Miracle-dz/UniFAR-FTUs huggingface dataset:
Growth evaluation of Oreochromis niloticus (Cichlidae, Neopterygii) exposed to Trichlorfon

O atual crescimento da piscicultura nacional tem aumentado a incidencia de enfermidades parasitarias que promovem perdas economicas. Atualmente sao utilizados tratamentos com organofosforados para o combate das parasitoses (e.g.: Trichlorfon). Este trabalho tem como objetivo avaliar a influencia de Trichlorfon sobre o crescimento de peixes criados em cativeiro. Utilizando como peixe modelo a tilapia (Oreochromis niloticus), foram realizados bioensaios em laboratorio e em ambiente de cultivo. Foram feitas biometrias do peso e comprimento total dos animais durante 16 semanas. O tratamento teve inicio apos a sexta semana de biometria, sendo aplicadas cinco doses de 0,25 ppm de Trichlorfon, uma por semana. Os dados foram gerenciados atraves de analises longitudinais. Em laboratorio, os peixes tratados apresentaram uma reducao das taxas de crescimento, sendo significativa apenas para a variavel peso. Em ambiente de cultivo, o crescimento foi maior nos peixes tratados do que nos controles. Tais resultados indicam que mesmo sendo toxico, a acao deleteria do Trichlorfon e diretamente proporcional a biomassa relativa (gramas de peixe por litro). Estes resultados sugerem que os tratamentos com produtos que tem como principio ativo o Trichlorfon devem ser realizados em viveiros/tanques com baixa biomassa relativa/densidade, para evitar o prejuizo da higidez e das caracteristicas zootecnicas do plantel.
Combating Packet Collisions Using Non-Stationary Signal Scaling in LPWANs

LoRa, a representative Low-Power Wide Area Network (LPWAN) technology, has been shown as a promising platform to connect Internet of Things. Practical LoRa deployments, however, suffer from collisions, especially in dense networks and wide coverage areas expected by LoRa applications. Existing collision resolving approaches do not exploit the modulation properties of LoRa and thus cannot work well for low-SNR LoRa signals. We propose NScale to decompose concurrent transmissions by leveraging subtle inter-packet time offsets for low SNR LoRa collisions. NScale (1) translates subtle time offsets, which are vulnerable to noise, to robust frequency features, and (2) further amplifies the time offsets by non-stationary signal scaling, i.e., scaling the amplitude of a symbol differently at different positions. In practical implementation, we propose a noise resistant iterative symbol recovery method to combat symbol distortion in low SNR, and address frequency shifts incurred by CFO and packet time offsets in decoding. We propose optimized designs for diminishing the time costs of computation-intensive tasks and meeting the real-time requirements of LoRa collision resolving. We theoretically show that NScale introduces < 1.7 dB SNR loss compared with the original LoRa. We implement NScale on USRP N210 and evaluate its performance in both indoor and outdoor networks. NScale is implemented in software at the gateway and can work for COTS LoRa nodes without any modification. The evaluation results show that NScale improves the network throughput by $3.3\times $ for low SNR collided signals compared with other state-of-the-art methods.
Monitoring the oxidation of unsaturated oils and formation of oxygenated aldehydes by proton NMR

The oxidation process of rapeseed, walnut and linseed oils, rich sources of linolenic acyl groups, was studied by means of 1 H nuclear magnetic resonance spectroscopy. The visible changes in some spectrum signals, especially in those of methylic, allylic and bis-allylic hydrogen atoms, provide information about the degradation of the different acyl groups throughout the oxidation process. The rate of the changes produced in the three oils was compared, not only in relation to the rate of degradation of the different acyl groups, but also to both the formation and degradation of primary as well as to the formation of secondary oxidation products. Among the secondary oxidation products, the study was focused on the formation of aldehydes, their nature and evolution throughout the oxidation process of these three oils. The formation of n-alkanals, trans-2-alkenals, trans,trans-2,4-alkadienals, as well as of the well known geno- and cytotoxic 4,5-epoxy-, 4-hydroperoxy- and 4-hydroxy-trans-2-alkenals, was proved to take place.
waveSense: Low Power Voxel-tracking Technique for Resource Limited Devices

In this paper, we describe waveSense, a power-efficient optical voxel tracking system for hand tracking in resource-limited devices. waveSense uses off-the-shelf, low-cost, non-focused infrared (IR) sensors and emitters. To achieve power efficiency, we introduce a novel selective volumetric illumination (SVI) technique, which illuminates different areas of a scene with variable power IR light and captures the reflected energy using non-focused IR sensors. Our technical evaluation suggests the feasibility of using waveSense for hand tracking performed within 45cm radius of the device. We believe with waveSense, gesture-based interactions would become more practical on resource-constrained wearables such as head-mounted displays (HMDs) and smartwatches.
TargetNet Backdoor: Attack on Deep Neural Network with Use of Different Triggers

Deep neural networks (DNNs) provide good performance in image recognition, speech recognition, and pattern analysis. However, DNNs are vulnerable to backdoor attacks. Backdoor attacks allow attackers to proactively access DNN training data to train it on additional data that are malicious, including a specific trigger. Normally, DNNs correctly classify normal data, but malicious data with a specific trigger trained by attackers can cause misclassification by DNNs. For example, if an attacker sets up a road sign that includes a specific trigger, an autonomous vehicle equipped with a DNN may misidentify the road sign and cause an accident. Thus, an attacker can use a backdoor attack to threaten the DNN at any time. However, in certain cases, when an attacker wants to perform a targeted attack, it may be desirable for the data introduced through the backdoor to be misrecognized as a particular class chosen by the attacker according to the position of a trigger. For example, if a specific trigger is attached to the top right side of the road sign, it may be misunderstood as a left-turn sign; if a specific trigger is attached to the top left side of the road sign, it may be misunderstood as a right-turn sign; and if a specific trigger is attached to the bottom left side of the road sign, it may be misunderstood as a U-turn sign. In this paper, we propose the TargetNet backdoor, which is designed to be misidentified as a particular target class chosen by the attacker according to a specific trigger location. The proposed method additionally trains the target classifier on the TargetNet backdoor data so that data with a trigger at a specific location will be misidentified as the target class selected by the attacker. We used MNIST and Fashion-MNIST as experimental datasets and Tensor-flow as a machine learning library. Experimental results show that the proposed method applied to MNIST and Fashion-MNIST has a 100% attack success rate for the TargetNet backdoor and 99.17% and 91.4% accuracy rates on normal test data, respectively.
Interfirm IT Capability Profiles and Communications for Cocreating Relational Value: Evidence from the Logistics Industry

This study seeks to identify the means by which information technology helps cocreate relational value in the context of interfirm relationships in the logistics industry — a large and information-intensive industry. We identify a set of IT functionalities — single-location shipping, multilocation shipping, supply chain visibility, and financial settlement — that can be used to manage the flows of physical goods, information, and finances across locations in interfirm logistics processes. Progressively more advanced sets of IT functionalities, when implemented and used in the interfirm relationship to execute logistics processes, are proposed to form four distinct IT capability profiles of increased sophistication. Interfirm IT capability profiles of higher sophistication are proposed to help cocreate greater relational value by facilitating the flows of physical goods, information, and finances across locations in the interfirm logistics process. Besides their direct role in helping cocreate relational value, these interfirm IT capability profiles are proposed to further enhance relational value cocreation when complemented by interfirm communications for business development and IT development. Our empirical study was situated in one of the world’s largest logistics suppliers and over 2,000 of its interfirm relationships with buyers across industries. Integrated data from four archival sources on the IT functionalities implemented and used in interfirm logistics relationships, interfirm communications, relational value (share of wallet and loyalty), and multiple control variables were collected. The results show that the proposed interfirm IT capability profiles and interfirm communications have both a direct and an interaction effect on relational value. Implications for cocreating relational value in interfirm relationships with the aid of IT are discussed.
Don't Forget Langmuir-Blodgett Films 2020: Interfacial Nanoarchitectonics with Molecules, Materials, and Living Objects.

Architecting interfacial structures with nanoscale (or molecular) components is one of the important tasks of the nanoarchitectonics concept. Especially, the Langmuir-Blodgett (LB) method can become a promising and powerful strategy in interfacial nanoarchitectonics. From this viewpoint, the status of LB films at 2020 will be discussed in this feature article. After one section on the basics of interfacial nanoarchitectonics with LB technique, various recent research examples of LB films are introduced according to classifications of (i) growing research, (ii) emerging research, and (iii) future research. In recent LB research, various materials other than traditional lipids and typical amphiphiles can be used as film components of the LB techniques. Low dimensional materials such as two-dimensional materials, supramolecular structures such as metal organic frameworks, and biomaterials such as DNA origami pieces are capable of working as functional components in the LB assemblies. Possible working areas of the LB methods would cover emerging demands including energy, environmental and biomedical applications upon uses of a wide range of functional materials. In addition, forefront research such as molecular manipulation and cell fate control is conducted in LB-related interfacial science. The LB technique is a traditional and well-develop methodology for molecular films with a ca 100-year history. However, there is plenty of room at the interfaces, as shown in LB research examples described in this feature articles. Continuous developments of science and technology of the LB method will hopefully keep this technique as unforgettable methodology.
Deep Semantic Multimodal Hashing Network for Scalable Image-Text and Video-Text Retrievals

Hashing has been widely applied to multimodal retrieval on large-scale multimedia data due to its efficiency in computation and storage. In this article, we propose a novel deep semantic multimodal hashing network (DSMHN) for scalable image-text and video-text retrieval. The proposed deep hashing framework leverages 2-D convolutional neural networks (CNN) as the backbone network to capture the spatial information for image-text retrieval, while the 3-D CNN as the backbone network to capture the spatial and temporal information for video-text retrieval. In the DSMHN, two sets of modality-specific hash functions are jointly learned by explicitly preserving both intermodality similarities and intramodality semantic labels. Specifically, with the assumption that the learned hash codes should be optimal for the classification task, two stream networks are jointly trained to learn the hash functions by embedding the semantic labels on the resultant hash codes. Moreover, a unified deep multimodal hashing framework is proposed to learn compact and high-quality hash codes by exploiting the feature representation learning, intermodality similarity-preserving learning, semantic label-preserving learning, and hash function learning with different types of loss functions simultaneously. The proposed DSMHN method is a generic and scalable deep hashing framework for both image-text and video-text retrievals, which can be flexibly integrated with different types of loss functions. We conduct extensive experiments for both single-modal- and cross-modal-retrieval tasks on four widely used multimodal-retrieval data sets. Experimental results on both image-text- and video-text-retrieval tasks demonstrate that the DSMHN significantly outperforms the state-of-the-art methods.
Multiple Amino Acid Supplementations to Low-Protein Diets: Effect on Performance, Carcass Yield, Meat Quality and Nitrogen Excretion of Finishing Broilers under Hot Climate Conditions

Simple Summary Crude protein is an essential nutrient in poultry feed. Reducing the use of crude protein not only reduces the feed cost, but also minimizes pollution during poultry production. Thus, finding the minimum protein requirement in broiler diet without compromising broiler growth is the objective in the present study. Supplementing essential amino acids including methionine and lysine to the low-protein diet showed comparable growth performance and carcass yield to the regular protein diet. Thus, reducing the crude protein level is possible if the essential amino acid balance is adequate for broiler growth. Abstract The objective of this study was to evaluate the effect of low-protein diets with amino acid supplementation on growth performance, carcass yield, meat quality and nitrogen excretion of broilers raised under hot climate conditions during the finisher period. In trial 1, broilers from 28 to 49 days of age were fed 18% crude protein (CP) as a positive control or 15% CP supplemented with (1) DL-methionine (Met) + L-lysine (Lys), (2) Met + Lys + L-Arginine (Arg), or (3) Met + Lys + L-Valine (Val). In trial 2, broilers from 30 to 45 days of age, were fed an 18% CP diet as a positive control or 15% CP supplemented with Met, Lys, Arg, Val, L-Isoleucine (Ile) or combination with glycine (Gly) and/or urea as nitrogen sources: (1) Met + Lys, (2) Met + Lys + Arg, (3) Met + Lys + Val, (4) Met + Lys + Ile, (5) Met + Lys + Arg +Val + Ile + Gly, and (6) Met+ Lys + Arg + Val + Ile + Gly + urea. Protein use was improved by feeding low-protein amino acid-supplemented diets as compared to the high-protein diet. Feeding 15% crude protein diet supplemented with only methionine and lysine had no negative effects on carcass yield, CP, total lipids and moisture% of breast meat while decreasing nitrogen excretion by 21%.
Effects of queen importation on the genetic diversity of Macaronesian island honey bee populations (Apis mellifera Linneaus 1758)

Summary Beekeeping practices such as the importation of non-native honey bee queens may interact with the conservation of honey bee biodiversity. Island honey bee populations are particularly appropriate to test the impact of the introduction of foreign subspecies into their genetic diversity and structure. Here we have used microsatellite markers to evaluate the temporal genetic variation over the last decade in Macaronesian honey bee populations, which have been exposed to different beekeeping strategies regarding queen importation as previously revealed by mitochondrial determination of the evolutionary lineage: a high level on Tenerife and São Miguel and absent or low on La Palma and Madeira. While genetic diversity remained low (from 0.402 to 0.483 in the previous survey and from 0.390 to 0.513 in the current survey), genetic introgression from foreign honey bees was detected on Tenerife and São Miguel based on Bayesian structure analysis. Nevertheless, the existence of endemic honey bee populations deserving conservation on the Macaronesian Islands can still be inferred from the aforementioned analyses.
Premières données sur la dynamique d'une population d'Oikopleura dioica Fol, 1872 (Appendiculaire) en élevage

Several parameters that characterize a population arising from a single male–female pair of Oikopleura dioica Fol, 1872 (Appendicularia) were studied in our culture system. Egg volume varied by a factor of 1.4. Daily size of individuals in a population followed a Gaussian distribution, and the spread of this distribution increased with time. In a population that arose at 18 °C from 252 eggs, mortality reduced the number of individuals to 162 after 5 days. During the following 4 days, the appendicularians reached sexual maturity and died after gametes were released. The first individuals to mature were males, 115 h after fertilization. Average trunk sizes of mature animals are 942 ± 95 μm for males and 1165 ± 104 μm for females. All appendicularians released their gametes and died 215 h after fertilization. The number of eggs per female showed considerable variation, ranging between 41 and 362, with an average of 170 ± 78. Different causes for mortality during the entire life cycle are analysed.
Amyotrophic lateral sclerosis and intestinal microbiota—toward establishing cause and effect

ABSTRACT The intestinal microbiota may be involved, through metabolic gut–brain interactions, in a variety of neurological conditions. In this addendum, we summarize the findings of our recent study investigating the potentially modulatory influence of the microbiome in a transgenic ALS mouse model, and the possible application to human disease. We found that transgenic mice show evidence of dysbiosis, even at the pre-symptomatic stage, and have a more severe disease course under germ-free conditions or after receiving broad-spectrum antibiotics. We demonstrated that Akkermansia muciniphila ameliorated the disease in mice and that this may be due to the production of nicotinamide. We then conducted a preliminary study in human ALS and identified functionally similar alterations within the metagenome. Furthermore, we found that patients with ALS had lower systemic and CSF levels of nicotinamide, suggesting that the changes observed in the mouse model may be relevant to human disease.
Large-Scale Drug Screening in Patient-Derived IDHmut Glioma Stem Cells Identifies Several Efficient Drugs among FDA-Approved Antineoplastic Agents

The discovery of the isocitrate dehydrogenase (IDH) mutation in glioma led to a paradigm shift on how we see glioma biology. Difficulties in cultivating IDH mutant glioma stem cells (IDHmut GSCs) resulted in a paucity of preclinical models in IDHmut glioma, limiting the discovery of new effective chemotherapeutic agents. To fill this gap, we used six recently developed patient-derived IDHmut GSC lines and performed a large-scale drug screening with 147 Food and Drug Administration (FDA)-approved anticancer drugs. GSCs were subjected to the test compounds for 72 h in concentrations ranging from 0.0001 to 1 µM. Cell viability was assessed by CellTiterGlo and the induction of apoptosis by flow cytometry with Annexin V/propidium iodide staining. The initial screen was performed with two IDHmut GSC lines and identified seven drugs (bortezomib, carfilzomib, daunorubicin, doxorubicin, epirubicin, omacetaxine, plicamycin) with a substantial antiproliferative activity, as reflected by half maximal inhibitory concentrations (IC50) below 1 µM and maximum inhibitory effects (Emax) below 25%. These findings were validated in an additional four IDHmut GSC lines. The candidate drugs, of which plicamycin and omacetaxine are known to cross the blood brain barrier, were used for subsequent cell death analyses. A significant induction of apoptosis was observed at IC50 values of the respective drugs. In summary, we were able to identify seven FDA-approved drugs that should be further taken into clinical investigations for the treatment of IDHmut gliomas.
Epidemiological characteristics and spatial-temporal clusters of hand, foot, and mouth disease in Qingdao City, China, 2013-2018

Background Hand, foot, and mouth disease (HFMD) has become one of the most important infectious diseases recent years. Qingdao City has suffered from serious HFMD epidemic. This study aimed to describe epidemiological characteristics and investigate spatial-temporal distribution at town level in Qingdao City. Method The surveillance data of HFMD during 2013–2018 were collected from the National Notifiable Disease Surveillance System. The global Moran’s I statistic was used to detect the spatial autocorrelation of HFMD cases by ArcGis 10.0 software. Purely spatial and spatial-temporal analysis was used to detect epidemic clusters by SatScanTM v9.6 software. Results The annual average incidence of HFMD cases in Qingdao City from 2013 to 2018 was 123.16 per 100000, while the incidence rate of children≤5years old was 2879.80 per 100000. The majority (88.97%) of HFMD cases were aged within 0–5 years old and the males were 60.20%. Other enterovirus (EV), enteriovirus 71(EV71), and Coxsackievirus A16 (CA16) accounted for 48.75%, 30.91% and 20.34%. The seasonal peak was between May and October. HFMD had positive spatial autocorrelation at town level with global Moran’s I from 0.19 to 0.31(P<0.001). Spatial-temporal cluster analysis detected six most likely clusters and three secondary clusters from 2013 to 2018. The most likely cluster was located in urban and urban-rural fringe areas. Conclusions Urban and urban-rural fringe areas were the major locations of the clusters with other EV as the dominant pathogen between May and October. The findings suggested that the prevention and control of HFMD in Qingdao City should be focus on these high-risk periods and locations which had important public health significance for the allocation of public health resources.
Quantile graphs for EEG-based diagnosis of Alzheimer’s disease

Known as a degenerative and progressive dementia, Alzheimer’s disease (AD) affects about 25 million elderly people around the world. This illness results in a decrease in the productivity of people and places limits on their daily lives. Electroencephalography (EEG), in which the electrical brain activity is recorded in the form of time series and analyzed using signal processing techniques, is a well-known neurophysiological AD biomarker. EEG is noninvasive, low-cost, has a high temporal resolution, and provides valuable information about brain dynamics in AD. Here, we present an original approach based on the use of quantile graphs (QGs) for classifying EEG data. QGs map frequency, amplitude, and correlation characteristics of a time series (such as the EEG data of an AD patient) into the topological features of a network. The five topological network metrics used here—clustering coefficient, mean jump length, betweenness centrality, modularity, and Laplacian Estrada index—showed that the QG model can distinguish healthy subjects from AD patients, with open or closed eyes. The QG method also indicates which channels (corresponding to 19 different locations on the patients’ scalp) provide the best discriminating power. Furthermore, the joint analysis of delta, theta, alpha, and beta wave results indicate that all AD patients under study display clear symptoms of the disease and may have it in its late stage, a diagnosis known a priori and supported by our study. Results presented here attest to the usefulness of the QG method in analyzing complex, nonlinear signals such as those generated from AD patients by EEGs.