Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcription Factors with Broad Expression in the Zebrafish Spinal Cord

Background The spinal cord is a crucial part of the vertebrate central nervous system, controlling movements and receiving and processing sensory information from the trunk and limbs. However, there is much we do not know about how this essential organ develops. Here, we describe expression of 22 transcription factor genes in the zebrafish spinal cord. Results We analyzed the spinal cord expression of aurkb, foxb1a, foxb1b, her8a, homeza, ivns1abpb, mybl2b, myt1a, nr2f1b, onecut1, sall1a, sall3a, sall3b, sall4, sox2, sox19b, sp8b, tsc22d1, wdhd1, zfhx3b, znf804a, and znf1032 in wild-type and MIB E3 ubiquitin protein ligase 1 zebrafish embryos. While all of these genes are broadly expressed in the spinal cord, they have distinct expression patterns from one another. Some are predominanatly expressed in progenitor domains, and others in subsets of post-mitotic cells. Given the conservation of spinal cord development, and the transcription factors that regulate it, we expect that these genes will have similar spinal cord expression patterns in other vertebrates, including mammals and humans. Conclusions Our data identify 22 different transcription factors that are strong candidates for playing different roles in spinal cord development. For several of these genes, this is the first published description of their spinal cord expression.
Data mining antibody sequences for database searching in bottom-up proteomics

Mass spectrometry (MS)-based proteomics allows identifying and quantifying thousands of proteins but suffers from challenges when measuring human antibodies due to their vast variety. The mainly used bottom-up proteomics approaches rely on database searches that compare experimental values of peptides and their fragments to theoretical values derived from protein sequences in a database. While the human body can produce millions of distinct antibodies, the current databases for human antibodies such as UniProtKB/Swiss-Prot are limited to only 1095 sequences (as of 2024 Jan). This limitation may hinder the identification of new antibodies using mass spectrometry. Therefore, extending the database for mass spectrometry is an important task for discovering new antibodies. Recent genomic studies have compiled millions of human antibody sequences publicly accessible through the Observed Antibody Space (OAS) database. However, this data has yet to be exploited to confirm the presence of these antibodies. In this study, we adopted this extensive collection of antibody sequences for conducting efficient database searches in publicly available proteomics data with a focus on the SARS-CoV-2 disease. Thirty million heavy antibody sequences from 146 SARS-CoV-2 patients in the OAS database were digested in silico to obtain 18 million unique peptides. These peptides were then used to create new databases for bottom-up proteomics. We used those databases for searching new antibody peptides in publicly available SARS-CoV-2 human plasma samples in the Proteomics Identification Database (PRIDE). This approach avoids false positives in antibody peptide identification as confirmed by searching against negative controls (brain samples) and employing different database sizes. We show that the found sequences provide valuable information to distinguish diseased from healthy and expect that the newly discovered antibody peptides can be further employed to develop therapeutic antibodies. The method will be broadly applicable to find characteristic antibodies for other diseases.
LbCas12-mediated multiplex gene editing and 2-fluoroadenine counter-selection in Phytophthora palmivora

CRISPR-Cas systems have moved forward genetic engineering in virtually any organism amenable to genetic modification. In particular, these systems have unlocked unprecedented possibilities to generate mutants in oomycetes, a group of filamentous microbes comprising over two hundred Phytophthora species, including the cacao killer Phytophthora palmivora. Here, we showcase multiplex gene editing in P. palmivora using LbCas12. We have developed a straightforward protocol to simultaneously knock out two genes encoding adenine phosphoribosyltransferase (APT), an essential enzyme of the purine salvage pathway. We show that APT knockouts (ΔPpATP1/2) are insensitive to 2-fluoroadenine (2-FA) and retain full virulence on Nicotiana benthamiana. We rely on zoospore electroporation using an all-in-one construct to facilitate the rapid editing of multiple genes. This work enhances the genetic toolbox for Phytophthora species and simplifies the exploration of gene function, laying the groundwork for future innovations aiming to tackle oomycete plant diseases.
Hyperactivity Induced By Vapor Inhalation of Nicotine in Male and Female Rats

Rationale Preclinical models of electronic nicotine delivery system (ENDS; “e-cigarette”) use have been rare, so there is an urgent need to develop experimental approaches to evaluate their effects. Objective To contrast the impact of inhaled nicotine across sex. Methods Male and female Wistar rats were exposed to vapor from a propylene glycol vehicle (PG), nicotine (NIC; 1-30 mg/mL in PG), or were injected with NIC (0.1-0.8 mg/kg, s.c.), and then assessed for changes in temperature and activity. The antagonist mecamylamine (2 mg/kg) was administered prior to NIC to verify pharmacological specificity. Plasma levels of nicotine and cotinine were determined after inhalation and injection. Results Activity increased in females for ∼60 minutes after nicotine inhalation, and this was blocked by mecamylamine. A similar magnitude of hyperlocomotion was observed after s.c. administration. Body temperature was reduced after nicotine inhalation by female rats but mecamylamine increased this hypothermia. Increased locomotor activity was observed in male rats if inhalation was extended to 40 minutes or when multiple inhalation epochs were used per session. The temperature of male rats was not altered by nicotine. Plasma nicotine concentrations were slightly lower in male rats than in female rats after 30-minute nicotine vapor inhalation and slightly higher after nicotine injection (1.0 mg/kg, s.c.). Conclusions Nicotine inhalation increases locomotor activity in male and female rats to a similar or greater extent than by subcutaneous injection. Sex differences were observed, which may be related to lower nicotine plasma levels, lower baseline activity and/or a higher vehicle response in males.
Hurricane Sandy (2012), the TRMM Satellite, and the Physics of the Hot Towers

Kelley: And that matters because air doesn't get lifted from the earth's surface to high up unless there is energy being transformed into strong updrafts. So [cloud top temperature] tells you something about the physics that is going on inside the clouds. If you are out on a sunny day and a little puffy cloud comes along, you don't get worried, but if you suddenly see it get dark and a cloud shoots up and starts to cover a lot of territory, then you know it's a big storm cloud and you might not want to set out on a hike.
COMPORTAMENTO INGESTIVO DE VACAS EM LACTAÇÃO EM PASTAGEM CONSORCIADA NO PERÍODO ESTIVAL INGESTIVE BEHAVIOR OF DAIRY COWS GRAZING INTERCROPPED PASTURES IN WARM SEASON

This research aimed to study the behavior of Holstein lactating cows, during the warm-season on a pasture constituted by elephant grass (Pennisetum purpureum Schum.) and spontaneous growing species intercropped with white clover (Trifolium repens L.) or forage peanut (Arachis pintoi Krap & Greg.). Evaluations were taken during three grazing in December, February, and April. Six cows between the second and fifth lactation month were used for each evaluation. The data were recorded at a 10 min-interval, from 6 P.M. to 6 A.M. and from 8 A.M. to 4 P.M. The grazing time on elephant grass, on species established in space between lines, total grazing (elephant grass and species established in space between lines), rumination and idleness were the behavioral parameters observed. The average of grazing was 3h 48 min and 2h 9 min for pastures intercropped with white clover and forage peanut, respectively, being higher (P <0.05) for the first intercropped forage. The average of the grazing on elephantgrass was 5h 32 min and 6h 27 min for the respective intercropped pastures systems, with
Delay-Aware Parallelized SFC Deployment in Space-Air-Ground-Integrated Network

As technology advances, traditional terrestrial wire-less communications, facing exponential growth in users and services, encounter challenges in remote areas due to geographi-cal constraints. Space-Air-Ground Integrated Networks (SAGIN) offer a flexible solution with extensive coverage. This paper addresses the critical task of deploying Service Function Chains (SFCs) in a resource-constrained SAGIN network, which requires a flexible deployment method. Leveraging Network Functions Virtualization (NFV), we propose a heuristic algorithm for Paral-lelized SFC (PSFC) deployment, strategically allocating resources to meet stringent delay requirements and enhance acceptance ratios. The proposed algorithm mitigates virtual network function (VNF) processing delays by deploying parallel VNFs on the same node. Extensive simulations validate the algorithm's efficacy, showcasing improvements in both acceptance ratio and service delay when compared to the state-of-the-art scheme.
Testing How Dating Apps Recommend a Potential Matches for User

In this age of globalization, the internet has gradually entered every aspect of our daily life even in our love lives. Dating has already entered the digital realm where people around the world could find the person of their type by swiping through multiple people on an application or even a website. Dating apps even have the feature of providing several recommendations based on the user's type. When a user created an account, they would be given several questions related to their type of people they want to date to know which type of person to recommend but sometimes, the application itself would recommend a user, a person that does not meet the initial requirements of the user itself. The goal of this research is to find the accuracy of the recommendation algorithm from the dating apps with the tag or interests that the user set with the recommended match and the fluctuation of the accuracy to conclude the most accurate matching based on aligned interest.
A Bandpass Filter with Low Loss Using Glide-Symmetric Holey Structure

A novel bandpass filter configuration in glide-symmetric periodic technology is presented in this work. The proposed filter design is based on a periodic structure that uses the glide-symmetric periodic holey structure as the unit cell to produce the desired frequency response. To achieve impedance matching, a three-order Chebyshev impedance matching block is proposed. The proposed bandpass filter configuration features a glide-symmetric periodic holey geometry between two layers of non-contacting metal plates, which exhibits stopband characteristics and reduced tolerance to processing and assembly errors. The filter topology containing two back-to-back transitions is simulated, and the passband results illustrate that the return loss is less than 18 dB and the insertion loss is smaller than 0.83 dB, ranging from 29 GHz to 40 GHz.
Research of hybrid active filter protection scheme for LCC-UHVDC transmission system

For traditional passive filters have many disadvantages, such as large occupation of area, prone to resonance with the impedance of AC power system, therefore the cascade H-bridge hybrid active filter was adopted as AC side filter of LCC-UHVDC transmission project in China, which adopts a scheme of 12 / 24 dual-tuned filter directly connected in series with the active filter based on cascade H-bridge. This article proposes a protection configuration scheme of hybrid active filter for LCC-UHVDC transmission, At the same time, this article optimize the design of the protection software and hardware architecture. The protection device uses dual-CPU 'AND' outputs to eliminate mis-action caused by the damage of a single device or a single loop. As the action time of hybrid active filter protection has become longer due to the addition of an active filter, this article utilizes differential protection comprehensive optimization technology, logic operation parallel processing technology, and relay asynchronous startup technology to compress the overall action time of protection system. The performance of hybrid active filter protection device developed based on the above design scheme can meet the technical specifications for UHV DC transmission system.
Efficiency Measurement of k-Partitioning of Mobile WSN

Network partitioning improves the energy efficiency of the network. In this work, the algorithm (KP-MWSN) can determine the no. of partitions (k) required based on the delay tolerance limit of each node of MWSN. It can be fruitful for the events where a small amount of delay can be accepted but efficiency has not been calculated in respect of a metric. So, we have measured the efficiency of KP-MWSN algorithm and the results are compared with the Topology Partitioning Scheme (TPS). The simulation result shows that in every randomly created event the efficiency of KP-MWSN is always better than other related state-of-the-art work.
PhishDetect: A BiLSTM based phishing URL detection framework using FastText embeddings

As per the Anti-Phishing Working Group's (APWG) report, there were approximately 4.7 million phishing attacks in the year 2022. A significant portion of these phishing attacks were carried out by alluring unsuspecting users into visiting counterfeit web-pages, which are hosted using malicious uniform resource locators (URLs). This paper proposes a lightweight Bidirectional Long Short Term Memory (BiLSTM) based phishing URL detection framework for mitigating the threats posed by phishing attacks. The proposed framework initially splits the URLs into four distinct components namely, Protocol type, domain, sub-domain and top level domain(TLD) using a set of special characters as delimiters. Different feature values corresponding to these four components are then used to build a vocabulary database of the URL corpus. Thereafter, a customized FastText word embedding technique is used to learn numeric feature vector representations of the tokens(URL features) present in the vocabulary database. These learned feature vectors, along with the pre-processed instances of the URL corpus are then provided as input to train a BiLSTM based classifier model for detection of malicious phishing URLs. Experimental results on a proprietary URL corpus comprising 200,000 normal and phishing URL instances show that the proposed framework achieves high accuracy in detecting malicious phishing URLs with minimal computational overhead.
Weak-shot Semantic Segmentation Based on Encoder-Decoder Structure

Semantic segmentation is a common and important computer vision task that requires pixel level annotation in supervised training manner. However, in a wide range of application scenarios, the cost of pixel level annotation is high, especially when extended to more classes. Therefore, this paper studies the problem of the so-called weak-shot semantic segmentation, which is how to learn better methods for semantic segmentation of new classes from weaker annotated image level category labels with the support of existing base classes. To address this issue, this paper optimizes the Transformer encoding and decoding structure and performs dual similarity transfer between proposal-pixels and pixel-pixels. Followed previous work, the semantic segmentation task is decomposed into proposal classification and proposal semantic segmentation. Proposal segmentation allows for the transfer of proposal-pixel similarity from the base class to the new class, thereby achieving mask learning for the new class. Furthermore, we edit the transformer decoder by swapping the order of self-attention and cross-attention, adopting learnable query features, and eliminating dropout in the decoder. These adjustment addresses potential performance issues of the previous model in handling small objects. The effectiveness of the proposed method was verified through experiments on two large-scale public datasets.
An Investigation on Internet of Things: Smart Grid Technology Framework's

The traditional grid networks could be upgraded to smart grids via Internet of Things (IoT) technology. This paper discusses the various grid system architecture's, technologies, and latest communication protocols for the IoT enabled smart grids. Literature survey shows that the technological advancements in power systems are making the grids better in terms of stability and sustainability; but one of the major issue related to such advancements, especially in the IoT enabled smart grids is related to data theft. This paper summarizes the IoT enabled mitigation techniques for such smart grid system vulnerabilities. Use of technologies such as bigdata, machine learning, augmented reality and blockchain, can give support to the IoT smart energy systems so that it become more flexible, safe, and secure. This paper summarizes the knowledge of such advanced technologies which are indeed helpful to make the IoT enabled smart grid system more effective technically, economically, and socially.
Human Activity Classification Using Recurrence Plot and Residual Network

Human Activity Classification (HAC) is a challenging time series classification task, aiming to discern specific behaviors or movements of individuals based on sensor data. This paper proposed an innovative method for HAC that leverages the power of Residual Network and the unique insights provided by Recurrence Plots (RP). Recurrence Plots are a visualization technique that transforms time series data into graphical representations, highlighting temporal patterns and dependencies. In this proposed method, Recurrence Plots play a crucial role by capturing intricate temporal relationships in the sensor data, enabling more precise activity recognition. Furthermore, Residual Network (ResNet), a prominent deep learning architecture, is employed to create a robust and efficient classification model. ResNet is able to mitigate the vanishing gradient problem through residual connections, which is particularly advantageous in the context of time series classification. The experimental results on MotionSense, UCI-HAR, and USC-HAD datasets demonstrate the superiority of our proposed approach. When compared to five existing methods and a self-constructed transfer learning model, the proposed method consistently outperforms others, achieving the highest average accuracy of 84.01%. Notably, it reaches accuracy rates of 92.31%, 84.72%, and 75.00% for MotionSense, UCI-HAR, and USC-HAD datasets, respectively.
Data Protection Using Optical System and Steganography Tool

Researchers have been using steganography, or the art of hiding secret information, extensively for the past few decades. However, because commercial stenography tools are readily available, most individuals use them. With the majority of these instruments, secret information can be easily unlocked by following a different procedure. Therefore, employing this kind of steganography tool to securely transfer personal information is a challenging undertaking. In this correspondence, we put forth a brand-new encryption technique that inserts concealed data into the text and staga images of the optical system (grating). There are two steps to the overall encryption process. Using the commercially available steganographic program Steghide, the images, and messages are first concealed among other images before being modulated in the optical grating system. The random phase matrix further processes the final object in the second section. We experimented with various decryption programs and found it impossible to successfully decrypt the secret text and image. As a result, the steganographic technique can safely be used to protect information that is hidden.
Fluctuating asymmetry employed in analyzing development instability of Cheilopogon pinnatibarbatus (Bangsi) from Cabadbaran City, Agusan del Norte, Philippines.

The study was aimed to analyze the developmental instability of Cheilopogon pinnatibarbatus collected from the coastal areas of Cabadbaran City, Agusan del Norte, Philippines. Reports from the fisherfolks in the area suggested decreasing fish catch of the species. The prevailing samples were essential in analyzing developmental instability. Landmark analyses were obtained using thin-plate spline (TPS) series and loaded into Symmetry and Asymmetry in Geometric Data (SAGE) software. Procrustes ANOVA showed a highly significant difference (p < 0.0001) on the three measured factors for fluctuating asymmetry (FA). These factors were individuals, sides, and interaction of individuals and sides which were noted to be asymmetrical. The results of principal components scores indicated high fluctuating asymmetry of female (68.0391%) and male (90.2138%) cumulative values. In females, affected landmarks largely came from posterior insertion of dorsal spine, dorsal and ventral insertion of caudal fin as well as midpoint of caudal border of hypural plate, and the anterior insertion of anal fin. In males, the commonly affected landmarks were the snout tip, posterior end of nuchal spine, anterior and posterior insertion of dorsal spine, anterior and posterior insertion of anal fin, and dorsal base of pelvic fin. High FA values provided evidence of a stressful adaptation of the fish species in an unstable environment during its development. The study presented the use of FA tool in evaluating the developmental instability of the species.
Classification of Pepper Bell into Healthy and Bacterial Spot Using Deep Learning

This study makes a strong case for dividing bell pepper leaves into two broad groups: those that are healthy and those plagued by disease centers. The system uses advanced machine learning techniques to achieve a classification accuracy of over 95 %. The importance of correct classification in agriculture cannot be denied because it plays an important role in early detection of diseases, preventing losses and ensuring product safety. Bacterial diseases pose a threat to pepper crops, so accurate identification is important for effective disease control. Our approach requires the use of deep learning models to classify bell peppers. The model adapts to different situations using different data, including both healthy and disease-affected leaves. Classification accuracy above 95 % demonstrates the effectiveness of our method. This high level of accuracy is required to develop disease on real farms. This advance allows farmers and experts to quickly implement intervention plans that protect crops and all the benefits.