Miracle-dz/UniFAR-FTUs huggingface dataset:
Zebrafish Embryo Toxicity of a Binary Mixture of Pyrethroid Insecticides: d-Tetramethrin and Cyphenothrin

Pesguard FG161™, a mixture of d-tetramethrin and cyphenothrin (1:3 ratio), is extensively used to achieve rapid control of adult dengue vector, Aedes aegypti, during the disease outbreaks. Both d-tetramethrin and cyphenothrin are synthetic pyrethroids that are known to have adverse effects on non-mammalian organisms such as fish. The present study intended to use zebrafish embryo toxicity model to investigate the toxic effect of the above binary mixture on fish. Particularly, zebrafish embryo toxicity model provides an alternative to acute fish toxicity tests in terms of animal welfare perspective as the embryos are not considered live until 5 days after fertilization. The zebrafish embryos (2 hrs after fertilization) were exposed to a binary mixture of pyrethroids at different concentrations (d-tetramethrin: 0.01 – 1.20 μmolL−1 and cyphenothrin: 0.03 – 3.20 μmolL−1) for 24, 48, and 72 hrs at room temperature (26°C) according to the OECD guideline no. 236. Percentage mortality of embryos were calculated by observing the lethal endpoints and LC50 values were calculated for each time interval employing the probit analysis. This binary mixture was highly toxic to zebrafish embryos and was found to be concentration and time dependent. LC50 values at 24 hrs (d-tet: 0.58 μmolL−1, cyp: 1.74 μmolL−1) were significantly reduced in 48 hrs (d-tet: 0.11 μmolL−1, cyp: 0.33 μmolL−1) and 72 hrs (d-tet: 0.03 μmolL−1, cyp: 0.09 μmolL−1). Coagulation of embryos was the most common lethal effect observed and lack of somite formation and lack of heartbeat were also observed. The present study revealed that the binary mixture is highly toxic to zebrafish embryos even when based on nominal concentrations. Hence, extensive use of these pesticides could be detrimental to fish population and integrated vector control methods which involve the minimum use of insecticides are recommended. Further, this study highlights the applicability of zebrafish embryo toxicity model as an alternative method to investigate the toxicity of pyrethroids to fish.
Therapeutic Properties of Stingless Bee Honey in Comparison with European Bee Honey

Both honeybees (Apis spp.) and stingless bees (Trigona spp.) produce honeys with high nutritional and therapeutics value. Until recently, the information regarding potential health benefits of stingless bee honey (SBH) in medical databases is still scarce as compared to the common European bee honey (EBH) which is well known for their properties as therapeutic agents. Although there have been very few reports on SBH, empirically these products would have similar therapeutic quality as the EBH. In addition, due to the structure of the nest, few studies reported that the antimicrobial activity of SBH is a little bit stronger than EBH. Therefore, the composition of both the types of honey as well as the traditional uses and clinical applications were compared. The results of various studies on EBH and SBH from tissue culture research to randomised control clinical trials were collated in this review. Interestingly, there are many therapeutic properties that are unique to SBH. Therefore, SBH has a great potential to be developed for modern medicinal uses.
Effects of a Meal Replacement on Body Composition and Metabolic Parameters among Subjects with Overweight or Obesity

Meal replacement plans are effective tools for weight loss and improvement of various clinical characteristics but not sustainable due to the severe energy restriction. The aim of the study was to evaluate the impact of meal replacement, specifically 388 kcal in total energy, on body composition and metabolic parameters in individuals with overweight and obesity from a Chinese population. A parallel, randomized controlled trial was performed with 174 participants (ChiCTR-OOC-17012000). The intervention group (N=86) was provided with a dinner meal replacement, and the control group (N=88) continued their routine diet as before. Body composition and blood parameters were assessed at 0, 4, 8, and 12 weeks. A post hoc analysis (least significant difference (LSD) test), repeated measurements, and paired T-test were used to compare each variable within and between groups. Significant (p < 0.001) improvements in body composition components were observed among the intervention group, including body weight (−4.3 ± 3.3%), body mass index (−4.3 ± 3.3%), waist circumference (−4.3 ± 4.4%), fat-free mass (−1.8 ± 2.9%), and body fat mass (−5.3 ± 8.8%). Body composition improvements corresponded with significant metabolic improvements of blood glucose (−4.7 ± 9.8%). Further improvements in visceral fat area (−7.7 ± 10.1%), accompanying with improvements in systolic (−3.7 ± 6.9%) and diastolic (−5.3 ± 7.7%) blood pressure, were only found in male subjects. To conclude, meal replacement intake with 388 kcal in total energy at dinner time for 12 weeks contributed to improvement in body composition and clinically significant metabolic parameters in both male and female participants with overweight/obesity. Additionally, glucose and blood pressure reduction were gender-specific highlighting the importance of gender stratification for design of nutritional intervention studies for improvement of health.
OmpA-Like Proteins of Porphyromonas gingivalis Mediate Resistance to the Antimicrobial Peptide LL-37

Subgingival bacteria are continually exposed to gingival crevicular fluids that are derived from serum, which contain various bactericidal agents. The periodontopathic bacterium Porphyromonas gingivalis has been demonstrated to possess a variety of abilities to resist bactericidal agents, due to which it is able to propagate in the subgingival environment. We previously demonstrated that the major surface glycoproteins of P. gingivalis—Pgm6 and Pgm7, also called outer membrane protein A-like proteins (OmpALPs)—mediate resistance to the bactericidal activity of human serum, but their precise role remains unknown. In this study, we investigated the sensitivity of the wild-type and Pgm6/Pgm7-deficient P. gingivalis strains toward major antimicrobial peptides in the oral cavity, human β-defensins (hBDs) 1-3, and human cathelicidin LL-37. hBDs showed a considerably weak bactericidal activity against both bacterial strains. LL-37 also showed a weak activity against the wild-type strain; however, it showed a significant activity against the Pgm6/Pgm7-deficient strain. In the Pgm6/Pgm7-deficient strain, LL-37 remarkably accumulated on the bacterial cell surface, which may result in the destruction of the outer membrane. Additionally, the bactericidal activity of hBDs against the Pgm6/Pgm7-deficient strain was found to be synergistically promoted in the presence of LL-37. Our results suggest that OmpALPs specifically protect P. gingivalis from the bactericidal activity of LL-37; thus, P. gingivalis may adeptly survive in LL-37-producing subgingival environments.
PlayeRank

The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this article, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players’ evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by PlayeRank and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank—i.e. searching players and player versatility—showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.
On the Complexity of Approximating Wasserstein Barycenter

We study the complexity of approximating Wassertein barycenter of $m$ discrete measures, or histograms of size $n$ by contrasting two alternative approaches, both using entropic regularization. The first approach is based on the Iterative Bregman Projections (IBP) algorithm for which our novel analysis gives a complexity bound proportional to $\frac{mn^2}{\varepsilon^2}$ to approximate the original non-regularized barycenter. Using an alternative accelerated-gradient-descent-based approach, we obtain a complexity proportional to $\frac{mn^{2.5}}{\varepsilon} $. As a byproduct, we show that the regularization parameter in both approaches has to be proportional to $\varepsilon$, which causes instability of both algorithms when the desired accuracy is high. To overcome this issue, we propose a novel proximal-IBP algorithm, which can be seen as a proximal gradient method, which uses IBP on each iteration to make a proximal step. We also consider the question of scalability of these algorithms using approaches from distributed optimization and show that the first algorithm can be implemented in a centralized distributed setting (master/slave), while the second one is amenable to a more general decentralized distributed setting with an arbitrary network topology.
Diffusion Variational Autoencoders

A standard Variational Autoencoder, with a Euclidean latent space, is structurally incapable of capturing topological properties of certain datasets. To remove topological obstructions, we introduce Diffusion Variational Autoencoders (DeltaVAE) with arbitrary (closed) manifolds as a latent space. A Diffusion Variational Autoencoder uses transition kernels of Brownian motion on the manifold. In particular, it uses properties of the Brownian motion to implement the reparametrization trick and fast approximations to the KL divergence. We show that the DeltaVAE is indeed capable of capturing topological properties for datasets with a known underlying latent structure derived from generative processes such as rotations and translations.
Improving Adversarial Robustness via Promoting Ensemble Diversity

Though deep neural networks have achieved significant progress on various tasks, often enhanced by model ensemble, existing high-performance models can be vulnerable to adversarial attacks. Many efforts have been devoted to enhancing the robustness of individual networks and then constructing a straightforward ensemble, e.g., by directly averaging the outputs, which ignores the interaction among networks. This paper presents a new method that explores the interaction among individual networks to improve robustness for ensemble models. Technically, we define a new notion of ensemble diversity in the adversarial setting as the diversity among non-maximal predictions of individual members, and present an adaptive diversity promoting (ADP) regularizer to encourage the diversity, which leads to globally better robustness for the ensemble by making adversarial examples difficult to transfer among individual members. Our method is computationally efficient and compatible with the defense methods acting on individual networks. Empirical results on various datasets verify that our method can improve adversarial robustness while maintaining state-of-the-art accuracy on normal examples.
On convergence rate of stochastic proximal point algorithm without strong convexity, smoothness or bounded gradients

Significant parts of the recent learning literature on stochastic optimization algorithms focused on the theoretical and practical behaviour of stochastic first order schemes under different convexity properties. Due to its simplicity, the traditional method of choice for most supervised machine learning problems is the stochastic gradient descent (SGD) method. Many iteration improvements and accelerations have been added to the pure SGD in order to boost its convergence in various (strong) convexity setting. However, the Lipschitz gradient continuity or bounded gradients assumptions are an essential requirement for most existing stochastic first-order schemes. In this paper novel convergence results are presented for the stochastic proximal point algorithm in different settings. In particular, without any strong convexity, smoothness or bounded gradients assumptions, we show that a slightly modified quadratic growth assumption is sufficient to guarantee for the stochastic proximal point $\mathcal{O}\left(\frac{1}{k}\right)$ convergence rate, in terms of the distance to the optimal set. Furthermore, linear convergence is obtained for interpolation setting, when the optimal set of expected cost is included in the optimal sets of each functional component.
Multi-stream Network With Temporal Attention For Environmental Sound Classification

Environmental sound classification systems often do not perform robustly across different sound classification tasks and audio signals of varying temporal structures. We introduce a multi-stream convolutional neural network with temporal attention that addresses these problems. The network relies on three input streams consisting of raw audio and spectral features and utilizes a temporal attention function computed from energy changes over time. Training and classification utilizes decision fusion and data augmentation techniques that incorporate uncertainty. We evaluate this network on three commonly used data sets for environmental sound and audio scene classification and achieve new state-of-the-art performance without any changes in network architecture or front-end preprocessing, thus demonstrating better generalizability.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining

Abstract Motivation Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. Results We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. Availability and implementation We make the pre-trained weights of BioBERT freely available at https://github.com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://github.com/dmis-lab/biobert.
Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs

Training recurrent neural networks (RNNs) on long sequence tasks is plagued with difficulties arising from the exponential explosion or vanishing of signals as they propagate forward or backward through the network. Many techniques have been proposed to ameliorate these issues, including various algorithmic and architectural modifications. Two of the most successful RNN architectures, the LSTM and the GRU, do exhibit modest improvements over vanilla RNN cells, but they still suffer from instabilities when trained on very long sequences. In this work, we develop a mean field theory of signal propagation in LSTMs and GRUs that enables us to calculate the time scales for signal propagation as well as the spectral properties of the state-to-state Jacobians. By optimizing these quantities in terms of the initialization hyperparameters, we derive a novel initialization scheme that eliminates or reduces training instabilities. We demonstrate the efficacy of our initialization scheme on multiple sequence tasks, on which it enables successful training while a standard initialization either fails completely or is orders of magnitude slower. We also observe a beneficial effect on generalization performance using this new initialization.
Near optimal finite time identification of arbitrary linear dynamical systems

We derive finite time error bounds for estimating general linear time-invariant (LTI) systems from a single observed trajectory using the method of least squares. We provide the first analysis of the general case when eigenvalues of the LTI system are arbitrarily distributed in three regimes: stable, marginally stable, and explosive. Our analysis yields sharp upper bounds for each of these cases separately. We observe that although the underlying process behaves quite differently in each of these three regimes, the systematic analysis of a self--normalized martingale difference term helps bound identification error up to logarithmic factors of the lower bound. On the other hand, we demonstrate that the least squares solution may be statistically inconsistent under certain conditions even when the signal-to-noise ratio is high.
Partitioned Paxos via the Network Data Plane

Consensus protocols are the foundation for building fault-tolerant, distributed systems, and services. They are also widely acknowledged as performance bottlenecks. Several recent systems have proposed accelerating these protocols using the network data plane. But, while network-accelerated consensus shows great promise, current systems suffer from an important limitation: they assume that the network hardware also accelerates the application itself. Consequently, they provide a specialized replicated service, rather than providing a general-purpose high-performance consensus that fits any off-the-shelf application. To address this problem, this paper proposes Partitioned Paxos, a novel approach to network-accelerated consensus. The key insight behind Partitioned Paxos is to separate the two aspects of Paxos, agreement, and execution, and optimize them separately. First, Partitioned Paxos uses the network forwarding plane to accelerate agreement. Then, it uses state partitioning and parallelization to accelerate execution at the replicas. Our experiments show that using this combination of data plane acceleration and parallelization, Partitioned Paxos is able to provide at least x3 latency improvement and x11 throughput improvement for a replicated instance of a RocksDB key-value store.
Post training 4-bit quantization of convolutional networks for rapid-deployment

Convolutional neural networks require significant memory bandwidth and storage for intermediate computations, apart from substantial computing resources. Neural network quantization has significant benefits in reducing the amount of intermediate results, but it often requires the full datasets and time-consuming fine tuning to recover the accuracy lost after quantization. This paper introduces the first practical 4-bit post training quantization approach: it does not involve training the quantized model (fine-tuning), nor it requires the availability of the full dataset. We target the quantization of both activations and weights and suggest three complementary methods for minimizing quantization error at the tensor level, two of whom obtain a closed-form analytical solution. Combining these methods, our approach achieves accuracy that is just a few percents less the state-of-the-art baseline across a wide range of convolutional models. The source code to replicate all experiments is available on GitHub: \url{this https URL}.
A minimal-length approach unifies rigidity in underconstrained materials

Significance What do a guitar string and a balloon have in common? They are both floppy unless rigidified by geometric incompatibility. The same kind of rigidity transition in underconstrained materials has more recently been discussed in the context of disordered biopolymer networks and models for biological tissues. Here, we propose a general approach to quantitatively describe such transitions. Based on a minimal length function, which scales linearly with intrinsic fluctuations in the system and quadratically with shear strain, we make concrete predictions about the elastic response of these materials, which we verify numerically and which are consistent with previous experiments. Finally, our approach may help develop methods that connect macroscopic elastic properties of disordered materials to their microscopic structure. We present an approach to understand geometric-incompatibility–induced rigidity in underconstrained materials, including subisostatic 2D spring networks and 2D and 3D vertex models for dense biological tissues. We show that in all these models a geometric criterion, represented by a minimal length ℓ¯min, determines the onset of prestresses and rigidity. This allows us to predict not only the correct scalings for the elastic material properties, but also the precise magnitudes for bulk modulus and shear modulus discontinuities at the rigidity transition as well as the magnitude of the Poynting effect. We also predict from first principles that the ratio of the excess shear modulus to the shear stress should be inversely proportional to the critical strain with a prefactor of 3. We propose that this factor of 3 is a general hallmark of geometrically induced rigidity in underconstrained materials and could be used to distinguish this effect from nonlinear mechanics of single components in experiments. Finally, our results may lay important foundations for ways to estimate ℓ¯min from measurements of local geometric structure and thus help develop methods to characterize large-scale mechanical properties from imaging data.