Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Transcriptomics Prediction from Histology jointly through Transformer and Graph Neural Networks

The rapid development of spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to simultaneously profile gene expression, spatial locations, and the corresponding hematoxylin and eosin-stained histology images. Since histology images are relatively easy and cheap to obtain, it is promising to leverage histology images for predicting gene expression. Though several methods have been devised to predict gene expression using histology images, they don’t simultaneously include the 2D vision features and the spatial dependency, limiting their performances. Here, we have developed Hist2ST, a deep learning-based model using histology images to predict RNA-seq expression. At each sequenced spot, the corresponding histology image is cropped into an image patch, from which 2D vision features are learned through convolutional operations. Meanwhile, the spatial relations with the whole image and neighbored patches are captured through Transformer and graph neural network modules, respectively. These learned features are then used to predict the gene expression by following the zero-inflated negative binomial (ZINB) distribution. To alleviate the impact by the small spatial transcriptomics data, a self-distillation mechanism is employed for efficient learning of the model. Hist2ST was tested on the HER2-positive breast cancer and the cutaneous squamous cell carcinoma datasets, and shown to outperform existing methods in terms of both gene expression prediction and following spatial region identification. Further pathway analyses indicated that our model could reserve biological information. Thus, Hist2ST enables generating spatial transcriptomics data from histology images for elucidating molecular signatures of tissues.
Canine lineages reveal genetic drivers of dog behavioral diversification

Selective breeding of domestic dogs has generated diverse breeds often optimized for performing specialized tasks. Despite the heritability of breed-typical behavioral traits, identification of causal loci has proven challenging due to the complexity of canine population structure. We overcome longstanding difficulties in identifying genetic drivers of canine behavior by developing an innovative framework for understanding relationships between breeds and the behaviors that define them utilizing a dataset of over 4,000 domestic, semi-feral and wild canids and behavioral survey data for over 46,000 dogs. We identify ten major canine genetic lineages and their behavioral correlates, and show that breed diversification is predominantly driven by non-coding regulatory variation. We determine that lineage-associated genes converge in neurodevelopmental co-expression networks, identifying a sheepdog-associated enrichment for interrelated axon guidance functions. This work presents a scaffold for canine diversification that positions the domestic dog as an unparalleled system for revealing the genetic origins of behavioral diversity.
The conserved histone chaperone Spt6 facilitates DNA replication and mediates genome instability

Histone chaperones are an important class of proteins that regulate chromatin accessibility for DNA-templated processes. Spt6 is a conserved histone chaperone and key regulator of transcription and chromatin structure. However, its functions outside of these roles have been little explored. In this work, we demonstrate a role for S. cerevisiae Spt6 in DNA replication and more broadly as a regulator of genome stability. Spt6 binds the replication machinery and depletion or mutation of Spt6 impairs DNA replication in vivo. Additionally, spt6 mutants are sensitive to DNA replication stress inducing agents, with increased sensitivity when combined with loss of DNA replication associated factors. Furthermore, spt6 mutants have elevated levels of DNA double strand breaks and recombination. These effects appear to be independent of R-loops, which are not elevated in spt6 mutants. Our results identify Spt6 as a regulator of genome stability, at least in part through a role in DNA replication.
The rediscovery of a relict unlocks the first global phylogeny of whip spiders (Amblypygi)

Asymmetrical rates of cladogenesis and extinction abound in the Tree of Life, resulting in numerous minute clades that are dwarfed by larger sister groups. Such taxa are commonly regarded as phylogenetic relicts or “living fossils” when they exhibit an ancient first appearance in the fossil record and prolonged external morphological stasis, particularly in comparison to their more diversified sister groups. Due to their special status, various phylogenetic relicts tend to be well-studied and prioritized for conservation. A notable exception to this trend is found within Amblypygi (“whip spiders”), a visually striking order of functionally hexapodous arachnids that are notable for their antenniform first walking leg pair (the eponymous “whips”). Paleoamblypygi, the putative sister group to the remaining Amblypygi, is known from Late Carboniferous and Eocene deposits, but is survived by a single living species, Paracharon caecus Hansen, 1921, that was last collected in 1899. Due to the absence of genomic sequence-grade tissue for this vital taxon, there is no global molecular phylogeny for Amblypygi to date, nor a fossil-calibrated estimation of divergences within the group. Here, we report several individuals of a previously unknown species of Paleoamblypygi from a cave site in Colombia. Capitalizing upon this discovery, we generated the first molecular phylogeny of Amblypygi, integrating ultraconserved element sequencing with legacy Sanger datasets and including described extant genera. To quantify the impact of sampling Paleoamblypygi on divergence time estimation, we performed in silico experiments with pruning of Paracharon. We demonstrate that the omission of relicts has a significant impact on the accuracy of node dating approaches that outweighs the impact of excluding ingroup fossils. Our results underscore the imperative for biodiversity discovery efforts in elucidating the phylogenetic relationships of “dark taxa”, and especially phylogenetic relicts in tropical and subtropical habitats.
Characterizing dynamic neural representations of scene attractiveness

Aesthetic experiences during natural vision are varied: they can arise from viewing scenic landscapes, interesting architecture, or attractive people. Recent research in the field of neuroaesthetics has taught us a lot about where in the brain such aesthetic experiences are represented. Much less is known about when such experiences arise during the cortical processing cascade. Particularly, the dynamic neural representation of perceived attractiveness for rich natural scenes is not well understood. Here, I present data from an EEG experiment, in which participants provided attractiveness judgments for a set of diverse natural scenes. Using multivariate pattern analysis, I demonstrate that scene attractiveness is mirrored in early brain signals that arise within 200ms of vision, suggesting that the aesthetic appeal of scenes is first resolved during perceptual processing. In more detailed analyses, I show that even such early neural correlates of scene attractiveness are partly related to inter-individual variation in aesthetic preferences and that they generalize across scene contents. Together, these results characterize the time-resolved neural dynamics that give rise to aesthetic experiences in complex natural environments.
Metabolomic Proﬁle of Citrus limon Leaves (‘Verna’ Variety) by 1H-NMR and Multivariate Analysis Technique

: The elaboration and deﬁnition of “metabolic ﬁngerprints” can subsidize both the identiﬁcation and determination of plant varieties, as well as the increase in knowledge about the responses and adaptations of plants to external and/or internal factors. The lemon tree ( Citrus limon Burm.) is one of the most important crops in the Spanish southeast and is often consumed around the world. Although the study and characterization of its fruits are common due to its economic interest, its leaves are limited to speciﬁc functionalized studies related to the objective of the work (extraction of essential oils, stabilizing agent, aromatic extracts, etc.). So, this study aimed to identify the primary and secondary metabolites of Citrus limon Burm. (‘Verna’ variety) leaf samples cultivated under different conditions (three rootstocks and three culture media). In total, 19 metabolites were identiﬁed for all samples, of which 9 were amino acids, 5 organic acids, 3 sugars and 2 intermediate metabolites. The results pointed to a limited inﬂuence, both of the substrate and of the crop rootstock, on the metabolomic differentiation of lemon leaves. Knowledge and foliar metabolomic differentiation can offer important information that supports the application of crop foliar treatments but also helps in the management of diseases and pests.
Chronic Nicotine Exposure Depresses Dopamine Release in Nonhuman Primate Nucleus Accumbens

Tobacco use is a leading cause of preventable deaths worldwide. However, current smoking cessation therapies have very limited long-term success rates. Considerable research effort is therefore focused on identification of CNS changes with nicotine exposure as this may lead to more successful treatment options. Although recent work suggests that α β 2* nAChRs play a dominant role in dopaminergic function in rodent nucleus accumbens, the effects of chronic nicotine exposure remain to be determined. Here, we used cyclic voltammetry to investigate α 6 β 2* nAChR-mediated release with chronic nicotine treatment in nonhuman primate nucleus accumbens shell. Control studies showed that nAChR-mediated dopamine release occurs predominantly through the α 6 β 2* receptor subtype. Unexpectedly, there was a complete loss of α 6 β 2* nAChR-mediated activity after several months of nicotine treatment. This decline in function was observed with both single and multiple pulse-stimulated dopamine release. Paired pulse studies showed that the facilitation of dopamine release with multiple pulsing observed in controls in the presence of nAChR antagonist was lost with chronic nicotine treatment. Nicotine-evoked 3 H-dopamine release from nucleus accumbens synaptosomes was similar in nicotine and vehicle-treated monkeys, indicating that long-term nicotine administration does not directly modify α 6 β 2* nAChR-mediated dopamine release. Dopamine uptake rates, as well as dopamine transporter and α 6 β 2* nAChRs levels were also not changed with nicotine administration. These data indicate that nicotine exposure, as occurs with smoking, has major effects on cellular mechanisms linked to α 6 β 2* nAChR-mediated dopamine release and that this receptor subtype may represent a novel therapeutic target for smoking cessation. Synaptosomes began. Release of 3 H-dopamine from synaptosomes on each evoked by sec to of of concentrations μ M-100 μ or K + α -CtxMII perfused 3 min before nicotine exposure to distinguish between α 4 β 2* and α 6 β 2* nAChR-mediated Twelve 18 sec fractions (including basal release before and after the stimulated release) collected, and radioactivity determined by liquid scintillation counting. nicotine-evoked in endogenously evoked dopamine release measured with voltammetry not effects the nAChR-linked dopamine release mechanism. These data suggest that the depression of dopamine release in accumbens slices assessed using cyclic voltammetry is not to functional alterations in the nAChR-mediated release machinery but involves nAChR-mediated changes neuronal + 3 H-dopamine release since depolarization terminals. + 3 H-dopamine release from striatal was similar compared on long term nicotine treatment. These results may imply that are not fully representative mechanism that control release in intact presynaptic
Maximization of the Minicircle DNA Vaccine Production Expressing SARS-CoV-2 RBD

Nucleic acid vaccines have been proven to be a revolutionary technology to induce an efficient, safe and rapid response against pandemics, like the coronavirus disease (COVID-19). Minicircle DNA (mcDNA) is an innovative vector more stable than messenger RNA and more efficient in cell transfection and transgene expression than conventional plasmid DNA. This work describes the construction of a parental plasmid (PP) vector encoding the receptor-binding domain (RBD) of the S protein from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the use of the Design of Experiments (DoE) to optimize PP recombination into mcDNA vector in an orbital shaker. First, the results revealed that host cells should be grown at 42 °C and the Terrific Broth (TB) medium should be replaced by Luria Broth (LB) medium containing 0.01% L-arabinose for the induction step. The antibiotic concentration, the induction time, and the induction temperature were used as DoE inputs to maximize the % of recombined mcDNA. The quadratic model was statistically significant (p-value < 0.05) and presented a non-significant lack of fit (p-value > 0.05) with a suitable coefficient of determination. The optimal point was validated using 1 h of induction, at 30 °C, without the presence of antibiotics, obtaining 93.87% of recombined mcDNA. Based on these conditions, the production of mcDNA was then maximized in a mini-bioreactor platform. The most favorable condition obtained in the bioreactor was obtained by applying 60% pO2 in the fermentation step during 5 h and 30% pO2 in the induction step, with 0.01% L-arabinose throughout 5 h. The yield of mcDNA-RBD was increased to a concentration of 1.15 g/L, when compared to the orbital shaker studies (16.48 mg/L). These data revealed that the bioreactor application strongly incremented the host biomass yield and simultaneously improved the recombination levels of PP into mcDNA. Altogether, these results contributed to improving mcDNA-RBD biosynthesis to make the scale-up of mcDNA manufacture simpler, cost-effective, and attractive for the biotechnology industry.
Relationships of People with Intellectual Disabilities in Times of Pandemic: An Inclusive Study

(1) Background: Since 2012, our Inclusive Research Team has developed several studies on various topics that interest the co-researchers with intellectual disabilities. In 2021, throughout the COVID-19 pandemic, the co-researchers decided to investigate the impact of the pandemic on the relationships of people with intellectual disabilities. The aim of this article is to disseminate how this inclusive study was developed and to explain its results; (2) Methods: Co-researchers and academic researchers met six times to make different decisions: decide on the research topic; discuss the topic; prepare an interview script for people with intellectual disabilities; analyse the data obtained; and decide how to disseminate the results of the research. The co-researchers interviewed 10 people with intellectual disabilities and participated as facilitators in 3 focus groups; (3) Results: During the pandemic, digital devices have enabled people with intellectual disabilities to maintain their social relationships. However, the lack of access or support in using them, as well as the restrictions imposed on people with intellectual disabilities living in institutions, have presented significant barriers to maintaining their social relationships; (4) Conclusions: This article shows the difficulties people with intellectual disabilities face in maintaining successful social relationships in times of pandemic, and how we undertook research in an inclusive, virtual manner.
Brand personality factors affecting purchasing decision for bottled drinking water in Bangkok and Bangkok peripheral area: Supervised machine learning approach

The study of brand personality factors affecting purchasing decision for bottled drinking water in Bangkok and Bangkok peripheral area aimed to investigate brand personality factors that affect purchasing decision for bottled water in Bangkok and Bangkok peripheral area by utilizing a supervised machine learning approach from data science discipline. The objective of the study was to develop and evaluate the most suitable and practical purchasing decision label prediction model from brand personality features. The samples were drawn from people living in Bangkok and Bangkok peripheral area experienced in consuming bottled drinking water. Research tools were questionnaire with rating scale, and convenience sampling method was applied. The analysis of results showed that 5 brand personality features positively and significantly affected the level of purchasing decision label. Furthermore, this well-developed prediction model from train set maintained a moderate level of generalization as per supervised machine learning model evaluation by unseen set. As per the results, bottled drinking water manufacturer should not ignore the importance of promoting drinking water brand personality to match target consumers’ various personality traits as all 5 brand personality traits had a significant effect on the decision to buy bottled drinking water. Predicting purchasing decision for bottled drinking water by adopting Supervised Machine Learning approach from data science discipline is considered as a contribution of this study. Future research should possibly broaden the results of this study by utilizing unsupervised machine learning technique to develop the classification algorithm to classify an unknown target label.
Equilibrium Biosorption of Zn2+ and Ni2+ Ions from Monometallic and Bimetallic Solutions by Crab Shell Biomass

This work explored the technical feasibility of using crab shell (CS) as a promising, low-cost biosorbent to individually and simultaneously remove Zn2+ and Ni2+ from aqueous solutions. It was found that in both monometallic and bimetallic systems, Zn2+ and Ni2+ biosorption by CS was strongly dependent on the solution pH, with the optimum biosorption occurring at a pH of 6.0 for both heavy metals. The obtained isotherms for Zn2+ and Ni2+ biosorption onto CS in monometallic and bimetallic systems demonstrated that CS has a higher affinity for Zn2+ than for Ni2+. The experimental equilibrium data for the bimetallic system revealed that when one heavy metal is present in the system, there is a decrease in the equilibrium biosorption capacity for the other heavy metal; therefore, the combined action of Zn2+ and Ni2+ was antagonistic. The Sips and Redlich–Peterson isotherm models best fitted the equilibrium biosorption data for Zn2+ and Ni2+ in the monometallic systems, while the modified Sips model best fitted the binary biosorption equilibrium data. DRIFTS analyses indicated that carbonate ion, chitin, and proteins are mainly involved in the biosorption of Zn2+ and Ni2+ by CS from aqueous solutions, as confirmed using a range of analytical techniques.
Using High-Frequency PAR Measurements to Assess the Quality of the SIF Derived from Continuous Field Observations

Fluctuations in illumination are one of the major sources for SIF retrieval errors during temporal continuous field measurements. In this study, we propose a method for evaluating the quality of SIF based on simultaneous measurements of photosynthetically active radiation (PAR), which are acquired using a quantum sensor at a sampling frequency higher than that obtained using spectral measurements. The proposed method is based on the coefficient of variation (known as relative standard deviation) of the high-frequency PAR during a SIF measurement to determine the quality of the SIF value. To evaluate the method, spectral and PAR data of a healthy maize canopy were collected under various illumination conditions, including clear, cloudy, and rapidly fluctuating illumination. The SIF values were retrieved by 3FLD, SFM, and SVD. The results showed that SFM and 3FLD were sensitive to illumination stability. The determination coefficients (R2) between PAR and SIF extracted by SFM and 3FLD were higher than 0.8 on clear and cloudy days and only approximately 0.48 on the day with rapidly fluctuating illumination. By removing the unqualified data using the proposed method, the R2 values of SFM and 3FLD on the day of rapidly fluctuating illumination significantly increased to 0.72. SVD was insensitive to illumination stability. The R2 values of SVD on days with clear, cloudy, and rapidly fluctuating illumination were 0.73, 0.76, and 0.61, respectively. By removing the unqualified data, the R2 values of SVD were increased to 0.66 on the day with rapidly fluctuating illumination. The results indicated that the quality assessment method based on high-frequency PAR data can eliminate the incorrect SIFs due to unstable illumination. The method can be used to extract more accurate and reliable SIF datasets from long-term field observations for the study of the relationship between SIF and vegetation photosynthesis.
Teacher Talk to Early Age Children Applied in PAUD Lab. Raudlatul Qur'an

: The current age demands the skills in many ways to cope with information enhancement, which makes the need for education even higher. Consequently, the children are invited as early as possible to start learning in a formal institution such as pre-kindergarten children, or known as Early Childhood Education (PAUD). This study employs a qualitative research method focusing on how to talk with early childhood so that effective communication occurs. The technique used in this study was observation and in-depth interviews with teachers in a PAUD. In this study, PAUD teachers were selected with the category of having adequate experience in PAUD teaching at least five years. The PAUD chosen uses bilingual, namely Indonesian and also teaches the introduction of Arabic through short letter learning and prayer. The finding shows that the strategy of talking to early childhood is conveying the message by first exemplifying what the teacher asks students and as much as possible without coercion. In this case, the suggested strategies cover (1) learning attitude with verbal, visual stimuli; (2) full interaction with the proper eye contact and gestures; (3) conditioning classes to support interaction; and (4) maximizing context functions to facilitate understanding.
PLC channel characterization and modelling

This OMEGA deliverable presents a detailed study of the channel characterization and modelling for the Power Line Communications (PLC) channel. The Channel Transfer Function (CTF) is first thoroughly investigated. The channel time-frequency characteristics are studied from a measurement campaign covering a frequency band up to 100 MHz. Different approaches for the modelling of the CTF are proposed. Statistical channel generators are derived from both experimental observations and analytical representations, and an approach based on the transmission line theory is provided to study the effect of the network topology. Then, the impulsive noise generated by different electrical appliances is experimentally characterized at the source, and used to generate a model at the receiver. Stationary noise is finally investigated by both literature overview and experimental observations, and a simple model is provided. The models proposed in this deliverable will be used to support future studies on advanced signal processing techniques for PLC systems.