Miracle-dz/UniFAR-FTUs huggingface dataset:
The assessment of basic blue 41 textile dye decolorisation using the Fenton process: response surface methodology optimisation and toxicity evaluation

ABSTRACT The biochemical biomarkers (Catalase (CAT), glutathione s-transferase (GST), cytochrome P4501A1 (CYP1A1450) activities and glutathione (GSH), malondialdehyde (MDA) levels) of D. polymorpha exposed to the basic blue 41 (BB41) solutions for 24 and 96 h before and after the Fenton process (FTP) were evaluated to investigate the efficiency of the FTP. The optimum conditions for the removal of BB41 were set using central composite design (CCD), which is a response surface methodology (RSM) module in the Design-Expert® software. The optimal conditions were found to be 27.87 min, 3.49 mg/L, and 0.04 for reaction time, initial BB41 concentration, and Fe2+/H2O2 molar ratio, respectively, resulting in maximum (99%) removal efficiency. After FTP, the MDA and GSH levels decreased significantly during the 24 and 96 h. CAT activities were increased after treatment. No statistically significant changes were observed in the CYP1A1 levels after FTP. GST activities decreased during 24 h but increased during 96 h after FTP. CYP1A1450, catalase, GST activities and MDA, GSH levels of D. polymorpha are useful biomarkers to evaluate the efficiency of FTP. This study suggests that BB41 dye must be further treated after the FTP to minimise the ecotoxicological risk.
A general approach for stabilizing nanobodies for intracellular expression

Conventional antibodies and their derived fragments are difficult to deploy against intracellular targets in live cells, due to their bulk and structural complexity. Nanobodies provide an alternative modality, with well documented examples of intracellular expression. Despite their promise as intracellular reagents, there has not been a systematic study of nanobody intracellular expression. Here, we examined intracellular expression of 75 nanobodies from the Protein Data Bank. Surprisingly, a majority of nanobodies were unstable in cells, illustrated by aggregation and clearance. Using comparative analysis and framework mutagenesis, we developed a general approach that stabilized a great majority of originally intracellularly unstable nanobodies, without compromising target-binding function. Additionally, we identified distinct sequence features impacting nanobody intracellular stability, and demonstrated mutationally stabilized nanobody expression in vivo, in the murine retina and in E. coli. This work will allow for standardized nanobody engineering for intracellular applications, potentiating a growing field of intracellular interrogation and intervention.
Intron Losses and Gains in Nematodes: Not Eccentric at All

The evolution of spliceosomal introns has been widely studied among various eukaryotic groups. Researchers nearly reached the consensuses on the pattern and the mechanisms of intron losses and gains across eukaryotes. However, according to previous studies that analyzed a few genes or genomes of nematodes, Nematoda seem to be an eccentric group. Taking advantage of the recent accumulation of sequenced genomes, we carried out an extensive analysis on the intron losses and gains using 104 nematodes genomes across all the five Clades of the phylum. Nematodes have a wide range of intron density, from less than one to more than nine per 1kbp coding sequence. The rates of intron losses and gains exhibit significant heterogeneity both across different nematode lineages and across different evolutionary stages of the same lineage. The frequency of intron losses far exceeds that of intron gains. Five pieces of evidence supporting the model of cDNA-mediated intron loss have been observed in ten Caenorhabditis species, the dominance of the precise intron losses, frequent loss of adjacent introns, and high-level expression of the intron-lost genes, preferential losses of short introns, and the preferential losses of introns close to 3′-ends of genes. Like studies in most eukaryotic groups, we cannot find the source sequences for the limited number of intron gains detected in the Caenorhabditis genomes. All the results indicate that nematodes are a typical eukaryotic group rather than an outlier in intron evolution.
Rapid neural encoding of the contrast between native and nonnative speech in the alpha band

More than half of the world’s population is multilingual, yet it is not known how the human brain encodes the perception of native vs. nonnative speech. To find out, we asked German native speakers to detect the onset of native and nonnative (English and Turkish) vowels in a roving standard stimulation. Using EEG, we show that nonnativeness is robustly registered by an increase in phase coherence in the alpha band (8-12 Hz), beginning as early as ∼100 ms after stimulus onset and lasting more than 200 ms. The alpha band effect is speech-specific, successfully predicts the response speed advantage of nonnative speech, and grants ∼90% decoding accuracy in distinguishing native vs. nonnative speech irrespective of language familiarity. We propose alpha phase coherence as a candidate neural channel for the online resolution of the native-nonnative contrast in the adult brain. Significance Statement We show that the human brain takes only ∼ 100 ms to distinguish native vs. nonnative speech. This difference is neurally encoded in the alpha band in a reliable and specific way, supporting a processing model in which the mixture of top-down and bottom-up information represented in the speech signal is concurrently partitioned into different frequency channels.
Complex effects of kinase localization revealed by compartment-specific regulation of protein kinase A activity

Kinase activity in signaling networks frequently depends on regulatory subunits that can both inhibit activity by interacting with the catalytic subunits and target the kinase to distinct molecular partners and subcellular compartments. Here, using a new synthetic molecular interaction system, we show that translocation of a regulatory subunit of the protein kinase A (PKA-R) to the plasma membrane has a paradoxical effect on the membrane kinase activity. It can both enhance it at lower translocation levels, even in the absence of signaling inputs, and inhibit it at higher translocation levels, suggesting its role as a linker that can both couple and decouple signaling processes in a concentration-dependent manner. We further demonstrate that superposition of gradients of PKA-R abundance across single cells can control the directionality of cell migration, reversing it at high enough input levels. Thus complex in vivo patterns of PKA-R localization can drive complex phenotypes, including cell migration.
Investigating the Interplay Between Affective, Phonatory and Motoric Subsystems in Autism Spectrum Disorder Using a Multimodal Dialogue Agent

We explore the utility of an on-demand multimodal conversational platform in extracting speech and facial metrics in children with Autism Spectrum Disorder (ASD). We investigate the extent to which these metrics correlate with objective clinical measures, particularly as they pertain to the interplay be-tween the affective, phonatory and motoric subsystems. 22 participants diagnosed with ASD engaged with a virtual agent in conversational affect production tasks designed to elicit facial and vocal affect. We found significant correlations between vocal pitch and loudness extracted by our platform during these tasks and accuracy in recognition of facial and vocal affect, as-sessed via the Diagnostic Analysis of Nonverbal Accuracy-2 (DANVA-2) neuropsychological task. We also found significant correlations between jaw kinematic metrics extracted using our platform and motor speed of the dominant hand assessed via a standardised neuropsychological finger tapping task. These findings offer preliminary evidence for the usefulness of these audiovisual analytic metrics and could help us better model the interplay between different physiological subsystems in individuals with ASD.
Conservation of Archaeological Ships and Boats: personal experiences

here they are older, mid-Holocene, and possibly reflect a reaction to deteriorating settlement conditions inland as the result of change in monsoon patterns. Climate change, but especially rising population densities are themes in research on the huge shell middens of the first millennium BC in south-west Africa. This paper, by Jerardino, comes closest to the kind of modelling in behavioural ecology that was once so prevalent, and rightly so, in midden analysis. Across the Atlantic, Schmitz considers a more traditional problem associated with shell mounds, the transition from hunter-gatherer to agricultural populations—in this case in Brazil. Down at the southern tip of South America, there are more results from a long-standing project involving the intricate dissection of shell mounds to understand better the constant problem of complex stratigraphy (Mansur and Piqué), along with ethnographical contextualization of the prehistoric data (Estévez, Vila and Piqué). In the Indian Ocean, relatively neglected in shell-mound studies, Datta and Ghosh provide one of the very few papers available on shell middens in the Andaman Islands and there is a brief glimpse of freshwater shell fishing in Malaysia (Saidin). The editors introduce themes prompted by this collection: the era of shell mounds and what it might mean; the wealth of new investigations which are now reaching toward a global distribution; the uneasy relationship of ethnography and archaeology; and the continuing issues of taphonomy, preservation, and the intentions of the shell dumpers. These and other themes are taken up in a thoughtful overview by Peter Woodman. Among them he emphasizes the argument that shellfish were always more than food and shell mounds more than refuse dumps. Shell Energy is rich in data and ideas and diverse in locality and approach. It is handsomely produced, has French and Spanish abstracts, and adds importantly to the venerable but still vital study of shell mounds.
The world’s hotspot of linguistic and biocultural diversity under threat

Papua New Guinea is home to >10% of the world’s languages and rich and varied biocultural knowledge, but the future of this diversity remains unclear. We measured language skills of 6,190 students speaking 392 languages (5.5% of the global total) and modelled their future trends, using individual-level variables characterizing family language use, socio-economic conditions, student’s skills, and language traits. This approach showed that only 58% of the students, compared to 91% of their parents, were fluent in indigenous languages, while the trends in key drivers of language skills (language use at home, proportion of mixed-language families, urbanization, students’ traditional skills) predicted accelerating decline of fluency, to an estimated 26% in the next generation of students. Ethnobiological knowledge declined in close parallel with language skills. Varied medicinal plant uses known to the students speaking indigenous languages are replaced by a few, mostly non-native species for the students speaking English or Tok Pisin, the national lingua franca. Most (88%) students want to teach indigenous language to their children. While crucial for keeping languages alive, this intention faces powerful external pressures as key factors (education, cash economy, road networks, urbanization) associated with language attrition are valued in contemporary society. Significance Statement Around the world, more than 7,000 languages are spoken, most of them by small populations of speakers in the tropics. Globalization puts small languages at a disadvantage, but our understanding of the drivers and rate of language loss remains incomplete. When we tested key factors causing language attrition among Papua New Guinean students speaking 392 different indigenous languages, we found an unexpectedly rapid decline in their language skills compared to their parents and predicted further acceleration of language loss in the next generation. Language attrition was accompanied by decline in the traditional knowledge of nature among the students, pointing to an uncertain future for languages and biocultural knowledge in the most linguistically diverse place on Earth.
Precise gene models using long-read sequencing reveal a unique poly(A) signal in Giardia lamblia

During pre-mRNA processing, the poly(A) signal is recognized by a protein complex that ensures precise cleavage and polyadenylation of the nascent transcript. The location of this cleavage event establishes the length and sequence of the 3′ UTR of an mRNA, thus determining much of its post-transcriptional fate. Here, using long-read sequencing, we characterize the polyadenylation signal and related sequences surrounding Giardia lamblia cleavage sites for over 2600 genes. We find that G. lamblia uses a AGURAA poly(A) signal, which differs from the mammalian AAUAAA. We also describe how G. lamblia lacks common auxiliary elements found in other eukaryotes, along with the proteins that recognize them. Further, we identify 133 genes that show evidence of alternative polyadenylation. These results suggest that despite pared down cleavage and polyadenylation machinery, 3′ end formation still appears to be an important regulatory step for gene expression in G. lamblia.
An Analysis of the Impact of Personality Traits towards Augmented Reality in Online Shopping

The Internet as a shopping and purchasing medium has become an extensively researched topic. Augmented realty, in particular, allows consumers to explore their options and make personalised changes while shopping online. Our study aims to analyse the symmetry between the attitudes towards using the traditional electronic online shopping and the electronic commerce that uses augmented reality. We also investigate the effects of personality traits and the attitudes towards the Internet on the two electronic commerce forms. Our results show that the buying intention in online shopping is significantly higher in the case of augmented reality. Our results also reveal associations between personality traits and online buying behaviours, i.e., neuroticism and the openness to experiences being associated with the willingness to buy online. On the other hand, personality traits are proved to predict buying impulsiveness, the highest weight being represented by low emotional stability and high external locus of control. Further research should also include other dimensions, such as the perceived risks associated with online purchasing, self-efficacy or anxiety towards technology.
Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

With the development of modern power systems (smart grid), energy consumption prediction becomes an essential aspect of resource planning and operations. In the last few decades, industrial and commercial buildings have thoroughly been investigated for consumption patterns. However, due to the unavailability of data, the residential buildings could not get much attention. During the last few years, many solutions have been devised for predicting electric consumption; however, it remains a challenging task due to the dynamic nature of residential consumption patterns. Therefore, a more robust solution is required to improve the model performance and achieve a better prediction accuracy. This paper presents an ensemble approach based on learning to a statistical model to predict the short-term energy consumption of a multifamily residential building. Our proposed approach utilizes Long Short-Term Memory (LSTM) and Kalman Filter (KF) to build an ensemble prediction model to predict short term energy demands of multifamily residential buildings. The proposed approach uses real energy data acquired from the multifamily residential building, South Korea. Different statistical measures are used, such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score, to evaluate the performance of the proposed approach and compare it with existing models. The experimental results reveal that the proposed approach predicts accurately and outperforms the existing models. Furthermore, a comparative analysis is performed to evaluate and compare the proposed model with conventional machine learning models. The experimental results show the effectiveness and significance of the proposed approach compared to existing energy prediction models. The proposed approach will support energy management to effectively plan and manage the energy supply and demands of multifamily residential buildings.
Recoil Reduction Method of Gun with Side to Rear Jet Controlled by Piston Motion

Excessive recoil severely restricts the loading of high-power traditional guns on modern vehicles. To reduce the recoil without breaking the continuous firing mode and reducing the projectile velocity, a recoil reduction method that controls the lateral ejecting of propellant gas by a piston was proposed. The recoil reduction device is symmetric about the barrel axis. First, a one-dimensional two-phase flow model of interior ballistic during the gun firing cycle was established. Next, the MacCormack scheme was used to simulate, and the piston motion was gained. Then the propagation of the rarefaction wave in the barrel was presented. Finally, the propulsion difference between the piston-controlled gun and the traditional gun was discussed. The results showed that the recoil momentum was reduced by 31.80%, and the muzzle velocity was decreased by just 1.30% under the reasonable matching of structural parameters.
Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things

In this paper, we apply the multi-class supervised machine learning techniques for classifying the agriculture farm machinery. The classification of farm machinery is important when performing the automatic authentication of field activity in a remote setup. In the absence of a sound machine recognition system, there is every possibility of a fraudulent activity taking place. To address this need, we classify the machinery using five machine learning techniques—K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Gradient Boosting (GB). For training of the model, we use the vibration and tilt of machinery. The vibration and tilt of machinery are recorded using the accelerometer and gyroscope sensors, respectively. The machinery included the leveler, rotavator and cultivator. The preliminary analysis on the collected data revealed that the farm machinery (when in operation) showed big variations in vibration and tilt, but observed similar means. Additionally, the accuracies of vibration-based and tilt-based classifications of farm machinery show good accuracy when used alone (with vibration showing slightly better numbers than the tilt). However, the accuracies improve further when both (the tilt and vibration) are used together. Furthermore, all five machine learning algorithms used for classification have an accuracy of more than 82%, but random forest was the best performing. The gradient boosting and random forest show slight over-fitting (about 9%), but both algorithms produce high testing accuracy. In terms of execution time, the decision tree takes the least time to train, while the gradient boosting takes the most time.
Leveraging Software-Defined Networking Approach for Future Information-Centric Networking Enhancement

Information-centric networking (ICN) has been developed as a potential candidate for future networks. In this model, users are provided with content rather than communication channels between the different hosts. The ICN network has several problems such as scalability issues and bandwidth consumption. However, software-defined networking (SDN) has been proposed to improve the networking architectures. The goal of our paper is to propose a new approach to named-data networking (NDN) based on the paradigm of SDN. Our work introduces various research studies carried out in the SDN and ICN contexts. We first present the SDN architecture. Then, we focus on work that combines ICN and SDN architectures. Finally, we show the effects of using the SDN architecture on the named-data network (NDN). Our experimental results show that the use of the SDN architecture has a positive effect on NDN network performance.
Synthesis of C3-Symmetric Cinchona-Based Organocatalysts and Their Applications in Asymmetric Michael and Friedel-Crafts Reactions

In this work, anchoring of cinchona derivatives to trifunctional cores (hub approach) was demonstrated to obtain size-enlarged organocatalysts. By modifying the cinchona skeleton in different positions, we prepared four C3-symmetric size-enlarged cinchona derivatives (hub-cinchonas), which were tested as organocatalysts and their catalytic activities were compared with the parent cinchona (hydroquinine) catalyst. We showed that in the hydroxyalkylation reaction of indole, hydroquinine provides good enantioselectivities (up to 73% ee), while the four new size-enlarged derivatives resulted in significantly lower values (up to 29% ee) in this reaction. Anchoring cinchonas to trifunctional cores was found to facilitate nanofiltration-supported catalyst recovery using the PolarClean alternative solvent. The C3-symmetric size-enlarged organocatalysts were completely rejected by all the applied membranes, whereas the separation of hydroquinine was found to be insufficient when using organic solvent nanofiltration. Furthermore, the asymmetric catalysis was successfully demonstrated in the case of the Michael reaction of 1,3-diketones and trans-β-nitrostyrene using Hub3-cinchona (up to 96% ee) as a result of the positive effect of the C3-symmetric structure using a bulkier substrate. This equates to an increased selectivity of the catalyst in comparison to hydroquinine in the latter Michael reaction.
Lightweight Secure Authentication and Key Distribution Scheme for Vehicular Cloud Computing

A vehicular ad-hoc network (VANET) is the basic block in building an intelligent transportation system that improves the traffic flow and makes needed information conveniently accessible. VANET depends on a dense exchange of sensed data between vehicles and Road Side Units (RSUs). A large amount of sensed data requires a huge computation and storage capabilities, which is provided by the vehicular cloud computing (VCC). However, the security problems of data confidentiality, access control, vehicles’ authentication, and conductors’ privacy in VCC are issues that need to be solved. In this paper, we propose an efficient algorithm to ensure VCC security and privacy. We use Pseudo-ID instead of vehicles’ real ID to provide conductors’ privacy, Identifier-Based Signature mechanism is used to guarantee vehicles’ authentication, and Ciphertext-Policy Attribute-Based Encryption (CP-ABE) algorithm is used for key distribution. Our liGhtweight secURe AutheNticaTion and keY distribution scheme for vehicular cloud computing (GUARANTY) ensures a secure keys distribution to minimize the encryption and decryption computation cost. Vehicles use a symmetrical cryptography in their communication. We analyze the security of our algorithm using AVISPA tool. We use this tool to simulate insiders and outsiders attacks. We evaluate our algorithm’s performance in terms of computation delay and reception rate.