Miracle-dz/UniFAR-FTUs huggingface dataset:
CounterFAccTual: How FAccT Undermines Its Organizing Principles

This essay joins recent scholarship in arguing that FAccT's fundamental framing of the potential to achieve the normative conditions for justice through bettering the design of algorithmic systems is counterproductive to achieving said justice in practice. Insofar as the FAccT community's research tends to prioritize design-stage interventions, it ignores the fact that the majority of the contextual factors that practically determine FAccT outcomes happen in the implementation and impact stages of AI/ML lifecycles. We analyze an emergent and widely-cited movement within the FAccT community for attempting to honor the centrality of contextual factors in shaping social outcomes, a set of strategies we term ‘metadata maximalism’. Symptomatic of design-centered approaches, metadata maximalism abstracts away its reliance on institutions and structures of justice that are, by every observable metric, already struggling (where not failing) to provide accessible, enforceable rights. These justice infrastructures, moreover, are currently wildly under-equipped to manage the disputes arising from digital transformation and machine learning. The political economy of AI/ML implementation provides further obstructions to realizing rights. Data and software supply chains, in tandem with intellectual property protections, introduce structural sources of opacity. Where duties of care to vulnerable persons should reign, profit incentives are given legal and regulatory primacy. Errors are inevitable and inextricable from the development of machine learning systems. In the face of these realities, FAccT programs, including metadata maximalism, tend to project their efforts in a fundamentally counter-factual universe: one in which functioning institutions and processes for due diligence in implementation and for redress of harms are working and ready to interoperate with. Unfortunately, in our world, these institutions and processes have been captured by the interests they are meant to hold accountable, intentionally hollowed-out, and/or were never designed to function in today's sociotechnical landscape. Continuing to produce (fair! accountable! transparent! ) data-enabled systems that operate in high-impact areas, irrespective of this landscape's radically insufficient paths to justice, given the unavoidability of errors and/or intentional misuse in implementation, and the exhaustively-demonstrated disproportionate distribution of resulting harms onto already-marginalized communities, is a choice - a choice to be CounterFAccTual.
Thermal heat acclimation in live yeast supplemented pigs Saccharomyces cerevisiae boulardii CNCM I-1079 supplementation in finishing male pigs helps to cope with heat stress through feeding behavior and gut microbiota modulation

Pigs subjected to heat stress (HS) decrease their feed intake and growth. The objectives of the experiment were to determine the effects of live yeast (LY) supplementation (Saccharomyces cerevisiae var boulardii CNCM I-1079) on feeding behavior, energy metabolism and fecal microbiota composition of finishing boars (n=10) housed in respiration chamber at thermoneutrality (seven days at 22°C) or during HS (seven plus six days at 28°C). Dietary LY supplementation increased dry matter intake (P = 0.01), whatever the ambient temperature, whereas HS decreased feed intake whatever dietary supplementation (P = 0.01). Dietary LY supplementation increased the number of meals (P = 0.02). Energy retention was higher with dietary LY supplementation (P < 0.01) but decreased during HS (P < 0.01). The skin temperature of the supplemented pigs was lower at thermoneutrality and increased during HS to a lesser extent than that of non-supplemented pigs (P < 0.01). Fecal microbiota composition was determined using 16S rRNA gene sequencing. Treponema, Christensenellaceae R-7, Ruminococcaceae UCG-002, Rikenellaceae RC9, Clostridium sensu stricto 1 and Romboutsia genera and some bacteria belonging to Alloprevotella, Oxalobacter and Anaeroplasma genera were more abundant under HS. LY supplementation attenuated HS effects on Romboutsia abundance, while decreasing the abundance of some bacteria from Ruminoccocus, Coprococcus, Peptococcus and Oxalobacter genera and increasing the abundance of beneficial bacteria from Lactococcus and Subdoligranulum genera. Our results suggest that higher level of the keystone species Ruminococcus Bromii at thermoneutrality may be one of the cause for higher energy retention observed under subsequent HS.
The meningeal transcriptional response to traumatic brain injury and aging

Emerging evidence suggests that the meningeal compartment plays instrumental roles in various neurological disorders and can modulate neurodevelopment and behavior. While this has sparked great interest in the meninges, we still lack fundamental knowledge about meningeal biology. Here, we utilized high-throughput RNA sequencing (RNA-seq) techniques to investigate the transcriptional response of the meninges to traumatic brain injury (TBI) and aging in the sub-acute and chronic time frames. Using single-cell RNA sequencing (scRNA-seq), we first explored how mild TBI affects the cellular and transcriptional landscape in the meninges in young mice at one week post-injury. Then, using bulk RNA sequencing, we assessed the differential long-term outcomes between young and aged mice following a TBI. In our scRNA-seq studies, we found that mild head trauma leads to an activation of type I interferon (IFN) signature genes in meningeal macrophages as well as the mobilization of multiple distinct sub-populations of meningeal macrophages expressing hallmarks of either classically activated or wound healing macrophages. We also revealed that dural fibroblasts in the meningeal compartment are highly responsive to TBI, and pathway analysis identified differential expression of genes linked to various neurodegenerative diseases. For reasons that remain poorly understood, the elderly are especially vulnerable to head trauma, where even mild injuries can lead to rapid cognitive decline and devastating neuropathology. To better understand the differential outcomes between the young and the elderly following brain injury, we performed bulk RNA-seq on young and aged meninges from mice that had received a mild TBI or Sham treatment 1.5 months prior. Notably, we found that aging alone induced massive upregulation of meningeal genes involved in antibody production by B cells and type I IFN signaling. Following injury, the meningeal transcriptome had largely returned to its pre-injury signature in young mice. In stark contrast, aged TBI mice still exhibited massive upregulation of immune-related genes and markedly reduced expression of genes involved in extracellular matrix remodeling and maintenance of cellular junctions. Overall, these findings illustrate the dynamic and complex transcriptional response of the meninges to mild head trauma. Moreover, we also reveal how aging modulates the meningeal response to TBI.
Identification of chemo-sensory genes from antennae of female banana pseudostem weevil, Odoiporous longicollis by transcriptomics approach

In the present study, the female O. longicollis antennal transcriptome was constructed by Illumina Hiseq 2500™ sequencing, with the aim of discovering olfaction-related genes. Totally 12, 411 unigenes were identified from the transcriptome assembly and the putative genes functions were annotated using gene ontology tools. We identified 46 putative chemosensory unigenes likely essential for insect olfaction including 19 odorant binding proteins, 3 Niemann–Pick type C (NPC) protein, 6 odorant receptors, 15 ionotropic receptors, and 3 sensory neuron membrane proteins. The key function of these chemosensory genes in the antennae are discussed. Phylogenetic analysis of NPC revealed that the identified proteins had a close relationship with Coleopterans than other insects. This is the first ever report on identification of olfactory genes from O. longicollis which may provide new leads for control of this major pest.
Landscape drives zoonotic malaria prevalence in non-human primates

Zoonotic disease dynamics in wildlife hosts are rarely quantified at macroecological scales due to the lack of systematic surveys. Non-human primates (NHPs) host Plasmodium knowlesi, a zoonotic malaria of public health concern and the main barrier to malaria elimination in Southeast Asia. Understanding of regional P. knowlesi infection dynamics in wildlife is limited. Here, we systematically assemble reports of NHP P. knowlesi and investigate geographic determinants of prevalence in reservoir species. Meta-analysis of 6322 NHPs from 148 sites reveals that prevalence is heterogeneous across Southeast Asia, with low overall prevalence and high estimates for Malaysian Borneo. We find that regions exhibiting higher prevalence in NHPs overlap with human infection hotspots. In wildlife and humans, parasite transmission is linked to land conversion and fragmentation. By assembling remote sensing data and fitting statistical models to prevalence at multiple spatial scales, we identify novel relationships between P. knowlesi in NHPs and forest fragmentation. This suggests that higher prevalence may be contingent on habitat complexity, which would begin to explain observed geographic variation in parasite burden. These findings address critical gaps in understanding regional P. knowlesi epidemiology and indicate that prevalence in simian reservoirs may be a key spatial driver of human spillover risk.
Dynamic top-down biasing implements rapid adaptive changes to individual movements

Complex behaviors depend on the coordinated activity of neural ensembles in interconnected brain areas. The behavioral function of such coordination, often measured as co-fluctuations in neural activity across areas, is poorly understood. One hypothesis is that rapidly varying co-fluctuations may be a signature of moment-by-moment task-relevant influences of one area on another. We tested this possibility for error-corrective adaptation of birdsong, a form of motor learning which has been hypothesized to depend on the top-down influence of a higher-order area, LMAN, in shaping moment-by-moment output from a primary motor area, RA. In paired recordings of LMAN and RA in singing birds, we discovered a neural signature of a top-down influence of LMAN on RA, quantified as an LMAN-leading co-fluctuation between these areas. During learning, this cofluctuation strengthened in a premotor temporal window linked to the specific movement, sequential context, and acoustic modification associated with learning. Moreover, transient perturbation of LMAN activity specifically within this premotor window caused rapid occlusion of pitch modifications, consistent with LMAN conveying a temporally localized motor-biasing signal. Combined, our results reveal a dynamic top-down influence of LMAN on RA that varies on the rapid timescale of individual movements and is flexibly linked to contexts associated with learning. This finding indicates that inter-area co-fluctuations can be a signature of dynamic top-down influences that support complex behavior and its adaptation.
Vulnerable Brain Networks Associated with Risk for Alzheimer’s Disease

Brain connectomes provide untapped potential for identifying individuals at risk for Alzheimer’s disease (AD), and can help provide novel targets based on selective circuit vulnerability. Age, APOE4 genotype, and female sex are thought to contribute to the selective vulnerability of brain networks in Alzheimer’s disease, in a manner that differentiates pathological versus normal aging. These brain networks may predict pathology otherwise hard to detect, decades before overt disease manifestation and cognitive decline. Uncovering network based biomarkers at prodromal, asymptomatic stages may offer new windows of opportunity for interventions, either therapeutic or preventive. We used a sample of 72 people across the age span to model the relationship between Alzheimer’s disease risk and vulnerable brain networks. Sparse Canonical Correlation analysis (SCCA) revealed relationships between brain subgraphs and AD risk, with bootstrap based confidence intervals. When constructing a composite AD risk factor based on sex, age, genotype, the highest weight was associated with genotype. Next, we mapped networks associated with auditory, visual, and olfactory memory, and identified networks extending beyond the main nodes known to be involved in these functions. The inclusion of cognitive metrics in a composite risk factor pointed to vulnerable networks, and associated with the specific memory tests. These regions with the highest cumulative degree of connectivity in our studies were the pericalcarine, insula, banks of the superior sulcus and cerebellum. To help scale up our approach, we extended Tensor Network Principal Component Analysis (TNPCA) to evaluate AD risk related subgraphs, introducing CCA components and sparsity. When constructing a composite AD risk factor based on sex, age, and genotype, and family risk factor the most significant risk was associated with age. Our sparse regression based predictive models revealed vulnerable networks associated with known risk factors. The prediction error was 17% for genotype, 24% for family risk factor, and 5 years for age. Age prediction in groups including MCI and AD subjects involved several regions that were not prominent for age prediction otherwise. These regions included the middle and transverse temporal, paracentral and superior banks of temporal sulcus, as well as the amygdala and parahippocampal gyrus. The joint estimation of AD risk and connectome based mappings involved the cuneus, temporal, and cingulate cortices known to be associated with AD, and add new candidates, such as the cerebellum, whose role in AD is to be understood. Our predictive modeling approaches for AD risk factors represent a stepping stone towards single subject prediction, based on distances from normative graphs.
De novo design of site-specific protein interactions with learned surface fingerprints

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic, and structural data grows. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction (PPI) networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications. We exploit a geometric deep learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features critical to drive PPIs. We hypothesized these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof-of-principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1, and CTLA-4. Several designs were experimentally optimized while others were purely generated in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling a novel approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.
Oligomerization of a plant helper NLR requires cell-surface and intracellular immune receptor activation

Significance Plant immunity involves defense activation upon pathogen perception by both cell-surface and intracellular NLR immune receptors. Most plant NLRs detect and respond to pathogen effectors, whereas “helper” NLRs transduce signals initiated by “sensor” NLRs. We report here that pathogen detection by both cell-surface and intracellular immune receptors is required for activation of NRG1, a helper NLR essential for signaling from a subset of sensor NLRs. NRG1 is a broadly conserved helper NLR that activates defense via EDS1 and SAG101 lipase-like immune signaling components. Here, we reveal a recognition-dependent oligomeric NRG1–EDS1–SAG101 “resistosome” involved in plant immune receptor signaling.
Distributing task-related neural activity across a cortical network through task-independent connections

Task-related neural activity is widespread across populations of neurons during goal-directed behaviors. However, little is known about the synaptic reorganization and circuit mechanisms that lead to broad activity changes. Here we trained a limited subset of neurons in a spiking network with strong synaptic interactions to reproduce the activity of neurons in the motor cortex during a decision-making task. We found that task-related activity, resembling the neural data, emerged across the network, even in the untrained neurons. Analysis of trained networks showed that strong untrained synapses, which were independent of the task and determined the dynamical state of the network, mediated the spread of task-related activity. Optogenetic perturbations suggest that the motor cortex is strongly-coupled, supporting the applicability of the mechanism to cortical networks. Our results reveal a cortical mechanism that facilitates distributed representations of task-variables by spreading the activity from a subset of plastic neurons to the entire network through task-independent strong synapses.
Characterizing phytoplankton communities in the absence of resource-based competition

Under most natural marine conditions, phytoplankton cells suspended in the water column are too distantly spaced for direct competition for resources to be a routine occurrence. Accordingly, resource-based competitive exclusion should be rare. In contrast, contemporary ecosystem models typically predict an exclusion of larger phytoplankton size classes under low-nutrient conditions, an outcome interpreted as reflecting the competitive advantage of small cells having much higher nutrient ‘affinities’ than larger cells. Here, we develop mechanistically-focused expressions for steady-state, nutrient-limited phytoplankton growth that are consistent with the discrete, distantly-spaced cells of natural populations. These expressions are then encompassed in an ecosystem model that sustains diversity across all size classes over the full range in nutrient concentrations observed in the ocean. In other words, our model does not exhibit resource-based competitive exclusion between size classes. We show that the basis for species exclusions in earlier models is not a reflection of size-dependent nutrient ‘affinities’, but rather a consequence of inappropriate descriptions of non-grazing phytoplankton mortality.
Epigenetic reprogramming of melanoma cell state through fatty acid β-oxidation and Toll-like receptor 4 signaling

Tumor cells respond to a host of factors from the local microenvironment. Microenvironmental fatty acids can be used by melanoma cells for fuel, but their effects on transcription and epigenetics are still unclear. Here, we show that the fatty acid β-oxidation (FAO) pathway integrates signaling and epigenetics to drive melanoma progression. Using transgenic zebrafish and human cell lines, we find that octanoate, a medium-chain fatty acid, increases tumorigenesis. Octanoate is metabolized via the FAO/ACLY axis into acetyl-CoA, leading to increased histone acetylation. Transcriptomic and epigenetic analyses demonstrate a convergence of inflammatory gene signatures in octanoate-treated melanoma cells. This signature is mediated by TLR4/MyD88 signaling, which is activated by saturated fatty acids like octanoate. Genetic inactivation of either FAO enzymes or TLR4/MyD88 inhibits alterations in histone acetylation, and rescues octanoate-tumor promoting effects. Together, these data demonstrate clear evidence linking fatty acid metabolism and epigenetics to melanoma pathogenesis through TLR4 signaling.
Multimodal monitoring of human cortical organoids implanted in mice using transparent graphene microelectrodes reveal functional connection between organoid and mouse visual cortex

Human cortical organoids, three-dimensional neuronal cell cultures derived from human induced pluripotent stem cells, have recently emerged as promising models of human brain development and dysfunction. Transplantation of human brain organoids into the mouse brain has been shown to be a successful in vivo model providing vascularization for long term chronic experiments. However, chronic functional connectivity and responses evoked by external sensory stimuli has yet to be demonstrated, due to limitations of chronic recording technologies. Here, we develop an experimental paradigm based on transparent graphene microelectrode arrays and two-photon imaging for longitudinal, multimodal monitoring of human organoids transplanted in the mouse cortex. The transparency of graphene microelectrodes permits visual and optical inspection of the transplanted organoid and the surrounding cortex throughout the chronic experiments where local field potentials and multi-unit activity (MUA) are recorded during spontaneous activity and visual stimuli. These experiments reveal that visual stimuli evoke electrophysiological responses in the organoid, matching the responses from the surrounding cortex. Increases in the power of the gamma and MUA bands as well as phase locking of MUA events to slow oscillations evoked by visual stimuli suggest functional connectivity established between the human and mouse tissue. Optical imaging through the transparent microelectrodes shows vascularization of the organoids. Postmortem histological analysis exhibits morphological integration and synaptic connectivity with surrounding mouse cortex as well as migration of organoid cells into the surrounding cortex. This novel combination of stem cell and neural recording technologies could serve as a unique platform for comprehensive evaluation of organoids as models of brain development and dysfunction and as personalized neural prosthetics to restore lost, degenerated, or damaged brain regions.
PTK7 is a positive allosteric modulator of GPR133 (ADGRD1) signaling in GBM

GPR133 (ADGRD1), an adhesion G protein-coupled receptor, supports growth of glioblastoma, a brain malignancy. We demonstrated that GPR133 is intramolecularly cleaved, and that dissociation of its N-terminal and C-terminal fragments (NTF and CTF) at the plasma membrane correlates with increased receptor signaling. However, how the extracellular interactome of GPR133 in glioblastoma modulates signaling remains unknown. Here, we use affinity purification and mass spectrometry to identify extracellular binding partners of GPR133 in patient-derived glioblastoma cells. We show that the transmembrane protein PTK7 binds the GPR133 NTF and its expression in trans increases GPR133 signaling. This effect requires the intramolecular cleavage of GPR133 and PTK7’s anchoring in the plasma membrane. The GPR133-PTK7 interaction facilitates orthosteric activation of GPR133 by soluble peptide mimicking the endogenous tethered Stachel agonist, suggesting PTK7 binding allosterically enhances accessibility of GPR133’s orthosteric Stachel binding pocket. GPR133 and PTK7 are expressed in adjacent cells in glioblastoma, where their knockdown phenocopies each other. We propose that this novel ligand-receptor interaction is relevant to the pathogenesis of glioblastoma, as well as physiological processes in several tissues.