Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial rank-based high-dimensional monitoring through random projection

Abstract High-dimensional process monitoring becomes increasingly important in many applications where the joint distribution of process variables is usually unknown and not normal, and requires nonparametric methods for analysis and monitoring. However, when the process dimension is much larger than the reference sample size, most traditional nonparametric multivariate control charts fail due to the curse of dimensionality. Furthermore, when the process goes out of control, only a few (sparse) dimensions will be influenced, which increases the difficulty for both detection and diagnosis. To address these problems, this article proposes a new nonparametric monitoring scheme for high-dimensional processes. This scheme first projects the high-dimensional process into several subprocesses using ensemble random projections for dimension reduction. Then, for each subprocess a local nonparametric control chart is constructed based on the spatial rank test. Finally, all the local charts are fused together for decision making. Furthermore, after an out-of-control alarm is triggered, a diagnostic framework is proposed based on the square-root LASSO algorithm. Numerical studies together with real-data examples demonstrate the efficacy and applicability of the proposed methodology.
Spatial Analysis of Relative Poverty: A Case Study of Slum Areas of Karachi

Purpose This study aims to explore the reasons for the existence of relative poverty in Karachi, a metropolitan city in Pakistan. Methodology A self-administered survey was conducted on 375 households in eight, major slum areas in Karachi (Orangi Town, Manghopir, New Karachi, Surjani Town, Bangali Para Gulshan, Lyari, Malir, and Korangi). Logistic regression was used for the data analysis. Findings The results confirm that secondary education for males, the uneducated males, the bread-winning members, and children having no education significantly impacts the dependent variable, i.e., total family income. Whereas, tertiary education of children, secondary education of children, tertiary education of male households, and house possession have no impact on total family income. The result of Andrews and Hosmer-Leme confirms that the model is a good fit. Conclusion The study concludes that the basic cause of relative poverty is unequal access to education, a highly dependent population, and low-earning members. Unequal access to housing facilities is another significant factor that needs to be assessed.
Restricted Isometry Property and lp sparse recovery failure

This paper considers conditions based on the restricted isometry constant (RIC) under which the solution of an underdetermined linear system with minimal lp norm, 0 < p [\leq] 1, is guaranteed to be also the sparsest one. Specifically matrices are identified that have RIC, [\delta_{2m}], arbitrarily close to 1/[{ \sqrt{2} \) \approx 0.707] where sparse recovery with p = 1 fails for at least one m-sparse vector. This indicates that there is limited room for improvement over the best known positive results of Foucart and Lai, which guarantee that `1-minimisation recovers all m-sparse vectors for any matrix with [\delta_{2m} <2(3-{ \sqrt{2})/7 \approx 0.4531]. We also present results that show, compared to [l~{1}] minimisation, [l~{p}] minimisation recovery failure is only slightly delayed in terms of the RIC values. Furthermore when `p optimisation is attempted using an iterative reweighted [l~{p}] scheme, failure can still occur for [\delta_{2m}] arbitrarily close to 1[{ \sqrt{2} \) .
Establishing thresholds for cytokine storm and defining their relationship to disease severity in respiratory viral infections

Previous studies have identified cytokines associated with respiratory virus infection illness outcome. However, few studies have included comprehensive cytokine panels, longitudinal analyses, and/or simultaneous assessment across the severity spectrum. This, coupled with subjective definitions of cytokine storm syndrome (CSS), have contributed to inconsistent findings of cytokine signatures, particularly with COVID severity. Here, we measured 38 plasma cytokines and compared profiles in healthy, SARS-CoV-2 infected, and multisystem inflammatory syndrome in children (MIS-C) patients (n = 169). Infected patients spanned the severity spectrum and were classified as Asymptomatic, Mild, Moderate or Severe. Our results showed acute cytokine profiles and longitudinal dynamics of IL1Ra, IL10, MIP1b, and IP10 can differentiate COVID severity groups. Only 4% of acutely infected patients exhibited hypercytokinemia. Of these subjects, 3 were Mild, 3 Moderate, and 1 Severe, highlighting the lack of association between CSS and COVID severity. Additionally, we identified IL1Ra and TNFa as potential biomarkers for patients at high risk for long COVID. Lastly, we compare hypercytokinemia profiles across COVID and influenza patients and show distinct elevated cytokine signatures, wherein influenza induces the most elevated cytokine profile. Together, these results identify key analytes that, if obtained at early time points, can predict COVID illness outcome and/or risk of complications, and provide novel insight for improving the conceptual framework of hypercytokinemia, wherein CSS is a subgroup that requires concomitant severe clinical manifestations, and including a list of cytokines that can distinguish between subtypes of hypercytokinemia.
SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design

Predicting synergistic drug combinations can help accelerate discovery of cancer treatments, particularly therapies personalized to a patient’s specific tumor via biopsied cells. In this paper, we propose a novel setting and models for in-context drug synergy learning. We are given a small “personalized dataset” of 10-20 drug synergy relationships in the context of specific cancer cell targets. Our goal is to predict additional drug synergy relationships in that context. Inspired by recent work that pre-trains a GPT language model (LM) to “in-context learn” common function classes, we devise novel pre-training schemes that enable a GPT model to in-context learn “drug synergy functions”. Our model—which does not use any textual corpora, molecular fingerprints, protein interaction or any other domain-specific knowledge— is able to achieve competitive results. We further integrate our in-context approach with a genetic algorithm to optimize model prompts and select synergy candidates to test after conducting a patient biopsy. Finally, we explore a novel task of inverse drug design which can potentially enable the design of drugs that synergize specifically to target a given patient’s “personalized dataset”. Our findings can potentially have an important impact on precision cancer medicine, and also raise intriguing questions on non-textual pre-training for LMs.
Coffee microsporogenesis and related small interfering RNAs biosynthesis have a unique pattern among eudicots suggesting a sensitivity to climate changes

Recently, the siRNAs pathways, and especially reproductive phasiRNAs, have attracted attention in eudicots since their biological roles are still unknown and their biogenesis took different evolutionary pathways compared to monocots. In this work, we used Coffea arabica L. , a recently allotetraploid formed from the hybridization of C. canephora and C. eugenioides unreduced gametes, to explore microsporogenesis and small RNAs related pathways in a eudicot crop. First, we identified the microsporogenesis stages during anther development revealing that pre-meiosis occurs in anthers of 1.5 mm inside floral buds (FBs), whereas meiosis between 1.5 and 4.2 mm FBs, and post-meiosis in FBs larger than 4.2mm. These stages coincide with the Brazilian winter, a period of FBs reduced growth which suggests temperature sensitivity. Next, we identified and quantified the expression of reproductive 21- and 24-nt phasiRNAs during coffee anther development together with their canonical and novel miRNA triggers, and characterized the DCL and AGO families. Our results showed that the pattern of reproductive phasiRNA abundance in C. arabica is unique among described eudicots and the canonical trigger car-miR2275 is involved in the processing of both 21 and 24 nt phasiRNAs. Fourteen DCL genes were identified, but DCL5, related to phasiRNA biosynthesis in monocots, was not according to its specificity for monocots. Thus, our work explored the knowledge gap about microsporogenesis and related siRNAs pathways in coffee, contributing to the control of reproductive development and to the improvement of fertility in eudicots.
FedCrack: Federated Transfer Learning With Unsupervised Representation for Crack Detection

Empowered by labeled datasets, supervised pre-training based transfer learning (SPTL) has made significant advances for image classification applications. However, due to privacy-preserving protocol and unaccessible annotation, it emerges as a novel problem in federated learning scenarios whether unsupervised pre-training based transfer learning (UPTL) is available for semantic segmentation. In this work, we define federated transfer learning (FedTL) in the absence of source domain label, and track research progress on pavement crack benchmark. The main challenges of FedTL include: i) a privacy-protecting distributed training framework that extends UPTL to the constraints of federated settings, and ii) a self-learning semantic segmentation approach that develops self-supervised learning paradigm to simultaneously learn category and shape representations. Motivated by that, we propose a FedCrack model to absorb feature disentanglement and prototype clustering into vision Transformer, which obtains the pre-trained encoder on source domain without accessing annotation. Thereafter, a fine-tuning stage is presented to learn decoder with scaling attention on target domain for fine-grained crack segmentation. The effectiveness of proposed FedCrack can be demonstrated with superior performance of 82.14% on mIoU and 9.85 FPS on speed in extensive experiments. To the best of our knowledge, it is the first work in FedTL to gain weights of unsupervised pre-training representations on source domain locally, gradients of which are then aggregated to a federated central model that also fine-tunes the transferable parameters by target domain.
First Eurasian cases of SARS-CoV-2 seropositivity in a free-ranging urban population of wild fallow deer

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infects wildlife. Recent studies highlighted that variants of concern (VOC) may expand into novel animal reservoirs with the potential for reverse zoonosis. North American white-tailed deer are the only deer species in which SARS-CoV-2 has been documented, raising the question whether further reservoir species exist as new VOC emerge. Here, we report the first cases of deer SARS-CoV-2 seropositivity in Eurasia, in a city population of fallow deer in Dublin, Ireland. Deer were seronegative in 2020 (circulating variant in humans: Alpha), one animal was seropositive in 2021 (Delta variant), and 57% of animals tested in 2022 were seropositive (Omicron variant). Ex vivo, a clinical isolate of Omicron BA.1 infected fallow deer precision cut lung slice type-2 pneumocytes, also a major target of infection in human lungs. Our findings suggest a change in host tropism as new variants emerged in the human reservoir, highlighting the importance of continued wildlife disease monitoring and limiting human-wildlife contacts. Teaser: This study is the first report of SARS-CoV-2 seropositivity in fallow deer, highlighting expansion of viral variants into new host reservoirs.
Enhancing Household Food Security and Dietary Diversity through Homegardens: A Case Study in Selected Districts of Sri Lanka

Homegardens are resourceful ecosystems that provide essential services to the environment and mankind. Their contribution to secure household food requirement is among the provisional services of contemporary relevance. A study was conducted in 429 homegardens in Jaffna, Matale, Ratnapura, and Hambantota districts to assess the scope of utilizing homegarden resources on household food security. A questionnaire survey was conducted to gather information on the consumption frequency and the number of food categories that were purchased, produced, and shared. The mean food consumption score (FCS) was 60±23, where 89% of the study population had an acceptable level of FCS (FCS>35). As per the dietary diversity score (HDDS), an average household consumed at least eight food categories per day. Coconut was the most common food commodity produced in homegardens, followed by jackfruit and other fruits (mango, guava, soursop and pomegranate). Households rely on the market-available foods as their primary food source. The study revealed that utilizing at least one food category as a homestead-resource produced, could improve FCS and assist to fulfil the household food demand significantly. However, the majority of the homestead-resources were identified as plant-based, except in Jaffna where the presence of livestock and poultry in homegarden was common. Many of the plant-based resources were from perennial fruit-bearing trees. Therefore, modifying and utilizing the tree composition and viable crops can be considered a timely option to avert household food insecurity. Further, incorporating livestock components (dairy, poultry) as a part of homegardens is beneficial in fulfilling the animal protein demand of the household.
Fine-Grained Position Helps Memorizing More, a Novel Music Compound Transformer Model with Feature Interaction Fusion

Due to the particularity of the simultaneous occurrence of multiple events in music sequences, compound Transformer is proposed to deal with the challenge of long sequences. However, there are two deficiencies in the compound Transformer. First, since the order of events is more important for music than natural language, the information provided by the original absolute position embedding is not precise enough. Second, there is an important correlation between the tokens in the compound word, which is ignored by the current compound Transformer. Therefore, in this work, we propose an improved compound Transformer model for music understanding. Specifically, we propose an attribute embedding fusion module and a novel position encoding scheme with absolute-relative consideration. In the attribute embedding fusion module, different attributes are fused through feature permutation by using a multi-head self-attention mechanism in order to capture rich interactions between attributes. In the novel position encoding scheme, we propose RoAR position encoding, which realizes rotational absolute position encoding, relative position encoding, and absolute-relative position interactive encoding, providing clear and rich orders for musical events. Empirical study on four typical music understanding tasks shows that our attribute fusion approach and RoAR position encoding brings large performance gains. In addition, we further investigate the impact of masked language modeling and casual language modeling pre-training on music understanding.
Clinical and Molecular Aspects of C2orf71/PCARE in Retinal Diseases

Mutations in the photoreceptor-specific C2orf71 gene (also known as photoreceptor cilium actin regulator protein PCARE) cause autosomal recessive retinitis pigmentosa type 54 and cone-rod dystrophy. No treatments are available for patients with C2orf71 retinal ciliopathies exhibiting a severe clinical phenotype. Our understanding of the disease process and the role of PCARE in the healthy retina significantly limits our capacity to transfer recent technical developments into viable therapy choices. This study summarizes the current understanding of C2orf71-related retinal diseases, including their clinical manifestations and an unclear genotype-phenotype correlation. It discusses molecular and functional studies on the photoreceptor-specific ciliary PCARE, focusing on the photoreceptor cell and its ciliary axoneme. It is proposed that PCARE is an actin-associated protein that interacts with WASF3 to regulate the actin-driven expansion of the ciliary membrane during the development of a new outer segment disk in photoreceptor cells. This review also introduces various cellular and animal models used to model these diseases and provides an overview of potential treatments.
Barriers to Basic School Teachers’ Implementation of Formative Assessment in the Cape Coast Metropolis of Ghana

Abstract Despite the identified benefits of formative assessment to teaching and learning, its implementation in the classroom remains a significant challenge to teachers. This has widened the gap between the theory and practice of formative assessment in the classroom to enhance students’ learning. This study investigated the potential resource-related factors that affect basic teachers’ adoption of formative assessment in their classrooms. A descriptive survey design with a multistage sampling procedure was employed to select 300 teachers from the six circuits in the Cape Coast Metropolis of Ghana. Data gathered were analyzed using descriptive (mean values, SD) and inferential statistics (two-way ANOVA). Time, workload, overcrowded curriculum, class size, and the number of lessons were reported as barriers to the effective implementation of formative assessment. Findings also revealed no statistically significant interaction effect of gender and grade level on the resource-related barriers. Moreover, grade level and gender had no statistically significant main effects with respect to the resource-related barriers. Recommendations and implications for policy, practice, and future research are discussed.
Commutators of multilinear Calderón–Zygmund operators with Dini type kernels on some function spaces

In this paper, we establish some new boundedness for commutators of multilinear Calderón-Zygmund operators with kernels of type ω from product of Lebesgue spaces into Lebesgue spaces, Lipschitz spaces, and Triebel-Lizorkin spaces, which extend some previous results. c ©2017 All rights reserved.
Cellular underpinnings of the selective vulnerability to tauopathic insults in Alzheimer’s disease

Neurodegenerative diseases such as Alzheimer’s disease (AD) exhibit pathological changes in the brain that proceed in a stereotyped and regionally specific fashion, but the cellular and molecular underpinnings of regional vulnerability are currently poorly understood. Recent work has identified certain subpopulations of neurons in a few focal regions of interest, such as the entorhinal cortex, that are selectively vulnerable to tau pathology in AD. However, the cellular underpinnings of regional susceptibility to tau pathology are currently unknown, primarily because whole-brain maps of a comprehensive collection of cell types have been inaccessible. Here, we deployed a recent cell-type mapping pipeline, Matrix Inversion and Subset Selection (MISS), to determine the brain-wide distributions of pan-hippocampal and neocortical neuronal and non-neuronal cells in the mouse using recently available single-cell RNA sequencing (scRNAseq) data. We then performed a robust set of analyses to identify general principles of cell-type-based selective vulnerability using these cell-type distributions, utilizing 5 transgenic mouse studies that quantified regional tau in 12 distinct PS19 mouse models. Using our approach, which constitutes the broadest exploration of whole-brain selective vulnerability to date, we were able to discover cell types and cell-type classes that conferred vulnerability and resilience to tau pathology. Hippocampal glutamatergic neurons as a whole were strongly positively associated with regional tau deposition, suggesting vulnerability, while cortical glutamatergic and GABAergic neurons were negatively associated. Among glia, we identified oligodendrocytes as the single-most strongly negatively associated cell type, whereas microglia were consistently positively correlated. Strikingly, we found that there was no association between the gene expression relationships between cell types and their vulnerability or resilience to tau pathology. When we looked at the explanatory power of cell types versus GWAS-identified AD risk genes, cell type distributions were consistently more predictive of end-timepoint tau pathology than regional gene expression. To understand the functional enrichment patterns of the genes that were markers of the identified vulnerable or resilient cell types, we performed gene ontology analysis. We found that the genes that are directly correlated to tau pathology are functionally distinct from those that constitutively embody the vulnerable cells. In short, we have demonstrated that regional cell-type composition is a compelling explanation for the selective vulnerability observed in tauopathic diseases at a whole-brain level and is distinct from that conferred by risk genes. These findings may have implications in identifying cell-type-based therapeutic targets.
Induction of plant disease resistance by mixed oligosaccharide elicitors prepared from plant cell wall and crustacean shells

Basal plant immune responses are activated by the recognition of the conserved pathogen-associated molecular patterns (PAMPs), or breakdown molecules released from plants after damage by pathogen infection, so-called danger-associated molecular patterns (DAMPs). While chitin-oligosaccharide (CHOS), a primary component of the fungal cell wall, is most known as PAMP, plant cell wall-derived oligosaccharides, cello-oligosaccharides (COS) from cellulose and xylo-oligosaccharide (XOS) from hemicellulose, are representative DAMPs, which activate signaling steps similar to PAMP-induced immunity to elicit defenses and provide protection against pathogens. In this study, elicitor activities of COS prepared from cotton linters, XOS prepared from corn cobs as well as chitin-oligosaccharide (CHOS) from crustacean shells were comparatively investigated. In Arabidopsis, treatment of COS, XOS or CHOS triggered typical defense responses such as reactive oxygen species (ROS) production, activation of MAP kinases phosphorylation, callose depositions, and activation of the promoter for defense-related transcription factor WRKY33. When COS, XOS and CHOS were used at concentrations with similar activity in inducing ROS production and callose depositions, CHOS was particularly highly potent in activating the MAPK kinases and WRKY33 promoters. Among the COS and XOS with different degrees of polymerization (DP), cellotriose (DP3) and xylotetraose (DP4) showed the highest activity for the activation of WRKY33 promoter. Simultaneous treatment of COS, XOS and CHOS leads to a strong transcriptional change for defense-related genes, and gene ontology (GO) enrichment analysis of RNAseq data revealed that a mixture of three oligosaccharide (oligo-mix) effectively activate the plants disease resistance. In practice, treatment of the oligo-mix enhanced the resistance of tomato to powdery mildew, but plant growth was not inhibited but rather tended to be promoted, providing evidence that mixed oligosaccharides have beneficial effects on improving disease resistance in plants, making them a promising class of compounds for practical application.