Miracle-dz/UniFAR-FTUs huggingface dataset:
Benchmarking of AlphaFold2 accuracy self-estimates as empirical quality measures and model ranking indicators and their comparison with independent model quality assessment programs

Motivation Despite an increase in the accuracy of predicted protein structures following the development of AlphaFold2, there remains a gap in the accuracy of predicted model quality assessment scores when compared to those generated with reference to experimental structures. The predictions of model accuracy scores generated by AlphaFold2, plDDT and pTM, have become familiar descriptors of model quality. However, at CASP15 some modelling groups noticed a variation in these scores for models of very similar observed quality, particularly for quaternary structures. There have also been a number of methods describing adaptations of the AlphaFold2 algorithm to purposes such as refinement by custom template recycling and model quality assessment using a similar method of template input. In this study we compare plDDT and pTM to their observed counterparts lDDT (including lDDT-Cα and lDDT-oligo) and TM-score to examine whether they retain their reliability across the whole scoring range for both tertiary and quaternary structures and in situations where the AlphaFold2 algorithm is adapted to customised functionality. In addition, we explore the accuracy with which plDDT and pTM rank AlphaFold2 tertiary and quaternary models and whether these can be improved by the independent model quality assessment programs ModFOLD9 and ModFOLDdock. Results For tertiary structures it was found that plDDT was an accurate descriptor of model quality when compared to observed lDDT-Cα scores (Pearson ρ = 0.97). Additionally, plDDT achieved a tertiary structure ranking agreement with observed scores of 0.34 as measured by true positive rate (TPR) and ModFOLD9 offered similar but not improved performance. However, the accuracy of plDDT (Pearson ρ = 0.67) and pTM (Pearson ρ = 0.70) became more variable for quaternary structures quality assessment where overprediction was seen with both scores for models of lower quality and underprediction was also seen with pTM for models of higher quality. Importantly, ModFOLDdock was able to improve upon AF2-Multimer quaternary structure model ranking as measured by both TM-score (TPR 0.34) and lDDT-oligo (TPR 0.43). Finally, evidence is presented for an increase in variability of both plDDT and pTM when custom template recycling is used, and that this variation is more pronounced for quaternary structures.
Chemotactile perception and associative learning of amino acids in yellowjacket workers

Learning and memory are essential for animal survival, influencing preferences, decision-making, and foraging behaviour. In this study, we explore the perceptual and learning abilities of Vespula germanica (yellowjacket wasps) to various amino acids. We hypothesize that V. germanica can qualitatively evaluate various amino acid solutions, given their scavenging habits and the possibility of metabolizing amino acids to fuel energy. Through chemo-tactile differential conditioning, we studied worker wasp’ maxilla labium extension response (MaLER) to essential (Lysine, Tryptophan, Arginine) and non-essential amino acids (Ornithine, Aspartic acid, Glycine). Conditioning sessions included individual amino acids against water and comparisons between different amino acids. Additionally, we tested retention, discrimination, and generalization abilities, 30 minutes later with conditioned and novel stimuli. Our results show that wasps exhibit the ability to learn and discriminate various amino acids. The discrimination capacity extended to differentiating between pairs of amino acids. Memory retention was generally robust, but certain associations observed during conditioning did not persist after a 30-minute interval. Moreover, when wasps were trained with essential amino acids, the acquired learning did not generally extend to other non-essential amino acids, except for Arginine, which exhibited generalization when tested with its precursor, Ornithine. Conversely, when trained with non-essential amino acids, the acquired learning generalized to other essential amino acids. These results suggest that, unlike other hymenopterans, wasps can detect, discriminate, and generalize free amino acids, crucial for their foraging decisions. This knowledge contributes to understanding the cognitive dimensions of V. germanica and their implications for targeted pest management. Summary statement This study on Vespula germanica’s amino acid perception enhances our understanding of foraging behaviours. The findings contribute to insect cognition studies, with potential implications for pest management.
An RXLR effector targets ER-Golgi interface to induce ER stress and necrotic cell death

To achieve successful colonization, the pathogen secretes hundreds of effectors into host cells to manipulate the host’s immune response. Despite numerous studies, the molecular mechanisms underlying effector-induced necrotic cell death remain elusive. In this study, we identified a novel virulent RXLR effector named Pc12 from P. capsici. Pc12 induces necrosis by triggering a distinct ER stress response through its interaction with Rab13-2. Unlike conventional hypersensitive response cell death associated with effector-triggered immunity, Pc12-induced cell death does not coincide with defense gene expression. Instead, it induces the relocalization of ER-resident proteins and confines secretory proteins within the ER. Pc12 interacts with Rab13-2, exhibiting a specific affinity for the active form of Rab13-2. Thus, it mimics the conformation of the inactive state of Rab13-2, subsequently recruiting the Rab-escort protein (REP). This process results in disruptions in vesicle formation within the ER-Golgi trafficking pathway. Furthermore, the substitution of a single amino acid of Rab13-2 structurally predicted to be crucial for the Pc12 interaction decreased the interaction with Pc12 while maintaining the interaction with REP and PRA1. These findings offer valuable insights into the ER stress-triggered cell death as well as a potential strategy for enhancing resistance against pathogens.
Selected humanization of yeast U1 snRNP leads to global suppression of pre-mRNA splicing and mitochondrial dysfunction in the budding yeast

The recognition of 5’ splice site (5’ ss) is one of the earliest steps of pre-mRNA splicing. To better understand the mechanism and regulation of 5’ ss recognition, we selectively humanized components of the yeast U1 snRNP to reveal the function of these components in 5’ ss recognition and splicing. We targeted U1C and Luc7, two proteins that interact with and stabilize the yeast U1 (yU1) snRNA and the 5’ ss RNA duplex. We replaced the Zinc-Finger (ZnF) domain of yU1C with its human counterpart, which resulted in cold-sensitive growth phenotype and moderate splicing defects. Next, we added an auxin-inducible degron to yLuc7 protein and found that Luc7-depleted yU1 snRNP resulted in the concomitant loss of PRP40 and Snu71 (two other essential yeast U1 snRNP proteins), and further biochemical analyses suggest a model of how these three proteins interact with each other in the U1 snRNP. The loss of these proteins resulted in a significant growth retardation accompanied by a global suppression of pre-mRNA splicing. The splicing suppression led to mitochondrial dysfunction as revealed by a release of Fe2+ into the growth medium and an induction of mitochondrial reactive oxygen species. Together, these observations indicate that the human U1C ZnF can substitute that of yeast, Luc7 is essential for the incorporation of the Luc7-Prp40-Snu71 trimer into yeast U1 snRNP, and splicing plays a major role in the regulation of mitochondria function in yeast.
Multilingual Knowledge Graphs and Low-Resource Languages: A Review

There is a lack of multilingual data to support applications in a large number of languages, especially for low-resource languages. Knowledge graphs (KG) could contribute to closing the gap of language support by providing easily accessible, machine-readable, multilingual linked data, which can be reused across applications. In this paper, we provide an overview of work in the domain of multi-lingual KGs with a focus on low-resource languages. We review the current state of multilingual KGs along with the different aspects that are crucial for creating KGs with language coverage in mind. Special consideration is given to challenges particular to low-resource languages in KGs. We further provide an overview of applications that yield multilingual KG information as well as downstream applications reusing such multilingual data. Finally, we explore open problems regarding multilingual KGs with a focus on low-resource languages.
Structural Summarization of Semantic Graphs Using Quotients

Graph summarization is the process of computing a compact version of an input graph while preserving chosen features of its structure. We consider semantic graphs where the features include edge labels and label sets associated with a vertex. Graph summaries are typically much smaller than the original graph. Applications that depend on the preserved features can perform their tasks on the summary, but much faster or with less memory overhead, while producing the same outcome as if they were applied on the original graph. In this survey, we focus on structural summaries based on quotients that organize vertices in equivalence classes of shared features. Structural summaries are particularly popular for semantic graphs and have the advantage of defining a precise graph-based output. We consider approaches and algorithms for both static and temporal graphs. A common example of quotient-based structural summaries is bisimulation, and we discuss this in detail. While there exist other surveys on graph summarization, to the best of our knowledge, we are the first to bring in a focused discussion on quotients, bisimulation, and their relation. Furthermore, structural summarization naturally connects well with formal logic due to the discrete structures considered. We complete the survey with a brief description of approaches beyond structural summaries.
Interaction Between Apigenin, Kaempferol and Trans-Cinnamic Acid: The Compounds Contributed to the Xanthine Oxidase Inhibitory Activity of Chrysanthemum morifolium

Chrysanthemum morifolium is commonly prescribe as one of the ingredients in traditional Chinese medicine for gout treatment and pain relief from gout. This study aimed to study the interaction between apigenin, kaempferol and trans-cinnamic acid identified from C. morifolium in xanthine oxidase (XO) inhibition. In our previous study, apigenin, kaempferol and trans-cinnamic acid have been shown to inhibit XO. In this study, an interaction assay showed that apigenin and kaempferol exhibited an additive effect, while trans-cinnamic acid exhibited an antagonistic effect against apigenin (or kaempferol) in XO inhibition. In conclusion, the additive effect of apigenin and kaempferol at the 8.2:2.7 ratio was responsible for the XO inhibitory effect of C. morifolium extract. Future study using the combination of apigenin and kaempferol for XO inhibition in the ratio similar to their natural composition in C. morifolium is recommended. The additive effect of these two compounds, each used at lower dose, could be beneficial due to its enhanced effectiveness and reduced negative impacts.
Footprint Biometrics on Person Identification Using CNN Algorithm

Footprint biometrics is as unique as a fingerprint in every human being. The prints on the sole of our foot can be used to identify individuals who have registered their information in our system. Biometric systems can be used as an alternative to password-protected profiles. With footprint as another option to strengthen a user's profile, the footprint biometric system can be used to secure confidential information of individuals. This study focuses on using Convolutional Neural Network Algorithm to acquire and identify footprints. The study uses the LeNet-5 architecture of CNN for data training and validation of the footprints registered. The system is composed of three major parts which are the platform where the user will step on to register their footprints and for the identification process as well. Raspberry Pi 3 Model B is used as the system's server where all necessary components are connected, and the UI of the system can be accessed in a computer. Raw registered images will undergo pre- processing where multiple filters will be applied before normalization such as Grayscale Filter, Anisotropic Diffusion Filter, and Gaussian Filter. 60 trials were done involving 10 test subjects in registering and identifying their footprints. The test yielded an accuracy of 91.67% considering the 5 misidentified trials.
Research On Tea Leaf Recognition Based On YOLOv5 Algorithm

Tea picking has always been mainly manual, but with the development of technology, it has been possible to use computer image recognition technology to assist robots in identifying tea leaves and picking them. However, the special characteristics of tea leaves make it difficult for the machine to recognize them with high accuracy and efficiency. In this paper, we compare the currently available algorithms and perform tea image recognition based on the YOLOv5 algorithm model. Based on the original YOLOv5 model, the CBAM attention mechanism is added to improve the recognition ability of tea. The mAP of our model can reach 89.1%, which is 13.6 percentage points higher than the original YOLOv5 model.
Low genetic variability and high isolation of a post-harvest South American pinniped population as revealed by genome-wide SNPs

Otaria flavescens has been one of the most heavily exploited pinnipeds during the last 200 years with depletions of about 90% in some colonies. After the prohibition on sealing in South America, populations became stabilized except for the Uruguayan population, which showed a constant decrease. The underlying causes of its decline have remained unknown. This study used the RAD-seq approach to assess the variability and connectivity of some of the most overexploited sea lion colonies in the Atlantic Ocean. Our results revealed low allelic richness, nucleotide diversity and heterozygosity in the Uruguayan population and evidence of complete isolation from the Argentinean populations under study. In contrast, the Patagonian populations showed a high degree of connectivity, which could explain their recovery and high levels of current diversity. Our research emphasizes the precarious genetic status of the Uruguayan sea lion population, calling for the immediate implementation of conservation measures.
Rumen protozoa are a hub for diverse hydrogenotrophic functions

Ciliate protozoa are an integral part of the rumen microbial community involved in a variety of metabolic processes. These processes are thought to be in part the outcome of interactions with their associated prokaryotic community. For example, methane production is facilitated by interspecies hydrogen transfer between protozoa and archaea. We hypothesize that ciliate protozoa are host to a stable prokaryotic community dictated by specific functions they carry. Here we modify the microbial community by varying the forage to concentrate ratios and show that, despite major changes in the prokaryotic community, several taxa remain stably associated with ciliate protozoa. By quantifying genes belonging to various known reduction pathways in the rumen, we find that the bacterial community associated with protozoa is enriched in genes belonging to hydrogen utilization pathways and that these genes correspond to the same taxonomic affiliations seen enriched in protozoa. Our results show that ciliate protozoa in the rumen may serve as a hub for various hydrogenotrophic functions and a better understanding of the processes driven by different protozoa may unveil potential role of ciliates in shaping rumen metabolism.
Rule Learning over Knowledge Graphs: A Review

Compared to black-box neural networks, logic rules express explicit knowledge, can provide human-understandable explanations for reasoning processes, and have found their wide application in knowledge graphs and other downstream tasks. As extracting rules manually from large knowledge graphs is labour-intensive and often infeasible, automated rule learning has recently attracted significant interest
SRRM2 splicing factor modulates cell fate in early development

Embryo development is an orchestrated process that relies on tight regulation of gene expression to guide cell differentiation and fate decisions. Alternative splicing is modulated during development as an additional layer of regulation to reprogram gene expression patterns. The Srrm2 splicing factor has recently been implicated in developmental disorders and diseases, but its role in early mammalian development remains unexplored. Here, we show that Srrm2 dosage is critical for maintaining embryonic stem cell pluripotency and cell identity. Srrm2 heterozygosity promotes loss of stemness, characterized by the coexistence of cells expressing naive and formative pluripotency markers, together with extensive changes in gene expression, including genes regulated by serum- response transcription factor and differentiation-related genes. Depletion of Srrm2 by RNA interference in embryonic stem cells shows that the earliest effects of Srrm2 half-dosage are specific alternative splicing events on a small number of genes, followed by expression changes in metabolism and differentiation-related genes. Our findings unveil molecular and cellular roles of Srrm2 in stemness and lineage commitment, shedding light on the roles of splicing regulators in early embryogenesis, developmental diseases and tumorigenesis. Summary statement This article emphasizes the importance of splicing regulators in early mammalian development by uncovering roles of SRRM2 splicing factor dosage in pluripotency, providing novel insights for a better understanding of Srrm2-related diseases.
Frequency-dependent phase entrainment of cortical cell types during tACS: converging modeling evidence

Background Transcranial alternating current stimulation (tACS) enables non-invasive modulation of brain activity, holding promise for clinical and research applications. Yet, it remains unclear how the stimulation frequency affects various neuron types. Objective To quantify the frequency-dependent behavior of key neocortical cell types. Methods We used both detailed (anatomical multicompartments) and simplified (three compartments) single-cell modeling approaches based on the Hodgkin–Huxley formalism to study neocortical excitatory and inhibitory cells under various-amplitude tACS frequencies within the 5–50 Hz range at rest and during basal 10 Hz activity. Results L5 pyramidal cells exhibited the highest polarizability at DC, ranging from 0.21 to 0.25mm and decaying exponentially with frequency. Inhibitory neurons displayed membrane resonance in the 5–15 Hz range with lower polarizability, although bipolar cells had higher polarizability. Layer 5 PC demonstrated the highest entrainment close to 10 Hz, which decayed with frequency. In contrast, inhibitory neurons entrainment increased with frequency, reaching level akin to PC. Results from simplified models could replicate the phase preferences, while amplitudes tend to follow opposite trends in PC. Conclusion tACS-induced membrane polarization is frequency-dependent, revealing observable resonance behavior. This finding motivates further experimental studies of cell-specific frequency-dependent membrane responses to weak electric stimuli. Whilst optimal phase entrainment of sustained activity is achieved in PC when tACS frequency matches the activity, inhibitory neurons tend to be entrained at higher frequencies. Consequently, this presents the potential for precise, cell-specific targeting. Single-cell models of frequency response to tACS for a wide variety of neurons Simplified and morphologically accurate models were compared Pyramidal cells (PC) show resonant-like response close to intrinsic firing frequency Entrainement of GABAergic neurons increases with the frequency of tACS Membrane polarization resonance in GABAergic neurons within the 5–15 Hz range
Manifold Regularized Tucker Decomposition Approach for Spatiotemporal Traffic Data Imputation

—Spatiotemporal trafﬁc data imputation (STDI), es- timating the missing data from partially observed trafﬁc data, is an inevitable and challenging task in data-driven intelligent transportation systems (ITS). Due to trafﬁc data’s multidimensional and spatiotemporal properties, we treat the missing data imputation as a tensor completion problem. Many studies have been on STDI based on tensor decomposition in the past decade. However, how to use spatiotemporal correlations and core tensor sparsity to improve the imputation performance still needs to be solved. This paper reshapes a 3rd/4th order Hankel tensor and proposes an innovative manifold regularized Tucker decomposition (ManiRTD) model for STDI. Expressly, we represent the sensory trafﬁc state data as the 3rd/4th tensors by introducing Multiway Delay Embedding Transforms. Then, ManiRTD improves the sparsity of the Tucker core using a sparse regularization term and employs manifold regularization and temporal constraint terms of factor matrices to characterize the spatiotemporal correlations. Finally, we address the ManiRTD model through a block coordinate descent framework under alternating proximal gradient updating rules with convergence-guaranteed. Numerical experiments are conducted on real-world spatiotemporal trafﬁc datasets (STDs). Our results demonstrate that the proposed model outperforms the other factorization approaches and reconstructs the STD more precisely under various missing scenarios.
Asymmetry and nonlinearity of the influence of ENSO on the northern winter stratosphere: 1. Observations

The El Niño–Southern Oscillation (ENSO) has significant effects on the extratropical stratosphere. This study explores the nonlinearity and the asymmetry of these influences by distinguishing the different effects of four types of ENSO: “moderate El Niño,” “strong El Niño,” “moderate La Niña,” and “strong La Niña. ” It is revealed that the moderate El Niño and the strong La Niña are much more efficient than the strong El Niño and the moderate La Niña, respectively, in modulating the northern winter stratospheric variability, resulting in significant nonlinearity and asymmetry. The tropical rainfall anomalies induced by a moderate El Niño or a strong La Niña are centered over the central equatorial Pacific region near the dateline, while the convection responses to a strong El Niño or a moderate La Niña are centered farther eastward. Accordingly, the anomalous Pacific‐North America wave train pattern is modulated by ENSO in a nonlinear and asymmetric way, which leads to the large nonlinear and asymmetric components of the vertical Eliassen‐Palm (E‐P) flux responses to ENSO. The increase of planetary wave activity in the extratropical stratosphere in moderate El Niño winters is thus greater than in strong El Niño winters, whereas a strong La Niña gives a larger decrease in propagation than a moderate La Niña. The relatively strong (weak) E‐P flux responses to moderate (strong), El Niño and strong (moderate) La Niña explains the remarkable nonlinearity and asymmetry in the response of the extratropical stratosphere to ENSO.