Miracle-dz/UniFAR-FTUs huggingface dataset:
ToxinPred 3.0: An improved method for predicting the toxicity of peptides

Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing the failure of numerous peptides during clinical trials. In 2013, our group developed ToxinPred, a computational method that has been extensively adopted by the scientific community for predicting peptide toxicity. In this paper, we propose a refined variant of ToxinPred that showcases improved reliability and accuracy in predicting peptide toxicity. Initially, we used BLAST for alignment-based toxicity prediction, yet coverage was limited. We adopted a motif-based approach with MERCI software to identify unique toxic patterns. Despite specificity gains, sensitivity was compromised. We developed alignment-free methods using machine/deep learning, achieving a balance sensitivity and specificity of prediction. A deep learning model (ANN – LSTM with fixed sequence length) developed using one-hot encoding attained a 0.93 AUROC and 0.71 MCC on independent data. The machine learning model (extra tree) developed using compositional features of peptides achieved 0.95 AUROC and 0.78 MCC. Lastly, we developed hybrid or ensemble methods combining two or more models to enhance performance. Hybrid approaches, including motif-based and machine learning, achieved a 0.98 AUROC and 0.81 MCC. Evaluation on independent data demonstrated our method’s superiority. To cater to the needs of the scientific community, we have developed a standalone software, pip package and web-based server ToxinPred3 (https://github.com/raghavagps/toxinpred3 and https://webs.iiitd.edu.in/raghava/toxinpred3/). Author’s Biography Anand Singh Rathore is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. Akanksha Arora is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. Shubham Choudhury is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. Purava Tijare is a Project Fellow in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. Gajendra P. S. Raghava is currently working as a Professor and Head of the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. Highlights Implementation of alignment or similarly based techniques for predicting toxic peptides. Discovery of toxicity-associated patterns and identification of toxic regions in peptides. Development of machine and deep learning-based models for toxicity prediction. Ensemble methods that combine alignment-based and alignment-free methods. Web server and standalone software package for screening toxicity in peptides/proteins.
Multi-omic stratification of the missense variant cysteinome

Cancer genomes are rife with genetic variants; one key outcome of this variation is gain-of-cysteine, which is the most frequently acquired amino acid due to missense variants in COSMIC. Acquired cysteines are both driver mutations and sites targeted by precision therapies. However, despite their ubiquity, nearly all acquired cysteines remain uncharacterized. Here, we pair cysteine chemoproteomics—a technique that enables proteome-wide pinpointing of functional, redox sensitive, and potentially druggable residues—with genomics to reveal the hidden landscape of cysteine acquisition. For both cancer and healthy genomes, we find that cysteine acquisition is a ubiquitous consequence of genetic variation that is further elevated in the context of decreased DNA repair. Our chemoproteogenomics platform integrates chemoproteomic, whole exome, and RNA-seq data, with a customized 2-stage false discovery rate (FDR) error controlled proteomic search, further enhanced with a user-friendly FragPipe interface. Integration of CADD predictions of deleteriousness revealed marked enrichment for likely damaging variants that result in acquisition of cysteine. By deploying chemoproteogenomics across eleven cell lines, we identify 116 gain-of-cysteines, of which 10 were liganded by electrophilic druglike molecules. Reference cysteines proximal to missense variants were also found to be pervasive, 791 in total, supporting heretofore untapped opportunities for proteoform-specific chemical probe development campaigns. As chemoproteogenomics is further distinguished by sample-matched combinatorial variant databases and compatible with redox proteomics and small molecule screening, we expect widespread utility in guiding proteoform-specific biology and therapeutic discovery.
Met58 and di-acidic motif located at C-terminal region of SARS-CoV-2 ORF6 plays a crucial role in its structural conformations

Despite being mostly neglected in structural biology, the C-terminal Regions (CTRs) are studied to be multifunctional in humans as well as in viruses. Their role in cellular processes such as trafficking, protein-protein interactions, and protein-lipid interactions are known due to their structural properties. In our previous findings on SARS-CoV-2 Spike and NSP1 proteins, the C-terminal regions (CTRs) are observed to be disordered and experimental evidence showed a gain of structure properties in different physiological environments. In this line, we have investigated the structural dynamics of CTR (residues 38-61) of SARS-CoV-2 ORF6 protein, disrupting bidirectional transport between the nucleus and cytoplasm. Like Spike and NSP1-CTR, the ORF6-CTR is also disordered in nature but possesses gain of structure properties in minimal physiological conditions. As per studies, the residue such as Methionine at 58th position in ORF6 is critical for interaction with Rae1-Nup98. Therefore, along with M58, we have identified a few other mutations from the literature and performed extensive structure modelling and dynamics studies using computational simulations. The exciting revelations in CTR models provide evidence of its structural flexibility and possible capabilities to perform multifunctionality inside the host.
Nearly Optimal Competitive Ratio for Online Allocation Problems with Two-sided Resource Constraints and Finite Requests

In this paper, we investigate the online allocation problem of maximizing the overall revenue subject to both lower and upper bound constraints. Compared to the extensively studied online problems with only resource upper bounds, the two-sided constraints affect the prospects of resource consumption more severely. As a result, only limited violations of constraints or pessimistic competitive bounds could be guaranteed. To tackle the challenge, we define a measure of feasibility ξ ∗ to evaluate the hardness of this problem, and estimate this measurement by an optimization routine with theoretical guarantees. We propose an on-line algorithm adopting a constructive framework, where we initialize a threshold price vector using the estimation, then dynamically update the price vector and use it for decision-making at each step. It can be shown that the proposed algorithm is (cid:0)
A trajectory is worth three sentences: multimodal transformer for offline reinforcement learning

Transformers hold tremendous promise in solving ofﬂine reinforcement learning (RL) by formulating it as a sequence modeling problem inspired by language modeling (LM). Prior works using trans-formers model a sample (trajectory) of RL as one sequence analogous to a sequence of words (one sentence) in LM, despite the fact that each trajectory includes tokens from three diverse modalities: state, action, and reward, while a sentence contains words only. Rather than taking a modality-agnostic approach which uniformly models the tokens from different modalities as one sequence, we propose a multimodal sequence modeling approach in which a trajectory (one “sentence”) of three modalities (state, action, reward) is disentangled into three unimodal ones (three “sentences”). We investigate the correlation of different modalities during sequential decision-making and use the insights to design a multimodal transformer, named Decision Transducer (DTd). DTd outperforms prior art in ofﬂine RL on the conducted D4RL benchmarks and enjoys better sample efﬁciency and algorithm ﬂexibility. Our code is made publicly here.
RLEG: Vision-Language Representation Learning with Diffusion-based Embedding Generation

Vision-language representation learning models ( e.g. , CLIP) have achieved state-of-the-art performance on various downstream tasks, which usually need large-scale training data to learn discriminative representation. Recent progress on generative diffusion models ( e.g. , DALL-E 2) has demonstrated that diverse high-quality samples can be synthesized by randomly sampling from generative distribution. By virtue of generative capability in this paper, we propose a novel vision-language R epresentation L earning method with diffusion-based E mbedding G eneration ( RLEG ), which exploits diffusion models to generate feature embedding online for learning effective vision-language representation. Specifically, we first adopt image and text encoders to extract the corresponding embeddings. Secondly, pretrained diffusion-based embedding generators are harnessed to transfer the embedding modality online between vision and language domains. The embeddings generated from the generators are then served as augmented embedding-level samples, which are applied to contrastive learning with the variant of the CLIP framework. Experimental re-sults show that the proposed method could learn effective representation and achieve state-of-the-art performance on various tasks including image classification, image-text retrieval, object detection, semantic segmentation, and text-conditional image generation.
Mapping Natural Product Biosynthetic Hotspots: Prioritizing Conservation for Medicinal Resources

Background Natural products (NPs) are vital for promoting human health. Given the increasing threats of biodiversity loss and ecosystem degradation, understanding the geographical hotspots of NPs is essential to strategically prioritize areas for conservation, ensuring the sustained availability of these invaluable medicinal resources. Methods We constructed a global diversity map for 1,434 woody angiosperm species, each represented by existing genomic or transcriptomic data. We curated a list of 166 enzymes essential for the biosynthesis and structural diversity of NPs, and identified geographical hotspots of NPs by averaging enzyme presence across the grids. We also examined the distribution pattern of each enzyme. To gain deeper insights into NP distribution patterns, we performed a comparative analysis of enzyme groups responsible for the biosynthesis of two pharmacologically significant compounds with distinct biosynthetic pathways, shikonin derivatives and benzylisoquinoline alkaloids. Findings Our study reveals a correlation between NP hotspots and biodiversity hotspots, with a subset of markers displaying unique, region-specific patterns. Comparative analysis of enzymes for shikonin derivatives and benzylisoquinoline alkaloids biosynthetic pathways shows similar pattern, with the former demonstrating a unique and region-specific distribution. Interpretation Our findings emphasize the importance of preserving biodiversity hotspots for sustaining NP-based medicinal resources. Additionally, the specific distribution of certain enzyme markers, such as those related to shikonin derivatives, suggests that some NPs may necessitate targeted conservation strategies. This study provides a foundational roadmap for identifying the geographical hotspots of NPs and developing targeted conservation strategies. Funding Japan Society for the Promotion of Science through the Grant-in-Aid for Scientific Research (A) 20H00651.
Goblet cell invasion promotes breaching of respiratory epithelia by an opportunistic human pathogen

While commensal bacteria generally respect natural barriers of the human body, pathogens are able to breach epithelia, invade deeper tissue layers and cause life-threatening infections. Pseudomonas aeruginosa, an opportunistic human pathogen, is a leading cause of severe hospital-acquired pneumonia, with mortality rates as high as 50% in mechanically ventilated patients1–3. Effective colonization and breaching of lung mucosa are hallmarks of P. aeruginosa pathogenesis4. Although virulence factors and behavioral strategies of P. aeruginosa have been described5,6, it has remained unclear how this pathogen disseminates on functional mucosal surfaces, how it avoids mucociliary clearance and how it invades the tissue barrier. Using fully differentiated human lung epithelia, we demonstrate that P. aeruginosa efficiently spreads on the apical tissue surface before it breaches epithelia by specifically invading mucus secreting goblet cells. Internalization leads to host cell death and expulsion and the formation of ruptures of the epithelial barrier. Rupture sites are rapidly colonized by extracellular bacteria through active chemotaxis, leading to increasing tissue damage and successful pathogen translocation to the unprotected basolateral side of the epithelium. We show that cell invasion is promoted by two Type-6 toxin secretion systems (T6SS), while Type-3 (T3SS) mediates cell death of infected goblet cells. T3SS mutants invade goblet cells normally, but internalized bacteria fail to trigger goblet cell expulsion and instead show unrestrained intracellular replication. While the effective shedding of infected host cells reveals potent tissue protection mechanisms, the discovery of an intracellular lifestyle of P. aeruginosa in human lung epithelia provides new entry points into investigating the intersection of antibiotic and immune mechanisms during lung infections. By demonstrating that P. aeruginosa uses a combination of specific virulence factors and collective behavior to invade goblet cells and breach the lung tissue barrier from within, these studies reveal novel mechanisms underlying lung infection dynamics under physiological conditions.
AUC Maximization in Imbalanced Lifelong Learning

Imbalanced data is ubiquitous in machine learning, such as medical or ﬁne-grained image datasets. The existing continual learning methods employ various techniques such as balanced sampling to improve classiﬁcation accuracy in this setting. However, classiﬁcation accuracy is not a suitable metric for imbalanced data, and hence these methods may not obtain a good classiﬁer as measured by other metrics (e.g., Area under the ROC Curve). In this paper, we propose a solution to enable efﬁcient imbalanced continual learning by designing an al-gorithm to effectively maximize one widely used metric in an imbalanced data setting: Area Under the ROC Curve (AUC). We ﬁnd that simply replacing accuracy with AUC will cause gradient interference problem due to the imbalanced data distribution. To address this issue, we propose a new al-gorithm, namely DIANA, which performs a novel synthesis of model DecouplIng ANd Alignment. In particular, the algorithm updates two models simultaneously: one focuses on learning the current knowledge while the other concentrates on reviewing previously-learned knowledge, and the two models gradually align during training. The results show that the proposed DIANA achieves state-of-the-art performance on all the imbalanced datasets compared with several competitive baselines. Code is available at https://github.com/ MingruiLiu-ML-Lab/Lifelong-AUC .
EXPLORING THE LIMITS OF CROP PRODUCTIVITY: A MODEL TO EVALUATE PROGRESS

The productivity of higher plants is determined by the photo-synthetic photon flux (PPF) and the efficiency of the following four physiological processes: PPF absorption by photosynthetic tissue, carbon fixation (photosynthesis), carbon use (respiration), and carbon partitioning (harvest index). These constituent processes can be integrated to establish potentially achievable productivity, which is estimated to be
Distribution Free Domain Generalization

Accurate prediction of the out-of-distribution data is desired for a learning algorithm. In domain generalization, training data from source domains tend to have different distributions from that of the target domain, while the target data are absence in the training process. We propose a Distribution Free Domain Generalization (DFDG) procedure for classification by conducting standardization to avoid the dominance of a few domains in the training process. The essence of the DFDG is its reformulating the cross domain/class discrepancy by pairwise two sample test statistics, and equally weights their importance or the covariance structures to avoid dominant domain/class. A theoretical generalization bound is established for the multi-class classification problem. The DFDG is shown to offer a superior performance in empirical studies with fewer hyperparameters, which means faster and easier implementation.
Semiparametrically Efficient Off-Policy Evaluation in Linear Markov Decision Processes

We study semiparametrically efficient estimation in off-policy evaluation (OPE) where the underlying Markov decision process (MDP) is linear with a known feature map. We characterize the variance lower bound for regular estimators in the linear MDP setting and propose an efficient estimator whose variance achieves that lower bound. Consistency and asymptotic normality of our estimator are established under mild conditions, which merely requires the only infinite-dimensional nuisance parameter to be estimated at a n − 1 / 4 convergence rate. We also construct an asymptotically valid confidence interval for statistical inference and conduct simulation studies to validate our results. To our knowledge, this is the first work that concerns efficient estimation in the presence of a known structure of MDPs in the OPE literature.
Existe Evidencia De Burbuja Inmobiliaria En El Ecuador? (Is There Evidence of a Housing Bubble in Ecuador?)

Spanish Abstract: El presente estudio analiza la evolucion de los precios del mercado de vivienda ecuatoriano de manera agregada, a partir de datos de fuentes secundarias, en el periodo enero 2008-octubre 2014. Se aplico el Price to Earnings Ratio (PER), utilizado en el analisis bursatil para valorar comparativamente las acciones, pero adaptado al mercado de vivienda como la relacion Precio/Alquiler. El PER mostro que los precios de la vivienda han crecido a mayor tasa que el alquiler, lo que genera una alerta temprana sobre una posible formacion de burbuja de precios. La segundo etapa del analisis consistio en estimar un modelo econometrico, en donde se buscaba explicar el movimiento del precio a partir de variables fundamentales de oferta y demanda: salario de la construccion, costos de la construccion, rendimiento de otros activos, tasa de interes de los creditos para vivienda y el indice de actividad economica (IDEAC). El resultado del modelo mostro que la subida de precios es explicada por los fundamentales de oferta y demanda en un 93%, luego de aplicar tests de cointegracion y de estabilidad de parametros. Basados en los resultados, concluimos que no hay evidencia de que las expectativas de los agentes sobre apreciacion futura de las viviendas sea el principal factor explicativo de la subida de precios; con lo cual rechazamos la hipotesis de formacion de una burbuja inmobiliaria en el Ecuador, de manera agregada.English Abstract: This study analyzes the evolution of aggregate prices in Ecuadorian housing market, using secondary data, since January 2008 to October 2014. The Price to Earnings Ratio (PER), used to comparatively assess stock prices, was adapted to the housing market as the ratio Price/Rent. The PER showed that housing prices have increased in greater proportion than rent, generating an early warning of an eventual bubble. The second stage of the analysis was to estimate an econometric model, which sought to explain the price movement from fundamentals of supply and demand: construction wages, construction costs, return over other assets, interest rate for housing loans and the index of economic activity (IDEAC). The model results showed that the price increase is explained by the fundamentals of supply and demand by 93%, after applying cointegration and parameters stability tests. Based on these results, we conclude that there is no evidence that agents expectations of future appreciation is the main factor explaining the aggregate price rise; thus rejecting the hypothesis of formation of a housing bubble in Ecuador.
Regularization-free Diffeomorphic Temporal Alignment Nets

In time-series analysis, nonlinear temporal mis-alignment is a major problem that forestalls even simple averaging. An effective learning-based solution for this problem is the Diffeomorphic Temporal Alignment Net (DTAN) (Shapira Weber et al., 2019), that, by relying on a diffeomorphic temporal transformer net and the amortization of the joint-alignment task, eliminates drawbacks of traditional alignment methods. Unfortunately, existing DTAN formulations crucially depend on a regularization term whose optimal hyperparameters are dataset-specific and usually searched via a large number of experiments. Here we pro-pose a regularization-free DTAN that obviates the need to perform such an expensive, and often impractical, search. Concretely, we propose a new well-behaved loss that we call the Inverse Consistency Averaging Error (ICAE), as well as a related new triplet loss. Extensive experiments on 128 UCR datasets show that the proposed method outperforms contemporary methods despite not using a regularization. Moreover, ICAE also gives rise to the first DTAN that supports variable-length signals. Our code is available at https: //github.com/BGU-CS-VIL/RF-DTAN .
The Age-Period-Cohort Conundrum and Verbal Ability: Empirical Relationships and Their Interpretation: Reply to Glenn and to Alwin and McCammon

Les AA. examinent de facon critique l'article de Norval D. Glenn : «Further Discussion of the Evidence for an Intercohort Decline in Education-Adjusted Vocabulary» et l'article de Duane F. Alwin et Ryan J. McCammon : «Aging versus Cohort Interpretations of Intercohort Differences in GSS Vocabulary Scores», publies tous deux en 1999. Les AA. rappellent que l'enquete par cohortes menees, aux Etats-Unis, entre 1974 et 1990 dans le cadre de la General Social Survey a mis en evidence une reduction des connaissances en terme de vocabulaire entre les cohortes nees avant 1920 et les cohortes nees apres 1920. Pour Glenn cette reduction n'est pas due a une baisse de la qualite de l'education mais a une diminution de la pratique de la lecture. Ils reconnaissent avec Alwin et McCammon que l'education affecte la connaissance verbale. Ils affirment que l'evolution du systeme educatif explique ce declin. Ils estiment que la methode de modelisation adoptee par Alwin et McCammon n'est pas adaptee. Ils examinent l'impact des changements linguistiques et plus particulierement de l'obsolescence du vocabulaire. Ils considerent qu'il convient de separer effet de l'âge, periode de l'enquete et cohorte de naissance dans l'analyse des donnees
Opening the Debate on Closure and Schooling Outcomes

In my previous study (Carbonaro 1998), "intergenerational closure" is defined as an individual attribute, reflecting an individual student's parents' connections to their children's friends' parents. If the effects of closure on educational outcomes are positive (as Coleman suggests), then students with high levels of closure will benefit, while those with low levels will not. In contrast, M&S view "social closure" as a group-level attribute that describes social relationships at the school (not individual) level. According to this view, students with low levels of individual closure who attend a school with a high overall level of closure will benefit as much as students with high individual levels of closure. This difference highlights an important theoretical issue: How does closure impact OPENING THE DEBATE ON CLOSURE AND SCHOOLING OUTCOMES*