Miracle-dz/UniFAR-FTUs huggingface dataset:
A reevaluation of the relationship between EGL-43 (EVI1/MECOM) and LIN-12 (Notch) during C. elegans anchor cell invasion

Development of the C. elegans reproductive tract is orchestrated by the anchor cell (AC). Among other things, this occurs through a cell invasion event that connects the uterine and vulval tissue. Several key transcription factors regulate AC invasion, such as EGL-43, HLH-2, and NHR-67. Specifically, these transcription factors function together to maintain the post-mitotic state of the AC, a requirement for AC invasion. EGL-43 is the C. elegans homolog of the human EVI1/MECOM proto-oncogene, and recently, a mechanistic connection has been made between its loss and AC cell-cycle entry. The current model states that EGL-43 represses LIN-12 (Notch) expression to prevent AC proliferation, suggesting that Notch signaling is mitogenic in the absence of EGL-43. To reevaluate the relationship between EGL-43 and LIN-12, we designed and implemented a heterologous co-expression system called AIDHB that combines the auxin-inducible degron (AID) system of plants with a live cell-cycle sensor based on human DNA helicase B (DHB). After validating the AIDHB approach using AID-tagged GFP, we sought to test this approach using AID-tagged alleles of egl-43 and lin-12. Auxin-inducible degradation of either EGL-43 or LIN-12 resulted in the expected AC phenotypes. Lastly, we seized the opportunity to pair AIDHB with RNAi to co-deplete LIN-12 and EGL-43, respectively. This combined approach revealed that LIN-12 is not required for AC proliferation following loss of EGL-43, which contrasts with a double RNAi experiment directed against these same targets. The addition of AIDHB to the C. elegans transgenic toolkit should facilitate functional in vivo imaging of cell-cycle associated phenomena.
The maximum solubility product marks the threshold for condensation of multivalent biomolecules

Clustering of weakly interacting multivalent biomolecules underlies the formation of membraneless compartments known as condensates. As opposed to single component (homotypic) systems, the concentration dependence of multi-component (heterotypic) condensate formation is not well understood. We previously proposed the solubility product (SP), the product of monomer concentrations in the dilute phase, as a tool for understanding the concentration dependence of multi-component systems. In the current study, we further explore the limits of the SP concept using spatial Langevin dynamics and rule-based stochastic simulations. We show, for a variety of idealized molecular structures, how the maximum SP coincides with the onset of the phase transition, i.e., the formation of large clusters. We reveal the importance of intra-cluster binding in steering the free and cluster phase molecular distributions. We also show how structural features of biomolecules shape the solubility product profiles. The interplay of flexibility, length and steric hindrance of linker regions controls the phase transition threshold. Remarkably, when solubility products are normalized to non-dimensional variables and plotted against the concentration scaled to the threshold for phase transition, the curves all coincide independent of the structural features of the binding partners. Similar coincidence is observed for the normalized clustering vs. concentration plots. Overall, the principles derived from these systematic models will help guide and interpret in vitro and in vivo experiments on the biophysics of biomolecular condensates. Significance Statement Biomolecular condensates are macroscopic intracellular structures that are composed of weakly interacting macromolecules. Because their composition can be complex, there are no simple rules for how condensates form as a function of the concentrations of their individual components. In this work, we show how the solubility product (SP), the product of monomer concentrations in the dilute phase, might serve as a tool for predicting the concentration dependence for condensation of multi-component systems. Specifically, Langevin dynamics simulations of the clustering of a series of multivalent binding partners reveals how the maximum SP is always attained at the same concentration as the appearance of large clusters. Experimental application of the SP concept should help rationalize the cellular formation of biomolecular condensates.
Entropic control of the free-energy landscape of an archetypal biomolecular machine

Biomolecular machines are complex macromolecular assemblies that utilize thermal and chemical energy to perform essential, multistep, cellular processes. Despite possessing different architectures and functions, an essential feature of the mechanisms-of-action of all such machines is that they require dynamic rearrangements of structural components. Surprisingly, biomolecular machines generally possess only a limited set of such motions, suggesting that these dynamics must be repurposed to drive different mechanistic steps. Although ligands that interact with these machines are known to drive such repurposing, the physical and structural mechanisms through which ligands achieve this remain unknown. Using temperature-dependent, single-molecule measurements analyzed with a time-resolution-enhancing algorithm, here we dissect the free energy landscape of an archetypal biomolecular machine, the bacterial ribosome, to reveal how its dynamics are repurposed to drive distinct steps during ribosome-catalyzed protein synthesis. Specifically, we show that the free energy landscape of the ribosome encompasses a network of allosterically coupled structural elements that coordinates the motions of these elements. Moreover, we reveal that ribosomal ligands which participate in disparate steps of the protein synthesis pathway repurpose this network by differentially modulating the structural flexibility of the ribosomal complex (i.e., the entropic component of the free energy landscape). We propose that such ligand-dependent entropic control of free energy landscapes has evolved as a general strategy through which ligands may regulate the functions of all biomolecular machines. Such entropic control is therefore an important driver in the evolution of naturally occurring biomolecular machines and a critical consideration for the design of synthetic molecular machines.
D R F T Competition between action plans tracks with evidence accumulation during flexible decision-making

The ability to change your mind when the local environment changes relies critically on cortico-basal ganglia-thalamic (CBGT) circuits. In silico experiments on the CBGT pathways show how shifts in decision policy are driven by learning-induced changes in competition between action plans, both within and across action representations. We empirically validate this idea, using whole-brain hemodynamic imaging in homo sapiens to show how competition between action representations in CBGT circuits adaptively shifts the rate of evidence accumulation in response to action-outcome contingency changes. uncertainty exploration cortico-basal ganglia-thalamic network
Evaluating the Potential of Marine Invertebrate and Insect Protein Hydrolysates to Reduce Fetal Bovine Serum in Cell Culture Media for Cultivated Fish Production

The use of fetal bovine serum (FBS) and the price of the cell culture media are the key constraints for developing serum-free cost-effective media. This study aims to replace or reduce the typical 10% serum application in fish cell culture media by applying protein hydrolysates from insects and marine invertebrate species for the growth of Zebrafish embryonic stem cells (ESC) as the model organism. Protein hydrolysates were produced from Black soldier fly (BSF), cricket, oyster, mussel, and lugworm with high protein content, suitable functional properties, adequate amino acids composition, and the degree of hydrolysis from 18.24 to 33.52%. Protein hydrolysates at low concentrations from 0.001 to 0.1 mg/mL in combination with 1 and 2.5% serum significantly increased cell growth compared to the control groups (5 and 10% serum) (P < 0.05). All protein hydrolysates with concentrations of 1 and 10 mg/mL were found to be toxic to cells and significantly reduced cell growth and performance (P < 0.05). However, except for cricket, all hydrolysates were able to restore or significantly increase cell growth and viability with 50% less serum at a concentration of 0.001, 0.01, and 0.1 mg/mL. Although cell growth was enhanced at lower concentrations of protein hydrolysates, cell morphology was altered due to the lack of serum. Lactate dehydrogenase (LDH) activity results indicated that BSF and lugworm hydrolysates did not alter the cell membrane. In addition, light and fluorescence imaging revealed that cell morphological features were comparable to the 10% serum control group. Overall, lugworm and BSF hydrolysates reduced serum by up to 90% while preserving excellent cell health.
Increased Sampling and Intracomplex Homologies Favor Vertical Over Horizontal Inheritance of the Dam1 Complex

Kinetochores connect chromosomes to spindle microtubules to ensure their correct segregation during cell division. Kinetochores of human and yeast are largely homologous, their ability to track depolymerizing microtubules however is carried out by the non-homologous complexes Ska1-C and Dam1-C, respectively. We previously reported the unique anti-correlating phylogenetic profiles of Dam1-C and Ska-C found amongst a wide variety of eukaryotes. Based on these profiles and the limited presence of Dam1-C, we speculated that horizontal gene transfer (HGT) could have played a role in the evolutionary history of Dam1-C. Here, we present expanded analyses of Dam1-c evolution, using additional genome as well as transcriptome sequences and recently acquired 3D structure data. This analysis revealed a wider and more complete presence of Dam1-C in Cryptista, Rhizaria, Ichthyosporea, CRuMs, and Colponemidia. The fungal Dam1-C cryo-EM structure supports earlier hypothesized intra-complex homologies, which enables the reconstruction of rooted and unrooted phylogenies. The rooted tree of concatenated Dam1-C subunits is statistically consistent with the species tree of eukaryotes, suggesting that Dam1-C is ancient, and that the present-day phylogenetic distribution is best explained by multiple, independent losses and no HGT was involved. Furthermore, we investigated the ancient origin of Dam1-C via profile-profile searches. Homology among eight out of the ten Dam1-C subunits suggested that the complex largely evolved from a single multimerizing subunit that diversified into an octameric core via stepwise duplication and sub-functionalization of the subunits before the origin of the Last Common Ancestor. Significance The Dam1 complex has a crucial role in cell division yet its distribution across species is very patchy. To resolve the evolutionary origin of this peculiar distribution, we used the recently acquired 3D structure to obtain a rooted phylogeny. This study makes an important step in discovering the evolutionary history of the Dam1 complex, by determining that Dam1-C was part of the Last eukaryotic Common Ancestor and arose via stepwise duplications during the transition from prokaryotes to eukaryotes.
Large magnetoresistance in La0.7Sr0.3MnO3/SrTiO3/La0.7Sr0.3MnO3 ramp-edge junctions

We report on the fabrication of ferromagnet–insulator–ferromagnet junction devices using a ramp-edge geometry based on (La0.7Sr0.3)MnO3 ferromagnetic electrodes and a SrTiO3 insulator. The maximum junction magnetoresistance (JMR) as large as 23% is observed below 300 Oe at low temperatures (T<100 K). Our ramp-edge junctions exhibit JMR of 6% at 200 K with a field less than 100 Oe. The device performance at room temperature is believed to be limited by both the nearly equivalent coercive fields in the electrodes and the magnetization process, rather than by the insulating barrier.
Phloem anatomy restricts root system architecture development: theoretical clues from in silico experiments

Plant growth and development involve the integration of numerous processes, influenced by both endogenous and exogenous factors. At any given time during a plant’s life cycle, the plant architecture is a readout of this continuous integration. However, untangling the individual factors and processes involved in the plant development and quantifying their influence on the plant developmental process is experimentally challenging. Here we used a combination of computational plant models to help understand experimental findings about how local phloem anatomical features influence the root system architecture. In particular, we simulated the mutual interplay between the root system architecture development and the carbohydrate distribution to provide a plausible mechanistic explanation for several experimental results. Our in silico study highlighted the strong influence of local phloem hydraulics on the root growth rates, growth duration and final length. The model result showed that a higher phloem resistivity leads to shorter roots due to the reduced flow of carbon within the root system. This effect was due to local properties of individual roots, and not linked to any of the pleiotropic effects at the root system level. Our results open the door to a better representation of growth processes in plant computational models.
Artificial neural network language models align neurally and behaviorally with humans even after a developmentally realistic amount of training

Artificial neural networks have emerged as computationally plausible models of human language processing. A major criticism of these models is that the amount of training data they receive far exceeds that of humans during language learning. Here, we use two complementary approaches to ask how the models’ ability to capture human neural and behavioral responses to language is affected by the amount of training data. First, we evaluate GPT-2 models trained on 1 million, 10 million, 100 million, or 1 billion tokens against two fMRI benchmarks and one behavioral (reading times) benchmark. Because children are exposed to approximately 100 million words during the first 10 years of life, we consider the 100-million-token model developmentally plausible. Second, we test the performance of a GPT-2 model that is trained on a 9-billion dataset to reach state-of-the-art next-word prediction performance against the same human benchmarks at different stages during training. Across both approaches, we find that (i) the models trained on a developmentally plausible amount of data already achieve near-maximal performance in capturing neural and behavioral responses to language. Further, (ii) lower perplexity—a measure of next-word prediction performance—is associated with stronger alignment with the human benchmarks, suggesting that models that have received enough training to achieve sufficiently high next-word prediction performance also acquire human-like representations of the linguistic input. In tandem, these findings establish that although some training is necessary for the models’ ability to predict human responses to language, a developmentally realistic amount of training (~100 million tokens) may suffice.
Psychological Distress among Healthcare Workers with Chronic Diseases during the COVID-19 Crisis

Background. Healthcare workers (HCWs) are a vulnerable group to psychological distress (PD) because they are subjected to strict working conditions, and the nature of the work implies a heavy level of emotional involvement. Objective. This study is aimed at determining psychological distress among healthcare workers with chronic diseases during the COVID-19 crisis. Methods. This was a quantitative cross-sectional correlation study. An online self-administered questionnaire was completed from July 2021 to October 2021 by a convenience sample of 302 HCWs at two primary healthcare centers in Saudi Arabia. The Kessler psychological distress scale was used to assess psychological distress among HCWs. Results. The prevalence of HCWs who contracted COVID-19 was 59.9%. The overall mean score of K10 was 4.38 ( SD = 0.706 ). The majority of HCWs that had the highest percentage of a very high level of distress were female (66.2%), between the ages of 30 and 40 years (35.1%), had more than 10 years of work experience (48.0%), and were nurses (49.7%), married (61.6%), and infected with COVID-19 (59.9%). There was a significant association between the age, working experience, and PD scores of the HCWs P value < 0.05. A significant correlation between each of the variables of (heart disease, high blood pressure, lung disease, diabetes, ulcer, anemia, depression, and back pain) and PD whereas the P value < 0.05 and the R value < 0.3. Conclusion. HCWs with associated comorbidities have been greatly affected by the COVID-19 pandemic. There was a very high level of PD among the studied population. HCWs with hypertension and back pain were the most affected by psychological distress, and emotional distress could be exacerbated if the HCWs contracted COVID-19.
Guanfacine Normalizes the Overexpression of Presynaptic α-2A Adrenoceptor Signaling and Ameliorates Neuropathic Pain in a Chronic Animal Model of Type 1 Diabetes

Background: Diabetes is associated with several complications, including neuropathic pain, which is difficult to manage with currently available drugs. Descending noradrenergic neurons possess antinociceptive activity; however, their involvement in diabetic neuropathic pain remains to be explored. Methods: To infer the regulatory role of this system, we examined as a function of diabetes, the expression and localization of alpha-2A adrenoceptors (α2-AR) in the dorsal root ganglia and key regions of the central nervous system, including pons and lumbar segment of the spinal cord using qRT-PCR, Western blotting, and immunofluorescence-based techniques. Results: The data revealed that presynaptic synaptosomal-associated protein-25 labeled α2-AR in the central and peripheral nervous system of streptozotocin diabetic rats was upregulated both at the mRNA and protein levels. Interestingly, the levels of postsynaptic density protein-95 labeled postsynaptic neuronal α2-AR remained unaltered as a function of diabetes. These biochemical abnormalities in the noradrenergic system of diabetic animals were associated with increased pain sensitivity as typified by the presence of thermal hyperalgesia and cold/mechanical allodynia. The pain-related behaviors were assessed using Hargreaves apparatus, cold-plate and dynamic plantar aesthesiometer. Chronically administered guanfacine, a selective α2-AR agonist, to diabetic animals downregulated the upregulation of neuronal presynaptic α2-AR and ameliorated the hyperalgesia and the cold/mechanical allodynia in these animals. Conclusion: Together, these findings demonstrate that guanfacine may function as a potent analgesic and highlight α2-AR, a key component of the descending neuronal autoinhibitory pathway, as a potential therapeutic target in the treatment of diabetic neuropathic pain.
PROBLEMS OF TRANSCRIPTION AND LABELLING IN THE SPECIFICATION OF SEGMENTAL AND PROSODIC STRUCTURE

A consensus transcription by two independent phoneticians of four speakers' readings of a two-minute passage was compared with the hand-labelling of the same recordings. A broad phonetic level of transcription was employed as this level of representation is usual in speech technology applications; the same symbol inventory was used for labelling. A number of differences between transcription and labelling are discussed with reference to the theoretical problems of relating auditory symbolic representation to the acoustic signal; the mapping of transcribed elements onto temporally defined acoustic segments is less than straight-
Analysis of large effective electric fields of weakly polar molecules for electron electric-dipole-moment searches

The electric dipole moment of an electron (eEDM) is one of the sensitive probes of physics beyond the standard model. The possible existence of the eEDM gives rise to an experimentally observed energy shift, which is proportional to the effective electric field (Eeff) of a target molecule. Hence, an analysis of the quantities that enhance Eeff is necessary to identify suitable molecules for eEDM searches. In the context of such searches, it is generally believed that a molecule with larger electric polarization also has a larger value of Eeff. However, our Dirac-Fock and relativistic coupled-cluster singles and doubles calculations show that the hydrides of Yb and Hg have larger Eeff than those of fluorides, even though their polarizations are smaller. This is due to significant mixing of valence s and p orbitals of the heavy atom in the molecules. This mixing has been attributed to the energy differences of the valence atomic orbitals and the overlap of the two atomic orbitals based on the orbital interaction theory.
Investigation of dust pollutants and the impact of suspended particulate matter on the performance of photovoltaic systems

The purpose of this study is to investigate the potential of airborne particulate matter (PM10 and PM2.5) and its impact on the performance of the photovoltaic (PV) system installed in the Sargodha region, being affected by the crushing activities in the hills. More than 100 stone crushers are operating in this region. Four stations within this region are selected for taking samples during the summer and winter seasons. Glass–fiber papers are used as a collection medium for particulate matter (PM) in a high-volume sampler. The concentration of PM is found above the permissible limit at all selected sites. The chemical composition, concentration, and the formation of particulate matter (PM10 and PM2.5) layers on the surface of the photovoltaic module varies significantly depending on the site’s location and time. The accumulation of PM layers on the PV module surface is one of the operating environmental factors that cause significant reduction in PV system performance. Consequently, it leads to power loss, reduction of service life, and increase in module temperature. For the PV system’s performance analysis, two PV systems are installed at the site, having higher PM concentration. One system is cleaned regularly, while the other remains dusty. The data of both PV systems are measured and compared for 4 months (2 months for the summer season and 2 months for the winter season). It is found that when the level of suspended particulate matter (PM10 and PM2.5) increases, the energy generation of the dusty PV system (compared to the cleaned one) is reduced by 7.48% in May, 7.342% in June, 10.68% in December, and 8.03% in January. Based on the obtained results, it is recommended that the negative impact of PM on the performance of the PV system should be considered carefully during the decision-making process of setting solar energy generation targets in the regions with a high level of particulate matter.
The two-component system CpxA/CpxR is critical for full virulence in Actinobacillus pleuropneumoniae

Actinobacillus pleuropneumoniae, a major bacterial porcine respiratory tract pathogen causing pig pleuropneumonia, has resulted in high economic losses worldwide. The mutation of the two-component system CpxAR strongly impacted the virulence of A. pleuropneumoniae, but the underlying regulatory mechanism remained unclear. Here, we found that CpxAR positively regulated the cpxDCBA gene cluster involved in polysaccharide capsule export. A capsular layer was confirmed in wild-type cells by transmission electron microscopy, whereas cpxAR and cpxD mutants were non-capsulated. The mutants for polysaccharide capsule export gene cpxD exhibited non-capsulated and were strongly impaired in virulence for mice, indicating a major role of CPS export system in virulence. We then demonstrated that CpxR directly regulated the transcription of the CPS export gene cluster cpxDCBA. Taken together, our data suggested that CpxAR is a key modulator of capsule export that facilitates A. pleuropneumoniae survival in the host.