Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of regulative noise exposure to enhanced lipid droplet assembly in Saccharomyces cerevisiae: Preliminary results from a laboratory experiment

Lipid Droplet (LD) is a ubiquitous cellular organelle that stores natural lipids as an energy and carbon source. It has emerged as a highly active organelle, engaged in lipid synthesis, protein storage, protein degradation, transportation, and metabolism. It stores natural lipids in the form of triacylglycerols (TAG) and steryl esters. TAGs consider promising biotechnological importance to produce biodiesel; thus, LD is considered a tremendous scientific concern in the modern era. As such, the LD assembly through microorganisms becomes an evident alternative against animal and plant-derived sources for economic reasons. Among microorganisms, the Saccharomyces cerevisiae is a better alternative for industrial utilization but has low production of TAGs. Thus, to enhance the LD concentration, novel research was designed to induce alternate high and low sound frequency at a regular interval on a yeast model organism. The control and treated yeast samples further investigated using biochemical, biophysical, and computational tools to conclude that cells increase lipid droplet production under noise exposure. Further, it regulates cell proliferation and maintains the ROS level; hence, persistent noise exposure acts as an inducer of LD accumulation with maintaining cell toxicity. The results endorsed that noise induces yeast LD yield is significantly higher than control, which could be considered a milestone in the biodiesel industry development and the biodiesel policy. This analysis also assists researchers in the understanding of the novel function of LDs.
A modified fluctuation test for elucidating drug resistance in microbial and cancer cells

Clonal populations of microbial and cancer cells are often driven into a drug-tolerant persister state in response to drug therapy, and these persisters can subsequently adapt to the new drug environment via genetic and epigenetic mechanisms. Estimating the frequency with which drug-tolerance states arise, and its transition to drug-resistance, is critical for designing efficient treatment schedules. Here we study a stochastic model of cell proliferation where drug-tolerant persister cells transform into a drug-resistant state with a certain adaptation rate, and the resistant cells can then proliferate in the presence of the drug. Assuming a random number of persisters to begin with, we derive an exact analytical expression for the statistical moments and the distribution of the total cell count (i.e., colony size) over time. Interestingly, for Poisson initial conditions the noise in the colony size (as quantified by the Fano factor) becomes independent of the initial condition and only depends on the adaptation rate. Thus, experimentally quantifying the fluctuations in the colony sizes provides an estimate of the adaptation rate, which then can be used to infer the starting persister numbers from the mean colony size. Overall, our analysis introduces a modification of the classical Luria–Delbrück experiment, also called the “Fluctuation Test”, providing a valuable tool to quantify the emergence of drug resistance in cell populations.
Loss of Active Neurogenesis in the Adult Shark Retina

Neurogenesis is the process by which progenitor cells generate new neurons. As development progresses neurogenesis becomes restricted to concrete neurogenic niches, where it persists during postnatal life. The retina of teleost fishes is thought to proliferate and produce new cells throughout life. Whether this capacity may be an ancestral characteristic of jawed vertebrates, shared with chondrichthyans, which diverged from osteichthyans prior to the gnathostome radiation is completely unknown. Previous work from our group revealed that the juvenile retina of the catshark Scyliorhinus canicula shows active proliferation and neurogenesis. Here, we compared the morphology and proliferative status of the retina between catshark juveniles and adults. Histological analyses revealed an important reduction in the size of the peripheral retina (where progenitor cells are mainly located), an increase in the thickness of the plexiform layers and a decrease in the thickness of the inner nuclear layer in adults. Contrary to what has been reported in teleost fish, we did not observe active mitotic activity in the catshark retina after sexual maturation, suggesting that there is no significant proliferation and neurogenesis in adult specimens. Based on these results, we carried out RNA-Sequencing (RNA-Seq) analyses comparing the retinal transcriptome of juveniles and adults, which revealed a statistically significant decrease in the expression of many genes involved in cell proliferation and neurogenesis in adult catsharks. Our RNA-Seq data provides an excellent resource to identify new signaling pathways controlling neurogenesis in the vertebrate retina.
Language network connectivity increases in prodromal Alzheimer’s disease

Language production deficits occur early in the course of Alzheimer’s disease (AD); however, only few studies have focused on language functional networks in prodromal AD. The current study aims to uncover the extent of language alteration at a prodromal stage, on a behavioral, structural and functional level, using univariate and multivariate analyses. Twenty-four AD participants and 24 matched healthy controls underwent a comprehensive language evaluation, a structural T1-3D MRI and resting-state fMRI. We performed seed-based analyses, using the left inferior frontal gyrus and left posterior temporal gyrus as seeds. Then, we analyzed connectivity between executive control networks and language network in each group. Finally, we used multivariate pattern analyses to test whether the two groups could be distinguished based on the pattern of atrophy within the language network; atrophy within the executive control networks, as well as the pattern of functional connectivity within the language network; and functional connectivity within executive control networks. AD participants had language impairment during standardized language tasks and connected-speech production. Univariate analyses were not able to discriminate participants at this stage, while multivariate pattern analyses could significantly predict the group membership of prodromal patients and healthy controls, both when classifying atrophy patterns or connectivity patterns of the language network. Language functional networks could discriminate AD participants better than executive control networks. Most notably, they revealed an increased connectivity at a prodromal stage. Multivariate analyses represent a useful tool for investigating the functional and structural (re-)organization of the neural bases of language. Highlights Language network connectivity discriminates prodromal AD from healthy controls Language network connectivity increases in prodromal AD Atrophy patterns in the language network do not correlate with connectivity patterns in AD
Rotational dynamics in motor cortex are consistent with a feedback controller

Recent studies hypothesize that motor cortical (MC) dynamics are generated largely through its recurrent connections based on observations that MC activity exhibits rotational structure. However, behavioural and neurophysiological studies suggest that MC behaves like a feedback controller where continuous sensory feedback and interactions with other brain areas contribute substantially to MC processing. We investigated these apparently conflicting theories by building recurrent neural networks that controlled a model arm and received sensory feedback about the limb. Networks were trained to counteract perturbations to the limb and to reach towards spatial targets. Network activities and sensory feedback signals to the network exhibited rotational structure even when the recurrent connections were removed. Furthermore, neural recordings in monkeys performing similar tasks also exhibited rotational structure not only in MC but also in somatosensory cortex. Our results argue that rotational structure may reflect dynamics throughout voluntary motor circuits involved in online control of motor actions. Highlights Neural networks with sensory feedback generate rotational dynamics during simulated posture and reaching tasks Rotational dynamics are observed even without recurrent connections in the network Similar dynamics are observed not only in motor cortex, but also in somatosensory cortex of non-huma n primates as well as sensory feedback signals Results highlight rotational dynamics may reflect internal dynamics, external inputs or any combination of the two.
Dramatic changes in mitochondrial substrate use at critically high temperatures: a comparative study using Drosophila

ABSTRACT Ectotherm thermal tolerance is critical to species distribution, but at present the physiological underpinnings of heat tolerance remain poorly understood. Mitochondrial function is perturbed at critically high temperatures in some ectotherms, including insects, suggesting that heat tolerance of these animals is linked to failure of oxidative phosphorylation (OXPHOS) and/or ATP production. To test this hypothesis, we measured mitochondrial oxygen consumption rate in six Drosophila species with different heat tolerance using high-resolution respirometry. Using a substrate–uncoupler–inhibitor titration protocol, we examined specific steps of the electron transport system to study how temperatures below, bracketing and above organismal heat limits affect mitochondrial function and substrate oxidation. At benign temperatures (19 and 30°C), complex I-supported respiration (CI-OXPHOS) was the most significant contributor to maximal OXPHOS. At higher temperatures (34, 38, 42 and 46°C), CI-OXPHOS decreased considerably, ultimately to very low levels at 42 and 46°C. The enzymatic catalytic capacity of complex I was intact across all temperatures and accordingly the decreased CI-OXPHOS is unlikely to be caused directly by hyperthermic denaturation/inactivation of complex I. Despite the reduction in CI-OXPHOS, maximal OXPHOS capacity was maintained in all species, through oxidation of alternative substrates – proline, succinate and, particularly, glycerol-3-phosphate – suggesting important mitochondrial flexibility at temperatures exceeding the organismal heat limit. Interestingly, this failure of CI-OXPHOS and compensatory oxidation of alternative substrates occurred at temperatures that correlated with species heat tolerance, such that heat-tolerant species could defend ‘normal’ mitochondrial function at higher temperatures than sensitive species. Future studies should investigate why CI-OXPHOS is perturbed and how this potentially affects ATP production rates. Highlighted Article: Drosophila mitochondrial functions persist at temperatures above organismal heat limits but turn to oxidation of alternative substrates as complex I-supported respiration is impaired.
γ-secretase promotes postsynaptic maturation through the cleavage of a Wnt receptor

An emerging feature of neurodegenerative disease is synaptic dysfunction and loss, leading to the suggestion that mechanisms required for synaptic maturation may be linked to disease. Synaptic maturation requires the transmission of signals between nascent synaptic sites and the nucleus, but how these signals are generated is not well understood. We posit that proteolytic cleavage of receptors, which enables their translocation to the nucleus, may be a shared molecular mechanism between the events that promote synaptic maturation and those linked to later-onset disorders of the nervous system, including neurodegenerative disease. Here we show during synaptic development, that cleavage of synaptic maturation molecules requires γ-secretase, a protein complex linked to Alzheimer’s Disease, a devastating neurodegenerative condition, is required for postsynaptic maturation. In the absence of γ-secretase, Drosophila neuromuscular synapses fail to appropriately recruit postsynaptic scaffolding and cytoskeletal proteins, and mutant larvae display behavioral deficits. At the NMJ, γ-secretase promotes synaptic maturation through the cleavage of the Wnt receptor Fz2, and the subsequent entry of its C-terminus into the nucleus. A developmental synaptic role for γ-secretase is also conserved in both the Drosophila central nervous system and mammalian cortical neuron dendrites. Finally, we found that similar maturation defects are evident in fly models for ALS, Alzheimer’s, Huntington’s, and Parkinson’s Diseases. The previously unknown, but conserved, role for γ-secretase coupled with its well-known role in neurodegenerative disease suggest that neurodevelopmental defects may be common to diverse neurodegenerative disease models.
More than the sum of its parts: Merging network psychometrics and network neuroscience with application in autism

Network approaches that investigate the interaction between symptoms and behaviours have opened new ways of understanding psychological phenomena in health and disorder in recent years. In parallel, network approaches that characterise the interaction between brain regions have become the dominant approach to understanding brain function in neuroimaging research. Combining these parallel approaches would enable new insights into the interaction between behaviours and their brain-level correlates. In this paper, we introduce a methodology for combining network psychometrics and network neuro-science. This approach utilises the information from the psychometric network to obtain neural correlates that are associated with each node in the psychometric network (network-based regression). Moreover, we combine the behavioural variables and their neural correlates in a joint network to characterise their interactions. We illustrate the approach by highlighting the interaction between the triad of autistic traits and their resting-state functional connectivity associations. To this end, we utilise data from 172 male autistic participants (10-21 years) from the autism brain data exchange (ABIDE, ABIDE-II) that completed resting-state fMRI and were assessed using the autism diagnostic interview (ADI-R). Our results indicate that the network-based regression approach can uncover both unique and shared neural correlates of behavioural measures. In addition, because the shared variance between behavioural measures is controlled for in the approach, the methodology enables us to isolate mechanisms at the brain-level that are unique to particular behavioural variables. For instance, our example analysis indicates that the overlap between communication and social difficulties is not reflected in the overlap between their functional brain correlates.
Lanthanide shift reagents in stereochemical analysis

An Introduction to Lanthanide Shift Reagents Computer Approaches to the Geometry of the LSR-Substrate Complex: Geometry of Shift Reagents-Substrate Complexes, Simulation of Induced Chemical Shifts The Nature of the LSR-Substrate Complex Solutions to Stereochemical Problems Binuclear Lanthanide(III)-Silver(I) NMR Shift Reagents Author Index Subject Index.
Reef development and Sea level changes drive Acanthaster Population Expansion in the Indo-Pacific region

Molecular clock calibration is central in population genetics as it provides an accurate inference of demographic history, whereby helping with the identification of driving factors of population changes in an ecosystem. This is particularly important for coral reef species that are seriously threatened globally and in need of conservation. Biogeographic events and fossils are the main source of calibration, but these are known to overestimate timing and parameters at population level, which leads to a disconnection between environmental changes and inferred reconstructions. Here, we propose the Last Glacial Maximum (LGM) calibration that is based on the assumptions that reef species went through a bottleneck during the LGM, which was followed by an early yet marginal increase in population size. We validated the LGM calibration using simulations and genetic inferences based on Extended Bayesian Skyline Plots. Applying it to mitochondrial sequence data of crown-of-thorns starfish Acanthaster spp. , we obtained mutation rates that were higher than phylogenetically based calibrations and varied among populations. The timing of the greatest increase in population size differed slightly among populations, but all started between 10 and 20 kya. Using a curve-fitting method, we showed that Acanthaster populations were more influenced by sea-level changes in the Indian Ocean and by reef development in the Pacific Ocean. Our results illustrate that the LGM calibration is robust and can probably provide accurate demographic inferences in many reef species. Application of this calibration has the potential to help identify population drivers that are central for the conservation and management of these threatened ecosystems.
Interneuron diversity is required for compartment-specific feedback inhibition

Cortical inhibitory interneurons consist of many subtypes that have been associated with different functions. Here we use an optimization approach to show that two classes of interneurons are necessary to implement compartment-specific feedback inhibition to pyramidal cells.The two classes resemble PV-expressing and SST-expressing interneurons in their connectivity and short-term plasticity, suggesting a functional role for their diverse characteristics.
Type I Interferons Promote Germinal Centers Through B Cell Intrinsic Signaling and Dendritic Cell Dependent Th1 and Tfh Cell Lineages

Type I interferons (IFNs) play an essential role in antiviral immunity, correlate with severity of systemic autoimmune disease, and are likely to represent a key component of mRNA vaccine-adjuvanticity. Relevant to all, type I IFNs can enhance germinal center (GC) B cell responses but underlying signaling pathways are incompletely understood due to pleiotropic effects in multiple cell types. Here, we demonstrate that a succinct type I IFN response promotes GC formation and associated IgG subclass distribution primarily through signaling in cDCs and B cells. Type I IFN signaling in cDCs, distinct from cDC1, stimulates development of separable Tfh and Th1 cell subsets. However, Th cell-derived IFN-γ induces T-bet expression and IgG2c isotype switching prior to this bifurcation and has no evident effects once GCs and bona fide Tfh cells developed. This pathway acts in synergy with early B cell-intrinsic type I IFN signaling, which reinforces T-bet expression in B cells and leads to a selective amplification of the IgG2c+ GC B cell response. Despite the strong Th1 polarizing effect of type I IFNs, the Tfh cell subset develops into IL-4 producing cells that control the overall magnitude of the GCs and promote generation of IgG1 + GC B cells. Thus, type I IFNs act on B cells and cDCs to drive GC formation and to coordinate IgG subclass distribution through parallel Th1 and Tfh cell-dependent pathways.
Probing the aggregated effects of purifying selection per individual on 1,250 medical phenotypes in the UK biobank

Understanding the relationship between natural selection and phenotypic variation has been a long-standing challenge in human population genetics. With the emergence of biobank-scale datasets, along with new statistical metrics to approximate strength of purifying selection at the variant level, it is now possible to correlate a proxy of individual relative fitness with a range of medical phenotypes. We calculated a per-individual deleterious load score by summing the total number of derived alleles per individual after incorporating a weight that approximates strength of purifying selection. We assessed four methods for the weight, including GERP, phyloP, CADD, and fitcons. By quantitatively tracking each of these scores with the site frequency spectrum, we identified phyloP as the most appropriate weight. The phyloP-weighted load score was then calculated across 15,129,142 variants in 335,161 individuals from the UK Biobank and tested for association on 1,250 medical phenotypes. After accounting for multiple test correction, we observed a strong association of the load score amongst coding sites only on 28 traits including body mass, adiposity and metabolic rate. We further observed that the association signals were driven by common variants (derived allele frequency > 5%) with high phyloP score (phyloP > 2). Finally, through permutation analyses, we showed that the deleterious load score had an excess of nominally significant associations on many medical phenotypes. These results suggest a broad impact of deleterious load on medical phenotypes and highlight the deleterious load score as a new tool to disentangle the complex relationship between natural selection and medical phenotypes. Author summary This study aims to augment our understanding between the complex relation between natural selection and human phenotypic variation. We developed a load score to approximate the relative fitness of an individual and correlate it with a set of medical phenotypes. Association tests between the load score amongst coding sites and 1,250 phenotypes in a sample of 335,161 individuals from the UK Biobank showed a strong association with 28 traits including body mass, adiposity and metabolic rate. Furthermore, an excess of nominal associations at suggestive levels was observed between the load score and medical phenotypes than would be expected under a null model. These results suggest that the aggregate effect of deleterious mutations as measured by the load score has a broad effect on human phenotypes.
Predicting Spatial Crime Occurrences through an Efficient Ensemble-Learning Model

While the use of crime data has been widely advocated in the literature, its availability is often limited to large urban cities and isolated databases that tend not to allow for spatial comparisons. This paper presents an efficient machine learning framework capable of predicting spatial crime occurrences, without using past crime as a predictor, and at a relatively high resolution: the U.S. Census Block Group level. The proposed framework is based on an in-depth multidisciplinary literature review allowing the selection of 188 best-fit crime predictors from socio-economic, demographic, spatial, and environmental data. Such data are published periodically for the entire United States. The selection of the appropriate predictive model was made through a comparative study of different machine learning families of algorithms, including generalized linear models, deep learning, and ensemble learning. The gradient boosting model was found to yield the most accurate predictions for violent crimes, property crimes, motor vehicle thefts, vandalism, and the total count of crimes. Extensive experiments on real-world datasets of crimes reported in 11 U.S. cities demonstrated that the proposed framework achieves an accuracy of 73% and 77% when predicting property crimes and violent crimes, respectively.