recent 5G NR-U design elements and discuss potential concerns, including fair coexistence with other unlicensed technologies such as Wi-Fi. We use simulations to study coexistence between Wi-Fi and 5G NR-U systems. Our evaluation indicates that NR-U often achieves higher throughput and lower delay than Wi-Fi (802.11ac). The two systems experience different buffer occupancies and spectrum utilization statistics. We also discuss the improvements that NR-U offers over LTE Licensed Assisted Access (LTE-LAA).
Who will accept my request? Predicting response of link initiation in two-way relation networks

Popularity of social networks has rapidly increased over the past few years, and daily lives interrupt without their proper functioning. Social networking platform provide multiple interaction types between individuals, such as creating and joining groups, sending and receiving messages, sharing interests and creating friendship relationships. This paper addresses an important problem in social networks analysis and mining that is how to predict link initiation feedback in two-way networks. Relationships between two individuals in a two-way network include a link invitation from one of the individuals, which will be an established link if it is accepted by the invitee. We consider a sport gaming social networking platform and construct a multilayer social network between a number of users. The network formed by the link initiation process is on one of the layers, while the other two layers include a messaging relationships and interactions between the users. We propose a methodology to solve the link initiation feedback prediction problem in this multilayer fashion. The proposed method is based on features extracted from meta-paths, i.e. paths defined between different individuals from multiples layers in multilayer networks. We proposed a clusterbased approach to handle the sparsity issue in the dataset. Experimental results show that the proposed method can provide accurate prediction that outperforms state-of-the-art methods.
Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism

In this paper, we provide a general framework for studying multi-agent online learning problems in the presence of delays and asynchronicities. Specifically, we propose and analyze a class of adaptive dual averaging schemes in which agents only need to accumulate gradient feedback received from the whole system, without requiring any between-agent coordination. In the single-agent case, the adaptivity of the proposed method allows us to extend a range of existing results to problems with potentially unbounded delays between playing an action and receiving the corresponding feedback. In the multi-agent case, the situation is significantly more complicated because agents may not have access to a global clock to use as a reference point; to overcome this, we focus on the information that is available for producing each prediction rather than the actual delay associated with each feedback. This allows us to derive adaptive learning strategies with optimal regret bounds, even in a fully decentralized, asynchronous environment. Finally, we also analyze an"optimistic"variant of the proposed algorithm which is capable of exploiting the predictability of problems with a slower variation and leads to improved regret bounds.