Miracle-dz/UniFAR-FTUs huggingface dataset:
Lateral Habenula Mediates Defensive Responses Only When Threat and Safety Memories Are in Conflict

Visual Abstract Survival depends on the ability to adaptively react or execute actions based on previous aversive salient experiences. Although lateral habenula (LHb) activity has been broadly implicated in the regulation of aversively motivated responses, it is not clear under which conditions this brain structure is necessary to regulate defensive responses to a threat. To address this issue, we combined pharmacological inactivations with behavioral tasks that involve aversive and appetitive events and evaluated defensive responses in rats. We found that LHb pharmacological inactivation did not affect cued threat conditioning (fear) and extinction (safety) learning and memory, anxiety-like or reward-seeking behaviors. Surprisingly, we found that LHb inactivation abolished reactive defensive responses (tone-elicited freezing) only when threat (conditioning) and safety memories (extinction and latent inhibition) compete during retrieval. Consistently, we found that LHb inactivation impaired active defensive responses [platform-mediated avoidance (PMA)], thereby biasing choice behavior (between avoiding a threat or approaching food) toward reward-seeking responses. Together, our findings suggest that LHb activity mediates defensive responses only when guided by competing threat and safety memories, consequently revealing a previously uncharacterized role for LHb in experience-dependent emotional conflict.
The Ecosystem Services of Marine Aquaculture: Valuing Benefits to People and Nature

As the world's population continues to grow, the way in which ocean industries interact with ecosystems will be key to supporting the longevity of food and social securities. Aquaculture is crucial to the future supply of seafood, but challenges associated with negative impacts could impede increased production, especially production that is efficient and safe for the environment. Using the typology established by The Economics of Ecosystems and Biodiversity Initiative, we describe how marine aquaculture could be influential in supporting ecosystem services beyond solely the production of goods, through provisioning services, regulating services, habitat or supporting services, and cultural services. The provision of these services will vary, depending on functional traits of culture species, biotic and abiotic characteristics of the surrounding environment, farm design, and operational standards. Increasing recognition, understanding, and accounting of ecosystem service provision by mariculture through innovative policies, financing, and certification schemes may incentivize active delivery of benefits and may enable effects at a greater scale.
Genetic Manipulation of the Brassicaceae Smut Fungus Thecaphora thlaspeos

Investigation of plant-microbe interactions greatly benefits from genetically tractable partners to address molecularly the virulence and defense mechanisms. The smut fungus Ustilago maydis is a model pathogen in that sense: efficient homologous recombination and a small genome allow targeted modification. On the host side, maize is limiting with regard to rapid genetic alterations. By contrast, the model plant Arabidopsis thaliana is an excellent model with a vast amount of information and techniques as well as genetic resources. Here, we present a transformation protocol for the Brassicaceae smut fungus Thecaphora thlaspeos. Using the well-established transformation of protoplasts, we generated the first reporter strains expressing fluorescent proteins to follow mating. As a proof-of-principle for homologous recombination, we deleted the pheromone receptor pra1. As expected, this mutant cannot mate. Further analysis will contribute to our understanding of the role of mating for infection biology in this novel model fungus. From now on, the genetic manipulation of T. thlaspeos, which is able to colonize the model plant A. thaliana, provides us with a pathosystem, in which both partners are genetically amenable to study smut infection biology.
Design and Validation of A Novel Adaptive Cruise Control Law for a Platoon Of Vehicles

Cooperative driving has attracted the attention of many research centers around the world as a continuation towards a world with fully autonomous vehicles as full autonomy requires cooperation and coordination among vehicles as well as the ability to move automatically. One of the well-known applications of the cooperative driving is vehicle platooning, where vehicles drive in a single lane with close distance gap for fuel reduction. The formation of platooning is a matter of vehicular communication as well as vehicle control. In this paper, a decentralized novel model-free controller is proposed based on the analysis of longitudinal vehicle and platoon dynamics. The proposed controller has been tested for stability in both analytical and graphical ways and it was tested on a nonlinear model in different simulation platforms; MATLAB/SIMULINK and on CARLA simulator and compared to Sliding Mode Control (SMC) to validate its performance. The results shown that the proposed controller is asymptotically stable and has an accuracy close to SMC but outperforming in computational efficiency.
Exploring Amharic Sentiment Analysis from Social Media Texts: Building Annotation Tools and Classification Models

This paper presents the study of sentiment analysis for Amharic social media texts. As the number of social media users is ever-increasing, social media platforms would like to understand the latent meaning and sentiments of a text to enhance decision-making procedures. However, low-resource languages such as Amharic have received less attention due to several reasons such as lack of well-annotated datasets, unavailability of computing resources, and fewer or no expert researchers in the area. This research addresses three main research questions. We first explore the suitability of existing tools for the sentiment analysis task. Annotation tools are scarce to support large-scale annotation tasks in Amharic. Also, the existing crowdsourcing platforms do not support Amharic text annotation. Hence, we build a social-network-friendly annotation tool called ‘ASAB’ using the Telegram bot. We collect 9.4k tweets, where each tweet is annotated by three Telegram users. Moreover, we explore the suitability of machine learning approaches for Amharic sentiment analysis. The FLAIR deep learning text classifier, based on network embeddings that are computed from a distributional thesaurus, outperforms other supervised classifiers. We further investigate the challenges in building a sentiment analysis system for Amharic and we found that the widespread usage of sarcasm and figurative speech are the main issues in dealing with the problem. To advance the sentiment analysis research in Amharic and other related low-resource languages, we release the dataset, the annotation tool, source code, and models publicly under a permissive.
Phytochemical screening and nutritional analysis of Nelumbo nucifera (Pink lotus) flower petals and seeds

Nelumbo nucifera flower petals and seed extracts were evaluated for phytochemical constituents (flower petals and seeds) such as primary (carbohydrate, protein, fat) and secondary metabolites, flavonoids, phenols, tannins, steroids and glycosides. In addition, the nutritive value of the seeds were evaluated. The healing properties of the lotus plant is due to the presence of active principles such as phenolics, tannins, alkaloids, and flavanoids that are constitutents of many pharmacologically active compounds. The Flower petals and seeds were extracted in solvents used for the extraction viz., ethanol, ethylacetate, hexane, chloroform, Acetone and aqueous for phytochemical screening. Lotus fower petals and seeds extract recorded the presence of primary metabolites viz., carbohydrates, proteins and lipids and secondary metabolites such as phenols, flavanoids, tannins, alkaloids, sterols, terpenoids, cardiac glycosides, coumarin (Petals) and quionone (Petals). The quantification of primary metabolites of the extract indicates its nutritive value while the secondary metabolites are responsible for the medicinal properties of the lotus seeds and flower petals. Quantification of phytochemicals revealed that ethanolic extract of flower and seed has the maximum phenols, flavanoids and tannin content compared to other solvents. This study indicates that lotus seeds can serve as an alternate protein source as it possess potential nutritional and medicinal properties.
Hybrid Detection Techniques for Skin Cancer Images

According to W.H. O, skin cancer is one of the most common types of human malignancy in medical sector. Application of deep learning is continuously being used to determine the accuracy of detecting different medical problems more effectively. A lot of new techniques have been discovered to fast forward the procedure with having highest percentage of accuracy. In this research work, we have proposed a model to detect skin cancer more effectively using image processing with convolutional neural network, a part of deep learning concept under machine learning. The dataset contains almost 3000+ images of the patients having skin diseases classified into two classes, malignant and benign. We have introduced CNN along with its seven different architectures to find the accuracy of the images of skin cancer and performed a comparative analysis to find out the best architecture that suits this type of problem. We ran ResNet50, VGG16, InceptionV3, VGG19, Xception, MobileNetV2, MobileNet architectures to find out the best model for that suits our type of dataset. Our model could be made even better if we changed the parameters, i.e. increasing the value of epochs, lowering batch size, changing the value of dropout etc. which would take longer period of times obviously. In our model, MobilenetV2 gave us the lowest accuracy with having almost 54.545% accuracy. The best architecture that we found for our dataset was the Xception. This architecture gave us about almost 85.303% accuracy for our dataset. Due to low power computer and other factors like, dataset image quality, image pre-processing, getting less accuracy can arise.
A Study of Jojoba Oil Extraction Based on a Fuzzy Logic Model

The physicochemical properties of jojoba oil extracted from the seeds harvested in a test plantation located in the Mesaoria Plain of Cyprus Island were found to be within the acceptable levels according to the Jojoba Export Council (IJEC) specifications. The product oil has already been commercialized. In case of a possible jojoba agriculture and industry in the region the optimum oil extraction parameters for maximum oil yield were examined. In the present study a fuzzy logic (FL) model and a multilinear regression model (MLR) for the prediction of jojoba oil yield was developed for the cases where the optimum conditions could not be attained easily in practice. Seed size, seed humidity level, seed temperature and residual discharge opening of the expeller for oil extraction was considered as input parameters and oil yield as the output. The data collected from 27 experiments were used to construct the models. The proposed FL model achieved R2 and RMSE values of 0.9751 and 1.5225 whereas the MLR model resulted in 0.8256 and 6.3528, respectively.
Self-Supervised Learning of Visual Odometry

Monocular visual odometry (VO) based on deep learning methods is one of the most important research tasks in computer vision in recent decades. Unlike unsupervised and supervised methods, Self-supervised learning can add additional image information to improve the accuracy and robustness of the model without real labels, so the self-supervised visual odometry methods have received widespread attention. However, most self-supervised VO methods do not make full use of self-supervised signals, especially optical flow as a self-supervised signal. In this paper, we make full use of optical flow as a self-supervised signal, a new self-supervised network structure is proposed. This self-supervised network structure adds optical flow as a self-supervised signal to the basic cascaded VO estimation network. While providing additional information to train the camera pose estimation, the optical flow is used to generate mask to solve the occlusion problems that often occur in self-supervised models. We evaluated the self-supervised visual odometry model proposed in this paper on the KITTI dataset. Experiments show that our method has achieved good performance in depth estimation and camera pose estimation.
Characterization of LC-MS based urine metabolomics in healthy subjects across the life span

Previous studies reported that gender and age could influence urine metabolomics, which should be considered in biomarker discovery. As a consequence, for the baseline of urine metabolomics characteristics, it becomes critical to avoid confounding effects in clinical cohort studies. In this study, we provided a comprehensive lifespan characterization of urine metabolomics in a cohort of 348 healthy children and 315 adults aged 1 to 70 years using liquid chromatography coupled with high resolution mass spectrometry. Our results suggested that gender-dependent urine metabolites are much greater in adults than in children. The pantothenate and CoA biosynthesis and alanine metabolism pathways were enriched in early life. Androgen and estrogen metabolism showed high activity during adolescence and youth stages. Pyrimidine metabolism was enriched in the old stage. Based on the above analysis, metabolomic characteristics of each age stage were provided. This work could help us understand the baseline of urine metabolism characteristics and contribute to further studies of clinical disease biomarker discovery.
Molecular characterisation of, and antimicrobial resistance in, Clostridioides difficile from Thailand, 2017-2018

Background Antimicrobial resistance (AMR) plays an important role in the pathogenesis and spread of Clostridioides difficile infection (CDI). Many antimicrobials, such as fluoroquinolones, have been associated with outbreaks of CDI globally. Objectives This study aimed to characterise AMR among clinical C. difficile strains in Thailand, a country where the use of antimicrobials remains inadequately regulated. Methods Stool samples were screened for tcdB and positives were cultured. C. difficile isolates were characterised by toxin profiling and PCR ribotyping. Antimicrobial susceptibility testing was performed using an agar incorporation method, and whole-genome sequencing and AMR genotyping performed on a subset of strains. Results A total of 321 C. difficile strains were isolated from 326 stool samples. The most common toxigenic ribotype (RT) was RT 017 (18%), followed by RTs 014 (12%) and 020 (7%). There was a high resistance prevalence (≥ 10%) to clindamycin, erythromycin, moxifloxacin and rifaximin, and resistance prevalence was greatest among RT 017 strains. AMR genotyping revealed a significant correlation between resistance genotype and phenotype for moxifloxacin and rifampicin. The presence of erm-class genes was associated with high-level clindamycin and erythromycin resistance. Point substitutions on the penicillin-binding proteins (PBP1 and PBP3) were not sufficient to confer meropenem resistance, however, a Y721S substitution in PBP3 was associated with a slight increase in meropenem MIC. No resistance to metronidazole, vancomycin or fidaxomicin was observed. Conclusion There was a large proportion of C. difficile RT 017 in Thailand and significant AMR among these strains. The concordance between AMR phenotype and genotype was significant.
Incidence and Risk Factors of Combined-Antiretroviral Therapy-Induced Hepatotoxicity among HIV Patients at the Bali District Hospital, Cameroon

Introduction The incidence of hepatotoxicity is life-threatening and can result to an end-stage liver disease in long-term patients on combined antiretroviral therapy (cART). Our study sought to evaluate the incidence and predictors of cART-induced hepatotoxicity (CIH) among long term users on cART in a rural District hospital. Methods This was a hospital-based cross-sectional study in the Bali District Hospital. Spectrophotometric method was use for the quantitative measurement of alanine-aminotransferase (ALT) and aspartate-aminotransferase (AST) levels. Patients with elevations of both ALT and AST were considered CIH. The Chi (χ2) square test, ANOVA and Kaplan Meier log-ranked/ survival analyses were used to analyse the data. Results Of the 350 participants enrolled [156 (44.6%) males and 194 (55.4%) females], aged 43.87 ± 0.79 years (range 20 – 84 years) included in this analysis, 26 (4.4%) experienced moderate CIH. We observed 57 (16.3%), 62 (17.7%) and 238 (68%) elevated levels ALT + AST, ALT and AST respectively. Two independent predictive factors of CIH were, the male sex and alcoholism during the study period. Conclusion The prevalence of CIH in HIV-infected patients in Bali was lower than that observed in previous studies. The duration of therapy had no influence on the frequency of CIH. Alcoholism and smoking showed significant differences in the development of CIH.
A Bidirectional Matrix-Based AC-DC Dual-Active Bridge for Modular Solid-State-Transformers

This paper proposes a Modular Solid-State-Transformer (SST) topology consisting of bidirectional Matrix-Based AC-DC Dual-Active-Bridge (DAB) submodules. Unlike the conventional SST topologies, the proposed topology offers a single stage Medium Voltage AC (MVAC) - Low Voltage DC (LVDC) conversion. The discussion is focused on the AC-DC DAB submodule of a 1 MVA SST for the Energy Control Centre (ECC) of a microgrid/nanogrid. The possible modulation modes along with the corresponding power transfer limits are presented and a suitable steady-state modulation scheme is identified for the required operating point. The modeling and small-signal analysis of the AC-DC submodule is comprehensively explained. Simulation results including the steady-state operation and the frequency response of the 17.6 kW submodule are presented.
Optimal Selection Techniques for Cloud Service Providers

Nowadays Cloud computing permeates almost every domain in Information and Communications Technology (ICT) and, increasingly, most of the action is shifting from large, dominant players toward independent, heterogeneous, private/hybrid deployments, in line with an ever wider range of business models and stakeholders. The rapid growth in the numbers and diversity of small and medium Cloud providers is bringing new challenges in the as-a-Services space. Indeed, significant hurdles for smaller Cloud service providers in being competitive with the incumbent market leaders induce some innovative players to “federate” deployments in order to pool a larger, virtually limitless, set of resources across the federation, and stand to gain in terms of economies of scale and resource usage efficiency. Several are the challenges that need to be addressed in building and managing a federated environment, that may go under the “Security”, “Interoperability”, “Versatility”, “Automatic Selection” and “Scalability” labels. The aim of this paper is to present a survey about the approaches and challenges belonging to the “Automatic Selection” category. This work provides a literature review of different approaches adopted in the “Automatic and Optimal Cloud Service Provider Selection”, also covering “Federated and Multi-Cloud” environments.
Lentimurisporaceae, a New Pleosporalean Family with Divergence Times Estimates

Abstract The new hyphomycetous genus Lentimurispora, based on L. urniformis sp. nov. collected from decaying wood in Phayao, Thailand, is introduced. It is characterized by micronematous conidiophores, monoblastic conidiogenous cells, and muriform, lenticular conidia with dark brown central cells and paler peripheral cells. Phylogenetic analysis based on the combined LSU, SSU and TEF1α sequences data of 110 taxa was carried out to infrer their phylogenetic relationships. The new genus formed a well-supported monophyletic clade with Bahusandhika indica, Berkleasmium micronesicum and Be. nigroapicale within Pleosporales. A new family, Lentimurisporaceae is established to accommodate this clade. Divergence times establishment was performed and the results showed that the new family was diverged approximately 78 (49–118) MYA.
Breaking the millisecond barrier: Robots and self-driving cars will need completely reengineered networks

FOR COMMUNICATIONS NETWORKS, bandwidth has long been king. With every generation of fiber optic, cellular, or Wi-Fi technology has come a jump in throughput that has enriched our online lives. Twenty years ago we were merely exchanging texts on our phones, but we now think nothing of streaming videos from YouTube and Netflix. No wonder, then, that video now consumes up to 60 percent of Internet bandwidth. If this trend continues, we might yet see full-motion holography delivered to our mobiles—a techie dream since Princess Leia's plea for help in Star Wars. • Recently, though, high bandwidth has begun to share the spotlight with a different metric of merit: low latency. The amount of latency varies drastically depending on how far in a network a signal travels, how many routers it passes through, whether it uses a wired or wireless connection, and so on. The typical latency in a 4G network, for example, is 50 milliseconds. Reducing latency to 10 milliseconds, as 5G and Wi-Fi are currently doing, opens the door to a whole slew of applications that high bandwidth alone cannot. With virtual-reality headsets, for example, a delay of more than about 10 milliseconds in rendering and displaying images in response to head movement is very perceptible, and it leads to a disorienting experience that is for some akin to seasickness. • Multiplayer games, autonomous vehicles, and factory robots also need extremely low latencies. Even as 5G and Wi-Fi make 10 milliseconds the new standard for latency, researchers, like my group at New York University's NYU Wireless research center, are already working hard on another order-of-magnitude reduction, to about 1 millisecond or less. • Pushing latencies down to 1 millisecond will require reengineering every step of the communications process. In the past, engineers have ignored sources of minuscule delay because they were inconsequential to the overall latency. Now, researchers will have to develop new methods for encoding, transmitting, and routing data to shave off even the smallest sources of delay. And immutable laws of physics—specifically the speed of light—will dictate firm restrictions on what networks with 1-millisecond latencies will look like. There's no one-size-fits-all technique that will enable these extremely low-latency networks. Only by combining solutions to all these sources of latency will it be possible to build networks where time is never wasted.